National Library of Energy BETA

Sample records for idm forecasts energy

  1. probabilistic energy production forecasts

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

    energy production forecasts - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary ...

  2. 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:...

  3. Acquisition Forecast | Department of Energy

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

    Acquisition Forecast Acquisition Forecast Acquisition Forecast It is the policy of the U.S. Department of Energy (DOE) to provide timely information to the public regarding DOE's forecast of future prime contracting opportunities and subcontracting opportunities which are available via the Department's major site and facilities management contractors. This forecast has been expanded to also provide timely status information for ongoing prime contracting actions that are valued in excess of the

  4. 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:...

  5. 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

  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 estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030. With declining costs and improving performance, LED products have been seeing increased adoption for general illumination applications. This is a positive development in terms of energy consumption, as LEDs use significantly

  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. Acquisition Forecast Download | Department of Energy

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

    Acquisition Forecast Download Acquisition Forecast Download Click on the link to download a copy of the DOE HQ Acquisition Forecast. Acquisition-Forecast-2016-07-20.xlsx (72.85 KB) More Documents & Publications Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment Assessment Report: OAS-V-15-01

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

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

    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

  10. DOE Publishes New Forecast of Energy Savings from LED Lighting

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy has just published the latest edition of its biannual report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, which models the...

  11. SUBMITTED TO IDMS 03/29/2016.

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

    SUBMITTED TO IDMS 03/29/2016. PAPERWORK REDUCTION ACT STATEMENT: Public nreoft burden fmr lIhe clW00" 0 Ifft R1rma1 I h aehnd to average-.0 hours per tesicnae hiduding "h Ibne for reiewing ini~rciormn, seawihNg exisig dale source, gallwbi mcd montaknki ft das neeaded, and aim"&*r and revavfti the =Aec~a n hd maln seid conmeen = di burden extirns or anV cow a-"dt of V*t coaeciin of kIformdr~on. hkdud~e suftaslona lot reducing V*~ burden, to U.S. GsswW Serd*ft Adminlsrafio,

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

    SciTech Connect (OSTI)

    Wilczak, James 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.

    2015-10-30

    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.

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

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

    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,

  14. Wind Energy Technology Trends: Comparing and Contrasting Recent Cost and Performance Forecasts (Poster)

    SciTech Connect (OSTI)

    Lantz, E.; Hand, M.

    2010-05-01

    Poster depicts wind energy technology trends, comparing and contrasting recent cost and performance forecasts.

  15. Making Wind Energy Predictable: New Profilers Provide Hourly Forecasts |

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

    Department of Energy Making Wind Energy Predictable: New Profilers Provide Hourly Forecasts Making Wind Energy Predictable: New Profilers Provide Hourly Forecasts May 11, 2016 - 6:48pm Addthis Balancing the power grid is an art-or at least a scientific study in chaos-and the Energy Department is hoping wind energy can take a greater role in the act. Yet, the intermittency of wind-sometimes it's blowing, sometimes it's not-makes adding it smoothly to the nation's electrical grid a challenge.

  16. 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.

  17. 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.

  18. OpenEI Community - energy data + forecasting

    Open Energy Info (EERE)

  19. 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...

  20. 2016 SSL Forecast Report | Department of Energy

    Energy Savers [EERE]

    of Energy Methods for Manufacturing Award Summaries 2016 NEET Advanced Methods for Manufacturing Award Summaries The Nuclear Energy Enabling Technologies Crosscutting Technology Development (NEET- CTD) Advanced Methods for Manufacturing (AMM) Award Summaries describe the research achievements and planned accomplishments for ongoing projects. This Award Summaries document will be updated annually, as needed. 2016 ADVANCED METHODS FOR MANUFACTURING AWARD SUMMARIES.pdf (1.23 MB) More Documents

  1. SSL Forecast Report | Department of Energy

    Office of Environmental Management (EM)

    Facility | Department of Energy Marks Successful Operational Startup of New Biomass Cogeneration Facility SRS Marks Successful Operational Startup of New Biomass Cogeneration Facility March 12, 2012 - 12:00pm Addthis Media Contacts Amy Caver (803) 952-7213 March 12, 2012 amy.caver@srs.gov CarolAnn Hibbard, (508) 661-2264 news@ameresco.com AIKEN, S.C. - Today, Under Secretary of Energy Thomas D'Agostino joined U.S. Representative Joe Wilson (R-SC) and other senior officials from the

  2. 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.

  3. 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.

  4. Beyond "Partly Sunny": A Better Solar Forecast | Department of Energy

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

    "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods

  5. Energy Department Announces $2.5 Million to Improve Wind Forecasting...

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

    turbines operate closer to maximum capacity, leading to lower energy costs for consumers. ... for the Weather Research and Forecasting model, a widely used weather prediction system. ...

  6. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    5 Aug 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specifications (In-Cash Procurement) Diagnostic In-Vessel Electrical system Engineering Diagnostic In-Vessel Electrical system Engineering IDM UID TMWPFQ VERSION CREATED ON / VERSION / STATUS 05 Aug 2016 / 1.0 / Approved EXTERNAL REFERENCE / VERSION Page 1 of 6 Table of Contents 1 PURPOSE

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

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

    ... renewable energy systems Nexus of natural gas and renewable energy Modeling of electric sector regulation and policy in capacity expansion and dispatch models, e.g. the ...

  8. New Forecasting Tools Enhance Wind Energy Integration In Idaho...

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

    ... RIT forecasting is saving costs and improving operational practices for IPC and helping integrate wind power more efficiently and cost effectively. Figure 3 shows how the ...

  9. 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

  10. 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

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

    Broader source: Energy.gov [DOE]

    The Wind Forecast Improvement Project (WFIP) is a U. S. Department of Energy (DOE) sponsored 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.

  12. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    5 May 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specifications (In-Cash Procurement) Mechanical and system engineering analysis for Diagnostics Magnetics systems This document describes technical needs for mechanical and system engineering analysis for ITER magnetic diagnostics systems. IDM UID T2CKM7 VERSION CREATED ON / VERSION / STATUS 29 Apr 2016 / 1.0 / Approved EXTERNAL REFERENCE / VERSION ITER_D_T2CKM7 Page 1 of 7 Table of

  13. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    7 Jul 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specifications (In-Cash Procurement) Technical specifications_Neutron Diagnostics project integration This document describes technical needs of ITER/TED/PPD Diagnostics Division, with particular reference to the requirement for engineering expertise for the Neutron Diagnostics project, integration and follow up activities, as appropriate. IDM UID TLA2Z4 VERSION CREATED ON / VERSION /

  14. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    11 Jul 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specifications (In-Cash Procurement) PLM Project. Framework Contract for Engineering Support This document define theoverall frame andrequirements for engineering service contracts in order to support ITER Organization Central Team (IO-CT) in the implementation the PLM project. IDM UID SSZSGR VERSION CREATED ON / VERSION / STATUS 07 Jul 2016 / 1.3 / Approved EXTERNAL REFERENCE / VERSION

  15. 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....

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

    Broader source: Energy.gov [DOE]

    Report forecasting the U.S. energy savings of LED white-light sources compared to conventional white-light sources (i.e., incandescent, halogen, fluorescent, and high-intensity discharge) over the...

  17. 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.)

  18. 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.)

  19. 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.

  20. 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.

  1. Final Report on California Regional Wind Energy Forecasting Project:Application of NARAC Wind Prediction System

    SciTech Connect (OSTI)

    Chin, H S

    2005-07-26

    Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather forecasting has been recognized as an important factor to further improve the wind energy forecast for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather forecasting capability to develop an improved regional wind energy forecasting system. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and

  2. Ammonia scrubber testing during IDMS SRAT and SME processing. Revision 1

    SciTech Connect (OSTI)

    Lambert, D.P.

    1995-04-28

    This report summarizes results of the Integrated DWPF (Defense Waste Processing Facility) Melter System (IDMS) ammonia scrubber testing during the PX-7 run (the 7th IDMS run with a Purex type sludge). Operation of the ammonia scrubber during IDMS Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) processing has been completed. The ammonia scrubber was successful in removing ammonia from the vapor stream to achieve NH3 concentrations far below the 10 ppM vapor exist design basis during SRAT processing. However, during SME processing, vapor NH3 concentrations as high as 450 ppM were measured exiting the scrubber. Problems during the SRAT and SME testing were vapor bypassing the scrubber and inefficient scrubbing of the ammonia at the end of the SME cycle (50% removal efficiency; 99.9% is design basis efficiency).

  3. “Nodal Gap” induced by the incommensurate diagonal spin density modulation in underdoped high- id='M1'>Tc superconductors

    SciTech Connect (OSTI)

    Zhou, Tao; Gao, Yi; Zhu, Jian -Xin

    2015-03-07

    Recently it was revealed that the whole Fermi surface is fully gapped for several families of underdoped cuprates. The existence of the finite energy gap along the id='M2'>d-wave nodal lines (nodal gap) contrasts the common understanding of the id='M3'>d-wave pairing symmetry, which challenges the present theories for the high-id='M4'>Tcsuperconductors. Here we propose that the incommensurate diagonal spin-density-wave order can account for the above experimental observation. The Fermi surface and the local density of states are also studied. Our results are in good agreement with many important experiments in high-id='M5'>Tcsuperconductors.

  4. 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

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

    SciTech Connect (OSTI)

    Not Available

    1992-09-01

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

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

    Broader source: Energy.gov [DOE]

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

  7. 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.

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

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energy’s 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 nation’s 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 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 2006 fiscal year.

  9. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    3 Aug 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specifications (In-Cash Procurement) RAMI Analysis for the Diagnostics systems The contract covers the following tasks (see details in Section 7 and 9): Determination of the electronics Quality Class (QC) and RAMI-based mitigation to achieve QC-3 for the electronics;Update of the RAMI analyses of ITER diagnostic systems, ports, port components and integrated assembly;Resolution of the

  10. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    Technical specifications for the expertise need in the design of Duel Wavelength Imaging Interferometer This document describes technical needs for expertise in the design of Duel Wavelength Imaging Interferometer, in in support of the ITER Erosion Monitor Diagnostics for ITER. It also describes the proof of principle experiments and R&D to be conducted at the experts site IDM UID TPSK7H VERSION CREATED ON / VERSION / STATUS 26 Aug 2016 / 1.1 / Approved EXTERNAL REFERENCE / VERSION

  11. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    9 Aug 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specifications (In-Cash Procurement) Design Justification and Engineering Validation Work_Technical specifications This document describes the technical needs for specialists in engineering of Diagnostics. Specifically the technical needs of the Diagnostics Division with particular reference to Design Justification and Engineering Validation Work. ITER is a major new device that is under

  12. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    9 Jul 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specification Summary Technical Specification - Requirement Management Framework Contract The following document provides the basic requirements to initiate the procurement of Requirement Management Services as part of a Call For Tender Process. Approval Process Name Action Affiliation Author Guigon A. 29 Jul 2016:signed IO/DG/COO/CIO/CMD Co-Authors Larrea J. 29 Jul 2016:signed

  13. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    8 Aug 2016 DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM Technical Specifications (In-Cash Procurement) Technical Summary for Call for Nomination - TPI Supervision and Inspection Services Contract This Call for Nomination is to seek companies interested in participating in the tender for the Quality Control Inspection services contract. This technical summary provides requirements for Supplier of inspections services and Quality Control supervision tasks

  14. 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.

  15. 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.

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

    SciTech Connect (OSTI)

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

    1983-07-01

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

  17. Today's Forecast: Improved Wind Predictions

    Broader source: Energy.gov [DOE]

    Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable.

  18. 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...

  19. Intelligent Data Management (IDM) for a Content-Based Image Retrieval System

    Energy Science and Technology Software Center (OSTI)

    2002-10-08

    With the availability of low-cost, high-performance computers, memory, and disk storage media, image libraries and content-based image retrieval (CBIR) technologies are becoming more prevalent. CBIR refers to technologies and systems that index large digital image libraries using image content derived from visual characteristics of the image such as color, texture and structure. Although large repositories can be readily assembled, the efficiency of these systems to retrieve the most relevant imagery is still a function ofmore » capacity and long-term storage. Due to the rapid growth in the size of image libraries and the high potential for data redundancy, the Intelligent Data Management (IDM) method has been developed to achieve a reduction in redundancy (IDM) method has been developed to achieve a reduction in redundancy that facilities either: (1) the long-term storage of the most information-rich image content (i.e., maintaining the same DB capacity but keeping data for a longer period of time), or (2) a reduction in the size of the repository capacity which results in improved performance (i.e., storage and retrieval efficiency) and reduced time for indexing.« less

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

    SciTech Connect (OSTI)

    Edwards, J.D.

    1997-08-01

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

  1. 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.

  2. IDM UID

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

    ... shall provide technical documentation and spare parts during at least 10 years. 9 Identification and valve ordering Each valve shall be given a unique Supplier part number. ...

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

    Gasoline and Diesel Fuel Update (EIA)

    Summer 2013 Outlook for Residential Electric Bills June 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Supplement: Summer 2013 Outlook for Residential Electric Bills 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

  4. Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

    SciTech Connect (OSTI)

    Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar; Marianno, Fernando J.; Shao, Xiaoyan; Zhang, Jie; Hodge, Bri-Mathias; Hamann, Hendrik F.

    2015-07-15

    With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

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

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

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

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

    SciTech Connect (OSTI)

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

    2015-06-23

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

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

    SciTech Connect (OSTI)

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

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter

  8. Wind Energy Management System 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.

    2010-09-01

    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 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 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. In order 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

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

    SciTech Connect (OSTI)

    Anggraeni, Novia Antika

    2015-04-24

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

  10. 2016 Solar Forecasting Workshop

    Office of Energy Efficiency and Renewable Energy (EERE)

    On August 3, 2016, the SunShot Initiative's systems integration subprogram hosted the Solar Forecasting Workshop to convene experts in the areas of bulk power system operations, distribution system operations, weather and solar irradiance forecasting, and photovoltaic system operation and modeling. The goal was to identify the technical challenges and opportunities in solar forecasting as a capability that can significantly reduce the integration cost of high levels of solar energy into the electricity grid. This will help SunShot to assess current technology and practices in this field and identify the gaps and needs for further research.

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

    Energy Savers [EERE]

    Supporting Jobs and Diversifying U.S. Energy Economy | Department of Energy Report: U.S. Wind Energy Production and Manufacturing Surges, Supporting Jobs and Diversifying U.S. Energy Economy Energy Report: U.S. Wind Energy Production and Manufacturing Surges, Supporting Jobs and Diversifying U.S. Energy Economy August 14, 2012 - 9:00am Addthis News Media Contact (202) 586-4940 WASHINGTON - The Energy Department released a new report today highlighting strong growth in the U.S. wind energy

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

    SciTech Connect (OSTI)

    Reno Harnish

    2011-08-16

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

  13. EIA lowers forecast for summer gasoline prices

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

    EIA lowers forecast for summer gasoline prices U.S. gasoline prices are expected to be ... according to the new monthly forecast from the U.S. Energy Information Administration. ...

  14. Solar Forecasting

    Broader source: Energy.gov [DOE]

    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....

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  16. 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.

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

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

    More Documents & Publications Energy Savings Potential of Solid-State Lighting in General Illumination Applications - Report LED ADOPTION REPORT Solid-State Lighting R&D Plan

  18. 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:...

  19. 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.

  20. Text-Alternative Version LED Lighting Forecast

    Office of Energy Efficiency and Renewable Energy (EERE)

    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....

  1. Forecast Change

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

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,151 3,302 4.8% Price (cents/kWh) 12.06 12.09 12.58 13.04 12.95 12.84 -0.9% Expenditures $415 $405 $393 $396 $408 $424 3.9% New England Usage (kWh) 2,122 2,188 2,173 1,930 1,992 2,082 4.5% Price (cents/kWh) 15.85 15.50 16.04 17.63 18.64 18.37 -1.5% Expenditures $336 $339 $348 $340 $371 $382 3.0% Mid-Atlantic Usage (kWh) 2,531 2,548 2,447 2,234 2,371 2,497 5.3% Price (cents/kWh) 16.39 15.63

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

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

    Department of Energy Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report.pdf (15.76 MB) 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. Assumptions to the Annual Energy Outlook 2015

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

    47 Industrial Demand Module The NEMS Industrial Demand Module (IDM) estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are subdivided further into the energy-intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure. The non-manufacturing

  4. Radar Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. ...

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

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

    DOE Announces Webinars on Solar Forecasting Metrics, the DOE ... from adopting the latest energy efficiency and renewable ... to liquids technology, advantages of using natural gas, ...

  6. Recently released EIA report presents international forecasting data

    SciTech Connect (OSTI)

    1995-05-01

    This report presents information from the Energy Information Administration (EIA). Articles are included on international energy forecasting data, data on the use of home appliances, gasoline prices, household energy use, and EIA information products and dissemination avenues.

  7. 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.

  8. 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...

  9. Forecasting Water Quality & Biodiversity

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

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability ... that measure feedstock production, water quality, water quantity, and biodiversity. ...

  10. 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...

  11. NREL: Transmission Grid Integration - Forecasting

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

    Forecasting NREL researchers use solar and wind resource assessment and forecasting techniques to develop models that better characterize the potential benefits and impacts of ...

  12. 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

  13. EIA revises up forecast for U.S. 2013 crude oil production by...

    Annual Energy Outlook [U.S. Energy Information Administration (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 ...

  14. 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.

  15. Solar Forecast Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE)

    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...

  16. 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.

  17. 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.

  18. 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.

  19. Contact Us - U.S. Energy Information Administration (EIA) - U.S. Energy

    Gasoline and Diesel Fuel Update (EIA)

    Information Administration (EIA) Forecasting & Analysis Short-Term (STEO) Energy Forecast Experts Long-Term (AEO) Energy Forecast Experts International (IEO) Energy Forecast Experts Renewable Energy Forecast Experts Long-Term (AEO) Analysis and Forecasting Experts Fax: (202) 586-3045 Annual Energy Outlook General questions/Executive summary Paul Holtberg 202-586-1284 paul.holtberg@eia.gov Carbon dioxide emissions Perry Lindstrom 202-586-0934 perry.lindstrom@eia.gov Coal supply and prices

  20. 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.

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

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

    The Wind Forecast Improvement Project (WFIP) is a U. S. Department of Energy (DOE) sponsored research project whose overarching goals are to improve the accuracy of short-term wind ...

  2. Baseline and Target Values for PV Forecasts: Toward Improved...

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

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

  3. 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)

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

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

    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

  5. 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.

  6. The forecast calls for flu

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

    Science on the Hill: The forecast calls for flu Using mathematics, computer programs, ... We're getting close. Using mathematics, computer programs, statistics and information ...

  7. New Climate Research Centers Forecast Changes and Challenges | Department

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

    of Energy Climate Research Centers Forecast Changes and Challenges New Climate Research Centers Forecast Changes and Challenges October 25, 2013 - 12:24pm Addthis This artist's rendering illustrates the full site installation, including a new aerosol observing system (far left) and a precipitation radar (far right, with 20-ft tower). The site is located near the Graciosa Island aiport terminal, hidden by the image inset. | Image courtesy of ARM Climate Research Facility. This artist's

  8. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect (OSTI)

    Not Available

    1981-05-27

    This report presents an overview of forecasts of domestic energy consumption, production, and prices for the year 1990. These results are selected from more detailed projections prepared and published in Volume 3 of the Energy Information Administration 1980 Annual Report to Congress. This report focuses specifically upon the 1980's and concentrates upon similarities and differences in the domestic energy system, as forecast, compared to the national experience in the years immediately following the 1973--1974 oil embargo. Interest in the 1980's stems not only from its immediacy in time, but also from its importance as a time in which certain adjustments to higher energy prices are expected to take place. The forecasts presented do not attempt to account for all of this wide range of potentially important forces that could conceivably alter the energy situation. Instead, the projections are based on a particular set of assumptions that seems reasonable in light of what is currently known. 9 figs., 25 tabs.

  9. NREL: Energy Analysis - Tyler Stehly

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

    Energy economic models Wind energy systems Cost of energy analysis Project cost estimation ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group ...

  10. 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.

  11. Microsoft Word - Documentation - Price Forecast Uncertainty.doc

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

    October 2009 1 October 2009 Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1 Summary It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energy- related financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the market- clearing process

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

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

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

  13. National Oceanic and Atmospheric Administration Provides Forecasting...

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

    ... will share their expertise with CLASIC and CHAPS forecasters and project leaders as they consult on the forecast that will determine the day's operations plan. -- Storm Prediction ...

  14. 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.

  15. 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

  16. Science on Tap - Forecasting illness

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

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

  17. 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....

  18. 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.

  19. 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.

  20. Onsemble | Open Energy Information

    Open Energy Info (EERE)

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

  1. NREL: Energy Analysis - Timothy Remo

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

    PV growth as cost competitive energy alternative Economics of changing energy costs and ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market and ...

  2. NREL: Energy Analysis - Ahmad Mayyas

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

    in the Strategic Energy Analysis Center. Clean Energy Manufacturing and Cost Analyst On staff since April, ... Data Analysis and Visualization Group Energy Forecasting and ...

  3. 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.

  4. Selected papers on fuel forecasting and analysis

    SciTech Connect (OSTI)

    Gordon, R.L.; Prast, W.G.

    1983-05-01

    Of the 19 presentations at this seminar, covering coal, uranium, oil, and gas issues as well as related EPRI research projects, eleven papers are published in this volume. Nine of the papers primarily address coal-market analysis, coal transportation, and uranium supply. Two additional papers provide an evaluation and perspective on the art and use of coal-supply forecasting models and on the relationship between coal and oil prices. The authors are energy analysts and EPRI research contractors from academia, the consulting profession, and the coal industry. A separate abstract was prepared for each of the 11 papers.

  5. 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.

  6. Wind Forecast Improvement Project Southern Study Area Final Report...

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

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

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

    Open Energy Info (EERE)

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

  8. 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

  9. Forecast of contracting and subcontracting opportunities. Fiscal year 1996

    SciTech Connect (OSTI)

    1996-02-01

    This forecast of prime and subcontracting opportunities with the U.S. Department of Energy and its MAO contractors and environmental restoration and waste management contractors, is the Department`s best estimate of small, small disadvantaged and women-owned small business procurement opportunities for fiscal year 1996. The information contained in the forecast is published in accordance with Public Law 100-656. It is not an invitation for bids, a request for proposals, or a commitment by DOE to purchase products or services. Each procurement opportunity is based on the best information available at the time of publication and may be revised or cancelled.

  10. The Value of Wind Power Forecasting

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

    ... day-ahead wind generation forecasts yields an average of 195M savings in annual operating costs. Figure 6 shows how operating cost savings vary with improvements in forecasting. ...

  11. UPF Forecast | Y-12 National Security Complex

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

    Subcontracting / Subcontracting Forecasts / 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

  12. Recent Trends in Variable Generation Forecasting and Its Value to the Power System

    SciTech Connect (OSTI)

    Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; Hodge, Bri-Mathias; Finley, Catherine; Nakafuji, Dora; Peterson, Jack L.; Maggio, David; Marquis, Melinda

    2014-12-23

    We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value of adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.

  13. Recent Trends in Variable Generation Forecasting and Its Value to the Power System

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

    Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; et al

    2014-12-23

    We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« less

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

    Office of Energy Efficiency and Renewable Energy (EERE)

    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...

  15. 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

  16. NREL: Energy Analysis - Jennie Jorgenson

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

    Jennie Jorgenson is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. ... Solar Power Technologies in a Production Cost Model. ...

  17. NREL: Energy Analysis - Aron Dobos

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

    Aron Dobos is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. ... Systems in 2015: Regional Cost Modeling of Installed Cost ...

  18. NREL: Energy Analysis - Aaron Bloom

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

    Aaron Bloom is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. ... in Production Cost Models for Renewable Integration Studies. ...

  19. NREL: Energy Analysis - Clayton Barrows

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

    Clayton Barrows is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. ... Domain Partitioning of Electricity Production Cost Simulations. ...

  20. NREL: Energy Analysis - Brady Stoll

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

    Brady Stoll is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. ... in Production Cost Models for Renewable Integration Studies. ...

  1. NREL: Energy Analysis - Nate Blair

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

    Nate Blair is the group manager of the Energy Forecasting and Modeling in the Strategic Energy Analysis Center. ... Power Trough Performance, Cost, and Financing with the Solar ...

  2. NREL: Energy Analysis - Parthiv Kurup

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

    Group in the Strategic Energy Analysis Center. CSP Cost Analyst and Engineer On staff since ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group ...

  3. NREL: Energy Analysis - Elaine Hale

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

    Elaine Hale is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Senior ... in Production Cost Models: Methodology and a Case Study. ...

  4. NREL: Energy Analysis - Carolyn Davidson

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

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

  5. Property:ProgramSector | Open Energy Information

    Open Energy Info (EERE)

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

  6. U.S. Energy Information Administration

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

    of the historical and forecasted data in EIA's Short-Term Energy Outlook ... U.S. Energy Information Administration www.eia.gov Energy production and other mining ...

  7. NREL: Energy Analysis - John (Jack) Mayernik

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

    Economic and market analysis of energy efficiency technologies Costbenefit analysis of energy ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market ...

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

    SciTech Connect (OSTI)

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

    2005-02-09

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

  9. 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,

  10. Short-term energy outlook annual supplement, 1993

    SciTech Connect (OSTI)

    1993-08-06

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

  11. Short-term energy outlook, annual supplement 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-01

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

  12. 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

  13. Development and Demonstration of Advanced Forecasting, Power...

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

    and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices 63wateruseoptimizationprojectanlgasper.ppt (7.72 MB) More ...

  14. 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...

  15. NREL: Resource Assessment and Forecasting - Webmaster

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

    email address: Your message: Send Message Printable Version Resource Assessment & Forecasting Home Capabilities Facilities Working with Us Research Staff Data & Resources Did...

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

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

    There is no cost to participate and all applicants are encouraged to attend. To join the ... Related Articles Upcoming Funding Opportunity for Wind Forecasting Improvement Project in ...

  17. Module 6 - Metrics, Performance Measurements and Forecasting...

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

    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 ...

  18. 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.

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

    SciTech Connect (OSTI)

    Tian; Tian; Chernyakhovskiy, Ilya

    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.

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

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

    forecasted unit sales of five lamp types is an action issued by the Department of Energy. ... with Forecasted Unit Sales of Five Lamp Types AGENCY: Office of Energy Efficiency ...

  1. Sensing, Measurement, and Forecasting | Grid Modernization | NREL

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

    Sensing, Measurement, and Forecasting NREL measures weather resources and power systems, forecasts renewable resources and grid conditions, and converts measurements into operational intelligence to support a modern grid. Photo of solar resource monitoring equipment Modernizing the grid involves assessing its health in real time, predicting its behavior and potential disruptions, and quickly responding to events-which requires understanding vital parameters throughout the electric

  2. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

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

    Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen MJ ... Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen, ...

  3. 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 ...

  4. Data Collection and Comparison with Forecasted Unit Sales of...

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

    Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types PDF icon Data Collection ...

  5. 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.

  6. 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.

  7. OSTIblog Articles in the wind Topic | OSTI, US Dept of Energy...

    Office of Scientific and Technical Information (OSTI)

    ... completed in 2011, to "develop a wind energy forecast system, and demonstrate its efficacy in ... geothermal, wind energy, energy storage, tidal and wave power, direct energy ...

  8. Key Activities in Wind Energy | Department of Energy

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

    help communities weigh the benefits and costs of wind energy, understand the deployment ... to electricity supply and demand, wind forecasting, and wind speed variability Develop ...

  9. Office of Energy Efficiency and Renewable Energy Fiscal Year...

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

    ... optimization studies for wind systems and operational forecasting tool development to understand and reduce costs associated with integrating variable wind energy into the ...

  10. NREL: Energy Analysis - Paul Denholm

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

    Paul Denholm is a member of the Energy Forecasting and Modeling Group in the Strategic ... Methods for Analyzing the Benefits and Costs of Distributed Photovoltaic Generation to ...

  11. NREL: Energy Analysis - Pieter Gagnon

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

    Pieter Gagnon is a member of the Energy Forecasting and Modeling Group in the Strategic ... Characterizing modern drivers of solar photovoltaic system costs Analyzing the economics ...

  12. NREL: Energy Analysis - Daniel Steinberg

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

    Daniel Steinberg is a section supervisor of the Energy Forecasting and Modeling Group in ... from lease and power-purchase agreement contract structures and costs in California." ...

  13. Past Opportunities | Department of Energy

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

    Access Greater Wind Resources and Lower Costs 4172015 6042015 RFI: Research and ... Energy 062514 072514 Wind Forecasting Improvement Project in Complex Terrain ...

  14. NREL: Energy Analysis - David Palchak

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

    Areas of expertise Electrical load forecasting with artificial neural networks Demand-side ... of Generator Flexibility on Electric System Costs and Integration of Renewable Energy. ...

  15. NREL: Energy Analysis - Paul Schwabe

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

    Senior Analyst, Consolidated Edison Inc., Volume and Revenue Forecasting New York, NY ... P. Schwabe, and K. Cory. 2012. Impact of Financial Structure on the Cost of Solar Energy. ...

  16. NREL: Energy Analysis - David Mooney

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

    technologies and industry Design and cost analysis of concentrating solar power system ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market and ...

  17. NREL: Energy Analysis - David Hurlbut

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

    and econometrics Optimization modeling Cost-benefit analysis Primary research interests Economic ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market ...

  18. NREL: Energy Analysis - Maureen Hand

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

    Wind technology development risk analysis Wind turbine cost and scaling model development ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group ...

  19. AL PRO | Open Energy Information

    Open Energy Info (EERE)

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

  20. NREL: Energy Analysis - Liz Torres

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

    and Visualization Group Energy Forecasting and Modeling Group Market and Policy Impact Analysis Group Technology Systems and Sustainability Analysis Group Washington D.C....

  1. NREL: Energy Analysis - Heidi Pawlowski

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

    and Visualization Group Energy Forecasting and Modeling Group Market and Policy Impact Analysis Group Technology Systems and Sustainability Analysis Group Washington D.C....

  2. NREL: Energy Analysis - Melissa Hudman

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

    and Visualization Group Energy Forecasting and Modeling Group Market and Policy Impact Analysis Group Technology Systems and Sustainability Analysis Group Washington D.C....

  3. NREL: Energy Analysis - Michael Bahl

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

    and Visualization Group Energy Forecasting and Modeling Group Market and Policy Impact Analysis Group Technology Systems and Sustainability Analysis Group Washington D.C....

  4. RES Anatolia | Open Energy Information

    Open Energy Info (EERE)

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

  5. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01

    This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

  6. 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.

  7. 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...

  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. 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.

  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. Flood Forecasting in River System Using ANFIS

    SciTech Connect (OSTI)

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  12. ARM - Field Campaign - Radar Wind Profiler for Cloud Forecasting at BNL

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

    govCampaignsRadar Wind Profiler for Cloud Forecasting at BNL Campaign Links Field Campaign Report 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 Campaign : Radar Wind Profiler for Cloud Forecasting at BNL 2013.07.15 - 2015.08.06 Lead Scientist : Michael Jensen For data sets, see below. Abstract In support of recent activities funded by the DOE Energy Efficiency and Renewable Energy (EERE) to produce short-term

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

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

    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

  14. 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.

  15. 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.

  16. Industrial end-use forecasting that incorporates DSM and air quality

    SciTech Connect (OSTI)

    Tutt, T.; Flory, J.

    1995-05-01

    The California Energy Commission (CEC) and major enregy utilities in California have generally depended on simple aggregate intensity or economic models to forecast energy use in the process industry sector (which covers large industries employing basic processes to transform raw materials, such as paper mills, glass plants, and cement plants). Two recent trends suggests that the time has come to develop a more disaggregate process industry forecasting model. First, recent efforts to improve air quality, especially by the South Coast Air Quality Management District (SCAQMD), could significantly affect energy use by the process industry by altering the technologies and processes employed in order to reduce emissions. Second, there is a renewed interest in Demand-Side Management (DSM), not only for utility least-cost planning, but also for improving the economic competitiveness and environmental compliance of the pro{minus}cess industries. A disaggregate forecasting model is critical to help the CEC and utilities evaluate both the air quality and DSM impacts on energy use. A crucial obstacle to the development and use of these detailed process industry forecasting models is the lack of good data about disaggregate energy use in the sector. The CEC is nearing completion of a project to begin to overcome this lack of data. The project is testing methds of developing detailed energy use data, collecting an initial database for a large portion of southern California, and providing recommendations and direction for further data collection efforts.

  17. Science on the Hill: The forecast calls for flu

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

    The forecast calls for flu The forecast calls for flu Using mathematics, computer programs, statistics and information about how disease develops and spreads, a research team at Los Alamos National Laboratory found a way to forecast the flu season and even next week's sickness trends. January 15, 2016 Forecasting flu A team from Los Alamos has developed a method to predict flu outbreaks based in part on influenza-related searches of Wikipedia. The forecast calls for flu Beyond the familiar flu,

  18. Baseline data for the residential sector and development of a residential forecasting database

    SciTech Connect (OSTI)

    Hanford, J.W.; Koomey, J.G.; Stewart, L.E.; Lecar, M.E.; Brown, R.E.; Johnson, F.X.; Hwang, R.J.; Price, L.K.

    1994-05-01

    This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. It provides a description of the methodology used to develop the database and describes the data used for heating and cooling end-uses as well as for typical household appliances. This report provides information on end-use unit energy consumption (UEC) values of appliances and equipment historical and current appliance and equipment market shares, appliance and equipment efficiency and sales trends, cost vs efficiency data for appliances and equipment, product lifetime estimates, thermal shell characteristics of buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings, baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and other economic drivers. Model inputs and outputs, as well as all other information in the database, are fully documented with the source and an explanation of how they were derived.

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

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

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

  20. 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.

  1. 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.

  2. 4273 | OSTI, US Dept of Energy Office of Scientific and Technical

    Office of Scientific and Technical Information (OSTI)

    Information 3 Energy in the Forecast Public Image File(s): Wind%20Power%20Map

  3. National Evaluation of the State Energy Program: An Evaluation...

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

    ... The Regional Economic Models, Inc (REMI) economic forecasting model used for this study is ... When energy efficiency or renewable generation programs reduce costs to energy consumers, ...

  4. Augustine Band of Cahuilla Mission Indians - Energy Conservation...

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

    ... * Design and prepare bid packages for alternative energy development project. ... for similar land uses? * How should we forecast and measure the energy and other ...

  5. NREL: Energy Analysis - Washington D.C. Office Staff

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

    R&D and commercialization of energy technologies Costbenefit analysis of renewable technology ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market ...

  6. DOE Announces Webinars on Real Time Energy Management, Solar...

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

    to Solicit Input on Solar Forecasting Metrics Webinar Sponsor: EERE's SunShot Initiative The Energy Department will ... on the National Geothermal Data System, Energy Efficiency ...

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

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

    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

  8. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    SciTech Connect (OSTI)

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

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was 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 in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.

  9. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

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

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

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

  10. Summer gasoline price forecast slightly higher, but drivers still pay less than last year

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

    Summer gasoline price forecast slightly higher, but drivers still pay less than last year Rising crude oil prices are likely to be passed on to consumers at the pump, but U.S. drivers are still expected to pay the lowest summer gasoline prices since 2004, and for all of 2016 the average household will spend $900 less on gasoline than it did two years ago." In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular grade gasoline will average

  11. Past Funding Opportunities | Department of Energy

    Office of Environmental Management (EM)

    Access Greater Wind Resources and Lower Costs 4172015 6042015 RFI: Research and ... Energy 062514 072514 Wind Forecasting Improvement Project in Complex Terrain ...

  12. AUDIT REPORT The Energy Information Administration's Information...

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

    range of data collection, analysis, forecasting, and dissemination of energy information. ... of EIA's IT costs had not been reported to the Office of Management and Budget (OMB). ...

  13. NREL: Energy Analysis - David J. Feldman

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

    ... Non-Hardware Balance of System (Soft) Costs for U.S. Photovoltaic Systems Using a ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market and ...

  14. NREL: Energy Analysis - Samantha Bench Reese

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

    System engineering and fundamentals Manufacturing cost models and cost reduction roadmaps ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market and ...

  15. Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01

    The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

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

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

    ... up-ramp reserves c down cost in MWh of down-ramp reserves R down MW range for ... power forecasting and the increased gas usage that comes with less-accurate forecasting. ...

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

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

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

  18. 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 ...

  19. 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.

  20. 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...

  1. 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.

  2. 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)

  3. NREL: Energy Analysis - Scott Jenne

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

    Jenne, D.S., Y.H. Yu, and V. Neary. 2015. Levelized Cost of Energy Analysis of Marine and ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group ...

  4. 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.

  5. 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.

  6. Short-term energy outlook quarterly projections. First quarter 1994

    SciTech Connect (OSTI)

    Not Available

    1994-02-07

    The Energy Information Administration (EIA) prepares quarterly, short- term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets.

  7. 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

  8. Forecasting hotspots using predictive visual analytics approach

    SciTech Connect (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.

  9. 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.

  10. 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

  11. OSTI, US Dept of Energy, Office of Scientific and Technical Informatio...

    Office of Scientific and Technical Information (OSTI)

    That was one objective of the "Great Plains Wind Energy Transmission Development Project," completed in 2011, to "develop a wind energy forecast system, and demonstrate its ...

  12. OSTIblog Articles in the weather Topic | OSTI, US Dept of Energy...

    Office of Scientific and Technical Information (OSTI)

    That was one objective of the "Great Plains Wind Energy Transmission Development Project," completed in 2011, to "develop a wind energy forecast system, and demonstrate its ...

  13. OSTIblog Articles in the maps Topic | OSTI, US Dept of Energy...

    Office of Scientific and Technical Information (OSTI)

    That was one objective of the "Great Plains Wind Energy Transmission Development Project," completed in 2011, to "develop a wind energy forecast system, and demonstrate its ...

  14. DOE Announces Webinars on Getting Buy-In for Energy Efficiency...

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

    to compute annualized energy and energy-cost savings, maintenance savings, greenhouse ... More DOE Announces Webinars on Solar Forecasting Metrics, the DOE Wind Vision, and More ...

  15. 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.

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

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

    Forecasts | Department of Energy NOAA Teams Up with Department of Energy & Industry to Improve Wind Forecasts NOAA Teams Up with Department of Energy & Industry to Improve Wind Forecasts July 2, 2014 - 3:51pm Addthis 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 the Wind Forecast Improvement Project, funded and led by DOE. "Our

  17. Energy

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

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

  18. 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.

  19. Energy

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

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

  20. Energy

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

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

  1. Energy Department Invests $6 Million to Support Commercial Building...

    Energy Savers [EERE]

    PEO uses data from weather forecasts, utility tariffs, demand response event signals, and occupant schedules to automatically adjust energy-consuming building systems. These ...

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

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

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

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

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

    Market Prices and Uncertainty Report This monthly supplement to the Short-Term Energy Outlook addresses price volatility and forecast uncertainty for crude oil and natural gas ...

  4. Wind Program Newsletter: Third Quarter 2011 | Department of Energy

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

    lower costs, and shorten the timeline for deploying offshore wind energy systems. ... Drivetrain Designs Current R&D Wind Forecasting Improvement Project New Model Examines ...

  5. Department of Energy to Provide Supercomputing Time to Run NOAA...

    Energy Savers [EERE]

    ... of climatic changes and the benefits and costs of alternative response options. ... on Renewable Energy Modeling and Forecasting Flying high Hunting Hurricanes...and ...

  6. Harnessing Sun, Wind and Lava for Islands' Energy Needs | Department...

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

    Cue in the Energy Development in Island Nations (EDIN) project - this international ... and is currently conducting a wind forecast to assess the potential of local trade ...

  7. The Shifting Landscape of Ratepayer-Funded Energy Efficiency...

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

    ... Rich Sedano (Regulatory Assistance Project), and Terry Singer (National Association ... Energy Efficiency Savings in AEO2009 Forecast...... 26 Table A - 3. ...

  8. Lessons Learned/Best Practices during the Department of Energy...

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

    ... to accelerate State Energy Program project implementation, including the development of an on-line management tool to forecast monthly expenditures and provision of a ...

  9. Making Wind Energy Predictable: New Profilers Provide Hourly...

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

    It is possible, however, to get better at predicting it, which is what the Energy Department's Wind Forecast Improvement Project (WFIP) seeks to accomplish. Under the second phase ...

  10. Federal Finance Facilities Available for Energy Efficiency Upgrades...

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

    ... Junta, Chief Distributions Branch H. Robert Lash, Chief Transmissions Branch Gerard Moore, Chief Energy Forecasting Branch Victor Vu, Director Power Supply Division Vacant ...

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

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

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

  12. 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...

  13. 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...

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

    Energy Savers [EERE]

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

  15. 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 ...

  16. 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 " " % % &...

  17. 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.

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

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

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

    ... Cost Assignment - Only a few respondents partly or fully recover forecasting costs from variable generators. Many simply absorb the costs, possibly viewing them as relatively ...

  20. 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.

  1. ANL Software Improves Wind Power Forecasting | Department of...

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

    ... The licensing arrangement helps to facilitate transfer of the statistical learning algorithms developed in the project to industry use. A leading forecast provider in the United ...

  2. 2014 Energy Campaign | Department of Energy

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

    Materials | Department of Energy Manufacturing R&D Workshop, held June 5-6 in Boston. Presentations Day 1 Welcome James Brodrick, Solid-State Lighting Program Manager, U.S. Department of Energy Introduction David Danielson, Assistant Secretary for Energy Efficiency and Renewable Energy, U.S. Department of Energy SSL Market Forecast Jed Dorsheimer, Canaccord Genuity LED Package Manufacturing Trends Iain Black, Philips Lumileds LED Luminaire Manufacturing Trends Ralph Tuttle, Cree Lessons

  3. 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.

  4. Market penetration of new energy technologies

    SciTech Connect (OSTI)

    Packey, D.J.

    1993-02-01

    This report examines the characteristics, advantages, disadvantages, and, for some, the mathematical formulas of forecasting methods that can be used to forecast the market penetration of renewable energy technologies. Among the methods studied are subjective estimation, market surveys, historical analogy models, cost models, diffusion models, time-series models, and econometric models. Some of these forecasting methods are more effective than others at different developmental stages of new technologies.

  5. Hawaii energy strategy report, October 1995

    SciTech Connect (OSTI)

    1995-10-01

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

  6. Hawaii energy strategy: Executive summary, October 1995

    SciTech Connect (OSTI)

    1995-10-01

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

  7. Recent Federal Register Notices | Department of Energy

    Office of Environmental Management (EM)

    ... 80FR52210 08272015 Average Unit Cost of Energy N Notice of forecasting the representative average unit costs of energy 80FR52039 08262015 Commercial Fans and Blowers N Notice ...

  8. Short-Term Energy Outlook January 2014

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

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

  9. Minority energy assessment report

    SciTech Connect (OSTI)

    Teotia, A.P.S.; Poyer, D.A.; Lampley, L.; Anderson, J.L.

    1992-12-01

    The purpose of this research is to project household energy consumption, energy expenditure, and energy expenditure as share of income for five population groups from 1991 to 2009. The approach uses the Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory for the US Department of Energy's Office of Minority Economic Impact. The MEAM provides a framework that can be used to forecast regional energy consumption and energy expenditure for majority, black, Hispanic, poor, and nonpoor households. The forecasts of key macroeconomic and energy variables used as exogenous variables in the MEAM were obtained from the Data Resources, Inc., Macromodel and Energy Model. Generally, the projections of household energy consumption, expenditure, and energy expenditure as share of income vary across population groups and census regions.

  10. Developing an industrial end-use forecast: A case study at the Los Angeles department of water and power

    SciTech Connect (OSTI)

    Mureau, T.H.; Francis, D.M.

    1995-05-01

    The Los Angeles Department of Water and Power (LADWP) uses INFORM 1.0 to forecast industrial sector energy. INFORM 1.0 provides an end-use framework that can be used to forecast electricity, natural gas or other fuels consumption. Included with INFORM 1.0 is a default date set including the input data and equations necessary to solve each model. LADWP has substituted service area specific data for the default data wherever possible. This paper briefly describes the steps LADWP follows in developing those inputs and application in INFORM 1.0.

  11. Technical analysis in short-term uranium price forecasting

    SciTech Connect (OSTI)

    Schramm, D.S.

    1990-03-01

    As market participants anticipate the end of the current uranium price decline and its subsequent reversal, increased attention will be focused upon forecasting future price movements. Although uranium is economically similar to other mineral commodities, it is questionable whether methodologies used to forecast price movements of such commodities may be successfully applied to uranium.

  12. 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.

  13. 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.

  14. 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.

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

    Open Energy Info (EERE)

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

  16. Incorporating Forecast Uncertainty in Utility Control Center

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

    2014-07-09

    Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

  17. The Role Of IC Engines In Future Energy Use | Department of Energy

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

    Of IC Engines In Future Energy Use The Role Of IC Engines In Future Energy Use Reviews future market trends and forecasts, and future engine challenges and research focus PDF icon ...

  18. 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

  19. ENERGY

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

    U.S. Department of ENERGY Department of Energy Quadrennial Technology Review-2015 Framing Document http:energy.govqtr 2015-01-13 Page 2 The United States faces serious ...

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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.

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  6. Energy

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

    Energy Energy National security depends on science and technology. The United States relies on Los Alamos National Laboratory for the best of both. No place on Earth pursues a broader array of world-class scientific endeavors. Energy Overview Charlie McMillan, Director of Los Alamos National Laboratory 0:50 Director McMillan on energy security With energy use increasing across the nation and the world, Los Alamos National Laboratory is using its world-class scientific capabilities to enhance

  7. Uncle Sam Turns to SAM for Solar Modeling | Department of Energy

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

    and read the GAO report. Addthis Related Articles Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% Solar Energy to Benefit from New FERC Interconnection...

  8. Energy Department Announces New SunShot Projects to Harness the...

    Energy Savers [EERE]

    ... in Golden, Colorado, will lead another project with Clean Power Finance to develop a ... cost reductions and better forecast future cost reductions for new energy technologies. ...

  9. Energy Savings Potential of Solid-State Lighting in General Illuminati...

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

    PDF icon sslenergy-savings-reportjan-2012.pdf More Documents & Publications Energy Savings Forecast of Solid-State Lighting in General Illumination Applications ISSUANCE ...

  10. Short-Term Energy Outlook Model Documentation: Coal Supply, Demand, and Prices

    Reports and Publications (EIA)

    2016-01-01

    The coal module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of U.S. production, consumption, imports, exports, inventories, and prices.

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

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

    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 ...

  12. 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...

  13. 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.

  14. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    U.S. DEPARTMENT OF HP IENERGY Office of Science DOESC-ARM-15-024 915-MHz Wind Profiler ... M Jensen et al., March 2016, DOESC-ARM-15-024 915-MHz Wind Profiler for Cloud Forecasting ...

  15. Improving the Accuracy of Solar Forecasting Funding Opportunity

    Broader source: Energy.gov [DOE]

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

  16. 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.

  17. 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.

  18. 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,...

  19. Issues in Midterm Analysis and Forecasting

    Reports and Publications (EIA)

    1999-01-01

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

  20. Asia-Pacific energy database

    SciTech Connect (OSTI)

    1997-06-01

    Statistical data is presented in graphic and tabular form on the petroleum market in Asia and Pacific nations. Seven major categories are reported: (1) primary energy production and consumption; (2) historical petroleum product demand and forecasts; (3) crude oil production and exports; (4) import dependence; (5) crude and product pricing assumptions; (6) market share of refined products by suppliers in selected countries; and (7) refining margins. Petroleum demand and forecasts and crude oil production and exports are reported by country. Historical data are presented from 1970 through 1996, and forecasts are made through 2010.

  1. 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

  2. Highlights - Energy Research

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

    January 2010 1 January 2010 Short-Term Energy Outlook January 12, 2010 Release Highlights This edition of the Short-Term Energy Outlook is the first to include monthly forecasts through December 2011. EIA expects that the price of West Texas Intermediate (WTI) crude oil, which averaged $62 per barrel in 2009, will average about $80 and $84 per barrel in 2010 and 2011, respectively. EIA's forecast assumes that U.S. real gross domestic product (GDP) grows by 2.0 percent in 2010 and by 2.7 percent

  3. State Energy Efficiency Program Evaluation Inventory - Energy Information

    Gasoline and Diesel Fuel Update (EIA)

    State Energy Efficiency Program Evaluation Inventory July 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | State Energy Efficiency Program Evaluation Inventory i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other

  4. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

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

  5. 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.

  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. Regional Short-Term Energy Model (RSTEM) Overview

    Reports and Publications (EIA)

    2009-01-01

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

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

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

    unit sales of five lamp types, is a rulemaking action issued by the Department of Energy. ... with Forecasted Unit Sales of Five Lamp Types AGENCY: Office of Energy Efficiency ...

  9. NREL: Resource Assessment and Forecasting - Capabilities

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

    Best Practices Handbook Helps Industry Collect and Interpret Solar Resource Data Read about this ... staff provides expertise in renewable energy measurement and instrumentation. ...

  10. Annual energy outlook 1995, with projections to 2010

    SciTech Connect (OSTI)

    1995-01-01

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

  11. Lawrence Livermore National Laboratory | Open Energy Information

    Open Energy Info (EERE)

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

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

    Gasoline and Diesel Fuel Update (EIA)

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

  13. NREL: Energy Analysis - Kelsey A. W. Horowitz

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

    H. Lee, and G.P. Smestad. 2015. A bottom-up cost analysis of a high concentration PV module. ... Data Analysis and Visualization Group Energy Forecasting and Modeling Group Market and ...

  14. 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

  15. 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.

  16. Energy

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

    Energy Energy Research into alternative forms of energy, and improving and securing the power grid, is a major national security imperative. News Releases Science Briefs Photos Picture of the Week Publications Social Media Videos Fact Sheets Pajarito Powder, LLC, a fuel-cell-catalyst company based in Albuquerque, is one of the voucher recipients that will partner with Los Alamos. Fuel-cell technology companies win small-business aid Pajarito Powder, LLC, (Albuquerque), NanoSonic (Pembroke, Va.)

  17. Energy

    Office of Legacy Management (LM)

    ..) ".. _,; ,' . ' , ,; Depar?.me.nt ,of.' Energy Washington; DC 20585 : . ' , - $$ o"\ ' ~' ,' DEC ?;$ ;y4,,, ~ ' .~ The Honorable John Kalwitz , 200 E. Wells Street Milwaukee, W~isconsin 53202, . . i :. Dear,Mayor 'Kalwitz: " . " Secretary of Energy Hazel' O'Leary has announceha new,approach 'to,openness in " the Department of Ene~rgy (DOE) and its communications with'the public. In -. support of~this initiative, we areipleased to forward the enclosed information

  18. 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.

  19. Short-term energy outlook. Quarterly projections, Third quarter 1995

    SciTech Connect (OSTI)

    1995-08-02

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent projections with those of other forecasting services, and discusses current topics related to the short-term energy markets. The forecast period for this issue of the Outlook extends from the third quarter of 1995 through the fourth quarter of 1996. Values for the second quarter of 1995, however, are preliminary EIA estimates.

  20. Electric-utility DSM programs: 1990 data and forecasts to 2000

    SciTech Connect (OSTI)

    Hirst, E.

    1992-06-01

    In April 1992, the Energy Information Administration (EIA) released data on 1989 and 1990 electric-utility demand-site management (DMS) programs. These data represent a census of US utility DSM programs, with reports of utility expenditures, energy savings, and load reductions caused by these programs. In addition, EIA published utility estimates of the costs and effects of these programs from 1991 to 2000. These data provide the first comprehensive picture of what utilities are spending and accomplishing by utility, state, and region. This report presents, summarizes, and interprets the 1990 data and the utility forecasts of their DSM-program expenditures and impacts to the year 2000. Only utilities with annual sales greater than 120 GWh were required to report data on their DSM programs to EIA. Of the 1194 such utilities, 363 reported having a DSM program that year. These 363 electric utilities spent $1.2 billion on their DSM programs in 1990, up from $0.9 billion in 1989. Estimates of energy savings (17,100 GWh in 1990 and 14,800 GWh in 1989) and potential reductions in peak demand (24,400 MW in 1990 and about 19,400 MW in 1989) also showed substantial increases. Overall, utility DSM expenditures accounted for 0.7% of total US electric revenues, while the reductions in energy and demand accounted for 0.6% and 4.9% of their respective 1990 national totals. The investor-owned utilities accounted for 70 to 90% of the totals for DSM costs, energy savings, and demand reductions. The public utilities reported larger percentage reductions in peak demand and energy smaller percentage DSM expenditures. These averages hide tremendous variations across utilities. Utility forecasts of DSM expenditures and effects show substantial growth in both absolute and relative terms.

  1. 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.

  2. NREL: Energy Analysis - Greg Brinkman

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

    Greg Brinkman Photo of Greg Brinkman Greg Brinkman is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Energy Analysis Engineer On staff since May 2009 Phone number: 303-384-7390 E-mail: gregory.brinkman@nrel.gov Areas of expertise Unit commitment and dispatch modeling of electric power systems Emission estimation from electric power systems Air quality modeling Primary research interests Integration of renewable energy, energy efficiency, and

  3. 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).

  4. 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).

  5. Energy

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

    M onthly Energy Re< view Ila A a m 0 II 8 IIIW *g U In this issue: New data on nuclear electricity in Eastern Europe (Table 10.4) 9'Ij a - Ordering Information This publication...

  6. Energy Markets Outlook

    Gasoline and Diesel Fuel Update (EIA)

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

  7. Final Report - Integration of Behind-the-Meter PV Fleet Forecasts...

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

    Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Final Report - Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System ...

  8. 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.

  9. 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.

  10. Annual energy outlook 1994: With projections to 2010

    SciTech Connect (OSTI)

    Not Available

    1994-01-01

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

  11. 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.

  12. 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.

  13. Mission Support Contract Section J Contract No. DE-AC06-09RL14728

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

    ... historically underutilized business zone IAEA International Atomic Energy Agency IAMIT Inter-Agency Management Integration Team IDMS Integrated Document Management System IR...

  14. U.S. diesel fuel price forecast to be 1 penny lower this summer at $3.94 a gallon

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

    diesel fuel price forecast to be 1 penny lower this summer at $3.94 a gallon The retail price of diesel fuel is expected to average $3.94 a gallon during the summer driving season that which runs from April through September. That's close to last summer's pump price of $3.95, according to the latest monthly energy outlook from the U.S. Energy Information Administration. Demand for distillate fuel, which includes diesel fuel, is expected to be up less than 1 percent from last summer. Daily

  15. Final Report- Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Office of Energy Efficiency and Renewable Energy (EERE)

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California independent system operator’s load forecasts by integrating behind-the-meter photovoltaic forecasts.

  16. 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.

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

    Gasoline and Diesel Fuel Update (EIA)

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

  18. NREL: Energy Analysis - Janine Freeman

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

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

  19. NREL: Energy Analysis - Josh Novacheck

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

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

  20. EIA - Energy Conferences & Presentations.

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

    5 EIA Conference 2010 Session 5: Energy and the Economy Moderator: Adam Sieminski, Deutsche Bank Speakers: Stephen P. A. Brown, Resources for the Future Donald L. Paul, University of Southern California Energy Institute Moderator and Speaker Biographies Adam Sieminski is the Chief Energy Economist for Deutsche Bank, working with the Bank's global commodities research and trading units. Drawing on extensive industry, government and academic sources, Mr. Sieminski forecasts energy market trends

  1. Minority energy assessment report. Fall 1992

    SciTech Connect (OSTI)

    Teotia, A.P.S.; Poyer, D.A.; Lampley, L.; Anderson, J.L.

    1992-12-01

    The purpose of this research is to project household energy consumption, energy expenditure, and energy expenditure as share of income for five population groups from 1991 to 2009. The approach uses the Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory for the US Department of Energy`s Office of Minority Economic Impact. The MEAM provides a framework that can be used to forecast regional energy consumption and energy expenditure for majority, black, Hispanic, poor, and nonpoor households. The forecasts of key macroeconomic and energy variables used as exogenous variables in the MEAM were obtained from the Data Resources, Inc., Macromodel and Energy Model. Generally, the projections of household energy consumption, expenditure, and energy expenditure as share of income vary across population groups and census regions.

  2. Systems Integration | Department of Energy

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

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

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

    Gasoline and Diesel Fuel Update (EIA)

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

  4. Assumptions to the Annual Energy Outlook 2015

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

    Assumptions to the Annual Energy Outlook 2015 September 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 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

  5. State Energy Efficiency Program Evaluation Inventory

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

    State Energy Efficiency Program Evaluation Inventory July 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | State Energy Efficiency Program Evaluation Inventory i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other

  6. OSTI, US Dept of Energy Office of Scientific and Technical Information...

    Office of Scientific and Technical Information (OSTI)

    Wind%20Power%20Map.png Energy in the Forecast Read more about 4273 If you can accurately predict the weather, you may be able to predict how much energy can be generated from wind ...

  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

  8. DOE Announces Webinars on Investment Funds for Clean Energy Programs...

    Office of Environmental Management (EM)

    ... During the webinar, Greg Brinkman, a member of the Energy Forecasting and Modeling Group ... on potential emissions and wear-and-tear costs of cycling fossil-fueled generators in ...

  9. DOE Announces Webinars on Resources for Tribal Energy Efficiency...

    Office of Environmental Management (EM)

    ... During the webinar, Greg Brinkman, a member of the Energy Forecasting and Modeling Group ... on potential emissions and wear-and-tear costs of cycling fossil-fueled generators in ...

  10. About EIA - Ourwork - U.S. Energy Information Administration...

    Gasoline and Diesel Fuel Update (EIA)

    EIA is the nation's premier source of energy information and, by law, its data, analyses, and forecasts are independent of approval by any other officer or employee of the U.S. ...

  11. About EIA - Budget - U.S. Energy Information Administration ...

    Gasoline and Diesel Fuel Update (EIA)

    EIA is the nation's premier source of energy information and, by law, its data, analyses, and forecasts are independent of approval by any other officer or employee of the United ...

  12. DOE Zero Energy Ready Home: Better Business for Builders Webinar...

    Energy Savers [EERE]

    ... and full compliance with that spec is part of the DOE Zero Energy Ready Home project. ... which is sort of the default industry forecast made up by statisticians and policy ...

  13. Solar Energy Resource Center | Department of Energy

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

    Resource Center Solar Energy Resource Center Solar Mapping Resources Featured Article There's a Map for That National laboratories and private companies have developed a number of tools to forecast the solar potential of homes and businesses. Learn more Icon of a person giving a presentation. Educating Consumers Tools designed to meet individual consumer needs Icon of a trending chart. Market Analysis State- and local-level challenges and solutions for establishing a thriving solar market Icon

  14. Towards a Science of Tumor Forecast for Clinical Oncology

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

    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 panoplymore » 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.« less

  15. Toward a science of tumor forecasting for clinical oncology

    SciTech Connect (OSTI)

    Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; Rericha, Erin C.

    2015-03-15

    We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for 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. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.

  16. Toward a science of tumor forecasting for clinical oncology

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

    Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; Rericha, Erin C.

    2015-03-15

    We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for 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 therapiesmore » 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. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.« less

  17. 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.

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

    SciTech Connect (OSTI)

    Pennock, K.

    2012-10-01

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

  19. OSTI, US Dept of Energy Office of Scientific and Technical Information |

    Office of Scientific and Technical Information (OSTI)

    Speeding access to science information from DOE and Beyond in the Forecast by Daphne Evans on Mon, August 13, 2012 4273 Wind%20Power%20Map.png Energy in the Forecast Read more about 4273 If you can accurately predict the weather, you may be able to predict how much energy can be generated from wind turbines. That was one objective of the "Great Plains Wind Energy Transmission Development Project," completed in 2011, to "develop a wind energy forecast system, and demonstrate

  20. 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.

  1. 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)

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

    Open Energy Info (EERE)

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

  3. Audit Report: IG-0499 | Department of Energy

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

    9 Audit Report: IG-0499 April 2, 2001 Department of Energy's Super Energy Savings Performance Contracts As you recently noted in both testimony before the Congress and in public statements, the United States is facing the most serious energy supply situation since the 1970s. And, current forecasts suggest that the demand for energy is increasing. As one of the largest energy consumers in the United States, the Federal Government has established several programs to reduce demand, specifically, by

  4. Direct Drive Wave Energy Buoy

    SciTech Connect (OSTI)

    Rhinefrank, Ken

    2011-11-02

    Presentation from the 2011 Water Peer Review in which principal investigator discusses project progress and results for this project which will be used to inform the utility-scale design process, improve cost estimates, accurately forecast energy production and to observe system operation and survivability.

  5. 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.

  6. Coal supply/demand, 1980 to 2000. Task 3. Resource applications industrialization system data base. Final review draft. [USA; forecasting 1980 to 2000; sector and regional analysis

    SciTech Connect (OSTI)

    Fournier, W.M.; Hasson, V.

    1980-10-10

    This report is a compilation of data and forecasts resulting from an analysis of the coal market and the factors influencing supply and demand. The analyses performed for the forecasts were made on an end-use-sector basis. The sectors analyzed are electric utility, industry demand for steam coal, industry demand for metallurgical coal, residential/commercial, coal demand for synfuel production, and exports. The purpose is to provide coal production and consumption forecasts that can be used to perform detailed, railroad company-specific coal transportation analyses. To make the data applicable for the subsequent transportation analyses, the forecasts have been made for each end-use sector on a regional basis. The supply regions are: Appalachia, East Interior, West Interior and Gulf, Northern Great Plains, and Mountain. The demand regions are the same as the nine Census Bureau regions. Coal production and consumption in the United States are projected to increase dramatically in the next 20 years due to increasing requirements for energy and the unavailability of other sources of energy to supply a substantial portion of this increase. Coal comprises 85 percent of the US recoverable fossil energy reserves and could be mined to supply the increasing energy demands of the US. The NTPSC study found that the additional traffic demands by 1985 may be met by the railways by the way of improved signalization, shorter block sections, centralized traffic control, and other modernization methods without providing for heavy line capacity works. But by 2000 the incremental traffic on some of the major corridors was projected to increase very significantly and is likely to call for special line capacity works involving heavy investment.

  7. 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

  8. The Value of Improved Wind Power Forecasting in the Western Interconne...

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

    of this research will facilitate a better functional understanding of wind forecasting accuracy and power system operations at various spatial and temporal scales.* Of particular ...

  9. 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.

  10. Integration of Behind-the-Meter PV Fleet Forecasts into Utility...

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

    Forecasting behind-the-meter distributed PV generation power production within a region ... This project is expected to reduce the costs of integrating higher penetrations of PV into ...

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

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

    electric system operators, and solar project owners better predict when and how much ... production varies, an accurate solar forecast is needed in order to maintain an ...

  12. 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.

  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. Short-term energy outlook. Quarterly projections, Third quarter 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-02

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202). The feature article for this issue is Demand, Supply and Price Outlook for Reformulated Gasoline, 1995.

  15. Supplement to the annual energy outlook 1994

    SciTech Connect (OSTI)

    1994-03-01

    This report is a companion document to the Annual Energy Outlook 1994 (AEO94), (DOE/EIA-0383(94)), released in Jan. 1994. Part I of the Supplement presents the key quantitative assumptions underlying the AEO94 projections, responding to requests by energy analysts for additional information on the forecasts. In Part II, the Supplement provides regional projections and other underlying details of the reference case projections in the AEO94. The AEO94 presents national forecasts of energy production, demand and prices through 2010 for five scenarios, including a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. These forecasts are used by Federal, State, and local governments, trade associations, and other planners and decisionmakers in the public and private sectors.

  16. Energy Analysis Program 1990 annual report

    SciTech Connect (OSTI)

    Not Available

    1992-01-01

    The Energy Analysis Program has played an active role in the analysis and discussion of energy and environmental issues at several levels. (1) at the international level, with programs as developing scenarios for long-term energy demand in developing countries and organizing leading an analytic effort, ``Energy Efficiency, Developing Countries, and Eastern Europe,`` part of a major effort to increase support for energy efficiency programs worldwide; (2) at national level, the Program has been responsible for assessing energy forecasts and policies affecting energy use (e.g., appliance standards, National Energy Strategy scenarios); and (3) at the state and utility levels, the Program has been a leader in promoting integrated resource utility planning; the collaborative process has led to agreement on a new generation of utility demand-site programs in California, providing an opportunity to use knowledge and analytic techniques of the Program`s researchers. We continue to place highest on analyzing energy efficiency, with particular attention given to energy use in buildings. The Program continues its active analysis of international energy issues in Asia (including China), the Soviet Union, South America, and Western Europe. Analyzing the costs and benefits of different levels of standards for residential appliances continues to be the largest single area of research within the Program. The group has developed and applied techniques for forecasting energy demand (or constructing scenarios) for the United States. We have built a new model of industrial energy demand, are in the process of making major changes in our tools for forecasting residential energy demand, have built an extensive and documented energy conservation supply curve of residential energy use, and are beginning an analysis of energy-demand forecasting for commercial buildings.

  17. Energy Analysis Program 1990 annual report

    SciTech Connect (OSTI)

    Not Available

    1992-01-01

    The Energy Analysis Program has played an active role in the analysis and discussion of energy and environmental issues at several levels. (1) at the international level, with programs as developing scenarios for long-term energy demand in developing countries and organizing leading an analytic effort, Energy Efficiency, Developing Countries, and Eastern Europe,'' part of a major effort to increase support for energy efficiency programs worldwide; (2) at national level, the Program has been responsible for assessing energy forecasts and policies affecting energy use (e.g., appliance standards, National Energy Strategy scenarios); and (3) at the state and utility levels, the Program has been a leader in promoting integrated resource utility planning; the collaborative process has led to agreement on a new generation of utility demand-site programs in California, providing an opportunity to use knowledge and analytic techniques of the Program's researchers. We continue to place highest on analyzing energy efficiency, with particular attention given to energy use in buildings. The Program continues its active analysis of international energy issues in Asia (including China), the Soviet Union, South America, and Western Europe. Analyzing the costs and benefits of different levels of standards for residential appliances continues to be the largest single area of research within the Program. The group has developed and applied techniques for forecasting energy demand (or constructing scenarios) for the United States. We have built a new model of industrial energy demand, are in the process of making major changes in our tools for forecasting residential energy demand, have built an extensive and documented energy conservation supply curve of residential energy use, and are beginning an analysis of energy-demand forecasting for commercial buildings.

  18. Assumptions to Annual Energy Outlook - Energy Information Administration

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

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

  19. State Energy Efficiency Program Evaluation Inventory - Energy Information

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

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

  20. Short-Term Energy Outlook- May 2003

    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

  1. Anna Brockway | Department of Energy

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

    Anna Brockway About Us Anna Brockway - SunShot Fellow, Solar Energy Technologies Office Anna Brockway is the point person on shared and community solar efforts at the SunShot Initiative. At SunShot, fellows act as an internal think-tank to develop and implement new projects to advance solar energy in the United States. Anna has also worked on initiatives related to developing metrics for solar forecasting and the chemical and physical reliability of solar system components. Previously, Anna

  2. 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 systems 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.

  3. 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.

  4. 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.

  5. 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

  6. Analysis of Energy Efficiency Program Impacts Based on Program Spending

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

    Analysis of Energy Efficiency Program Impacts Based on Program Spending May 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are

  7. Behavioral Economics Applied to Energy Demand Analysis: A Foundation

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

    Behavioral Economics Applied to Energy Demand Analysis: A Foundation October 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Behavioral Economics Applied to Energy Demand Analysis i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  8. Analysis of the Clean Energy Standard Act of 2012

    Gasoline and Diesel Fuel Update (EIA)

    Energy Efficiency Program Impacts Based on Program Spending May 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  9. 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.

  10. Energy 101: Geothermal Energy | Department of Energy

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

    Geothermal Energy Energy 101: Geothermal Energy

  11. 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.

  12. 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

  13. 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

  14. 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

  15. Hawaii demand-side management resource assessment. Final report, Reference Volume 5: The DOETRAN user`s manual; The DOE-2/DBEDT DSM forecasting model interface

    SciTech Connect (OSTI)

    1995-04-01

    The DOETRAN model is a DSM database manager, developed to act as an intermediary between the whole building energy simulation model, DOE-2, and the DBEDT DSM Forecasting Model. DOETRAN accepts output data from DOE-2 and TRANslates that into the format required by the forecasting model. DOETRAN operates in the Windows environment and was developed using the relational database management software, Paradox 5.0 for Windows. It is not necessary to have any knowledge of Paradox to use DOETRAN. DOETRAN utilizes the powerful database manager capabilities of Paradox through a series of customized user-friendly windows displaying buttons and menus with simple and clear functions. The DOETRAN model performs three basic functions, with an optional fourth. The first function is to configure the user`s computer for DOETRAN. The second function is to import DOE-2 files with energy and loadshape data for each building type. The third main function is to then process the data into the forecasting model format. As DOETRAN processes the DOE-2 data, graphs of the total electric monthly impacts for each DSM measure appear, providing the user with a visual means of inspecting DOE-2 data, as well as following program execution. DOETRAN provides three tables for each building type for the forecasting model, one for electric measures, gas measures, and basecases. The optional fourth function provided by DOETRAN is to view graphs of total electric annual impacts by measure. This last option allows a comparative view of how one measure rates against another. A section in this manual is devoted to each of the four functions mentioned above, as well as computer requirements and exiting DOETRAN.

  16. The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

    SciTech Connect (OSTI)

    Martin Wilde, Principal Investigator

    2012-12-31

    ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most wind plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational

  17. Short-Term Energy Outlook Model Documentation: Petroleum Product Prices Module

    Reports and Publications (EIA)

    2015-01-01

    The petroleum products price module of the Short-Term Energy Outlook (STEO) model is designed to provide U.S. average wholesale and retail price forecasts for motor gasoline, diesel fuel, heating oil, and jet fuel.

  18. Short-Term Energy Outlook Model Documentation: Regional Residential Propane Price Model

    Reports and Publications (EIA)

    2009-01-01

    The regional residential propane price module of the Short-Term Energy Outlook (STEO) model is designed to provide residential retail price forecasts for the 4 Census regions: Northeast, South, Midwest, and West.

  19. DOE Zero Energy Ready Home High-Performance Home Sales Training...

    Energy Savers [EERE]

    ... For instance, a smart phone is introduced, costs 600 vs. 6 for a normal cell phone, and ... All the innovations that it's forecasting about zero energy ready, and of course, you're ...

  20. EERE Success Story-Solar Energy to Benefit from New FERC Interconnecti...

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

    solar energy cost-competitive with other forms of electricity by the end of the decade. ... R&D 100 Award EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy ...

  1. Short-Term Energy Outlook Model Documentation: Motor Gasoline Consumption Model

    Reports and Publications (EIA)

    2011-01-01

    The motor gasoline consumption module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of total U.S. consumption of motor gasolien based on estimates of vehicle miles traveled and average vehicle fuel economy.

  2. Short-Term Energy Outlook Model Documentation: Hydrocarbon Gas Liquids Supply and Demand

    Reports and Publications (EIA)

    2015-01-01

    The hydrocarbon gas liquids (ethane, propane, butanes, and natural gasoline) module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of U.S. production, consumption, refinery inputs, net imports, and inventories.

  3. Short-Term Energy Outlook Model Documentation: Regional Residential Heating Oil Price Model

    Reports and Publications (EIA)

    2009-01-01

    The regional residential heating oil price module of the Short-Term Energy Outlook (STEO) model is designed to provide residential retail price forecasts for the 4 census regions: Northeast, South, Midwest, and West.

  4. Short-term energy outlook, Annual supplement 1995

    SciTech Connect (OSTI)

    1995-07-25

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

  5. 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.

  6. NREL: Energy Analysis - Brian W Bush

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

    Brian W Bush Photo of Brian Bush Brian W Bush is a member of the Energy Forecasting and Modeling Group in the Strategic Energy Analysis Center. Principal Strategic Analyst On staff from January 2008 Phone number: (303) 384-7472 E-mail: brian.bush@nrel.gov Areas of expertise Energy and infrastructure modeling, simulation, and analysis High performance computing Software architecture, design, implementation, and testing Discrete-event and continuous simulation Statistical analysis Geographic

  7. Assumptions to the Annual Energy Outlook 2015

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

    International Energy Module The National Energy Modeling System International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the IEM computes Brent and WTI prices, provides a supply curve of world

  8. 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.

  9. Demand for oil and energy in developing countries

    SciTech Connect (OSTI)

    Wolf, C. Jr.; Relles, D.A.; Navarro, J.

    1980-05-01

    How much of the world's oil and energy supply will the non-OPEC less-developed countries (NOLDCs) demand in the next decade. Will their requirements be small and thus fairly insignificant compared with world demand, or large and relatively important. How will world demand be affected by the economic growth of the NOLDCs. In this report, we try to develop some reasonable forecasts of NOLDC energy demands in the next 10 years. Our focus is mainly on the demand for oil, but we also give some attention to the total commercial energy requirements of these countries. We have tried to be explicit about the uncertainties associated with our forecasts, and with the income and price elasticities on which they are based. Finally, we consider the forecasts in terms of their implications for US policies concerning the NOLDCs and suggest areas of future research on NOLDC energy issues.

  10. Demand forecasting and revenue requirements, with implications for consideration in British Columbia

    SciTech Connect (OSTI)

    Acton, J.P.

    1983-05-01

    This paper was filed as an exhibit on behalf of The Consumers' Association of Canada (B.C. Branch), The Federated Anti-Poverty Groups of B.C., The Sierra Club of Western Canada, and the B.C. Old Age Pensioners' Organization. It was subjected to cross-examination on October 29, 1982, during Phase I of the hearings. The Utilities Commission had designated Phase I for consideration of (1) demand, (2) assets in service, (3) revenue requirements excluding return, and (4) financing and capital requirements. This paper presents a general discussion of the elements of a rate structure and their relationship to the demand for electricity, a systematic review of some 50 empirical studies of the demand for electricity as a function of price and other factors by the three principal classes of customers, and a discussion of the notion of revenue requirements. The paper should be of interest to utility regulators, rate specialists, and forecasters for its review of demand models and to academics concerned with the study of energy demand.

  11. International energy outlook 1994

    SciTech Connect (OSTI)

    Not Available

    1994-07-01

    The International Energy Outlook 1994 (IEO94) presents an assessment by the Energy Information Administration (EIA) of the outlook for international energy markets between 1990 and 2010. The report is provided as a statistical service to assist energy managers and analysts, both in government and in the private sector. These forecasts are used by international agencies, Federal and State governments, trade associations, and other planners and decisionmakers. They are published pursuant to the Depart. of Energy Organization Act of 1977 (Public Law 95-91), Section 205(c). The IEO94 projections are based on US and foreign government policies in effect on October 1, 1993-which means that provisions of the Climate Change Action Plan unveiled by the Administration in mid-October are not reflected by the US projections.

  12. Response to several FOIA requests - Renewable Energy. | Department of

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

    Energy Response to several FOIA requests - Renewable Energy. Response to several FOIA requests - Renewable Energy. Response to several FOIA requests - Renewable Energy. Excess Capacity from LADWP Control Area (LADWP, Glendale, Burbank),Summer 2001 nepdg_751_1000.pdf Total Load (CEC Draft Demand Forecast 10/16/2000 Response to several FOIA requests - Renewable Energy. (9.83 MB) More Documents & Publications An Assessment of Heating Fuels And Electricity Markets During the Winters of

  13. Waste Generation Forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1994--FY 2001. Environmental Restoration Program, September 1993 Revision

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

    This Waste Generation Forecast for DOE-ORO`s Environmental Restoration OR-1 Project. FY 1994--FY 2001 is the third in a series of documents that report current estimates of the waste volumes expected to be generated as a result of Environmental Restoration activities at Department of Energy, Oak Ridge Operations Office (DOE-ORO), sites. Considered in the scope of this document are volumes of waste expected to be generated as a result of remedial action and decontamination and decommissioning activities taking place at these sites. Sites contributing to the total estimates make up the DOE-ORO Environmental Restoration OR-1 Project: the Oak Ridge K-25 Site, the Oak Ridge National Laboratory, the Y-12 Plant, the Paducah Gaseous Diffusion Plant, the Portsmouth Gaseous Diffusion Plant, and the off-site contaminated areas adjacent to the Oak Ridge facilities (collectively referred to as the Oak Ridge Reservation Off-Site area). Estimates are available for the entire fife of all waste generating activities. This document summarizes waste estimates forecasted for the 8-year period of FY 1994-FY 2001. Updates with varying degrees of change are expected throughout the refinement of restoration strategies currently in progress at each of the sites. Waste forecast data are relatively fluid, and this document represents remediation plans only as reported through September 1993.

  14. Power Technologies Energy Data Book - Fourth Edition

    SciTech Connect (OSTI)

    Aabakken, J.

    2006-08-01

    This report, prepared by NREL's Strategic Energy Analysis Center, includes up-to-date information on power technologies, including complete technology profiles. The data book also contains charts on electricity restructuring, power technology forecasts, electricity supply, electricity capability, electricity generation, electricity demand, prices, economic indicators, environmental indicators, and conversion factors.

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

    Broader source: Energy.gov [DOE]

    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.

  16. 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.

  17. U.S. oil production forecast update reflects lower rig count

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

    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 ...

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

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

    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 ...

  19. 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%.

  20. 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.

  1. Short-Term Energy Outlook September 2013

    Gasoline and Diesel Fuel Update (EIA)

    (STEO) Highlights  Monthly average crude oil prices increased for the fourth consecutive month in August 2013, as supply disruptions in Libya increased and concerns over the conflict in Syria intensified. The U.S. Energy Information Administration's (EIA) forecast for Brent crude oil spot price, which averaged $108 per barrel during the first half of 2013, averages $109 per barrel over the second half of 2013 and $102 per barrel in 2014, $5 per barrel and $2 per barrel higher than forecast in

  2. 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.

  3. Annual energy outlook, 1986

    SciTech Connect (OSTI)

    Not Available

    1987-02-11

    The forecast summary highlights the principal components of the Energy Information Administration's projections of energy supply, demand, and prices. Key issues are addressed for each of the major energy markets (petroleum, natural gas, coal, and electricity), along with the developments anticipated for end-use energy consumption and new technologies. The overview lists the principal conclusions followed by discussions of the important elements of the projections. This information revises production forecasts for 1986, 1987, and 1988, but its effect on later years should be much less, because some portion of the production fall amounts to production delays (Table 1). Lower well completions and delayed work-overs remove production from significantly sized wells, but little of this production is lost permanently. To the extent that higher prices encourage higher development activity, well completions and work-overs should resume at a more normal level. In the short run, shut-in wells, which are mostly marginal stripper wells, can also be returned to production. With the liberalization of State and Federal regulations on when shut-in wells must be permanently abandoned, a window of about one year exists during which these stripper wells can be restored. Some of these wells will never resume production, but with prices in the $18 plus range, most will again be economic.

  4. Annual energy outlook 1999, with projections to 2020

    SciTech Connect (OSTI)

    1998-12-01

    The Annual Energy Outlook 1999 (AEO99) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA`s National Energy Modeling System (NEMS). The report begins with an Overview summarizing the AEO99 reference case. The next section, Legislation and Regulations, describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. Issues in Focus discusses current energy issues--the economic decline in East Asia, growth in demand for natural gas, vehicle emissions standards, competitive electricity pricing, renewable portfolio standards, and carbon emissions. It is followed by the analysis of energy market trends. The analysis in AEO99 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present a summary of the reference case forecasts in units of oil equivalence and household energy expenditures. The AEO99 projections are based on Federal, State, and local laws and regulations in effect on July 1, 1998. Pending legislation and sections of existing legislation for which funds have not been appropriated are not reflected in the forecasts. Historical data used for the AEOI99 projections were the most current available as of July 31, 1998, when most 1997 data but only partial 1998 data were available.

  5. Short-term energy outlook. Quarterly projections, second quarter 1996

    SciTech Connect (OSTI)

    1996-04-01

    The Energy Information Administration prepares quarterly, short-term energy supply, demand, and price projections. The forecasts in this issue cover the second quarter of 1996 through the fourth quarter of 1997. Changes to macroeconomic measures by the Bureau of Economic Analysis have been incorporated into the STIFS model used.

  6. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    and Summer Fuels Outlook April 2015 1 April 2015 Short-Term Energy and Summer Fuels Outlook (STEO) Highlights * On April 2, Iran and the five permanent members of the United Nations Security Council plus Germany (P5+1) reached a framework agreement that could result in the lifting of oil- related sanctions against Iran. Lifting sanctions could substantially change the STEO forecast for oil supply, demand, and prices by allowing a significantly increased volume of Iranian barrels to enter the

  7. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    5 1 October 2015 Short-Term Energy and Winter Fuels Outlook (STEO) Highlights  EIA projects average U.S. household expenditures for natural gas, heating oil, and propane during the upcoming winter heating season (October 1 through March 31) will be 10%, 25%, and 18% lower, respectively, than last winter, because of lower fuel prices and lower heating demand. Forecast lower heating demand and relatively unchanged prices contribute to electricity expenditures that are 3% lower than last winter

  8. Validation and Analysis of HRRR Insolation Forecasts using Surfrad...

    Office of Scientific and Technical Information (OSTI)

    Resource Type: Conference Resource Relation: Conference: Proceedings of the World Renewable Energy Forum, 13-17 May 2012, Denver, Colorado (CD-ROM) Publisher: Boulder, CO: American ...

  9. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds ...

  10. Supplement to the Annual Energy Outlook 1993

    SciTech Connect (OSTI)

    Not Available

    1993-02-17

    The Supplement to the Annual Energy Outlook 1993 is a companion document to the Energy Information Administration`s (EIA) Annual Energy Outlook 1993 (AEO). Supplement tables provide the regional projections underlying the national data and projections in the AEO. The domestic coal, electric power, commercial nuclear power, end-use consumption, and end-use price tables present AEO forecasts at the 10 Federal Region level. World coal tables provide data and projections on international flows of steam coal and metallurgical coal, and the oil and gas tables provide the AEO oil and gas supply forecasts by Oil and Gas Supply Regions and by source of supply. All tables refer to cases presented in the AEO, which provides a range of projections for energy markets through 2010.

  11. Short-term energy outlook: Quarterly projections. Second quarter 1995

    SciTech Connect (OSTI)

    1995-05-02

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent projections with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the second quarter of 1995 through the fourth quarter of 1996. Values for the first quarter of 1995, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the second quarter 1995 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service.

  12. International Energy Outlook 2014

    Gasoline and Diesel Fuel Update (EIA)

    Household heating bills expected to be lower this winter U.S. consumers are expected to pay less this winter on their home heating bills because of lower oil and natural gas prices and projected milder temperatures than last winter. In its new forecast, the U.S. Energy Information Administration said households that rely on heating oil which are mainly located in the Northeast will pay the lowest heating expenditures in 9 years down 25% from last winter as consumers are expected to save about

  13. Inventing an Energy Internet: Concepts, Architectures and Protocols for Smart Energy Utilization

    ScienceCinema (OSTI)

    Tsoukalas, Lefteri [Purdue University, Purdue, Indiana, United States

    2010-01-08

    In recent years, the Internet is revolutionizing information availability much like the Power Grid revolutionized energy availability a century earlier. We will explore the differences and similarities of these two critical infrastructures and identify ways for convergence which may lead to an energy internet. Pricing signals, nodal forecasting, and short-term elasticities are key concepts in smart energy flows respecting the delicate equilibrium involved in generation-demand and aiming at higher efficiencies. We will discuss how intelligent forecasting approaches operating at multiple levels (including device or nodal levels) can ameliorate the challenges of power storage. In addition to higher efficiencies, an energy internet may achieve significant reliability and security improvements and offer greater flexibility and transparency in the overall energy-environmental relation.

  14. Inventing an Energy Internet: Concepts, Architectures, and Protocols for Smart Energy Utilization

    SciTech Connect (OSTI)

    Tsoukalas, Lefteri

    2009-04-29

    In recent years, the Internet is revolutionizing information availability much like the Power Grid revolutionized energy availability a century earlier. We will explore the differences and similarities of these two critical infrastructures and identify ways for convergence which may lead to an energy internet. Pricing signals, nodal forecasting, and short-term elasticities are key concepts in smart energy flows respecting the delicate equilibrium involved in generation-demand and aiming at higher efficiencies. We will discuss how intelligent forecasting approaches operating at multiple levels (including device or nodal levels) can ameliorate the challenges of power storage. In addition to higher efficiencies, an energy internet may achieve significant reliability and security improvements and offer greater flexibility and transparency in the overall energy-environmental relation.

  15. Inventing an Energy Internet: Concepts, Architectures and Protocols for Smart Energy Utilization

    SciTech Connect (OSTI)

    Tsoukalas, Lefteri

    2009-04-29

    In recent years, the Internet is revolutionizing information availability much like the Power Grid revolutionized energy availability a century earlier. We will explore the differences and similarities of these two critical infrastructures and identify ways for convergence which may lead to an energy internet. Pricing signals, nodal forecasting, and short-term elasticities are key concepts in smart energy flows respecting the delicate equilibrium involved in generation-demand and aiming at higher efficiencies. We will discuss how intelligent forecasting approaches operating at multiple levels (including device or nodal levels) can ameliorate the challenges of power storage. In addition to higher efficiencies, an energy internet may achieve significant reliability and security improvements and offer greater flexibility and transparency in the overall energy-environmental relation.

  16. Sandia Energy - Energy Surety

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

    Energy, Energy Assurance, Energy Surety, Grid Integration, Infrastructure Security, Microgrid, News, News & Events, Renewable Energy, Systems Analysis, Systems Engineering,...

  17. Sandia Energy - Energy Assurance

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

    Energy, Energy Assurance, Energy Surety, Grid Integration, Infrastructure Security, Microgrid, News, News & Events, Renewable Energy, Systems Analysis, Systems Engineering,...

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

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

    short-term forecasts to 2016 Annual projections to 2040 International projections All projections reports Analysis & Projections Major Topics Most popular Annual Energy Outlook related Congressional & other requests International Energy Outlook related Presentations Recurring Short-Term Outlook Related Special outlooks Testimony All reports Browse by Tag Alphabetical Frequency Tag Cloud Full report Appendix A-Request Letter Appendix B-Summary Tables Scenario Case Data Reference Reference

  19. Assumptions to the Annual Energy Outlook 2015

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

    00 Appendix A: Handling of federal and selected state legislation and regulations in the AEO Residential sector Legislation Brief description AEO handling Basis A. National Appliance Energy Conservation Act of 1987 (NAECA87) Requires Secretary of Energy to set minimum efficiency standards for various appliance categories with periodic updates Include categories represented in the AEO residential sector forecast Public Law 100-12 a. Room air conditioners Sets standards for room air conditioners

  20. R&D Issues in the Office of Energy Efficiency and Renewable Energy |

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

    Department of Energy R&D Issues in the Office of Energy Efficiency and Renewable Energy R&D Issues in the Office of Energy Efficiency and Renewable Energy 2003 DEER Conference Presentation: U.S. Department of Energy FreedomCAR and Vehicle Technologies Program deer_2003_baldwin.pdf (691.4 KB) More Documents & Publications DOE Benefits Forecasts: Report of the External Peer Review Panel Details of the FY 2014 Congressional Budget Request for OE Download the SunShot Initiative 2014

  1. Short-Term Energy Outlook - U.S. Energy Information Administration (EIA)

    Gasoline and Diesel Fuel Update (EIA)

    Renewables and Carbon Dioxide Emissions Electricity and Heat Generation from Renewables EIA expects total renewables used in the electric power sector to increase by 10.5% in 2016 and by 4.3% in 2017. Forecast hydropower generation in the electric power sector increases by 7.8% in 2016 and then falls by 2.0% in 2017. Consumption of renewable energy other than hydropower in the electric power sector is forecast to grow by 12.9% in 2016 and by 9.6% in 2017. EIA expects that utility-scale solar

  2. Calendar Year 2009 Program Benefits for ENERGY STAR Labeled Products

    SciTech Connect (OSTI)

    Homan, Gregory K; Sanchez, Marla C.; Brown, Richard E.

    2010-11-15

    ENERGY STAR is a voluntary energy efficiency labeling program operated jointly by the Environmental Protection Agency (US EPA) and the U.S. Department of Energy (US DOE), designed to identify and promote energy-efficient products, buildings and practices. Since the program inception in 1992, ENERGY STAR has become a leading international brand for energy efficient products, and currently labels more than thirty products, spanning office equipment, heating, cooling and ventilation equipment, commercial and residential lighting, home electronics, and major appliances. ENERGY STAR's central role in the development of regional, national and international energy programs necessitates an open process whereby its program achievements to date as well as projected future savings are shared with stakeholders. This report presents savings estimates from the use ENERGY STAR labeled products. We present estimates of energy, dollar, and carbon savings achieved by the program in the year 2009, annual forecasts for 2010 and 2011, and cumulative savings estimates for the period 1993 through 2009 and cumulative forecasts for the period 2010 through 2015. Through 2009 the program saved 9.5 Quads of primary energy and avoided the equivalent of 170 million metric tons carbon (MMTC). The forecast for the period 2009-2015 is 11.5 Quads or primary energy saved and 202 MMTC emissions avoided. The sensitivity analysis bounds the best estimate of carbon avoided between 110 MMTC and 231 MMTC (1993 to 2009) and between 130 MMTC and 285 MMTC (2010 to 2015).

  3. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    SciTech Connect (OSTI)

    Zulkepli, Jafri Abidin, Norhaslinda Zainal; Fong, Chan Hwa

    2015-12-11

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

  4. Price Elasticities for Energy Use in Buildings of the United States

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

    Price Elasticities for Energy Use in Buildings of the United States October 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Price Elasticities for Energy Use in Buildings of the United States 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

  5. Assumptions and Expectations for Annual Energy Outlook 2015: Oil and Gas Working Group

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

    Assumptions and Expectations for Annual Energy Outlook 2016: Oil and Gas Working Group AEO2016 Oil and Gas Supply Working Group Meeting Office of Petroleum, Gas, and Biofuels Analysis December 1, 2015| Washington, DC http://www.eia.gov/forecasts/aeo/workinggroup/ WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES DO NOT QUOTE OR CITE AS RESULTS ARE SUBJECT TO CHANGE We welcome feedback on our assumptions and documentation * The AEO Assumptions report http://www.eia.gov/forecasts/aeo/assumptions/

  6. Hawaii Energy Strategy: Program guide

    SciTech Connect (OSTI)

    Not Available

    1992-09-01

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

  7. Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP): An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

    SciTech Connect (OSTI)

    Blair, Nate; Jenkin, Thomas; Milford, James; Short, Walter; Sullivan, Patrick; Evans, David; Lieberman, Elliot; Goldstein, Gary; Wright, Evelyn; Jayaraman, Kamala R.; Venkatesh, Boddu; Kleiman, Gary; Namovicz, Christopher; Smith, Bob; Palmer, Karen; Wiser, Ryan; Wood, Frances

    2009-09-01

    Energy system modeling can be intentionally or unintentionally misused by decision-makers. This report describes how both can be minimized through careful use of models and thorough understanding of their underlying approaches and assumptions. The analysis summarized here assesses the impact that model and data choices have on forecasting energy systems by comparing seven different electric-sector models. This analysis was coordinated by the Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP), a collaboration among governmental, academic, and nongovernmental participants.

  8. Regional four-dimensional variational data assimilation in a quasi-operational forecasting environment

    SciTech Connect (OSTI)

    Zupanski, M. )

    1993-08-01

    Four-dimensional variational data assimilation is applied to a regional forecast model as part of the development of a new data assimilation system at the National Meteorological Center (NMC). The assimilation employs an operational version of the NMC's new regional forecast model defined in eta vertical coordinates, and data used are operationally produced optimal interpolation (OI) analyses (using the first guess from the NMC's global spectral model), available every 3 h. Humidity and parameterized processes are not included in the adjoint model integration. The calculation of gradients by the adjoint model is approximate since the forecast model is used in its full-physics operational form. All experiments are over a 12-h assimilation period with subsequent 48-h forecast. Three different types of assimilation experiments are performed: (a) adjustment of initial conditions only (standard [open quotes]adjoint[close quotes] approach), (b) adjustment of a correction to the model equations only (variational continuous assimilation), and (c) simultaneous or sequential adjustment of both initial conditions and the correction term. Results indicate significantly better results when the correction term is included in the assimilation. It is shown, for a single case, that the new technique [experiment (c)] is able to produce a forecast better than the current conventional OI assimilation. It is very important to note that these results are obtained with an approximate gradient, calculated from a simplified adjoint model. Thus, it may be possible to perform an operational four-dimensional variational data assimilation of realistic forecast models, even before more complex adjoint models are developed. Also, the results suggest that it may be possible to reduce the large computational cost of assimilation by using only a few iterations of the minimization algorithm. This fast convergence is encouraging from the prospective of operational use. 37 refs., 10 figs., 1 tab.

  9. Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy

    SciTech Connect (OSTI)

    Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

    2014-08-15

    Purpose: To create models that forecast longitudinal trends in changing tumor morphology and to evaluate and compare their predictive potential throughout the course of radiation therapy. Methods: Two morphology feature vectors were used to describe 35 gross tumor volumes (GTVs) throughout the course of intensity-modulated radiation therapy for oropharyngeal tumors. The feature vectors comprised the coordinates of the GTV centroids and a description of GTV shape using either interlandmark distances or a spherical harmonic decomposition of these distances. The change in the morphology feature vector observed at 33 time points throughout the course of treatment was described using static, linear, and mean models. Models were adjusted at 0, 1, 2, 3, or 5 different time points (adjustment points) to improve prediction accuracy. The potential of these models to forecast GTV morphology was evaluated using leave-one-out cross-validation, and the accuracy of the models was compared using Wilcoxon signed-rank tests. Results: Adding a single adjustment point to the static model without any adjustment points decreased the median error in forecasting the position of GTV surface landmarks by the largest amount (1.2 mm). Additional adjustment points further decreased the forecast error by about 0.4 mm each. Selection of the linear model decreased the forecast error for both the distance-based and spherical harmonic morphology descriptors (0.2 mm), while the mean model decreased the forecast error for the distance-based descriptor only (0.2 mm). The magnitude and statistical significance of these improvements decreased with each additional adjustment point, and the effect from model selection was not as large as that from adding the initial points. Conclusions: The authors present models that anticipate longitudinal changes in tumor morphology using various models and model adjustment schemes. The accuracy of these models depended on their form, and the utility of these models

  10. ARM - Field Campaign - 915 MHz Wind Profiler for Cloud Forecasting at BNL

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

    govCampaigns915 MHz Wind Profiler for Cloud Forecasting at BNL Campaign Links Field Campaign Report 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 Campaign : 915 MHz Wind Profiler for Cloud Forecasting at BNL 2011.05.31 - 2012.05.31 Lead Scientist : Michael Jensen For data sets, see below. Abstract In support of the installation of a 37 MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study

  11. California energy prices 1980-2000. Staff report

    SciTech Connect (OSTI)

    Cauchois, S.; Ward, D.; Merritt, M.

    1981-07-01

    The report provides semiannual 20-year forecasts of electricity and primary fuel prices. Its purposes is to review current and past trends in energy prices as well as report on the California Energy Commission staff's periodic price forecasts. Both in actual and inflation-adjusted terms, energy prices are expected to continue to rise in the next 20 years, although not at the extreme rates witnessed during the 1973-1974 oil embargo and again in 1979. The impact of rising energy prices of electricity, natural gas, gasoline, coal, and petroleum on a hypothetical California household's energy bill (based on estimated 1980 consumption) is characterized. Although price increases will not be easy to sustain, the proven and anticipated abilities of households and businesses to adjust to higher prices will mitigate the impacts of higher energy prices so that they are consistent with a growing state economy and improved standard of living.

  12. A Buildings Module for the Stochastic Energy Deployment System

    SciTech Connect (OSTI)

    Lacommare, Kristina S H; Marnay, Chris; Stadler, Michael; Borgeson, Sam; Coffey, Brian; Komiyama, Ryoichi; Lai, Judy

    2008-05-15

    The U.S. Department of Energy (USDOE) is building a new long-range (to 2050) forecasting model for use in budgetary and management applications called the Stochastic Energy Deployment System (SEDS), which explicitly incorporates uncertainty through its development within the Analytica(R) platform of Lumina Decision Systems. SEDS is designed to be a fast running (a few minutes), user-friendly model that analysts can readily run and modify in its entirety through a visual programming interface. Lawrence Berkeley National Laboratory is responsible for implementing the SEDS Buildings Module. The initial Lite version of the module is complete and integrated with a shared code library for modeling demand-side technology choice developed by the National Renewable Energy Laboratory (NREL) and Lumina. The module covers both commercial and residential buildings at the U.S. national level using an econometric forecast of floorspace requirement and a model of building stock turnover as the basis for forecasting overall demand for building services. Although the module is fundamentally an engineering-economic model with technology adoption decisions based on cost and energy performance characteristics of competing technologies, it differs from standard energy forecasting models by including considerations of passive building systems, interactions between technologies (such as internal heat gains), and on-site power generation.

  13. University of Arizona Compressed Air Energy Storage

    SciTech Connect (OSTI)

    Simmons, Joseph; Muralidharan, Krishna

    2012-12-31

    Boiled down to its essentials, the grant’s purpose was to develop and demonstrate the viability of compressed air energy storage (CAES) for use in renewable energy development. While everyone agrees that energy storage is the key component to enable widespread adoption of renewable energy sources, the development of a viable scalable technology has been missing. The Department of Energy has focused on expanded battery research and improved forecasting, and the utilities have deployed renewable energy resources only to the extent of satisfying Renewable Portfolio Standards. The lack of dispatchability of solar and wind-based electricity generation has drastically increased the cost of operation with these components. It is now clear that energy storage coupled with accurate solar and wind forecasting make up the only combination that can succeed in dispatchable renewable energy resources. Conventional batteries scale linearly in size, so the price becomes a barrier for large systems. Flow batteries scale sub-linearly and promise to be useful if their performance can be shown to provide sufficient support for solar and wind-base electricity generation resources. Compressed air energy storage provides the most desirable answer in terms of scalability and performance in all areas except efficiency. With the support of the DOE, Tucson Electric Power and Science Foundation Arizona, the Arizona Research Institute for Solar Energy (AzRISE) at the University of Arizona has had the opportunity to investigate CAES as a potential energy storage resource.

  14. Application of global weather and climate model output to the design and operation of wind-energy systems

    SciTech Connect (OSTI)

    Curry, Judith

    2015-05-21

    This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatory environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.

  15. Press Room - U.S. Energy Information Administration (EIA)

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

    Press Releases EIA now using near-real-time export data to provide better weekly petroleum consumption estimates August 31, 2016 Hourly information on U.S. electricity supply, demand, and flows is now available July 25, 2016 MEDIA ADVISORY: 2016 EIA Energy Conference June 29, 2016 MEDIA ADVISORY: EIA presents updated energy projections to 2040 June 21, 2016 More press releases... Congressional Testimony Renewable Fuel Standard pdf Subject: EIA, forecasts, energy markets Presented by: Howard

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

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

    Department of Energy Risk Predictions for the 2015 Hurricane Season (June 2015) Energy Risk Predictions for the 2015 Hurricane Season (June 2015) This presentation is from a DOE-NASEO webinar held June 23, 2015, on forecasting energy infrastructure risk for the 2015 hurricane season. A variety of sources predict a below-normal season, with hurricane intensity lower than the 1981-2010 averages. The presentation includes an overview of hurricane season classification, historic impacts, and

  17. Analysis & Projections - U.S. Energy Information Administration (EIA) -

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

    U.S. Energy Information Administration (EIA) Short-Term Forecasts to 2012 Annual Projections to 2035 International Projections Analysis & Projections Most Requested All Reports Models & Documentation Technical Workshop on Behavior Economics Presentations November 15, 2013 About the workshop The U.S. Department of Energy's Energy Information Administration (EIA) conducted a technical workshop on July 17, 2013 in Washington, D.C. to assess recent methodological developments in the

  18. Project SEEBECK Saving Energy Effectively By Engaging in Collaborative

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

    Campaigns to Promote Solar Technology Diffusion through Data-Driven Behavior Modeling | Department of Energy Logos of Sandia National Laboratories, the Center of Sustainable Energy California, National Renewable Energy Laboratory, and the University of Pennsylvania Wharton School. A graph that highlights the solar social networks and helps to forecast how solar adoption patterns change under different business or policy scenarios. Sandia National Laboratories, along with partners at the

  19. CX-100737 Categorical Exclusion Determination | Department of Energy

    Office of Environmental Management (EM)

    7 Categorical Exclusion Determination CX-100737 Categorical Exclusion Determination High penetration DER system forecasting with hybrid models Award Number: DE-EE0007597 CX(s) Applied: A9 Solar Energy Technologies Office Date: 8/19/2016 Location(s): NJ Office(s): Golden Field Office The U.S. Department of Energy (DOE) is proposing to provide federal funding to Qado Energy, Inc. to provide a unified approach to the interconnection planning and assessment of system reinforcement needs of electric

  20. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2003 THRU FY2046 VERSION 2003.1 VOLUME 2 [SEC 1 & 2

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2003-12-01

    This report includes data requested on September 10, 2002 and includes radioactive solid waste forecasting updates through December 31, 2002. The FY2003.0 request is the primary forecast for fiscal year FY 2003.

  1. Sandia Energy - Energy Surety

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

    Energy Storage Systems, Energy Surety, Grid Integration, Infrastructure Security, Microgrid, Modeling & Analysis, News, News & Events, Partnership, Renewable Energy, SMART...

  2. Model documentation report: Residential sector demand module of the national energy modeling system

    SciTech Connect (OSTI)

    1998-01-01

    This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This reference document provides a detailed description for energy analysts, other users, and the public. The NEMS Residential Sector Demand Module is currently used for mid-term forecasting purposes and energy policy analysis over the forecast horizon of 1993 through 2020. The model generates forecasts of energy demand for the residential sector by service, fuel, and Census Division. Policy impacts resulting from new technologies, market incentives, and regulatory changes can be estimated using the module. 26 refs., 6 figs., 5 tabs.

  3. Energy reference handbook. Third edition

    SciTech Connect (OSTI)

    Not Available

    1985-01-01

    The energy field has exploded since the OPEC oil embargo of 1973. Terms that did not even exist several years ago are now being used. In addition, many words have developed interpretations somewhat different from their commonly accepted meanings. The 3rd Edition of the Energy Reference Handbook records and standardizes these terms in a comprehensive glossary. Special emphasis is placed on providing terms and definitions in the area of alternative fuels-synthetics from coal and oil shale; solar; wind; biomass; geothermal; and more - as well as traditional fossil fuels. In total, more than 3,500 terms, key words, and phrases used daily in energy literature are referenced. In addition to these definitions, conversion tables, diagrams, maps, tables, and charts on various aspects of energy which forecast the reserves of fuel resources, plus other information relevant to energy resources and technologies are found in this reference.

  4. Transportation Sector Model of the National Energy Modeling System. Volume 1

    SciTech Connect (OSTI)

    1998-01-01

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. The NEMS Transportation Model comprises a series of semi-independent models which address different aspects of the transportation sector. The primary purpose of this model is to provide mid-term forecasts of transportation energy demand by fuel type including, but not limited to, motor gasoline, distillate, jet fuel, and alternative fuels (such as CNG) not commonly associated with transportation. The current NEMS forecast horizon extends to the year 2010 and uses 1990 as the base year. Forecasts are generated through the separate consideration of energy consumption within the various modes of transport, including: private and fleet light-duty vehicles; aircraft; marine, rail, and truck freight; and various modes with minor overall impacts, such as mass transit and recreational boating. This approach is useful in assessing the impacts of policy initiatives, legislative mandates which affect individual modes of travel, and technological developments. The model also provides forecasts of selected intermediate values which are generated in order to determine energy consumption. These elements include estimates of passenger travel demand by automobile, air, or mass transit; estimates of the efficiency with which that demand is met; projections of vehicle stocks and the penetration of new technologies; and estimates of the demand for freight transport which are linked to forecasts of industrial output. Following the estimation of energy demand, TRAN produces forecasts of vehicular emissions of the following pollutants by source: oxides of sulfur, oxides of nitrogen, total carbon, carbon dioxide, carbon monoxide, and volatile organic compounds.

  5. Sandia Energy Energy Assurance

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

    DOE International Energy Storage Database Has Logged 420 Energy Storage Projects Worldwide with 123 GW of Installed Capacity http:energy.sandia.govdoe-international-energy-stora...

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

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

    Short-Term Energy Outlook 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

  7. Oil and Gas Supply Module of the National Energy Modeling System: Model Documentation 2014

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

    Oil and Gas Supply Module of the National Energy Modeling System: Model Documentation 2014 July 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | NEMS Model Documentation 2014: Oil and Gas Supply Module 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

  8. Coal Market Module of the National Energy Modeling System Model Documentation 2013

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

    Coal Market Module of the National Energy Modeling System Model Documentation 2013 June 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Model Documentation: Coal Market Module 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

  9. Residential Demand Module of the National Energy Modeling System: Model Documentation 2014

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

    Residential Demand Module of the National Energy Modeling System: Model Documentation 2014 August 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | NEMS Residential Demand Module Documentation Report 2014 ii 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

  10. Expectations models of electric utilities' forecasts: a case study of econometric estimation with influential data points

    SciTech Connect (OSTI)

    Vellutini, R. de A.S.; Mount, T.D.

    1983-01-01

    This study develops an econometric model for explaining how electric utilities revise their forecasts of future electricity demand each year. The model specification is developed from the adaptive expectations hypothesis and it relates forecasted growth rates to actual lagged growth rates of electricity demand. Unlike other studies of the expectation phenomenon, expectations of future demand levels constitute an observable variable and thus can be incorporated explicitly into the model. The data used for the analysis were derived from the published forecasts of the nine National Electric Reliability Councils in the US for the years 1974 to 1980. Three alternative statistical methods are used for estimation purposes: ordinary least-squares, robust regression and a diagnostic analysis to identify influential observations. The results obtained with the first two methods are very similar, but are both inconsistent with the underlying economic logic of the model. The estimated model obtained from the diagnostics approach after deleting two aberrant observations is consistent with economic logic, and supports the hypothesis that the low growth demand experienced immediately following the oil embargo in 1973 were disregarded by the industry for forecasting purposes. The model includes transitory effects associated with the oil embargo that gradually disappear over time, the estimated coefficients for the lagged values of actual growth approach a structure with declining positive weights. The general shape of this asymptotic structure is similar to the findings in many economic applications using distributed lag models.

  11. 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.

  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. Annual Energy Review 1999

    SciTech Connect (OSTI)

    Seiferlein, Katherine E.

    2000-07-01

    A generation ago the Ford Foundation convened a group of experts to explore and assess the Nation’s energy future, and published their conclusions in A Time To Choose: America’s Energy Future (Cambridge, MA: Ballinger, 1974). The Energy Policy Project developed scenarios of U.S. potential energy use in 1985 and 2000. Now, with 1985 well behind us and 2000 nearly on the record books, it may be of interest to take a look back to see what actually happened and consider what it means for our future. The study group sketched three primary scenarios with differing assumptions about the growth of energy use. The Historical Growth scenario assumed that U.S. energy consumption would continue to expand by 3.4 percent per year, the average rate from 1950 to 1970. This scenario assumed no intentional efforts to change the pattern of consumption, only efforts to encourage development of our energy supply. The Technical Fix scenario anticipated a “conscious national effort to use energy more efficiently through engineering know-how." The Zero Energy Growth scenario, while not clamping down on the economy or calling for austerity, incorporated the Technical Fix efficiencies plus additional efficiencies. This third path anticipated that economic growth would depend less on energy-intensive industries and more on those that require less energy, i.e., the service sector. In 2000, total energy consumption was projected to be 187 quadrillion British thermal units (Btu) in the Historical Growth case, 124 quadrillion Btu in the Technical Fix case, and 100 quadrillion Btu in the Zero Energy Growth case. The Annual Energy Review 1999 reports a preliminary total consumption for 1999 of 97 quadrillion Btu (see Table 1.1), and the Energy Information Administration’s Short-Term Energy Outlook (April 2000) forecasts total energy consumption of 98 quadrillion Btu in 2000. What energy consumption path did the United States actually travel to get from 1974, when the scenarios were drawn

  14. DISCLAIMER : UNCONTROLLED WHEN PRINTED - PLEASE CHECK THE STATUS OF THE DOCUMENT IN IDM

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

    Physics Modelling and Analysis expertise on CXRS Diagnostic The work described below is related to the Physics Modelling and Analysis expertise of the ITER CXRS Diagnostics: 55.E1 CXRS Core, 55.EC CXRS Edge and 55.EF CXRS Pedestal. The 3 diagnostics are being developed by 3 IO-DA's (respectively EU-DA, RF-DA and IN- DA). However, to support the diagnostic development input of CXRS spectral modelling is required, that needs to be cross checked against existing Fusion experiments. Moreover,

  15. Energy Technologies on the Horizon (released in AEO2006)

    Reports and Publications (EIA)

    2006-01-01

    A key issue in mid-term forecasting is the representation of changing and developing technologies. How existing technologies will evolve, and what new technologies might emerge, cannot be known with certainty. The issue is of particular importance in Annual Energy Outlook 2006 (AEO), the first AEO with projections out to 2030.

  16. Forecast of contracting and subcontracting opportunities: Fiscal year 1998

    SciTech Connect (OSTI)

    1998-01-01

    This report describes procurement procedures and opportunities for small businesses with the Department of Energy (DOE). It describes both prime and subcontracting opportunities of $100,000 and above which are being set aside for 8(a) and other small business concerns. The report contains sections on: SIC codes; procurement opportunities with headquarters offices; procurement opportunities with field offices; subcontracting opportunities with major contractors; 8(a) contracts expiring in FY 1998; other opportunities to do business with DOE; management and operating contractors--expiration dates; Office of Small and Disadvantaged Business Utilization (OSDBU) staff directory; and small business survey. This document will be updated quarterly on the home page.

  17. 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.

  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. Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting

    SciTech Connect (OSTI)

    Zack, J; Natenberg, E; Young, S; Manobianco, J; Kamath, C

    2010-02-21

    The operators of electrical grids, sometimes referred to as Balancing Authorities (BA), typically make critical decisions on how to most reliably and economically balance electrical load and generation in time frames ranging from a few minutes to six hours ahead. At higher levels of wind power generation, there is an increasing need to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts on this time scale have typically been strongly dependent on short-term trends indicated by the time series of power production and meteorological data from a wind farm. Additional input information is often available from the output of Numerical Weather Prediction (NWP) models and occasionally from off-site meteorological towers in the region surrounding the wind generation facility. A widely proposed approach to improve short-term forecasts is the deployment of off-site meteorological towers at locations upstream from the wind generation facility in order to sense approaching wind perturbations. While conceptually appealing, it turns out that, in practice, it is often very difficult to derive significant benefit in forecast performance from this approach. The difficulty is rooted in the fact that the type, scale, and amplitude of the processes controlling wind variability at a site change from day to day if not from hour to hour. Thus, a location that provides some useful forecast information for one time may not be a useful predictor a few hours later. Indeed, some processes that cause significant changes in wind power production operate predominantly in the vertical direction and thus cannot be monitored by employing a network of sensors at off-site locations. Hence, it is very challenging to determine the type of sensors and deployment locations to get the most benefit for a specific short-term forecast application. Two tools recently developed in the meteorological research community have the potential to help determine the locations and parameters to

  20. Historical Procurement Information | Department of Energy

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

    Historical Procurement Information Historical Procurement Information Use the documents on this page to learn more about historically procured goods and services in various industry sectors at the Department of Energy. Note - historical means what the Department has bought in the past. Our procurement Forecast shows what we will buy in the future. Construction Forestry Goods Manufacturing Mining Services Information Transportation and Warehousing Leasing and Real Estate Professional, Scientific,

  1. Stochastic Energy Deployment System (SEDS) World Oil Model (WOM)

    Energy Science and Technology Software Center (OSTI)

    2009-08-07

    The function of the World Oil Market Model (WOMM) is to calculate a world oil price. SEDS will set start and end dates for the forecast period, and a time increment (assumed to be 1 year in the initial version). The WOMM will then randomly select an Annual Energy Outlook (AEO) oil price case and calibrate itself to that case. As it steps through each year, the WOMM will generate a stochastic supply shock tomore » OPEC output and accept a new estimate of U.S. petroleum demand from SEDS. The WOMM will then calculate a new oil market equilibrium for the current year. The world oil price at the new equilibrium will be sent back to SEDS. When the end year is reached, the process will begin again with the selection of a new AEO forecast. Iterations over forecasts will continue until SEDS has completed all its simulation runs.« less

  2. Stochastic Energy Deployment System (SEDS) World Oil Model (WOM)

    SciTech Connect (OSTI)

    2009-08-07

    The function of the World Oil Market Model (WOMM) is to calculate a world oil price. SEDS will set start and end dates for the forecast period, and a time increment (assumed to be 1 year in the initial version). The WOMM will then randomly select an Annual Energy Outlook (AEO) oil price case and calibrate itself to that case. As it steps through each year, the WOMM will generate a stochastic supply shock to OPEC output and accept a new estimate of U.S. petroleum demand from SEDS. The WOMM will then calculate a new oil market equilibrium for the current year. The world oil price at the new equilibrium will be sent back to SEDS. When the end year is reached, the process will begin again with the selection of a new AEO forecast. Iterations over forecasts will continue until SEDS has completed all its simulation runs.

  3. Short-Term Energy Outlook July 2013

    Gasoline and Diesel Fuel Update (EIA)

    1 July 2013 Short-Term Energy Outlook (STEO) Highlights  The U.S. Energy Information Administration (EIA) expects that the Brent crude oil spot price will average $102 per barrel over the second half of 2013, and $100 per barrel in 2014. This forecast assumes there are no disruptions to energy markets arising from the recent unrest in Egypt. After increasing to $119 per barrel in early February 2013, the Brent crude oil spot price fell to a low of $97 per barrel in mid-April and then

  4. Calendar Year 2008 Program Benefits for ENERGY STAR Labeled Products

    SciTech Connect (OSTI)

    Homan, GregoryK; Sanchez, Marla; Brown, RichardE; Lai, Judy

    2010-08-24

    This paper presents current and projected savings for ENERGY STAR labeled products, and details the status of the model as implemented in the September 2009 spreadsheets. ENERGY STAR is a voluntary energy efficiency labeling program operated jointly by the Environmental Protection Agency (US EPA) and the U.S. Department of Energy (US DOE), designed to identify and promote energy-efficient products, buildings and practices. Since the program inception in 1992, ENERGY STAR has become a leading international brand for energy efficient products, and currently labels more than thirty products, spanning office equipment, heating, cooling and ventilation equipment, commercial and residential lighting, home electronics, and major appliances. ENERGY STAR's central role in the development of regional, national and international energy programs necessitates an open process whereby its program achievements to date as well as projected future savings are shared with stakeholders. This report presents savings estimates for ENERGY STAR labeled products. We present estimates of energy, dollar, and carbon savings achieved by the program in the year 2008, annual forecasts for 2009 and 2010, and cumulative savings estimates for the period 1993 through 2008 and cumulative forecasts for the period 2009 through 2015. Through 2008 the program saved 8.8 Quads of primary energy and avoided the equivalent of 158 metric tones carbon (MtC). The forecast for the period 2009-2015 is 18.1 Quads or primary energy saved and 316 MtC emissions avoided. The sensitivity analysis bounds the best estimate of carbon avoided between 104 MtC and 213 MtC (1993 to 2008) and between 206 MtC and 444 MtC (2009 to 2015). In this report we address the following questions for ENERGY STAR labeled products: (1) How are ENERGY STAR impacts quantified; (2) What are the ENERGY STAR achievements; and (3) What are the limitations to our method?

  5. Wind energy systems: program summary

    SciTech Connect (OSTI)

    1980-05-01

    The Federal Wind Energy Program (FWEP) was initiated to provide focus, direction and funds for the development of wind power. Each year a summary is prepared to provide the American public with an overview of government sponsored activities in the FWEP. This program summary describes each of the Department of Energy's (DOE) current wind energy projects initiated or renewed during FY 1979 (October 1, 1978 through September 30, 1979) and reflects their status as of April 30, 1980. The summary highlights on-going research, development and demonstration efforts and serves as a record of progress towards the program objectives. It also provides: the program's general management structure; review of last year's achievements; forecast of expected future trends; documentation of the projects conducted during FY 1979; and list of key wind energy publications.

  6. Regional forecasting with global atmospheric models; Third year report

    SciTech Connect (OSTI)

    Crowley, T.J.; North, G.R.; Smith, N.R.

    1994-05-01

    This report was prepared by the Applied Research Corporation (ARC), College Station, Texas, under subcontract to Pacific Northwest Laboratory (PNL) as part of a global climate studies task. The task supports site characterization work required for the selection of a potential high-level nuclear waste repository and is part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work is under the overall direction of the Office of Civilian Radioactive Waste Management (OCRWM), US Department of Energy Headquarters, Washington, DC. The scope of the report is to present the results of the third year`s work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain several studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals.

  7. Energy conservation from regenerative incineration

    SciTech Connect (OSTI)

    Pennington, R.L.

    1982-06-01

    The oil embargo in the winter of 1973 covered the nation with a serious energy crisis. Although the ''gas lines'' have subsided, sky-rocketing fuel costs and diminishing energy supplies linger on. Projected U.S. energy demands indicate normal energy requirements over a normal growth rate. However, when compared with the projected U.S. energy supplies, a very significant energy deficit may exist in the near future. Although coal and nuclear show substantial potential as energy sources, it is unlikely that they will fill the gap between energy demands and the gas and oil supplies. In view of the Three-Mile Island nuclear incident, and cutbacks in the state of Washington, it is doubtful that the 13% contribution to the energy supply in the part of nuclear power will ever materialize. Although coal supplies are very abundant, the development of coal technology will not meet the next decade's energy requirements as it is indicated by the fact that coal is supplying far less energy than forecasted by the government.

  8. Energy Information Administration/Short-Term Energy Outlook - May 2005

    Gasoline and Diesel Fuel Update (EIA)

    5 1 Short-Term Energy Outlook May 2005 2005 Summer Motor Gasoline Outlook Update (Figure 1) A considerable break in the expected strength of near-term crude oil prices has resulted in a lower forecast for retail gasoline prices this spring. Gasoline prices may well have seen their peak for the year, barring sharp disruptions in crude oil supply or refinery operations. Pump prices for the summer (April-September) are now projected to average $2.17 per gallon, still high by historical standards

  9. Annual energy outlook 1997 with projections to 2015

    SciTech Connect (OSTI)

    1996-12-01

    The Annual Energy Outlook 1997 (AEO97) presents midterm forecasts of energy supply, demand, and prices through 2015 prepared by the Energy Information Administration (EIA). These projections are based on results of EIA`s National Energy Modeling System (NEMS). This report begins with a summary of the reference case, followed by a discussion of the legislative assumptions and evolving legislative and regulatory issues. ``Issues in Focus`` discusses emerging energy issues and other topics of particular interest. It is followed by the analysis of energy market trends. The analysis in AEO97 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present summaries of the reference case forecasts in units of oil equivalence and household energy expenditures. Twenty-three other cases explore the impacts of varying key assumptions in NEMS--generally, technology penetration, with the major results shown in Appendix F. Appendix G briefly describes NEMS and the major AEO97 assumptions, with a summary table. 114 figs., 22 tabs.

  10. Sandia Energy - Energy Assurance

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

    Energy Surety, Facilities, Global Climate & Energy, Grid Integration, Mesa del Sol, Microgrid, News, News & Events, Renewable Energy, SMART Grid, Solar Mesa del Sol Unveils First...

  11. Sandia Energy Energy Storage

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

    Sandia Participates in Preparation of New Mexico Renewable Energy Storage Report http:energy.sandia.govsandia-participates-in-preparation-of-new-mexico-renewable-energy-storage-...

  12. Sandia Energy Energy Efficiency

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

    Sandia's Energy Program Wins Two Federal Laboratory Consortium 2015 Awards http:energy.sandia.govsandias-energy-program-wins-two-federal-laboratory-consortium-2015-awards...

  13. Sandia Energy - Nuclear Energy

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

    Sandia's Brayton-Cycle Turbine Boosts Small Nuclear Reactor Efficiency Energy, Energy Efficiency, News, News & Events, Nuclear Energy Sandia's Brayton-Cycle Turbine Boosts Small...

  14. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    March 2015 Short-Term Energy Outlook (STEO) Highlights  North Sea Brent crude oil prices averaged $58/barrel (bbl) in February, an increase of $10/bbl from the January average, and the first monthly average price increase since June 2014. The price increase reflects news of falling U.S. crude oil rig counts and announced reductions in capital expenditures by major oil companies, along with lower-than-expected Iraqi crude oil exports.  EIA forecasts that Brent crude oil prices will average

  15. Short-Term Energy Outlook Supplement: Summer 2013 Outlook for Residential Electric Bills

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

    Summer 2013 Outlook for Residential Electric Bills June 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Supplement: Summer 2013 Outlook for Residential Electric Bills 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

  16. Analysis Insights, August 2015: Sustainable Transportation (Brochure), NREL (National Renewable Energy Laboratory)

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

    Analysis & Projections Glossary › FAQS › Overview Projection Data Monthly Short-Term Forecasts to 2012 Annual Projections to 2035 International Projections Analysis & Projections Most Requested All Reports Models & Documentation AEO Working Groups Purpose of Working Groups The Annual Energy Outlook, prepared by the U.S. Energy Information Administration (EIA), presents long-term projections of energy supply, demand, and prices, based on results from EIA's National Energy Modeling

  17. Short-Term Energy Outlook Model Documentation: Electricity Generation and Fuel Consumption Models

    Gasoline and Diesel Fuel Update (EIA)

    Model Documentation: Electricity Generation and Fuel Consumption Models January 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Model Documentation: Electricity Generation and Fuel Consumption Models 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

  18. 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.

  19. 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:

  20. Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

    SciTech Connect (OSTI)

    Intharathirat, Rotchana; Abdul Salam, P.; Kumar, S.; Untong, Akarapong

    2015-05-15

    Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.