National Library of Energy BETA

Sample records for base forecast natural

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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)

  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. 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.

  15. Base Oil Market Segment Forecasts up to 2020,Research Reports...

    Open Energy Info (EERE)

    Market Research Home > Groups > Future of Condition Monitoring for Wind Turbines Wayne31jan's picture Submitted by Wayne31jan(150) Contributor 11 June, 2015 - 03:19 Base Oil...

  16. 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

  17. 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.

  18. 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)

  19. 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...

  20. Community-Based Forest (Natural) Resource Management: A Path...

    Open Energy Info (EERE)

    Based Forest (Natural) Resource Management: A Path to Sustainable Environment and Development Jump to: navigation, search Name Community-Based Forest (Natural) Resource Management:...

  1. Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms

    SciTech Connect (OSTI)

    Hou, Zhangshuan; Makarov, Yuri V.; Samaan, Nader A.; Etingov, Pavel V.

    2013-03-19

    Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (stochastic) model with the weather forecast model (deterministic) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.

  2. 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.

  3. 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

  4. 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. ...

  5. West Virginia Natural Gas Plant Liquids, Reserves Based Production...

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

    Reserves Based Production (Million Barrels) West Virginia Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  6. Texas Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Texas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 134,707 134,707...

  7. Washington Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Washington Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 21,300 21,300...

  8. Alaska Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Alaska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 7,622 14,197...

  9. Utah Natural Gas in Underground Storage (Base Gas) (Million Cubic...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Utah Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,944 46,944...

  10. Virginia Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 0 0 0 0 0 0 0 0...

  11. Missouri Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Missouri Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 21,600 21,600...

  12. Maryland Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Maryland Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,677 46,677...

  13. Indiana Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Indiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 74,572 74,572...

  14. Kentucky Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Kentucky Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 105,889 105,889...

  15. Michigan Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Michigan Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 395,529 395,529...

  16. Minnesota Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Minnesota Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 4,655 4,655...

  17. Ohio Natural Gas in Underground Storage (Base Gas) (Million Cubic...

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

    Base Gas) (Million Cubic Feet) Ohio Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 338,916 338,916...

  18. Wyoming Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Wyoming Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 31,205 31,205...

  19. Arkansas Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Arkansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 19,202 19,202...

  20. Oklahoma Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Oklahoma Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 167,385 163,458...

  1. Oregon Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Oregon Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 3,291 3,291 3,291...

  2. Mississippi Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Mississippi Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 46,050...

  3. Louisiana Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Louisiana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 262,136...

  4. Alabama Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Alabama Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1995 880 880 880 880...

  5. Nebraska Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Nebraska Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 27,312 27,312...

  6. Iowa Natural Gas in Underground Storage (Base Gas) (Million Cubic...

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

    Base Gas) (Million Cubic Feet) Iowa Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 153,933 153,933...

  7. Colorado Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Colorado Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 39,062 39,062...

  8. Tennessee Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) Tennessee Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 0 0 0 0 0 0 0...

  9. New York Natural Gas in Underground Storage (Base Gas) (Million...

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) New York Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 88,911 88,911...

  10. ASSESSING AND FORECASTING, BY PLAY, NATURAL GAS ULTIMATE RECOVERY GROWTH AND QUANTIFYING THE ROLE OF TECHNOLOGY ADVANCEMENTS IN THE TEXAS GULF COAST BASIN AND EAST TEXAS

    SciTech Connect (OSTI)

    William L. Fisher; Eugene M. Kim

    2000-12-01

    A detailed natural gas ultimate recovery growth (URG) analysis of the Texas Gulf Coast Basin and East Texas has been undertaken. The key to such analysis was determined to be the disaggregation of the resource base to the play level. A play is defined as a conceptual geologic unit having one or more reservoirs that can be genetically related on the basis of depositional origin of the reservoir, structural or trap style, source rocks and hydrocarbon generation, migration mechanism, seals for entrapment, and type of hydrocarbon produced. Plays are the geologically homogeneous subdivision of the universe of petroleum pools within a basin. Therefore, individual plays have unique geological features that can be used as a conceptual model that incorporates geologic processes and depositional environments to explain the distribution of petroleum. Play disaggregation revealed important URG trends for the major natural gas fields in the Texas Gulf Coast Basin and East Texas. Although significant growth and future potential were observed for the major fields, important URG trends were masked by total, aggregated analysis based on a broad geological province. When disaggregated by plays, significant growth and future potential were displayed for plays that were associated with relatively recently discovered fields, deeper reservoir depths, high structural complexities due to fault compartmentalization, reservoirs designated as tight gas/low-permeability, and high initial reservoir pressures. Continued technology applications and advancements are crucial in achieving URG potential in these plays.

  11. 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.

  12. 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...

  13. 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...

  14. Other States Natural Gas Coalbed Methane, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Billion Cubic Feet) Other States Natural Gas Coalbed Methane, Reserves Based Production (Billion Cubic Feet) Other States Natural Gas Coalbed Methane, Reserves Based Production (Billion Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 0 1990's 1 3 10 18 34 47 56 70 99 130 2000's 0 -- -- -- - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next

  15. Utah Natural Gas Plant Liquids, Reserves Based Production (Million Barrels)

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Utah Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 3 3 7 2010's 8 11 11 11 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Natural Gas Plant Liquids Production

  16. Wyoming Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Wyoming Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 51 58 64 2010's 63 66 71 53 55 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Natural Gas Plant Liquids Production

  17. 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.

  18. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

    SciTech Connect (OSTI)

    Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.; Kaderali, Lars

    2015-09-30

    People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our research highlight the importance of analyzing sub-populations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

  19. Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

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

    Gutfraind, Alexander; Boodram, Basmattee; Prachand, Nikhil; Hailegiorgis, Atesmachew; Dahari, Harel; Major, Marian E.; Kaderali, Lars

    2015-09-30

    People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID tomore » build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our research highlight the importance of analyzing sub-populations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.« less

  20. 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...

  1. 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...

  2. 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...

  3. Miscellaneous States Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Miscellaneous States Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 1 1 1 2 2010's 3 2 4 8 16 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  4. Miscellaneous States Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Miscellaneous States Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 8 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 1 1 1 1 0 2010's 0 0 0 1 24 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release

  5. Mississippi (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Mississippi (with State Offshore) Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 1 1 1 1 1 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 1 1 0 0 0 0 0 0 0 2010's 0 1 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  6. New Mexico Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) New Mexico Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 6 1980's 5 5 4 4 6 4 3 4 4 4 1990's 5 3 4 4 4 3 4 5 5 7 2000's 7 7 7 6 6 7 10 10 7 7 2010's 7 8 10 11 8 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next

  7. New Mexico Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) New Mexico Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 43 1980's 44 45 42 40 41 38 34 44 43 43 1990's 46 47 53 58 60 59 75 75 74 74 2000's 77 77 75 76 73 70 68 66 64 65 2010's 63 62 58 60 61 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  8. North Dakota Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) North Dakota Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 0 0 1990's 1 1 1 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 1 0 2010's 1 0 1 1 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  9. North Dakota Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) North Dakota Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 3 4 4 5 6 6 5 6 5 5 1990's 5 5 5 5 4 4 4 4 4 4 2000's 5 5 5 4 5 5 6 6 6 8 2010's 9 11 19 26 36 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

  10. Oklahoma Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Oklahoma Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 8 1980's 8 9 10 10 11 11 12 11 11 11 1990's 9 9 8 8 8 8 8 8 10 9 2000's 8 9 11 11 11 13 14 15 17 17 2010's 19 21 24 30 35 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  11. Oklahoma Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Oklahoma Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 59 1980's 62 65 67 70 75 77 76 76 79 73 1990's 75 76 77 77 76 70 74 71 69 70 2000's 69 66 61 59 64 65 67 69 74 77 2010's 82 88 96 99 117 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  12. Colorado Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Colorado Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 2 1 1 1 1990's 1 1 1 2 3 2 2 2 2 3 2000's 3 3 4 5 6 5 6 6 7 7 2010's 7 8 8 16 16 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next

  13. Colorado Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Colorado Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 10 1980's 10 11 10 9 8 9 8 8 9 10 1990's 10 12 13 14 15 18 17 21 18 19 2000's 21 22 23 24 26 26 26 27 38 48 2010's 58 63 57 52 61 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015

  14. Federal Offshore--California Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Federal Offshore--California Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 1990's 0 0 1 1 1 1 1 1 1 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  15. Federal Offshore--Texas Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Federal Offshore--Texas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 2 2 4 4 5 5 4 4 1990's 4 5 6 6 5 5 6 6 4 5 2000's 5 4 5 3 3 2 2 1 1 0 2010's 0 0 7 7 5 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  16. Kansas Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Kansas Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 1 0 0 2000's 0 0 0 1 0 1 1 1 1 1 2010's 2 1 1 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release

  17. Kansas Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Kansas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 29 1980's 26 24 14 17 20 20 19 19 18 18 1990's 17 26 27 27 29 29 31 24 28 30 2000's 28 26 25 22 22 19 18 18 18 16 2010's 16 16 15 11 12 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  18. Louisiana (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Louisiana (with State Offshore) Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 52 38 40 40 39 1990's 45 44 49 42 43 68 65 41 37 45 2000's 41 35 35 33 31 29 28 30 27 26 2010's 25 23 24 29 26 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  19. Louisiana--North Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Louisiana--North Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 5 1980's 5 4 4 3 3 2 2 3 3 3 1990's 3 4 3 3 4 4 5 5 3 5 2000's 5 4 3 3 3 3 3 4 4 4 2010's 3 3 3 3 3 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  20. Louisiana--North Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Louisiana--North Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 7 1980's 6 8 7 6 6 6 5 5 6 5 1990's 6 6 6 5 6 7 8 7 5 4 2000's 4 3 3 4 4 4 5 6 6 6 2010's 5 5 5 6 7 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release

  1. Louisiana--South Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Louisiana--South Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 59 1980's 39 38 32 33 29 28 29 30 30 28 1990's 33 33 36 34 34 58 48 31 29 37 2000's 32 23 23 20 20 20 19 18 15 15 2010's 15 14 16 20 17 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  2. Louisiana--State Offshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Louisiana--State Offshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 6 4 5 4 6 1990's 6 5 7 3 3 3 9 3 3 4 2000's 5 9 9 9 7 5 4 6 6 5 2010's 5 4 3 3 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release

  3. Lower 48 Federal Offshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Lower 48 Federal Offshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 54 47 51 48 49 1990's 46 51 48 52 52 37 42 71 68 80 2000's 93 91 94 70 81 61 67 69 53 61 2010's 66 57 61 49 52 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  4. Lower 48 States Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Lower 48 States Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 147 1980's 159 161 157 157 179 168 169 162 162 165 1990's 158 153 147 153 157 145 162 174 178 199 2000's 208 215 207 191 182 174 182 181 173 178 2010's 224 211 254 295 326 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  5. Lower 48 States Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Lower 48 States Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 579 1980's 572 580 564 568 597 580 566 569 572 549 1990's 556 577 599 608 608 616 655 655 631 649 2000's 688 655 657 593 627 597 615 637 654 701 2010's 734 773 854 920 1,107 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  6. Michigan Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Michigan Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 2 1 1 1 1 1 1 1 1 2 1990's 1 2 2 1 1 1 1 1 1 0 2000's 0 0 1 0 1 0 1 0 0 1 2010's 1 1 1 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next

  7. Michigan Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Michigan Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 11 1980's 12 12 11 10 10 8 9 8 8 8 1990's 6 6 6 5 5 5 5 4 4 4 2000's 4 4 3 3 3 3 2 3 3 2 2010's 3 2 2 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date:

  8. Alabama (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Alabama (with State Offshore) Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 3 3 4 4 4 4 4 4 4 4 1990's 4 4 4 4 4 4 4 4 4 8 2000's 10 3 3 2 2 2 3 2 7 5 2010's 6 6 5 6 5 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  9. Alaska (with Total Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Alaska (with Total Offshore) Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 0 0 0 0 0 5 3 16 20 17 1990's 18 24 27 27 26 30 33 35 24 21 2000's 22 20 20 18 18 17 14 13 13 13 2010's 11 11 11 11 17 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  10. Arkansas Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Arkansas Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 1 1 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next

  11. California (with State Offshore) Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) California (with State Offshore) Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 9 1980's 7 6 12 12 12 12 11 9 1990's 9 8 10 9 8 9 9 8 7 8 2000's 8 8 10 10 10 11 11 11 11 11 2010's 10 10 10 11 10 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  12. Utah Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Utah Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 2 3 3 2010's 3 3 4 3 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Lease Condensate Estimated Production Utah

  13. Utah and Wyoming Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Liquids Lease Condensate, Reserves Based Production (Million Barrels) Utah and Wyoming Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 4 1980's 5 11 8 20 26 31 31 28 25 23 1990's 16 17 15 14 14 9 8 8 8 14 2000's 7 11 11 10 10 12 13 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  14. Utah and Wyoming Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Utah and Wyoming Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 14 1980's 14 16 15 18 24 27 27 28 38 35 1990's 35 34 32 32 34 37 44 49 40 45 2000's 55 54 55 52 52 50 49 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next

  15. Wyoming Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Wyoming Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 12 12 13 2010's 13 13 13 13 12 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Lease Condensate Estimated

  16. 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

  17. California--State Offshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Natural Gas Plant Liquids

  18. 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.

  19. Montana Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Montana Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 1 1 1 1 1 0 0 0 0 0 2000's 0 0 1 1 1 1 1 1 1 1 2010's 1 1 1 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  20. Florida Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Florida Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 10 1980's 10 5 4 3 2 2 1 1 1 1990's 1 1 1 1 1 1 1 1 1 1 2000's 1 1 1 1 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  1. Kentucky Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Kentucky Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 3 1980's 3 2 3 2 2 2 2 1 2 1 1990's 1 2 2 2 3 3 3 3 3 3 2000's 2 3 3 3 3 3 3 3 3 4 2010's 5 4 5 5 5 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  2. Arkansas Natural Gas Plant Liquids, Reserves Based Production (Million

    Gasoline and Diesel Fuel Update (EIA)

    Barrels) Reserves Based Production (Million Barrels) Arkansas Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 1 0 0 0 0 0 0 0 0 0 2000's 0 1 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016

  3. Formulation of cracking catalyst based on zeolite and natural clays

    SciTech Connect (OSTI)

    Aliev, R.R.; Lupina, M.I.

    1995-11-01

    Domestically manufactured cracking catalysts are based on a synthetic amorphous aluminosilicate matrix and Y zeolite. A multistage {open_quotes}gel{close_quotes} technology is used in manufacturing the catalysts. The process includes mixing solutions of sodium silicate and acidic aluminum sulfate, forming, syneresis, and activation of the beaded gel. In the manufacture of bead catalysts, the next steps in the process are washing, drying, and calcining; in the manufacture of microbead catalysts, the next steps are dispersion and formation of a hydrogel slurry, spray-drying, and calcining. The Y zeolite is either introduced into the alumina-silica sol in the stage of forming the beads, or introduced in the dispersion stage. With the aim of developing an active and selective cracking catalyst based on Y zeolite and natural clays, with improved physicomechanical properties, the authors carried out a series of studies, obtaining results that are set forth in the present article.

  4. California Natural Gas in Underground Storage (Base Gas) (Million Cubic

    Gasoline and Diesel Fuel Update (EIA)

    Feet) Base Gas) (Million Cubic Feet) California Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 1991 243,944 243,944 243,944 243,944 243,944 243,944 243,944 243,944 248,389 248,389 248,389 248,389 1992 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 248,389 250,206 1993 250,206 250,206

  5. Pennsylvania Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Pennsylvania Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 352,686 352,686 352,686 351,920 352,686 352,686 353,407 353,407 353,407 353,407 359,236 358,860 1991 349,459 348,204 334,029 335,229 353,405 349,188 350,902 352,314 353,617 354,010 353,179 355,754 1992 358,198 353,313 347,361 341,498 344,318 347,751 357,498 358,432 359,300 359,504 359,321 362,275 1993 362,222 358,438

  6. 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 ...

  7. 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.

  8. 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.

  9. 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. ...

  10. Montana Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Montana Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Lease

  11. Florida Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Florida Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Lease

  12. Kentucky Natural Gas Liquids Lease Condensate, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next Release Date: 12/31/2016 Referring Pages: Lease Condensate

  13. 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

  14. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers

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

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are onemore » of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments.« less

  15. Microtubule-based nanomaterials: Exploiting nature's dynamic biopolymers.

    SciTech Connect (OSTI)

    Bachand, George D.; Stevens, Mark J.; Spoerke, Erik David

    2015-04-09

    For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. In addition, one intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are one of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments. Biotechnol. Bioeng.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. Control method for mixed refrigerant based natural gas liquefier

    DOE Patents [OSTI]

    Kountz, Kenneth J. (Palatine, IL); Bishop, Patrick M. (Chicago, IL)

    2003-01-01

    In a natural gas liquefaction system having a refrigerant storage circuit, a refrigerant circulation circuit in fluid communication with the refrigerant storage circuit, and a natural gas liquefaction circuit in thermal communication with the refrigerant circulation circuit, a method for liquefaction of natural gas in which pressure in the refrigerant circulation circuit is adjusted to below about 175 psig by exchange of refrigerant with the refrigerant storage circuit. A variable speed motor is started whereby operation of a compressor is initiated. The compressor is operated at full discharge capacity. Operation of an expansion valve is initiated whereby suction pressure at the suction pressure port of the compressor is maintained below about 30 psig and discharge pressure at the discharge pressure port of the compressor is maintained below about 350 psig. Refrigerant vapor is introduced from the refrigerant holding tank into the refrigerant circulation circuit until the suction pressure is reduced to below about 15 psig, after which flow of the refrigerant vapor from the refrigerant holding tank is terminated. Natural gas is then introduced into a natural gas liquefier, resulting in liquefaction of the natural gas.

  2. 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)

  3. 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:...

  4. 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.

  5. 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....

  6. 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.

  7. 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:...

  8. 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...

  9. 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...

  10. Texas--RRC District 1 Natural Gas Plant Liquids, Reserves Based...

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

    Reserves Based Production (Million Barrels) Texas--RRC District 1 Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  11. Texas--RRC District 8A Natural Gas Plant Liquids, Reserves Based...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Texas--RRC District 8A Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  12. Texas--RRC District 10 Natural Gas Plant Liquids, Reserves Based...

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

    Reserves Based Production (Million Barrels) Texas--RRC District 10 Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  13. Texas--RRC District 7C Natural Gas Plant Liquids, Reserves Based...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Texas--RRC District 7C Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  14. Texas--RRC District 9 Natural Gas Plant Liquids, Reserves Based...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Texas--RRC District 9 Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  15. Texas--RRC District 8 Natural Gas Plant Liquids, Reserves Based...

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

    Reserves Based Production (Million Barrels) Texas--RRC District 8 Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  16. Texas--RRC District 5 Natural Gas Plant Liquids, Reserves Based...

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

    Reserves Based Production (Million Barrels) Texas--RRC District 5 Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  17. Texas--RRC District 7B Natural Gas Plant Liquids, Reserves Based...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Texas--RRC District 7B Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4...

  18. U.S. Natural Gas Non-Salt Underground Storage - Base Gas (Million...

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

    - Base Gas (Million Cubic Feet) U.S. Natural Gas Non-Salt Underground Storage - Base Gas (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 4,310,511...

  19. U.S. Total Natural Gas in Underground Storage (Base Gas) (Million...

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

    Base Gas) (Million Cubic Feet) U.S. Total Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1973 NA NA NA NA...

  20. U.S. Natural Gas Salt - Underground Storage - Base Gas (Million...

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

    - Underground Storage - Base Gas (Million Cubic Feet) U.S. Natural Gas Salt - Underground Storage - Base Gas (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov...

  1. 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

  2. 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.

  3. Pacific Region Natural Gas in Underground Storage (Base Gas)...

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

    Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 258,736 258,541 258,456 258,619 258,736 258,736 258,736 258,736 258,736...

  4. Mountain Region Natural Gas in Underground Storage (Base Gas...

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

    Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 421,075 420,615 419,767 420,250 420,606 420,353 422,402 422,811 423,525...

  5. Efficient Use of Natural Gas Based Fuels in Heavy-Duty Engines | Department

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

    of Energy Use of Natural Gas Based Fuels in Heavy-Duty Engines Efficient Use of Natural Gas Based Fuels in Heavy-Duty Engines Natural gas and other liquid feedstocks for transportation fuels are compared for use in a dual-fuel engine. Benefits include economic stability, national security, environment, and cost. PDF icon deer12_kargul.pdf More Documents & Publications A Universal Dual-Fuel Controller for OEM/Aftermarket Diesel Engineswith Comprehensive Fuel & Emission Control Natural

  6. 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.

  7. 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.

  8. Natural Gas Weekly Update

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

    regions of the country and weather forecasts indicated colder temperatures are here to stay until at least the end of the month. Natural gas in storage declined to 2,195 Bcf with...

  9. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Btu (MMBtu). The NEB noted the contrast of this forecast to the market prices of last summer, when natural gas prices peaked at more than 13 per MMBtu and crude oil reached a...

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. Effects of soot-induced snow albedo change on snowpack and hydrological cycle in western United States based on Weather Research and Forecasting chemistry and regional climate simulations

    SciTech Connect (OSTI)

    Qian, Yun; Gustafson, William I.; Leung, Lai-Yung R.; Ghan, Steven J.

    2009-02-14

    Radiative forcing induced by soot on snow is a major anthropogenic forcing affecting the global climate. However, it is uncertain how the soot-induced snow albedo perturbation affects regional snowpack and the hydrological cycle. In this study we simulated the deposition of soot aerosol on snow and investigated the resulting impact on snowpack and the surface water budget in the western United States. A yearlong simulation was performed using the chemistry version of the Weather Research and Forecasting model (WRF-Chem) to determine an annual budget of soot deposition, followed by two regional climate simulations using WRF in meteorology-only mode, with and without the soot-induced snow albedo perturbations. The chemistry simulation shows large spatial variability in soot deposition that reflects the localized emissions and the influence of the complex terrain. The soot-induced snow albedo perturbations increase the net solar radiation flux at the surface during late winter to early spring, increase the surface air temperature, reduce snow water equivalent amount, and lead to reduced snow accumulation and less spring snowmelt. These effects are stronger over the central Rockies and southern Alberta, where soot deposition and snowpack overlap the most. The indirect forcing of soot accelerates snowmelt and alters stream flows, including a trend toward earlier melt dates in the western United States. The soot-induced albedo reduction initiates a positive feedback process whereby dirty snow absorbs more solar radiation, heating the surface and warming the air. This warming causes reduced snow depth and fraction, which further reduces the regional surface albedo for the snow covered regions. Our simulations indicate that the change of maximum snow albedo induced by soot on snow contributes to 60% of the net albedo reduction over the central Rockies. Snowpack reduction accounts for the additional 40%.

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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.

  20. 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,

  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. 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.

  3. 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.

  4. 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.

  5. Holographic imaging based on time-domain data of natural-fiber-containing materials

    DOE Patents [OSTI]

    Bunch, Kyle J.; McMakin, Douglas L.

    2012-09-04

    Methods and apparatuses for imaging material properties in natural-fiber-containing materials can utilize time-domain data. In particular, images can be constructed that provide quantified measures of localized moisture content. For example, one or more antennas and at least one transceiver can be configured to collect time-domain data from radiation interacting with the natural-fiber-containing materials. The antennas and the transceivers are configured to transmit and receive electromagnetic radiation at one or more frequencies, which are between 50 MHz and 1 THz, according to a time-domain impulse function. A computing device is configured to transform the time-domain data to frequency-domain data, to apply a synthetic imaging algorithm for constructing a three-dimensional image of the natural-fiber-containing materials, and to provide a quantified measure of localized moisture content based on a pre-determined correlation of moisture content to frequency-domain data.

  6. Natural language processing-based COTS software and related technologies survey.

    SciTech Connect (OSTI)

    Stickland, Michael G.; Conrad, Gregory N.; Eaton, Shelley M.

    2003-09-01

    Natural language processing-based knowledge management software, traditionally developed for security organizations, is now becoming commercially available. An informal survey was conducted to discover and examine current NLP and related technologies and potential applications for information retrieval, information extraction, summarization, categorization, terminology management, link analysis, and visualization for possible implementation at Sandia National Laboratories. This report documents our current understanding of the technologies, lists software vendors and their products, and identifies potential applications of these technologies.

  7. 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

  8. 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

  9. 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.

  10. 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:...

  11. 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...

  12. New Mexico--East Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) New Mexico--East Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 3 1980's 3 3 2 2 4 2 2 2 2 2 1990's 3 2 2 2 2 1 2 3 3 4 2000's 5 5 5 4 4 5 5 5 6 6 2010's 6 7 8 9 6 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  13. New Mexico--East Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) New Mexico--East Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 28 1980's 28 29 28 28 28 27 24 23 24 23 1990's 24 25 28 32 34 34 44 40 39 37 2000's 38 38 38 38 35 33 32 32 30 32 2010's 32 30 29 32 35 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  14. New Mexico--West Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) New Mexico--West Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 3 1980's 2 2 2 2 2 2 1 2 2 2 1990's 2 1 2 2 2 2 2 2 2 3 2000's 2 2 2 2 2 2 5 5 1 1 2010's 1 1 2 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  15. New Mexico--West Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) New Mexico--West Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 15 1980's 16 16 14 12 13 11 10 21 19 20 1990's 22 22 25 26 26 25 31 35 35 37 2000's 39 39 37 38 38 37 36 34 34 33 2010's 31 32 29 28 26 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  16. Calif--Coastal Region Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Calif--Coastal Region Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 2 1 2 2 2 2 1 1 1 1 1990's 1 1 1 1 1 1 1 1 1 1 2000's 1 1 1 1 1 1 1 1 1 1 2010's 1 1 1 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  17. Calif--Los Angeles Basin Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Calif--Los Angeles Basin Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 0 1990's 0 0 1 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company

  18. Calif--San Joaquin Basin Onshore Natural Gas Plant Liquids, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Plant Liquids, Reserves Based Production (Million Barrels) Calif--San Joaquin Basin Onshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 6 1980's 4 4 9 9 9 10 10 10 9 8 1990's 8 7 8 8 7 8 8 7 6 7 2000's 7 7 9 9 9 10 10 10 10 10 2010's 9 9 9 10 9 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  19. Texas--RRC District 1 Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 1 Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 2 1 1 1 1 0 0 1 1 1 2000's 1 0 0 0 1 1 1 1 2 1 2010's 1 12 26 38 46 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  20. Texas--RRC District 10 Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 10 Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 3 2 2 2 2 2 2 2 2 2 1990's 2 2 1 2 3 1 1 1 1 1 2000's 1 1 1 2 2 3 5 5 8 8 2010's 11 15 18 20 14 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data.

  1. Texas--RRC District 5 Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 5 Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 2 1990's 2 2 2 2 1 1 1 0 0 1 2000's 1 1 1 1 1 1 1 1 1 1 2010's 1 1 1 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  2. Texas--RRC District 6 Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 6 Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 5 1980's 5 5 5 5 5 4 5 4 4 5 1990's 5 5 4 5 6 6 6 5 5 5 2000's 4 5 5 5 5 6 7 7 8 7 2010's 7 6 7 6 6 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  3. Texas--RRC District 7B Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 7B Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 1 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 1 2000's 0 0 0 0 0 0 0 1 1 1 2010's 1 1 1 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  4. Texas--RRC District 7C Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 7C Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 2 1980's 2 2 2 1 2 1 1 1 1 1 1990's 4 1 1 2 2 2 2 2 3 2 2000's 1 3 2 3 3 3 2 2 3 3 2010's 4 3 2 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  5. Texas--RRC District 8 Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 8 Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 7 1980's 6 5 4 3 3 2 3 2 2 2 1990's 2 2 2 1 2 1 1 2 2 2 2000's 2 2 2 2 1 2 2 2 2 3 2010's 38 5 5 7 9 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  6. Texas--RRC District 8A Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 8A Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 0 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 1 1 1 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  7. Texas--RRC District 9 Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--RRC District 9 Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 1 1980's 1 1 1 1 1 1 1 1 1 1 1990's 0 1 0 1 1 1 0 0 0 0 2000's 0 1 1 2 2 1 2 2 2 2 2010's 2 2 2 3 3 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  8. Texas--State Offshore Natural Gas Liquids Lease Condensate, Reserves Based

    Gasoline and Diesel Fuel Update (EIA)

    Production (Million Barrels) Reserves Based Production (Million Barrels) Texas--State Offshore Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 2 2 1 1 1 1 1 1 1 1990's 1 1 0 1 1 0 0 0 0 0 2000's 0 1 1 1 1 0 0 0 1 1 2010's 1 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date:

  9. Texas--State Offshore Natural Gas Plant Liquids, Reserves Based Production

    Gasoline and Diesel Fuel Update (EIA)

    (Million Barrels) Reserves Based Production (Million Barrels) Texas--State Offshore Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 0 1980's 0 0 1 1 1 1 1 1 0 1 1990's 1 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 11/19/2015 Next

  10. U.S. Natural Gas Plant Liquids, Reserves Based Production (Million Barrels)

    Gasoline and Diesel Fuel Update (EIA)

    Based Production (Million Barrels) U.S. Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 580 1980's 572 580 564 568 597 585 569 585 592 566 1990's 574 601 626 635 634 646 688 690 655 697 2000's 710 675 677 611 645 614 629 650 667 714 2010's 745 784 865 931 1,124 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release

  11. ,"U.S. Natural Gas Non-Salt Underground Storage - Base Gas (MMcf)"

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

    - Base Gas (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","U.S. Natural Gas Non-Salt Underground Storage - Base Gas (MMcf)",1,"Monthly","12/2015" ,"Release Date:","2/29/2016" ,"Next Release Date:","3/31/2016" ,"Excel File

  12. U.S. Natural Gas Liquids Lease Condensate, Reserves Based Production

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

    (Million Barrels) Based Production (Million Barrels) U.S. Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 147 1980's 159 161 157 157 179 168 169 162 162 165 1990's 158 153 147 153 157 145 162 174 178 199 2000's 208 215 207 191 182 174 182 181 173 178 2010's 224 231 274 311 326 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of

  13. 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.

  14. AGA Eastern Consuming Region Natural Gas in Underground Storage (Base Gas)

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

    (Million Cubic Feet) Base Gas) (Million Cubic Feet) AGA Eastern Consuming Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 2,700,245 2,697,308 2,696,823 2,698,489 2,699,802 2,699,840 2,700,331 2,701,227 2,701,285 2,702,703 2,702,571 2,703,149 1995 2,699,674 2,699,575 2,696,880 2,695,400 2,726,268 2,726,255 2,668,312 2,671,818 2,672,399 2,672,258 2,671,362 2,672,808 1996 2,670,906 2,670,070 2,646,056 2,654,836

  15. AGA Producing Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) AGA Producing Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 1,039,864 1,032,160 1,033,297 1,032,517 1,037,294 1,037,338 1,038,940 1,036,193 1,037,422 1,035,931 1,035,050 1,043,103 1995 1,051,669 1,054,584 1,051,120 1,051,697 1,052,949 1,062,613 1,058,260 1,054,218 1,054,870 1,051,687 1,056,704 1,060,588 1996 1,067,220 1,062,343 1,027,692 1,040,511 1,055,164

  16. AGA Western Consuming Region Natural Gas in Underground Storage (Base Gas)

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

    (Million Cubic Feet) Base Gas) (Million Cubic Feet) AGA Western Consuming Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 607,596 607,629 612,749 613,849 614,562 614,534 615,937 617,412 614,732 615,667 615,712 613,840 1995 613,874 613,874 613,898 613,357 613,699 616,811 613,151 613,413 613,504 613,752 613,514 615,837 1996 616,124 616,330 616,610 617,033 616,902 617,159 616,822 615,039 616,632 616,849 617,148

  17. Montana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

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

    Base Gas) (Million Cubic Feet) Montana Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 109,573 109,573 109,573 109,573 112,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 1991 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 109,573 1992 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 169,892 1993 169,892 169,892 169,892 169,892

  18. Illinois Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Illinois Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 571,959 571,959 571,959 571,959 572,425 572,423 572,421 572,421 572,419 572,419 573,776 577,424 1991 577,418 577,418 577,418 568,227 568,178 568,160 568,158 568,157 568,157 568,158 568,158 568,158 1992 576,257 576,227 576,227 576,227 576,227 576,227 576,227 576,234 576,234 577,202 577,202 579,715 1993 620,575 620,856 620,777 621,051

  19. Kansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    Base Gas) (Million Cubic Feet) Kansas Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 179,462 191,402 190,669 1991 188,597 191,203 191,198 191,198 191,126 192,733 192,736 192,798 192,798 192,805 192,563 192,563 1992 190,943 190,963 190,914 190,591 190,765 190,714 190,611 190,578 190,606 190,643 189,320 186,399 1993 184,254 180,510 181,152 186,315

  20. Midwest Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) Midwest Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,505,645 1,504,979 1,497,798 1,502,556 1,498,128 1,498,610 1,498,610 1,498,610 1,498,887 1,496,791 1,496,848 1,497,021 2015 1,496,719 1,496,419 1,495,862 1,495,321 1,495,206 1,496,379 1,496,378 1,488,787 1,489,658 1,487,866 1,487,894 1,488,055 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  1. South Central Region Natural Gas in Underground Storage (Base Gas) (Million

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

    Cubic Feet) Base Gas) (Million Cubic Feet) South Central Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,050,691 1,049,083 1,049,047 1,049,443 1,049,496 1,053,249 1,054,073 1,058,479 1,060,363 1,060,181 1,060,298 1,059,866 2015 1,057,760 1,057,807 1,054,816 1,054,786 1,057,044 1,058,973 1,059,103 1,058,987 1,058,721 1,060,652 1,061,199 1,055,894 - = No Data Reported; -- = Not Applicable; NA = Not Available;

  2. East Region Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) East Region Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,113,096 1,112,811 1,110,723 1,111,592 1,111,730 1,113,003 1,113,262 1,113,458 1,113,383 1,113,607 1,113,589 1,113,356 2015 1,110,947 1,110,440 1,112,459 1,112,585 1,112,921 1,114,082 1,118,936 1,118,750 1,118,923 1,119,041 1,118,912 1,118,877 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld

  3. Lower 48 States Total Natural Gas in Underground Storage (Base Gas)

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

    (Million Cubic Feet) Base Gas) (Million Cubic Feet) Lower 48 States Total Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2011 4,302,792 4,302,341 4,302,108 4,303,570 4,304,364 4,301,779 4,300,139 4,300,269 4,301,291 4,301,737 4,299,727 4,301,752 2012 4,309,129 4,309,505 4,321,454 4,325,195 4,332,383 4,338,100 4,342,905 4,347,859 4,351,797 4,365,049 4,372,359 4,372,412 2013 4,369,851 4,369,819 4,368,153 4,367,022

  4. West Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) West Virginia Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 310,640 310,640 310,640 310,640 310,640 310,640 311,765 311,765 311,765 311,765 312,670 309,331 1991 331,618 332,229 331,898 332,278 332,288 332,288 331,275 332,283 332,269 332,264 332,259 332,070 1992 336,854 336,689 335,303 335,602 335,965 336,044 336,309 336,528 336,527 336,526 336,525 305,441 1993 305,478 304,578

  5. 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

  6. 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....

  7. 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...

  8. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  9. 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...

  10. 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.

  11. 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.

  12. World Natural Gas Model

    Energy Science and Technology Software Center (OSTI)

    1994-12-01

    RAMSGAS, the Research and Development Analysis Modeling System World Natural Gas Model, was developed to support planning of unconventional gaseoues fuels research and development. The model is a scenario analysis tool that can simulate the penetration of unconventional gas into world markets for oil and gas. Given a set of parameter values, the model estimates the natural gas supply and demand for the world for the period from 1980 to 2030. RAMSGAS is based onmore » a supply/demand framwork and also accounts for the non-renewable nature of gas resources. The model has three fundamental components: a demand module, a wellhead production cost module, and a supply/demand interface module. The demand for gas is a product of total demand for oil and gas in each of 9 demand regions and the gas share. Demand for oil and gas is forecast from the base year of 1980 through 2030 for each demand region, based on energy growth rates and price-induced conservation. For each of 11 conventional and 19 unconventional gas supply regions, wellhead production costs are calculated. To these are added transportation and distribution costs estimates associated with moving gas from the supply region to each of the demand regions and any economic rents. Based on a weighted average of these costs and the world price of oil, fuel shares for gas and oil are computed for each demand region. The gas demand is the gas fuel share multiplied by the total demand for oil plus gas. This demand is then met from the available supply regions in inverse proportion to the cost of gas from each region. The user has almost complete control over the cost estimates for each unconventional gas source in each year and thus can compare contributions from unconventional resources under different cost/price/demand scenarios.« less

  13. Natural Gas Transmission and Distribution Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Documents the archived version of the Natural Gas Transmission and Distribution Model that was used to produce the natural gas forecasts used in support of the Annual Energy Outlook 2014.

  14. New Mexico Natural Gas in Underground Storage (Base Gas) (Million Cubic

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

    Feet) Base Gas) (Million Cubic Feet) New Mexico Natural Gas in Underground Storage (Base Gas) (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1990 20,204 20,204 20,204 20,204 16,500 20,204 20,204 20,204 20,204 20,204 20,204 20,204 1991 20,204 20,204 20,204 30,426 30,426 30,426 30,413 30,410 30,410 30,426 30,426 30,426 1992 30,426 30,426 30,426 30,426 30,426 30,426 30,426 30,426 30,426 30,426 30,426 30,426 1993 30,426 30,426 30,426 30,426 30,426 30,426 30,426 30,426

  15. Study on systems based on coal and natural gas for producing dimethyl ether

    SciTech Connect (OSTI)

    Zhou, L.; Hu, S.Y.; Chen, D.J.; Li, Y.R.; Zhu, B.; Jin, Y.

    2009-04-15

    China is a coal-dependent country and will remain so for a long time. Dimethyl ether (DME), a potential substitute for liquid fuel, is a kind of clean diesel motor fuel. The production of DME from coal is meaningful and is studied in this article. Considering the C/H ratios of coal and natural gas (NG), the cofeed (coal and NG) system (CFS), which does not contain the water gas shift process, is studied. It can reduce CO{sub 2} emission and increase the conversion rate of carbon, producing more DME. The CFS is simulated and compared with the coal-based and NG-based systems with different recycling ratios. The part of the exhaust gas that is not recycled is burned, producing electricity. On the basis of the simulation results, the thermal efficiency, economic index, and CO{sub 2} emission ratio are calculated separately. The CFS with a 100% recycling ratio has the best comprehensive evaluation index, while the energy, economy, and environment were considered at the same time.

  16. 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.

  17. Pollutant Exposures from Natural Gas Cooking Burners: A Simulation-Based Assessment for Southern California

    SciTech Connect (OSTI)

    Logue, Jennifer M.; Klepeis, Neil E.; Lobscheid, Agnes B.; Singer, Brett C.

    2014-06-01

    Residential natural gas cooking burners (NGCBs) can emit substantial quantities of pollutants and they are typically used without venting. The objective of this study is to quantify pollutant concentrations and occupant exposures resulting from NGCB use in California homes. A mass balance model was applied to estimate time-dependent pollutant concentrations throughout homes and the "exposure concentrations" experienced by individual occupants. The model was applied to estimate nitrogen dioxide (NO{sub 2}), carbon monoxide (CO), and formaldehyde (HCHO) concentrations for one week each in summer and winter for a representative sample of Southern California homes. The model simulated pollutant emissions from NGCBs, NO{sub 2} and CO entry from outdoors, dilution throughout the home, and removal by ventilation and deposition. Residence characteristics and outdoor concentrations of CO and NO{sub 2} were obtained from available databases. Ventilation rates, occupancy patterns, and burner use were inferred from household characteristics. Proximity to the burner(s) and the benefits of using venting range hoods were also explored. Replicate model executions using independently generated sets of stochastic variable values yielded estimated pollutant concentration distributions with geometric means varying less than 10%. The simulation model estimates that in homes using NGCBs without coincident use of venting range hoods, 62%, 9%, and 53% of occupants are routinely exposed to NO{sub 2}, CO, and HCHO levels that exceed acute health-based standards and guidelines. NGCB use increased the sample median of the highest simulated 1-hr indoor concentrations by 100, 3000, and 20 ppb for NO{sub 2}, CO, and HCHO, respectively. Reducing pollutant exposures from NGCBs should be a public health priority. Simulation results suggest that regular use of even moderately effective venting range hoods would dramatically reduce the percentage of homes in which concentrations exceed health-based standards.

  18. Computerized map-based information management system for natural resource management

    SciTech Connect (OSTI)

    Miller, K.

    1995-12-01

    Federal agencies, states and resource managers have control and stewardship responsibility over a significant inventory of natural resources. A number of federal regulations require the review, protection and preservation of natural resource protection. Examples of such actions include the reauthorization of the Clean Water Act and the modification of the National Contingency Plan to incorporate the requirements of the Oil Pollution Act of 1990. To successfully preserve conserve and restore natural resources on federal reservations, and state and private lands, and to comply with Federal regulations designed to protect natural resources located on their sites, and the type of information on these resources required by environmental regulations. This paper presents an approach using a computerized, graphical information management system to catalogue and track data for the management of natural resources under Federal and state regulations, and for promoting resource conservation, preservation and restoration. The system is designed for use by Federal facility resource managers both for the day-to-day management of resources under their control, and for the longer-term management of larger initiatives, including restoration of significant or endangered resources, participation in regional stewardship efforts, and general ecosystem management. The system will be valuable for conducting natural resource baseline inventories an implementing resource management plans on lands other than those controlled by the Federal government as well. The system can provide a method for coordinating the type of natural resource information required by major federal environmental regulations--thereby providing a cost-effective means for managing natural resource information.

  19. STEO December 2012 - natural gas production

    Gasoline and Diesel Fuel Update (EIA)

    2012 natural gas production seen at record 69 billion cubic feet per day U.S. natural gas production is expected to increase 4.5 percent this year to a record 69 billion cubic feet per day, according to the new monthly energy forecast from the U.S. Energy Information Administration. A big portion of that natural gas is going to the U.S. electric power sector, which is generating more electricity from gas in place of coal. Consumption of natural gas for power generation this year is forecast to

  20. Technology-Based Oil and Natural Gas Plays: Shale Shock! Could There Be Billions in the Bakken?

    Reports and Publications (EIA)

    2006-01-01

    This report presents information about the Bakken Formation of the Williston Basin: its location, production, geology, resources, proved reserves, and the technology being used for development. This is the first in a series intending to share information about technology-based oil and natural gas plays.

  1. 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 ...

  2. 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...

  3. 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...

  4. Accounting for fuel price risk when comparing renewable togas-fired generation: the role of forward natural gas prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2004-07-17

    Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then--contrary to common practice--any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000-2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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)

  12. 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...

  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. 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

  15. 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

  16. 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

  17. EIA - Natural Gas Pipeline Network - Natural Gas Transmission...

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

    Transmission Path Diagram About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates Natural Gas Transmission Path Natural...

  18. 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.

  19. 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.

  20. 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.

  1. Natural gas transmission and distribution model of the National Energy Modeling System

    SciTech Connect (OSTI)

    1997-02-01

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. NEMS was developed in the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA). NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the EIA and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. From 1982 through 1993, the Intermediate Future Forecasting System (IFFS) was used by the EIA for its analyses, and the Gas Analysis Modeling System (GAMS) was used within IFFS to represent natural gas markets. Prior to 1982, the Midterm Energy Forecasting System (MEFS), also referred to as the Project Independence Evaluation System (PIES), was employed. NEMS was developed to enhance and update EIA`s modeling capability by internally incorporating models of energy markets that had previously been analyzed off-line. In addition, greater structural detail in NEMS permits the analysis of a broader range of energy issues. The time horizon of NEMS is the midterm period (i.e., through 2015). In order to represent the regional differences in energy markets, the component models of NEMS function at regional levels appropriate for the markets represented, with subsequent aggregation/disaggregation to the Census Division level for reporting purposes.

  2. Natural Gas Weekly Update, Printer-Friendly Version

    Gasoline and Diesel Fuel Update (EIA)

    regions of the country and weather forecasts indicated colder temperatures are here to stay until at least the end of the month. Natural gas in storage declined to 2,195 Bcf with...

  3. Natural Gas Weekly Update, Printer-Friendly Version

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

    Btu (MMBtu). The NEB noted the contrast of this forecast to the market prices of last summer, when natural gas prices peaked at more than 13 per MMBtu and crude oil reached a...

  4. 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

  5. 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)

  6. 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

  7. 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

  8. 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

  9. 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.

  10. 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...

  11. 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...

  12. 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 " " % % &...

  13. 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.

  14. EIA - Natural Gas Pipeline Network - Natural Gas Pipeline Mileage...

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

    Mileage by State About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates Estimated Natural Gas Pipeline Mileage in the...

  15. 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.

  16. 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

  17. LCO Nature | Open Energy Information

    Open Energy Info (EERE)

    LCO Nature Jump to: navigation, search Name: LCO Nature Place: Germany Sector: Renewable Energy Product: Based in Hamburg, LCO Nature is a consultancy and renewable energy project...

  18. 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.

  19. 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.

  20. 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...

  1. 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...

  2. Weekly Natural Gas Storage Report

    Weekly Natural Gas Storage Report (EIA)

    Weekly Natural Gas Storage Report PERFORMANCE EVALUATION for 2011 through 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 September 2014 U.S. Energy Information Administration | PERFORMANCE EVALUATION for 2011 through 2013 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

  3. High natural gas output and inventories contribute to lower prices

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

    High natural gas output and inventories contribute to lower prices High natural gas production and ample gas inventories are expected to keep natural gas prices relatively low for the rest of 2015. In its new monthly forecast, the U.S. Energy Information Administration says that while expected production growth is slowing from last year's torrid pace, domestic natural gas production in 2015 is still expected to be almost 6 percent above the 2014 level. Higher production has pushed U.S. natural

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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,...

  10. 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.

  11. 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.

  12. 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 ...

  13. 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...

  14. 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...

  15. 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 ...

  16. 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.

  17. 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

  18. EIA - Natural Gas Pipeline Network - Natural Gas Supply Basins...

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

    Corridors About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates U.S. Natural Gas Supply Basins Relative to Major Natural...

  19. 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.

  20. 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

  1. 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,

  2. 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

  3. Waste-to-wheel analysis of anaerobic-digestion-based renewable natural gas pathways with the GREET model.

    SciTech Connect (OSTI)

    Han, J.; Mintz, M.; Wang, M.

    2011-12-14

    In 2009, manure management accounted for 2,356 Gg or 107 billion standard cubic ft of methane (CH{sub 4}) emissions in the United States, equivalent to 0.5% of U.S. natural gas (NG) consumption. Owing to the high global warming potential of methane, capturing and utilizing this methane source could reduce greenhouse gas (GHG) emissions. The extent of that reduction depends on several factors - most notably, how much of this manure-based methane can be captured, how much GHG is produced in the course of converting it to vehicular fuel, and how much GHG was produced by the fossil fuel it might displace. A life-cycle analysis was conducted to quantify these factors and, in so doing, assess the impact of converting methane from animal manure into renewable NG (RNG) and utilizing the gas in vehicles. Several manure-based RNG pathways were characterized in the GREET (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model, and their fuel-cycle energy use and GHG emissions were compared to petroleum-based pathways as well as to conventional fossil NG pathways. Results show that despite increased total energy use, both fossil fuel use and GHG emissions decline for most RNG pathways as compared with fossil NG and petroleum. However, GHG emissions for RNG pathways are highly dependent on the specifics of the reference case, as well as on the process energy emissions and methane conversion factors assumed for the RNG pathways. The most critical factors are the share of flared controllable CH{sub 4} and the quantity of CH{sub 4} lost during NG extraction in the reference case, the magnitude of N{sub 2}O lost in the anaerobic digestion (AD) process and in AD residue, and the amount of carbon sequestered in AD residue. In many cases, data for these parameters are limited and uncertain. Therefore, more research is needed to gain a better understanding of the range and magnitude of environmental benefits from converting animal manure to RNG via AD.

  4. EIA - Natural Gas Pipeline Network - Combined Natural Gas Transportation

    Gasoline and Diesel Fuel Update (EIA)

    Maps Combined Natural Gas Transportation Maps About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Pipeline Network Map of U.S. Natural Gas Pipeline Network Major Natural Gas Supply Basins Relative to Natural Gas Pipeline Transportation Corridors Map of Major Natural Gas Supply Basins Relative to Natural Gas Pipeline Transportation Corridors see related text enlarge see related text enlarge U.S. Regional Breakdown

  5. 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.

  6. 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

  7. 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.

  8. Natural Gas Value-Chain and Network Assessments

    SciTech Connect (OSTI)

    Kobos, Peter H.; Outkin, Alexander V.; Beyeler, Walter E.; Walker, LaTonya Nicole; Malczynski, Leonard A.; Myerly, Melissa M.; Vargas, Vanessa N.; Tenney, Craig M.; Borns, David J.

    2015-09-01

    The current expansion of natural gas (NG) development in the United States requires an understanding of how this change will affect the natural gas industry, downstream consumers, and economic growth in order to promote effective planning and policy development. The impact of this expansion may propagate through the NG system and US economy via changes in manufacturing, electric power generation, transportation, commerce, and increased exports of liquefied natural gas. We conceptualize this problem as supply shock propagation that pushes the NG system and the economy away from its current state of infrastructure development and level of natural gas use. To illustrate this, the project developed two core modeling approaches. The first is an Agent-Based Modeling (ABM) approach which addresses shock propagation throughout the existing natural gas distribution system. The second approach uses a System Dynamics-based model to illustrate the feedback mechanisms related to finding new supplies of natural gas - notably shale gas - and how those mechanisms affect exploration investments in the natural gas market with respect to proven reserves. The ABM illustrates several stylized scenarios of large liquefied natural gas (LNG) exports from the U.S. The ABM preliminary results demonstrate that such scenario is likely to have substantial effects on NG prices and on pipeline capacity utilization. Our preliminary results indicate that the price of natural gas in the U.S. may rise by about 50% when the LNG exports represent 15% of the system-wide demand. The main findings of the System Dynamics model indicate that proven reserves for coalbed methane, conventional gas and now shale gas can be adequately modeled based on a combination of geologic, economic and technology-based variables. A base case scenario matches historical proven reserves data for these three types of natural gas. An environmental scenario, based on implementing a $50/tonne CO 2 tax results in less proven reserves being developed in the coming years while demand may decrease in the absence of acceptable substitutes, incentives or changes in consumer behavior. An increase in demand of 25% increases proven reserves being developed by a very small amount by the end of the forecast period of 2025.

  9. 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.

  10. 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).

  11. 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).

  12. Model documentation natural gas transmission and distribution model (NGTDM) of the national energy modeling system. Volume II: Model developer`s report

    SciTech Connect (OSTI)

    Not Available

    1995-01-03

    To partially fulfill the requirements for {open_quotes}Model Acceptance{close_quotes} as stipulated in EIA Standard 91-01-01 (effective February 3, 1991), the Office of Integrated Analysis and Forecasting has conducted tests of the Natural Gas Transmission and Distribution Model (NGTDM) for the specific purpose of validating the forecasting model. This volume of the model documentation presents the results of {open_quotes}one-at-a-time{close_quotes} sensitivity tests conducted in support of this validation effort. The test results are presented in the following forms: (1) Tables of important model outputs for the years 2000 and 2010 are presented with respect to change in each input from the reference case; (2) Tables of percent changes from base case results for the years 2000 and 2010 are presented for important model outputs; (3) Tables of conditional sensitivities (percent change in output/percent change in input) for the years 2000 and 2010 are presented for important model outputs; (4) Finally, graphs presenting the percent change from base case results for each year of the forecast period are presented for selected key outputs. To conduct the sensitivity tests, two main assumptions are made in order to test the performance characteristics of the model itself and facilitate the understanding of the effects of the changes in the key input variables to the model on the selected key output variables: (1) responses to the amount demanded do not occur since there are no feedbacks of inputs from other NEMS models in the stand-alone NGTDM run. (2) All the export and import quantities from and to Canada and Mexico, and liquefied natural gas (LNG) imports and exports are held fixed (i.e., there are no changes in imports and exports between the reference case and the sensitivity cases) throughout the forecast period.

  13. 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.

  14. 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...

  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. Natural gas inventories at record levels

    Gasoline and Diesel Fuel Update (EIA)

    Natural gas inventories at record levels U.S. natural gas inventories at the end of October tied the all-time record high and inventories could climb to 4 trillion cubic feet in November for the first time. In its new monthly forecast, the U.S. Energy Information Administration said weekly injections of natural gas into storage may continue into November, after inventories at the close of October matched the record high of just over 3.9 trillion cubic feet. High inventories, along with rising

  18. Model documentation: Natural gas transmission and distribution model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    1995-02-17

    The Natural Gas Transmission and Distribution Model (NGTDM) is the component of the National Energy Modeling System (NEMS) that is used to represent the domestic natural gas transmission and distribution system. NEMS was developed in the Office of integrated Analysis and Forecasting of the Energy information Administration (EIA). NEMS is the third in a series of computer-based, midterm energy modeling systems used since 1974 by the EIA and its predecessor, the Federal Energy Administration, to analyze domestic energy-economy markets and develop projections. The NGTDM is the model within the NEMS that represents the transmission, distribution, and pricing of natural gas. The model also includes representations of the end-use demand for natural gas, the production of domestic natural gas, and the availability of natural gas traded on the international market based on information received from other NEMS models. The NGTDM determines the flow of natural gas in an aggregate, domestic pipeline network, connecting domestic and foreign supply regions with 12 demand regions. The methodology employed allows the analysis of impacts of regional capacity constraints in the interstate natural gas pipeline network and the identification of pipeline capacity expansion requirements. There is an explicit representation of core and noncore markets for natural gas transmission and distribution services, and the key components of pipeline tariffs are represented in a pricing algorithm. Natural gas pricing and flow patterns are derived by obtaining a market equilibrium across the three main elements of the natural gas market: the supply element, the demand element, and the transmission and distribution network that links them. The NGTDM consists of four modules: the Annual Flow Module, the Capacity F-expansion Module, the Pipeline Tariff Module, and the Distributor Tariff Module. A model abstract is provided in Appendix A.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. Reducing Onshore Natural Gas and Oil Exploration and Production Impacts Using a Broad-Based Stakeholder Approach

    SciTech Connect (OSTI)

    Amy Childers

    2011-03-30

    Never before has the reduction of oil and gas exploration and production impacts been as important as it is today for operators, regulators, non-governmental organizations and individual landowners. Collectively, these stakeholders are keenly interested in the potential benefits from implementing effective environmental impact reducing technologies and practices. This research project strived to gain input and insight from such a broad array of stakeholders in order to identify approaches with the potential to satisfy their diverse objectives. The research team examined three of the most vital issue categories facing onshore domestic production today: (1) surface damages including development in urbanized areas, (2) impacts to wildlife (specifically greater sage grouse), and (3) air pollution, including its potential contribution to global climate change. The result of the research project is a LINGO (Low Impact Natural Gas and Oil) handbook outlining approaches aimed at avoiding, minimizing, or mitigating environmental impacts. The handbook identifies technical solutions and approaches which can be implemented in a practical and feasible manner to simultaneously achieve a legitimate balance between environmental protection and fluid mineral development. It is anticipated that the results of this research will facilitate informed planning and decision making by management agencies as well as producers of oil and natural gas. In 2008, a supplemental task was added for the researchers to undertake a 'Basin Initiative Study' that examines undeveloped and/or underdeveloped oil and natural gas resources on a regional or geologic basin scope to stimulate more widespread awareness and development of domestic resources. Researchers assessed multi-state basins (or plays), exploring state initiatives, state-industry partnerships and developing strategies to increase U.S. oil and gas supplies while accomplishing regional economic and environmental goals.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Natural Gas Weekly Update

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

    to last week's relatively moderate temperatures and a forecast for a continuation of this weather pattern have contributed to lower prices at most markets. After remaining...

  12. 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.

  13. 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

  14. 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

  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. EIA - Natural Gas Pipeline Network - Underground Natural Gas Storage

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

    Facilities Map Storage > U.S. Underground Natural Gas Storage Facilities Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Underground Natural Gas Storage Facilities, Close of 2007 more recent map U.S. Underground Natural Gas Storage Facilities, 2008 The EIA has determined that the informational map displays here do not raise security concerns, based on the application of the Federal Geographic Data Committee's

  17. 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

  18. 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.

  19. EIA - Natural Gas Pipeline Network - Natural Gas Transportation Corridors

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

    Map Corridors > Major U.S. Natural Gas Transportation Corridors Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Major U.S. Natural Gas Transportation Corridors, 2008

  20. EIA - Natural Gas Pipeline Network - Generalized Natural Gas Pipeline

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

    Capacity Design Schematic Generalized Design Schematic About U.S. Natural Gas Pipelines- Transporting Natural Gas based on data through 2007/2008 with selected updates Generalized Natural Gas Pipeline Capacity Design Schematic Generalized Natural Gas Pipeline Capcity Design Schematic

  1. Natural gas inventories heading to record levels at start of winter heating season

    Gasoline and Diesel Fuel Update (EIA)

    Natural gas inventories heading to record levels at start of winter heating season U.S. natural gas inventories are expected to be at record levels to start the winter heating season. In its new forecast, the U.S. Energy Information Administration said the amount of natural gas stored underground should total almost 4 trillion cubic feet by the beginning of November, reflecting record high natural gas production. Inventories could go even higher if heating demand is not strong during October

  2. 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.

  3. Economic feasibility analysis of distributed electric power generation based upon the natural gas-fired fuel cell. Final report

    SciTech Connect (OSTI)

    Not Available

    1994-03-01

    The final report provides a summary of results of the Cost of Ownership Model and the circumstances under which a distributed fuel cell is economically viable. The analysis is based on a series of micro computer models estimate the capital and operations cost of a fuel cell central utility plant configuration. Using a survey of thermal and electrical demand profiles, the study defines a series of energy user classes. The energy user class demand requirements are entered into the central utility plant model to define the required size the fuel cell capacity and all supporting equipment. The central plant model includes provisions that enables the analyst to select optional plant features that are most appropriate to a fuel cell application, and that are cost effective. The model permits the choice of system features that would be suitable for a large condominium complex or a residential institution such as a hotel, boarding school or prison. Other applications are also practical; however, such applications have a higher relative demand for thermal energy, a characteristic that is well-suited to a fuel cell application with its free source of hot water or steam. The analysis combines the capital and operation from the preceding models into a Cost of Ownership Model to compute the plant capital and operating costs as a function of capacity and principal features and compares these estimates to the estimated operating cost of the same central plant configuration without a fuel cell.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. 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)

  9. 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.

  10. 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

  11. 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.

  12. Natural Gas Weekly Update

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

    of 1 Tcf from the 1994 estimate of 51 Tcf. Ultimate potential for natural gas is a science-based estimate of the total amount of conventional gas in the province and is an...

  13. EIA - Natural Gas Pipeline Network - Major Natural Gas Transportation

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

    Corridors Major Natural Gas Transportation Corridors About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Major Natural Gas Transportation Corridors Corridors from the Southwest | From Canada | From Rocky Mountain Area | Details about Transportation Corridors The national natural gas delivery network is intricate and expansive, but most of the major transportation routes can be broadly categorized into 11 distinct corridors or flow

  14. EIA - Natural Gas Pipeline Network - Underground Natural Gas Storage

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

    Storage About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Underground Natural Gas Storage Overview | Regional Breakdowns Overview Underground natural gas storage provides pipelines, local distribution companies, producers, and pipeline shippers with an inventory management tool, seasonal supply backup, and access to natural gas needed to avoid imbalances between receipts and deliveries on a pipeline network. There are three

  15. 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.

  16. 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

  17. EIA - Natural Gas Pipeline Network - Regional Definitions

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

    Definitions Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 20072008 with selected updates Regional Definitions The regions defined in the...

  18. 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.

  19. The Future of U.S. Natural Gas: Supply, Demand & Infrastructure

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

    Developments | Department of Energy The Future of U.S. Natural Gas: Supply, Demand & Infrastructure Developments The Future of U.S. Natural Gas: Supply, Demand & Infrastructure Developments This analysis forecasts natural gas supply, demand, and infrastructure developments through 2030 using an inventory and cell model. After introduction of methodology and market approach, the analysis describes expectations of production and supply and demand. This includes how production shifts in

  20. Natural Gas Weekly Update

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

    cooler temperatures all the way down into Texas, Tuesday's release of the Salomon Smith Barney (SSB) forecast for a colder-than-normal winter, and rumors that the federal...

  1. 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

  2. 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

  3. 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)

  4. Thermoacoustic natural gas liquefier

    SciTech Connect (OSTI)

    Swift, G.; Gardner, D.; Hayden, M.; Radebaugh, R.; Wollan, J.

    1996-07-01

    This is the final report of a two-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). This project sought to develop a natural-gas-powered natural-gas liquefier that has absolutely no moving parts and requires no electrical power. It should have high efficiency, remarkable reliability, and low cost. The thermoacoustic natural-gas liquefier (TANGL) is based on our recent invention of the first no-moving-parts cryogenic refrigerator. In short, our invention uses acoustic phenomena to produce refrigeration from heat, with no moving parts. The required apparatus comprises nothing more than heat exchangers and pipes, made of common materials, without exacting tolerances. Its initial experimental success in a small size lead us to propose a more ambitious application: large-energy liquefaction of natural gas, using combustion of natural gas as the energy source. TANGL was designed to be maintenance-free, inexpensive, portable, and environmentally benign.

  5. EIA - Natural Gas Pipeline Network - Aquifer Storage Reservoir...

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

    Transporting Natural Gas based on data through 20072008 with selected updates Aquifer Underground Natural Gas Storage Reservoir Configuration Aquifer Underground Natural Gas Well

  6. EIA - Natural Gas Pipeline Network - Natural Gas Pipeline Development &

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

    Expansion Pipelinesk > Development & Expansion About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipeline Development and Expansion Timing | Determining Market Interest | Expansion Options | Obtaining Approval | Prefiling Process | Approval | Construction | Commissioning Timing and Steps for a New Project An interstate natural gas pipeline construction or expansion project takes an average of about three years

  7. 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.

  8. EIA - Natural Gas Pipeline Network - Natural Gas Supply Basins Relative to

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

    Major Natural Gas Pipeline Transportation Corridors Corridors About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Supply Basins Relative to Major Natural Gas Pipeline Transportation Corridors, 2008 U.S. Natural Gas Transporation Corridors out of Major Supply Basins

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. Study forecasts disappearance of conifers due to climate change

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

    due to climate change New results, reported in a paper released today in the journal Nature Climate Change, suggests that global models may underestimate predictions of forest...

  14. 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.

  15. 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.

  16. 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 ...

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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....

  2. 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 ...

  3. 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...

  4. Betting on the Future: The authors compare natural gas forecaststo futures buys

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-01-20

    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. The goal is 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. Below is a discussion of 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--.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 have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this article we 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. 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. 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. Natural Gas Processing Plants in the United States: 2010 Update...

    Gasoline and Diesel Fuel Update (EIA)

    3. Natural Gas Processing Plants Utilization Rates Based on 2008 Flows Figure 3. Natural Gas Processing Plants Utilization Rates Based on 2008 Flows Note: Average utilization rates...

  6. 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.

  7. 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

  8. 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.

  9. 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.)

  10. 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.)

  11. 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

  12. Quantifying the value that energy efficiency and renewable energy provide as a hedge against volatile natural gas prices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Bachrach, Devra; Golove, William

    2002-05-15

    Advocates of energy efficiency and renewable energy have long argued that such technologies can mitigate fuel price risk within a resource portfolio. Such arguments--made with renewed vigor in the wake of unprecedented natural gas price volatility during the winter of 2000/2001--have mostly been qualitative in nature, however, with few attempts to actually quantify the price stability benefit that these sources provide. In evaluating this benefit, it is important to recognize that alternative price hedging instruments are available--in particular, gas-based financial derivatives (futures and swaps) and physical, fixed-price gas contracts. Whether energy efficiency and renewable energy can provide price stability at lower cost than these alternative means is therefore a key question for resource acquisition planners. In this paper we evaluate the cost of hedging gas price risk through financial hedging instruments. To do this, we compare the price of a 10-year natural gas swap (i.e., what it costs to lock in prices over the next 10 years) to a 10-year natural gas price forecast (i.e., what the market is expecting spot natural gas prices to be over the next 10 years). We find that over the past two years natural gas users have had to pay a premium as high as $0.76/mmBtu (0.53/242/kWh at an aggressive 7,000 Btu/kWh heat rate) over expected spot prices to lock in natural gas prices for the next 10 years. This incremental cost to hedge gas price risk exposure is potentially large enough - particularly if incorporated by policymakers and regulators into decision-making practices - to tip the scales away from new investments in variable-price, natural gas-fired generation and in favor of fixed-price investments in energy efficiency and renewable energy.

  13. 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.

  14. Natural Gas

    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

  15. 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.

  16. EIA - Natural Gas Pipeline Network - Aquifer Storage Reservoir

    Gasoline and Diesel Fuel Update (EIA)

    Configuration Aquifer Storage Reservoir Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Aquifer Underground Natural Gas Storage Reservoir Configuration Aquifer Underground Natural Gas Well

  17. Short-Term Energy Outlook Supplement: Weather Sensitivity in Natural Gas Markets

    Gasoline and Diesel Fuel Update (EIA)

    Supplement: Weather Sensitivity in Natural Gas Markets October 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Supplement: Weather Sensitivity in Natural Gas Markets 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

  18. 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.

  19. EIA - Natural Gas Pipeline Network - Largest Natural Gas Pipeline Systems

    Gasoline and Diesel Fuel Update (EIA)

    Interstate Pipelines Table About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Thirty Largest U.S. Interstate Natural Gas Pipeline Systems, 2008 (Ranked by system capacity) Pipeline Name Market Regions Served Primary Supply Regions States in Which Pipeline Operates Transported in 2007 (million dekatherm)1 System Capacity (MMcf/d) 2 System Mileage Columbia Gas Transmission Co. Northeast Southwest, Appalachia DE, PA, MD, KY, NC, NJ, NY,

  20. Natural Gas Industry and Markets

    Reports and Publications (EIA)

    2006-01-01

    This special report provides an overview of the supply and disposition of natural gas in 2004 and is intended as a supplement to the Energy Information Administration's (EIA) Natural Gas Annual 2004 (NGA). Unless otherwise stated, all data and figures in this report are based on summary statistics published in the NGA 2004.

  1. Market-Based Indian Grid Integration Study Options: Preprint

    SciTech Connect (OSTI)

    Stoltenberg, B.; Clark, K.; Negi, S. K.

    2012-03-01

    The Indian state of Gujarat is forecasting solar and wind generation expansion from 16% to 32% of installed generation capacity by 2015. Some states in India are already experiencing heavy wind power curtailment. Understanding how to integrate variable generation (VG) into the grid is of great interest to local transmission companies and India's Ministry of New and Renewable Energy. This paper describes the nature of a market-based integration study and how this approach, while new to Indian grid operation and planning, is necessary to understand how to operate and expand the grid to best accommodate the expansion of VG. Second, it discusses options in defining a study's scope, such as data granularity, generation modeling, and geographic scope. The paper also explores how Gujarat's method of grid operation and current system reliability will affect how an integration study can be performed.

  2. ,"Natural Gas Consumption",,,"Natural Gas Expenditures"

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

    Census Division, 1999" ,"Natural Gas Consumption",,,"Natural Gas Expenditures" ,"per Building (thousand cubic feet)","per Square Foot (cubic feet)","per Worker (thousand cubic...

  3. 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

  4. 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

  5. Effects of Alaska Oil and Natural Gas Provisions of H. R. 4 and S. 1766 on U.S. Energy Markets

    Reports and Publications (EIA)

    2002-01-01

    On December 20, 2001, Sen. Frank Murkowski, the Ranking Minority Member of the Senate Committee on Energy and Natural Resources requested an analysis of selected portions of Senate Bill 1766 (S. 1766, the Energy Policy Act of 2002) and House Bill H.R. 4 (the Securing America's Future Energy Act of 2001). In response, the Energy Information Administration (EIA) has prepared a series of analyses showing the impacts of each of the selected provisions of the bills on energy supply, demand, and prices, macroeconomic variables where relevant, import dependence, and emissions. The analysis provided is based on the Annual Energy Outlook 2002 (AEO2002) midterm forecasts of energy supply, demand, and prices through 2020.

  6. EIA - Natural Gas Pipeline System - Midwest Region

    Gasoline and Diesel Fuel Update (EIA)

    Midwest Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Midwest Region Overview | Domestic Gas | Canadian Imports | Regional Pipeline Companies & Links Overview Twenty-six interstate and at least eight intrastate natural gas pipeline companies operate within the Midwest Region (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin). The principal sources of natural gas supply for the

  7. Heat distribution by natural convection

    SciTech Connect (OSTI)

    Balcomb, J.D.

    1985-01-01

    Natural convection can provide adequate heat distribution in many situations that arise in buildings. This is appropriate, for example, in passive solar buildings where some rooms tend to be more strongly solar heated than others. Natural convection can also be used to reduce the number of auxiliary heating units required in a building. Natural airflow and heat transport through doorways and other internal building apertures are predictable and can be accounted for in the design. The nature of natural convection is described, and a design chart is presented appropriate to a simple, single-doorway situation. Experimental results are summarized based on the monitoring of 15 passive solar buildings which employ a wide variety of geometrical configurations including natural convective loops.

  8. Natural Gas Weekly Update

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

    natural gas demand, thereby contributing to larger net injections of natural gas into storage. Other Market Trends: EIA Releases The Natural Gas Annual 2006: The Energy...

  9. 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.

  10. 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.

  11. Atena Tecnologias em Energia Natural | Open Energy Information

    Open Energy Info (EERE)

    Atena Tecnologias em Energia Natural Jump to: navigation, search Name: Atena Tecnologias em Energia Natural Place: Martinopolis, Sao Paulo, Brazil Product: Brazil based ethanol...

  12. EIA - Natural Gas Pipeline Network - States Dependent on Interstate...

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

    Natural Gas based on data through 20072008 with selected updates States in grey which are at least 85% dependent on the interstate pipeline network for their natural...

  13. SECURING OIL AND NATURAL GAS INFRASTRUCTURES IN THE NEW ECONOMY...

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

    SECURING OIL AND NATURAL GAS INFRASTRUCTURES IN THE NEW ECONOMY SECURING OIL AND NATURAL GAS INFRASTRUCTURES IN THE NEW ECONOMY Based on the finding of a growing potential ...

  14. Natural Gas Basics

    SciTech Connect (OSTI)

    NREL Clean Cities

    2010-04-01

    Fact sheet answers questions about natural gas production and use in transportation. Natural gas vehicles are also described.

  15. 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.

  16. 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.

  17. Builds in U.S. natural gas storage running above five-year average

    Gasoline and Diesel Fuel Update (EIA)

    Builds in U.S. natural gas storage running above five-year average The amount of natural gas put into underground storage since the beginning of the so-called "injection season" in April has been above the five-year average by a wide margin. In its new forecast, the U.S. Energy Information Administration said natural gas inventories, which are running more than 50% above year ago levels, are on track to reach almost 4 trillion cubic feet by the end of October which marks the start of

  18. North American Natural Gas Markets

    SciTech Connect (OSTI)

    Not Available

    1989-02-01

    This report summarizes die research by an Energy Modeling Forum working group on the evolution of the North American natural gas markets between now and 2010. The group's findings are based partly on the results of a set of economic models of the natural gas industry that were run for four scenarios representing significantly different conditions: two oil price scenarios (upper and lower), a smaller total US resource base (low US resource case), and increased potential gas demand for electric generation (high US demand case). Several issues, such as the direction of regulatory policy and the size of the gas resource base, were analyzed separately without the use of models.

  19. Nature Elements Capital | Open Energy Information

    Open Energy Info (EERE)

    Capital Jump to: navigation, search Name: Nature Elements Capital Place: Beijing, Beijing Municipality, China Zip: 100125 Product: Beijing-based private equity firm investing in...

  20. Natural Fuel Energy Inc | Open Energy Information

    Open Energy Info (EERE)

    Fuel Energy Inc Jump to: navigation, search Name: Natural Fuel & Energy Inc Place: Boston, Massachusetts Zip: 2100 Product: Boston - based biodiesel producer that operates a...

  1. Natural Solutions Pvt Ltd | Open Energy Information

    Open Energy Info (EERE)

    Jump to: navigation, search Name: Natural Solutions Pvt. Ltd. Place: Mumbai, Maharashtra, India Sector: Renewable Energy Product: Mumbai-based IT consultant. The firm plans to set...

  2. 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.

  3. EIA - Natural Gas Pipeline Network - Depleted Reservoir Storage

    Gasoline and Diesel Fuel Update (EIA)

    Configuration Depleted Reservoir Storage Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Depleted Production Reservoir Underground Natural Gas Storage Well Configuration Depleted Production Reservoir Storage

  4. EIA-914 Monthly Crude Oil, Lease Condensate, and Natural Gas Production Report Revision Policy

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

    Revision Policy December 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | EIA-94 Monthly Crude Oil, Lease Condensate, and Natural Gas Production Report Methodology i This revision policy 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

  5. 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.

  6. 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.

  7. 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).

  8. EIA - Natural Gas Pipeline System - Central Region

    Gasoline and Diesel Fuel Update (EIA)

    Central Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Central Region Overview | Domestic Gas | Exports | Regional Pipeline Companies & Links Overview Twenty-two interstate and at least thirteen intrastate natural gas pipeline companies (see Table below) operate in the Central Region (Colorado, Iowa, Kansas, Missouri, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming). Twelve

  9. EIA - Natural Gas Pipeline System - Northeast Region

    Gasoline and Diesel Fuel Update (EIA)

    Northeast Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Northeast Region Overview | Domestic Gas | Canadian Imports | Regional Pipeline Companies & Links Overview Twenty interstate natural gas pipeline systems operate within the Northeast Region (Connecticut, Delaware, Massachusetts, Maine, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Virginia, and West Virginia). These

  10. EIA - Natural Gas Pipeline System - Southeast Region

    Gasoline and Diesel Fuel Update (EIA)

    Southeast Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Southeast Region Overview | Transportation to Atlantic & Gulf States | Gulf of Mexico Transportation Corridor | Transportation to the Northern Tier | Regional Pipeline Companies & Links Overview Twenty-three interstate, and at least eight intrastate, natural gas pipeline companies operate within the Southeast Region (Alabama,

  11. EIA - Natural Gas Pipeline System - Southwest Region

    Gasoline and Diesel Fuel Update (EIA)

    Southwest Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Southwest Region Overview | Export Transportation | Intrastate | Connection to Gulf of Mexico | Regional Pipeline Companies & Links Overview Most of the major onshore interstate natural gas pipeline companies (see Table below) operating in the Southwest Region (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) are primarily

  12. EIA - Natural Gas Pipeline System - Western Region

    Gasoline and Diesel Fuel Update (EIA)

    Western Region About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Pipelines in the Western Region Overview | Transportation South | Transportation North | Regional Pipeline Companies & Links Overview Ten interstate and nine intrastate natural gas pipeline companies provide transportation services to and within the Western Region (Arizona, California, Idaho, Nevada, Oregon, and Washington), the fewest number serving

  13. EIA - Natural Gas Pipeline Network - Regulatory Authorities

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

    Regulatory Authorities About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Regulatory Authorities Beginning | Regulations Today | Coordinating Agencies | Regulation of Mergers and Acquisitions Beginning of Industry Restructuring In April 1992, the Federal Energy Regulatory Commission (FERC) issued its Order 636 and transformed the interstate natural gas transportation segment of the industry forever. Under it,

  14. 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.

  15. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Sources & Uses Petroleum & Other Liquids Crude oil, gasoline, heating oil, diesel, propane, and other liquids including biofuels and natural gas liquids. Natural Gas...

  16. Natural Gas Weekly Update

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

    6, 2009 Next Release: August 13, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, August 5, 2009) Natural...

  17. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    , 2008 Next Release: July 10, 2008 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview Since Wednesday, June 25, natural gas spot prices...

  18. Historical Natural Gas Annual

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

    6 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at...

  19. Historical Natural Gas Annual

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

    7 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at...

  20. Historical Natural Gas Annual

    Gasoline and Diesel Fuel Update (EIA)

    8 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at...

  1. Natural Gas Weekly Update

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

    cooling demand for natural gas. Meanwhile, it became increasingly clear that Hurricane Frances likely would not pose a significant threat to natural gas production in the Gulf of...

  2. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    of natural gas vehicles. The Department of Energys Office of Energy Efficiency and Renewable Energy reports that there were 841 compressed natural gas (CNG) fuel stations and 41...

  3. Natural Gas Weekly Update

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

    wide array of affiliates in all sectors of the energy market. Along with the changing nature of the energy affiliates, the changing nature of the transmission providers themselves...

  4. Natural Gas Weekly Update

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

    5, 2009 Next Release: July 2, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, June 24, 2009) Natural gas...

  5. Natural Gas Weekly Update

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

    Next Release: Thursday, May 19, 2011 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, May 11, 2011) Natural...

  6. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Release: Thursday, April 28, 2011 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, April 20, 2011) Natural...

  7. Natural Gas Weekly Update

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

    Release: Thursday, August 26, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, August 18, 2010) Natural...

  8. Natural Gas Weekly Update

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

    2008 Next Release: November 6, 2008 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the week ending Wednesday, October 29) Natural gas...

  9. NETL: Natural Gas Resources

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

    Natural Gas Resources Useful for heating, manufacturing, and as chemical feedstock, natural gas has the added benefit of producing fewer greenhouse gas emissions than other fossil...

  10. Natural Gas Weekly Update

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

    9, 2008 Next Release: June 26, 2008 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview Since Wednesday, June 11, natural gas spot prices...

  11. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Weekly Underground Natural Gas Storage Report. The sample change occurred over a transition period that began with the release of the Weekly Natural Gas Storage Report (WNGSR)...

  12. Natural Gas Applications

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

    Gas Applications. If you need assistance viewing this page, please call (202) 586-8800. Energy Information Administration Home Page Home > Natural Gas > Natural Gas Applications...

  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. 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. 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

  16. 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)

  17. New Tools for Forecasting Old Physics at the LHC

    ScienceCinema (OSTI)

    None

    2011-10-06

    For the LHC to uncover many types of new physics, the "old physics" produced by the Standard Model must be understood very well. For decades, the central theoretical tool for this job was the Feynman diagram expansion. However, Feynman diagrams are just too slow, even on fast computers, to allow adequate precision for complicated LHC events with many jets in the final state. Such events are already visible in the initial LHC data. Over the past few years, alternative methods to Feynman diagrams have come to fruition. These new "on-shell" methods are based on the old principles of unitarity and factorization. They can be much more efficient because they exploit the underlying simplicity of scattering amplitudes, and recycle lower-loop information. I will describe how and why these methods work, and present some of the recent state-of-the-art results that have been obtained with them.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. NATURAL RESOURCES ASSESSMENT

    SciTech Connect (OSTI)

    D.F. Fenster

    2000-12-11

    The purpose of this report is to summarize the scientific work that was performed to evaluate and assess the occurrence and economic potential of natural resources within the geologic setting of the Yucca Mountain area. The extent of the regional areas of investigation for each commodity differs and those areas are described in more detail in the major subsections of this report. Natural resource assessments have focused on an area defined as the ''conceptual controlled area'' because of the requirements contained in the U.S. Nuclear Regulatory Commission Regulation, 10 CFR Part 60, to define long-term boundaries for potential radionuclide releases. New requirements (proposed 10 CFR Part 63 [Dyer 1999]) have obviated the need for defining such an area. However, for the purposes of this report, the area being discussed, in most cases, is the previously defined ''conceptual controlled area'', now renamed the ''natural resources site study area'' for this report (shown on Figure 1). Resource potential can be difficult to assess because it is dependent upon many factors, including economics (demand, supply, cost), the potential discovery of new uses for resources, or the potential discovery of synthetics to replace natural resource use. The evaluations summarized are based on present-day use and economic potential of the resources. The objective of this report is to summarize the existing reports and information for the Yucca Mountain area on: (1) Metallic mineral and mined energy resources (such as gold, silver, etc., including uranium); (2) Industrial rocks and minerals (such as sand, gravel, building stone, etc.); (3) Hydrocarbons (including oil, natural gas, tar sands, oil shales, and coal); and (4) Geothermal resources. Groundwater is present at the Yucca Mountain site at depths ranging from 500 to 750 m (about 1,600 to 2,500 ft) below the ground surface. Groundwater resources are not discussed in this report, but are planned to be included in the hydrology section of future revisions of the ''Yucca Mountain Site Description'' (CRWMS M&O 2000c).

  6. 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,

  7. Forecast of Standard Atomic Weights for the Mononuclidic Elements 2011

    SciTech Connect (OSTI)

    Holden, N.E.; Holden, N.; Holden,N.E.

    2011-07-27

    In this short report, I will provide an early warning about potential changes to the standard atomic weight values for the twenty mononuclidic and the so-called pseudo-mononuclidic ({sup 232}Th and {sup 231}Pa) chemical elements due to the estimated changes in the mass values to be published in the next Atomic Mass Tables within the next two years. There have been many new measurements of atomic masses, since the last published Atomic Mass Table. The Atomic Mass Data Center has released an unpublished version of the present status of the atomic mass values as a private communication. We can not update the Standard Atomic Weight Table at this time based on these unpublished values but we can anticipate how many changes are probably going to be expected in the next few years on the basis of the forthcoming publication of the Atomic Mass Table. I will briefly discuss the procedures that the Atomic Weights Commission used in deriving the recommended Standard Atomic Weight values and their uncertainties from the atomic mass values. I will also discuss some concern raised about a proposed change in the definition of the mole. The definition of the mole is now connected directly to the mass of a {sup 12}C isotope (which is defined as 12 exactly) and to the kilogram. A change in the definition of the mole will probably impact the mass of {sup 12}C.

  8. Electrospinning of PVC with natural rubber

    SciTech Connect (OSTI)

    Othman, Muhammad Hariz; Abdullah, Ibrahim; Mohamed, Mahathir

    2013-11-27

    Polyvinyl chloride (PVC) was mixed with natural rubbers which are liquid natural rubber (LNR), liquid epoxidised natural rubber (LENR) and liquid epoxidised natural rubber acrylate (LENRA) for a preparation of a fine non-woven fibers mat. PVC and each natural rubbers(PVC:LENR, PVC:LNR and PVC:LENRA) were mixed based on ratio of 70:30. Electrospinning method was used to prepare the fiber. The results show that the spinnable concentration of PVC/ natural rubber/THF solution is 16 wt%. The morphology, diameter, structure and degradation temperature of electrospun fibers were investigated by scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). SEM photos showed that the morphology and diameter of the fibers were mainly affected by the addition of natural rubber. TGA results suggested that PVC electrospun fiber has higher degradation temperature than those electrospun fibers that contain natural rubber.

  9. Short-Term Energy Outlook: Changes to the Natural Gas Storage Regions

    Gasoline and Diesel Fuel Update (EIA)

    Short-Term Energy Outlook: Changes to the Natural Gas Storage Regions December 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Short-Term Energy Outlook: Changes to the Natural Gas Storage Regions 1 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

  10. U.S. Crude Oil and Natural Gas Proved Reserves, 2014

    Gasoline and Diesel Fuel Update (EIA)

    and Natural Gas Proved Reserves, 2014 November 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | U.S. Crude Oil and Natural Gas Proved Reserves, 2014 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 officer or

  11. Base Natural Gas in Underground Storage (Summary)

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

    Monthly Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 View History U.S. 4,371,340 4,363,455 4,364,233 4,364,778 4,367,380 4,362,559 1973-2015 Alabama 9,640 9,640 9,640 9,640 9,640 9,640 1995-2015 Alaska 14,197 14,197 14,197 14,197 14,197 14,197 2013-2015 Arkansas 9,648 9,648 9,648 10,841 11,213 11,664 1990-2015 California 225,550 225,550 225,550 225,845

  12. Texas (with State Offshore) Natural Gas Plant Liquids, Reserves...

    Gasoline and Diesel Fuel Update (EIA)

    Reserves Based Production (Million Barrels) Texas (with State Offshore) Natural Gas Plant Liquids, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3...

  13. West Virginia Natural Gas Liquids Lease Condensate, Reserves...

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

    Reserves Based Production (Million Barrels) West Virginia Natural Gas Liquids Lease Condensate, Reserves Based Production (Million Barrels) Decade Year-0 Year-1 Year-2 Year-3...

  14. Natural gas monthly, July 1995

    SciTech Connect (OSTI)

    1995-07-21

    The Natural Gas Monthly (NGM) highlights activities, events, and analyses of interest to public and private sector organizations associated with the natural gas industry. Volume and price data are presented each month for natural gas production, distribution, consumption, and interstate pipeline activities. Producer-related activities and underground storage data are also reported. From time to time, the NGM features articles designed to assist readers in using and interpreting natural gas information. The data in this publication are collected on surveys conducted by the EIA to fulfill its responsibilities for gathering and reporting energy data. Some of the data are collected under the authority of the Federal Energy Regulatory Commission (FERC), an independent commission within the DOE, which has jurisdiction primarily in the regulation of electric utilities and the interstate natural gas industry. Geographic coverage is the 50 States and the District of Columbia. Explanatory Notes supplement the information found in tables of the report. A description of the data collection surveys that support the NGM is provided in the Data Sources section. A glossary of the terms used in this report is also provided to assist readers in understanding the data presented in this publication. All natural gas volumes are reported at a pressure base of 14.73 pounds per square inch absolute (psia) and at 60 degrees Fahrenheit. Cubic feet are converted to cubic meters by applying a factor of 0.02831685.

  15. EIA - Natural Gas Pipeline Network - Natural Gas Imports/Exports Pipelines

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

    Export Pipelines About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Natural Gas Import/Export Pipelines As of the close of 2008 the United States has 58 locations where natural gas can be exported or imported. 24 locations are for imports only 18 locations are for exports only 13 locations are for both imports and exports 8 locations are liquefied natural gas (LNG) import facilities Imported natural gas in 2007 represented almost 16

  16. Natural gas will account for biggest share of U.S. electricity for first time in 2016

    Gasoline and Diesel Fuel Update (EIA)

    Natural gas will account for biggest share of U.S. electricity for first time in 2016 For the first time on an annual basis, the amount of U.S. electricity generated by natural gas- fired power plants is expected to exceed coal-fired generation. In its new monthly forecast, the U.S. Energy Information Administration said 33% of U.S. electricity will come from natural gas this year while 32% will come from coal. The electric power sector's use of coal this year is expected to decline by 29

  17. Appraisal of transport and deformation in shale reservoirs using natural noble gas tracers

    SciTech Connect (OSTI)

    Heath, Jason E.; Kuhlman, Kristopher L.; Robinson, David G.; Bauer, Stephen J.; Gardner, William Payton

    2015-09-01

    This report presents efforts to develop the use of in situ naturally-occurring noble gas tracers to evaluate transport mechanisms and deformation in shale hydrocarbon reservoirs. Noble gases are promising as shale reservoir diagnostic tools due to their sensitivity of transport to: shale pore structure; phase partitioning between groundwater, liquid, and gaseous hydrocarbons; and deformation from hydraulic fracturing. Approximately 1.5-year time-series of wellhead fluid samples were collected from two hydraulically-fractured wells. The noble gas compositions and isotopes suggest a strong signature of atmospheric contribution to the noble gases that mix with deep, old reservoir fluids. Complex mixing and transport of fracturing fluid and reservoir fluids occurs during production. Real-time laboratory measurements were performed on triaxially-deforming shale samples to link deformation behavior, transport, and gas tracer signatures. Finally, we present improved methods for production forecasts that borrow statistical strength from production data of nearby wells to reduce uncertainty in the forecasts.

  18. EIA - Natural Gas Publications

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

    these data from 2005 to 2009 are presented for each State. (12282010) U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Proved Reserves: 2009 National and State...

  19. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    York Mercantile Exchange (NYMEX), the August 2011 natural gas contract price also lost ground over the week, closing at 4.217 per MMBtu on Wednesday. The natural gas rotary rig...

  20. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    York Mercantile Exchange (NYMEX), the August 2011 natural gas contract price also lost ground over the week, closing at 4.315 per MMBtu on Wednesday. The natural gas rotary rig...

  1. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    of natural gas into storage. However, shut-in natural gas production in the Gulf of Mexico reduced available current supplies, and so limited net injections during the report...

  2. Natural Gas Weekly Update

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

    a high degree of price volatility seems inherent in natural gas markets because of the nature of the commodity. However, the annual volatility during 1994 and 2006 does not exhibit...

  3. Natural Gas Weekly Update

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

    heating-related demand for natural gas that limited the size of the net addition to storage. The economic incentives for storing natural gas for next winter are considerably...

  4. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    The report provides an overview of U.S. international trade in 2008 as well as historical data on natural gas imports and exports. Net natural gas imports accounted for only 13...

  5. Natural Gas Weekly Update

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

    next heating season. Net injections reported in today's release of EIA's Weekly Natural Gas Storage Report brought natural gas storage supplies to 2,163 Bcf as of Friday, June 1,...

  6. Natural gas annual 1996

    SciTech Connect (OSTI)

    1997-09-01

    This document provides information on the supply and disposition of natural gas to a wide audience. The 1996 data are presented in a sequence that follows natural gas from it`s production to it`s end use.

  7. Natural Phenomena Hazards Program

    Broader source: Energy.gov [DOE]

    The Department of Energy (DOE) Natural Phenomena Hazards Program develops and maintains state-of-the-art program standards and guidance for DOE facilities exposed to natural phenomena hazards (NPHs).

  8. 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.

  9. 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.

  10. 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.

  11. Natural gas annual 1995

    SciTech Connect (OSTI)

    1996-11-01

    The Natural Gas Annual provides information on the supply and disposition of natural gas to a wide audience including industry, consumers, Federal and State agencies, and educational institutions. The 1995 data are presented in a sequence that follows natural gas (including supplemental supplies) from its production to its end use. This is followed by tables summarizing natural gas supply and disposition from 1991 to 1995 for each Census Division and each State. Annual historical data are shown at the national level.

  12. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    . Home | Petroleum | Gasoline | Diesel | Propane | Natural Gas | Electricity | Coal | Nuclear Renewables | Alternative Fuels | Prices | States | International | Country Analysis...

  13. Natural Gas Weekly Update

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

    Independence Avenue, SW Washington, DC 20585 . Home | Petroleum | Gasoline | Diesel | Propane | Natural Gas | Electricity | Coal | Nuclear Renewables | Alternative Fuels |...

  14. EIA - Natural Gas Pipeline Network - Salt Cavern Storage Reservoir

    Gasoline and Diesel Fuel Update (EIA)

    Configuration Salt Cavern Storage Reservoir Configuration About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Salt Cavern Underground Natural Gas Storage Reservoir Configuration Salt Cavern Underground Natural Gas Storage Reservoir Configuration Source: PB Energy Storage Services Inc.

  15. 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.

  16. Arctic Oil and Natural Gas Potential

    Reports and Publications (EIA)

    2009-01-01

    This paper examines the discovered and undiscovered Arctic oil and natural gas resource base with respect to their location and concentration. The paper also discusses the cost and impediments to developing Arctic oil and natural gas resources, including those issues associated with environmental habitats and political boundaries.

  17. 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.

  18. EIA - Natural Gas Pipeline Network - Natural Gas Import/Export Locations

    Gasoline and Diesel Fuel Update (EIA)

    List Pipelines > Import/Export Location List About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Currently, there are 58 locations at which natural gas can be exported or imported into the United States, including 9 LNG (liquefied natural gas) facilities in the continental United States and Alaska (There is a tenth U.S. LNG import facility located in Puerto Rico). At 28 of these locations natural gas or LNG currently can only

  19. Oil & Natural Gas Technology

    Office of Scientific and Technical Information (OSTI)

    Oil & Natural Gas Technology DOE A ward N o.: D E---FE0001243 Topical R eport DEVELOPMENT OF CFD-BASED SIMULATION TOOLS FOR IN SITU THERMAL PROCESSING OF OIL SHALE/SANDS Submitted b y: University of Utah Institute f or C lean a nd S ecure E nergy 155 S outh 1 452 E ast, R oom 3 80 Salt L ake C ity, U tah 8 4112 Prepared for: United S tates D epartment o f E nergy National E nergy T echnology L aboratory February 2012 Office of Fossil Energy TOPICAL REPORT: DEVELOPMENT OF CFD-BASED

  20. Natural Gas Weekly Update

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

    sand with very little natural rock bottom and reef habitat. Without artificial reefs, fish and marine life typically would become widely dispersed. In the Gulf of Mexico...

  1. ,"Total Natural Gas Consumption

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

    Gas Consumption (billion cubic feet)",,,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"Total ","Space Heating","Water Heating","Cook- ing","Other","Total ","Space...

  2. Natural gas dehydration apparatus

    DOE Patents [OSTI]

    Wijmans, Johannes G; Ng, Alvin; Mairal, Anurag P

    2006-11-07

    A process and corresponding apparatus for dehydrating gas, especially natural gas. The process includes an absorption step and a membrane pervaporation step to regenerate the liquid sorbent.

  3. Natural Gas Weekly Update

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

    P.M. Next Release: Thursday, July 1, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, June 23, 2010)...

  4. Natural Gas Weekly Update

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

    Next Release: Thursday, September 9, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, September 1,...

  5. Natural Gas Weekly Update

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

    P.M. Next Release: Thursday, January 14, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, January 6,...

  6. Natural Gas Weekly Update

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

    Next Release: Thursday, November 19, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, November 11,...

  7. Natural Gas Weekly Update

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

    P.M. Next Release: Thursday, June 17, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, June 9, 2010)...

  8. Natural Gas Weekly Update

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

    P.M. Next Release: Thursday, August 12, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, August 4, 2010)...

  9. Natural Gas Weekly Update

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

    Next Release: Thursday, September 2, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, August 25,...

  10. Natural Gas Weekly Update

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

    P.M. Next Release: Thursday, August 5, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, July 28, 2010)...

  11. Natural Gas Weekly Update

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

    P.M. Next Release: Thursday, May 13, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, May 5, 2010)...

  12. Natural Gas Weekly Update

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

    9, 2009 Next Release: April 16, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, April 8, 2009) Since...

  13. Natural Gas Weekly Update

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

    15, 2009 Next Release: January 23, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, January 14, 2009) In...

  14. Natural Gas Weekly Update

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

    16, 2009 Next Release: April 23, 2009 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, April 15, 2009) Since...

  15. Natural Gas Weekly Update

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

    5 to Wednesday, December 12) Released: December 13 Next release: December 20, 2007 Natural gas spot and futures prices increased this report week (Wednesday to Wednesday,...

  16. Natural Gas Weekly Update

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

    Release: Thursday, April 15, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, April 7, 2010) Since...

  17. Natural Gas Weekly Update

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

    Release: Thursday, February 25, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, February 17, 2010)...

  18. Natural Gas Weekly Update

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

    Release: Thursday, March 18, 2010 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, March 10, 2010) Since...

  19. Natural Gas Weekly Update

    Gasoline and Diesel Fuel Update (EIA)

    Release: Thursday, March 24, 2011 Overview Prices Storage Other Market Trends Natural Gas Transportation Update Overview (For the Week Ending Wednesday, March 16, 2011) With...

  20. Natural gas annual 1997

    SciTech Connect (OSTI)

    1998-10-01

    The Natural Gas Annual provides information on the supply and disposition of natural gas to a wide audience including industry, consumers, Federal and State agencies, and educational institutions. The 1997 data are presented in a sequence that follows natural gas (including supplemental supplies) from its production to its end use. This is followed by tables summarizing natural gas supply and disposition from 1993 to 1997 for each Census Division and each State. Annual historical data are shown at the national level. 27 figs., 109 tabs.