National Library of Energy BETA

Sample records for forecast comparisons energy

  1. Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

    Broader source: Energy.gov [DOE]

    Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

  2. probabilistic energy production forecasts

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    probabilistic energy production forecasts - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Fuel Cycle Defense Waste Management

  3. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

    Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast AgencyCompany Organization: United States Department of Energy Sector:...

  4. Solar Forecasting | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Systems Integration » Solar Forecasting Solar Forecasting On December 7, 2012, DOE announced $8 million to fund two solar projects that are helping utilities and grid operators better forecast when, where, and how much solar power will be produced at U.S. solar energy plants. Part of the SunShot Systems Integration efforts, the Solar Forecasting projects will allow power system operators to integrate more solar energy into the electricity grid, and ensure the economic and reliable delivery of

  5. LED Lighting Forecast | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Publications Market Studies LED Lighting Forecast LED Lighting Forecast The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications ...

  6. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

    Zip: 94965 Region: Bay Area Sector: Services Product: Intelligent Monitoring and Forecasting Services Year Founded: 2010 Website: www.forecastenergy.net Coordinates:...

  7. Wind Forecasting Improvement Project | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Forecasting Improvement Project Wind Forecasting Improvement Project October 3, 2011 - 12:12pm Addthis This is an excerpt from the Third Quarter 2011 edition of the Wind Program R&D Newsletter. In July, the Department of Energy launched a $6 million project with the National Oceanic and Atmospheric Administration (NOAA) and private partners to improve wind forecasting. Wind power forecasting allows system operators to anticipate the electrical output of wind plants and adjust the electrical

  8. energy data + forecasting | OpenEI Community

    Open Energy Info (EERE)

    energy data + forecasting Home FRED Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in...

  9. Wind Power Forecasting Error Distributions: An International Comparison; Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Lew, D.; Milligan, M.; Holttinen, H.; Sillanpaa, S.; Gomez-Lazaro, E.; Scharff, R.; Soder, L.; Larsen, X. G.; Giebel, G.; Flynn, D.; Dobschinski, J.

    2012-09-01

    Wind power forecasting is expected to be an important enabler for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that do occur can be critical to system operation functions, such as the setting of operating reserve levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations.

  10. Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Florita, A.; Orwig, K.; Lew, D.; Milligan, M.

    2012-07-01

    The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenced by weather patterns. In this work we make a comparison between the day-ahead forecasting errors encountered in wind power forecasting and load forecasting. The study examines the distribution of errors from operational forecasting systems in two different Independent System Operator (ISO) regions for both wind power and load forecasts at the day-ahead timeframe. The day-ahead timescale is critical in power system operations because it serves the unit commitment function for slow-starting conventional generators.

  11. Energy Conservation Program: Data Collection and Comparison with...

    Energy Savers [EERE]

    Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability Energy Conservation Program: Data Collection ...

  12. Energy Conservation Program: Data Collection and Comparison with...

    Energy Savers [EERE]

    EERE-2011-BT-NOA-0013 Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types AGENCY: Office of Energy Efficiency and Renewable ...

  13. DOE Taking Wind Forecasting to New Heights | Department of Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Taking Wind Forecasting to New Heights DOE Taking Wind Forecasting to New Heights May 18, 2015 - 3:24pm Addthis A 2013 study conducted for the U.S. Department of Energy (DOE) by the National Oceanic and Atmospheric Administration (NOAA), AWS Truepower, and WindLogics in the Great Plains and Western Texas, demonstrated that wind power forecasts can be improved substantially using data collected from tall towers, remote sensors, and other devices, and incorporated into improved forecasting models

  14. Today's Forecast: Improved Wind Predictions | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical

  15. Solar Forecast Improvement Project | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Solar Forecast Improvement Project Solar Forecast Improvement Project NOAA.png For the Solar Forecast Improvement Project (SFIP), the Earth System Research Laboratory (ESRL) is partnering with the National Center for Atmospheric Research (NCAR) and IBM to develop more accurate methods for solar forecasts using their state-of-the-art weather models. APPROACH NOAA solar.png SFIP has three main goals: 1) to develop solar forecasting metrics tailored to the utility sector; 2) to improve solar

  16. NREL: Energy Analysis - Energy Forecasting and Modeling Staff

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Energy Forecasting and Modeling The following includes summary bios of staff expertise and interests in analysis relating to energy economics, energy system planning, risk and uncertainty modeling, and energy infrastructure planning. Team Lead: Nate Blair Administrative Support: Elizabeth Torres Clayton Barrows Dave Bielen Aaron Bloom Greg Brinkman Brian W Bush Stuart Cohen Wesley Cole Paul Denholm Victor Diakov Nicholas DiOrio Aron Dobos Kelly Eurek Janine Freeman Bethany Frew Pieter Gagnon

  17. The Wind Forecast Improvement Project (WFIP): A Public-Private Partnership Addressing Wind Energy Forecast Needs

    SciTech Connect (OSTI)

    Wilczak, J. M.; Finley, Cathy; Freedman, Jeff; Cline, Joel; Bianco, L.; Olson, J.; Djalaova, I.; Sheridan, L.; Ahlstrom, M.; Manobianco, J.; Zack, J.; Carley, J.; Benjamin, S.; Coulter, R. L.; Berg, Larry K.; Mirocha, Jeff D.; Clawson, K.; Natenberg, E.; Marquis, M.

    2015-10-11

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

  18. Energy Department Forecasts Geothermal Achievements in 2015 | Department of

    Office of Environmental Management (EM)

    Energy Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector, including Jay Nathwani, Acting Director of the Energy Department's Geothermal Technologies Office. Nathwani shared achievements and challenges in the program's technical portfolio. The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector,

  19. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Gomez-Lazaro, E.; Lovholm, A. L.; Berge, E.; Miettinen, J.; Holttinen, H.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Dobschinski, J.

    2013-10-01

    One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year; (ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted to characterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.

  20. Energy Savings Forecast of Solid-State Lighting in General Illuminatio...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Forecast of Solid-State Lighting in General Illumination Applications Energy Savings Forecast of Solid-State Lighting in General Illumination Applications PDF icon...

  1. Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2005-12-19

    On December 12, 2005, the reference case projections from ''Annual Energy Outlook 2006'' (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past five AEO releases (AEO 2001-AEO 2005), we once again find that the AEO 2006 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEX-AEO 2006 reference case comparison yields by far the largest premium--$2.3/MMBtu levelized over five years--that we have seen over the last six years. In other words, on average, one would have had to pay $2.3/MMBtu more than the AEO 2006 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

  2. Comparison of AEO 2005 natural gas price forecast to NYMEX futures prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

    On December 9, the reference case projections from ''Annual Energy Outlook 2005 (AEO 2005)'' were posted on the Energy Information Administration's (EIA) web site. As some of you may be aware, we at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk. As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past four years, forward natural gas contracts (e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past four years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation (presuming that long-term price stability is valued). In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2005. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or, more recently (and briefly), http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past four AEO releases (AEO 2001-AE0 2004), we once again find that the AEO 2005 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEXAEO 2005 reference case comparison yields by far the largest premium--$1.11/MMBtu levelized over six years--that we have seen over the last five years. In other words, on average, one would have to pay $1.11/MMBtu more than the AEO 2005 reference case natural gas price forecast in order to lock in natural gas prices over the coming six years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation. Fixed-price renewables obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of six years.

  3. Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

    Reports and Publications (EIA)

    2010-01-01

    The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

  4. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

    On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we once again find that the AEO 2007 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. Specifically, the NYMEX-AEO 2007 premium is $0.73/MMBtu levelized over five years. In other words, on average, one would have had to pay $0.73/MMBtu more than the AEO 2007 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

  5. Forecast of transportation energy demand through the year 2010

    SciTech Connect (OSTI)

    Mintz, M.M.; Vyas, A.D.

    1991-04-01

    Since 1979, the Center for Transportation Research (CTR) at Argonne National Laboratory (ANL) has produced baseline projections of US transportation activity and energy demand. These projections and the methodologies used to compute them are documented in a series of reports and research papers. As the lastest in this series of projections, this report documents the assumptions, methodologies, and results of the most recent projection -- termed ANL-90N -- and compares those results with other forecasts from the current literature, as well as with the selection of earlier Argonne forecasts. This current forecast may be used as a baseline against which to analyze trends and evaluate existing and proposed energy conservation programs and as an illustration of how the Transportation Energy and Emission Modeling System (TEEMS) works. (TEEMS links disaggregate models to produce an aggregate forecast of transportation activity, energy use, and emissions). This report and the projections it contains were developed for the US Department of Energy's Office of Transportation Technologies (OTT). The projections are not completely comprehensive. Time and modeling effort have been focused on the major energy consumers -- automobiles, trucks, commercial aircraft, rail and waterborne freight carriers, and pipelines. Because buses, rail passengers services, and general aviation consume relatively little energy, they are projected in the aggregate, as other'' modes, and used primarily as scaling factors. These projections are also limited to direct energy consumption. Projections of indirect energy consumption, such as energy consumed in vehicle and equipment manufacturing, infrastructure, fuel refining, etc., were judged outside the scope of this effort. The document is organized into two complementary sections -- one discussing passenger transportation modes, and the other discussing freight transportation modes. 99 refs., 10 figs., 43 tabs.

  6. DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting

    Broader source: Energy.gov [DOE]

    DOE has published a new report forecasting the energy savings of LED white-light sources compared with conventional white-light sources. The sixth iteration of the Energy Savings Forecast of Solid...

  7. OpenEI Community - energy data + forecasting

    Open Energy Info (EERE)

  8. Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets

    SciTech Connect (OSTI)

    Wong-Parodi, Gabrielle; Lekov, Alex; Dale, Larry

    2005-02-09

    This paper evaluates the accuracy of two methods to forecast natural gas prices: using the Energy Information Administration's ''Annual Energy Outlook'' forecasted price (AEO) and the ''Henry Hub'' compared to U.S. Wellhead futures price. A statistical analysis is performed to determine the relative accuracy of the two measures in the recent past. A statistical analysis suggests that the Henry Hub futures price provides a more accurate average forecast of natural gas prices than the AEO. For example, the Henry Hub futures price underestimated the natural gas price by 35 cents per thousand cubic feet (11.5 percent) between 1996 and 2003 and the AEO underestimated by 71 cents per thousand cubic feet (23.4 percent). Upon closer inspection, a liner regression analysis reveals that two distinct time periods exist, the period between 1996 to 1999 and the period between 2000 to 2003. For the time period between 1996 to 1999, AEO showed a weak negative correlation (R-square = 0.19) between forecast price by actual U.S. Wellhead natural gas price versus the Henry Hub with a weak positive correlation (R-square = 0.20) between forecasted price and U.S. Wellhead natural gas price. During the time period between 2000 to 2003, AEO shows a moderate positive correlation (R-square = 0.37) between forecasted natural gas price and U.S. Wellhead natural gas price versus the Henry Hub that show a moderate positive correlation (R-square = 0.36) between forecast price and U.S. Wellhead natural gas price. These results suggest that agencies forecasting natural gas prices should consider incorporating the Henry Hub natural gas futures price into their forecasting models along with the AEO forecast. Our analysis is very preliminary and is based on a very small data set. Naturally the results of the analysis may change, as more data is made available.

  9. Weather forecast-based optimization of integrated energy systems.

    SciTech Connect (OSTI)

    Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

    2009-03-01

    In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

  10. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect (OSTI)

    Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

    2011-03-28

    Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

  11. Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices

    SciTech Connect (OSTI)

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04

    On December 14, 2009, the reference-case projections from Annual Energy Outlook 2010 were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in itigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings.

  12. ANL Software Improves Wind Power Forecasting | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ANL Software Improves Wind Power Forecasting ANL Software Improves Wind Power Forecasting May 1, 2012 - 3:19pm Addthis This is an excerpt from the Second Quarter 2012 edition of the Wind Program R&D Newsletter. Since 2008, Argonne National Laboratory and INESC TEC (formerly INESC Porto) have conducted a research project to improve wind power forecasting and better use of forecasting in electricity markets. One of the main results from the project is ARGUS PRIMA (PRediction Intelligent

  13. Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2009-01-28

    On December 17, 2008, the reference-case projections from Annual Energy Outlook 2009 (AEO 2009) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof), differences in capital costs and O&M expenses, or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired or nuclear generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers; and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal, uranium, and other fuel prices. Finally, we caution readers about drawing inferences or conclusions based solely on this memo in isolation: to place the information contained herein within its proper context, we strongly encourage readers interested in this issue to read through our previous, more-detailed studies, available at http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf.

  14. Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices

    SciTech Connect (OSTI)

    Bolinger, Mark A; Bolinger, Mark; Wiser, Ryan

    2008-01-07

    On December 12, 2007, the reference-case projections from Annual Energy Outlook 2008 (AEO 2008) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof) or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers (though its appeal has diminished somewhat as prices have increased); and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal and other fuel prices. Finally, we caution readers about drawing inferences or conclusions based solely on this memo in isolation: to place the information contained herein within its proper context, we strongly encourage readers interested in this issue to read through our previous, more-detailed studies, available at http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf.

  15. Energy Savings Forecast of Solid-State Lighting in General Illumination

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Applications | Department of Energy Forecast of Solid-State Lighting in General Illumination Applications Energy Savings Forecast of Solid-State Lighting in General Illumination Applications PDF icon energysavingsforecast14.pdf More Documents & Publications Energy Savings Potential of Solid-State Lighting in General Illumination Applications - Report LED ADOPTION REPORT Solid-State Lighting R&D

  16. Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

    SciTech Connect (OSTI)

    Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.

  17. Energy Department Announces $2.5 Million to Improve Wind Forecasting |

    Office of Environmental Management (EM)

    Department of Energy .5 Million to Improve Wind Forecasting Energy Department Announces $2.5 Million to Improve Wind Forecasting January 8, 2015 - 12:00pm Addthis The Energy Department today announced $2.5 million for a new project to research the atmospheric processes that generate wind in mountain-valley regions. This in-depth research, conducted by Vaisala of Louisville, Colorado, will be used to improve the wind industry's weather models for short-term wind forecasts, especially for

  18. Energy Forecasting Framework and Emissions Consensus Tool (EFFECT...

    Open Energy Info (EERE)

    Tool (EFFECT) EFFECT is an open, Excel-based modeling tool used to forecast greenhouse gas emissions from a range of development scenarios at the regional and national levels....

  19. NOAA Teams Up with Department of Energy & Industry to Improve Wind Forecasts

    Broader source: Energy.gov [DOE]

    The growth of wind-generated power in the United States  is creating greater demand for improved wind forecasts. To address this need, the Department of Energy is working with NOAA and industry on...

  20. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  1. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect (OSTI)

    1995-01-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  2. A comparison of water vapor quantities from model short-range forecasts and ARM observations

    SciTech Connect (OSTI)

    Hnilo, J J

    2006-03-17

    Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the 'Merged-sounding' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.

  3. A comparison of model short-range forecasts and the ARM Microbase data

    SciTech Connect (OSTI)

    Hnilo, J J

    2006-09-22

    For the fourth quarter ARM metric we will make use of new liquid water data that has become available, and called the 'Microbase' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Tropical West Pacific (TWP) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both TWP and NSA. The Microbase data have been averaged to 35 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3hourly data for direct comparison to our model output.

  4. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect (OSTI)

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

  5. U.S. Department of Energy Workshop Report: Solar Resources and Forecasting

    SciTech Connect (OSTI)

    Stoffel, T.

    2012-06-01

    This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

  6. The impact of forecasted energy price increases on low-income consumers

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energys Energy Information Administration (EIA) recently released its short term forecast for residential energy prices for the winter of 2005-2006. The forecast indicates significant increases in fuel costs, particularly for natural gas, propane, and home heating oil, for the year ahead. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nations low-income households by primary heating fuel type, nationally and by Census Region. The statistics are intended for the use of policymakers in the Department of Energys Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2006 fiscal year.

  7. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison (Presentation)

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B.; Miettinen, J.; Holttinen, H.; Gomez-Lozaro, E.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Lovholm, A.; Berge, E.; Dobschinski, J.

    2013-10-01

    This presentation summarizes the work to investigate the uncertainty in wind forecasting at different times of year and compare wind forecast errors in different power systems using large-scale wind power prediction data from six countries: the United States, Finland, Spain, Denmark, Norway, and Germany.

  8. The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

    SciTech Connect (OSTI)

    Finley, Cathy

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.

  9. New Forecasting Tools Enhance Wind Energy Integration In Idaho and Oregon

    Office of Environmental Management (EM)

    New Forecasting Tools Enhance Wind Energy Integration in Idaho and Oregon Page 1 Under the American Recovery and Reinvestment Act of 2009, the U.S. Department of Energy and the electricity industry have jointly invested over $7.9 billion in 99 cost-shared Smart Grid Investment Grant projects to modernize the electric grid, strengthen cybersecurity, improve interoperability, and collect an unprecedented level of data on smart grid and customer operations. 1. Summary Idaho Power Company (IPC)

  10. Short-Term Energy Outlook Supplement: Uncertainties in the Short-Term Global Petroleum and Other Liquids Supply Forecast

    Gasoline and Diesel Fuel Update (EIA)

    Uncertainties in the Short-Term Global Petroleum and Other Liquids Supply Forecast February 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Supplement: Uncertainties in the Global Petroleum and Other Liquids Supply Forecast i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data,

  11. Short-Term Energy Carbon Dioxide Emissions Forecasts August 2009

    Reports and Publications (EIA)

    2009-01-01

    Supplement to the Short-Term Energy Outlook. Short-term projections for U.S. carbon dioxide emissions of the three fossil fuels: coal, natural gas, and petroleum.

  12. Project Profile: Forecasting and Influencing Technological Progress in Solar Energy

    Broader source: Energy.gov [DOE]

    The University of North Carolina at Charlotte, along with their partners at Arizona State University and the University of Oxford, under the Solar Energy Evolution and Diffusion Studies (SEEDS)...

  13. Hawaii Energy Strategy: Program guide. [Contains special sections on analytical energy forecasting, renewable energy resource assessment, demand-side energy management, energy vulnerability assessment, and energy strategy integration

    SciTech Connect (OSTI)

    Not Available

    1992-09-01

    The Hawaii Energy Strategy program, or HES, is a set of seven projects which will produce an integrated energy strategy for the State of Hawaii. It will include a comprehensive energy vulnerability assessment with recommended courses of action to decrease Hawaii's energy vulnerability and to better prepare for an effective response to any energy emergency or supply disruption. The seven projects are designed to increase understanding of Hawaii's energy situation and to produce recommendations to achieve the State energy objectives of: Dependable, efficient, and economical state-wide energy systems capable of supporting the needs of the people, and increased energy self-sufficiency. The seven projects under the Hawaii Energy Strategy program include: Project 1: Develop Analytical Energy Forecasting Model for the State of Hawaii. Project 2: Fossil Energy Review and Analysis. Project 3: Renewable Energy Resource Assessment and Development Program. Project 4: Demand-Side Management Program. Project 5: Transportation Energy Strategy. Project 6: Energy Vulnerability Assessment Report and Contingency Planning. Project 7: Energy Strategy Integration and Evaluation System.

  14. Ocean thermal energy conversion: Historical highlights, status, and forecast

    SciTech Connect (OSTI)

    Dugger, G.L.; Avery, W.H.; Francis, E.J.; Richards, D.

    1983-07-01

    In 1881, d'Arsonval conceived the closed-Rankine-cycle ocean thermal energy conversion (OTEC) system in which a working fluid is vaporized by heat exchange with cold water drawn from a 700-1200 m depth. In 1930, Claude demonstrated an open-cycle process in Cuba. Surface water was flash-vaporized at 3 kPa to drive a turbine directly (no secondary working fluid) and then was condensed by direct contact with water drawn from a 700-m depth through a 1.6m-diam, 1.75-km-long cold-water pipe (CWP). From a delta T of 14/sup 0/C his undersized turbine generated 22 kW. In 1956 a French team designed a 3.5-MW (net) open-cycle plant for installation off Abidjan on the Ivory Coast of Africa and demonstrated the necessary CWP deployment. The at-sea demonstrations by Mini-OTEC and OTEC-1 and other recent advances in OTEC technology summarized herein represent great progress. All of the types of plants proposed for the DOE's PON program may be worthy of development; certainly work on a grazing plant is needed. Our estimates indicate that the U.S. goals established by Public Law 96-310 leading to 10 GW of OTEC power and energy product equivalents by 1999 are achievable, provided that adequate federal financial incentives are retained to assure the building of the first few plants.

  15. Energy Savings Forecast of Solid-State Lighting in General Illumination Applications

    SciTech Connect (OSTI)

    none,

    2014-08-29

    With declining production costs and increasing technical capabilities, LED adoption has recently gained momentum in general illumination applications. This is a positive development for our energy infrastructure, as LEDs use significantly less electricity per lumen produced than many traditional lighting technologies. The U.S. Department of Energy’s Energy Savings Forecast of Solid-State Lighting in General Illumination Applications examines the expected market penetration and resulting energy savings of light-emitting diode, or LED, lamps and luminaires from today through 2030.

  16. 3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information

    Open Energy Info (EERE)

    TIER Environmental Forecast Group Inc 3TIER Jump to: navigation, search Name: 3TIER Environmental Forecast Group Inc (3TIER) Place: Seattle, Washington Zip: 98121 Sector: Renewable...

  17. Energy consumption and expenditure projections by population group on the basis of the annual energy outlook 1999 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Balsley, J.H.

    2000-01-07

    This report presents an analysis of the relative impact of the base-case scenario used in Annual Energy Outlook 1999 on different population groups. Projections of energy consumption and expenditures, as well as energy expenditure as a share of income, from 1996 to 2020 are given. The projected consumption of electricty, natural gas, distillate fuel, and liquefied petroleum gas during this period is also reported for each population group. In addition, this report compares the findings of the Annual Energy Outlook 1999 report with the 1998 report. Changes in certain indicators and information affect energy use forecasts, and these effects are analyzed and discussed.

  18. Acquisition Forecast

    Broader source: Energy.gov [DOE]

    It is the policy of the Department of Energy (DOE) and the National Nuclear Security Administration (NNSA) to provide timely information to the public regarding DOE/NNSA’s forecast of future prime contracting opportunities and subcontracting opportunities which are available via the Department’s major site and facilities management contractors.

  19. The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

    SciTech Connect (OSTI)

    Freedman, Jeffrey M.; Manobianco, John; Schroeder, John; Ancell, Brian; Brewster, Keith; Basu, Sukanta; Banunarayanan, Venkat; Hodge, Bri-Mathias; Flores, Isabel

    2014-04-30

    This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.

  20. Crude oil and alternate energy production forecasts for the twenty-first century: The end of the hydrocarbon era

    SciTech Connect (OSTI)

    Edwards, J.D.

    1997-08-01

    Predictions of production rates and ultimate recovery of crude oil are needed for intelligent planning and timely action to ensure the continuous flow of energy required by the world`s increasing population and expanding economies. Crude oil will be able to supply increasing demand until peak world production is reached. The energy gap caused by declining conventional oil production must then be filled by expanding production of coal, heavy oil and oil shales, nuclear and hydroelectric power, and renewable energy sources (solar, wind, and geothermal). Declining oil production forecasts are based on current estimated ultimate recoverable conventional crude oil resources of 329 billion barrels for the United States and close to 3 trillion barrels for the world. Peak world crude oil production is forecast to occur in 2020 at 90 million barrels per day. Conventional crude oil production in the United States is forecast to terminate by about 2090, and world production will be close to exhaustion by 2100.

  1. Comparison of Real World Energy Consumption to Models and DOE...

    Energy Savers [EERE]

    Comparison of Real World Energy Consumption to Models and DOE Test Procedures Comparison of Real World Energy Consumption to Models and DOE Test Procedures This study investigates ...

  2. Forecasting Flu

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Forecasting Flu March 6, 2016 Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? Using real-time data from Wikipedia and social media, Sara del Valle and her team from Los Alamos National Laboratory have developed a global disease-forecasting system that will improve the way we respond to epidemics. Using this model, individuals and public health officials can monitor

  3. Short-term energy outlook, annual supplement 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-01

    The Short-Term Energy Outlook Annual Supplement (Supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

  4. Short-term energy outlook annual supplement, 1993

    SciTech Connect (OSTI)

    1993-08-06

    The Short-Term Energy Outlook Annual Supplement (supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

  5. RACORO Forecasting

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Hartsock CIMMS, University of Oklahoma  ARM AAF Wiki page  Weather Briefings  Observed Weather  Cloud forecasting models  BUFKIT forecast soundings + guidance from Norman NWS enhanced pages and discussions NAM-WRF updated twice/day (12Z and 00Z) Forecast out to 84-hours RUC (updated every 3 hours) Operational RUC forecast only goes out 12 hours (developmental out 24 hours)

  6. Ramping Effect on Forecast Use: Integrated Ramping as a Mitigation Strategy; NREL (National Renewable Energy Laboratory)

    SciTech Connect (OSTI)

    Diakov, Victor; Barrows, Clayton; Brinkman, Gregory; Bloom, Aaron; Denholm, Paul

    2015-06-23

    Power generation ramping between forecasted (net) load set-points shift the generation (MWh) from its scheduled values. The Integrated Ramping is described as a method that mitigates this problem.

  7. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the flying brick technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.

  8. Distributed Wind Policy Comparison Tool Website | Open Energy...

    Open Energy Info (EERE)

    TOOL Name: Distributed Wind Policy Comparison Tool Website Focus Area: Renewable Energy Topics: Security & Reliability Website: www.eformativeoptions.comdwpolicytool...

  9. Seismic energy data analysis of Merapi volcano to test the eruption time prediction using materials failure forecast method (FFM)

    SciTech Connect (OSTI)

    Anggraeni, Novia Antika

    2015-04-24

    The test of eruption time prediction is an effort to prepare volcanic disaster mitigation, especially in the volcano’s inhabited slope area, such as Merapi Volcano. The test can be conducted by observing the increase of volcanic activity, such as seismicity degree, deformation and SO2 gas emission. One of methods that can be used to predict the time of eruption is Materials Failure Forecast Method (FFM). Materials Failure Forecast Method (FFM) is a predictive method to determine the time of volcanic eruption which was introduced by Voight (1988). This method requires an increase in the rate of change, or acceleration of the observed volcanic activity parameters. The parameter used in this study is the seismic energy value of Merapi Volcano from 1990 – 2012. The data was plotted in form of graphs of seismic energy rate inverse versus time with FFM graphical technique approach uses simple linear regression. The data quality control used to increase the time precision employs the data correlation coefficient value of the seismic energy rate inverse versus time. From the results of graph analysis, the precision of prediction time toward the real time of eruption vary between −2.86 up to 5.49 days.

  10. Project Profile: Forecasting and Influencing Technological Progress...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Influencing Technological Progress in Solar Energy Project Profile: Forecasting and ... energy technologies based on estimates of future rates of progress and adoption. ...

  11. Department of Energy award DE-SC0004164 Climate and National Security: Securing Better Forecasts

    SciTech Connect (OSTI)

    Reno Harnish

    2011-08-16

    The Climate and National Security: Securing Better Forecasts symposium was attended by senior policy makers and distinguished scientists. The juxtaposition of these communities was creative and fruitful. They acknowledged they were speaking past each other. Scientists were urged to tell policy makers about even improbable outcomes while articulating clearly the uncertainties around the outcomes. As one policy maker put it, we are accustomed to making these types of decisions. These points were captured clearly in an article that appeared on the New York Times website and can be found with other conference materials most easily on our website, www.scripps.ucsd.edu/cens/. The symposium, generously supported by the NOAA/JIMO, benefitted the public by promoting scientifically informed decision making and by the transmission of objective information regarding climate change and national security.

  12. Energy Savings Forecast of Solid-State Lighting in General Illuminatio...

    Energy Savers [EERE]

    Prepared for the U.S. Department of Energy August 2014 Prepared by Navigant Consulting, ... James R. Brodrick of the U.S. Department of Energy, Building Technologies Office offered ...

  13. DOE Announces Webinars on Real Time Energy Management, Solar Forecasting Metrics, and More

    Broader source: Energy.gov [DOE]

    EERE offers webinars to the public on a range of subjects, from adopting the latest energy efficiency and renewable energy technologies to training for the clean energy workforce. Webinars are free; however, advanced registration is typically required. You can also watch archived webinars and browse previously aired videos, slides, and transcripts.

  14. Short and Long-Term Perspectives: The Impact on Low-Income Consumers of Forecasted Energy Price Increases in 2008 and A Cap & Trade Carbon Policy in 2030

    SciTech Connect (OSTI)

    Eisenberg, Joel Fred

    2008-01-01

    The Department of Energy's Energy Information Administration (EIA) recently released its short-term forecast for residential energy prices for the winter of 2007-2008. The forecast indicates increases in costs for low-income consumers in the year ahead, particularly for those using fuel oil to heat their homes. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nation's low-income households by primary heating fuel type, nationally and by Census Region. The report provides an update of bill estimates provided in a previous study, "The Impact Of Forecasted Energy Price Increases On Low-Income Consumers" (Eisenberg, 2005). The statistics are intended for use by policymakers in the Department of Energy's Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2008 fiscal year. In addition to providing expenditure forecasts for the year immediately ahead, this analysis uses a similar methodology to give policy makers some insight into one of the major policy debates that will impact low-income energy expenditures well into the middle decades of this century and beyond. There is now considerable discussion of employing a cap-and-trade mechanism to first limit and then reduce U.S. emissions of carbon into the atmosphere in order to combat the long-range threat of human-induced climate change. The Energy Information Administration has provided an analysis of projected energy prices in the years 2020 and 2030 for one such cap-and-trade carbon reduction proposal that, when integrated with the RECS 2001 database, provides estimates of how low-income households will be impacted over the long term by such a carbon reduction policy.

  15. NREL: Resource Assessment and Forecasting Home Page

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    are used to plan and develop renewable energy technologies and support climate change research. Learn more about NREL's resource assessment and forecasting research:...

  16. Comparison Table of Department of Energy Mentor-Protege Program |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy Programs » Mentor-Protégé Program » Comparison Table of Department of Energy Mentor-Protege Program Comparison Table of Department of Energy Mentor-Protege Program Program/Field Offices and Labs Alliance for Sustainable Energy (NREL) Argonne National Laboratory * DOE Headquarters Fermi National Accelerator Laboratory Los Alamos Nat'l Lab Oak Ridge Institute for Science and Education (ORISE) managed by ORAU Oak Ridge Nat'l Lab Thomas Jefferson Nat'l Accelerator

  17. The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership

    Energy Savers [EERE]

    for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations | Department of Energy The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations The Wind Forecast Improvement

  18. Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Lew, D.; Milligan, M.

    2013-01-01

    Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of the day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.

  19. Automated Comparison of Building Energy Simulation Engines (Presentation)

    SciTech Connect (OSTI)

    Polly, B.; Horowitz, S.; Booten, B.; Kruis, N.; Christensen, C.

    2012-08-01

    This presentation describes the BEopt comparative test suite, which is a tool that facilitates the automated comparison of building energy simulation engines. It also demonstrates how the test suite is improving the accuracy of building energy simulation programs. Building energy simulation programs inform energy efficient design for new homes and energy efficient upgrades for existing homes. Stakeholders rely on accurate predictions from simulation programs. Previous research indicates that software tends to over-predict energy usage for poorly-insulated leaky homes. NREL is identifying, investigating, and resolving software inaccuracy issues. Comparative software testing is one method of many that NREL uses to identify potential software issues.

  20. Distributed Wind Policy Comparison Tool | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Distributed Wind Policy Comparison Tool Distributed Wind Policy Comparison Tool Power through Policy: 'Best Practices' for Cost-Effective Distributed Wind is a U.S. Department of Energy (DOE)-funded project to identify distributed wind technology policy best practices and to help policymakers, utilities, advocates, and consumers examine their effectiveness using a pro forma model. Incorporating a customized feed from the Database of State Incentives for Renewables and Efficiency (DSIRE), the

  1. Intermediate future forecasting system

    SciTech Connect (OSTI)

    Gass, S.I.; Murphy, F.H.; Shaw, S.H.

    1983-12-01

    The purposes of the Symposium on the Department of Energy's Intermediate Future Forecasting System (IFFS) were: (1) to present to the energy community details of DOE's new energy market model IFFS; and (2) to have an open forum in which IFFS and its major elements could be reviewed and critiqued by external experts. DOE speakers discussed the total system, its software design, and the modeling aspects of oil and gas supply, refineries, electric utilities, coal, and the energy economy. Invited experts critiqued each of these topics and offered suggestions for modifications and improvement. This volume documents the proceedings (papers and discussion) of the Symposium. Separate abstracts have been prepared for each presentation for inclusion in the Energy Data Base.

  2. Text-Alternative Version LED Lighting Forecast

    Broader source: Energy.gov [DOE]

    The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030....

  3. Up and down: energy and cost comparison

    SciTech Connect (OSTI)

    Shapira, H.B.; Brite, S.E.; Yost, M.B.

    1981-01-01

    A study comparing cost and energy performance of equal aboveground and earth-sheltered homes is being conducted at the Oak Ridge National Laboratory. Five cities were selected to represent five regions of the US. A module of a basic 138 m/sup 2/ (1480-sq-ft) living unit was designed to adapt to both conventional, well-insulated housing and earth-sheltered (ES) housing. The homes were designed to represent the popular home on the market in the particular neighborhood. The designs vary to conform with regional requirements for heating and cooling loads as well as style, construction materials, finish, etc. Finished sets of detailed drawings were prepared for all the sites.

  4. Comparison of Software Models for Energy Savings from Cool Roofs

    SciTech Connect (OSTI)

    New, Joshua Ryan; Miller, William A; Huang, Yu; Levinson, Ronnen

    2014-01-01

    A web-based Roof Savings Calculator (RSC) has been deployed for the United States Department of Energy as an industry-consensus tool to help building owners, manufacturers, distributors, contractors and researchers easily run complex roof and attic simulations. This tool employs modern web technologies, usability design, and national average defaults as an interface to annual simulations of hour-by-hour, whole-building performance using the world-class simulation tools DOE-2.1E and AtticSim in order to provide estimated annual energy and cost savings. In addition to cool reflective roofs, RSC simulates multiple roof and attic configurations including different roof slopes, above sheathing ventilation, radiant barriers, low-emittance roof surfaces, duct location, duct leakage rates, multiple substrate types, and insulation levels. A base case and energy-efficient alternative can be compared side-by-side to estimate monthly energy. RSC was benchmarked against field data from demonstration homes in Ft. Irwin, California; while cooling savings were similar, heating penalty varied significantly across different simulation engines. RSC results reduce cool roofing cost-effectiveness thus mitigating expected economic incentives for this countermeasure to the urban heat island effect. This paper consolidates comparison of RSC s projected energy savings to other simulation engines including DOE-2.1E, AtticSim, Micropas, and EnergyPlus, and presents preliminary analyses. RSC s algorithms for capturing radiant heat transfer and duct interaction in the attic assembly are considered major contributing factors to increased cooling savings and heating penalties. Comparison to previous simulation-based studies, analysis on the force multiplier of RSC cooling savings and heating penalties, the role of radiative heat exchange in an attic assembly, and changes made for increased accuracy of the duct model are included.

  5. The Wind Forecast Improvement Project (WFIP): A Public/Private...

    Broader source: Energy.gov (indexed) [DOE]

    research project whose overarching goals are to improve the accuracy of short-term wind energy forecasts, and to demonstrate the economic value of these improvements. WFIP Round...

  6. Wind Power Forecasting

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    data Presentations BPA Super Forecast Methodology Related Links Near Real-time Wind Animation Meteorological Data Customer Supplied Generation Imbalance Dynamic Transfer Limits...

  7. Wind Power Forecasting Data

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Operations Call 2012 Retrospective Reports 2012 Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email...

  8. EIA lowers forecast for summer gasoline prices

    Gasoline and Diesel Fuel Update (EIA)

    EIA lowers forecast for summer gasoline prices U.S. gasoline prices are expected to be lower this summer than previously thought. The price for regular gasoline this summer is now expected to average $3.53 a gallon, according to the new monthly forecast from the U.S. Energy Information Administration. That's down 10 cents from last month's forecast and 16 cents cheaper than last summer. After reaching a weekly peak of $3.78 a gallon in late February, pump prices fell nine weeks in a row to $3.52

  9. Funding Opportunity Announcement for Wind Forecasting Improvement Project

    Office of Environmental Management (EM)

    in Complex Terrain | Department of Energy Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain April 4, 2014 - 9:47am Addthis On April 4, 2014 the U.S. Department of Energy announced a $2.5 million funding opportunity entitled "Wind Forecasting Improvement Project in Complex Terrain." By researching the physical processes that take place in complex

  10. ENERGY INVESTMENTS UNDER CLIMATE POLICY: A COMPARISON OF GLOBAL MODELS

    SciTech Connect (OSTI)

    McCollum, David; Nagai, Yu; Riahi, Keywan; Marangoni, Giacomo; Calvin, Katherine V.; Pietzcker, Robert; Van Vliet, Jasper; van der Zwaan, Bob

    2013-11-01

    The levels of investment needed to mobilize an energy system transformation and mitigate climate change are not known with certainty. This paper aims to inform the ongoing dialogue and in so doing to guide public policy and strategic corporate decision making. Within the framework of the LIMITS integrated assessment model comparison exercise, we analyze a multi-IAM ensemble of long-term energy and greenhouse gas emissions scenarios. Our study provides insight into several critical but uncertain areas related to the future investment environment, for example in terms of where capital expenditures may need to flow regionally, into which sectors they might be concentrated, and what policies could be helpful in spurring these financial resources. We find that stringent climate policies consistent with a 2C climate change target would require a considerable upscaling of investments into low-carbon energy and energy efficiency, reaching approximately $45 trillion (range: $30$75 trillion) cumulative between 2010 and 2050, or about $1.1 trillion annually. This represents an increase of some $30 trillion ($10$55 trillion), or $0.8 trillion per year, beyond what investments might otherwise be in a reference scenario that assumes the continuation of present and planned emissions-reducing policies throughout the world. In other words, a substantial "clean-energy investment gap" of some $800 billion/yr exists notably on the same order of magnitude as present-day subsidies for fossil energy and electricity worldwide ($523 billion). Unless the gap is filled rather quickly, the 2C target could potentially become out of reach.

  11. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

    This document consists of papers which cover topics in analysis and modeling that underlie the Annual Energy Outlook 1996. Topics include: The Potential Impact of Technological Progress on U.S. Energy Markets; The Outlook for U.S. Import Dependence; Fuel Economy, Vehicle Choice, and Changing Demographics, and Annual Energy Outlook Forecast Evaluation.

  12. NREL: Transmission Grid Integration - Forecasting

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    generation output by using forecasts that incorporate meteorological data to predict production. Such systems typically provide forecasts at a number of timescales, ranging from...

  13. National Oceanic and Atmospheric Administration Provides Forecasting Support for CLASIC and CHAPS 2007

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    NOAA Provides Forecasting Support for CLASIC and CHAPS 2007 Forecasting Challenge While weather experiments in the heart of Tornado Alley typically focus on severe weather, the CLASIC and CHAPS programs will have different emphases. Forecasters from the National Oceanic and Atmospheric Administration in Norman, Okla. will provide weather forecasting support to these two Department of Energy experiments based in the state. Forecasting support for meteorological research field programs usually

  14. Upcoming Funding Opportunity for Wind Forecasting Improvement Project in

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Complex Terrain | Department of Energy Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am Addthis On February 11, 2014 the Wind Program announced a Notice of Intent to issue a funding opportunity entitled "Wind Forecasting Improvement Project in Complex Terrain." By researching the physical processes that take place in complex terrain, this funding would improve

  15. Module 6 - Metrics, Performance Measurements and Forecasting | Department

    Energy Savers [EERE]

    of Energy 6 - Metrics, Performance Measurements and Forecasting Module 6 - Metrics, Performance Measurements and Forecasting This module focuses on the metrics and performance measurement tools used in Earned Value. This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices. In addition, this module will outline forecasting tools such as estimate to complete (ETC) and estimate at completion (EAC)

  16. DOE Benefits Forecasts: Report of the External Peer Review Panel |

    Office of Environmental Management (EM)

    Department of Energy Benefits Forecasts: Report of the External Peer Review Panel DOE Benefits Forecasts: Report of the External Peer Review Panel A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts. PDF icon Report of the External Peer Review Panel More Documents & Publications Industrial Technologies Funding Profile by Subprogram Survey of Emissions Models for Distributed Combined Heat and Power

  17. Chapter 9: Enabling Capabilities for Science and Energy | A Comparison of Research Funding Modalities

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Funding Modalities High-Performance Computing Capabilities and Allocations User Facility Statistics Examples and Case Studies ENERGY U.S. DEPARTMENT OF Quadrennial Technology Review 2015 1 Quadrennial Technology Review 2015 A Comparison of Research Center Funding Modalities Chapter 9: Enabling Capabilities for Science and Energy A Comparison of Multi-disciplinary, Multi-scale Research Center Funding Modalities Three Department of Energy (DOE) research center modalities-the Energy Frontier

  18. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

    Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`s National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.

  19. Comparison of Fuel Cell Technologies | Department of Energy

    Office of Environmental Management (EM)

    Comparison of Fuel Cell Technologies Comparison of Fuel Cell Technologies Each fuel cell technology has advantages and disadvantages. See how fuel cell technologies compare with one another. This comparison chart is also available as a fact sheet. Fuel Cell Type Common Electrolyte Operating Temperature Typical Stack Size Electrical Efficiency (LHV) Applications Advantages Disadvantages Polymer Electrolyte Membrane (PEM) Perfluorosulfonic acid <120°C <1 kW-100 kW 60% direct H2;a 40%

  20. Comparison of Software Models for Energy Savings from Cool Roofs...

    Office of Scientific and Technical Information (OSTI)

    RSC results reduce cool roofing cost-effectiveness thus mitigating expected economic ... Comparison to previous simulation-based studies, analysis on the force multiplier of RSC ...

  1. Distributed Wind Policy Comparison Tool | Open Energy Information

    Open Energy Info (EERE)

    URI: cleanenergysolutions.orgcontentdistributed-wind-policy-comparison-to Language: English Policies: "Deployment Programs,Financial Incentives,Regulations" is not in...

  2. Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Florita, A.; Lu, S.; Hamann, H. F.; Banunarayanan, V.

    2013-10-01

    Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.

  3. Pathways to low-cost electrochemical energy storage: a comparison of

    Office of Scientific and Technical Information (OSTI)

    aqueous and nonaqueous flow batteries (Journal Article) | SciTech Connect Journal Article: Pathways to low-cost electrochemical energy storage: a comparison of aqueous and nonaqueous flow batteries Citation Details In-Document Search Title: Pathways to low-cost electrochemical energy storage: a comparison of aqueous and nonaqueous flow batteries Energy storage is increasingly seen as a valuable asset for electricity grids composed of high fractions of intermittent sources, such as wind power

  4. Pathways to low-cost electrochemical energy storage: a comparison of

    Office of Scientific and Technical Information (OSTI)

    aqueous and nonaqueous flow batteries (Journal Article) | SciTech Connect Journal Article: Pathways to low-cost electrochemical energy storage: a comparison of aqueous and nonaqueous flow batteries Citation Details In-Document Search Title: Pathways to low-cost electrochemical energy storage: a comparison of aqueous and nonaqueous flow batteries × You are accessing a document from the Department of Energy's (DOE) SciTech Connect. This site is a product of DOE's Office of Scientific and

  5. Chapter 9: Enabling Capabilities for Science and Energy | A Comparison...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Three Department of Energy (DOE) research center modalities-the Energy Frontier Research ... and Energy. The EFRCs are addressing high-risk, high-reward challenges in basic energy ...

  6. Annual energy outlook 1995, with projections to 2010

    SciTech Connect (OSTI)

    1995-01-01

    The Annual Energy Outlook 1995 (AEO95) presents the midterm energy forecasts of the Energy Information Administration (EIA). This year`s report presents projections and analyses of energy supply, demand, and prices through 2010, based on results from the National Energy Modeling System (NEMS). Quarterly forecasts of energy supply and demand for 1995 and 1996 are published in the Short-Term Energy Outlook (February 1995). Forecast tables for the five cases examined in the AEO95 are provided in Appendixes A through C. Appendix A gives historical data and forecasts for selected years from 1992 through 2010 for the reference case. Appendix B presents two additional cases, which assume higher and lower economic growth than the reference case. Appendix C presents two cases that assume higher and lower world oil prices. Appendix D presents a summary of the forecasts in units of oil equivalence. Appendix E presents a summary of household energy expenditures. Appendix F provides detailed comparisons of the AEO95 forecasts with those of other organizations. Appendix G briefly describes NEMS and the major AEO95 forecast assumptions. Appendix H presents a stand-alone high electricity demand case. Appendix 1 provides a table of energy conversion factors and a table of metric conversion factors. 89 figs., 23 tabs.

  7. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

    Templeton, K.J.; Clary, J.L.

    1994-09-01

    This report describes a 30-year forecast of the solid waste volumes by container type. The volumes described are low-level mixed waste (LLMW) and transuranic/transuranic mixed (TRU/TRUM) waste. These volumes and their associated container types will be generated or received at the US Department of Energy Hanford Site for storage, treatment, and disposal at Westinghouse Hanford Company`s Solid Waste Operations Complex (SWOC) during a 30-year period from FY 1994 through FY 2023. The forecast data for the 30-year period indicates that approximately 307,150 m{sup 3} of LLMW and TRU/TRUM waste will be managed by the SWOC. The main container type for this waste is 55-gallon drums, which will be used to ship 36% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of 55-gallon drums is Past Practice Remediation. This waste will be generated by the Environmental Restoration Program during remediation of Hanford`s past practice sites. Although Past Practice Remediation is the primary generator of 55-gallon drums, most waste generators are planning to ship some percentage of their waste in 55-gallon drums. Long-length equipment containers (LECs) are forecasted to contain 32% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of LECs is the Long-Length Equipment waste generator, which is responsible for retrieving contaminated long-length equipment from the tank farms. Boxes are forecasted to contain 21% of the waste. These containers are primarily forecasted for use by the Environmental Restoration Operations--D&D of Surplus Facilities waste generator. This waste generator is responsible for the solid waste generated during decontamination and decommissioning (D&D) of the facilities currently on the Surplus Facilities Program Plan. The remaining LLMW and TRU/TRUM waste volume is planned to be shipped in casks and other miscellaneous containers.

  8. NREL: Resource Assessment and Forecasting - Capabilities

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Capabilities Best Practices Handbook Helps Industry Collect and Interpret Solar Resource Data Read about this new comprehensive resource for the solar industry. NREL's resource assessment and forecasting research staff provides expertise in renewable energy measurement and instrumentation. Major capabilities include solar resource measurement, instrument calibration, instrument characterization, solar monitoring training, and standards development and information dissemination. Solar Resource

  9. U.S. Regional Demand Forecasts Using NEMS and GIS

    SciTech Connect (OSTI)

    Cohen, Jesse A.; Edwards, Jennifer L.; Marnay, Chris

    2005-07-01

    The National Energy Modeling System (NEMS) is a multi-sector, integrated model of the U.S. energy system put out by the Department of Energy's Energy Information Administration. NEMS is used to produce the annual 20-year forecast of U.S. energy use aggregated to the nine-region census division level. The research objective was to disaggregate this regional energy forecast to the county level for select forecast years, for use in a more detailed and accurate regional analysis of energy usage across the U.S. The process of disaggregation using a geographic information system (GIS) was researched and a model was created utilizing available population forecasts and climate zone data. The model's primary purpose was to generate an energy demand forecast with greater spatial resolution than what is currently produced by NEMS, and to produce a flexible model that can be used repeatedly as an add-on to NEMS in which detailed analysis can be executed exogenously with results fed back into the NEMS data flow. The methods developed were then applied to the study data to obtain residential and commercial electricity demand forecasts. The model was subjected to comparative and statistical testing to assess predictive accuracy. Forecasts using this model were robust and accurate in slow-growing, temperate regions such as the Midwest and Mountain regions. Interestingly, however, the model performed with less accuracy in the Pacific and Northwest regions of the country where population growth was more active. In the future more refined methods will be necessary to improve the accuracy of these forecasts. The disaggregation method was written into a flexible tool within the ArcGIS environment which enables the user to output the results in five year intervals over the period 2000-2025. In addition, the outputs of this tool were used to develop a time-series simulation showing the temporal changes in electricity forecasts in terms of absolute, per capita, and density of demand.

  10. The forecast calls for flu

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Laboratory found a way to forecast the flu season and even next week's sickness trends. ... Laboratory found a way to forecast the flu season and even next week's sickness trends. ...

  11. A Public-Private-Academic Partnership to Advance Solar Power Forecasting |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy A Public-Private-Academic Partnership to Advance Solar Power Forecasting A Public-Private-Academic Partnership to Advance Solar Power Forecasting UCAR logo2.jpg The University Corporation for Atmospheric Research (UCAR) will develop a solar power forecasting system that advances the state of the science through cutting-edge research. APPROACH UCAR value chain.png The team will develop a solar power forecasting system that advances the state of the science through

  12. Operational forecasting based on a modified Weather Research and Forecasting model

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

  13. DOE Announces Webinars on Solar Forecasting Metrics, the DOE...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    The Energy Department will present a live webinar titled "Solar Forecasting Metrics" on Thursday, February 13, from 3:00 p.m. to 5:00 p.m. Eastern Standard Time. During this ...

  14. Comparison of Energy Efficiency Incentive Programs: Rebates and...

    Open Energy Info (EERE)

    Energy Efficiency, - Utility Topics: Environmental Website: www.sciencedirect.comsciencearticlepiiS0957178709000460 Equivalent URI: cleanenergysolutions.orgcontent...

  15. Expert Panel: Forecast Future Demand for Medical Isotopes | Department of

    Office of Environmental Management (EM)

    Energy Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes The Expert Panel has concluded that the Department of Energy and National Institutes of Health must develop the capability to produce a diverse supply of radioisotopes for medical use in quantities sufficient to support research and clinical activities. Such a capability would prevent shortages of isotopes, reduce American dependence on foreign radionuclide sources and

  16. Annual energy outlook 1994: With projections to 2010

    SciTech Connect (OSTI)

    Not Available

    1994-01-01

    The Annual Energy Outlook 1994 (AEO94) presents the midterm energy forecasts of the Energy Information Administration (EIA). This year`s report presents projects and analyses of energy supply, demand, and prices through 2010, based for the first time on results from the National Energy Modeling System (NEMS). NEMS is the latest in a series of computer-based energy modeling systems used over the past 2 decades by EIA and its predecessor organization, the Federal Energy Administration, to analyze and forecast energy consumption and supply in the midterm period (about 20 years). Quarterly forecasts of energy supply and demand for 1994 and 1995 are published in the Short-Term Energy Outlook (February 1994). Forecast tables for 2000, 2005, and 2010 for each of the five scenarios examined in the AEO94 are provided in Appendices A through E. The five scenarios include a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. Appendix F provides detailed comparisons of the AEO94 forecasts with those of other organizations. Appendix G briefly described the NEMS and the major AEO94 forecast assumptions. Appendix H summarizes the key results for the five scenarios.

  17. Pathways to low-cost electrochemical energy storage: a comparison...

    Office of Scientific and Technical Information (OSTI)

    Energy storage is increasingly seen as a valuable asset for electricity grids composed of high fractions of intermittent sources, such as wind power or, in developing economies, ...

  18. Cost and Performance Comparison Baseline for Fossil Energy Power...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    blocks together into a new, revolutionary concept for future coal-based power and energy production. Objective To establish baseline performance and cost estimates for today's...

  19. Deep Energy Retrofit Performance Metric Comparison: Eight California...

    Office of Scientific and Technical Information (OSTI)

    the home were performed and the homes were monitored for total and individual end-use energy consumption for approximately one year. Annual performance in site and source...

  20. Cost and Performance Comparison Baseline for Fossil Energy Plants...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    energy security. A broad portfolio of technologies is being developed within the Clean Coal Program to accomplish this objective. Ever increasing technological enhancements...

  1. An Energy Evolution:Alternative Fueled Vehicle Comparisons

    Broader source: Energy.gov [DOE]

    Presented at the U.S. Department of Energy Light Duty Vehicle Workshop in Washington, D.C. on July 26, 2010.

  2. Deep Energy Retrofit Performance Metric Comparison: Eight California...

    Office of Scientific and Technical Information (OSTI)

    switched from natural gas to electricity for heating and hot water, resulting in energy consumption dominated by electricity use. This demonstrates the crucial importance of...

  3. Comparison of International Energy Intensities across the G7 and other parts of Europe, including Ukraine

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Comparison of International Energy Intensities across the G7 and other parts of Europe, including Ukraine Elizabeth Sendich November 2014 Independent Statistics & Analysis www.eia.gov U.S. Energy Information Administration Washington, DC 20585 This paper is released to encourage discussion and critical comment. The analysis and conclusions expressed here are those of the authors and not necessarily those of the U.S. Energy Information Administration. WORKING PAPER SERIES November 2014

  4. Energy Use and Carbon Emissions: Some International Comparisons

    Reports and Publications (EIA)

    1994-01-01

    Presents energy use and carbon emissions patterns in a world context. The report contrasts trends in economically developed and developing areas of the world since 1970, presents a disaggregated view of the "Group of Seven" (G7) key industrialized countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and examines sectoral energy use patterns within each of the G7 countries.

  5. Using Wikipedia to forecast diseases

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Using Wikipedia to forecast diseases Using Wikipedia to forecast diseases Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. November 13, 2014 Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Contact Nancy Ambrosiano Communications Office (505)

  6. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01

    This presentation describes the importance of good forecasting for variable generation, the different approaches used by industry, and the importance of validated high-quality data.

  7. Using Wikipedia to forecast diseases

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    based on today's forecast." Del Valle and her team were able to successfully monitor influenza in the United States, Poland, Japan and Thailand, dengue fever in Brazil and...

  8. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System

    Office of Environmental Management (EM)

    Operations | Department of Energy Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Clean Power Research logo.jpg This project will address the need for a more accurate approach to forecasting net utility load by taking into consideration the contribution of customer-sited PV energy generation. Tasks within the project are designed to integrate novel PV power

  9. Comparison of energy efficiency between variable refrigerant flow systems and ground source heat pump systems

    SciTech Connect (OSTI)

    Hong, Tainzhen; Liu, Xaiobing

    2009-11-01

    With the current movement toward net zero energy buildings, many technologies are promoted with emphasis on their superior energy efficiency. The variable refrigerant flow (VRF) and ground source heat pump (GSHP) systems are probably the most competitive technologies among these. However, there are few studies reporting the energy efficiency of VRF systems compared with GSHP systems. In this article, a preliminary comparison of energy efficiency between the air-source VRF and GSHP systems is presented. The computer simulation results show that GSHP system is more energy efficient than the air-source VRF system for conditioning a small office building in two selected US climates. In general, GSHP system is more energy efficient than the air-source VRV system, especially when the building has significant heating loads. For buildings with less heating loads, the GSHP system could still perform better than the air-source VRF system in terms of energy efficiency, but the resulting energy savings may be marginal.

  10. Comparison of Real World Energy Consumption to Models and DOE Test Procedures

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Comparison of Real World Energy Consumption to Models and Department of Energy Test Procedures September 2011 i NOTICE This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness,

  11. Funding Opportunity Announcement for Wind Forecasting Improvement...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex...

  12. Upcoming Funding Opportunity for Wind Forecasting Improvement...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    for Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am...

  13. Comparison of efficiency: Cogeneration vs. utility-supplied energy

    SciTech Connect (OSTI)

    Kolanowski, B.F.

    1996-06-01

    In order to understand the benefits of cogeneration -- the on site production of electricity and hot water -- it is beneficial to know the overall efficiency of the energy media presently being used when compared to cogeneration. Virtually every commercial and industrial establishment purchases their electricity from the local utility company and heat their water by using on site boilers and hot water heaters fired by natural gas or propane -- which they also purchase from an outside supplier. When on-site cogeneration is compared to purchased power the results in fuel usage efficiency are: cogeneration -- 89.2%; purchased power -- 52.6%. The overall result of on site, properly applied cogeneration is an economical, environmental, and conservational tool that preserves an establishment`s cash, helps reduce pollution and conserves a precious natural resource.

  14. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J.

    2011-02-23

    The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

  15. Comparison of building energy use data between the United States and China

    SciTech Connect (OSTI)

    Xia , Jianjun; Hong , Tianzhen; Shen, Qi; Feng , Wei; Yang, Le; Im , Piljae; Lu, Alison; Bhandari , Mahabir

    2013-10-30

    Buildings in the United States and China consumed 41percent and 28percent of the total primary energy in 2011, respectively. Good energy data are the cornerstone to understanding building energy performance and supporting research, design, operation, and policy making for low energy buildings. This paper presents initial outcomes from a joint research project under the U.S.-China Clean Energy Research Center for Building Energy Efficiency. The goal is to decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders. This paper first reviews and compares several popular existing building energy monitoring systems in both countries. Next a standard energy data model is presented. A detailed, measured building energy data comparison was conducted for a few office buildings in both countries. Finally issues of data collection, quality, sharing, and analysis methods are discussed. It was found that buildings in both countries performed very differently, had potential for deep energy retrofit, but that different efficiency measures should apply.

  16. Science on Tap - Forecasting illness

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Science on Tap - Forecasting illness Science on Tap - Forecasting illness WHEN: Mar 17, 2016 5:30 PM - 7:00 PM WHERE: UnQuarked Wine Room 145 Central Park Square, Los Alamos, New Mexico 87544 USA CONTACT: Linda Anderman (505) 665-9196 CATEGORY: Bradbury INTERNAL: Calendar Login Event Description Mark your calendars for this event held every third Thursday from 5:30 to 7 p.m. A short presentation is followed by a lively discussion on a different subject each month. Forecasting the flu (and other

  17. Economic Evaluation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results; Preprint

    SciTech Connect (OSTI)

    Orwig, K.; Hodge, B. M.; Brinkman, G.; Ela, E.; Milligan, M.; Banunarayanan, V.; Nasir, S.; Freedman, J.

    2012-09-01

    Historically, a number of wind energy integration studies have investigated the value of using day-ahead wind power forecasts for grid operational decisions. These studies have shown that there could be large cost savings gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter-term (0 to 6-hour-ahead) wind power forecasts. In 2010, the Department of Energy and National Oceanic and Atmospheric Administration partnered to fund improvements in short-term wind forecasts and to determine the economic value of these improvements to grid operators, hereafter referred to as the Wind Forecasting Improvement Project (WFIP). In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined, then the economic results of a production cost model simulation are analyzed.

  18. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

    Lew, D.; Milligan, M.; Jordan, G.; Piwko, R.

    2011-04-01

    This study, building on the extensive models developed for the Western Wind and Solar Integration Study (WWSIS), uses these WECC models to evaluate the operating cost impacts of improved day-ahead wind forecasts.

  19. Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Technology | Department of Energy Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting Technology Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting Technology IBM logo.png As part of this project, new solar forecasting technology will be developed that leverages big data processing, deep machine learning, and cloud modeling integrated in a universal platform with an open architecture. Similar to the Watson computer system, this proposed technology

  20. Energy use in Poland, 1970--1991: Sectoral analysis and international comparison

    SciTech Connect (OSTI)

    Meyers, S.; Schipper, L.; Salay, J.

    1993-07-01

    This report provides an analysis of how and why energy use has changed in Poland since the 1970s, with particular emphasis on changes since the country began its transition from a centrally planned to a market economy in 1989. The most important factors behind the large decline in Polish energy use in 1990 were a sharp fall in industrial output and a huge drop in residential coal use driven by higher prices. The structural shift away from heavy industry was slight. Key factors that worked to increase energy use were the rise in energy intensity in many heavy industries and the shift toward more energy intensive modes of transport. The growth in private activities in 1991 was nearly sufficient to balance out continued decline in industrial energy use in that year. We compared energy use in Poland and the factors that shape it with similar elements in the West. We made a number of modifications to the Polish energy data to bring it closer to a Western energy accounting framework, and augmented these with a variety of estimates in order to construct a sufficiently detailed portrait of Polish energy use to allow comparison with Western data. Per capita energy use in Poland was not much below W. European levels despite Poland`s much lower GDP per capita. Poland has comparatively high energy intensities in manufacturing and residential space heating, and a large share of heavy industries in manufacturing output, all factors that contribute to higher energy use per capita. The structure of passenger and freight transportation and the energy intensity of automobiles contribute to lower energy use per capita in Poland than in Western Europe, but the patterns in Poland are moving closer to those that prevail in the West.

  1. Resource Information and Forecasting Group; Electricity, Resources, & Building Systems Integration (ERBSI) (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2009-11-01

    Researchers in the Resource Information and Forecasting group at NREL provide scientific, engineering, and analytical expertise to help characterize renewable energy resources and facilitate the integration of these clean energy sources into the electricity grid.

  2. U.S. Crude Oil Production Forecast-Analysis of Crude Types

    U.S. Energy Information Administration (EIA) Indexed Site

    of Energy Washington, DC 20585 U.S. Energy Information Administration | U.S. Crude Oil Production Forecast-Analysis of Crude Types i This report was prepared by the U.S....

  3. Improving energy efficiency via smart building energy management systems. A comparison with policy measures

    SciTech Connect (OSTI)

    Rocha, Paula; Siddiqui, Afzal; Stadler, Michael

    2014-12-09

    In this study, to foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end, we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures.

  4. Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-08-13

    Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e.g., futures, swaps, and fixed-price physical supply contracts) to contemporaneous forecasts of spot natural gas prices, with the purpose of identifying any systematic differences between the two. Although our data set is quite limited, we find that over the past three years, forward gas prices for durations of 2-10 years have been considerably higher than most natural gas spot price forecasts, including the reference case forecasts developed by the Energy Information Administration (EIA). This difference is striking, and implies that resource planning and modeling exercises based on these forecasts over the past three years have yielded results that are biased in favor of gas-fired generation (again, presuming that long-term stability is desirable). As discussed later, these findings have important ramifications for resource planners, energy modelers, and policy-makers.

  5. DOE Announces Webinars on Solar Forecasting Metrics, the DOE Wind Vision,

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    and More | Department of Energy Solar Forecasting Metrics, the DOE Wind Vision, and More DOE Announces Webinars on Solar Forecasting Metrics, the DOE Wind Vision, and More February 12, 2014 - 7:38pm Addthis EERE offers webinars to the public on a range of subjects, from adopting the latest energy efficiency and renewable energy technologies to training for the clean energy workforce. Webinars are free; however, advanced registration is typically required. You can also watch archived webinars

  6. Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

    SciTech Connect (OSTI)

    Hodge, B.

    2013-12-01

    Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

  7. A Comparison of Iron and Steel Production Energy Use and Energy Intensity in China and the U.S.

    SciTech Connect (OSTI)

    Hasanbeigi, Ali; Price, Lynn; Aden, Nathaniel; Chunxia, Zhang; Xiuping, Li; Fangqin, Shangguan

    2011-06-15

    Production of iron and steel is an energy-intensive manufacturing process. In 2006, the iron and steel industry accounted for 13.6% and 1.4% of primary energy consumption in China and the U.S., respectively (U.S. DOE/EIA, 2010a; Zhang et al., 2010). The energy efficiency of steel production has a direct impact on overall energy consumption and related carbon dioxide (CO2) emissions. The goal of this study is to develop a methodology for making an accurate comparison of the energy intensity (energy use per unit of steel produced) of steel production. The methodology is applied to the steel industry in China and the U.S. The methodology addresses issues related to boundary definitions, conversion factors, and indicators in order to develop a common framework for comparing steel industry energy use. This study uses a bottom-up, physical-based method to compare the energy intensity of China and U.S. crude steel production in 2006. This year was chosen in order to maximize the availability of comparable steel-sector data. However, data published in China and the U.S. are not always consistent in terms of analytical scope, conversion factors, and information on adoption of energy-saving technologies. This study is primarily based on published annual data from the China Iron & Steel Association and National Bureau of Statistics in China and the Energy Information Agency in the U.S. This report found that the energy intensity of steel production is lower in the United States than China primarily due to structural differences in the steel industry in these two countries. In order to understand the differences in energy intensity of steel production in both countries, this report identified key determinants of sector energy use in both countries. Five determinants analyzed in this report include: share of electric arc furnaces in total steel production, sector penetration of energy-efficiency technologies, scale of production equipment, fuel shares in the iron and steel industry, and final steel product mix in both countries. The share of lower energy intensity electric arc furnace production in each country was a key determinant of total steel sector energy efficiency. Overall steel sector structure, in terms of average plant vintage and production capacity, is also an important variable though data were not available to quantify this in a scenario. The methodology developed in this report, along with the accompanying quantitative and qualitative analyses, provides a foundation for comparative international assessment of steel sector energy intensity.

  8. Actual and Estimated Energy Savings Comparison for Deep Energy Retrofits in the Pacific Northwest

    SciTech Connect (OSTI)

    Blanchard, Jeremy; Widder, Sarah H.; Giever, Elisabeth L.; Baechler, Michael C.

    2012-10-01

    Seven homes from the Pacific Northwest were selected to evaluate the differences between estimated and actual energy savings achieved from deep energy retrofits. The energy savings resulting from these retrofits were estimated, using energy modeling software, to save at least 30% on a whole-house basis. The modeled pre-retrofit energy use was trued against monthly utility bills. After the retrofits were completed, each of the homes was extensively monitored, with the exception of one home which was monitored pre-retrofit. This work is being conducted by Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy Building Technologies Program as part of the Building America Program. This work found many discrepancies between actual and estimated energy savings and identified the potential causes for the discrepancies. The differences between actual energy use and modeled energy use also suggest improvements to improve model accuracy. The difference between monthly whole-house actual and estimated energy savings ranged from 75% more energy saved than predicted by the model to 16% less energy saved for all the monitored homes. Similarly, the annual energy savings difference was between 36% and -14%, which was estimated based on existing monitored savings because an entire year of data is not available. Thus, on average, for all six monitored homes the actual energy use is consistently less than estimates, indicating home owners are saving more energy than estimated. The average estimated savings for the eight month monitoring period is 43%, compared to an estimated savings average of 31%. Though this average difference is only 12%, the range of inaccuracies found for specific end-uses is far greater and are the values used to directly estimate energy savings from specific retrofits. Specifically, the monthly post-retrofit energy use differences for specific end-uses (i.e., heating, cooling, hot water, appliances, etc.) ranged from 131% under-predicted to 77% over-predicted by the model with respect to monitored energy use. Many of the discrepancies were associated with occupant behavior which influences energy use, dramatically in some cases, actual versus modeled weather differences, modeling input limitations, and complex homes that are difficult to model. The discrepancy between actual and estimated energy use indicates a need for better modeling tools and assumptions. Despite the best efforts of researchers, the estimated energy savings are too inaccurate to determine reliable paybacks for retrofit projects. While the monitored data allows researchers to understand why these differences exist, it is not cost effective to monitor each home with the level of detail presented here. Therefore an appropriate balance between modeling and monitoring must be determined for more widespread application in retrofit programs and the home performance industry. Recommendations to address these deficiencies include: (1) improved tuning process for pre-retrofit energy use, which currently utilized broad-based monthly utility bills; (2) developing simple occupant-based energy models that better address the many different occupant types and their impact on energy use; (3) incorporating actual weather inputs to increase accuracy of the tuning process, which uses utility bills from specific time period; and (4) developing simple, cost-effective monitoring solutions for improved model tuning.

  9. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

    Bird, L.; Holt, E.; Sumner, J.; Kreycik, C.

    2010-05-01

    Various factors influence the development of the voluntary 'green' power market--the market in which consumers purchase or produce power from non-polluting, renewable energy sources. These factors include climate policies, renewable portfolio standards (RPS), renewable energy prices, consumers' interest in purchasing green power, and utilities' interest in promoting existing programs and in offering new green options. This report presents estimates of voluntary market demand for green power through 2015 that were made using historical data and three scenarios: low-growth, high-growth, and negative-policy impacts. The resulting forecast projects the total voluntary demand for renewable energy in 2015 to range from 63 million MWh annually in the low case scenario to 157 million MWh annually in the high case scenario, representing an approximately 2.5-fold difference. The negative-policy impacts scenario reflects a market size of 24 million MWh. Several key uncertainties affect the results of this forecast, including uncertainties related to growth assumptions, the impacts that policy may have on the market, the price and competitiveness of renewable generation, and the level of interest that utilities have in offering and promoting green power products.

  10. Picture of the Week: Forecasting Flu

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3 Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? March 6, 2016 flu epidemics modellled using social media Watch the video on YouTube. Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? Using real-time data from Wikipedia and social media, Sara del

  11. Improving energy efficiency via smart building energy management systems. A comparison with policy measures

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Rocha, Paula; Siddiqui, Afzal; Stadler, Michael

    2014-12-09

    In this study, to foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end,more » we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures.« less

  12. Comparison of Real World Energy Consumption to Models and Department of Energy Test Procedures

    SciTech Connect (OSTI)

    Goetzler, William; Sutherland, Timothy; Kar, Rahul; Foley, Kevin

    2011-09-01

    This study investigated the real-world energy performance of appliances and equipment as it compared with models and test procedures. The study looked to determine whether the U.S. Department of Energy and industry test procedures actually replicate real world conditions, whether performance degrades over time, and whether installation patterns and procedures differ from the ideal procedures. The study first identified and prioritized appliances to be evaluated. Then, the study determined whether real world energy consumption differed substantially from predictions and also assessed whether performance degrades over time. Finally, the study recommended test procedure modifications and areas for future research.

  13. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

    SciTech Connect (OSTI)

    Mellit, Adel; Pavan, Alessandro Massi

    2010-05-15

    Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became - with reference to the Grid Connected Photovoltaic Plants (GCPV) - fundamental in making power dispatching plans and - with reference to stand alone and hybrid systems - also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45 40'N, longitude 13 46'E), Italy. In order to check the generalization capability of the MLP-forecaster, a K-fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98-99% for sunny days and 94-96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model. (author)

  14. Wind Forecast Improvement Project Southern Study Area Final Report |

    Broader source: Energy.gov (indexed) [DOE]

    Department of Energy PDF icon Wind Forecast Improvement Project Southern Study Area Final Report.pdf More Documents & Publications QER - Comment of Edison Electric Institute (EEI) 1 QER - Comment of Canadian Hydropower Association QER - Comment of Edison Electric Institute (EEI) 2

  15. Forecasting Water Quality & Biodiversity

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability Platform Review Principle Investigator: Dr. Henriette I. Jager Organization: Oak Ridge National Laboratory This presentation does not contain any proprietary, confidential, or otherwise restricted information 2015 DOE Bioenergy Technologies Office (BETO) Project Peer Review Goal Statement Addresses the following MYPP BETO goals:  Advance scientific methods and models for measuring and understanding

  16. Short-term energy outlook, Annual supplement 1995

    SciTech Connect (OSTI)

    1995-07-25

    This supplement is published once a year as a complement to the Short- Term Energy Outlook, Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts. Chap. 2 analyzes the response of the US petroleum industry to the recent four Federal environmental rules on motor gasoline. Chap. 3 compares the EIA base or mid case energy projections for 1995 and 1996 (as published in the first quarter 1995 Outlook) with recent projections made by four other major forecasting groups. Chap. 4 evaluates the overall accuracy. Chap. 5 presents the methology used in the Short- Term Integrated Forecasting Model for oxygenate supply/demand balances. Chap. 6 reports theoretical and empirical results from a study of non-transportation energy demand by sector. The empirical analysis involves the short-run energy demand in the residential, commercial, industrial, and electrical utility sectors in US.

  17. The Value of Improved Short-Term Wind Power Forecasting

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    The Value of Improved Short- Term Wind Power Forecasting B.-M. Hodge and A. Florita National Renewable Energy Laboratory J. Sharp Sharply Focused, LLC M. Margulis and D. Mcreavy Lockheed Martin Technical Report NREL/TP-5D00-63175 February 2015 NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL)

  18. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

    Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V.

    2011-11-29

    The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

  19. Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM Youtube Video | Courtesy of IBM Remember when IBM's super computer Watson defeated Jeopardy! champions Ken Jennings and Brad Rutter? With funding from the U.S. Department of Energy SunShot Initiative, IBM researchers are using Watson-like technology to improve solar forecasting accuracy by as much

  20. Renewable Energy and Climate Change

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... infrastructure * Generation flexibility * Energy storage technologies * Demand side management * Improved forecasting and operational planning methods Check the ...

  1. Aerial Measuring System (AMS)/Israel Atomic Energy Commission (IAEC) Joint Comparison Study Report

    SciTech Connect (OSTI)

    Wasiolek, P.; Halevy, I.

    2013-12-23

    Under the 13th Bilateral Meeting to Combat Nuclear Terrorism conducted on January 89, 2013, the committee approved the development of a cost-effective proposal to conduct a Comparison Study of the Aerial Measuring System (AMS) of the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA) and Israel Atomic Energy Commission (IAEC). The study was to be held at the Remote Sensing Laboratory (RSL), Nellis Air Force Base, Las Vegas, Nevada, with measurements at the Nevada National Security Site (NNSS). The goal of the AMS and the IAEC joint survey was to compare the responses of the two agencies aerial radiation detection systems to varied radioactive surface contamination levels and isotopic composition experienced at the NNSS, and the differing data processing techniques utilized by the respective teams. Considering that for the comparison both teams were using custom designed and built systems, the main focus of the short campaign was to investigate the impact of the detector size and data analysis techniques used by both teams. The AMS system, SPectral Advanced Radiological Computer System, Model A (SPARCS-A), designed and built by RSL, incorporates four different size sodium iodide (NaI) crystals: 1" 1", 2" 4" 4", 2" 4" 16", and an up-looking 2" 4" 4". The Israel AMS System, Air RAM 2000, was designed by the IAEC Nuclear Research Center Negev (NRCN) and built commercially by ROTEM Industries (Israel) and incorporates two 2" diameter 2" long NaI crystals. The operational comparison was conducted at RSL-Nellis in Las Vegas, Nevada, during week of June 2427, 2013. The Israeli system, Air RAM 2000, was shipped to RSL-Nellis and mounted together with the DOE SPARCS on a DOE Bell-412 helicopter for a series of aerial comparison measurements at local test ranges, including the Desert Rock Airport and Area 3 at the NNSS. A 4-person Israeli team from the IAEC NRCN supported the activity together with 11 members of the RSL team, which consisted of pilots, mechanics, scientists, a data analyst, equipment operators, and operation specialists. All planned flight activities followed by scientific discussions on the collected data were completed. For IAEC, the joint survey provided an opportunity to characterize their systems response to extended sources of various fission products at the NNSS. As both systems play an important role in their respective countries (United States and Israel) national framework of radiological emergency response and are subject to multiple mutual cooperation agreements, it was important for each country to obtain more thorough knowledge of how they would employ these important assets and define the roles that they would each play in an actual response.

  2. Supply Forecast and Analysis (SFA)

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Matthew Langholtz Science Team Leader Oak Ridge National Laboratory DOE Bioenergy Technologies Office (BETO) 2015 Project Peer Review Supply Forecast and Analysis (SFA) 2 | Bioenergy Technologies Office Goal Statement * Provide timely and credible estimates of feedstock supplies and prices to support - the development of a bioeconomy; feedstock demand analysis of EISA, RFS2, and RPS mandates - the data and analysis of other projects in Analysis and Sustainability, Feedstock Supply and Logistics,

  3. Onsemble | Open Energy Information

    Open Energy Info (EERE)

    Colorado Zip: 80302 Region: Rockies Area Sector: Wind energy Product: wind energy forecasting Website: www.onsemble.ws Coordinates: 40.010492, -105.276843 Show Map Loading...

  4. ARM - CARES - Tracer Forecast for CARES

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    CampaignsCarbonaceous Aerosols and Radiative Effects Study (CARES)Tracer Forecast for CARES Related Links CARES Home AAF Home ARM Data Discovery Browse Data Post-Campaign Data Sets Field Updates CARES Wiki Campaign Images Experiment Planning Proposal Abstract and Related Campaigns Science Plan Operations Plan Measurements Forecasts News News & Press Backgrounder (PDF, 1.45MB) G-1 Aircraft Fact Sheet (PDF, 1.3MB) Contacts Rahul Zaveri, Lead Scientist Tracer Forecasts for CARES This webpage

  5. UPF Forecast | Y-12 National Security Complex

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    UPF Forecast UPF Forecast UPF Procurement provides the following forecast of subcontracting opportunities. Keep in mind that these requirements may be revised or cancelled, depending on program budget funding or departmental needs. If you have questions or would like to express an interest in any of the opportunities listed below, contact UPF Procurement. Descriptiona Methodb NAICS Est. Dollar Range RFP release/ Award datec Buyer/ Phone Commodities Equipment Rental FOC 238910 TBD 3Q FY15/ 3Q

  6. Forecast and Funding Arrangements - Hanford Site

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Annual Waste Forecast and Funding Arrangements About Us Hanford Site Solid Waste Acceptance Program What's New Acceptance Criteria Acceptance Process Becoming a new Hanford...

  7. California's 6th congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    in California's 6th congressional district A10 Power Akuacom Alternative Energy Inc Bio Energy Systems LLC Bioil Energy Matters LLC Enphase Energy Inc Forecast Energy Geysers...

  8. NREL: Resource Assessment and Forecasting - Data and Resources

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Data and Resources National Solar Radiation Database NREL resource assessment and forecasting research information is available from the following sources. Renewable Resource Data Center (RReDC) Provides information about biomass, geothermal, solar, and wind energy resources. Measurement and Instrumentation Data Center Provides irradiance and meteorological data from stations throughout the United States. Baseline Measurement System (BMS) Provides live solar radiation data from approximately 70

  9. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    SciTech Connect (OSTI)

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  10. Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Florita, A.

    2013-05-01

    Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

  11. Nuclear Theory Helps Forecast Neutron Star Temperatures | U.S. DOE Office

    Office of Science (SC) Website

    of Science (SC) Nuclear Theory Helps Forecast Neutron Star Temperatures Nuclear Physics (NP) NP Home About Research Facilities Science Highlights Benefits of NP Funding Opportunities Nuclear Science Advisory Committee (NSAC) Community Resources Contact Information Nuclear Physics U.S. Department of Energy SC-26/Germantown Building 1000 Independence Ave., SW Washington, DC 20585 P: (301) 903-3613 F: (301) 903-3833 E: Email Us More Information » 05.01.14 Nuclear Theory Helps Forecast Neutron

  12. Assessment and comparison of waste management costs for nuclear and fossil energy sources

    SciTech Connect (OSTI)

    Long, F.G.; Zaccai, H.; Ward, R.D.; McNicholas, P.; Albers, R.W.

    1993-12-31

    This paper presents the key results of an assessment of waste management costs undertaken by a group of international experts on behalf of the IAEA, Vienna. The objective of this work is to provide an assessment and comparison of the impact of waste management on the cost of electricity production from nuclear and other energy sources. The study focuses on the cost of managing wastes arising from the production of electricity from a PWR, with and without reprocessing, a coal-fueled conventional steam cycle, and a gas-fueled combined cycle; using data available in the open literature. This study has only assessed the impact of those waste management costs which are typically internalized by an electric utility and passed on as part of the price charged to customers. The data utilized in the study is typically in range form, reflecting worldwide experience with such factors as technology, regulatory requirements and economic parameters. To the extent that estimates can be identified in the literature the study has attempted to include costs associated with waste management from all stages of the fuel cycles. This paper also includes a discussion of future developments which may influence the results of this work including the effect of technology advances and changes in regulatory requirements.

  13. Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets

    SciTech Connect (OSTI)

    Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

    2005-06-30

    The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

  14. Property:ProgramSector | Open Energy Information

    Open Energy Info (EERE)

    + AGI-32 + Energy + ANL Wind Power Forecasting and Electricity Markets + Energy + APEC-Alternative Transport Fuels: Implementation Guidelines + Energy + APFED-Good Practice...

  15. NREL: Energy Analysis - Carolyn Davidson

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Carolyn Davidson Photo of Carolyn Davidson Carolyn Davidson is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Economic Analyst On...

  16. Comparison of the ENERGYGAUGE USA and BEopt Building Energy Simulation Programs

    SciTech Connect (OSTI)

    Parker, Danny S.; Cummings, Jamie E.

    2009-08-01

    This report compares two hourly energy simulation softwares, BEopt and Energy Gauge USA, to ensure accuracy and evaluate agreement on the impact of various energy efficiency improvements.

  17. Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

    SciTech Connect (OSTI)

    Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.

    2013-11-01

    As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

  18. A theoretical comparison of x-ray angiographic image quality using energy-dependent and conventional subtraction methods

    SciTech Connect (OSTI)

    Tanguay, Jesse; Kim, Ho Kyung; Cunningham, Ian A.

    2012-01-15

    Purpose: X-ray digital subtraction angiography (DSA) is widely used for vascular imaging. However, the need to subtract a mask image can result in motion artifacts and compromised image quality. The current interest in energy-resolving photon-counting (EPC) detectors offers the promise of eliminating motion artifacts and other advanced applications using a single exposure. The authors describe a method of assessing the iodine signal-to-noise ratio (SNR) that may be achieved with energy-resolved angiography (ERA) to enable a direct comparison with other approaches including DSA and dual-energy angiography for the same patient exposure. Methods: A linearized noise-propagation approach, combined with linear expressions of dual-energy and energy-resolved imaging, is used to describe the iodine SNR. The results were validated by a Monte Carlo calculation for all three approaches and compared visually for dual-energy and DSA imaging using a simple angiographic phantom with a CsI-based flat-panel detector. Results: The linearized SNR calculations show excellent agreement with Monte Carlo results. While dual-energy methods require an increased tube heat load of 2x to 4x compared to DSA, and photon-counting detectors are not yet ready for angiographic imaging, the available iodine SNR for both methods as tested is within 10% of that of conventional DSA for the same patient exposure over a wide range of patient thicknesses and iodine concentrations. Conclusions: While the energy-based methods are not necessarily optimized and further improvements are likely, the linearized noise-propagation analysis provides the theoretical framework of a level playing field for optimization studies and comparison with conventional DSA. It is concluded that both dual-energy and photon-counting approaches have the potential to provide similar angiographic image quality to DSA.

  19. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Offshore Lubricants Market Forecast Home There are currently no posts in this category. Syndicate...

  20. Coal Fired Power Generation Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Coal Fired Power Generation Market Forecast Home There are currently no posts in this category. Syndicate...

  1. Distribution of Wind Power Forecasting Errors from Operational Systems (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Ela, E.; Milligan, M.

    2011-10-01

    This presentation offers new data and statistical analysis of wind power forecasting errors in operational systems.

  2. Metrics for Evaluating the Accuracy of Solar Power Forecasting (Presentation)

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B.; Florita, A.; Lu, S.; Hamann, H.; Banunarayanan, V.

    2013-10-01

    This presentation proposes a suite of metrics for evaluating the performance of solar power forecasting.

  3. Assessment of the possibility of forecasting future natural gas curtailments

    SciTech Connect (OSTI)

    Lemont, S.

    1980-01-01

    This study provides a preliminary assessment of the potential for determining probabilities of future natural-gas-supply interruptions by combining long-range weather forecasts and natural-gas supply/demand projections. An illustrative example which measures the probability of occurrence of heating-season natural-gas curtailments for industrial users in the southeastern US is analyzed. Based on the information on existing long-range weather forecasting techniques and natural gas supply/demand projections enumerated above, especially the high uncertainties involved in weather forecasting and the unavailability of adequate, reliable natural-gas projections that take account of seasonal weather variations and uncertainties in the nation's energy-economic system, it must be concluded that there is little possibility, at the present time, of combining the two to yield useful, believable probabilities of heating-season gas curtailments in a form useful for corporate and government decision makers and planners. Possible remedial actions are suggested that might render such data more useful for the desired purpose in the future. The task may simply require the adequate incorporation of uncertainty and seasonal weather trends into modeling systems and the courage to report projected data, so that realistic natural gas supply/demand scenarios and the probabilities of their occurrence will be available to decision makers during a time when such information is greatly needed.

  4. Comparison of Clean Diesel Buses to CNG Buses | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Comparison of Clean Diesel Buses to CNG Buses Comparison of Clean Diesel Buses to CNG Buses 2003 DEER Conference Presentation: New York City Transit Department of Buses PDF icon deer_2003_lowell.pdf More Documents & Publications Comparative Study on Exhaust Emissions from Diesel- and CNG-Powered Urban Buses Summary of Swedish Experiences on CNG and "Clean" Diesel Buses CNG and Diesel Transite Bus Emissions in Review

  5. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01

    This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

  6. EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy

    Office of Environmental Management (EM)

    Improved by 30% | Department of Energy Forecasting Gets a Boost from Watson, Accuracy Improved by 30% EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM Youtube Video | Courtesy of IBM Remember when IBM's super computer Watson defeated Jeopardy! champions Ken Jennings and Brad Rutter? With funding from the U.S. Department of Energy SunShot Initiative, IBM researchers are using Watson-like technology to improve solar

  7. Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Florita, A.; Hodge, B. M.; Freedman, J.

    2014-05-01

    The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.

  8. National Renewable Energy Laboratory Pyrheliometer Comparisons: 24 September - 5 October 2012 (NPC-2012)

    SciTech Connect (OSTI)

    Stoffel, T.; Reda, I.

    2013-05-01

    The NREL Pyrheliometer Comparisons for 2012 (NPC-2012) were held at the Solar Radiation Research Laboratory in Golden, Colorado, from September 24 through October 5 for the purpose of transferring the World Radiometric Reference (WRR) to participating instrument. Twenty scientists and engineers operated 32 absolute cavity radiometers and 18 conventional thermopile-based pyrheliometers to simultaneously measure clear-sky direct normal irradiance during the comparisons. The transfer standard group of reference radiometers for NPC-2012 consisted of four NREL radiometers with direct traceability to the WRR, having participated in the Eleventh International Pyrheliometer Comparisons (IPC-XI) hosted by the World Radiation Center in the fall of 2010. As the result of NPC-2012, each participating absolute cavity radiometer was assigned a new WRR transfer factor, computed as the reference irradiance computed by the transfer standard group divided by the observed irradiance from the participating radiometer. The performance of the transfer standard group during NPC-2012 was consistent with previous comparisons, including IPC-XI. The measurement performance of the transfer standard group allowed the transfer of the WRR to each participating radiometer with an estimated uncertainty of +/- 0.33% with respect to the International System of Units.

  9. Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)

    Reports and Publications (EIA)

    1998-01-01

    The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

  10. EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day

    Gasoline and Diesel Fuel Update (EIA)

    EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day The forecast for U.S. crude oil production keeps going higher. The U.S. Energy Information Administration revised upward its projection for crude oil output in 2013 by 70,000 barrels per day and for next year by 190,000 barrels per day. U.S. oil production is now on track to average 7.5 million barrels per day this year and rise to 8.4 million barrels per day in 2014, according to EIA's latest monthly forecast.

  11. Comparisons of HVAC Simulations between EnergyPlus and DOE-2.2 for Data Centers

    SciTech Connect (OSTI)

    Hong, Tianzhen; Sartor, Dale; Mathew, Paul; Yazdanian, Mehry

    2008-08-13

    This paper compares HVAC simulations between EnergyPlus and DOE-2.2 for data centers. The HVAC systems studied in the paper are packaged direct expansion air-cooled single zone systems with and without air economizer. Four climate zones are chosen for the study - San Francisco, Miami, Chicago, and Phoenix. EnergyPlus version 2.1 and DOE-2.2 version 45 are used in the annual energy simulations. The annual cooling electric consumption calculated by EnergyPlus and DOE-2.2 are reasonablely matched within a range of -0.4percent to 8.6percent. The paper also discusses sources of differences beween EnergyPlus and DOE-2.2 runs including cooling coil algorithm, performance curves, and important energy model inputs.

  12. Comparison of Real World Energy Consumption to Models and DOE Test Procedures

    Broader source: Energy.gov [DOE]

    This study investigates the real-world energy performance of appliances and equipment as it compares with models and test procedures.

  13. Are there Gains from Pooling Real-Time Oil Price Forecasts?

    Gasoline and Diesel Fuel Update (EIA)

    Are there Gains from Pooling Real- Time Oil Price Forecasts? Christiane Baumeister, Bank of Canada Lutz Kilian, University of Michigan Thomas K. Lee, U.S. Energy Information Administration February 12, 2014 Independent Statistics & Analysis www.eia.gov U.S. Energy Information Administration Washington, DC 20585 This paper is released to encourage discussion and critical comment. The analysis and conclusions expressed here are those of the authors and not necessarily those of the U.S. Energy

  14. Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building

    SciTech Connect (OSTI)

    Dudley, Junqiao Han; Black, Doug; Apte, Mike; Piette, Mary Ann; Berkeley, Pam

    2010-05-14

    We have studied a low energy building on a campus of the University of California. It has efficient heating, ventilation, and air conditioning (HVAC) systems, consisting of a dual-fan/dual-duct variable air volume (VAV) system. As a major building on the campus, it was included in two demand response (DR) events in the summers of 2008 and 2009. With chilled water supplied by thermal energy storage in the central plant, cooling fans played a critical role during DR events. In this paper, an EnergyPlus model of the building was developed and calibrated. We compared both whole-building and HVAC fan energy consumption with model predictions to understand why demand savings in 2009 were much lower than in 2008. We also used model simulations of the study building to assess pre-cooling, a strategy that has been shown to improve demand saving and thermal comfort in many types of building. This study indicates a properly calibrated EnergyPlus model can reasonably predict demand savings from DR events and can be useful for designing or optimizing DR strategies.

  15. Solar Forecasting Gets a Boost from Watson, Accuracy Improved...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM ...

  16. A Review of Variable Generation Forecasting in the West: July...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    rely on an array of VG forecasts suited to different purposes. Some of the most common types of VG forecasts are defined below: 2 This report is available at no cost from the...

  17. PBL FY 2003 Second Quarter Review Forecast of Generation Accumulated...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    the rate period (i.e., FY 2002-2006), a forecast of that end-of-year Accumulated Net Revenue (ANR) will be completed. If the ANR at the end of the forecast year falls below the...

  18. Gasoline price forecast to stay below 3 dollar a gallon in 2015

    Gasoline and Diesel Fuel Update (EIA)

    Gasoline price forecast to stay below $3 a gallon in 2015 The national average pump price of gasoline is expected to stay below $3 per gallon during 2015. In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular gasoline should average $2.33 per gallon this year. The price of gasoline increased in early February after falling for 17 weeks in a row. But gasoline prices will continue to remain low in 2015 when compared with pump prices in recent

  19. RES Anatolia | Open Energy Information

    Open Energy Info (EERE)

    navigation, search Name: RES Anatolia Place: Istanbul, Turkey Zip: 34398 Sector: Solar, Wind energy Product: Istanbul-based subsidiary formed due to positive forecasts for the...

  20. AL PRO | Open Energy Information

    Open Energy Info (EERE)

    search Name: AL-PRO Place: Grossheide, Lower Saxony, Germany Zip: 26532 Sector: Wind energy Product: AL-PRO is an inndependent expert office for wind forecasts, wind...

  1. NREL: Energy Analysis - David Palchak

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Electrical load forecasting with artificial neural networks Demand-side management optimization with Matlab Primary research interests Demand response and renewable energy ...

  2. Buildings Energy Data Book: 1.5 Generic Fuel Quad and Comparison

    Buildings Energy Data Book [EERE]

    2 Consumption Comparisons in 2010 One quad equals: - 50.2 million short tons of coal = enough coal to fill a train of railroad cars 4,123 miles long (about one and a half times across the U.S.) - 974.7 billion cubic feet natural gas - 8.2 billion gallons of gasoline = 21.2 days of U.S. gasoline use = 22.89 million passenger cars each driven 12,400 miles = 20.12 million light-duty vehicles each driven 12,200 miles = all new passenger cars sold, each driven 50,000 miles = 13.69 million stock

  3. Buildings Energy Data Book: 1.5 Generic Fuel Quad and Comparison

    Buildings Energy Data Book [EERE]

    3 Carbon Emission Comparisons One million metric tons of carbon dioxide-equivalent emissions equals: - the combustion of 530 thousand short tons of coal - the coal input to 1 coal plant (200-MW) in about 1 year - the combustion of 18 billion cubic feet of natural gas - the combustion of 119 million gallons of gasoline = the combustion of gasoline for 7 hours in the U.S. = 323 thousand new cars, each driven 12,400 miles = 282 thousand new light-duty vehicles, each driven 12,200 miles = 274

  4. Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01

    This presentation presents some statistical analysis of wind power forecast errors and error distributions, with examples using ERCOT data.

  5. Wind power forecasting in U.S. electricity markets.

    SciTech Connect (OSTI)

    Botterud, A.; Wang, J.; Miranda, V.; Bessa, R. J.; Decision and Information Sciences; INESC Porto

    2010-04-01

    Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts.

  6. Wind power forecasting in U.S. Electricity markets

    SciTech Connect (OSTI)

    Botterud, Audun; Wang, Jianhui; Miranda, Vladimiro; Bessa, Ricardo J.

    2010-04-15

    Wind power forecasting is becoming an important tool in electricity markets, but the use of these forecasts in market operations and among market participants is still at an early stage. The authors discuss the current use of wind power forecasting in U.S. ISO/RTO markets, and offer recommendations for how to make efficient use of the information in state-of-the-art forecasts. (author)

  7. Combined Heat And Power Installation Market Forecast | OpenEI...

    Open Energy Info (EERE)

    Combined Heat And Power Installation Market Forecast Home There are currently no posts in this category. Syndicate...

  8. Uncertainty Reduction in Power Generation Forecast Using Coupled

    Office of Scientific and Technical Information (OSTI)

    Wavelet-ARIMA (Conference) | SciTech Connect Conference: Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA Citation Details In-Document Search Title: Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction

  9. BIOENERGIZEME INFOGRAPHIC CHALLENGE: Comparison of Bio-fuels to Other Commonly Used Forms of Energy

    Broader source: Energy.gov [DOE]

    This infographic was created by students from Sun Valley High School in Aston, PA, as part of the U.S. Department of Energy-BioenergizeME Infographic Challenge. The BioenergizeME Infographic...

  10. Buildings Energy Data Book: 1.5 Generic Fuel Quad and Comparison

    Buildings Energy Data Book [EERE]

    1 Key Definitions Quad: Quadrillion Btu (10^15 or 1,000,000,000,000,000 Btu) Generic Quad for the Buildings Sector: One quad of primary energy consumed in the buildings sector (includes the residential and commercial sectors), apportioned between the various primary fuels used in the sector according to their relative consumption in a given year. To obtain this value, electricity is converted into its primary energy forms according to relative fuel contributions (or shares) used to produce

  11. Comparison of the calorimetric and kinematic methods of neutrino energy reconstruction in disappearance experiments

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Ankowski, Artur M.; Benhar, Omar; Coloma, Pilar; Huber, Patrick; Jen, Chun -Min; Mariani, Camillo; Meloni, Davide; Vagnoni, Erica

    2015-10-22

    To be able to achieve their physics goals, future neutrino-oscillation experiments will need to reconstruct the neutrino energy with very high accuracy. In this work, we analyze how the energy reconstruction may be affected by realistic detection capabilities, such as energy resolutions, efficiencies, and thresholds. This allows us to estimate how well the detector performance needs to be determined a priori in order to avoid a sizable bias in the measurement of the relevant oscillation parameters. We compare the kinematic and calorimetric methods of energy reconstruction in the context of two νμ → νμ disappearance experiments operating in different energymore » regimes. For the calorimetric reconstruction method, we find that the detector performance has to be estimated with an O(10%) accuracy to avoid a significant bias in the extracted oscillation parameters. Thus, in the case of kinematic energy reconstruction, we observe that the results exhibit less sensitivity to an overestimation of the detector capabilities.« less

  12. Pathways to low-cost electrochemical energy storage: a comparison of aqueous and nonaqueous flow batteries

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Darling, Robert M.; Gallagher, Kevin G.; Kowalski, Jeffrey A.; Ha, Seungbum; Brushett, Fikile R.

    2014-11-01

    Energy storage is increasingly seen as a valuable asset for electricity grids composed of high fractions of intermittent sources, such as wind power or, in developing economies, unreliable generation and transmission services. However, the potential of batteries to meet the stringent cost and durability requirements for grid applications is largely unquantified. We investigate electrochemical systems capable of economically storing energy for hours and present an analysis of the relationships among technological performance characteristics, component cost factors, and system price for established and conceptual aqueous and nonaqueous batteries. We identified potential advantages of nonaqueous flow batteries over those based on aqueousmore » electrolytes; however, new challenging constraints burden the nonaqueous approach, including the solubility of the active material in the electrolyte. Requirements in harmony with economically effective energy storage are derived for aqueous and nonaqueous systems. The attributes of flow batteries are compared to those of aqueous and nonaqueous enclosed and hybrid (semi-flow) batteries. Flow batteries are a promising technology for reaching these challenging energy storage targets owing to their independent power and energy scaling, reliance on facile and reversible reactants, and potentially simpler manufacture as compared to established enclosed batteries such as lead–acid or lithium-ion.« less

  13. Comparison of explosive and vibroseis source energy penetration during COCORP deep seismic reflection profiling in the Williston basin

    SciTech Connect (OSTI)

    Steer, D.N.; Brown, L.D.; Knapp, J.H.; Baird, D.J. [Cornell Univ., Ithaca, NY (United States)] [Cornell Univ., Ithaca, NY (United States)

    1996-01-01

    Comparison of high-fold (50) vibroseis recordings with coincident low-fold (6) explosive source data from deep reflection surveys in the Williston Basin indicates that while vibroseis generated energy decays to ambient noise levels at 7--9 s two-way traveltime (twtt) (20--30 km depth), energy from explosive sources remains above ambient levels to 35--60 s twtt (105--180 km depth). Moreover, single, moderately sized (30 kg) and well-placed charges proved to be as effective as larger (90 kg) sources at penetrating to mantle traveltimes in this area. However, the explosive source energy proved highly variable, with source-to-ground coupling being a major limiting factor in shot efficacy. Stacked results from the vibroseis sources provide superior imagery of shallow and moderate crustal levels by virtue of greater redundancy and shot-to-shot uniformity; shot statics, low fold, and ray-path distortion across the relatively large (24--30 km aperture) spreads used during the explosive recording have proven to be especially problematic in producing conventional seismic sections. In spite of these complications, the explosive source recording served its primary purpose in confirming Moho truncation and the presence of a dipping reflection fabric in the upper mantle along the western flank of the Trans-Hudson orogen buried beneath the Williston Basin.

  14. Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01

    This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

  15. Comparison of energy expenditures by elderly and non-elderly households: 1975 and 1985

    SciTech Connect (OSTI)

    Siler, A.

    1980-05-01

    The relative position of the elderly in the population is examined and their characteristic use of energy in relation to the total population and their non-elderly counterparts is observed. The 1985 projections are based on demographic, economic, and socio-economic, and energy data assumptions contained in the 1978 Annual Report to Congress. The model used for estimating household energy expenditure is MATH/CHRDS - Micro-Analysis of Transfers to Households/Comprehensive Human Resources Data System. Characteristics used include households disposable income, poverty status, location by DOE region and Standard Metropolitan Statistical Area (SMSA), and race and sex of the household head as well as age. Energy use by fuel type will be identified for total home fuels, including electricity, natural gas, bottled gas and fuel oil, and for all fuels, where gasoline use is also included. Throughout the analysis, both income and expenditure-dollar amounts for 1975 and 1985 are expressed in constant 1978 dollars. Two appendices contain statistical information.

  16. Executive Summary: Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    5060 Sargent & Lundy LLC Consulting Group Chicago, Illinois Executive Summary: Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401-3393 NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute * Battelle * Bechtel Contract No. DE-AC36-99-GO10337 October 2003 * NREL/SR-550-35060 Executive Summary: Assessment of Parabolic Trough and Power Tower

  17. Overall Energy Considerations for Algae Species Comparison and Selection in Algae-to-Fuels Processes

    SciTech Connect (OSTI)

    Link, D.; Kail, B.; Curtis, W.; Tuerk,A.

    2011-01-01

    The controlled growth of microalgae as a feedstock for alternative transportation fuel continues to receive much attention. Microalgae have the characteristics of rapid growth rate, high oil (lipid) content, and ability to be grown in unconventional scenarios. Algae have also been touted as beneficial for CO{sub 2} reuse, as algae can be grown using CO{sub 2} emissions from fossil-based energy generation. Moreover, algae does not compete in the food chain, lessening the 'food versus fuel' debate. Most often, it is assumed that either rapid production rate or high oii content should be the primary factor in algae selection for algae-to-fuels production systems. However, many important characteristics of algae growth and lipid production must be considered for species selection, growth condition, and scale-up. Under light limited, high density, photoautotrophic conditions, the inherent growth rate of an organism does not affect biomass productivity, carbon fixation rate, and energy fixation rate. However, the oil productivity is organism dependent, due to physiological differences in how the organisms allocate captured photons for growth and oil production and due to the differing conditions under which organisms accumulate oils. Therefore, many different factors must be considered when assessing the overall energy efficiency of fuel production for a given algae species. Two species, Chlorella vulgaris and Botryococcus braunii, are popular choices when discussing algae-to-fuels systems. Chlorella is a very robust species, often outcompeting other species in mixed-culture systems, and produces a lipid that is composed primarily of free fatty acids and glycerides. Botryococcus is regarded as a slower growing species, and the lipid that it produces is characterized by high hydrocarbon content, primarily C28-C34 botryococcenes. The difference in growth rates is often considered to be an advantage oiChlorella. However, the total energy captured by each algal species in the same photobioreactor system should be similar at light limited growth conditions based on photon flux. It is how the algae 'allocate' this energy captured that will vary: Data will be presented that shows that Botryococcus invests greater energy in oil production than Chlorella under these growth conditions. In essence, the Chlorella can grow 'fast and lean' or can be slowed to grow 'slow and fat'. The overall energy potential between the Chlorella and Botryococcus, then, becomes much more equivalent on a per-photon basis. This work will indicate an interesting relationship between two very different algae species, in terms of growth rate, lipid content and composition, and energy efficiency of the overall process. The presentation will indicate that in light-limited growth, it cannot be assumed that either rapid growth rate or lipid production rate can be used as stand-alone indicators of which species-lipid relationships will truly be more effective in algae-to-fuels scenarios.

  18. Cost and energy comparison study of above- and below-ground dwellings

    SciTech Connect (OSTI)

    Shapira, H.B.; Cristy, G.A.; Brite, S.E.; Yost, M.B.

    1983-08-01

    Designs of earth-sheltered (ES) homes were examined and compared with identical aboveground (AG) homes. The homes are identical except where changes were necessitated by earth-sheltering and energy conservation. The study involved design, construction costing, energy analysis, and life-cycle costing (LCC). It was concluded from this study that under present market conditions, if aboveground and earth-sheltered dwellings of equal size and quality are built on similar lots, the construction cost of the earth-sheltered structure compares poorly with that of the aboveground structure. Lowered operation and maintenance costs, including the lower fuel bills of the earth-sheltered structure, are outweighed by the current high interest rates, which cause an increase in monthly payments. 24 references.

  19. Departments of Energy and Commerce Announce New Partnership to Further

    Energy Savers [EERE]

    Cooperation on Renewable Energy Modeling and Forecasting | Department of Energy Commerce Announce New Partnership to Further Cooperation on Renewable Energy Modeling and Forecasting Departments of Energy and Commerce Announce New Partnership to Further Cooperation on Renewable Energy Modeling and Forecasting January 24, 2011 - 12:00am Addthis WASHINGTON - The Department of Energy and the Department of Commerce today announced a new agreement to further collaboration between the agencies on

  20. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

    Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

    2014-12-30

    A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

  1. Enhanced Short-Term Wind Power Forecasting and Value to Grid Operations: Preprint

    SciTech Connect (OSTI)

    Orwig, K.; Clark, C.; Cline, J.; Benjamin, S.; Wilczak, J.; Marquis, M.; Finley, C.; Stern, A.; Freedman, J.

    2012-09-01

    The current state of the art of wind power forecasting in the 0- to 6-hour time frame has levels of uncertainty that are adding increased costs and risk on the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: 1) a 1-year field measurement campaign within two regions; 2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and 3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provides an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis.

  2. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

  3. Global disease monitoring and forecasting with Wikipedia

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: accessmore » logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.« less

  4. Model documentation Natural Gas Transmission and Distribution Model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    1996-02-26

    The Natural Gas Transmission and Distribution Model (NGTDM) of the National Energy Modeling System is developed and maintained by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting. This report documents the archived version of the NGTDM that was used to produce the natural gas forecasts presented in the Annual Energy Outlook 1996, (DOE/EIA-0383(96)). The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic approach, and provides detail on the methodology employed. Previously this report represented Volume I of a two-volume set. Volume II reported on model performance, detailing convergence criteria and properties, results of sensitivity testing, comparison of model outputs with the literature and/or other model results, and major unresolved issues.

  5. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-08-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  6. Comparison of Energy Efficiency and Power Density in Pressure Retarded Osmosis and Reverse Electrodialysis

    SciTech Connect (OSTI)

    Yip, NY; Elimelech, M

    2014-09-16

    Pressure retarded osmosis (PRO) and reverse electrodialysis (RED) are emerging membrane-based technologies that can convert chemical energy in salinity gradients to useful work. The two processes have intrinsically different working principles: controlled mixing in PRO is achieved by water permeation across salt-rejecting membranes, whereas RED is driven by ion flux across charged membranes. This study compares the energy efficiency and power density performance of PRO and RED with simulated technologically available membranes for natural, anthropogenic, and engineered salinity gradients (seawater-river water, desalination brine-wastewater, and synthetic hypersaline solutions, respectively). The analysis shows that PRO can achieve both greater efficiencies (54-56%) and higher power densities (2.4-38 W/m(2)) than RED (18-38% and 0.77-1.2 W/m(2)). The superior efficiency is attributed to the ability of PRO membranes to more effectively utilize the salinity difference to drive water permeation and better suppress the detrimental leakage of salts. On the other hand, the low conductivity of currently available ion exchange membranes impedes RED ion flux and, thus, constrains the power density. Both technologies exhibit a trade-off between efficiency and power density: employing more permeable but less selective membranes can enhance the power density, but undesired entropy production due to uncontrolled mixing increases and some efficiency is sacrificed. When the concentration difference is increased (i.e., natural -> anthropogenic -> engineered salinity gradients), PRO osmotic pressure difference rises proportionally but not so for RED Nernst potential, which has logarithmic dependence on the solution concentration. Because of this inherently different characteristic, RED is unable to take advantage of larger salinity gradients, whereas PRO power density is considerably enhanced. Additionally, high solution concentrations suppress the Donnan exclusion effect of the charged RED membranes, severely reducing the permselectivity and diminishing the energy conversion efficiency. This study indicates that PRO is more suitable to extract energy from a range of salinity gradients, while significant advancements in ion exchange membranes are likely necessary for RED to be competitive with PRO.

  7. Cost comparison of energy projects: discounted cash flow and revenue requirement methods

    SciTech Connect (OSTI)

    Phung, D.L.

    1980-05-01

    Both the discounted-cash-flow (DCF) and the revenue-requirement (RR) methods are frequently used in the cost analysis of energy projects. Each is uniquely needed in special circumstances, but in the early stages of most ventures, the RR method appears to be more useful. This paper provides simple formulations for the two methods and some special cases of interest to costing practices. Both formulations are applicable to either free or regulated enterprises and in constant or inflated dollars. It is stressed that the interpretation of cost results depends on the selection of cash-flow streams and/or the intent of revenue requirements. Several numerical examples are given.

  8. Comparison of the Department of Energy's 2007, 2008, & 2009 Annual Employee Survey Results

    Office of Environmental Management (EM)

    of the Department of Energy's 2007, 2008, & 2009 Annual Employee Survey Results Item # Personal Work Experiences Year Favorable Neutral Unfavorable DNK/NBJ 2009 87% 7% 6% 0% 2008 86% 8% 6% 0% 1 The people I work with cooperate to get the job done. 2007 78% 13% 9% 0% 2009 68% 17% 15% 0% 2008 66% 17% 17% 0% 2 I am given a real opportunity to improve my skills in my organization. 2007 57% 25% 19% 0% 2009 76% 13% 11% 0% 2008 72% 14% 14% 0% 3 My work gives me a feeling of personal accomplishment.

  9. Press Room - Press Releases - U.S. Energy Information Administration...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    1, 2015 MEDIA ADVISORY: EIA to Release Updated Energy Forecasts to 2040 WHO: Adam Sieminski, Administrator U.S. Energy Information Administration (EIA) WHAT: EIA presents cases...

  10. U.S. Energy Information Administration (EIA) - Ap

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    (2) Congressional & other requests (1) consumption (8) demand (1) end-use (1) energy (2) Energy Perspectives (5) exports (7) Extended Policies Case (1) forecast (1)...

  11. SU-E-I-98: Dose Comparison for Pulmonary Embolism CT Studies: Single Energy Vs. Dual Energy

    SciTech Connect (OSTI)

    Mahmood, U; Erdi, Y

    2014-06-01

    Purpose: The purpose of this study was to assess and compare the size specific dose estimate (SSDE), dose length product (DLP) and noise relationship for pulmonary embolism studies evaluated by single source dual energy computed tomography (DECT) against conventional CT (CCT) studies in a busy cancer center and to determine the dose savings provided by DECT. Methods: An IRB-approved retrospective study was performed to determine the CTDIvol and DLP from a subset of patients scanned with both DECT and CCT over the past five years. We were able to identify 30 breast cancer patients (6 male, 24 female, age range 24 to 81) who had both DECT and CCT studies performed. DECT scans were performed with a GE HD 750 scanner (140/80 kVp, 480 mAs and 40 mm) and CCT scans were performed with a GE Lightspeed 16 slice scanner (120 kVp, 352 mAs, 20 mm). Image noise was measured by placing an ROI and recording the standard deviation of the mean HU along the descending aorta. Results: The average DECT patient size specific dose estimate was to be 14.2 1.7 mGy as compared to 22.4 2.7 mGy from CCT PE studies, which is a 37% reduction in the SSDE. The average DECT DLP was 721.8 84.6 mGy-cm as compared to 981.8 106.1 mGy-cm for CCT, which is a 26% decrease. Compared to CCT the image noise was found to decrease by 19% when using DECT for PE studies. Conclusion: DECT SSDE and DLP measurements indicate dose savings and image noise reduction when compared to CCT. In an environment that heavily debates CT patient doses, this study confirms the effectiveness of DECT in PE imaging.

  12. Entropy vs. energy waveform processing: A comparison based on the heat equation

    SciTech Connect (OSTI)

    Hughes, Michael S.; McCarthy, John E.; Bruillard, Paul J.; Marsh, Jon N.; Wickline, Samuel A.

    2015-05-25

    Virtually all modern imaging devices collect electromagnetic or acoustic waves and use the energy carried by these waves to determine pixel values to create what is basically an “energy” picture. However, waves also carry “information”, as quantified by some form of entropy, and this may also be used to produce an “information” image. Numerous published studies have demonstrated the advantages of entropy, or “information imaging”, over conventional methods. The most sensitive information measure appears to be the joint entropy of the collected wave and a reference signal. The sensitivity of repeated experimental observations of a slowly-changing quantity may be defined as the mean variation (i.e., observed change) divided by mean variance (i.e., noise). Wiener integration permits computation of the required mean values and variances as solutions to the heat equation, permitting estimation of their relative magnitudes. There always exists a reference, such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an “optimal” reference for the joint entropy emerges, which also has been validated in several studies.

  13. Entropy vs. energy waveform processing: A comparison based on the heat equation

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Hughes, Michael S.; McCarthy, John E.; Bruillard, Paul J.; Marsh, Jon N.; Wickline, Samuel A.

    2015-05-25

    Virtually all modern imaging devices collect electromagnetic or acoustic waves and use the energy carried by these waves to determine pixel values to create what is basically an “energy” picture. However, waves also carry “information”, as quantified by some form of entropy, and this may also be used to produce an “information” image. Numerous published studies have demonstrated the advantages of entropy, or “information imaging”, over conventional methods. The most sensitive information measure appears to be the joint entropy of the collected wave and a reference signal. The sensitivity of repeated experimental observations of a slowly-changing quantity may be definedmore » as the mean variation (i.e., observed change) divided by mean variance (i.e., noise). Wiener integration permits computation of the required mean values and variances as solutions to the heat equation, permitting estimation of their relative magnitudes. There always exists a reference, such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an “optimal” reference for the joint entropy emerges, which also has been validated in several studies.« less

  14. Sensitivity to Energy Technology Costs: A Multi-model comparison analysis

    SciTech Connect (OSTI)

    Bosetti, Valentina; Marangoni, Giacomo; Borgonovo, Emanuele; Anadon, Laura Diaz; Barron, Robert W.; McJeon, Haewon C.; Politis, Savvas; Friley, Paul

    2015-05-01

    In the present paper we use the output of multiple expert elicitation surveys on the future cost of key low-carbon technologies and use it as input of three Integrated Assessment models, GCAM, MARKAL_US and WITCH. By means of a large set of simulations we aim to assess the implications of these subjective distributions of technological costs over key model outputs. We are able to detect what sources of technology uncertainty are more influential, how this differs across models, and whether and how results are affected by the time horizon, the metric considered or the stringency of the climate policy. In unconstrained emission scenarios, within the range of future technology performances considered in the present analysis, the cost of nuclear energy is shown to dominate all others in affecting future emissions. Climate-constrained scenarios, stress the relevance, in addition to that of nuclear energy, of biofuels, as they represent the main source of decarbonization of the transportation sector and bioenergy, since the latter can be coupled with CCS to produce negative emissions.

  15. Wind Levelized Cost of Energy: A Comparison of Technical and Financing Input Variables

    SciTech Connect (OSTI)

    Cory, K.; Schwabe, P.

    2009-10-01

    The expansion of wind power capacity in the United States has increased the demand for project development capital. In response, innovative approaches to financing wind projects have emerged and are proliferating in the U.S. renewable energy marketplace. Wind power developers and financiers have become more efficient and creative in structuring their financial relationships, and often tailor them to different investor types and objectives. As a result, two similar projects may use very different cash flows and financing arrangements, which can significantly vary the economic competitiveness of wind projects. This report assesses the relative impact of numerous financing, technical, and operating variables on the levelized cost of energy (LCOE) associated with a wind project under various financing structures in the U.S. marketplace. Under this analysis, the impacts of several financial and technical variables on the cost of wind electricity generation are first examined individually to better understand the relative importance of each. Then, analysts examine a low-cost and a high-cost financing scenario, where multiple variables are modified simultaneously. Lastly, the analysis also considers the impact of a suite of financial variables versus a suite of technical variables.

  16. Wind power forecasting : state-of-the-art 2009.

    SciTech Connect (OSTI)

    Monteiro, C.; Bessa, R.; Miranda, V.; Botterud, A.; Wang, J.; Conzelmann, G.; Decision and Information Sciences; INESC Porto

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and uncertainty in wind power and to more efficiently operate power systems with large wind power penetrations. Moreover, in a market environment, the wind power contribution to the generation portofolio becomes important in determining the daily and hourly prices, as variations in the estimated wind power will influence the clearing prices for both energy and operating reserves. With the increasing penetration of wind power, WPF is quickly becoming an important topic for the electric power industry. System operators (SOs), generating companies (GENCOs), and regulators all support efforts to develop better, more reliable and accurate forecasting models. Wind farm owners and operators also benefit from better wind power prediction to support competitive participation in electricity markets against more stable and dispatchable energy sources. In general, WPF can be used for a number of purposes, such as: generation and transmission maintenance planning, determination of operating reserve requirements, unit commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and energy trading. The objective of this report is to review and analyze state-of-the-art WPF models and their application to power systems operations. We first give a detailed description of the methodologies underlying state-of-the-art WPF models. We then look at how WPF can be integrated into power system operations, with specific focus on the unit commitment problem.

  17. NREL: Resource Assessment and Forecasting - Webmaster

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Webmaster Use this form to send us your comments and questions, report problems with the site, or ask for help finding information on the site. Please enter your name and email address in the boxes provided, then type your message below. When you are finished, click "Send Message." NOTE: If you enter your e-mail address incorrectly, we will be unable to reply. Your name: Your email address: Your message: Send Message Printable Version Resource Assessment & Forecasting Home

  18. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    SciTech Connect (OSTI)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  19. Improving the Accuracy of Solar Forecasting Funding Opportunity...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Opportunity, DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and other stakeholders better forecast when, where, and how much...

  20. FY 2004 Second Quarter Review Forecast of Generation Accumulated...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Bonneville Power Administration Power Business Line Generation (PBL) Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

  1. PBL FY 2003 Third Quarter Review Forecast of Generation Accumulated...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    2003 Bonneville Power Administration Power Business Line Generation Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

  2. Roel Neggers European Centre for Medium-range Weather Forecasts

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    transition from shallow to deep convection using a dual mass flux boundary layer scheme Roel Neggers European Centre for Medium-range Weather Forecasts Introduction " " % % &...

  3. Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

    SciTech Connect (OSTI)

    Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M.

    2009-10-09

    We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

  4. Comparison of the Energy Efficiency Prescribed by ASHRAE/ANSI/IESNA Standard 90.1-1999 and ASHRAE/ANSI/IESNA Standard 90.1-2004

    SciTech Connect (OSTI)

    Halverson, Mark A.; Liu, Bing; Richman, Eric E.; Winiarski, David W.

    2006-12-01

    This document presents the qualitative comparison of DOEs formal determination of energy savings of ANSI/ASHRAE/IESNA Standard 90.1-2004. The term qualitative is used in the sense of identifying whether or not changes have a positive, negative, or neutral impact on energy efficiency of the standard, with no attempt made to quantify that impact. A companion document will present the quantitative comparison of DOEs determination. The quantitative comparison will be based on whole building simulation of selected building prototypes in selected climates. This document presents a comparison of the energy efficiency requirements in ANSI/ASHRAE/IESNA 90.1-1999 (herein referred to as Standard 90.1-1999) and ANSI/ASHRAE/IESNA 90.1-2004 (herein referred to as Standard 90.1-2004). The comparison was done through a thorough review of all addenda to Standard 90.1-1999 that were included in the published ANSI/ASHRAE/IESNA Standard 90.1-2001 (herein referred to as Standard 90.1-2001) and also all addenda to Standard 90.1-2001 that were included in the published Standard 90.1-2004. A summary table showing the impact of each addendum is provided. Each addendum to both Standards 90.1-1999 and 90.1-2001 was evaluated as to its impact on the energy efficiency requirements of the standard (greater efficiency, lesser efficiency) and as to significance. The final section of this document summarizes the impacts of the various addenda and proposes which addenda should be included in the companion quantitative portion of DOEs determination. Addenda are referred to with the nomenclature addendum 90.1-xxz, where xx is either 99 for 1999 or 01 for 2001, and z is the ASHRAE letter designation for the addendum. Addenda names are shown in bold face in text. DOE has chosen not to prepare a separate evaluation of Standard 90.1-2001 as that standard does not appear to improve energy efficiency in commercial buildings. What this means for the determination of energy savings for Standard 90.1-2004 is that the baseline standard for comparison is Standard 90.1-1999 and all addenda to both Standards 90.1-1999 and 90.1-2001 must be considered to determine the overall change in efficiency between Standard 90.1-1999 and Standard 90.1-2004.

  5. Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

    SciTech Connect (OSTI)

    Woolsey, Lauren N.; Cranmer, Steven R.

    2014-06-01

    Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvn waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

  6. Compiler Comparisons

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Comparisons Compiler Comparisons Compiler Comparisons on Hopper There are five compilers available to users on Hopper, the NERSC XE6. All of the compilers on this system are...

  7. National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

    SciTech Connect (OSTI)

    Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

    1982-03-31

    The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

  8. Short-Term Energy Outlook - U.S. Energy Information Administration...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Feature Articles Changes to the Natural Gas Storage Regions December 2015 PDF 2015 Outlook ... Liquids Supply Forecast February 2014 PDF Energy-weighted Industrial Production Indices ...

  9. Waste generation forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1995-FY 2002, September 1994 revision

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

    A comprehensive waste-forecasting task was initiated in FY 1991 to provide a consistent, documented estimate of the volumes of waste expected to be generated as a result of U.S. Department of Energy-Oak Ridge Operations (DOE-ORO) Environmental Restoration (ER) OR-1 Project activities. Continual changes in the scope and schedules for remedial action (RA) and decontamination and decommissioning (D&D) activities have required that an integrated data base system be developed that can be easily revised to keep pace with changes and provide appropriate tabular and graphical output. The output can then be analyzed and used to drive planning assumptions for treatment, storage, and disposal (TSD) facilities. The results of this forecasting effort and a description of the data base developed to support it are provided herein. The initial waste-generation forecast results were compiled in November 1991. Since the initial forecast report, the forecast data have been revised annually. This report reflects revisions as of September 1994.

  10. 2007 Wholesale Power Rate Case Final Proposal : Market Price Forecast Study.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    2006-07-01

    This study presents BPA's market price forecasts for the Final Proposal, which are based on AURORA modeling. AURORA calculates the variable cost of the marginal resource in a competitively priced energy market. In competitive market pricing, the marginal cost of production is equivalent to the market-clearing price. Market-clearing prices are important factors for informing BPA's power rates. AURORA was used as the primary tool for (a) estimating the forward price for the IOU REP Settlement benefits calculation for fiscal years (FY) 2008 and 2009, (b) estimating the uncertainty surrounding DSI payments and IOU REP Settlements benefits, (c) informing the secondary revenue forecast and (d) providing a price input used for the risk analysis. For information about the calculation of the secondary revenues, uncertainty regarding the IOU REP Settlement benefits and DSI payment uncertainty, and the risk run, see Risk Analysis Study WP-07-FS-BPA-04.

  11. Development and testing of improved statistical wind power forecasting methods.

    SciTech Connect (OSTI)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J.

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

  12. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema (OSTI)

    Gonzalez, Frank

    2010-01-08

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

  13. Comparison of Methods for Estimating the NOx Emission Impacts of Energy Efficiency and Renewable Energy Projects: Shreveport, Louisiana Case Study (Revised)

    SciTech Connect (OSTI)

    Chambers, A.; Kline, D. M.; Vimmerstedt, L.; Diem, A.; Dismukes, D.; Mesyanzhinov, D.

    2005-07-01

    This is a case study comparing methods of estimating the NOx emission impacts of energy efficiency and renewable energy projects in Shreveport, Louisiana.

  14. Sandia Energy - Sandia PV Team Publishes Book Chapter

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Book Chapter Previous Next Sandia PV Team Publishes Book Chapter The book, Solar Energy Forecasting and Resource Assessment, provides an authoritative voice on the...

  15. Analysis & Projections - U.S. Energy Information Administration...

    U.S. Energy Information Administration (EIA) Indexed Site

    Cooperation on Energy Information site internationalUnited StatesCanadaenergyMexico Electricity generation from ... Price summary (historical and forecast) 2014 2015 2016 2017 ...

  16. About EIA - Policies - U.S. Energy Information Administration...

    U.S. Energy Information Administration (EIA) Indexed Site

    through surveys, reports and articles detailing energy data analyses, and forecasts Currently Available 1 PrivacySecurity Privacy policy and security policy Currently ...

  17. Visiting Speaker Program - July 28, 2010 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Robert Atkinson Panelists More Documents & Publications Expert Panel: Forecast Future Demand for Medical Isotopes Clark Atlanta Universities (CAU) Energy Related Research...

  18. Energy Risk Predictions for the 2015 Hurricane Season (June 2015)

    Broader source: Energy.gov [DOE]

    This presentation is from a DOE-NASEO webinar held June 23, 2015, on forecasting energy infrastructure risk for the 2015 hurricane season.

  19. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  20. Solar Trackers Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Solar Trackers Market Forecast Home John55364's picture Submitted by John55364(100) Contributor 12 May, 2015 - 03:54 Solar Trackers Market - Global Industry Analysis, Size, Share,...

  1. Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels

    Reports and Publications (EIA)

    2003-01-01

    This paper presents a short-term monthly forecasting model of West Texas Intermediate crude oil spot price using Organization for Economic Cooperation and Development (OECD) petroleum inventory levels.

  2. Value of Improved Short-Term Wind Power Forecasting

    SciTech Connect (OSTI)

    Hodge, B. M.; Florita, A.; Sharp, J.; Margulis, M.; Mcreavy, D.

    2015-02-01

    This report summarizes an assessment of improved short-term wind power forecasting in the California Independent System Operator (CAISO) market and provides a quantification of its potential value.

  3. Improving the Accuracy of Solar Forecasting Funding Opportunity

    Broader source: Energy.gov [DOE]

    Through the Improving the Accuracy ofSolar Forecasting Funding Opportunity,DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and other...

  4. PBL FY 2002 Second Quarter Review Forecast of Generation Accumulated...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Slice true-ups, and actual expense levels. Any variation of these can change the net revenue situation. FY 2002 Forecasted Second Quarter Results 170 (418) FY 2002 Unaudited...

  5. World oil inventories forecast to grow significantly in 2016...

    U.S. Energy Information Administration (EIA) Indexed Site

    World oil inventories forecast to grow significantly in 2016 and 2017 Global oil inventories are expected to continue strong growth over the next two years which should keep oil ...

  6. Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

    Broader source: Energy.gov [DOE]

    Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

  7. Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models

    SciTech Connect (OSTI)

    Hannay, C; Williamson, D L; Hack, J J; Kiehl, J T; Olson, J G; Klein, S A; Bretherton, C S; K?hler, M

    2008-01-24

    We examine forecasts of Southeast Pacific stratocumulus at 20S and 85W during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized from ECMWF analyses and each model is run for 3 days to determine the differences with the EPIC field data. Observations during the EPIC cruise show a stable and well-mixed boundary layer under a sharp inversion. The inversion height and the cloud layer have a strong and regular diurnal cycle. A key problem common to the four models is that the forecasted planetary boundary layer (PBL) height is too low when compared to EPIC observations. All the models produce a strong diurnal cycle in the Liquid Water Path (LWP) but there are large differences in the amplitude and the phase compared to the EPIC observations. This, in turn, affects the radiative fluxes at the surface. There is a large spread in the surface energy budget terms amongst the models and large discrepancies with observational estimates. Single Column Model (SCM) experiments with the CAM show that the vertical pressure velocity has a large impact on the PBL height and LWP. Both the amplitude of the vertical pressure velocity field and its vertical structure play a significant role in the collapse or the maintenance of the PBL.

  8. Study forecasts disappearance of conifers due to climate change

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. December 21, 2015 Los Alamos scientist Nate McDowell discusses how climate change is killing trees with PBS NewsHour reporter Miles O'Brien. Los Alamos scientist Nate McDowell discusses how climate change is

  9. Forecasting neutrino masses from combining KATRIN and the CMB observations:

    Office of Scientific and Technical Information (OSTI)

    Frequentist and Bayesian analyses (Journal Article) | SciTech Connect SciTech Connect Search Results Journal Article: Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses Citation Details In-Document Search Title: Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses We present a showcase for deriving bounds on the neutrino masses from laboratory experiments and cosmological

  10. ANL Wind Power Forecasting and Electricity Markets | Open Energy...

    Open Energy Info (EERE)

    Company Organization Argonne National Laboratory Partner Institute for Systems and Computer Engineering of Porto (INESC Porto) in Portugal, Midwest Independent System Operator...

  11. Issues in Midterm Analysis and Forecasting

    Reports and Publications (EIA)

    1999-01-01

    Final issue of this report. Presents a series of eight papers, which cover topics in analysis and modeling that underlie the Annual Energy Outlook 1999, as well as other significant issues in midterm energy markets.

  12. Development of Energy Balances for the State of California

    SciTech Connect (OSTI)

    Murtishaw, Scott; Price, Lynn; de la Rue du Can, Stephane; Masanet, Eric; Worrell, Ernst; Sahtaye, Jayant

    2005-12-01

    Analysts assessing energy policies and energy modelers forecasting future trends need to have access to reliable and concise energy statistics. Lawrence Berkeley National Laboratory evaluated several sources of California energy data, primarily from the California Energy Commission and the U.S. Energy Information Administration, to develop the California Energy Balance Database (CALEB). This database manages highly disaggregated data on energy supply, transformation, and end-use consumption for each type of energy commodity from 1990 to the most recent year available (generally 2001) in the form of an energy balance, following the methodology used by the International Energy Agency. This report presents the data used for CALEB and provides information on how the various data sources were reconciled. CALEB offers the possibility of displaying all energy flows in numerous ways (e.g.,physical units, Btus, petajoules, different levels of aggregation), facilitating comparisons among the different types of energy commodities and different end-use sectors. In addition to displaying energy data, CALEB can also be used to calculate state-level energy-related carbon dioxide emissions using the methodology of the Intergovernmental Panel on Climate Change.

  13. AVLIS: a technical and economic forecast

    SciTech Connect (OSTI)

    Davis, J.I.; Spaeth, M.L.

    1986-01-01

    The AVLIS process has intrinsically large isotopic selectivity and hence high separative capacity per module. The critical components essential to achieving the high production rates represent a small fraction (approx.10%) of the total capital cost of a production facility, and the reference production designs are based on frequent replacement of these components. The specifications for replacement frequencies in a plant are conservative with respect to our expectations; it is reasonable to expect that, as the plant is operated, the specifications will be exceeded and production costs will continue to fall. Major improvements in separator production rates and laser system efficiencies (approx.power) are expected to occur as a natural evolution in component improvements. With respect to the reference design, such improvements have only marginal economic value, but given the exigencies of moving from engineering demonstration to production operations, we continue to pursue these improvements in order to offset any unforeseen cost increases. Thus, our technical and economic forecasts for the AVLIS process remain very positive. The near-term challenge is to obtain stable funding and a commitment to bring the process to full production conditions within the next five years. If the funding and commitment are not maintained, the team will disperse and the know-how will be lost before it can be translated into production operations. The motivation to preserve the option for low-cost AVLIS SWU production is integrally tied to the motivation to maintain a competitive nuclear option. The US industry can certainly survive without AVLIS, but our tradition as technology leader in the industry will certainly be lost.

  14. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect (OSTI)

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

  15. Forecasting the 2013–2014 influenza season using Wikipedia

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

  16. Short-Term Energy Outlook January 2014

    U.S. Energy Information Administration (EIA) Indexed Site

    4 1 January 2014 Short-Term Energy Outlook (STEO) Highlights This edition of the Short-Term Energy Outlook is the first to include forecasts for 2015. After falling to the...

  17. NREL: Resource Assessment and Forecasting - Facilities

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    energy research and development. The 2,600-square-foot facility was completed in 2000 and houses the Metrology Laboratory, Optical Metrology Laboratory, Data Acquisition...

  18. Regional Analysis of Building Distributed Energy Costs and CO2 Abatement: A U.S. - China Comparison

    SciTech Connect (OSTI)

    Mendes, Goncalo; Feng, Wei; Stadler, Michael; Steinbach, Jan; Lai, Judy; Zhou, Nan; Marnay, Chris; Ding, Yan; Zhao, Jing; Tian, Zhe; Zhu, Neng

    2014-04-09

    The following paper conducts a regional analysis of the U.S. and Chinese buildings? potential for adopting Distributed Energy Resources (DER). The expected economics of DER in 2020-2025 is modeled for a commercial and a multi-family residential building in different climate zones. The optimal building energy economic performance is calculated using the Distributed Energy Resources Customer Adoption Model (DER CAM) which minimizes building energy costs for a typical reference year of operation. Several DER such as combined heat and power (CHP) units, photovoltaics, and battery storage are considered. The results indicate DER have economic and environmental competitiveness potential, especially for commercial buildings in hot and cold climates of both countries. In the U.S., the average expected energy cost savings in commercial buildings from DER CAM?s suggested investments is 17percent, while in Chinese buildings is 12percent. The electricity tariffs structure and prices along with the cost of natural gas, represent important factors in determining adoption of DER, more so than climate. High energy pricing spark spreads lead to increased economic attractiveness of DER. The average emissions reduction in commercial buildings is 19percent in the U.S. as a result of significant investments in PV, whereas in China, it is 20percent and driven by investments in CHP. Keywords: Building Modeling and Simulation, Distributed Energy Resources (DER), Energy Efficiency, Combined Heat and Power (CHP), CO2 emissions 1. Introduction The transition from a centralized and fossil-based energy paradigm towards the decentralization of energy supply and distribution has been a major subject of research over the past two decades. Various concerns have brought the traditional model into question; namely its environmental footprint, its structural inflexibility and inefficiency, and more recently, its inability to maintain acceptable reliability of supply. Under such a troubled setting, distributed energy resources (DER) comprising of small, modular, electrical renewable or fossil-based electricity generation units placed at or near the point of energy consumption, has gained much attention as a viable alternative or addition to the current energy system. In 2010, China consumed about 30percent of its primary energy in the buildings sector, leading the country to pay great attention to DER development and its applications in buildings. During the 11th Five Year Plan (FYP), China has implemented 371 renewable energy building demonstration projects, and 210 photovoltaics (PV) building integration projects. At the end of the 12th FYP, China is targeting renewable energy to provide 10percent of total building energy, and to save 30 metric tons of CO2 equivalents (mtce) of energy with building integrated renewables. China is also planning to implement one thousand natural gas-based distributed cogeneration demonstration projects with energy utilization rates over 70percent in the 12th FYP. All these policy targets require significant DER systems development for building applications. China?s fast urbanization makes building energy efficiency a crucial economic issue; however, only limited studies have been done that examine how to design and select suitable building energy technologies in its different regions. In the U.S., buildings consumed 40percent of the total primary energy in 2010 [1] and it is estimated that about 14 billion m2 of floor space of the existing building stock will be remodeled over the next 30 years. Most building?s renovation work has been on building envelope, lighting and HVAC systems. Although interest has emerged, less attention is being paid to DER for buildings. This context has created opportunities for research, development and progressive deployment of DER, due to its potential to combine the production of power and heat (CHP) near the point of consumption and delivering multiple benefits to customers, such as cost

  19. Solid waste integrated forecast technical (SWIFT) report: FY1997 to FY 2070, Revision 1

    SciTech Connect (OSTI)

    Valero, O.J.; Templeton, K.J.; Morgan, J.

    1997-01-07

    This web site provides an up-to-date report on the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons with previous forecasts and with other national data sources. This web site does not include: liquid waste (current or future generation); waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); or waste that has been received by the WM Project to date (i.e., inventory waste). The focus of this web site is on low-level mixed waste (LLMW), and transuranic waste (both non-mixed and mixed) (TRU(M)). Some details on low-level waste and hazardous waste are also provided. Currently, this web site is reporting data th at was requested on 10/14/96 and submitted on 10/25/96. The data represent a life cycle forecast covering all reported activities from FY97 through the end of each program's life cycle. Therefore, these data represent revisions from the previous FY97.0 Data Version, due primarily to revised estimates from PNNL. There is some useful information about the structure of this report in the SWIFT Report Web Site Overview.

  20. Residential sector end-use forecasting with EPRI-Reeps 2.1: Summary input assumptions and results

    SciTech Connect (OSTI)

    Koomey, J.G.; Brown, R.E.; Richey, R.

    1995-12-01

    This paper describes current and projected future energy use by end-use and fuel for the U.S. residential sector, and assesses which end-uses are growing most rapidly over time. The inputs to this forecast are based on a multi-year data compilation effort funded by the U.S. Department of Energy. We use the Electric Power Research Institute`s (EPRI`s) REEPS model, as reconfigured to reflect the latest end-use technology data. Residential primary energy use is expected to grow 0.3% per year between 1995 and 2010, while electricity demand is projected to grow at about 0.7% per year over this period. The number of households is expected to grow at about 0.8% per year, which implies that the overall primary energy intensity per household of the residential sector is declining, and the electricity intensity per household is remaining roughly constant over the forecast period. These relatively low growth rates are dependent on the assumed growth rate for miscellaneous electricity, which is the single largest contributor to demand growth in many recent forecasts.

  1. Hawaii energy strategy report, October 1995

    SciTech Connect (OSTI)

    1995-10-01

    This is a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

  2. Hawaii energy strategy: Executive summary, October 1995

    SciTech Connect (OSTI)

    1995-10-01

    This is an executive summary to a report on the Hawaii Energy Strategy Program. The topics of the report include the a description of the program including an overview, objectives, policy statement and purpose and objectives; energy strategy policy development; energy strategy projects; current energy situation; modeling Hawaii`s energy future; energy forecasts; reducing energy demand; scenario assessment, and recommendations.

  3. Comparison of the Supplement to the 2004 IECC to the Current New York Energy Conservation Code - Residential Buildings

    SciTech Connect (OSTI)

    Lucas, Robert G.

    2004-09-01

    The New York State Department of State requested the U.S. Department of Energy (DOE) to prepare a report consisting of two components. The first component is an analysis comparing the effects on energy usage as a result of implementation of the 2004 Supplement to the IECC with the current New York code. The second component is an engineering analysis to determine whether additional costs of compliance with the proposal would be equal to or less than the present value of anticipated energy savings over a 10-year period. Under DOE's direction, Pacific Northwest National Laboratory (PNNL) completed the requested assessment of the potential code upgrade.

  4. Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010

    SciTech Connect (OSTI)

    Jacobs, David E. . E-mail: dejacobs@starpower.net; Nevin, Rick

    2006-11-15

    We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels {>=}10 {mu}g/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

  5. Comparison of energy consumption between displacement and mixing ventilation systems for different U.S. buildings and climates

    SciTech Connect (OSTI)

    Hu, S.; Chen, Q.; Glicksman, L.R.

    1999-07-01

    A detailed computer simulation method was used to compare the energy consumption of a displacement ventilation system with that of a mixing ventilation system for three types of US buildings: a small office, a classroom, and an industrial workshop. The study examined five typical climatic regions as well as different building zones. It was found that a displacement ventilation system may use more fan energy and less chiller and boiler energy than a mixing ventilation system. The total energy consumption is slightly less using a displacement ventilation system. Both systems can use a similarly sized boiler. However, a displacement ventilation system requires a larger air-handling unit and a smaller chiller than the mixing ventilation system. The overall first costs are lower for the displacement ventilation if the system is applied for the core region of a building.

  6. Survey of Variable Generation Forecasting in the West: August 2011 - June 2012

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2012-04-01

    This report surveyed Western Interconnection Balancing Authorities regarding their implementation of variable generation forecasting, the lessons learned to date, and recommendations they would offer to other Balancing Authorities who are considering variable generation forecasting. Our survey found that variable generation forecasting is at an early implementation stage in the West. Eight of the eleven Balancing Authorities interviewed began forecasting in 2008 or later. It also appears that less than one-half of the Balancing Authorities in the West are currently utilizing variable generation forecasting, suggesting that more Balancing Authorities in the West will engage in variable generation forecasting should more variable generation capacity be added.

  7. Solid Waste Integrated Forecast Technical (SWIFT) Report FY2001 to FY2046 Volume 1

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2000-08-31

    This report provides up-to-date life cycle information about the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons to previous forecasts and other national data sources. This report does not include: waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); waste that has been received by the WM Project to date (i.e., inventory waste); mixed low-level waste that will be processed and disposed by the River Protection Program; and liquid waste (current or future generation). Although this report currently does not include liquid wastes, they may be added as information becomes available.

  8. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Klein, Stephen

    2008-01-15

    Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

  9. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Klein, Stephen

    Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

  10. A Comparison of the 2003 and 2006 International Energy Conservation Codes to Determine the Potential Impact on Residential Building Energy Efficiency

    SciTech Connect (OSTI)

    Stovall, Therese K; Baxter, Van D

    2008-03-01

    The IECC was updated in 2006. As required in the Energy Conservation and Production Act of 1992, Title 3, DOE has a legislative requirement to "determine whether such revision would improve energy efficiency in residential buildings" within 12 months of the latest revision. This requirement is part of a three-year cycle of regular code updates. To meet this requirement, an independent review was completed using personnel experienced in building science but not involved in the code development process.

  11. A comparison of global optimization algorithms with standard benchmark functions and real-world applications using Energy Plus

    SciTech Connect (OSTI)

    Kamph, Jerome Henri; Robinson, Darren; Wetter, Michael

    2009-09-01

    There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimise the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility.

  12. Comparison of ground-derived and satellite-derived surface energy fluxes from a shrub-steppe site

    SciTech Connect (OSTI)

    Kirkham, R.R.; Gee, G.W. [Pacific Northwest Lab., Richland, WA (United States); Fritschen, L.J. [Washington Univ., Seattle, WA (United States)

    1994-03-01

    Efforts to measure evapotranspiration (ET) remotely are common in agriculture, and the application of such data to irrigation scheduling is readily apparent. Extending this methodology to arid environments is primarily of use as a mechanism for validation of ET algorithms used in large-scale watershed and global climate change modeling efforts. To facilitate testing of the remote sensing method for ET, measurements of sensible and latent heat flux were made at four sites located on the US Department of Energy`s Hanford Site using a combination of lysimeter and Bowen Ratio Energy Balance (BREB) stations. The objective was to calibrate an aerodynamic transport equation that relates sensible heat flux to radiant surface temperature, and to map sensible heat flux using Landsat data.

  13. Using contingent valuation to explore willingness to pay for renewable energy: A comparison of collective and voluntary payment vehicles

    SciTech Connect (OSTI)

    Wiser, Ryan H.

    2002-07-28

    Some of the most basic questions about the organization and functioning of society involve issues raised by the existence of public goods. With respect to environmental public goods, how should funds used to support environmental improvement be collected and used? In particular, are collective, mandatory payments superior to voluntary, charitable payments due to the possibility of free riding? And to what degree should the government be involved in spending these funds: should the government directly fund environmental improvement projects or should the private sector be used to collect funds and determine funding priorities? This report explores these questions from the perspective of renewable energy: wind, geothermal, biomass, hydropower, and solar. In particular, this report analyzes the payment preferences of U.S. households through the implementation of a large-scale contingent valuation (CV) survey of willingness to pay (WTP) for renewable energy. Renewable energy can be supported through a mandatory ''tax'' on electric bills or through voluntary payments via green power marketing; the government may or may not be heavily involved in the collection and expenditure of such funds. The question of how households prefer to pay for renewable energy is therefore highly relevant. The primary objective of this study is to explore variations in stated WTP for renewable energy under the following four payment and provision contexts: (1) A mandatory increase in the electricity bills of all customers, the funds from which are collected and spent by the government on renewable energy projects. (2) A voluntary increase in the electricity bills of those customers who choose to pay, the funds from which are collected and spent by the government on renewable energy projects. (3) A voluntary increase in the electricity bills of those customers who choose to pay, the funds from which are collected and spent by electricity suppliers on renewable energy projects. (4) A mandatory increase in the electricity bills of all customers, the funds from which are collected and spent by electricity suppliers on renewable energy projects. These payment and provision scenarios are consistent with contemporary forms of support for renewable energy. The first scenario--mandatory payments and government provision--is consistent with a system-benefits charge policy, a policy that has been adopted in 15 U.S. states. The third scenario--voluntary payments to an electricity supplier--is consistent with competitive green power marketing. The fourth scenario--mandatory payments through electricity suppliers--is consistent with a renewables portfolio standard, a policy adopted in thirteen U.S. states as of mid 2003. The second scenario--voluntary payments and government provision--has only been used in a limited fashion in the United States. In addition to having contemporary policy relevance, these four contingent valuation scenarios allow one to distinguish differences in stated WTP based on: (1) the payment method--is WTP affected by whether payments are to be made collectively or voluntarily? and (2) the provision arrangement--does the manner in which a good is provided, in this case through the government or the private sector, affect stated WTP? A split-sample, dichotomous choice contingent valuation survey of 1,574 U.S. residents was developed and implemented to test the sensitivity of stated WTP to these variables at three different payment levels, or bid points. Three secondary objectives also influenced research design, and are discussed in this report. First, this study indirectly and tentatively evaluates the importance of ''participation expectations'' in contingent valuation surveys: specifically, are individuals who state a WTP for renewable energy more likely to think that others will also contribute? Such relationships are commonly discussed in the sociology, social psychology, and marketing literatures, and are also frequently referenced in the collective action literature, but have yet to be tested thoroughly in a contingent valuation co

  14. Impacts of Improved Day-Ahead Wind Forecasts on Power Grid Operations: September 2011

    SciTech Connect (OSTI)

    Piwko, R.; Jordan, G.

    2011-11-01

    This study analyzed the potential benefits of improving the accuracy (reducing the error) of day-ahead wind forecasts on power system operations, assuming that wind forecasts were used for day ahead security constrained unit commitment.

  15. Status of Centralized Wind Power Forecasting in North America: May 2009-May 2010

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2010-04-01

    Report surveys grid wind power forecasts for all wind generators, which are administered by utilities or regional transmission organizations (RTOs), typically with the assistance of one or more wind power forecasting companies.

  16. EERE Success Story-Solar Forecasting Gets a Boost from Watson...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Forecasting Gets a Boost from Watson, Accuracy Improved by 30% EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am ...

  17. NREL: Resource Assessment and Forecasting - Metrology Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Metrology Laboratory Photo of Solar Radiation Research Laboratory researchers inspecting radiometers mounted to calibration tables at the outside test site. Researchers at the Solar Radiation Research Laboratory use pyranometers, pyrheliometers, pyrgeometers, photometers, and spectroradiometers to provide the solar resource information necessary for renewable energy research and development. Metrology, the science of measurement, is a critical part of providing accurate and repeatable data.

  18. Comparisons of neutrino event generators from an oscillation...

    Office of Scientific and Technical Information (OSTI)

    of high energy physics data, ideally giving a baseline comparison between the state-of-art theoretical models and experimental data. Presented here is a comparison between three...

  19. Weather Research and Forecasting Model with the Immersed Boundary Method

    Energy Science and Technology Software Center (OSTI)

    2012-05-01

    The Weather Research and Forecasting (WRF) Model with the immersed boundary method is an extension of the open-source WRF Model available for wwww.wrf-model.org. The new code modifies the gridding procedure and boundary conditions in the WRF model to improve WRF's ability to simutate the atmosphere in environments with steep terrain and additionally at high-resolutions.

  20. Central Wind Power Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2009-12-01

    The report addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America.

  1. Comparison of the incentives used to stimulate energy production in Japan, France, West Germany, and the United States

    SciTech Connect (OSTI)

    Cole, R.J.; Sommers, P.; Eschbach, C.; Sheppard, W.J.; Lenerz, D.E.; Huelshoff, M.; Marcus, A.A.

    1981-09-01

    This volume represents the culmination of a five-year research effort examining the incentives used to stimulate energy production in four countries, and the incentives used to stimulate energy consumption in one country. Following the theoretical approach developed for studying US energy incentives, the researchers in each country classified incentives into the following six categories: (1) Taxation, including exemption from or reduction of existing taxes; (2) Disbursements, in which the national government distributes money without requiring anything in return; (3) Requirements, including demands made by the government, backed by civil or criminal sanctions; (4) Traditional Services, including those almost always provided exclusively by a governmental entity; (5) Nontraditional Services, including those sometimes performed by non-governmental entities, as well as governmental entities (e.g., research and development); and (6) Market Activities, including government involvement in the market under conditions similar to those faced by non-governmental producers or consumers. A complete list of research reports prepared in the Federal Incentives series is provided in the Appendix.

  2. A Field Study Comparison of the Energy and Moisture Performance Characteristics of Ventilated Versus Sealed Crawl Spaces in the South

    SciTech Connect (OSTI)

    Bruce Davis; Cyrus Dastur; William E. Warren; Shawn Fitzpatrick; Christine Maurer; Rob Stevens; Terry Brennan; William Rose

    2005-06-22

    This study compared the performance of closed crawl spaces, which had sealed foundation wall vents, a sealed polyethylene film liner and various insulation and drying strategies, to traditional wall-vented crawl spaces with perimeter wall vents and polyethylene film covering 100% of the ground surface. The study was conducted at 12 owner-occupied, all electric, single-family detached houses with the same floor plan located on one cul-de-sac in the southeastern United States. Using the matched pairs approach, the houses were divided into three study groups of four houses each. Comparative data was recorded for each house to evaluate sub-metered heat pump energy consumption, relative humidity, wood moisture content, duct infiltration, house infiltration, temperature, radon, and bioaerosol levels. Findings indicated that in the humid conditions of the southeastern United States, a properly closed crawl space is a robust construction measure that produces a substantially drier crawl space and significantly reduces occupied space conditioning energy use on an annual basis.

  3. Compiler Comparisons

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Compiler Comparisons Compiler Comparisons Compiler Comparisons on Hopper There are five compilers available to users on Hopper, the NERSC XE6. All of the compilers on this system are provided by Cray, and they are invoked with wrapper modules that ensure that each compiler links with the proper system and MPI libraries. Each of the compilers have a wide variety of options that control the level of optimization of the exectuable code they produce. We have collected several optimization

  4. Comparison of TG-43 and TG-186 in breast irradiation using a low energy electronic brachytherapy source

    SciTech Connect (OSTI)

    White, Shane A.; Landry, Guillaume; Reniers, Brigitte; Fonseca, Gabriel Paiva; Beaulieu, Luc; Verhaegen, Frank

    2014-06-15

    Purpose: The recently updated guidelines for dosimetry in brachytherapy in TG-186 have recommended the use of model-based dosimetry calculations as a replacement for TG-43. TG-186 highlights shortcomings in the water-based approach in TG-43, particularly for low energy brachytherapy sources. The Xoft Axxent is a low energy (<50 kV) brachytherapy system used in accelerated partial breast irradiation (APBI). Breast tissue is a heterogeneous tissue in terms of density and composition. Dosimetric calculations of seven APBI patients treated with Axxent were made using a model-based Monte Carlo platform for a number of tissue models and dose reporting methods and compared to TG-43 based plans. Methods: A model of the Axxent source, the S700, was created and validated against experimental data. CT scans of the patients were used to create realistic multi-tissue/heterogeneous models with breast tissue segmented using a published technique. Alternative water models were used to isolate the influence of tissue heterogeneity and backscatter on the dose distribution. Dose calculations were performed using Geant4 according to the original treatment parameters. The effect of the Axxent balloon applicator used in APBI which could not be modeled in the CT-based model, was modeled using a novel technique that utilizes CAD-based geometries. These techniques were validated experimentally. Results were calculated using two dose reporting methods, dose to water (D{sub w,m}) and dose to medium (D{sub m,m}), for the heterogeneous simulations. All results were compared against TG-43-based dose distributions and evaluated using dose ratio maps and DVH metrics. Changes in skin and PTV dose were highlighted. Results: All simulated heterogeneous models showed a reduced dose to the DVH metrics that is dependent on the method of dose reporting and patient geometry. Based on a prescription dose of 34 Gy, the average D{sub 90} to PTV was reduced by between ?4% and ?40%, depending on the scoring method, compared to the TG-43 result. Peak skin dose is also reduced by 10%15% due to the absence of backscatter not accounted for in TG-43. The balloon applicator also contributed to the reduced dose. Other ROIs showed a difference depending on the method of dose reporting. Conclusions: TG-186-based calculations produce results that are different from TG-43 for the Axxent source. The differences depend strongly on the method of dose reporting. This study highlights the importance of backscatter to peak skin dose. Tissue heterogeneities, applicator, and patient geometries demonstrate the need for a more robust dose calculation method for low energy brachytherapy sources.

  5. Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Fuel Cycle Defense Waste Management Programs Advanced Nuclear Energy Nuclear

  6. Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    2 - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Fuel Cycle Defense Waste Management Programs Advanced Nuclear Energy Nuclear

  7. Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3 - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Fuel Cycle Defense Waste Management Programs Advanced Nuclear Energy Nuclear

  8. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    (STEO) Highlights  This edition of the Short-Term Energy Outlook is the first to include forecasts for 2016.  December was the sixth consecutive month in which monthly average Brent prices decreased, falling $17/barrel (bbl) from November to a monthly average of $62/bbl, the lowest since May 2009. The December price decline reflects continued growth in U.S. tight oil production, strong global supply, and weakening outlooks for the global economy and oil demand growth.  EIA forecasts

  9. Regional Short-Term Energy Model (RSTEM) Overview

    Reports and Publications (EIA)

    2009-01-01

    The Regional Short-Term Energy Model (RSTEM) utilizes estimated econometric relationships for demand, inventories and prices to forecast energy market outcomes across key sectors and selected regions throughout the United States.

  10. Energy Standard

    Gasoline and Diesel Fuel Update (EIA)

    Standard as requested by Chairman Bingaman November 2011 Analysis of Impacts of a Clean www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as

  11. Long History of IAM Comparisons

    SciTech Connect (OSTI)

    Smith, Steven J.; Clarke, Leon E.; Edmonds, James A.; Kejun, Jiang; Kriegler, Elmar; Masui, Toshihiko; Riahi, Keywan; Shukla, Priyadarshi R.; Tavoni, Massimo; Van Vuuren, Detlef; Weyant, John

    2015-04-23

    Correspondence to editor: We agree with the editors that the assumptions behind models of all types, including integrated assessment models (IAMs), should be as transparent as possible. The editors were in error, however, when they implied that the IAM community is just now emulating the efforts of climate researchers by instigating their own model inter-comparison projects (MIPs). In fact, model comparisons for integrated assessment and climate models followed a remarkably similar trajectory. Early General Circulation Model (GCM) comparison efforts, evolved to the first Atmospheric Model Inter-comparison Project (AMIP), which was initiated in the early 1990s. Atmospheric models evolved to coupled atmosphere-ocean models (AOGCMs) and results from the first Coupled Model Inter-Comparison Project (CMIP1) become available about a decade later. Results of first energy model comparison exercise, conducted under the auspices of the Stanford Energy Modeling Forum, were published in 1977. A summary of the first comparison focused on climate change was published in 1993. As energy models were coupled to simple economic and climate models to form IAMs, the first comparison exercise for IAMs (EMF-14) was initiated in 1994, and IAM comparison exercises have been on-going since this time.

  12. Lawrence Livermore National Laboratory | Open Energy Information

    Open Energy Info (EERE)

    4.7 Geothermal 4.8 Wind forecasting 4.9 Underground coal gasification 4.10 Geologic carbon sequestration 4.11 Vehicle aerodynamics 5 Analysis in energy and environment 5.1...

  13. U.S. Energy Information Administration (EIA)

    Gasoline and Diesel Fuel Update (EIA)

    | PricesDemandSupply | Storage In the News: EIA projects lower natural gas use this winter The U.S. Energy Information Administration (EIA) forecasts that reduced natural gas...

  14. Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    national energy security by developing energy sources with limited impacts on environment improving efficiency and reliability of nation's energy infrastructure Research...

  15. Towards a Science of Tumor Forecast for Clinical Oncology

    SciTech Connect (OSTI)

    Yankeelov, Tom; Quaranta, Vito; Evans, Katherine J; Rericha, Erin

    2015-01-01

    We propose that the quantitative cancer biology community make a concerted effort to apply the methods of weather forecasting to develop an analogous theory for predicting tumor growth and treatment response. Currently, the time course of response is not predicted, but rather assessed post hoc by physical exam or imaging methods. This fundamental limitation of clinical oncology makes it extraordinarily difficult to select an optimal treatment regimen for a particular tumor of an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful theory of tumor forecasting, it should be possible to integrate large tumor specific datasets of varied types, and effectively defeat cancer one patient at a time.

  16. Solid waste integrated forecast technical (SWEFT) report: FY1997 to FY 2070 - Document number changed to HNF-0918 at revision 1 - 1/7/97

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-10-03

    This web site provides an up-to-date report on the radioactive solid waste expected to be managed at Hanford`s Solid Waste (SW) Program from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the SW Program; program- level and waste class-specific estimates; background information on waste sources; and Li comparisons with previous forecasts and with other national data sources. The focus of this web site is on low- level mixed waste (LLMW), and transuranic waste (both non-mixed and mixed) (TRU(M)). Some details on low-level waste and hazardous waste are also provided. Currently, this site is reporting data current as of 9/96. The data represent a life cycle forecast covering all reported activities from FY97 through the end of each program`s life cycle.

  17. NREL: Energy Analysis - Jordan Macknick

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Jordan Macknick Photo of Jordan Macknick Jordan Macknick is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy and Environmental Analyst On staff since September 2009 Phone number: 303-275-3828 E-mail: jordan.macknick@nrel.gov Areas of expertise Renewable energy technological characterizations Database development Policy analysis Primary research interests Interface of energy and water in policy planning Environmental impacts of renewable energy

  18. NREL: Energy Analysis - Wesley Cole

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Wesley Cole Photo of Wesley Cole Wesley Cole is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy Systems Modeler and Analyst On staff since 2014 Phone number: 303-275-3915 E-mail: wesley.cole@nrel.gov Areas of expertise Dynamic and steady-state modeling of energy systems Optimization and advanced control of energy systems Analysis of integrated energy systems Primary research interests Capacity expansion modeling of the electricity sector

  19. ENERGY

    Office of Environmental Management (EM)

    U.S. Department of ENERGY Department of Energy Quadrennial Technology Review-2015 Framing Document http://energy.gov/qtr 2015-01-13 Page 2 The United States faces serious energy-linked challenges as well as substantial energy opportunities. Disruptions, both natural and man-made, threaten our aging energy infrastructure; global patterns of energy use are changing our climate; and our nation's dependence on foreign sources of energy comes at a significant cost to our economy. We need clean,

  20. Risk-Based Comparison of Carbon Capture Technologies

    SciTech Connect (OSTI)

    Engel, David W.; Dalton, Angela C.; Dale, Crystal; Jones, Edward

    2013-05-01

    In this paper, we describe an integrated probabilistic risk assessment methodological framework and a decision-support tool suite for implementing systematic comparisons of competing carbon capture technologies. Culminating from a collaborative effort among national laboratories under the Carbon Capture Simulation Initiative (CCSI), the risk assessment framework and the decision-support tool suite encapsulate three interconnected probabilistic modeling and simulation components. The technology readiness level (TRL) assessment component identifies specific scientific and engineering targets required by each readiness level and applies probabilistic estimation techniques to calculate the likelihood of graded as well as nonlinear advancement in technology maturity. The technical risk assessment component focuses on identifying and quantifying risk contributors, especially stochastic distributions for significant risk contributors, performing scenario-based risk analysis, and integrating with carbon capture process model simulations and optimization. The financial risk component estimates the long-term return on investment based on energy retail pricing, production cost, operating and power replacement cost, plan construction and retrofit expenses, and potential tax relief, expressed probabilistically as the net present value distributions over various forecast horizons.

  1. Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

    SciTech Connect (OSTI)

    Pennock, K.

    2012-10-01

    AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

  2. Control and Size Energy Storage for Managing Energy balance of Variable Generation Resources

    SciTech Connect (OSTI)

    Ke, Xinda; Lu, Ning; Jin, Chunlian

    2015-01-01

    This paper presents control algorithms and sizing strategies for using energy storage to manage energy balance for variable generation resources. The control objective is to minimize the hourly generation imbalance between the actual and the scheduled generation of the wind farm. Three control algorithms are compared: tracking power imbalance, post-compensation, and pre-compensation. Measurement data from a wind farm located in South-central Washington State are used in the study. The results show that tracking power imbalance yields the best performance by keeping the hourly energy imbalances zero. However, the energy storage system (ESS) will be significantly oversized. Post-compensation reduces power rating of the ESS but the hourly imbalance may not be kept as zero when large and long-lasting energy imbalances occur. A linear regression forecasting algorithm is developed for the pre-compensation algorithm to pre-charge or pre-discharge the ESS based on predicted energy imbalances. The performance comparison shows that the pre-compensation method significantly reduces the size of the ESS while maintaining satisfactory performance.

  3. Compiler Comparisons

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Comparisons Compiler Comparisons Using a set of benchmarks described below, different optimization options for the different compilers on Edison are compared. The compilers are also compared against one another on the benchmarks. Benchmarks used Using a set of benchmarks described below, different optimization options for the different compilers on Edison. The compilers are also compared against one another on the benchmarks. NERSC6 Benchmarks We used these benchmarks from the NERSC6

  4. Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1

    SciTech Connect (OSTI)

    Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

    1994-05-01

    This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the US residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute. In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70% of electricity consumption and 30% of natural gas consumption in the US residential sector. Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific technologies within those end-uses, developing cost data for the various technologies, and specifying decision models to forecast future purchase decisions by households. Our implementation of the REEPS 2.1 modeling framework draws on the extensive technology, cost and market data assembled by LBL for the purpose of analyzing federal energy conservation standards. The resulting residential appliance forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national level.

  5. A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 1. Wind and Turbulence

    SciTech Connect (OSTI)

    Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.; Bieringer, Paul E.; Annunzio, Andrew; Bieberbach, George; Meech, Scott

    2015-09-25

    We found that numerical-weather-prediction models are often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense horizontally- and vertically-resolved geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City and the simulated fields important to transport and dispersion models were compared to measurements from a number of sodars, tower-based sonic anemometers, and balloon soundings located in the greater metropolitan area. Time histories of computed wind speed, wind direction, turbulent kinetic energy (e), friction velocity (u* ), and reciprocal Obukhov length (1 / L) were compared to measurements over the 1-month field campaign. Vertical profiles of wind speed, potential temperature (? ), and e were compared during short intensive operating periods. The WRF model was typically able to replicate the measured diurnal variation of the wind fields, but with an average absolute wind direction and speed difference of 35 and 1.9 m s-1 , respectively. Then, using the Mellor-Yamada-Janjic (MYJ) surface-layer scheme, the WRF model was found to generally underpredict surface-layer TKE but overpredict u* that was observed above a suburban region of Oklahoma City. The TKE-threshold method used by the WRF models MYJ surface-layer scheme to compute the boundary-layer height (h) consistently overestimated h derived from a ? gradient method whether using observed or modelled ? profiles.

  6. A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 1. Wind and Turbulence

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.; Bieringer, Paul E.; Annunzio, Andrew; Bieberbach, George; Meech, Scott

    2015-09-25

    We found that numerical-weather-prediction models are often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense horizontally- and vertically-resolved geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City and the simulated fields important to transport and dispersion models were compared to measurements from a number of sodars, tower-based sonic anemometers, and balloon soundings located in the greater metropolitan area. Time histories of computed windmore » speed, wind direction, turbulent kinetic energy (e), friction velocity (u* ), and reciprocal Obukhov length (1 / L) were compared to measurements over the 1-month field campaign. Vertical profiles of wind speed, potential temperature (θ ), and e were compared during short intensive operating periods. The WRF model was typically able to replicate the measured diurnal variation of the wind fields, but with an average absolute wind direction and speed difference of 35° and 1.9 m s-1 , respectively. Then, using the Mellor-Yamada-Janjic (MYJ) surface-layer scheme, the WRF model was found to generally underpredict surface-layer TKE but overpredict u* that was observed above a suburban region of Oklahoma City. The TKE-threshold method used by the WRF model’s MYJ surface-layer scheme to compute the boundary-layer height (h) consistently overestimated h derived from a θ gradient method whether using observed or modelled θ profiles.« less

  7. Central Wind Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities: Revised Edition

    SciTech Connect (OSTI)

    Rogers, J.; Porter, K.

    2011-03-01

    The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.

  8. Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Energy /newsroom/_assets/images/energy-icon.png Energy Research into alternative forms of energy, and improving and securing the power grid, is a major national security imperative. Health Space Computing Energy Earth Materials Science Technology The Lab All The Grid Modernization Initiative represents a comprehensive DOE effort to help shape the future of our nation's grid and solve the challenges of integrating conventional and renewable sources with energy storage and smart buildings. Los

  9. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2005 THRU FY2035 2005.0 VOLUME 2

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2005-08-17

    This report provides up-to-date life cycle information about the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: (1) an overview of Hanford-wide solid waste to be managed by the WM Project; (2) multi-level and waste class-specific estimates; (3) background information on waste sources; and (4) comparisons to previous forecasts and other national data sources. The focus of this report is low-level waste (LLW), mixed low-level waste (MLLW), and transuranic waste, both non-mixed and mixed (TRU(M)). Some details on hazardous waste are also provided, however, this information is not considered comprehensive. This report includes data requested in December, 2004 with updates through March 31,2005. The data represent a life cycle forecast covering all reported activities from FY2005 through the end of each program's life cycle and are an update of the previous FY2004.1 data version.

  10. U.S. Energy Information Administration (EIA) - Projectiondata

    Gasoline and Diesel Fuel Update (EIA)

    Find data from forecast models on crude oil and petroleum liquids, gasoline, diesel, natural gas, electricity, coal prices, supply, and demand projections and more. + EXPAND ALL Monthly short-term forecasts to 2016 additional formats Short-Term Energy Outlook Released: the first Tuesday following the first Thursday of each month. WF01. Average consumer prices and expenditures for heating fuels during the winter PDF 1. U.S. energy market summary PDF 2. U.S. energy prices PDF 3a. International

  11. ARM - PI Product - CCPP-ARM Parameterization Testbed Model Forecast Data

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ProductsCCPP-ARM Parameterization Testbed Model Forecast Data ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send PI Product : CCPP-ARM Parameterization Testbed Model Forecast Data Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are

  12. Report of the external expert peer review panel: DOE benefits forecasts

    SciTech Connect (OSTI)

    None, None

    2006-12-20

    A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts.

  13. Cloudy Sky RRTM Shortwave Radiative Transfer and Comparison to the Revised ECMWF Shortwave Model

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Cloudy Sky RRTM Shortwave Radiative Transfer and Comparison to the Revised ECMWF Shortwave Model M. J. Iacono, J. S. Delamere, E. J. Mlawer, and S. A. Clough Atmospheric and Environmental Research, Inc. Lexington, Massachusetts J.-J. Morcrette European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Introduction An important step toward improving radiative transfer codes in general circulation models (GCMs) is their thorough evaluation by comparison to measurements directly, or

  14. Annual Energy Outlook Retrospective Review - Energy Information

    Gasoline and Diesel Fuel Update (EIA)

    Administration Analysis & Projections Glossary › FAQS › Overview Projection Data Monthly short-term forecasts to 2016 Annual projections to 2040 International projections All projections reports Analysis & Projections Major Topics Most popular Annual Energy Outlook related Congressional & other requests International Energy Outlook related Presentations Recurring Short-Term Outlook Related Special outlooks Testimony All reports Browse by Tag Alphabetical Frequency Tag Cloud

  15. Energy Markets Outlook

    Gasoline and Diesel Fuel Update (EIA)

    Energy Markets Outlook For National Association for Business Economics March 7, 2016 | Washington, D.C. By Adam Sieminski, Administrator Forecast -3 -2 -1 0 1 2 3 4 5 6 82 84 86 88 90 92 94 96 98 100 2011-Q1 2012-Q1 2013-Q1 2014-Q1 2015-Q1 2016-Q1 2017-Q1 Implied stock change and balance (right axis) World production (left axis) World consumption (left axis) world supply and demand million barrels per day implied stock change million barrels per day Global oil inventories are forecast to

  16. Electric power transmission for a Hanford Nuclear Energy Center (HNEC)

    SciTech Connect (OSTI)

    Harty, H.; Dowis, W.J.

    1983-06-01

    The original study of transmission for a Hanford Nuclear Energy Center (HNEC), which was completed in September 1975, was updated in June 1978. The present 1983 revision takes cognizance of recent changes in the electric power situation of the PNW with respect to: (1) forecasts of load growth, (2) the feasibility of early use of 1100 kV transmission, and (3) the narrowing opportunities for siting nuclear plants in the region. The purpose of this update is to explore and describe additions to the existing transmission system that would be necessary to accommodate three levels of generation at HNEC. Comparisons with a PNW system having new thermal generating capacity distributed throughout the marketing region are not made as was done in earlier versions.

  17. Apples with apples: accounting for fuel price risk in comparisons of gas-fired and renewable generation

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2003-12-18

    For better or worse, natural gas has become the fuel of choice for new power plants being built across the United States. According to the US Energy Information Administration (EIA), natural gas combined-cycle and combustion turbine power plants accounted for 96% of the total generating capacity added in the US between 1999 and 2002--138 GW out of a total of 144 GW. Looking ahead, the EIA expects that gas-fired technology will account for 61% of the 355 GW new generating capacity projected to come on-line in the US up to 2025, increasing the nationwide market share of gas-fired generation from 18% in 2002 to 22% in 2025. While the data are specific to the US, natural gas-fired generation is making similar advances in other countries as well. Regardless of the explanation for (or interpretation of) the empirical findings, however, the basic implications remain the same: one should not blindly rely on gas price forecasts when comparing fixed-price renewable with variable-price gas-fired generation contracts. If there is a cost to hedging, gas price forecasts do not capture and account for it. Alternatively, if the forecasts are at risk of being biased or out of tune with the market, then one certainly would not want to use them as the basis for resource comparisons or investment decisions if a more certain source of data (forwards) existed. Accordingly, assuming that long-term price stability is valued, the most appropriate way to compare the levelized cost of these resources in both cases would be to use forward natural gas price data--i.e. prices that can be locked in to create price certainty--as opposed to uncertain natural gas price forecasts. This article suggests that had utilities and analysts in the US done so over the sample period from November 2000 to November 2003, they would have found gas-fired generation to be at least 0.3-0.6 cents/kWh more expensive (on a levelized cost basis) than otherwise thought. With some renewable resources, in particular wind power, now largely competitive with gas-fired generation in the US (including the impact of the federal production tax credit and current high gas prices), a margin of 0.3-0.6 cents/kWh may in some cases be enough to sway resource decisions in favor of renewables.

  18. Material World: Forecasting Household Appliance Ownership in a Growing Global Economy

    SciTech Connect (OSTI)

    Letschert, Virginie; McNeil, Michael A.

    2009-03-23

    Over the past years the Lawrence Berkeley National Laboratory (LBNL) has developed an econometric model that predicts appliance ownership at the household level based on macroeconomic variables such as household income (corrected for purchase power parity), electrification, urbanization and climate variables. Hundreds of data points from around the world were collected in order to understand trends in acquisition of new appliances by households, especially in developing countries. The appliances covered by this model are refrigerators, lighting fixtures, air conditioners, washing machines and televisions. The approach followed allows the modeler to construct a bottom-up analysis based at the end use and the household level. It captures the appliance uptake and the saturation effect which will affect the energy demand growth in the residential sector. With this approach, the modeler can also account for stock changes in technology and efficiency as a function of time. This serves two important functions with regard to evaluation of the impact of energy efficiency policies. First, it provides insight into which end uses will be responsible for the largest share of demand growth, and therefore should be policy priorities. Second, it provides a characterization of the rate at which policies affecting new equipment penetrate the appliance stock. Over the past 3 years, this method has been used to support the development of energy demand forecasts at the country, region or global level.

  19. Mesoscale and Large-Eddy Simulations for Wind Energy

    SciTech Connect (OSTI)

    Marjanovic, N

    2011-02-22

    Operational wind power forecasting, turbine micrositing, and turbine design require high-resolution simulations of atmospheric flow over complex terrain. The use of both Reynolds-Averaged Navier Stokes (RANS) and large-eddy (LES) simulations is explored for wind energy applications using the Weather Research and Forecasting (WRF) model. To adequately resolve terrain and turbulence in the atmospheric boundary layer, grid nesting is used to refine the grid from mesoscale to finer scales. This paper examines the performance of the grid nesting configuration, turbulence closures, and resolution (up to as fine as 100 m horizontal spacing) for simulations of synoptically and locally driven wind ramping events at a West Coast North American wind farm. Interestingly, little improvement is found when using higher resolution simulations or better resolved turbulence closures in comparison to observation data available for this particular site. This is true for week-long simulations as well, where finer resolution runs show only small changes in the distribution of wind speeds or turbulence intensities. It appears that the relatively simple topography of this site is adequately resolved by all model grids (even as coarse as 2.7 km) so that all resolutions are able to model the physics at similar accuracy. The accuracy of the results is shown in this paper to be more dependent on the parameterization of the land-surface characteristics such as soil moisture rather than on grid resolution.

  20. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

  1. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect (OSTI)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  2. Energy Information Administration/Short-Term Energy Outlook - August 2005

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    5 1 Short-Term Energy Outlook August 2005 Short-Term Energy Outlook - Regional Enhancements Starting with this edition of the Short-Term Energy Outlook (STEO), EIA is introducing regional projections (the scope of which will vary by fuel) of energy prices, consumption, and production. The addition of regional data and forecasts will allow us to examine regional fuel demands and prices, regional fuel inventory trends, the interaction between regional electricity demand shifts, and regional

  3. Systems Integration | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Systems Integration Systems Integration Hawaii DREAMS of New Solar Technologies Hawaii DREAMS of New Solar Technologies Read more Plug and Play Solar PV for American Homes Plug and Play Solar PV for American Homes Read more Watt-Sun: A Multi-Scale, Multi-Modal, Machine-Learning Solar Forecasting Technology Watt-Sun: A Multi-Scale, Multi-Modal, Machine-Learning Solar Forecasting Technology Read more High PV Penetration with Energy Storage in Flagstaff, AZ High PV Penetration with Energy Storage

  4. Energy Forecast, ForskEL (Smart Grid Project) | Open Energy Informatio...

    Open Energy Info (EERE)

    meeting both the clients' demand for cost stability and at the same time encourages a demand response. 2. Lack of information and awareness about the possibilities and...

  5. NREL: Energy Analysis - Bethany Frew

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Frew Photo of Bethany Frew Bethany Frew is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Postdoctoral Researcher - Electric System Analyst On staff since October 2014 Phone number: 303-275-3819 E-mail: Bethany.Frew@nrel.gov Areas of expertise Energy systems modeling and analysis Linear programming Primary research interests Understand and determine how to meet system needs for integrating high penetrations of renewable energy, including demand-

  6. NREL: Energy Analysis - Daniel Steinberg

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Steinberg Photo of Daniel Steinberg Daniel Steinberg is a section supervisor of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Senior Policy and Economic Analyst - Electricity Planning Section Supervisor On staff since September 2009 Phone number: 303-275-4287 E-mail: daniel.steinberg@nrel.gov Areas of expertise Energy policy and regulatory analysis Econometric analysis Modeling of interactions between climate change and the energy sector Primary research

  7. NREL: Energy Analysis - Dave Bielen

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Dave Bielen Photo of Dave Bielen Dave Bielen is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy and Environmental Policy Analyst On staff since June 2015 Phone number: 303-275-4921 E-mail: david.bielen@nrel.gov Areas of expertise Environmental policy design Dynamic programming Time series and microeconometric analysis Primary research interests Distributional impact analysis of environmental and energy policy GHG emissions mitigation in the

  8. NREL: Energy Analysis - Janine Freeman

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Janine Freeman Photo of Janine Freeman Janine Freeman is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy System Modeling Engineer On staff since March 2013 Phone number: 303-275-4694 E-mail: janine.freeman@nrel.gov Areas of expertise Solar instrumentation PV energy production modeling Solar resource estimates Vertical axis wind turbine analysis/wind tunnel experimentation PVsyst CAD modeling Campbell Scientific LoggerNet Matlab Primary

  9. NREL: Energy Analysis - Josh Novacheck

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Josh Novacheck Photo of Josh Novacheck Josh Novacheck is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Electricity System Flexibility Analyst On staff since April 2015 Phone number: 303-275-3269 E-mail: joshua.novacheck@nrel.gov Areas of expertise Unit commitment and economic dispatch model of the power system Systems modeling and optimization Primary research interests Wind and solar energy integration into the grid Sustainable energy systems

  10. NREL: Energy Analysis - Kelly Eurek

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Kelly Eurek Photo of Kelly Eurek Kelly Eurek is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy Analyst On staff since August 2010 Phone number: 303-275-4289 E-mail: kelly.eurek@nrel.gov Areas of expertise Linear programming with General Algebraic Modeling System (GAMS) Mathematical programming Primary research interests Capacity expansion and dispatch modeling of the electricity sector, e.g. the Regional Energy Deployment Systems (ReEDS)

  11. NREL: Energy Analysis - Trieu Mai

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Trieu Mai Photo of Trieu Mai Trieu Mai is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy Engineer On staff since 2009 Phone number: 303-384-7566 E-mail: trieu.mai@nrel.gov Areas of expertise Electric sector capacity expansion modeling High penetration renewable scenario analysis Linear programming Primary research interests Implications of large-scale deployment of renewable energy Integration of renewable technologies into the power system

  12. Press Room - Presentations - U.S. Energy Information Administration...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Administrator Presented to: Oil and Money London, UK-October 6, 2015 EIA Short-Term Energy and Winter Fuels Outlook pdf ppt Subject: EIA, petroleum, natural gas, forecasts...

  13. Press Room - Radio - U.S. Energy Information Administration ...

    U.S. Energy Information Administration (EIA) Indexed Site

    ... In its new monthly forecast, the U.S. Energy Information Administration said 33% of U.S. electricity will come ... Residential heating oil price decreases mp3 Date: December 30, 2015 ...

  14. Thirty-Year Solid Waste Generation Maximum and Minimum Forecast for SRS

    SciTech Connect (OSTI)

    Thomas, L.C.

    1994-10-01

    This report is the third phase (Phase III) of the Thirty-Year Solid Waste Generation Forecast for Facilities at the Savannah River Site (SRS). Phase I of the forecast, Thirty-Year Solid Waste Generation Forecast for Facilities at SRS, forecasts the yearly quantities of low-level waste (LLW), hazardous waste, mixed waste, and transuranic (TRU) wastes generated over the next 30 years by operations, decontamination and decommissioning and environmental restoration (ER) activities at the Savannah River Site. The Phase II report, Thirty-Year Solid Waste Generation Forecast by Treatability Group (U), provides a 30-year forecast by waste treatability group for operations, decontamination and decommissioning, and ER activities. In addition, a 30-year forecast by waste stream has been provided for operations in Appendix A of the Phase II report. The solid wastes stored or generated at SRS must be treated and disposed of in accordance with federal, state, and local laws and regulations. To evaluate, select, and justify the use of promising treatment technologies and to evaluate the potential impact to the environment, the generic waste categories described in the Phase I report were divided into smaller classifications with similar physical, chemical, and radiological characteristics. These smaller classifications, defined within the Phase II report as treatability groups, can then be used in the Waste Management Environmental Impact Statement process to evaluate treatment options. The waste generation forecasts in the Phase II report includes existing waste inventories. Existing waste inventories, which include waste streams from continuing operations and stored wastes from discontinued operations, were not included in the Phase I report. Maximum and minimum forecasts serve as upper and lower boundaries for waste generation. This report provides the maximum and minimum forecast by waste treatability group for operation, decontamination and decommissioning, and ER activities.

  15. Weather Research and Forecasting Model with Vertical Nesting Capability

    Energy Science and Technology Software Center (OSTI)

    2014-08-01

    The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improvesmore » WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundary conditions to be provided through the nesting procedure.« less

  16. Energy Markets

    Gasoline and Diesel Fuel Update (EIA)

    Cyclical and Structural Changes in Oil Markets for Columbia University January 29, 2016 | New York, NY by Adam Sieminski, Administrator U.S. Energy Information Administration Forecast -2 -1 0 1 2 3 4 5 6 7 82 84 86 88 90 92 94 96 98 100 2011-Q1 2012-Q1 2013-Q1 2014-Q1 2015-Q1 2016-Q1 2017-Q1 Implied stock change and balance (right axis) World production (left axis) World consumption (left axis) world supply and demand million barrels per day implied stock change million barrels per day Oil

  17. Short-Term Energy Outlook Supplement: Energy-weighted industrial production indices

    Gasoline and Diesel Fuel Update (EIA)

    Energy-weighted industrial production indices December 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Short-Term Energy Outlook Supplement: Energy-weighted industrial production indices i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  18. State Energy Efficiency Program Evaluation Inventory

    U.S. Energy Information Administration (EIA) Indexed Site

    State Energy Efficiency Program Evaluation Inventory July 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | State Energy Efficiency Program Evaluation Inventory i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other

  19. NREL: Energy Analysis - Matthew O'Connell

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Matthew O'Connell Photo of Matthew O'Connell. Matthew O'Connell is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy Systems Engineer On staff since December 2014 Phone number: 303-384-7445 E-mail: matthew.oconnell@nrel.gov Areas of expertise Wind ramp detection methods Energy storage systems for renewable energy generation Statistical data analysis and modeling Primary research interests Increased renewable energy penetration in the electric

  20. Energy

    Office of Legacy Management (LM)

    ..) ".. _,; ,' . ' , ,; Depar?.me.nt ,of.' Energy Washington; DC 20585 : . ' , - $$ o"\ ' ~' ,' DEC ?;$ ;y4,,, ~ ' .~ The Honorable John Kalwitz , 200 E. Wells Street Milwaukee, W~isconsin 53202, . . i :. Dear,Mayor 'Kalwitz: " . " Secretary of Energy Hazel' O'Leary has announceha new,approach 'to,openness in " the Department of Ene~rgy (DOE) and its communications with'the public. In -. support of~this initiative, we areipleased to forward the enclosed information

  1. Technology data characterizing water heating in commercial buildings: Application to end-use forecasting

    SciTech Connect (OSTI)

    Sezgen, O.; Koomey, J.G.

    1995-12-01

    Commercial-sector conservation analyses have traditionally focused on lighting and space conditioning because of their relatively-large shares of electricity and fuel consumption in commercial buildings. In this report we focus on water heating, which is one of the neglected end uses in the commercial sector. The share of the water-heating end use in commercial-sector electricity consumption is 3%, which corresponds to 0.3 quadrillion Btu (quads) of primary energy consumption. Water heating accounts for 15% of commercial-sector fuel use, which corresponds to 1.6 quads of primary energy consumption. Although smaller in absolute size than the savings associated with lighting and space conditioning, the potential cost-effective energy savings from water heaters are large enough in percentage terms to warrant closer attention. In addition, water heating is much more important in particular building types than in the commercial sector as a whole. Fuel consumption for water heating is highest in lodging establishments, hospitals, and restaurants (0.27, 0.22, and 0.19 quads, respectively); water heating`s share of fuel consumption for these building types is 35%, 18% and 32%, respectively. At the Lawrence Berkeley National Laboratory, we have developed and refined a base-year data set characterizing water heating technologies in commercial buildings as well as a modeling framework. We present the data and modeling framework in this report. The present commercial floorstock is characterized in terms of water heating requirements and technology saturations. Cost-efficiency data for water heating technologies are also developed. These data are intended to support models used for forecasting energy use of water heating in the commercial sector.

  2. Hawaii demand-side management resource assessment. Final report, Reference Volume 5: The DOETRAN user`s manual; The DOE-2/DBEDT DSM forecasting model interface

    SciTech Connect (OSTI)

    1995-04-01

    The DOETRAN model is a DSM database manager, developed to act as an intermediary between the whole building energy simulation model, DOE-2, and the DBEDT DSM Forecasting Model. DOETRAN accepts output data from DOE-2 and TRANslates that into the format required by the forecasting model. DOETRAN operates in the Windows environment and was developed using the relational database management software, Paradox 5.0 for Windows. It is not necessary to have any knowledge of Paradox to use DOETRAN. DOETRAN utilizes the powerful database manager capabilities of Paradox through a series of customized user-friendly windows displaying buttons and menus with simple and clear functions. The DOETRAN model performs three basic functions, with an optional fourth. The first function is to configure the user`s computer for DOETRAN. The second function is to import DOE-2 files with energy and loadshape data for each building type. The third main function is to then process the data into the forecasting model format. As DOETRAN processes the DOE-2 data, graphs of the total electric monthly impacts for each DSM measure appear, providing the user with a visual means of inspecting DOE-2 data, as well as following program execution. DOETRAN provides three tables for each building type for the forecasting model, one for electric measures, gas measures, and basecases. The optional fourth function provided by DOETRAN is to view graphs of total electric annual impacts by measure. This last option allows a comparative view of how one measure rates against another. A section in this manual is devoted to each of the four functions mentioned above, as well as computer requirements and exiting DOETRAN.

  3. An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study

    SciTech Connect (OSTI)

    De Mello, Phillip; Lu, Ning; Makarov, Yuri V.

    2011-01-17

    This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.

  4. The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

    SciTech Connect (OSTI)

    Martin Wilde, Principal Investigator

    2012-12-31

    ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most wind plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational setting. It shall be demonstrated that when used properly, the real-time offsite measurements materially improve wind ramp capture and prediction statistics, when compared to traditional wind forecasting techniques and to a simple persistence model.

  5. Solar Energy Resource Center | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Resource Center Solar Energy Resource Center Solar Mapping Resources Featured Article There's a Map for That National laboratories and private companies have developed a number of tools to forecast the solar potential of homes and businesses. Learn more Icon of a person giving a presentation. Educating Consumers Tools designed to meet individual consumer needs Icon of a trending chart. Market Analysis State- and local-level challenges and solutions for establishing a thriving solar market Icon

  6. Review of Variable Generation Forecasting in the West: July 2013 - March 2014

    SciTech Connect (OSTI)

    Widiss, R.; Porter, K.

    2014-03-01

    This report interviews 13 operating entities (OEs) in the Western Interconnection about their implementation of wind and solar forecasting. The report updates and expands upon one issued by NREL in 2012. As in the 2012 report, the OEs interviewed vary in size and character; the group includes independent system operators, balancing authorities, utilities, and other entities. Respondents' advice for other utilities includes starting sooner rather than later as it can take time to plan, prepare, and train a forecast; setting realistic expectations; using multiple forecasts; and incorporating several performance metrics.

  7. Energy Efficiency and Renewable Energy Benefits

    Office of Environmental Management (EM)

    § Objective § Background § Methods § Indian Canyons Trading Post § History § Renewable Energy § Energy Efficiency § Comparisons § Conclusion 2 Objective § Benefits of renewable energy & energy efficiency § Energy demand § Cost § Emissions 3 Background § Global warming § Climate change § Non-renewable energy § Biggest energy users: buildings § Solutions: energy efficiency & renewable energy 4 Methods § Site visit § Approval from Agua Caliente Band Tribal Council §

  8. Energy

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    M onthly Energy Re< view Ila A a m 0 II 8 IIIW *g U In this issue: New data on nuclear electricity in Eastern Europe (Table 10.4) 9'Ij a - Ordering Information This publication...

  9. NREL: Energy Analysis - Ben Sigrin

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Ben Sigrin Photo of Ben Sigrin Ben Sigrin is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy Systems Modeling Engineer On staff since May 2013 Phone number: 303-275-3244 E-mail: Benjamin.Sigrin@nrel.gov Areas of expertise Financial modeling and analysis Risk and uncertainty modeling Distributed photovoltaics and consumer decision-making Primary research interests Consumer decision-making in the adoption of novel technologies Capacity

  10. NREL: Energy Analysis - Owen Zinaman

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Owen Zinaman Photo of Owen Zinaman Owen Zinaman is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. International Policy and Regulatory Specialist On staff since March 2012 Phone number: 303-275-4589 E-mail: owen.zinaman@nrel.gov Areas of expertise Executes program of international collaboration with South Africa power sector stakeholders under the 21st Century Power Partnership Coordinates NREL support for the Clean Energy Regulators Initiative Grid

  11. NREL: Energy Analysis - Paul Schwabe

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Schwabe Photo of Paul Schwabe Paul Schwabe is a member of the Market and Policy Impact Analysis Group in the Strategic Energy Analysis Center. Energy and Financial Analyst On staff since August 2008 Phone number: 303-384-7468 E-mail: paul.schwabe@nrel.gov Areas of expertise Electricity market financial analysis Natural gas volume and revenue forecasting Energy efficiency and conservation, including electric and natural gas rate decoupling Primary research interests Market penetration and

  12. NREL: Energy Analysis - Pieter Gagnon

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Pieter Gagnon Photo of Pieter Gagnon Pieter Gagnon is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Engineering Analyst of Solar Policy and Technoeconomics On staff since June 2015 Phone number: 303-275-4910 E-mail: Pieter.Gagnon@nrel.gov Areas of expertise Design and economic analysis of solar photovoltaic systems Energy system modeling in the hydraulic, pneumatic, mechanical, and solar domains Primary research interests Characterizing modern

  13. NREL: Energy Analysis - Venkat Kirshnan

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Venkat Krishnan Photo of Venkat K. Krishnan Venkat Krishnan is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy System Analyst On staff since August 11, 2014 Phone number: 303-275-3237 E-mail: Venkat.Krishnan@nrel.gov Areas of expertise Interdependent infrastructure planning Power system operational planning and stability assessment Statistical simulation and data mining Primary research interests Co-optimization of transmission and supply

  14. Demand forecasting and revenue requirements, with implications for consideration in British Columbia

    SciTech Connect (OSTI)

    Acton, J.P.

    1983-05-01

    This paper was filed as an exhibit on behalf of The Consumers' Association of Canada (B.C. Branch), The Federated Anti-Poverty Groups of B.C., The Sierra Club of Western Canada, and the B.C. Old Age Pensioners' Organization. It was subjected to cross-examination on October 29, 1982, during Phase I of the hearings. The Utilities Commission had designated Phase I for consideration of (1) demand, (2) assets in service, (3) revenue requirements excluding return, and (4) financing and capital requirements. This paper presents a general discussion of the elements of a rate structure and their relationship to the demand for electricity, a systematic review of some 50 empirical studies of the demand for electricity as a function of price and other factors by the three principal classes of customers, and a discussion of the notion of revenue requirements. The paper should be of interest to utility regulators, rate specialists, and forecasters for its review of demand models and to academics concerned with the study of energy demand.

  15. Comparison of Test Procedures and Energy Efficiency Criteria in Selected International Standards & Labeling Programs for Copy Machines, External Power Supplies, LED Displays, Residential Gas Cooktops and Televisions

    SciTech Connect (OSTI)

    Zheng, Nina; Zhou, Nan; Fridley, David

    2012-03-01

    This report presents a technical review of international minimum energy performance standards (MEPS), voluntary and mandatory energy efficiency labels and test procedures for five products being considered for new or revised MEPS in China: copy machines, external power supply, LED displays, residential gas cooktops and flat-screen televisions. For each product, an overview of the scope of existing international standards and labeling programs, energy values and energy performance metrics and description and detailed summary table of criteria and procedures in major test standards are presented.

  16. Direct Drive Wave Energy Buoy

    SciTech Connect (OSTI)

    Rhinefrank, Ken

    2011-11-02

    Presentation from the 2011 Water Peer Review in which principal investigator discusses project progress and results for this project which will be used to inform the utility-scale design process, improve cost estimates, accurately forecast energy production and to observe system operation and survivability.

  17. Integration of Behind-the-Meter PV Fleet Forecasts into Utility...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Clean Power Research logo.jpg This project will address the need for a more accurate approach ...

  18. Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Orwig, K.; Milligan, M.

    2012-06-01

    In this paper, we examine the parameters associated with the calculation of the Renyi entropy in order to further the understanding of its application to assessing wind power forecasting errors.

  19. U.S. diesel fuel price forecast to be 1 penny lower this summer...

    U.S. Energy Information Administration (EIA) Indexed Site

    That's down 12 percent from last summer's record exports. Biodiesel production, which averaged 68,000 barrels a day last summer, is forecast to jump to 82,000 barrels a day this ...

  20. Waste Generation Forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1994--FY 2001. Environmental Restoration Program, September 1993 Revision

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

    This Waste Generation Forecast for DOE-ORO`s Environmental Restoration OR-1 Project. FY 1994--FY 2001 is the third in a series of documents that report current estimates of the waste volumes expected to be generated as a result of Environmental Restoration activities at Department of Energy, Oak Ridge Operations Office (DOE-ORO), sites. Considered in the scope of this document are volumes of waste expected to be generated as a result of remedial action and decontamination and decommissioning activities taking place at these sites. Sites contributing to the total estimates make up the DOE-ORO Environmental Restoration OR-1 Project: the Oak Ridge K-25 Site, the Oak Ridge National Laboratory, the Y-12 Plant, the Paducah Gaseous Diffusion Plant, the Portsmouth Gaseous Diffusion Plant, and the off-site contaminated areas adjacent to the Oak Ridge facilities (collectively referred to as the Oak Ridge Reservation Off-Site area). Estimates are available for the entire fife of all waste generating activities. This document summarizes waste estimates forecasted for the 8-year period of FY 1994-FY 2001. Updates with varying degrees of change are expected throughout the refinement of restoration strategies currently in progress at each of the sites. Waste forecast data are relatively fluid, and this document represents remediation plans only as reported through September 1993.

  1. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  2. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  3. Short-term energy outlook. Quarterly projections, Third quarter 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-02

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202). The feature article for this issue is Demand, Supply and Price Outlook for Reformulated Gasoline, 1995.

  4. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    SciTech Connect (OSTI)

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  5. LED ADOPTION REPORT | Department of Energy

    Energy Savers [EERE]

    ADOPTION REPORT LED ADOPTION REPORT PDF icon Report: Adoption of Light-Emitting Diodes in Common Lighting Applications PDF icon Report Summary PDF icon Full Infographic More Documents & Publications Energy Savings Forecast of Solid-State Lighting in General Illumination Applications 2015 SSL TECHNOLOGY DEVELOPMENT WORKSHOP PRESENTATIONS - Day 1 Energy Savings Estimates of Light Emitting Diodes

  6. Building Energy Asset Score | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Commercial Buildings » Analysis Tools » Building Energy Asset Score Building Energy Asset Score Building Energy Asset Score The U.S. Department of Energy's Building Energy Asset Score (Asset Score) is a national standardized tool for assessing the physical and structural energy efficiency of commercial and multifamily residential buildings. The Asset Score generates a simple energy efficiency rating that enables comparison among buildings, and identifies opportunities to invest in energy

  7. Supplement to the annual energy outlook 1994

    SciTech Connect (OSTI)

    1994-03-01

    This report is a companion document to the Annual Energy Outlook 1994 (AEO94), (DOE/EIA-0383(94)), released in Jan. 1994. Part I of the Supplement presents the key quantitative assumptions underlying the AEO94 projections, responding to requests by energy analysts for additional information on the forecasts. In Part II, the Supplement provides regional projections and other underlying details of the reference case projections in the AEO94. The AEO94 presents national forecasts of energy production, demand and prices through 2010 for five scenarios, including a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. These forecasts are used by Federal, State, and local governments, trade associations, and other planners and decisionmakers in the public and private sectors.

  8. Comparison Report

    Office of Environmental Management (EM)

    Report 2009 Department of Energy Annual Employee Survey Results -vs- 2006 & 2008 All Federal Government Federal Human Capital Survey Results This is a summary-by-question of DOE's responses to the 2009 Annual Employee Survey compared to corresponding items on the 2006 and 2008 Federal Human Capital Surveys. This summary displays results by Positive, Neutral, Negative, and where applicable, Do Not Know or No Basis to Judge responses. As shown below, for each response scale two responses are

  9. Statement from Secretary of Energy Samuel W. Bodman Regarding EIA's Updated

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Annual Energy Outlook | Department of Energy Regarding EIA's Updated Annual Energy Outlook Statement from Secretary of Energy Samuel W. Bodman Regarding EIA's Updated Annual Energy Outlook December 12, 2007 - 4:44pm Addthis WASHINGTON, DC - Earlier today the Department of Energy's Energy Information Administration released their Annual Energy Outlook 2008. Energy Secretary Samuel Bodman made the following statement regarding the EIA's Annual Energy Outlook which forecasts to 2030:

  10. Life-Cycle Analysis Results of Geothermal Systems in Comparison...

    Office of Environmental Management (EM)

    Systems in Comparison to Other Power Systems A life-cycle energy and greenhouse gas emissions analysis has been conducted with Argonne National Laboratory's GREET model...

  11. State Energy Efficiency Program Evaluation Inventory - Energy Information

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Administration Analysis & Projections Glossary › FAQS › Overview Projection Data Monthly short-term forecasts to 2016 Annual projections to 2040 International projections All projections reports Analysis & Projections Major Topics Most popular Annual Energy Outlook related Congressional & other requests International Energy Outlook related Presentations Recurring Short-Term Outlook Related Special outlooks Testimony All reports Browse by Tag Alphabetical Frequency Tag Cloud

  12. State Energy Efficiency Program Evaluation Inventory - Energy Information

    U.S. Energy Information Administration (EIA) Indexed Site

    Administration Analysis & Projections Glossary › FAQS › Overview Projection Data Monthly short-term forecasts to 2016 Annual projections to 2040 International projections All projections reports Analysis & Projections Major Topics Most popular Annual Energy Outlook related Congressional & other requests International Energy Outlook related Presentations Recurring Short-Term Outlook Related Special outlooks Testimony All reports Browse by Tag Alphabetical Frequency Tag Cloud

  13. Short-Term Energy Outlook Model Documentation: Petroleum Product Prices Module

    Reports and Publications (EIA)

    2015-01-01

    The petroleum products price module of the Short-Term Energy Outlook (STEO) model is designed to provide U.S. average wholesale and retail price forecasts for motor gasoline, diesel fuel, heating oil, and jet fuel.

  14. Short-Term Energy Outlook Model Documentation: Motor Gasoline Consumption Model

    Reports and Publications (EIA)

    2011-01-01

    The motor gasoline consumption module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of total U.S. consumption of motor gasolien based on estimates of vehicle miles traveled and average vehicle fuel economy.

  15. Short-Term Energy Outlook Model Documentation: Regional Residential Heating Oil Price Model

    Reports and Publications (EIA)

    2009-01-01

    The regional residential heating oil price module of the Short-Term Energy Outlook (STEO) model is designed to provide residential retail price forecasts for the 4 census regions: Northeast, South, Midwest, and West.

  16. Short-Term Energy Outlook Model Documentation: Hydrocarbon Gas Liquids Supply and Demand

    Reports and Publications (EIA)

    2015-01-01

    The hydrocarbon gas liquids (ethane, propane, butanes, and natural gasoline) module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of U.S. production, consumption, refinery inputs, net imports, and inventories.

  17. Short-Term Energy Outlook Model Documentation: Regional Residential Propane Price Model

    Reports and Publications (EIA)

    2009-01-01

    The regional residential propane price module of the Short-Term Energy Outlook (STEO) model is designed to provide residential retail price forecasts for the 4 Census regions: Northeast, South, Midwest, and West.

  18. OSTI, US Dept of Energy, Office of Scientific and Technical Informatio...

    Office of Scientific and Technical Information (OSTI)

    Topic Energy in the Forecast by Daphne Evans 13 Aug, 2012 in Science Communications Wind Resource Map of the United States If you can accurately predict the weather, you may be...

  19. EIA - Energy Conferences & Presentations.

    Gasoline and Diesel Fuel Update (EIA)

    3 EIA Conference 2010 Session 3: EIA's 2010 Annual Energy Outlook Highlights Moderator: Paul Holtberg, EIA Speakers: John Conti, EIA Tom R. Eizember, Exxon Mobil Corporation Mary Novak, IHS Global Insight Moderator and Speaker Biographies Paul Holtberg, EIA Paul D. Holtberg is Director of the Demand and Integration Division in the Office of Integrated Analysis and Forecasting at the U.S. Energy Information Administration. Mr. Holtberg joined EIA in July of 2002. At EIA, he works with three other

  20. NREL: Energy Analysis - Andrew Weekley

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Andrew Weekley Photo of Andrew Weekley (UCAR, photo by Carlye Calvin) Andrew Weekley is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Data Scientist On staff since January 2013 Phone number: 303-275-3680 E-mail: andrew.weekley@nrel.gov Areas of expertise MATLAB Expert systems Scientific data analysis Primary research interests Time series segmentation and classification Image segmentation and classification Machine intelligent algorithms Education

  1. NREL: Energy Analysis - Dheepak Krishnamurthy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Dheepak Krishnamurthy Photo of Dheepak Krishnamurthy Dheepak Krishnamurthy is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Developer of Agent-Based Models On staff since January 2015 Phone number: 303-384-7394 E-mail: dheepak.krishnamurthy@nrel.gov Areas of expertise Power System Operation, Optimization and Control High performance computing Computer programming and software development Primary research interests Integrated Retail and Wholesale

  2. NREL: Energy Analysis - Liz Torres

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Liz Torres Photo of Liz Torres Liz Torres is administrative assisant for the Data Analysis and Visualization and Energy Forecasting and Modeling groups in the Strategic Energy Analysis Center. Administrative Assistant On staff since August 2014 Phone number: 303-275-3210 E-mail: elizabeth.torres@nrel.gov Areas of expertise Customer service Technically savvy Event planning Word processing/desktop publishing Database management Primary research interests Website design Database design

  3. NREL: Energy Analysis - Nate Blair

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Nate Blair Photo of Nate Blair. Nate Blair is the group manager of the Energy Forecasting and Modeling in the Strategic Energy Analysis Center. Group Manager On staff since September 2002 Phone number: 303-384-7426 E-mail: nate.blair@nrel.gov Areas of expertise Linear programming with General Algebraic Modeling System (GAMS) Building and system simulation tools, especially the TRaNsient SYstem Simulation Program (TRNSYS) EES Concentrating solar power system modeling Experimental solar

  4. NREL: Energy Analysis - Stuart Cohen

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Stuart Cohen Photo of Stuart Cohen Stuart Cohen is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Electric System Modeler On staff since September 2012 Phone number: 303-275-4369 E-mail: stuart.cohen@nrel.gov Areas of expertise Electricity system analysis Optimization and data analysis in GAMS and MATLAB Carbon capture and sequestration Primary research interests Multi-scale electricity systems modeling and optimization (e.g. ReEDS) Optimizing

  5. Natural Gas Consumption and Prices Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    Natural Gas Consumption and Prices Short-Term Energy Outlook June 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Natural Gas Consumption and Prices - Short-Term Energy Outlook Model i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  6. Analysis of Energy Efficiency Program Impacts Based on Program Spending

    Gasoline and Diesel Fuel Update (EIA)

    Energy Efficiency Program Impacts Based on Program Spending May 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  7. Behavioral Economics Applied to Energy Demand Analysis: A Foundation

    Gasoline and Diesel Fuel Update (EIA)

    Behavioral Economics Applied to Energy Demand Analysis: A Foundation October 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Behavioral Economics Applied to Energy Demand Analysis i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  8. Long-Term U.S. Energy Outlook: Different Perspectives

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Paul Holtberg, Moderator April 26, 2011 | Washington, D.C. Long-Term U.S. Energy Outlook: Different Perspectives Speakers 2 Paul Holtberg, 2011 EIA Energy Conference Washington, D.C., April 26, 2011 * John Conti, Assistant Administrator of Energy Analysis, Energy Information Administration * Mark Finley, General Manager, Global Energy Markets and U.S. Economics, BP * Douglas Meade, Director of Research, INFORUM Forecasts/projections and uncertainty 3 Paul Holtberg, 2011 EIA Energy Conference

  9. Comparison of electric and magnetic quadrupole focusing for the low energy end of an induction-linac-ICF (Inertial-Confinement-Fusion) driver

    SciTech Connect (OSTI)

    Kim, C.H.

    1987-04-01

    This report compares two physics designs of the low energy end of an induction linac-ICF driver: one using electric quadrupole focusing of many parallel beams followed by transverse combining; the other using magnetic quadrupole focusing of fewer beams without beam combining. Because of larger head-to-tail velocity spread and a consequent rapid current amplification in a magnetic focusing channel, the overall accelerator size of the design using magnetic focusing is comparable to that using electric focusing.

  10. New XDM-corrected potential energy surfaces for ArNO(X{sup 2}?): A comparison with CCSD(T) calculations and experiments

    SciTech Connect (OSTI)

    Warehime, Michael; Johnson, Erin R.; K?os, Jacek

    2015-01-14

    We report new potential energy surfaces for the ground state ArNO(X{sup 2}?) van der Waals system calculated using the unrestricted Hartree-Fock (UHF) method with the addition of the Becke-Roussel correlation functional and exchange-hole dipole moment dispersion correction (XDM). We compare UHFBR-XDM surfaces and those previously reported by Alexander from coupled cluster CCSD(T) calculations [J. Chem. Phys. 111, 7426 (1999)]. The bound states of ArNO have been investigated with these new UHFBR-XDM surfaces, including relative energy-level spacing, adiabatic bender states and wave functions, and spectroscopic data. These results have been found to be in good agreement with calculations based on the CCSD(T) PESs. These new PESs are used to investigate the inelastic scattering of NO(X) by Ar. Full close-coupling integral cross sections at collision energies of 442 cm{sup ?1}, 1774 cm{sup ?1} and differential cross sections at collision energy of 530 cm{sup ?1} were determined for transitions out of the lowest NO(X) rotational level (j = ? = 1/2,f). These cross sections are in good agreement with those calculated with CCSD(T) and accordingly in good agreement with the most recent initial and final state resolved experimental data. The UHFBR-XDM scheme yields high-quality potential surfaces with computational cost comparable to the Hartree-Fock method and our results may serve as a benchmark for application of this scheme to collisions between larger molecules.

  11. Simulations of Clouds and Sensitivity Study by Weather Research and Forecast Model for Atmospheric Radiation Measurement Case 4

    SciTech Connect (OSTI)

    Wu, J.; Zhang, M.

    2005-03-18

    One of the large errors in general circulation models (GCMs) cloud simulations is from the mid-latitude, synoptic-scale frontal cloud systems. Now, with the availability of the cloud observations from Atmospheric Radiation Measurement (ARM) 2000 cloud Intensive Operational Period (IOP) and other observational datasets, the community is able to document the model biases in comparison with the observations and make progress in development of better cloud schemes in models. Xie et al. (2004) documented the errors in midlatitude frontal cloud simulations for ARM Case 4 by single-column models (SCMs) and cloud resolving models (CRMs). According to them, the errors in the model simulated cloud field might be caused by following reasons: (1) lacking of sub-grid scale variability; (2) lacking of organized mesoscale cyclonic advection of hydrometeors behind a moving cyclone which may play important role to generate the clouds there. Mesoscale model, however, can be used to better under stand these controls on the subgrid variability of clouds. Few studies have focused on applying mesoscale models to the forecasting of cloud properties. Weaver et al. (2004) used a mesoscale model RAMS to study the frontal clouds for ARM Case 4 and documented the dynamical controls on the sub-GCM-grid-scale cloud variability.

  12. Sandia Energy - News

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    forecasts automatically from deterministic historical forecasts for load, solar, andor wind power production and their respective actuals, using a technology known as...

  13. Discussion on a Code Comparison Effort for the Geothermal Technologies

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Program | Department of Energy Discussion on a Code Comparison Effort for the Geothermal Technologies Program Discussion on a Code Comparison Effort for the Geothermal Technologies Program Code comparison presentation by Mark White of PNNL at the 2012 Peer Review meeting on May 10. PDF icon gtp_2012peerreview_pnnl_white.pdf More Documents & Publications PNNL Support of the DOE GTO Model Comparison Activity Methane Hydrate Annual Reports Fiscal Year 2014 ASCEM Status Report

  14. Comparison of heating and cooling energy consumption by HVAC system with mixing and displacement air distribution for a restaurant dining area in different climates

    SciTech Connect (OSTI)

    Zhivov, A.M.; Rymkevich, A.A.

    1998-12-31

    Different ventilation strategies to improve indoor air quality and to reduce HVAC system operating costs in a restaurant with nonsmoking and smoking areas and a bar are discussed in this paper. A generic sitting-type restaurant is used for the analysis. Prototype designs for the restaurant chain with more than 200 restaurants in different US climates were analyzed to collect the information on building envelope, dining area size, heat and contaminant sources and loads, occupancy rates, and current design practices. Four constant air volume HVAC systems wit h a constant and variable (demand-based) outdoor airflow rate, with a mixing and displacement air distribution, were compared in five representative US climates: cold (Minneapolis, MN); Maritime (Seattle, WA); moderate (Albuquerque, NM); hot-dry (Phoenix, AZ); and hot-humid (Miami, FL). For all four compared cases and climatic conditions, heating and cooling consumption by the HVAC system throughout the year-round operation was calculated and operation costs were compared. The analysis shows: Displacement air distribution allows for better indoor air quality in the breathing zone at the same outdoor air supply airflow rate due to contaminant stratification along the room height. The increase in outdoor air supply during the peak hours in Miami and Albuquerque results in an increase of both heating and cooling energy consumption. In other climates, the increase in outdoor air supply results in reduced cooling energy consumption. For the Phoenix, Minneapolis, and Seattle locations, the HVAC system operation with a variable outdoor air supply allows for a decrease in cooling consumption up to 50% and, in some cases, eliminates the use of refrigeration machines. The effect of temperature stratification on HVAC system parameters is the same for all locations; displacement ventilation systems result in decreased cooling energy consumption but increased heating consumption.

  15. Energy Sources | Department of Energy

    Office of Environmental Management (EM)

    Sources Energy Sources December 17, 2015 Top 5 Interactives and Maps of 2015 From rapidly rising renewables to carbon emission comparisons, these powerful visual aids illustrate the biggest energy stories of 2015. August 21, 2015 This gigantic animated globe will soon take environmental awareness to a whole new level! The National Oceanic & Atmospheric Administration's (NOAA's) Science On a Sphere, like this one at their headquarters in Maryland, will soon feature new energy datasets to

  16. Monthly energy review, April 1994

    SciTech Connect (OSTI)

    Not Available

    1994-04-01

    The Monthly Energy Review contains statistical data on the following: energy consumption, petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, and international energy. In addition, an energy overview is provided, and, for the April issue, Energy use and carbon emissions; Some international comparisons.

  17. Demand for oil and energy in developing countries

    SciTech Connect (OSTI)

    Wolf, C. Jr.; Relles, D.A.; Navarro, J.

    1980-05-01

    How much of the world's oil and energy supply will the non-OPEC less-developed countries (NOLDCs) demand in the next decade. Will their requirements be small and thus fairly insignificant compared with world demand, or large and relatively important. How will world demand be affected by the economic growth of the NOLDCs. In this report, we try to develop some reasonable forecasts of NOLDC energy demands in the next 10 years. Our focus is mainly on the demand for oil, but we also give some attention to the total commercial energy requirements of these countries. We have tried to be explicit about the uncertainties associated with our forecasts, and with the income and price elasticities on which they are based. Finally, we consider the forecasts in terms of their implications for US policies concerning the NOLDCs and suggest areas of future research on NOLDC energy issues.

  18. Assumptions to the Annual Energy Outlook 2015

    Gasoline and Diesel Fuel Update (EIA)

    International Energy Module The National Energy Modeling System International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the IEM computes Brent and WTI prices, provides a supply curve of world

  19. NREL: Energy Analysis - Brian W Bush

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Brian W Bush Photo of Brian Bush Brian W Bush is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Principal Strategic Analyst On staff from January 2008 Phone number: (303) 384-7472 E-mail: brian.bush@nrel.gov Areas of expertise Energy and infrastructure modeling, simulation, and analysis High performance computing Software architecture, design, implementation, and testing Discrete-event and continuous simulation Statistical analysis Geographic

  20. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2003 THRU FY2046 VERSION 2003.1 VOLUME 2 [SEC 1 & 2

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2003-12-01

    This report includes data requested on September 10, 2002 and includes radioactive solid waste forecasting updates through December 31, 2002. The FY2003.0 request is the primary forecast for fiscal year FY 2003.

  1. Fossil Fuel-Generated Energy Consumption Reduction for New Federal...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Buildings OIRA Comparison Document Fossil Fuel-Generated Energy Consumption Reduction for New Federal Buildings and Major Renovations of Federal Buildings OIRA Comparison Document...

  2. Response to several FOIA requests - Renewable Energy. | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy Response to several FOIA requests - Renewable Energy. Response to several FOIA requests - Renewable Energy. Response to several FOIA requests - Renewable Energy. Excess Capacity from LADWP Control Area (LADWP, Glendale, Burbank),Summer 2001 nepdg_751_1000.pdf Total Load (CEC Draft Demand Forecast 10/16/2000 PDF icon Response to several FOIA requests - Renewable Energy. More Documents & Publications An Assessment of Heating Fuels And Electricity Markets During the Winters of

  3. About EIA - Ourwork - U.S. Energy Information Administration (EIA)

    Gasoline and Diesel Fuel Update (EIA)

    Mission and Overview The U.S. Energy Information Administration (EIA) is the statistical and analytical agency within the U.S. Department of Energy. EIA collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment. EIA is the nation's premier source of energy information and, by law, its data, analyses, and forecasts are independent of

  4. Power Technologies Energy Data Book - Fourth Edition

    SciTech Connect (OSTI)

    Aabakken, J.

    2006-08-01

    This report, prepared by NREL's Strategic Energy Analysis Center, includes up-to-date information on power technologies, including complete technology profiles. The data book also contains charts on electricity restructuring, power technology forecasts, electricity supply, electricity capability, electricity generation, electricity demand, prices, economic indicators, environmental indicators, and conversion factors.

  5. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

    SciTech Connect (OSTI)

    Brown, C. W.; Hood, Raleigh R.; Long, Wen; Jacobs, John M.; Ramers, D. L.; Wazniak, C.; Wiggert, J. D.; Wood, R.; Xu, J.

    2013-09-01

    The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanisticempirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.

  6. Short-Term Energy Outlook September 2013

    Gasoline and Diesel Fuel Update (EIA)

    (STEO) Highlights  Monthly average crude oil prices increased for the fourth consecutive month in August 2013, as supply disruptions in Libya increased and concerns over the conflict in Syria intensified. The U.S. Energy Information Administration's (EIA) forecast for Brent crude oil spot price, which averaged $108 per barrel during the first half of 2013, averages $109 per barrel over the second half of 2013 and $102 per barrel in 2014, $5 per barrel and $2 per barrel higher than forecast in

  7. How regulators should use natural gas price forecasts

    SciTech Connect (OSTI)

    Costello, Ken

    2010-08-15

    Natural gas prices are critical to a range of regulatory decisions covering both electric and gas utilities. Natural gas prices are often a crucial variable in electric generation capacity planning and in the benefit-cost relationship for energy-efficiency programs. High natural gas prices can make coal generation the most economical new source, while low prices can make natural gas generation the most economical. (author)

  8. Annual energy outlook 1999, with projections to 2020

    SciTech Connect (OSTI)

    1998-12-01

    The Annual Energy Outlook 1999 (AEO99) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA`s National Energy Modeling System (NEMS). The report begins with an Overview summarizing the AEO99 reference case. The next section, Legislation and Regulations, describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. Issues in Focus discusses current energy issues--the economic decline in East Asia, growth in demand for natural gas, vehicle emissions standards, competitive electricity pricing, renewable portfolio standards, and carbon emissions. It is followed by the analysis of energy market trends. The analysis in AEO99 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present a summary of the reference case forecasts in units of oil equivalence and household energy expenditures. The AEO99 projections are based on Federal, State, and local laws and regulations in effect on July 1, 1998. Pending legislation and sections of existing legislation for which funds have not been appropriated are not reflected in the forecasts. Historical data used for the AEOI99 projections were the most current available as of July 31, 1998, when most 1997 data but only partial 1998 data were available.

  9. SU-E-T-537: Comparison of Intra-Operative Soft X-Rays to Low Energy Electron Beams for Treatment of Superficial Lesions

    SciTech Connect (OSTI)

    Chinsky, B; Diak, A; Gros, S; Sethi

    2014-06-01

    Purpose: Superficial soft x-ray applicators have recently been designed for use with existing intra-operative radiotherapy systems. These applicators may be used in treating superficial lesions which are conventionally treated with electron beams. The purpose of this abstract is to compare dose distributions of an intra-operative 50kV x-ray unit with low energy electrons for the treatment of superficial lesions. Methods: Dosimetric parameters for 1 and 3-cm diameter Intrabeam superficial x-ray applicators were measured with EBT3 Gafchromic film in a solid water phantom. Depth dose distributions and profiles (d=2, 5, 10 and 15mm) were obtained by prescribing a dose of 400cGy at 5mm depth below the phantom surface. Corresponding dose profiles for 6-MeV electrons were acquired from a Varian Clinac 21EX at 100 SSD. H and D calibration curves were generated for each modality for 0-800cGy. Results: Dose coverage, penumbra, dose uniformity, surface dose, and dose fall-off were examined. Compared to electrons, Intrabeam lateral dose coverage at 5mm depth was 70% larger with a much sharper (1/4) penumbra. Electron isodose levels bulged with depth, whereas Intrabeam isodose levels exhibited a convex cone shape. The Intrabeam dose profiles demonstrated horns in the dose distribution up to a 5mm depth and an exponential dose fall-off. Relative surface dose was higher for the Intrabeam applicators. Treatment times were comparable for both modalities. Conclusions: The very small penumbra of Intrabeam at shallow depths could be useful in treating superficial lesions adjacent to critical structures. The exponential dose fall-off of Intrabeam makes it appealing in the sparing of structures beyond the lesion. However, for lesions past a depth of 5mm, electrons would be desirable as they penetrate farther and provide skin sparing. Intrabeam may be preferable for sites that are difficult to treat with electrons due to mechanical and physical limitations.

  10. US energy flow, 1991

    SciTech Connect (OSTI)

    Borg, I.Y.; Briggs, C.K.

    1992-06-01

    Trends in energy consumption and assessment of energy sources are discussed. Specific topics discussed include: energy flow charts; comparison of energy use with 1990 and earlier years; supply and demand of fossil fuels (oils, natural gas, coal); electrical supply and demand; and nuclear power.

  11. International energy outlook 1994

    SciTech Connect (OSTI)

    Not Available

    1994-07-01

    The International Energy Outlook 1994 (IEO94) presents an assessment by the Energy Information Administration (EIA) of the outlook for international energy markets between 1990 and 2010. The report is provided as a statistical service to assist energy managers and analysts, both in government and in the private sector. These forecasts are used by international agencies, Federal and State governments, trade associations, and other planners and decisionmakers. They are published pursuant to the Depart. of Energy Organization Act of 1977 (Public Law 95-91), Section 205(c). The IEO94 projections are based on US and foreign government policies in effect on October 1, 1993-which means that provisions of the Climate Change Action Plan unveiled by the Administration in mid-October are not reflected by the US projections.

  12. Energy Videos

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Energy Videos Energy

  13. Forecast of contracting and subcontracting opportunities: Fiscal year 1998

    SciTech Connect (OSTI)

    1998-01-01

    This report describes procurement procedures and opportunities for small businesses with the Department of Energy (DOE). It describes both prime and subcontracting opportunities of $100,000 and above which are being set aside for 8(a) and other small business concerns. The report contains sections on: SIC codes; procurement opportunities with headquarters offices; procurement opportunities with field offices; subcontracting opportunities with major contractors; 8(a) contracts expiring in FY 1998; other opportunities to do business with DOE; management and operating contractors--expiration dates; Office of Small and Disadvantaged Business Utilization (OSDBU) staff directory; and small business survey. This document will be updated quarterly on the home page.

  14. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect (OSTI)

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  15. RTU Comparison Calculator Enhancement Plan

    SciTech Connect (OSTI)

    Miller, James D.; Wang, Weimin; Katipamula, Srinivas

    2014-03-31

    Over the past two years, Department of Energys Building Technologies Office (BTO) has been investigating ways to increase the operating efficiency of the packaged rooftop units (RTUs) in the field. First, by issuing a challenge to the RTU manufactures to increase the integrated energy efficiency ratio (IEER) by 60% over the existing ASHRAE 90.1-2010 standard. Second, by evaluating the performance of an advanced RTU controller that reduces the energy consumption by over 40%. BTO has previously also funded development of a RTU comparison calculator (RTUCC). RTUCC is a web-based tool that provides the user a way to compare energy and cost savings for two units with different efficiencies. However, the RTUCC currently cannot compare savings associated with either the RTU Challenge unit or the advanced RTU controls retrofit. Therefore, BTO has asked PNNL to enhance the tool so building owners can compare energy and savings associated with this new class of products. This document provides the details of the enhancements that are required to support estimating energy savings from use of RTU challenge units or advanced controls on existing RTUs.

  16. EAC Presentation: Comparison Among Three Studies (July 12, 2011) |

    Office of Environmental Management (EM)

    Department of Energy Comparison Among Three Studies (July 12, 2011) EAC Presentation: Comparison Among Three Studies (July 12, 2011) Presentation by L. T. Papay before the Electricity Advisorty Committee on July 12, 2011, analyzing three studies: Roadmap 2050: a practical guide to a prosperous, low carbon Europe(ECF) 2010 America's Energy Future(National Academies) 2009 California's Energy Future: Reducing GHG emissions 80% below 1990(CCST) 2011 PDF icon Charge 7: 2020 Vision, Early Start-up

  17. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    and Summer Fuels Outlook April 2015 1 April 2015 Short-Term Energy and Summer Fuels Outlook (STEO) Highlights * On April 2, Iran and the five permanent members of the United Nations Security Council plus Germany (P5+1) reached a framework agreement that could result in the lifting of oil- related sanctions against Iran. Lifting sanctions could substantially change the STEO forecast for oil supply, demand, and prices by allowing a significantly increased volume of Iranian barrels to enter the

  18. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    5 1 October 2015 Short-Term Energy and Winter Fuels Outlook (STEO) Highlights  EIA projects average U.S. household expenditures for natural gas, heating oil, and propane during the upcoming winter heating season (October 1 through March 31) will be 10%, 25%, and 18% lower, respectively, than last winter, because of lower fuel prices and lower heating demand. Forecast lower heating demand and relatively unchanged prices contribute to electricity expenditures that are 3% lower than last winter

  19. Backup Power Cost of Ownership Analysis and Incumbent Technology Comparison

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Backup Power Cost of Ownership Analysis and Incumbent Technology Comparison J. Kurtz, G. Saur, S. Sprik, and C. Ainscough National Renewable Energy Laboratory NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Technical Report NREL/TP-5400-60732 September 2014

  20. Energy 101: Geothermal Energy | Department of Energy

    Office of Environmental Management (EM)

    Geothermal Energy Energy 101: Geothermal Energy

  1. Short-term energy outlook: Quarterly projections. Second quarter 1995

    SciTech Connect (OSTI)

    1995-05-02

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent projections with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the second quarter of 1995 through the fourth quarter of 1996. Values for the first quarter of 1995, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the second quarter 1995 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service.

  2. A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    System A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast System Morcrette, Jean-Jacques European Centre for Medium-Range Weather Forecasts Category: Radiation A new processor for evaluating the UV-B and UV-A radiation at the surface, based on modifications to the current shortwave radiation scheme of the ECMWF forecast system is described. Sensitivity studies of the UV surface irradiance and Erythemal Dose Rate to spectral resolution, representation and atmospheric

  3. Assumptions and Expectations for Annual Energy Outlook 2015: Oil and Gas Working Group

    Gasoline and Diesel Fuel Update (EIA)

    Assumptions and Expectations for Annual Energy Outlook 2016: Oil and Gas Working Group AEO2016 Oil and Gas Supply Working Group Meeting Office of Petroleum, Gas, and Biofuels Analysis December 1, 2015| Washington, DC http://www.eia.gov/forecasts/aeo/workinggroup/ WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE We welcome feedback on our assumptions and documentation * The AEO Assumptions report http://www.eia.gov/forecasts/aeo/assumptions/

  4. Regional forecasting with global atmospheric models; Third year report

    SciTech Connect (OSTI)

    Crowley, T.J.; North, G.R.; Smith, N.R.

    1994-05-01

    This report was prepared by the Applied Research Corporation (ARC), College Station, Texas, under subcontract to Pacific Northwest Laboratory (PNL) as part of a global climate studies task. The task supports site characterization work required for the selection of a potential high-level nuclear waste repository and is part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work is under the overall direction of the Office of Civilian Radioactive Waste Management (OCRWM), US Department of Energy Headquarters, Washington, DC. The scope of the report is to present the results of the third year`s work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain several studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals.

  5. U.S. Energy Information Administration | Short-Term Energy Outlook January 2016 1

    Gasoline and Diesel Fuel Update (EIA)

    (STEO) Highlights  This edition of the Short-Term Energy Outlook is the first to include forecasts for 2017.  North Sea Brent crude oil prices averaged $38/barrel (b) in December, a $6/b decrease from November, and the lowest monthly average price since June 2004. Brent crude oil prices averaged $52/b in 2015, down $47/b from the average in 2014, as growth in global liquids inventories put downward pressure on Brent prices throughout much of the year.  Forecast Brent crude oil prices

  6. Short-Term Energy Outlook - U.S. Energy Information Administration (EIA)

    U.S. Energy Information Administration (EIA) Indexed Site

    9b : U.S. Regional Macroeconomic Data Either scripts and active content are not permitted to run or Adobe Flash Player version ${version_major}.${version_minor}.${version_revision} or greater is not installed. Get Adobe Flash Player - = no data available Notes: The approximate break between historical and forecast values is shown with estimates and forecasts in italics. Regions refer to U.S. Census divisions. See "Census division" in EIA's Energy Glossary for a list of states in each

  7. NREL: Energy Analysis - Aron Dobos

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Aron Dobos Photo of Aron Dobos Aron Dobos is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Programming Engineer On staff since August 2008 Phone number: 303-384-7422 E-mail: aron.dobos@nrel.gov Areas of expertise Detailed modeling of PV, CSP, and solar water heating systems Flat-plate PV and HCPV modeling and engineering Systems simulation using TRNSYS and SAM Economic analysis of renewable energy systems Power system scheduling and economics

  8. Supplement to the Annual Energy Outlook 1993

    SciTech Connect (OSTI)

    Not Available

    1993-02-17

    The Supplement to the Annual Energy Outlook 1993 is a companion document to the Energy Information Administration`s (EIA) Annual Energy Outlook 1993 (AEO). Supplement tables provide the regional projections underlying the national data and projections in the AEO. The domestic coal, electric power, commercial nuclear power, end-use consumption, and end-use price tables present AEO forecasts at the 10 Federal Region level. World coal tables provide data and projections on international flows of steam coal and metallurgical coal, and the oil and gas tables provide the AEO oil and gas supply forecasts by Oil and Gas Supply Regions and by source of supply. All tables refer to cases presented in the AEO, which provides a range of projections for energy markets through 2010.

  9. Inventing an Energy Internet: Concepts, Architectures and Protocols for Smart Energy Utilization

    ScienceCinema (OSTI)

    Tsoukalas, Lefteri [Purdue University, Purdue, Indiana, United States

    2010-01-08

    In recent years, the Internet is revolutionizing information availability much like the Power Grid revolutionized energy availability a century earlier. We will explore the differences and similarities of these two critical infrastructures and identify ways for convergence which may lead to an energy internet. Pricing signals, nodal forecasting, and short-term elasticities are key concepts in smart energy flows respecting the delicate equilibrium involved in generation-demand and aiming at higher efficiencies. We will discuss how intelligent forecasting approaches operating at multiple levels (including device or nodal levels) can ameliorate the challenges of power storage. In addition to higher efficiencies, an energy internet may achieve significant reliability and security improvements and offer greater flexibility and transparency in the overall energy-environmental relation.

  10. Inventing an Energy Internet: Concepts, Architectures, and Protocols for Smart Energy Utilization

    SciTech Connect (OSTI)

    Tsoukalas, Lefteri

    2009-04-29

    In recent years, the Internet is revolutionizing information availability much like the Power Grid revolutionized energy availability a century earlier. We will explore the differences and similarities of these two critical infrastructures and identify ways for convergence which may lead to an energy internet. Pricing signals, nodal forecasting, and short-term elasticities are key concepts in smart energy flows respecting the delicate equilibrium involved in generation-demand and aiming at higher efficiencies. We will discuss how intelligent forecasting approaches operating at multiple levels (including device or nodal levels) can ameliorate the challenges of power storage. In addition to higher efficiencies, an energy internet may achieve significant reliability and security improvements and offer greater flexibility and transparency in the overall energy-environmental relation.

  11. Inventing an Energy Internet: Concepts, Architectures and Protocols for Smart Energy Utilization

    SciTech Connect (OSTI)

    Tsoukalas, Lefteri

    2009-04-29

    In recent years, the Internet is revolutionizing information availability much like the Power Grid revolutionized energy availability a century earlier. We will explore the differences and similarities of these two critical infrastructures and identify ways for convergence which may lead to an energy internet. Pricing signals, nodal forecasting, and short-term elasticities are key concepts in smart energy flows respecting the delicate equilibrium involved in generation-demand and aiming at higher efficiencies. We will discuss how intelligent forecasting approaches operating at multiple levels (including device or nodal levels) can ameliorate the challenges of power storage. In addition to higher efficiencies, an energy internet may achieve significant reliability and security improvements and offer greater flexibility and transparency in the overall energy-environmental relation.

  12. Integration of Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Chakrabarti, Bhujanga B.; Subbarao, Krishnappa; Loutan, Clyde; Guttromson, Ross T.

    2010-04-20

    In this paper, a new approach to evaluate the uncertainty ranges for the required generation performance envelope, including the balancing capacity, ramping capability and ramp duration is presented. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (CAISO) real life data have shown the effectiveness and efficiency of the proposed approach.

  13. Application of global weather and climate model output to the design and operation of wind-energy systems

    SciTech Connect (OSTI)

    Curry, Judith

    2015-05-21

    This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatory environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.

  14. Validation of Global Weather Forecast and Climate Models Over the North

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Slope of Alaska Validation of Global Weather Forecast and Climate Models Over the North Slope of Alaska Xie, Shaocheng Lawrence Livermore National Laboratory Klein, Stephen Lawrence Livermore National Laboratory Boyle, Jim Lawrence Livermore National Laboratory Fiorino, Michael DOE/Lawrence Livermore National Laboratory Hnilo, Justin DOE/Lawrence Livermore National Laboratory Phillips, Thomas PCMDI/LLNL Potter, Gerald Lawrence Livermore National Laboratory Beljaars, Anton ECMWF Category:

  15. Calendar Year 2009 Program Benefits for ENERGY STAR Labeled Products

    SciTech Connect (OSTI)

    Homan, Gregory K; Sanchez, Marla C.; Brown, Richard E.

    2010-11-15

    ENERGY STAR is a voluntary energy efficiency labeling program operated jointly by the Environmental Protection Agency (US EPA) and the U.S. Department of Energy (US DOE), designed to identify and promote energy-efficient products, buildings and practices. Since the program inception in 1992, ENERGY STAR has become a leading international brand for energy efficient products, and currently labels more than thirty products, spanning office equipment, heating, cooling and ventilation equipment, commercial and residential lighting, home electronics, and major appliances. ENERGY STAR's central role in the development of regional, national and international energy programs necessitates an open process whereby its program achievements to date as well as projected future savings are shared with stakeholders. This report presents savings estimates from the use ENERGY STAR labeled products. We present estimates of energy, dollar, and carbon savings achieved by the program in the year 2009, annual forecasts for 2010 and 2011, and cumulative savings estimates for the period 1993 through 2009 and cumulative forecasts for the period 2010 through 2015. Through 2009 the program saved 9.5 Quads of primary energy and avoided the equivalent of 170 million metric tons carbon (MMTC). The forecast for the period 2009-2015 is 11.5 Quads or primary energy saved and 202 MMTC emissions avoided. The sensitivity analysis bounds the best estimate of carbon avoided between 110 MMTC and 231 MMTC (1993 to 2009) and between 130 MMTC and 285 MMTC (2010 to 2015).

  16. Forecasting the Magnitude of Sustainable Biofeedstock Supplies: the Challenges and the Rewards

    SciTech Connect (OSTI)

    Graham, Robin Lambert

    2007-01-01

    Forecasting the magnitude of sustainable biofeedstock supplies is challenging because of 1) the myriad of potential feedstock types and their management 2) the need to account for the spatial variation of both the supplies and their environmental and economic consequences, and 3) the inherent challenges of optimizing across economic and environmental considerations. Over the last two decades U.S. biomass forecasts have become increasingly complex and sensitive to environmental and economic considerations. More model development and research is needed however, to capture the landscape and regional tradeoffs of differing biofeedstock supplies especially with regards water quality concerns and wildlife/biodiversity. Forecasts need to be done in the context of the direction of change and what the probable land use and attendant environmental and economic outcomes would be if biofeedstocks were not being produced. To evaluate sustainability, process-oriented models need to be coupled or used to inform sector models and more work needs to be done on developing environmental metrics that are useful for evaluating economic and environmental tradeoffs. These challenges are exciting and worthwhile as they will enable the bioenergy industry to capture environmental and social benefits of biofeedstock production and reduce risks.

  17. Why Models Don%3CU%2B2019%3Et Forecast.

    SciTech Connect (OSTI)

    McNamara, Laura A.

    2010-08-01

    The title of this paper, Why Models Don't Forecast, has a deceptively simple answer: models don't forecast because people forecast. Yet this statement has significant implications for computational social modeling and simulation in national security decision making. Specifically, it points to the need for robust approaches to the problem of how people and organizations develop, deploy, and use computational modeling and simulation technologies. In the next twenty or so pages, I argue that the challenge of evaluating computational social modeling and simulation technologies extends far beyond verification and validation, and should include the relationship between a simulation technology and the people and organizations using it. This challenge of evaluation is not just one of usability and usefulness for technologies, but extends to the assessment of how new modeling and simulation technologies shape human and organizational judgment. The robust and systematic evaluation of organizational decision making processes, and the role of computational modeling and simulation technologies therein, is a critical problem for the organizations who promote, fund, develop, and seek to use computational social science tools, methods, and techniques in high-consequence decision making.

  18. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equationsmore » at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.« less

  19. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

    SciTech Connect (OSTI)

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equations at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.

  20. Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay

    SciTech Connect (OSTI)

    Jacobs, John M.; Rhodes, M.; Brown, C. W.; Hood, Raleigh R.; Leight, A.; Long, Wen; Wood, R.

    2014-11-01

    The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.

  1. Building Energy Asset Score: Energy Services Companies, Engineers and

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Consultants | Department of Energy Energy Services Companies, Engineers and Consultants Building Energy Asset Score: Energy Services Companies, Engineers and Consultants The U.S. Department of Energy's Building Energy Asset Score is a national standardized tool for evaluating the physical and structural energy efficiency of commercial and multifamily residential buildings. The Asset Score generates a simple energy efficiency rating that enables comparison among buildings, and identifies

  2. Building Energy Asset Score: Utilities and Energy Efficiency Program

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Administrators | Department of Energy Utilities and Energy Efficiency Program Administrators Building Energy Asset Score: Utilities and Energy Efficiency Program Administrators The U.S. Department of Energy's Building Energy Asset Score is a national standardized tool for evaluating the physical and structural energy efficiency of commercial and multifamily residential buildings. The Asset Score generates a simple energy efficiency rating that enables comparison among buildings, and

  3. R&D Issues in the Office of Energy Efficiency and Renewable Energy |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy R&D Issues in the Office of Energy Efficiency and Renewable Energy R&D Issues in the Office of Energy Efficiency and Renewable Energy 2003 DEER Conference Presentation: U.S. Department of Energy FreedomCAR and Vehicle Technologies Program PDF icon deer_2003_baldwin.pdf More Documents & Publications DOE Benefits Forecasts: Report of the External Peer Review Panel Details of the FY 2014 Congressional Budget Request for OE QTR Public Webinar 2

  4. Workplace Charging: Comparison of Sustainable Commuting Options

    Broader source: Energy.gov (indexed) [DOE]

    Workplace Charging: Comparison of Sustainable Commuting Options November 18, 2014 Austin Brown National Renewable Energy Laboratory vehicles.energy.gov Relevance of ROI calculation * Value Proposition for Employers - How are Lifecycle/Scope 3 GHG emissions affected? - What are my (employer) direct costs? - What is the Return on Investment (ROI)? - What are possible ancillary benefits? * How does Workplace Charging compare to: - Transit Subsidies - Vanpool Subsidies - Bike Purchase Subsidies 2 -

  5. FY 1996 solid waste integrated life-cycle forecast characteristics summary. Volumes 1 and 2

    SciTech Connect (OSTI)

    Templeton, K.J.

    1996-05-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the physical waste forms, hazardous waste constituents, and radionuclides of the waste expected to be shipped to the CWC from 1996 through the remaining life cycle of the Hanford Site (assumed to extend to 2070). In previous years, forecast data has been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to two previous reports: the more detailed report on waste volumes, WHC-EP-0900, FY1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary and the report on expected containers, WHC-EP-0903, FY1996 Solid Waste Integrated Life-Cycle Forecast Container Summary. All three documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on two main characteristics: the physical waste forms and hazardous waste constituents of low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major generators for each waste category and waste characteristic are also discussed. The characteristics of low-level waste (LLW) are described in Appendix A. In addition, information on radionuclides present in the waste is provided in Appendix B. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste is expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters. The range is primarily due to uncertainties associated with the Tank Waste Remediation System (TWRS) program, including uncertainties regarding retrieval of long-length equipment, scheduling, and tank retrieval technologies.

  6. RTU Comparison Calculator Enhancement Plan

    SciTech Connect (OSTI)

    Miller, James D.; Wang, Weimin; Katipamula, Srinivas

    2015-07-01

    Over the past two years, Department of Energy’s Building Technologies Office (BTO) has been investigating ways to increase the operating efficiency of the packaged rooftop units (RTUs) in the field. First, by issuing a challenge to the RTU manufactures to increase the integrated energy efficiency ratio (IEER) by 60% over the existing ASHRAE 90.1-2010 standard. Second, by evaluating the performance of an advanced RTU controller that reduces the energy consumption by over 40%. BTO has previously also funded development of a RTU comparison calculator (RTUCC). RTUCC is a web-based tool that provides the user a way to compare energy and cost savings for two units with different efficiencies. However, the RTUCC currently cannot compare savings associated with either the RTU Challenge unit or the advanced RTU controls retrofit. Therefore, BTO has asked PNNL to enhance the tool so building owners can compare energy and savings associated with this new class of products. This document provides the details of the enhancements that are required to support estimating energy savings from use of RTU challenge units or advanced controls on existing RTUs.

  7. Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP): An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    SciTech Connect (OSTI)

    Blair, Nate; Jenkin, Thomas; Milford, James; Short, Walter; Sullivan, Patrick; Evans, David; Lieberman, Elliot; Goldstein, Gary; Wright, Evelyn; Jayaraman, Kamala R.; Venkatesh, Boddu; Kleiman, Gary; Namovicz, Christopher; Smith, Bob; Palmer, Karen; Wiser, Ryan; Wood, Frances

    2009-09-01

    Energy system modeling can be intentionally or unintentionally misused by decision-makers. This report describes how both can be minimized through careful use of models and thorough understanding of their underlying approaches and assumptions. The analysis summarized here assesses the impact that model and data choices have on forecasting energy systems by comparing seven different electric-sector models. This analysis was coordinated by the Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP), a collaboration among governmental, academic, and nongovernmental participants.

  8. Model documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System; Volume 1

    SciTech Connect (OSTI)

    1994-02-24

    The Natural Gas Transmission and Distribution Model (NGTDM) is a component of the National Energy Modeling System (NEMS) used to represent the domestic natural gas transmission and distribution system. NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the Energy Information Administration (EIA) and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. This report documents the archived version of NGTDM that was used to produce the natural gas forecasts used in support of the Annual Energy Outlook 1994, DOE/EIA-0383(94). The purpose of this report is to provide a reference document for model analysts, users, and the public that defines the objectives of the model, describes its basic design, provides detail on the methodology employed, and describes the model inputs, outputs, and key assumptions. It is intended to fulfill the legal obligation of the EIA to provide adequate documentation in support of its models (Public Law 94-385, Section 57.b.2). This report represents Volume 1 of a two-volume set. (Volume 2 will report on model performance, detailing convergence criteria and properties, results of sensitivity testing, comparison of model outputs with the literature and/or other model results, and major unresolved issues.) Subsequent chapters of this report provide: (1) an overview of the NGTDM (Chapter 2); (2) a description of the interface between the National Energy Modeling System (NEMS) and the NGTDM (Chapter 3); (3) an overview of the solution methodology of the NGTDM (Chapter 4); (4) the solution methodology for the Annual Flow Module (Chapter 5); (5) the solution methodology for the Distributor Tariff Module (Chapter 6); (6) the solution methodology for the Capacity Expansion Module (Chapter 7); (7) the solution methodology for the Pipeline Tariff Module (Chapter 8); and (8) a description of model assumptions, inputs, and outputs (Chapter 9).

  9. Enduse Global Emissions Mitigation Scenarios (EGEMS): A New Generation of Energy Efficiency Policy Planning Models

    SciTech Connect (OSTI)

    McNeil, Michael A.; de la Rue du Can, Stephane; McMahon, James E.

    2009-05-29

    This paper presents efforts to date and prospective goals towards development of a modelling and analysis framework which is comprehensive enough to address the global climate crisis, and detailed enough to provide policymakers with concrete targets and achievable outcomes. In terms of energy efficiency policy, this requires coverage of the entire world, with emphasis on countries and regions with large and/or rapidly growing energy-related emissions, and analysis at the 'technology' level-building end use, transport mode or industrial process. These elements have not been fully addressed by existing modelling efforts, which usually take either a top-down approach, or concentrate on a few fully industrialized countries where energy demand is well-understood. Inclusion of details such as appliance ownership rates, use patterns and efficiency levels throughout the world allows for a deeper understanding of the demand for energy today and, more importantly, over the coming decades. This is a necessary next step for energy analysts and policy makers in assessment of mitigation potentials. The modelling system developed at LBNL over the past 3 years takes advantage of experience in end use demand and in forecasting markets for energy-consuming equipment, in combination with known technology-based efficiency opportunities and policy types. A particular emphasis has been placed on modelling energy growth in developing countries. Experiences to date include analyses covering individual countries (China and India), end uses (refrigerators and air conditioners) and policy types (standards and labelling). Each of these studies required a particular effort in data collection and model refinement--they share, however, a consistent approach and framework which allows comparison, and forms the foundation of a comprehensive analysis system leading to a roadmap to address the greenhouse gas mitigation targetslikely to be set in the coming years.

  10. Buildings Energy Data Book

    Buildings Energy Data Book [EERE]

    1.1 Buildings Sector Energy Consumption 1.2 Building Sector Expenditures 1.3 Value of Construction and Research 1.4 Environmental Data 1.5 Generic Fuel Quad and Comparison 1.6 Embodied Energy of Building Assemblies 2The Residential Sector 3Commercial Sector 4Federal Sector 5Envelope and Equipment 6Energy Supply 7Laws, Energy Codes, and Standards 8Water 9Market Transformation Glossary Acronyms and Initialisms Technology Descriptions Building Descriptions Other Data Books Biomass Energy

  11. Operating Reserves and Wind Power Integration: An International Comparison; Preprint

    SciTech Connect (OSTI)

    Milligan, M.; Donohoo, P.; Lew, D.; Ela, E.; Kirby, B.; Holttinen, H.; Lannoye, E.; Flynn, D.; O'Malley, M.; Miller, N.; Eriksen, P. B.; Gottig, A.; Rawn, B.; Gibescu, M.; Lazaro, E. G.; Robitaille, A.; Kamwa, I.

    2010-10-01

    This paper provides a high-level international comparison of methods and key results from both operating practice and integration analysis, based on an informal International Energy Agency Task 25: Large-scale Wind Integration.

  12. Transportation Sector Model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    1998-01-01

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. The current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.

  13. Comparison between MSW ash and RDF ash from incineration process

    Office of Scientific and Technical Information (OSTI)

    (Conference) | SciTech Connect Comparison between MSW ash and RDF ash from incineration process Citation Details In-Document Search Title: Comparison between MSW ash and RDF ash from incineration process × You are accessing a document from the Department of Energy's (DOE) SciTech Connect. This site is a product of DOE's Office of Scientific and Technical Information (OSTI) and is provided as a public service. Visit OSTI to utilize additional information resources in energy science and

  14. The Role Of IC Engines In Future Energy Use | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Of IC Engines In Future Energy Use The Role Of IC Engines In Future Energy Use Reviews future market trends and forecasts, and future engine challenges and research focus PDF icon deer11_blaxill.pdf More Documents & Publications Next-generation Ultra-Lean Burn Powertrain Vehicle Technologies Office Merit Review 2015: Next-generation Ultra-Lean Burn Powertrain Vehicle Technologies Office Merit Review 2014: Next-Generation Ultra Lean Burn Powertrain

  15. OSTI, US Dept of Energy, Office of Scientific and Technical Information |

    Office of Scientific and Technical Information (OSTI)

    Speeding access to science information from DOE and Beyond in the Forecast by Daphne Evans on Mon, Aug 13, 2012 Wind Resource Map of the United States If you can accurately predict the weather, you may be able to predict how much energy can be generated from wind turbines. That was one objective of the "Great Plains Wind Energy Transmission Development Project," completed in 2011, to "develop a wind energy forecast system, and demonstrate its efficacy in scheduling power

  16. Analysis & Projections - U.S. Energy Information Administration (EIA) -

    U.S. Energy Information Administration (EIA) Indexed Site

    U.S. Energy Information Administration (EIA) Analysis & Projections Glossary › FAQS › Overview Projection Data Monthly Short-Term Forecasts to 2012 Annual Projections to 2035 International Projections Analysis & Projections Most Requested All Reports Models & Documentation Technical Workshop on Behavior Economics Presentations November 15, 2013 About the workshop The U.S. Department of Energy's Energy Information Administration (EIA) conducted a technical workshop on July 17,

  17. Hawaii Energy Strategy: Program guide

    SciTech Connect (OSTI)

    Not Available

    1992-09-01

    The Hawaii Energy Strategy program, or HES, is a set of seven projects which will produce an integrated energy strategy for the State of Hawaii. It will include a comprehensive energy vulnerability assessment with recommended courses of action to decrease Hawaii`s energy vulnerability and to better prepare for an effective response to any energy emergency or supply disruption. The seven projects are designed to increase understanding of Hawaii`s energy situation and to produce recommendations to achieve the State energy objectives of: Dependable, efficient, and economical state-wide energy systems capable of supporting the needs of the people, and increased energy self-sufficiency. The seven projects under the Hawaii Energy Strategy program include: Project 1: Develop Analytical Energy Forecasting Model for the State of Hawaii. Project 2: Fossil Energy Review and Analysis. Project 3: Renewable Energy Resource Assessment and Development Program. Project 4: Demand-Side Management Program. Project 5: Transportation Energy Strategy. Project 6: Energy Vulnerability Assessment Report and Contingency Planning. Project 7: Energy Strategy Integration and Evaluation System.

  18. Use of Data Denial Experiments to Evaluate ESA Forecast Sensitivity Patterns

    SciTech Connect (OSTI)

    Zack, J; Natenberg, E J; Knowe, G V; Manobianco, J; Waight, K; Hanley, D; Kamath, C

    2011-09-13

    The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region which encompasses the Bonneville Power Administration (BPA) wind generation area shown in Figure 1 that includes Klondike, Stateline, and Hopkins Ridge wind plants. The Ensemble Sensitivity Analysis (ESA) approach uses data generated by a set (ensemble) of perturbed numerical weather prediction (NWP) simulations for a sample time period to statistically diagnose the sensitivity of a specified forecast variable (metric) for a target location to parameters at other locations and prior times referred to as the initial condition (IC) or state variables. The ESA approach was tested on the large-scale atmospheric prediction problem by Ancell and Hakim 2007 and Torn and Hakim 2008. ESA was adapted and applied at the mesoscale by Zack et al. (2010a, b, and c) to the Tehachapi Pass, CA (warm and cools seasons) and Mid-Colombia Basin (warm season only) wind generation regions. In order to apply the ESA approach at the resolution needed at the mesoscale, Zack et al. (2010a, b, and c) developed the Multiple Observation Optimization Algorithm (MOOA). MOOA uses a multivariate regression on a few select IC parameters at one location to determine the incremental improvement of measuring multiple variables (representative of the IC parameters) at various locations. MOOA also determines how much information from each IC parameter contributes to the change in the metric variable at the target location. The Zack et al. studies (2010a, b, and c), demonstrated that forecast sensitivity can be characterized by well-defined, localized patterns for a number of IC variables such as 80-m wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective.

  19. University of Arizona Compressed Air Energy Storage

    SciTech Connect (OSTI)

    Simmons, Joseph; Muralidharan, Krishna

    2012-12-31

    Boiled down to its essentials, the grants purpose was to develop and demonstrate the viability of compressed air energy storage (CAES) for use in renewable energy development. While everyone agrees that energy storage is the key component to enable widespread adoption of renewable energy sources, the development of a viable scalable technology has been missing. The Department of Energy has focused on expanded battery research and improved forecasting, and the utilities have deployed renewable energy resources only to the extent of satisfying Renewable Portfolio Standards. The lack of dispatchability of solar and wind-based electricity generation has drastically increased the cost of operation with these components. It is now clear that energy storage coupled with accurate solar and wind forecasting make up the only combination that can succeed in dispatchable renewable energy resources. Conventional batteries scale linearly in size, so the price becomes a barrier for large systems. Flow batteries scale sub-linearly and promise to be useful if their performance can be shown to provide sufficient support for solar and wind-base electricity generation resources. Compressed air energy storage provides the most desirable answer in terms of scalability and performance in all areas except efficiency. With the support of the DOE, Tucson Electric Power and Science Foundation Arizona, the Arizona Research Institute for Solar Energy (AzRISE) at the University of Arizona has had the opportunity to investigate CAES as a potential energy storage resource.

  20. Energy Savings Potential of Solid-State Lighting in General Illumination

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Applications - Report | Department of Energy Potential of Solid-State Lighting in General Illumination Applications - Report Energy Savings Potential of Solid-State Lighting in General Illumination Applications - Report A U.S. DOE SSL report on Energy Savings Potential of Solid-State Lighting in General Illumination Applications. PDF icon ssl_energy-savings-report_jan-2012.pdf More Documents & Publications Energy Savings Forecast of Solid-State Lighting in General Illumination

  1. Purchasing a New Energy-Efficient Central Heating System | Department of

    Energy Savers [EERE]

    Energy Purchasing a New Energy-Efficient Central Heating System Purchasing a New Energy-Efficient Central Heating System October 21, 2008 - 4:00am Addthis John Lippert Energy prices are skyrocketing. According to the Energy Information Administration's October 7, 2008 forecast, heating fuel expenditures for the average household using oil as its primary heating fuel are expected to increase by $449 over last winter. Households using natural gas to heat their homes can expect to pay $155 more

  2. Climatic Forecasting of Net Infiltration at Yucca Montain Using Analogue Meteororological Data

    SciTech Connect (OSTI)

    B. Faybishenko

    2006-09-11

    At Yucca Mountain, Nevada, future changes in climatic conditions will most likely alter net infiltration, or the drainage below the bottom of the evapotranspiration zone within the soil profile or flow across the interface between soil and the densely welded part of the Tiva Canyon Tuff. The objectives of this paper are to: (a) develop a semi-empirical model and forecast average net infiltration rates, using the limited meteorological data from analogue meteorological stations, for interglacial (present day), and future monsoon, glacial transition, and glacial climates over the Yucca Mountain region, and (b) corroborate the computed net-infiltration rates by comparing them with the empirically and numerically determined groundwater recharge and percolation rates through the unsaturated zone from published data. In this paper, the author presents an approach for calculations of net infiltration, aridity, and precipitation-effectiveness indices, using a modified Budyko's water-balance model, with reference-surface potential evapotranspiration determined from the radiation-based Penman (1948) formula. Results of calculations show that net infiltration rates are expected to generally increase from the present-day climate to monsoon climate, to glacial transition climate, and then to the glacial climate. The forecasting results indicate the overlap between the ranges of net infiltration for different climates. For example, the mean glacial net-infiltration rate corresponds to the upper-bound glacial transition net infiltration, and the lower-bound glacial net infiltration corresponds to the glacial transition mean net infiltration. Forecasting of net infiltration for different climate states is subject to numerous uncertainties-associated with selecting climate analogue sites, using relatively short analogue meteorological records, neglecting the effects of vegetation and surface runoff and runon on a local scale, as well as possible anthropogenic climate changes.

  3. Energy Technologies on the Horizon (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    A key issue in mid-term forecasting is the representation of changing and developing technologies. How existing technologies will evolve, and what new technologies might emerge, cannot be known with certainty. The issue is of particular importance in Annual Energy Outlook 2006 (AEO), the first AEO with projections out to 2030.

  4. FY 1996 solid waste integrated life-cycle forecast container summary volume 1 and 2

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-04-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the containers expected to be used for these waste shipments from 1996 through the remaining life cycle of the Hanford Site. In previous years, forecast data have been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to the more detailed report on waste volumes: WHC-EP0900, FY 1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary. Both of these documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on the types of containers that will be used for packaging low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major waste generators for each waste category and container type are also discussed. Containers used for low-level waste (LLW) are described in Appendix A, since LLW requires minimal treatment and storage prior to onsite disposal in the LLW burial grounds. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste are expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters.

  5. U.S. oil production forecast update reflects lower rig count

    Gasoline and Diesel Fuel Update (EIA)

    U.S. oil production forecast update reflects lower rig count Lower oil prices and fewer rigs drilling for crude oil are expected to slow U.S. oil production growth this year and in 2016. U.S. crude oil production is still expected to average 9.2 million barrels per day this year. That's up half a million barrels per day from last year and the highest output level in more than four decades. A substantial part of the year-over-year increase reflects rapid production growth throughout 2014.

  6. A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China

    SciTech Connect (OSTI)

    Xu, Lilai; Gao, Peiqing; Cui, Shenghui; Liu, Chun

    2013-06-15

    Highlights: ? We propose a hybrid model that combines seasonal SARIMA model and grey system theory. ? The model is robust at multiple time scales with the anticipated accuracy. ? At month-scale, the SARIMA model shows good representation for monthly MSW generation. ? At medium-term time scale, grey relational analysis could yield the MSW generation. ? At long-term time scale, GM (1, 1) provides a basic scenario of MSW generation. - Abstract: Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term.

  7. Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    1998-01-01

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. The NEMS Commercial Sector Demand Module is a simulation tool based upon economic and engineering relationships that models commercial sector energy demands at the nine Census Division level of detail for eleven distinct categories of commercial buildings. Commercial equipment selections are performed for the major fuels of electricity, natural gas, and distillate fuel, for the major services of space heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The algorithm also models demand for the minor fuels of residual oil, liquefied petroleum gas, steam coal, motor gasoline, and kerosene, the renewable fuel sources of wood and municipal solid waste, and the minor services of office equipment. Section 2 of this report discusses the purpose of the model, detailing its objectives, primary input and output quantities, and the relationship of the Commercial Module to the other modules of the NEMS system. Section 3 of the report describes the rationale behind the model design, providing insights into further assumptions utilized in the model development process to this point. Section 3 also reviews alternative commercial sector modeling methodologies drawn from existing literature, providing a comparison to the chosen approach. Section 4 details the model structure, using graphics and text to illustrate model flows and key computations.

  8. Summary of available waste forecast data for the Environmental Restoration Program at the Oak Ridge National Laboratory, Oak Ridge, Tennessee

    SciTech Connect (OSTI)

    Not Available

    1994-08-01

    This report identifies patterns of Oak Ridge National Laboratory (ORNL) Environmental Restoration (ER) waste generation that are predicted by the current ER Waste Generation Forecast data base. It compares the waste volumes to be generated with the waste management capabilities of current and proposed treatment, storage, or disposal (TSD) facilities. The scope of this report is limited to wastes generated during activities funded by the Office of the Deputy Assistant Secretary for Environmental Restoration (EM-40) and excludes wastes from the decontamination and decommissioning of facilities. Significant quantities of these wastes are expected to be generated during ER activities. This report has been developed as a management tool supporting communication and coordination of waste management activities at ORNL. It summarizes the available data for waste that will be generated as a result of remediation activities under the direction of the U.S. Department of Energy Oak Ridge Operations Office and identifies areas requiring continued waste management planning and coordination. Based on the available data, it is evident that most remedial action wastes leaving the area of contamination can be managed adequately with existing and planned ORR waste management facilities if attention is given to waste generation scheduling and the physical limitations of particular TSD facilities. Limited use of off-site commercial TSD facilities is anticipated, provided the affected waste streams can be shown to satisfy the requirements of the performance objective for certification of non-radioactive hazardous waste and the waste acceptance criteria of the off-site facilities. Ongoing waste characterization will be required to determine the most appropriate TSD facility for each waste stream.

  9. Changes in Natural Gas Monthly Consumption Data Collection and the Short-Term Energy Outlook

    Reports and Publications (EIA)

    2010-01-01

    Beginning with the December 2010 issue of the Short-Term Energy Outlook (STEO), the Energy Information Administration (EIA) will present natural gas consumption forecasts for the residential and commercial sectors that are consistent with recent changes to the Form EIA-857 monthly natural gas survey.

  10. Stochastic Energy Deployment System (SEDS) World Oil Model (WOM)

    Energy Science and Technology Software Center (OSTI)

    2009-08-07

    The function of the World Oil Market Model (WOMM) is to calculate a world oil price. SEDS will set start and end dates for the forecast period, and a time increment (assumed to be 1 year in the initial version). The WOMM will then randomly select an Annual Energy Outlook (AEO) oil price case and calibrate itself to that case. As it steps through each year, the WOMM will generate a stochastic supply shock tomore » OPEC output and accept a new estimate of U.S. petroleum demand from SEDS. The WOMM will then calculate a new oil market equilibrium for the current year. The world oil price at the new equilibrium will be sent back to SEDS. When the end year is reached, the process will begin again with the selection of a new AEO forecast. Iterations over forecasts will continue until SEDS has completed all its simulation runs.« less

  11. Sandia Energy - Energy Surety

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Energy, Energy Assurance, Energy Surety, Grid Integration, Infrastructure Security, Microgrid, News, News & Events, Renewable Energy, Systems Analysis, Systems Engineering,...

  12. Sandia Energy - Energy Assurance

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Energy, Energy Assurance, Energy Surety, Grid Integration, Infrastructure Security, Microgrid, News, News & Events, Renewable Energy, Systems Analysis, Systems Engineering,...

  13. Building Energy Asset Score: Architects | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Architects Building Energy Asset Score: Architects The U.S. Department of Energy's Building Energy Asset Score is a national standardized tool for evaluating the physical and structural energy efficiency of commercial and multifamily residential buildings. The Asset Score generates a simple energy efficiency rating that enables comparison among buildings, and identifices opportunities for users to invest in energy efficiency upgrades. It is web-based and free to use. View additional information

  14. Building Energy Asset Score: Building Owners | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Building Owners Building Energy Asset Score: Building Owners The U.S. Department of Energy's Building Energy Asset Score is a national standardized tool for evaluating the physical and structural energy efficiency of commercial and multifamily residential buildings. The Asset Score generates a simple energy efficiency rating that enables comparison among buildings, and identifies opportunities for users to invest in energy efficiency upgrades. It is web-based and free to use. View information

  15. Building Energy Asset Score: Real Estate Managers | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Real Estate Managers Building Energy Asset Score: Real Estate Managers The U.S. Department of Energy's Building Energy Asset Score is a national standardized tool for evaluating the physical and structural energy efficiency of commercial and multifamily residential buildings. The Asset Score generates a simple energy efficiency rating that enables comparison among buildings, and identifies opportunities for users to invest in energy efficiency upgrades. It is web-based and free to use. View

  16. Historical Procurement Information | Department of Energy

    Energy Savers [EERE]

    Historical Procurement Information Historical Procurement Information Use the documents on this page to learn more about historically procured goods and services in various industry sectors at the Department of Energy. Note - historical means what the Department has bought in the past. Our procurement Forecast shows what we will buy in the future. Construction Forestry Goods Manufacturing Mining Services Information Transportation and Warehousing Leasing and Real Estate Professional, Scientific,

  17. Energy Information Administration/Petroleum Marketing Annual

    U.S. Energy Information Administration (EIA) Indexed Site

    . . . . . . . . . . . . November 1994 400 Energy Information AdministrationPetroleum Marketing Annual 1998 A Comparison of Selected EIA-782 Data With Other Data Sources . . . . ....

  18. Natural Gas Monthly (NGM) - Energy Information Administration...

    U.S. Energy Information Administration (EIA) Indexed Site

    solar, wind, geothermal, biomass and ethanol. Nuclear & Uranium Uranium fuel, nuclear reactors, generation, spent fuel. Total Energy Comprehensive data summaries, comparisons,...

  19. Fossil Fuel-Generated Energy Consumption Reduction for New Federal

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Buildings and Major Renovations of Federal Buildings OIRA Comparison Document | Department of Energy Buildings OIRA Comparison Document Fossil Fuel-Generated Energy Consumption Reduction for New Federal Buildings and Major Renovations of Federal Buildings OIRA Comparison Document Document details the Fossil Fuel-Generated Energy Consumption Reduction for New Federal Buildings and Major Renovations of Federal Buildings in an OIRA Comparison Document. File fossilfuel_compare2014.docx More

  20. Calendar Year 2008 Program Benefits for ENERGY STAR Labeled Products

    SciTech Connect (OSTI)

    Homan, GregoryK; Sanchez, Marla; Brown, RichardE; Lai, Judy

    2010-08-24

    This paper presents current and projected savings for ENERGY STAR labeled products, and details the status of the model as implemented in the September 2009 spreadsheets. ENERGY STAR is a voluntary energy efficiency labeling program operated jointly by the Environmental Protection Agency (US EPA) and the U.S. Department of Energy (US DOE), designed to identify and promote energy-efficient products, buildings and practices. Since the program inception in 1992, ENERGY STAR has become a leading international brand for energy efficient products, and currently labels more than thirty products, spanning office equipment, heating, cooling and ventilation equipment, commercial and residential lighting, home electronics, and major appliances. ENERGY STAR's central role in the development of regional, national and international energy programs necessitates an open process whereby its program achievements to date as well as projected future savings are shared with stakeholders. This report presents savings estimates for ENERGY STAR labeled products. We present estimates of energy, dollar, and carbon savings achieved by the program in the year 2008, annual forecasts for 2009 and 2010, and cumulative savings estimates for the period 1993 through 2008 and cumulative forecasts for the period 2009 through 2015. Through 2008 the program saved 8.8 Quads of primary energy and avoided the equivalent of 158 metric tones carbon (MtC). The forecast for the period 2009-2015 is 18.1 Quads or primary energy saved and 316 MtC emissions avoided. The sensitivity analysis bounds the best estimate of carbon avoided between 104 MtC and 213 MtC (1993 to 2008) and between 206 MtC and 444 MtC (2009 to 2015). In this report we address the following questions for ENERGY STAR labeled products: (1) How are ENERGY STAR impacts quantified; (2) What are the ENERGY STAR achievements; and (3) What are the limitations to our method?