National Library of Energy BETA

Sample records for yearly energy forecasts

  1. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

    ....................................................................................................1-16 Energy Consumption Data...............................................1-15 Data Sources for Energy Demand Forecasting ModelsCALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report

  2. Univariate Modeling and Forecasting of Monthly Energy Demand Time Series

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural networks, Neural networks, Modeling, Forecasting, Energy demand, Time series forecasting, Power system demand time series based only on data for six years to forecast the demand for the seventh year. Both

  3. SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS

    E-Print Network [OSTI]

    Heinemann, Detlev

    SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS Detlev Heinemann Oldenburg in irradiance forecasting have been presented more than twenty years ago (Jensenius and Cotton, 1981), when or progress with respect to the development of solar irradiance forecasting methods. Heck and Takle (1987

  4. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EA Jump to:ofEnia SpAFlex Fuels Energy JumpVyncke Jump to:Forecast

  5. Forecast of Contracting and Subcontracting Opportunities, Fiscal year 1995

    SciTech Connect (OSTI)

    Not Available

    1995-02-01

    Welcome to the US Department of Energy`s Forecast of Contracting and Subcontracting Opportunities. This forecast, which is published pursuant to Public Low 100--656, ``Business Opportunity Development Reform Act of 1988,`` is intended to inform small business concerns, including those owned and controlled by socially and economically disadvantaged individuals, and women-owned small business concerns, of the anticipated fiscal year 1995 contracting and subcontracting opportunities with the Department of Energy and its management and operating contractors and environmental restoration and waste management contractors. This document will provide the small business contractor with advance notice of the Department`s procurement plans as they pertain to small, small disadvantaged and women-owned small business concerns.Opportunities contained in the forecast support the mission of the Department, to serve as advocate for the notion`s energy production, regulation, demonstration, conservation, reserve maintenance, nuclear weapons and defense research, development and testing, when it is a national priority. The Department`s responsibilities include long-term, high-risk research and development of energy technology, the marketing of Federal power, and maintenance of a central energy data collection and analysis program. A key mission for the Department is to identify and reduce risks, as well as manage waste at more than 100 sites in 34 states and territories, where nuclear energy or weapons research and production resulted in radioactive, hazardous, and mixed waste contamination. Each fiscal year, the Department establishes contracting goals to increase contracts to small business concerns and meet our mission objectives.

  6. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  7. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

  8. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    /demographic growth, relatively low electricity and natural gas rates, and relatively low efficiency program CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity Manager Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY

  9. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P. Oglesby Executive

  10. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    high economic/demographic growth, relatively low electricity and natural gas rates, and relatively low CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION

  11. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

  12. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand Gough Office Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  13. AUTOMATION OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S.

    E-Print Network [OSTI]

    Povinelli, Richard J.

    AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand of the energy demand forecasting are achieved by integrating nonlinear transformations within the models

  14. DOE Releases Latest Report on Energy Savings Forecast of Solid...

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

    Latest Report on Energy Savings Forecast of Solid-State Lighting DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting September 12, 2014 - 2:06pm Addthis...

  15. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

    of transportation fuel and crude oil import requirements. The transportation energy demand forecasts make. The transportation fuel and crude oil import requirement assessments build on assumptions about California crude oil forecasts, transportation energy, gasoline, diesel, jet fuel, crude oil production, fuel imports, crude oil

  16. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    . Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data Office. Andrea Gough ran the summary energy model and supervised data preparation. Glen Sharp prepared models. Both the staff revised energy consumption and peak forecasts are slightly higher than

  17. Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center

    E-Print Network [OSTI]

    Washington at Seattle, University of

    Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime at wind energy sites are becoming paramount. Regime-switching space-time (RST) models merge meteorological forecast regimes at the wind energy site and fits a conditional predictive model for each regime

  18. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

    /Individuals Providing Comments California Natural Gas Vehicle Coalition/ Mike Eaves League of Women VotersCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY AND TRANSPORTATION DIVISION B. B. Blevins Executive Director DISCLAIMER This report was prepared by a California

  19. 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.

  20. Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids

    E-Print Network [OSTI]

    Hwang, Kai

    1 Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids Yogesh Simmhan. One of the characteristic applications of Smart Grids is demand response optimization (DR). The goal of DR is to use the power consumption time series data to reliable forecast the future consumption

  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 Northern Study Area

    SciTech Connect (OSTI)

    Finley, Cathy

    2014-04-30

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

  2. PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022

    E-Print Network [OSTI]

    low electricity and natural gas rates, and relatively low efficiency program and self Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert Oglesby Executive Director DISCLAIMER Staff for electric vehicles. #12;ii #12;iii ABSTRACT The Preliminary California Energy Demand Forecast 2012

  3. MPC for Wind Power Gradients --Utilizing Forecasts, Rotor Inertia, and Central Energy Storage

    E-Print Network [OSTI]

    MPC for Wind Power Gradients -- Utilizing Forecasts, Rotor Inertia, and Central Energy Storage the control of a wind power plant, possibly consisting of many individual wind turbines. The goal. INTRODUCTION Today, wind power is the most important renewable energy source. For the years to come, many

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

    SciTech Connect (OSTI)

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

    2006-11-15

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

  5. 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.

  6. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EA EIS ReportEurope GmbH JumpSlough HeatMccoyProject-Energy

  7. Comparison of Bottom-Up and Top-Down Forecasts: Vision Industry Energy Forecasts with ITEMS and NEMS 

    E-Print Network [OSTI]

    Roop, J. M.; Dahowski, R. T

    2000-01-01

    Comparisons are made of energy forecasts using results from the Industrial module of the National Energy Modeling System (NEMS) and an industrial economic-engineering model called the Industrial Technology and Energy Modeling System (ITEMS), a model...

  8. 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.

  9. Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

    E-Print Network [OSTI]

    Shenoy, Prashant

    Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems Navin Sharma,gummeson,irwin,shenoy}@cs.umass.edu Abstract--To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands

  10. Leveraging Weather Forecasts in Renewable Energy Navin Sharmaa,

    E-Print Network [OSTI]

    Shenoy, Prashant

    Leveraging Weather Forecasts in Renewable Energy Systems Navin Sharmaa, , Jeremy Gummesonb , David, Binghamton, NY 13902 Abstract Systems that harvest environmental energy must carefully regulate their us- age to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic, since

  11. Short-Term Solar Energy Forecasting Using Wireless Sensor Networks

    E-Print Network [OSTI]

    Cerpa, Alberto E.

    Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Stefan Achleitner, Tao Liu an advantage for output power prediction. Solar Energy Prediction System Our prediction model is based variability of more then 100 kW per minute. For practical usage of solar energy, predicting times of high

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

    SciTech Connect (OSTI)

    Eisenberg, Joel F. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    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.

  13. Tiree Energy Pulse: Exploring Renewable Energy Forecasts on the Edge of the Grid

    E-Print Network [OSTI]

    MacDonald, Mark

    Tiree Energy Pulse: Exploring Renewable Energy Forecasts on the Edge of the Grid Will Simm1 , Maria energy consumption with supply, and together built a prototype renewable energy forecast display. A num local renewable energy was expected to be available, despite having no financial in- centive to do so

  14. MM5 Aids Forecasters Over the past five years a group in the Atmospheric

    E-Print Network [OSTI]

    Doty, Sharon Lafferty

    Jaeglé's specialty is atmospheric chemistry. Her research deals with analysis and modelingMM5 Aids Forecasters Over the past five years a group in the Atmospheric Sciences department has around the region. (see Page 8) New Faculty Join Atmospheric Sciences In the past year, Atmospheric

  15. Testing Automated Solar Flare Forecasting With 13 Years of MDI Synoptic Magnetograms

    E-Print Network [OSTI]

    Hoeksema, Todd

    becomes more technologically dependent on complex global systems, the potential risk posedTesting Automated Solar Flare Forecasting With 13 Years of MDI Synoptic Magnetograms J.P. Mason1 is statistically associated with changes in several characteris- tics of the line-of-sight magnetic field in solar

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

    SciTech Connect (OSTI)

    NONE

    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. Large-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random Fields

    E-Print Network [OSTI]

    Kolter, J. Zico

    in a wide range of energy systems, including forecasting demand, renewable generation, and electricityLarge-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random demonstrated that in the context of electrical demand and wind power, probabilistic forecasts can offer

  18. 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.

  19. 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.

  20. 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.

  1. Wind Forecasting Improvement Project | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LIST OFAMERICA'S FUTURE. regulatorsEnergyDepartmentEnergyWideWind

  2. Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching

    E-Print Network [OSTI]

    Genton, Marc G.

    Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime at a wind energy site and fits a conditional predictive model for each regime. Geographically dispersed was applied to 2-hour-ahead forecasts of hourly average wind speed near the Stateline wind energy center

  3. Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators Department of Computer Science

    E-Print Network [OSTI]

    Hwang, Kai

    peak demand periods using pricing incentives. Reliable building energy forecast models can help predictImproving Energy Use Forecast for Campus Micro-grids using Indirect Indicators Saima Aman prasanna@usc.edu Abstract--The rising global demand for energy is best addressed by adopting and promoting

  4. Solar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting Questionnaire As someone who is familiar with solar energy issues, we hope that you will tak

    E-Print Network [OSTI]

    Islam, M. Saif

    is familiar with solar energy issues, we hope that you will take a few moments to answer this short survey on your needs for information on solar energy resources and forecasting. This survey is conducted with the California Solar Energy Collaborative (CSEC) and the California Solar Initiative (CSI) our objective

  5. Fitting and forecasting non-linear coupled dark energy

    E-Print Network [OSTI]

    Casas, Santiago; Baldi, Marco; Pettorino, Valeria; Vollmer, Adrian

    2015-01-01

    We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range $z=0-1.6$ and wave modes below $k=10 \\text{h/Mpc}$. These fitting formulas can be used to test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and w...

  6. 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

  7. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    and forecasting of solar radiation data: a review,”forecasting of solar- radiation data,” Solar Energy, vol.sequences of global solar radiation data for isolated sites:

  8. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People`s Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  9. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People's Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  10. ENERGY ANALYSIS PROGRAM FY-1979.

    E-Print Network [OSTI]

    Authors, Various

    2013-01-01

    of 25 years energy demand forecasts, and for a preliminary13143) In addition, energy demand forecasts will be reviseddemand forecasts as well as severly limiting their utility in assessing impacts of energy

  11. 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).

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

    SciTech Connect (OSTI)

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

    2011-10-01

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

  13. ENERGY & ENVIRONMENT DIVISION ANNUAL REPORT 1979

    E-Print Network [OSTI]

    Cairns, E.J.

    2010-01-01

    of 25 years energy demand forecasts, and for a preliminaryIn addition, energy demand forecasts will be revised to takedemand forecasts as well as severly limiting their utility in assessing impacts of energy

  14. On-line economic optimization of energy systems using weather forecast information.

    SciTech Connect (OSTI)

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

    2009-01-01

    We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.

  15. 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.

  16. China's Energy and Carbon Emissions Outlook to 2050

    E-Print Network [OSTI]

    Zhou, Nan

    2011-01-01

    effects. The forecast of energy demand underlying bothEnergy demand in residential buildings has been rising very fast in recent years. The forecast

  17. 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.

  18. European Wind Energy Conference -Brussels, Belgium, April 2008 Data mining for wind power forecasting

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    European Wind Energy Conference - Brussels, Belgium, April 2008 Data mining for wind power-term forecasting of wind energy produc- tion up to 2-3 days ahead is recognized as a major contribution the improvement of predic- tion systems performance is recognised as one of the priorities in wind energy research

  19. 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?

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power Administration wouldMass map shines light on771/6/14 Contact:NewsWebmasterWorkingElla Zhou Photo of EllaEnergy

  1. Bet and Energy -From Load Forecasting to Demand Response in a Web of Things

    E-Print Network [OSTI]

    Beigl, Michael

    Bet and Energy - From Load Forecasting to Demand Response in a Web of Things Yong Ding TECO (DSM) [7, 19]. Within DSM, mainly two principal activities i.e. load shifting (demand response programs) and load reduction (energy efficiency and conser- vation programs) can be realized [4]. 1.1 Demand Response

  2. Program Year 2008 State Energy Program Formula

    Broader source: Energy.gov [DOE]

    U.S. Department of Energy (DOE) State Energy Program (SEP), SEP Program Guidance Fiscal Year 2008, Program Year 2008, energy efficiency and renewable energy programs in the states, DOE Office of Energy Efficiency and Renewable Energy

  3. 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 [ORNL

    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.

  4. European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. Forecasting of Regional Wind Generation by a Dynamic

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. Forecasting of Regional Wind. Abstract-Short-term wind power forecasting is recognized nowadays as a major requirement for a secure and economic integration of wind power in a power system. In the case of large-scale integration, end users

  5. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

    s economy. Demand Forecasts The three energy futures wereto meet the forecast demand in each energy futurE2. e e1£~energy saved through improved appliance efficiencies. Also icit in our demand forecasts

  6. Implementation of a Corporate Energy Accounting and Forecasting Model 

    E-Print Network [OSTI]

    Kympton, H. W.; Bowman, B. M.

    1981-01-01

    pieces of equipment which are used as energy performance 'yardsticks'. Monthly reports permit equitable comparisons of plant energy consumption and isolation of those plants with the lowest efficiencies. The financial impact of increasing energy...

  7. 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.

  8. Energy: a historical perspective and 21st century forecast

    SciTech Connect (OSTI)

    Salvador, Amos [University of Texas, Austin, TX (United States)

    2005-07-01

    Contents are: Preface; Chapter 1: introduction, brief history, and chosen approach; Chapter 2: human population and energy consumption: the future; Chapter 4: sources of energy (including a section on coal); Chapter 5: electricity: generation and consumption; and Chapter 6: energy consumption and probable energy sources during the 21st century.

  9. 1993 Solid Waste Reference Forecast Summary

    SciTech Connect (OSTI)

    Valero, O.J.; Blackburn, C.L. [Westinghouse Hanford Co., Richland, WA (United States); Kaae, P.S.; Armacost, L.L.; Garrett, S.M.K. [Pacific Northwest Lab., Richland, WA (United States)

    1993-08-01

    This report, which updates WHC-EP-0567, 1992 Solid Waste Reference Forecast Summary, (WHC 1992) forecasts the volumes of solid wastes to be generated or received at the US Department of Energy Hanford Site during the 30-year period from FY 1993 through FY 2022. The data used in this document were collected from Westinghouse Hanford Company forecasts as well as from surveys of waste generators at other US Department of Energy sites who are now shipping or plan to ship solid wastes to the Hanford Site for disposal. These wastes include low-level and low-level mixed waste, transuranic and transuranic mixed waste, and nonradioactive hazardous waste.

  10. 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.

  11. Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, AlabamaETEC GmbH JumpEllenville, NewLtd EILEnergy Datadata TypeEnergyFocus IncEnergy

  12. 2004 Pollock Year-Class Prediction: Average Recruitment This forecast is based on five data sources: three physical properties and two biological data sets.

    E-Print Network [OSTI]

    1 2004 Pollock Year-Class Prediction: Average Recruitment DATA This forecast is based on five data sources: three physical properties and two biological data sets. The sources are: 1) Observed 2004 Kodiak precipitation totals (inches) from hourly observations. Data for 2004 were obtained from the NOAA National

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

    Energy Savers [EERE]

    Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget Rollout - Energy Saving Homes, Buildings, and Manufacturing Office of Energy Efficiency and Renewable...

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergyInformation Form EmployeeAvailableExplores Deep Direct Use

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i nAand DOEDepartment ofProgramImportsEnergyForecasting Tools Enhance Wind

  16. DOE Announces Webinars on Real Time Energy Management, Solar Forecasting

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsofProgram:Y-12Power,5Energyof| DepartmentCell ElectricHurricane

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home RoomPreservationBio-Inspired Solar FuelTechnologyTel:FebruaryEIA's Today In Energy stories at

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-in electricLaboratory | Department ofPotawatomi Community |Barrels3 study

  19. Google Archives by Fiscal YearEnergy Saver

    Broader source: Energy.gov [DOE]

    From the EERE Web Statistics Archive: Corporate sites, retired Google Analytics profiles for the Energy Saver sites by fiscal year.

  20. 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.

  1. US Energy Production over the Years Data | Department of Energy

    Energy Savers [EERE]

    US Energy Production over the Years Data US Energy Production over the Years Data totalstateslink.xlsx totalsectorslink.xls us9302v3.json More Documents & Publications...

  2. 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.

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

    Energy Savers [EERE]

    Sustainable Transportation Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget Rollout - Sustainable Transportation Office of Energy Efficiency and Renewable...

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

    Energy Savers [EERE]

    Renewable Electricity Generation Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget Rollout - Renewable Electricity Generation Office of Energy Efficiency and...

  5. 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.

  6. 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.

  7. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

    Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required in electricity demand is, of course, crucial to determining the need for new electricity resources and helping of any forecast of electricity demand and developing ways to reduce the risk of planning errors

  8. Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines

    E-Print Network [OSTI]

    Cañizares, Claudio A.

    for forecasting the Spanish electricity market prices. On the other hand, ARIMA, dynamic regression and transfer been used to forecast the Spanish market prices [7], [9], Californian market prices [9], Leipzig power have been used for forecasting the Spanish and Californian market prices [11] and the PJM market prices

  9. International Nuclear Energy Research Initiative, Fiscal Year...

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

    Area: Reactor Concepts RD&D Project Start Date: January 2011 Project End Date: December 2013 38 | International Nuclear Energy Research Initiative (I-NERI) Fiscal Year 2011...

  10. Sustainable energy Academic year 2014-2015

    E-Print Network [OSTI]

    Ernst, Damien

    @ulg.ac.be 1 #12;1. Motivations Typical quotes about energy: · There is an impending energy crisis. We conceivably survive on their own renewable energy sources? 2. If everyone turns their thermostats one degreeSustainable energy Academic year 2014-2015 Damien Ernst ­ University of Li`ege Email: dernst

  11. Energy markets Academic year 2014-2015

    E-Print Network [OSTI]

    Ernst, Damien

    Energy markets Academic year 2014-2015 Damien Ernst ­ University of Li`ege Email: dernst@ulg.ac.be 1 #12;Energy markets Goal of this class: to learn about energy markets. Class every Thursday between operations for both: electrical energy systems & markets. Solve all the technical problems appearing

  12. Predictability of European air quality: Assessment of 3 years of operational forecasts and analyses by the PREV'AIR system

    E-Print Network [OSTI]

    Menut, Laurent

    , is proved to improve ozone forecasts, especially when photochemical pollution episodes occur. The PREV'AIR and laws regarding the pollutants of utmost importance in relation to human health, air pollution is still- ments are still needed to manage and control the impacts of air pollution on health. [3] Facing

  13. 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.

  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. Year-in-Review: 2012 Energy Infrastructure Events and Expansions...

    Energy Savers [EERE]

    2 Energy Infrastructure Events and Expansions (July 2013) Year-in-Review: 2012 Energy Infrastructure Events and Expansions (July 2013) The Year-in-Review (YIR): 2012 Energy...

  16. FORECASTS ON THE DARK ENERGY AND PRIMORDIAL NON-GAUSSIANITY OBSERVATIONS WITH THE TIANLAI CYLINDER ARRAY

    SciTech Connect (OSTI)

    Xu, Yidong; Chen, Xuelei [National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 (China); Wang, Xin [Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218 (United States)

    2015-01-01

    The Tianlai experiment is dedicated to the observation of large-scale structures (LSS) by the 21 cm intensity mapping technique. In this paper, we make forecasts concerning its ability to observe or constrain the dark energy parameters and the primordial non-Gaussianity. From the LSS data, one can use the baryon acoustic oscillation (BAO) and growth rate derived from the redshift space distortion (RSD) to measure the dark energy density and equation of state. The primordial non-Gaussianity can be constrained either by looking for scale-dependent bias in the power spectrum, or by using the bispectrum. Here, we consider three cases: the Tianlai cylinder array pathfinder that is currently being built, an upgrade of the Pathfinder Array with more receiver units, and the full-scale Tianlai cylinder array. Using the full-scale Tianlai experiment, we expect ?{sub w{sub 0}}?0.082 and ?{sub w{sub a}}?0.21 from the BAO and RSD measurements, ?{sub f{sub N{sub L}{sup local}}}?14 from the power spectrum measurements with scale-dependent bias, and ?{sub f{sub N{sub L}{sup local}}}?22 and ?{sub f{sub N{sub L}{sup equil}}}?157 from the bispectrum measurements.

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

    Office of Environmental Management (EM)

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

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

    SciTech Connect (OSTI)

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

    2010-01-01

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

  19. 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.

  20. 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.

  1. Celebrating Two Years of Building America's Clean Energy Manufacturing...

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

    Two Years of Building America's Clean Energy Manufacturing Future Celebrating Two Years of Building America's Clean Energy Manufacturing Future March 27, 2015 - 3:23pm Addthis An...

  2. Fiscal Year 2013 Department of Energy Annual Occupational Safety...

    Office of Environmental Management (EM)

    Fiscal Year 2013 Department of Energy Annual Occupational Safety and Health Report for Federal Employees to the Secretary of Labor Fiscal Year 2013 Department of Energy Annual...

  3. 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....

  4. Calendar Year 2004 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 2004

  5. Calendar Year 2005 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 20045

  6. Calendar Year 2006 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 200456

  7. Calendar Year 2007 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 2004567

  8. Calendar Year 2008 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 20045678

  9. Calendar Year 2009 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 200456789

  10. Calendar Year 2010 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 2004567890

  11. Calendar Year 2011 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year 20045678901

  12. Calendar Year 2012 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year

  13. Calendar Year 2013 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year3 Calendar

  14. Calendar Year 2014 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year3 Calendar4

  15. Calendar Year 2015 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office04 Calendar Year3 Calendar45

  16. New Years Revolutions | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousMathematicsEnergyInterested Parties -DepartmentAvailable for PublicDepartmentTindall HomesNewNew Years

  17. Bidding wind energy exploiting wind speed forecasts Antonio Giannitrapani, Simone Paoletti,

    E-Print Network [OSTI]

    Garulli, Andrea

    -ahead generation profile for a wind power producer by exploiting wind speed forecasts provided by a meteorological service. In the con- sidered framework, the wind power producer is called to take part integration in the grid is causing serious problems to transmission and distribution system operators [2]. One

  18. Global Potential of Energy Efficiency Standards and Labeling Programs

    E-Print Network [OSTI]

    McNeil, Michael A

    2008-01-01

    together the forecasts of energy demand with unit savingsDemand Forecast . 73 Potential EnergyDemand Forecast A forecast of delivered energy demand is an

  19. Modeling and Forecasting Electric Daily Peak Loads

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    for the same data. Two methods are described for forecasting daily peak loads up to one week ahead through, including generator unit commitment, hydro-thermal coordination, short-term maintenance, fuel allocation forecasting accuracies. STLF forecasting covers the daily peak load, total daily energy, and daily load curve

  20. Energy Forecast, ForskEL (Smart Grid Project) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, AlabamaETEC GmbH JumpEllenville, NewLtd EILEnergy Datadata TypeEnergyFocus Inc

  1. Building Electricity Load Forecasting via Stacking Ensemble Learning Method with Moving Horizon Optimization

    E-Print Network [OSTI]

    Burger, Eric M.; Moura, Scott J.

    2015-01-01

    K. W. Yau, “Predicting electricity energy con- sumption: Afor building-level electricity load forecasts,” Energy andannealing algorithms in electricity load forecasting,”

  2. Energy Learning Exchange 2014-15 Year-End Report

    E-Print Network [OSTI]

    Branoff, Theodore J.

    Energy Learning Exchange 2014-15 Year-End Report June 2015 #12;#12;Introduction to the Energy ................................................................................................2 What is the Energy Learning Exchange (ELE......................................................................................................................................6 Achieving the Objectives of the Energy Learning Exchange

  3. State Energy Program Program Year 2014 Administrative and Legal...

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

    of Energy Energy Efficiency and Renewable Energy Golden Service Center State Energy Program (SEP) Program Year 2014 Formula Awards SEP-ALRD-2014 CFDA Number: 81.041, State...

  4. State Energy Program (SEP) Program Year 2015 Formula Awards Administra...

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

    of Energy Energy Efficiency and Renewable Energy Golden Service Center State Energy Program (SEP) Program Year 2015 Formula Awards SEP-ALRD-2015 CFDA Number: 81.041, State...

  5. Energy Department Announces Five Year Renewal of Funding for...

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

    DOENews@hq.doe.gov Energy Department Announces Five Year Renewal of Funding for First Energy Innovation Hub Consortium for Advanced Simulation of Light Water Reactors to...

  6. Fiscal Year 2012 Congressional Budget | Department of Energy

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

    More Documents & Publications FY 2012 Budget Request to Congress (Volume 3) Fiscal Year 2011 Congressional Budget Office of Energy Efficiency and Renewable Energy Overview...

  7. Home Performance with ENERGY STAR -- 10 Years of Continued Growth...

    Energy Savers [EERE]

    ENERGY STAR -- 10 Years of Continued Growth Home Performance with ENERGY STAR -- 10 Years of Continued Growth Provides an overview of the HPwES program, HPwES successes, and...

  8. Year-in-Review: 2011 Energy Infrastructure Events and Expansions...

    Energy Savers [EERE]

    1 Energy Infrastructure Events and Expansions (April 2012) Year-in-Review: 2011 Energy Infrastructure Events and Expansions (April 2012) The 2011 Year-in-Review (YIR) provides a...

  9. EERE's Fiscal Year 2004 Budget in Brief | Department of Energy

    Office of Environmental Management (EM)

    EERE's Fiscal Year 2004 Budget in Brief EERE's Fiscal Year 2004 Budget in Brief This document provides an overview of the U.S. Department of Energy Office of Energy Efficiency and...

  10. Ten Year Site Plans | Department of Energy

    Energy Savers [EERE]

    Ten Year Site Plans Ten Year Site Plans A Ten Year Site Plan (TYSP) is the essential planning document linking a site's real property requirements to its mission in support of the...

  11. Webtrends Archives by Fiscal YearEnergy Innovation Portal

    Office of Energy Efficiency and Renewable Energy (EERE)

    From the EERE Web Statistics Archive: Corporate sites, Webtrends archives for the Energy Innovation Portal by fiscal year.

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

    Energy Savers [EERE]

    R&D translates into improved performance and reduced costs for energy technologies. Motivation Technological forecasts, which plot the anticipated performance and costs of...

  13. Calendar Year 2007 Program Benefits for ENERGY STAR Labeled Products

    SciTech Connect (OSTI)

    Sanchez, Marla Christine; Homan, Gregory; Brown, Richard

    2008-10-31

    ENERGY STAR is a voluntary energy efficiency-labeling program operated jointly by the United States Department of Energy and the United States Environmental Protection Agency (US EPA). Since the program inception in 1992, ENERGY STAR has become a leading international brand for energy efficient products. 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 committed stakeholders. Through 2007, the program saved 7.1 Quads of primary energy and avoided 128 MtC equivalent. The forecast shows that the program is expected to save 21.2 Quads of primary energy and avoid 375 MtC equivalent over the period 2008-2015. The sensitivity analysis bounds the best estimate of carbon avoided between 84 MtC and 172 MtC (1993 to 2007) and between 243 MtC and 519 MtC (2008 to 2015).

  14. 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.

  15. California Baseline Energy Demands to 2050 for Advanced Energy Pathways

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    CEC (2005b) Energy demand forecast methods report.growth in California energy demands forecast in the baseline2006-2016: Staff energy demand forecast (Revised September

  16. Energy Production Over the Years | Department of Energy

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

    about how much energy it produces Pick an energy source Total Energy Produced Coal Crude Oil Natural Gas Total Renewable Energy Non-Biofuel Renewable Energy Biofuels Nuclear...

  17. Property:EnergyAccessYearInitiated | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to: navigation, search PropertyIsoOther JumpEndDateEnergyAccessYearInitiated

  18. The Role of Multimodel Climate Forecasts in Improving Water and Energy Management over the Tana River Basin, Kenya

    E-Print Network [OSTI]

    Arumugam, Sankar

    - logical ensembles are used in a reservoir model to allocate water for power generation by ensuring clima. Retrospective reservoir analysis shows that inflow forecasts developed from single GCM and multiple GCMs perform the single- model inflow forecasts by reducing uncertainty and the overconfidence of individual model

  19. Motivation Methods Model configuration Results Forecasting Summary & Outlook Retrieving direct and diffuse radiation with the

    E-Print Network [OSTI]

    Heinemann, Detlev

    Motivation Methods Model configuration Results Forecasting Summary & Outlook 1/ 14 Retrieving. 17, 2015 #12;Motivation Methods Model configuration Results Forecasting Summary & Outlook 2/ 14 Motivation Sky Imager based shortest-term solar irradiance forecasts for local solar energy applications

  20. State Energy Program Formula Grant Guidance Program Year 2007

    Broader source: Energy.gov [DOE]

    This document provides instructions to the states for program year 2007 about how they should administer their DOE grants provided through the State Energy Program.

  1. Northwest public utilities, BPA top five-year energy savings...

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

    Northwest-public-utilities-BPA-top-five-year-energy-savings-target Sign In About | Careers | Contact | Investors | bpa.gov Search News & Us Expand News & Us Projects &...

  2. Energy Smart Industrial: five years of enormous savings

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

    2.5 million kWh a year. JD Hisey, the plant's continuous improvement manager, says Energy Smart Industrial did more than just cut Fitesa's energy costs. "The new equipment reduced...

  3. Innovative Energy Research Grants Program Year 2 Request for Proposals

    E-Print Network [OSTI]

    Firestone, Jeremy

    of interdisciplinary energy researchers to collect preliminary data, to formulate and confirm hypotheses, inexpensive traditional energy reserves. Superimposed on this reality is the fact that world energy demandInnovative Energy Research Grants Program ­ Year 2 Request for Proposals Deadline: 5:00 PM, Monday

  4. Calendar Year 2003 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0

  5. National Renewable Energy Laboratory: 35 Years of Innovation (Brochure)

    SciTech Connect (OSTI)

    Not Available

    2012-04-01

    This brochure is an overview of NREL's innovations over the last 35 years. It includes the lab's history and a description of the laboratory of the future. The National Renewable Energy Laboratory (NREL) is the U.S. Department of Energy's (DOE) primary national laboratory for renewable energy and energy efficiency. NREL's work focuses on advancing renewable energy and energy efficiency technologies from concept to the commercial marketplace through industry partnerships. The Alliance for Sustainable Energy, LLC, a partnership between Battelle and MRIGlobal, manages NREL for DOE's Office of Energy Efficiency and Renewable Energy.

  6. Multi-Year Schedule | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy AEnergy Managing SwimmingMicrosoft Word1 2DepartmentPreparedEnergy2 presents

  7. Calendar Year 1995 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical26912345

  8. Calendar Year 1996 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical269123456

  9. Calendar Year 1997 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical2691234567

  10. Calendar Year 1998 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical26912345678

  11. Calendar Year 1999 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical269123456789

  12. Calendar Year 2000 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical2691234567890

  13. Calendar Year 2001 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical26912345678901

  14. Calendar Year 2002 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETOof Energy Office0 Categorical269123456789012

  15. Photovoltaic energy program overview: Fiscal year 1994

    SciTech Connect (OSTI)

    1995-03-01

    This is the 1994 overview for the Photovoltaic Energy Program. The topics of this overview include cooperative research projects to improve PV systems and develop pre-commercial prototypes of new PV products, expanding understanding of the fundamental mechanisms governing the formation and performance of PV materials, and helping US industry enhance its leadership position in the PV market.

  16. ENERGY & ENVIRONMENT DIVISION. ANNUAL REPORT FY 1980

    E-Print Network [OSTI]

    Authors, Various

    2010-01-01

    The results of the energy demand forecasts are presented inthe "low-growth" energy-demand forecasts that have recentlyprovide detailed forecasts of energy demand for the state's

  17. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

    McCarthy, Ryan; Yang, Christopher; Ogden, Joan M.

    2008-01-01

    2006-2016: Staff energy demand forecast (Revised SeptemberCEC (2005b) Energy demand forecast methods report.California energy demand 2003-2013 forecast. California

  18. Wind Energy Program overview, Fiscal year 1993

    SciTech Connect (OSTI)

    Not Available

    1994-05-01

    Wind energy research has two goals: (1) to gain a fundamental understanding of the interactions between wind and wind turbines; and (2) to develop the basic design tools required to develop advanced technologies. A primary objective of applied research activities is to develop sophisticated computer codes and integrate them into the design, testing, and evaluation of advanced components and systems, Computer models have become a necessary and integral part of developing new high-tech wind energy systems. A computer-based design strategy allows designers to model different configurations and explore new designs before building expensive hardware. DOE works closely with utilities and the wind industry in setting its applied research agenda. As soon as research findings become available, the national laboratories transfer the information to industry through workshops, conferences, and publications.

  19. Calendar Year Reports | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels Data Center HomeVehicle ReplacementStatesA CaseNovemberto one thatCalendar Year

  20. Text-Alternative Version LED Lighting Forecast

    Broader source: Energy.gov [DOE]

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

  1. Property:StartYear | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop Inc Jump to:Newberg,Energy LLCALLETE Inc dEA EISProject JumpRegion Jump

  2. Secretary Moniz's First Year | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment ofOffice|inWestMay 13,Discuss theDepartment of EnergyMarissa Newhall

  3. 60 Years of Computing | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar: Demonstration ofDepartment1of5Department of5Proposed -60

  4. 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.

  5. Forecasting and Capturing Emission Reductions Using Industrial Energy Management and Reporting Systems 

    E-Print Network [OSTI]

    Robinson, J.

    2010-01-01

    The Mandatory 2010 Green House Gas (GHG) Reporting Regulations and pending climate change legislation has increased interest in Energy Management and Reporting Systems (EMRS) as a means of both reducing and reporting GHG emissions. This paper...

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

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousMathematics And StatisticsProgramof-SA-02:Innovative Energy Apps BuiltSavers: Fireplaces EnergyApplications |

  7. Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01

    of Solar 2011, American Solar Energy Society, Raleigh, NC.Description and validation. Solar Energy, 73 (5), 307-317.forecast database. Solar Energy, Perez, R. , S. Kivalov, J.

  8. 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.

  9. Japan's Residential Energy Demand Outlook to 2030 Considering Energy Efficiency Standards "Top-Runner Approach"

    E-Print Network [OSTI]

    Komiyama, Ryoichi

    2008-01-01

    developed a residential energy demand forecast for 2030, theIn order to forecast energy service demand based on energy

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i n cEnergy (AZ,LocalEfficiency |<Technologies |CleanGeothermal

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment of EnergyResearchers atDayWhenBethany Sparn, M.S.Logo.jpg ThisworkingThe

  12. BESAC Subcommittee Workshop Report 20-Year Basic Energy Sciences

    E-Print Network [OSTI]

    Knowles, David William

    BESAC Subcommittee Workshop Report on 20-Year Basic Energy Sciences Facilities Roadmap Co Labs/Lucent (BESAC) Gabrielle Long, NIST (BESAC) Gerhard Materlik, Diamond Light Source Ltd. Les Price (U. of CA San Diego) 8:15am - 8:30am Introduction from BES Pat Dehmer, Basic Energy Sciences 8:30am

  13. Paths to fusion energy The next 30 years, the next 10 years

    E-Print Network [OSTI]

    roadmaps agree on Gme scale, differ in details Common views on an aggressive at a demonstraGon power plant in ~ 25 years · Most roadmaps agree on Gme scale, differ The fusion era A roadmap to fusion energy discussed in US present GA PPPL MIT

  14. STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES 2005 TO 2018 Mignon Marks Principal Author Mignon Marks Project Manager David Ashuckian Manager ELECTRICITY ANALYSIS OFFICE Sylvia Bender Acting Deputy Director ELECTRICITY SUPPLY DIVISION B.B. Blevins Executive Director

  15. Wind Speed Forecasting for Power System Operation 

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22

    In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system...

  16. Energy consumption and expenditure projections by income quintile on the basis of the Annual Energy Outlook 1997 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Allison, T.

    1998-03-01

    This report presents an analysis of the relative impacts of the base-case scenario used in the Annual Energy Outlook 1997, published by the US Department of Energy, Energy Information Administration, on income quintile groups. Projected energy consumption and expenditures, and projected energy expenditures as a share of income, for the period 1993 to 2015 are reported. Projected consumption of electricity, natural gas, distillate fuel, and liquefied petroleum gas over this period is also reported for each income group. 33 figs., 11 tabs.

  17. Distributed Energy Resource Optimization Using a Software as Service (SaaS) Approach at the University of California, Davis Campus

    E-Print Network [OSTI]

    Michael, Stadler

    2011-01-01

    18 Energy Demand Forecast for Week-Aheadon the PI system. Energy Demand Forecast for Week-AheadOnly load Figure 18 Forecast Energy Demand and Energy Demand

  18. ENERGY AND ENVIRONMENT DIVISION ANNUAL REPORT 1978

    E-Print Network [OSTI]

    Cairns, E.L.

    2011-01-01

    be incorporated in future energy demand forecasts and supplyshows two sets of energy demand forecasts for residential

  19. YEAR

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    4 YEAR 2012 Males 65 Females 29 YEAR 2012 SES 3 EJEK 5 EN 04 3 NN (Engineering) 21 NQ (ProfTechAdmin) 61 NU (TechAdmin Support) 1 YEAR 2012 American Indian Male 0 American...

  20. YEAR

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    4 YEAR 2011 Males 21 Females 23 YEAR 2011 SES 3 EJEK 1 EN 03 1 NN (Engineering) 3 NQ (ProfTechAdmin) 31 NU (TechAdmin Support) 5 YEAR 2011 American Indian Male 0 American...

  1. YEAR

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    92 YEAR 2012 Males 52 Females 40 YEAR 2012 SES 1 EJEK 7 EN 04 13 EN 03 1 NN (Engineering) 27 NQ (ProfTechAdmin) 38 NU (TechAdmin Support) 5 YEAR 2012 American Indian Male 0...

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    558 YEAR 2013 Males 512 Females 46 YEAR 2013 SES 2 EJEK 2 EN 04 1 NN (Engineering) 11 NQ (ProfTechAdmin) 220 NU (TechAdmin Support) 1 NV (Nuc Mat Courier) 321 YEAR 2013...

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    National Nuclear Security Administration (NNSA)

    of Employees 14 GENDER YEAR 2012 Males 9 Females 5 YEAR 2012 SES 2 EJEK 2 NN (Engineering) 4 NQ (ProfTechAdmin) 6 YEAR 2012 American Indian Male 0 American Indian Female 0...

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    National Nuclear Security Administration (NNSA)

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    5 YEAR 2014 Males 61 Females 24 PAY PLAN YEAR 2014 SES 1 EJEK 8 EN 04 22 NN (Engineering) 23 NQ (ProfTechAdmin) 28 NU (TechAdmin Support) 3 YEAR 2014 American Indian Alaska...

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  6. Energy Production Over the Years | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergyInformation FormManufacturingEnergy |October 15,ofPolicy

  7. Presentation: JCESR: One Year Later | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousMathematicsEnergyInterested PartiesBuilding energyDepartmentNEA-2011-03Presentation: DOEJCESR: One Year

  8. ENERGY ANALYSIS PROGRAM. CHAPTER FROM THE ENERGY AND ENVIRONMENT DIVISION ANNUAL REPORT 1978

    E-Print Network [OSTI]

    Various, Various,

    2011-01-01

    be incorporated in future energy demand forecasts and supplyshows two sets of energy demand forecasts for residential

  9. Webtrends Archives by Fiscal Year - Energy Basics | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LIST OFAMERICA'S FUTURE. regulators consumerWaste1ofFebruaryEnergyDepartmentEnergy

  10. Enduse Global Emissions Mitigation Scenarios (EGEMS): A New Generation of Energy Efficiency Policy Planning Models

    E-Print Network [OSTI]

    McNeil, Michael A.

    2010-01-01

    driver for the energy demand forecast. The basic assumptionglobal bottom-up energy demand forecasts, and a frameworkin modelling energy demand is to forecast activity. Activity

  11. 50 Years of Fusion Research Fusion Innovation Research and Energy

    E-Print Network [OSTI]

    , .... · Controlled Thermonuclear Fusion had great potential ­ Uncontrolled Thermonuclear fusion demonstrated in 19521 50 Years of Fusion Research Dale Meade Fusion Innovation Research and Energy® Princeton, NJ SOFE 2009 June 1, 2009 San Diego, CA 92101 #12;2 #12;2 #12;3 Fusion Prior to Geneva 1958 · A period of rapid

  12. "50" Years of Fusion Research Fusion Innovation Research and Energy

    E-Print Network [OSTI]

    Classified US Program on Controlled Thermonuclear Fusion (Project Sherwood) carried out until 1958 when"50" Years of Fusion Research Dale Meade Fusion Innovation Research and Energy® Princeton, NJ Fi P th SFusion Fire Powers the Sun "W d t if k f i k ""We need to see if we can make fusion work

  13. THE DESIRE TO ACQUIRE: FORECASTING THE EVOLUTION OF HOUSEHOLD

    E-Print Network [OSTI]

    THE DESIRE TO ACQUIRE: FORECASTING THE EVOLUTION OF HOUSEHOLD ENERGY SERVICES by Steven Groves BASc of Research Project: The Desire to Acquire: Forecasting the Evolution of Household Energy Services Report No, and gasoline. A fixed effects panel model was used to examine the relationship of demand for energy

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    Males 139 Females 88 YEAR 2012 SES 13 EX 1 EJEK 8 EN 05 23 EN 04 20 EN 03 2 NN (Engineering) 91 NQ (ProfTechAdmin) 62 NU (TechAdmin Support) 7 YEAR 2012 American Indian...

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  16. YEAR

    National Nuclear Security Administration (NNSA)

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  17. YEAR

    National Nuclear Security Administration (NNSA)

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  18. YEAR

    National Nuclear Security Administration (NNSA)

    7 YEAR 2012 Males 64 Females 33 YEAR 2012 SES 2 EJEK 3 EN 05 1 EN 04 30 EN 03 1 NN (Engineering) 26 NQ (ProfTechAdmin) 32 NU (TechAdmin Support) 2 YEAR 2012 American Indian...

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    National Nuclear Security Administration (NNSA)

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  20. YEAR

    National Nuclear Security Administration (NNSA)

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    National Nuclear Security Administration (NNSA)

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    National Nuclear Security Administration (NNSA)

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    National Nuclear Security Administration (NNSA)

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  5. 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...

  6. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    2.1.2 European Solar Radiation Atlas (ESRA)2.4 Evaluation of Solar Forecasting . . . . . . . . .2.4.1 Solar Variability . . . . . . . . . . . . .

  7. Forecasting Water Quality & Biodiversity

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

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability Platform Review Principle Investigator: Dr. Henriette I. Jager Organization: Oak Ridge National...

  8. US Energy Production over the Years Data | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment| Department of Energy Office ofProductionPresidenton

  9. Twenty years of energy policy: What should we have learned?

    SciTech Connect (OSTI)

    Greene, D.L. [Oak Ridge National Lab., TN (United States). Center for Transportation Analysis

    1994-07-01

    This report examines the past twenty years of energy market events and energy policies to determine what may be useful for the future. The author focuses on two important lessons that should have been learned but which the author feels have been seriously misunderstood. The first is that oil price shocks were a very big and very real problem for oil importing countries, a problem the has not gone away. The second is that automobile fuel economy regulation has worked and worked effectively to reduce oil consumption and the externalities associated with it, and can still work effectively in the future.

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    National Nuclear Security Administration (NNSA)

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  11. YEAR

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    9 Females 24 PAY PLAN YEAR 2014 SES 1 EJEK 4 EN 05 3 EN 04 22 EN 03 8 NN (Engineering) 15 NQ (ProfTechAdmin) 27 NU (TechAdmin Support) 3 YEAR 2014 American Indian Alaska Native...

  12. YEAR

    National Nuclear Security Administration (NNSA)

    8 Females 25 PAY PLAN YEAR 2014 SES 1 EJEK 3 EN 05 1 EN 04 25 EN 03 1 NN (Engineering) 25 NQ (ProfTechAdmin) 25 NU (TechAdmin Support) 2 YEAR 2014 American Indian Alaska Native...

  13. 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.

  14. Forecasting Hot Water Consumption in Residential Houses

    E-Print Network [OSTI]

    MacDonald, Mark

    and technological advancement in energy-intensive applications are causing fast electric energy consumption growth and consumption of electricity [8], as long as there is no significant correlation between intermittent energyArticle Forecasting Hot Water Consumption in Residential Houses Linas Gelazanskas * and Kelum A

  15. GOES Aviation Products Aviation Weather Forecasting

    E-Print Network [OSTI]

    Kuligowski, Bob

    GOES Aviation Products · The GOES aviation forecast products are based on energy measured in different characteristics #12;GOES Aviation Products Quiz · What is a geostationary satellite? · What generates energy received by the satellite in the visible band? · What generates energy received by the satellite

  16. Fiscal Year 2014 - Conference Reporting Activities | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof Energy Services » ProgramPolicySenate Appropriations CommitteeFiscal Year

  17. Fuel Price Forecasts INTRODUCTION

    E-Print Network [OSTI]

    Fuel Price Forecasts INTRODUCTION Fuel prices affect electricity planning in two primary ways and water heating, and other end-uses as well. Fuel prices also influence electricity supply and price turbines. This second effect is the primary use of the fuel price forecast for the Council's Fifth Power

  18. Weather Forecasting Spring 2014

    E-Print Network [OSTI]

    Hennon, Christopher C.

    ATMS 350 Weather Forecasting Spring 2014 Professor : Dr. Chris Hennon Office : RRO 236C Phone : 232 of atmospheric physics and the ability to include this understanding into modern numerical weather prediction agencies, forecast tools, numerical weather prediction models, model output statistics, ensemble

  19. Forecasting 65+ travel : an integration of cohort analysis and travel demand modeling

    E-Print Network [OSTI]

    Bush, Sarah, 1973-

    2003-01-01

    Over the next 30 years, the Boomers will double the 65+ population in the United States and comprise a new generation of older Americans. This study forecasts the aging Boomers' travel. Previous efforts to forecast 65+ ...

  20. Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling

    SciTech Connect (OSTI)

    Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao

    2011-09-29

    This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way.

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

    E-Print Network [OSTI]

    Homan, Gregory K

    2011-01-01

    Energy. 1996a. Annual energy outlook 1996 with projectionsEnergy. 1996b. Annual energy outlook 1997 with projectionsof Energy. 1997. Annual energy outlook 1998 with projections

  2. Calendar Year 2007 Program Benefits for ENERGY STAR Labeled Products

    E-Print Network [OSTI]

    Sanchez, Marla Christine

    2008-01-01

    Energy. 1996a. Annual energy outlook 1996 with projectionsEnergy. 1996b. Annual energy outlook 1997 with projectionsof Energy. 1997. Annual energy outlook 1998 with projections

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

    E-Print Network [OSTI]

    Homan, GregoryK

    2010-01-01

    Energy. 1996a. Annual energy outlook 1996 with projectionsEnergy. 1996b. Annual energy outlook 1997 with projectionsof Energy. 1997. Annual energy outlook 1998 with projections

  4. Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa

    E-Print Network [OSTI]

    Revised 1997 Retail Electricity Price Forecast March 1998 Principal Author: Ben Arikawa Electricity 1997 FORE08.DOC Page 1 CALIFORNIA ENERGY COMMISSION ELECTRICITY ANALYSIS OFFICE REVISED 1997 RETAIL ELECTRICITY PRICE FORECAST Introduction The Electricity Analysis Office of the California Energy Commission

  5. Forecasting wind speed financial return

    E-Print Network [OSTI]

    D'Amico, Guglielmo; Prattico, Flavio

    2013-01-01

    The prediction of wind speed is very important when dealing with the production of energy through wind turbines. In this paper, we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model that has been shown to be able to reproduce accurately the statistical behavior of wind speed. The model is used to forecast, one step ahead, wind speed. In order to check the validity of the model we show, as indicator of goodness, the root mean square error and mean absolute error between real data and predicted ones. We also compare our forecasting results with those of a persistence model. At last, we show an application of the model to predict financial indicators like the Internal Rate of Return, Duration and Convexity.

  6. The addition of a US Rare Earth Element (REE) supply-demand model improves the characterization and scope of the United States Department of Energy's effort to forecast US REE Supply and Demand

    E-Print Network [OSTI]

    Mancco, Richard

    2012-01-01

    This paper presents the development of a new US Rare Earth Element (REE) Supply-Demand Model for the explicit forecast of US REE supply and demand in the 2010 to 2025 time period. In the 2010 Department of Energy (DOE) ...

  7. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed

    E-Print Network [OSTI]

    2011-01-01

    solener.2011.02.014, Solar Energy. Lave, M. , Kleissl, J. ,smoothing. Submitted to Solar Energy. Linke, F. , 1922.24th European Photovoltaic Solar Energy Conference, Hamburg,

  8. YEAR

    National Nuclear Security Administration (NNSA)

    -9.09% YEAR 2012 2013 SES 1 1 0.00% EN 05 1 1 0.00% EN 04 11 11 0.00% NN (Engineering) 8 8 0.00% NQ (ProfTechAdmin) 17 14 -17.65% NU (TechAdmin Support) 2 2...

  9. YEAR

    National Nuclear Security Administration (NNSA)

    Females 863 YEAR 2013 SES 102 EX 3 SL 1 EJEK 89 EN 05 41 EN 04 170 EN 03 18 NN (Engineering) 448 NQ (ProfTechAdmin) 1249 NU (TechAdmin Support) 76 NV (Nuc Mat Courier) 321...

  10. YEAR

    National Nuclear Security Administration (NNSA)

    Females 942 YEAR 2012 SES 108 EX 4 SL 1 EJEK 96 EN 05 45 EN 04 196 EN 03 20 NN (Engineering) 452 NQ (ProfTechAdmin) 1291 NU (TechAdmin Support) 106 NV (Nuc Mat Courier) 335...

  11. YEAR

    National Nuclear Security Administration (NNSA)

    YEAR 2012 2013 SES 2 1 -50.00% EN 05 0 1 100.00% EN 04 4 4 0.00% NN (Engineering) 13 12 -7.69% NQ (ProfTechAdmin) 13 9 -30.77% NU (TechAdmin Support) 1 1...

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

    E-Print Network [OSTI]

    Homan, Gregory K

    2011-01-01

    and Sustainability, Renewable Energies Unit. Ispra, Italy.of Energy Efficiency and Renewable Energy. Washington, D. C.of Energy Efficiency and Renewable Energy. Washington, D. C.

  13. Short-Term Load Forecasting at the Local Level using Smart Meter Data

    E-Print Network [OSTI]

    Tronci, Enrico

    ]; electric vehicle integration [8]; and microgrid and virtual power plant applications [7], [11]. In addition, forecast uncertainty, power demand. I. INTRODUCTION Short-Term Load Forecasting (STLF) is the forecasting is considered to be critical for power system operation, particularly for energy balancing, energy market

  14. Solar irradiance forecasting at multiple time horizons and novel methods to evaluate uncertainty

    E-Print Network [OSTI]

    Marquez, Ricardo

    2012-01-01

    and forecasting of solar radiation data: a review. Int. J.beam and global solar radiation data. Solar Energy , 81:768–forecasting of solar radiation data: a review. International

  15. Property:Building/YearConstruction1 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceIIInformationEnergy InformationInformation Total JumpYearConstruction1

  16. One Year Anniversary, Office of the Ombudsman | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousMathematicsEnergyInterested Parties -DepartmentAvailableHighOffice ofProject |(FebruaryOneOneOne Year

  17. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed

    E-Print Network [OSTI]

    2011-01-01

    from meteorological satellite data. Solar Energy 37, 31–39.16 independent data banks. Solar Energy 80, 468– 478.

  18. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed

    E-Print Network [OSTI]

    2011-01-01

    solar irradiation in Brazil, Solar Energy, 68, 91- 107, ISSNmaps for Brazil under SWERA project, Solar Energy, 81, 517-

  19. Draft for Public Comment Appendix A. Demand Forecast

    E-Print Network [OSTI]

    in the forecast of electricity consumption for those years has been less than one half of a percent. Figure A-1 forecast of electricity demand is a required component of the Council's Northwest Regional Conservation and Electric Power Plan.1 Understanding growth in electricity demand is, of course, crucial to determining

  20. 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...

  1. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3 6370-Rev.National26 YEAR

  2. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3 6370-Rev.National26 YEAR93

  3. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3 6370-Rev.National26 YEAR93

  4. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3 6370-Rev.National26 YEAR9374

  5. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3 6370-Rev.National268 YEAR

  6. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3 6370-Rev.National268 YEAR17

  7. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3 6370-Rev.National268255 YEAR

  8. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3446 YEAR 2014 Males 1626

  9. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3446 YEAR 2014 Males 16268

  10. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3446 YEAR 2014 Males 16268563

  11. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3446 YEAR 2014 Males 162685638

  12. YEAR

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4)9 Federal RegisterStorm1 3446 YEAR 2014 Males

  13. 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.

  14. Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed

    E-Print Network [OSTI]

    2011-01-01

    smoothing. Submitted to Solar Energy. Linke, F. , 1922.24th European Photovoltaic Solar Energy Conference, Hamburg,solener.2011.02.014, Solar Energy. Lave, M. , Kleissl, J. ,

  15. Forecasting Hot Water Consumption in Dwellings Using Artifitial Neural Networks

    E-Print Network [OSTI]

    MacDonald, Mark

    electricity consumption in time. This paper investigates the ability on Artificial Neural Networks to predict shift electric energy. Keywords--Hot Water Consumption; Forecasting; Artifitial Neural Networks; SmartForecasting Hot Water Consumption in Dwellings Using Artifitial Neural Networks Linas Gelazanskas

  16. PROCEEDINGS OF THE WORKSHOP ON NATIONAL/REGIONAL ENERGY-ENVIRONMENTAL MODELING CONCEPTS, MAY 30 - JUNE 1, 1979

    E-Print Network [OSTI]

    Ritschard, R.L.

    2010-01-01

    system with an energy demand forecast. T. R. Lakshmanan,economic and total energy demand forecasts. with basic

  17. Tribes Provide Input on 10-Year Plan for Renewable Energy in...

    Office of Environmental Management (EM)

    Tribes Provide Input on 10-Year Plan for Renewable Energy in the Arctic Region Tribes Provide Input on 10-Year Plan for Renewable Energy in the Arctic Region June 2, 2015 - 1:56pm...

  18. Resolving to Make Earth Day Last All Year | Department of Energy

    Energy Savers [EERE]

    new furnace. Or maybe you liked Chris' idea, and you resolved do whatever you could to save energy and money this year. When we talk about saving energy throughout the year,...

  19. NREL's Record-Setting Year Highlights Clean Energy Innovation...

    Office of Environmental Management (EM)

    David Danielson, the Energy Department's Assistant Secretary for Energy Efficiency and Renewable Energy (EERE), speaks at the annual Innovation and Technology Transfer Awards on...

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

    E-Print Network [OSTI]

    Homan, Gregory K

    2011-01-01

    of regional, national and international energy programsEnergy Commission Public Interest Research Program (PIER) by Lawrence Berkeley NationalEnergy Star Office Equipment Program. Lawrence Berkeley National

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

    E-Print Network [OSTI]

    Homan, GregoryK

    2010-01-01

    of regional, national and international energy programsEnergy Commission Public Interest Research Program (PIER) by Lawrence Berkeley NationalEnergy Star Office Equipment Program. Lawrence Berkeley National

  2. Calendar Year 2007 Program Benefits for ENERGY STAR Labeled Products

    E-Print Network [OSTI]

    Sanchez, Marla Christine

    2008-01-01

    using the energy consumption test data collected by theon manufacturer energy consumption test data for qualifiedTo date, energy consumption test data for non-qualified

  3. 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.

  4. Integration of Renewable Distributed Energy Resources into Microgrids

    E-Print Network [OSTI]

    Huang, Rui

    2015-01-01

    Francisco, “Forecast of hourly average wind speed with armawind turbine with energy storage management, the development of forecast-

  5. Calendar Year 2007 Program Benefits for ENERGY STAR Labeled Products

    E-Print Network [OSTI]

    Sanchez, Marla Christine

    2008-01-01

    of regional, national, and international energy programsEnergy Commission Public Interest Research Program (PIER) by Lawrence Berkeley Nationalnational and international stakeholders can use these results to evaluate energy efficiency opportunities associated with the ENERGY STAR program.

  6. 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.

  7. FTCP Annual Plan - Fiscal Year 2005 | Department of Energy

    Office of Environmental Management (EM)

    FTCP Annual Plan - Fiscal Year 2005 FTCP Annual Plan - Fiscal Year 2005 The objective of the Federal Technical Capability Program is to recruit, deploy, develop, and retain Federal...

  8. Improving automotive battery sales forecast

    E-Print Network [OSTI]

    Bulusu, Vinod

    2015-01-01

    Improvement in sales forecasting allows firms not only to respond quickly to customers' needs but also to reduce inventory costs, ultimately increasing their profits. Sales forecasts have been studied extensively to improve ...

  9. Appendix A: Fuel Price Forecast Introduction..................................................................................................................................... 1

    E-Print Network [OSTI]

    Appendix A: Fuel Price Forecast Introduction................................................................................................................................. 3 Price Forecasts ............................................................................................................................ 5 U.S. Natural Gas Commodity Prices

  10. From Energy Audits to Home Performance: 30 Years of Articles in Home Energy Magazine

    SciTech Connect (OSTI)

    Meier, Alan

    2014-08-11

    Home Energy Magazine has been publishing articles about residential energy efficiency for 30 years. Its goal has been to disseminate technically reliable and neutral information to the practitioners, that is, professionals in the business of home energy efficiency. The articles, editorials, letters, and advertisements are a kind of window on the evolution of energy conservation technologies, policies, and organizations. Initially, the focus was on audits and simple retrofits, such as weatherstripping and insulation. Instrumentation was sparse sometimes limited to a ruler to measure depth of attic insulation and a blower door was exotic. CFLs were heavy, awkward bulbs which might, or might not, fit in a fixture. Saving air conditioning energy was not a priority. Solar energy was only for the most adventurous. Thirty years on, the technologies and business have moved beyond just insulating attics to the larger challenge of delivering home performance and achieving zero net energy. This shift reflects the success in reducing space heating energy and the need to create a profitable industry by providing more services. The leading edge of the residential energy services market is becoming much more sophisticated, offering both efficiency and solar systems. The challenge is to continue providing relevant and reliable information in a transformed industry and a revolutionized media landscape.

  11. 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.

  12. Consensus Coal Production Forecast for

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    Consensus Coal Production Forecast for West Virginia 2009-2030 Prepared for the West Virginia Summary 1 Recent Developments 2 Consensus Coal Production Forecast for West Virginia 10 Risks References 27 #12;W.Va. Consensus Coal Forecast Update 2009 iii List of Tables 1. W.Va. Coal Production

  13. 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.

  14. Publication of "Year in Review 2010: Energy Infrastructure Events...

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

    changes in: U.S. energy infrastructure Energy flows within and into the United States Oil and gas exploration, production, movement, and demand worldwide Published daily,...

  15. Home Performance with ENERGY STAR -- 10 Years of Continued Growth...

    Energy Savers [EERE]

    perception by being an advocate for energy efficiency and renewable energy * Reduce air pollution and greenhouse gas emissions Brand, Platform, Network, and DOE Resources 12...

  16. New Year, New Certification Opportunities for Home Energy Workers...

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

    Virginia. The Energy Department has developed a new certification program for quality control inspectors, energy auditors, crew leaders, and retrofit installer...

  17. New Year, New Certification Opportunities for Home Energy Workers...

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

    cover the four most common home energy and weatherization job classifications: quality control inspectors, energy auditors, crew leaders, and retrofit installer...

  18. Fiscal Year 2011 Congressional Budget | Department of Energy

    Office of Environmental Management (EM)

    More Documents & Publications Office of Energy Efficiency and Renewable Energy Overview Appropriation Summary by Program for FY 2011 Congressional Budget IT...

  19. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    Rutledge, Steven

    AMERICAN METEOROLOGICAL SOCIETY Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary microbursts than in many previously documented microbursts. Alignment of Doppler radar data to reports of wind-related damage to electrical power infrastructure in Phoenix allowed a comparison of microburst wind damage

  20. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

    AMERICAN METEOROLOGICAL SOCIETY Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary and interpretation of information from National Weather Service watches and warnings by10 decision makers such an outlier to the regional severe weather climatology. An analysis of the synoptic and13 mesoscale

  1. 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.

  2. Program Year 2013 State Energy Program Formula Grant Guidance

    Broader source: Energy.gov [DOE]

    This document contains State Energy Program Formula grant guidance for 2013, effective April 16, 2013.

  3. I strongly urge that the forecasts recognize the high oil prices and gas prices experienced in 2008 and not treat them as an unusual occurrence in the next 20 years. In the long term with cap and

    E-Print Network [OSTI]

    I strongly urge that the forecasts recognize the high oil prices and gas prices experienced in 2008 and the development of carbon capture and storage applied to new coal fired generating stations, gas prices will only that biofuels are made from food crops, the discussion is correct that fertilizer demands will drive gas prices

  4. Energy policies and their consequences after 25 years

    E-Print Network [OSTI]

    Joskow, Paul L.

    2003-01-01

    Hans Landsberg and Sam Schurr each led research teams that produced two important energy futures policy studies that were published in 1979. The conclusions, policy recommendations, and energy demand, supply, and price ...

  5. Marine and Hydrokinetic Energy Projects, Fiscal Years 2008-2014

    SciTech Connect (OSTI)

    2014-03-24

    This report covers the Wind and Water Power Technologies Office's Marine and Hydrokinetic Energy Projects from 2008 to 2014.

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

    E-Print Network [OSTI]

    Homan, GregoryK

    2010-01-01

    market shares are an industry estimate provided by Honeywell 3) ENERGY STAR exit signs, traffic signals, and transformers

  7. Calendar Year 2007 Program Benefits for ENERGY STAR Labeled Products

    E-Print Network [OSTI]

    Sanchez, Marla Christine

    2008-01-01

    market shares are an industry estimate provided by Honeywell 3) ENERGY STAR exit signs, traffic signals, and transformers

  8. Offshore Wind Energy Projects, Fiscal Years 2006–2014

    SciTech Connect (OSTI)

    2014-04-01

    This report covers the Wind and Water Power Technologies Office's Offshore Wind Energy Projects from 2006 to 2014.

  9. Calendar Year 2007 Program Benefits for U.S. EPA Energy Star Labeled Products: Expanded Methodology

    SciTech Connect (OSTI)

    Sanchez, Marla; Homan, Gregory; Lai, Judy; Brown, Richard

    2009-09-24

    This report provides a top-level summary of national savings achieved by the Energy Star voluntary product labeling program. To best quantify and analyze savings for all products, we developed a bottom-up product-based model. Each Energy Star product type is characterized by product-specific inputs that result in a product savings estimate. Our results show that through 2007, U.S. EPA Energy Star labeled products saved 5.5 Quads of primary energy and avoided 100 MtC of emissions. Although Energy Star-labeled products encompass over forty product types, only five of those product types accounted for 65percent of all Energy Star carbon reductions achieved to date, including (listed in order of savings magnitude)monitors, printers, residential light fixtures, televisions, and furnaces. The forecast shows that U.S. EPA?s program is expected to save 12.2 Quads of primary energy and avoid 215 MtC of emissions over the period of 2008?2015.

  10. Assessment and added value estimation of an ensemble approach with a focus on global radiation forecasts

    E-Print Network [OSTI]

    Bouallegue, Zied Ben

    2015-01-01

    The assessment of the high-resolution ensemble weather prediction system COSMO-DE-EPS is achieved with the perspective of using it for renewable energy applications. The performance of the ensemble forecast is explored focusing on global radiation, the main weather variable affecting solar power production, and on quantile forecasts, key probabilistic products for the energy sector. First, the ability of the ensemble system to capture and resolve the observation variability is assessed. Secondly, the potential benefit of the ensemble forecasting strategy compared to a single forecast approach is quantitatively estimated. A new metric called ensemble added value is proposed, aiming at a fair comparison of an ensemble forecast with a single forecast, when optimized to the users' needs. Hourly mean forecasts are verified against pyranometer measurements over verification periods covering 2013. The results show in particular that the added value of the ensemble approach is season-dependent and increases with the ...

  11. Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast

    E-Print Network [OSTI]

    Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast Energy Demand .............................................................. 23 Electricity Demand Growth in the West............................................................................................................................... 28 Estimating Electricity Demand in Data Centers

  12. Calendar Year 2007 Program Benefits for U.S. EPA Energy Star Labeled Products: Expanded Methodology

    E-Print Network [OSTI]

    Sanchez, Marla

    2010-01-01

    energy price in year t (in $/kWh or $/MBtu) C t = The carbonenergy price in year t (in $/kWh or $/MBtu) C t = The carbon

  13. 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.

  14. Trends in energy use in commercial buildings -- Sixteen years of EIA's commercial buildings energy consumption survey

    SciTech Connect (OSTI)

    Davis, J.; Swenson, A.

    1998-07-01

    The Commercial Buildings Energy Consumption Survey (CBECS) collects basic statistical information on energy consumption and energy-related characteristics of commercial buildings in the US. The first CBECS was conducted in 1979 and the most recent was completed in 1995. Over that period, the number of commercial bindings and total amount of floorspace increased, total consumption remained flat, and total energy intensity declined. By 1995, there were 4.6 million commercial buildings and 58.8 billion square feet of floorspace. The buildings consumed a total of 5.3 quadrillion Btu (site energy), with a total intensity of 90.5 thousand Btu per square foot per year. Electricity consumption exceeded natural gas consumption (2.6 quadrillion and 1.9 quadrillion Btu, respectively). In 1995, the two major users of energy were space heating (1.7 quadrillion Btu) and lighting (1.2 quadrillion Btu). Over the period 1979 to 1995, natural gas intensity declined from 71.4 thousand to 51.0 thousand Btu per square foot per year. Electricity intensity did not show a similar decline (44.2 thousand Btu per square foot in 1979 and 45.7 thousand Btu per square foot in 1995). Two types of commercial buildings, office buildings and mercantile and service buildings, were the largest consumers of energy in 1995 (2.0 quadrillion Btu, 38% of total consumption). Three building types, health care, food service, and food sales, had significantly higher energy intensities. Buildings constructed since 1970 accounted for half of total consumption and a majority (59%) of total electricity consumption.

  15. High energy particle colliders: past 20 years, next 20 years and beyond

    SciTech Connect (OSTI)

    Shiltsev, Vladimir D.; /Fermilab

    2012-04-01

    Particle colliders for high energy physics have been in the forefront of scientific discoveries for more than half a century. The accelerator technology of the collider has progressed immensely, while the beam energy, luminosity, facility size and the cost have grown by several orders of magnitude. The method of colliding beams has not fully exhausted its potential but its pace of progress has greatly slowed down. In this paper we very briefly review the method and the history of colliders, discuss in detail the developments over the past two decades and the directions of the R and D toward near future colliders which are currently being explored. Finally, we make an attempt to look beyond the current horizon and outline the changes in the paradigm required for the next breakthroughs.

  16. Minorities in Energy-Year One Anniversary Forum | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousMathematicsEnergyInterested Parties - WAPAEnergy6-09.doc MicrosoftMinisterialMinorities in Energy-Year

  17. Webtrends Archives by Fiscal Year - Tribal Energy Program | Department...

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

    Vacancies Internships & Fellowships Related Links FAQs Contact Us Offices From the EERE Web Statistics Archive: Corporate sites, Webtrends archives for the Tribal Energy Program...

  18. 10 Years after the 2003 Northeast Blackout | Department of Energy

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

    years ago today, large portions of the Midwest and Northeast United States and into Canada went dark. The cascading event, which started shortly after 4:00 PM on August 14,...

  19. Table 1. State energy-related carbon dioxide emissions by year...

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

    State energy-related carbon dioxide emissions by year (2000-2011)" "million metric tons of carbon dioxide" ,,,"Change" ,,,"2000 to 2011" "State",2000,2001,2002,...

  20. J2.6 A SPATIAL DATA MINING APPROACH FOR VERIFICATION AND UNDERSTANDING OF ENSEMBLE PRECIPITATION FORECASTING

    E-Print Network [OSTI]

    Gruenwald, Le

    FORECASTING Xuechao Yu* 1,2 and Ming Xue 2,3 1 NOAA/NWS/WDTB Cooperative Institute for Mesoscale is placed on meso- scale ensemble forecasting in recent years [e.g., the Storm and Mesoscale Ensemble complicated for mesoscale quantitative precipitation forecast (QPF), since QPF is a discontinuous field. Em

  1. Energy development and demonstration program: year-end report

    SciTech Connect (OSTI)

    Albright, B.

    1981-07-01

    The purpose of the Energy Development and Demonstration Program is to support projects for the development and demonstration of alternative energy sources available in Wisconsin and of energy conservation methods appropriate for Wisconsin. In September, eleven projects were selected for support in the program. Programs proposed include: monitoring an earth-sheltered dwelling; demonstrating a residential wood pellet eating system; energy management and control system on a dairy farm; three wind energy demonstrations; live-in solar collector; timber utilization project; continuous burn, induced-draft, condensing, modulating natural gas furnace; passive solar prototype for commercial-scale greenhouse; and high performance heat exchange device applied to fuel alcohol distillation processing. The benefits of the projects are briefly summarized. The location of the projects in Wisconsin is identified.

  2. Energy Management: A Corporate Objective in the Five-Year Business Plan 

    E-Print Network [OSTI]

    Mulhern, T. A.

    1982-01-01

    This paper presents the Energy Management Program developed by the Western Electric Company. The program includes managerial accountabilities; administrative and technical activities; and a most recently implemented comprehensive five-year energy...

  3. Sandia Energy - "Solid-state Lighting: 'The case' 10 Years After...

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

    "Solid-state Lighting: 'The case' 10 Years After and Future Prospects" paper will be translated in Chinese Home Energy Solid-State Lighting EC Energy Efficiency News & Events...

  4. 10 Ways to Save Money and Energy in the New Year | Department...

    Energy Savers [EERE]

    save money and energy throughout the New Year. So here goes, my personal top 10 ways to save money and energy in 2011 Research alternatives for saving money on fuel, decreasing...

  5. 10 Ways to Save Money and Energy in the New Year | Department...

    Energy Savers [EERE]

    save money and energy throughout the New Year. So here goes, my personal top 10 ways to save money and energy in 2011. Research alternatives for saving money on fuel, decreasing...

  6. Proceedings: Twenty years of energy policy: Looking toward the twenty-first century

    SciTech Connect (OSTI)

    Not Available

    1992-12-31

    In 1973, immediately following the Arab Oil Embargo, the Energy Resources Center, University of Illinois at Chicago initiated an innovative annual public service program called the Illinois Energy Conference. The objective was to provide a public forum each year to address an energy or environmental issue critical to the state, region and nation. Twenty years have passed since that inaugural program, and during that period we have covered a broad spectrum of issues including energy conservation nuclear power, Illinois coal, energy policy options, natural gas, alternative fuels, new energy technologies, utility deregulation and the National Energy Strategy.

  7. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27

    This paper presents a nonparametric diffusion modeling approach for forecasting partially observed noisy turbulent modes. The proposed forecast model uses a basis of smooth functions (constructed with the diffusion maps algorithm) to represent probability densities, so that the forecast model becomes a linear map in this basis. We estimate this linear map by exploiting a previously established rigorous connection between the discrete time shift map and the semi-group solution associated to the backward Kolmogorov equation. In order to smooth the noisy data, we apply diffusion maps to a delay embedding of the noisy data, which also helps to account for the interactions between the observed and unobserved modes. We show that this delay embedding biases the geometry of the data in a way which extracts the most predictable component of the dynamics. The resulting model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and in the observation noise limit. We will show numerical examples on a wide-range of well-studied turbulent modes, including the Fourier modes of the energy conserving Truncated Burgers-Hopf (TBH) model, the Lorenz-96 model in weakly chaotic to fully turbulent regimes, and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. In these examples, forecasting skills of the nonparametric diffusion model are compared to a wide-range of stochastic parametric modeling approaches, which account for the nonlinear interactions between the observed and unobserved modes with white and colored noises.

  8. Energy Division progress report, fiscal years 1994--1995

    SciTech Connect (OSTI)

    Moser, C.I. [ed.

    1996-06-01

    At ORNL, the Energy Division`s mission is to provide innovative solutions to energy and related issues of national and global importance through interdisciplinary research and development. Its goals and accomplishments are described in this progress report for FY 1994 and FY 1995. The Division`s expenditures in FY 1995 totaled 44.9 million. Sixty percent of the divisions work was supported by the US DOE. Other significant sponsors include the US DOT, the US DOD, other federal agencies, and some private organizations. The Division`s programmatic activities cover three main areas: (1) analysis and assessment, (2) transportation systems, and (3) energy use and delivery technologies. Analysis and assessment activities involve energy and resource analysis, preparation of environmental assessments and impact statements, and impact statements, research on emergency preparedness, analysis of energy and environmental needs in developing countries, and transportation analysis. Transportation systems research seeks to improve the quality of both civilian and military transportation efforts. Energy use and delivery technologies focus on building equipment, building envelopes, (walls, roofs, attics, and materials), improvement of energy efficiency in buildings, and electric power systems.

  9. 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.

  10. Fact Sheets Fiscal Year 2017 Project Prioritization | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i n cEnergyNaturaldefinesMay 4,of Energy mission behind

  11. Classification CommuniQué - Year: 2013 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i n c i p aDepartment of Energy <ofEnergy Today,FAST

  12. Classification CommuniQué - Year: 2014 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i n c i p aDepartment of Energy <ofEnergy Today,FASTconsisting of the

  13. Classification CommuniQué - Year: 2015 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i n c i p aDepartment of Energy <ofEnergy Today,FASTconsisting of

  14. NSAR Ten Year Renewable Energy Plan - Integration Planning

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousMathematicsEnergyInterested Parties -Department of EnergyNEW1forEnergy NRECA's ExDepartment

  15. 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.

  16. VIDEO: Bringing This Year's Energy Pumpkins to Life | Department...

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

    to the test and carved some energy pumpkins of our own. In the video above, see a CFL, solar panels, an atom and a wind turbine come to life in spooky, candlelit time-lapse --...

  17. Federal Government's Energy Consumption Lowest in Almost 40 Years...

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

    the U.S. federal government continues to be one of the largest energy consumers in the world, its consumption has been steadily declining for nearly four decades, and now stands...

  18. Publication of "Year in Review 2010: Energy Infrastructure Events...

    Energy Savers [EERE]

    report provides a snapshot of newsworthy events and broad trends that shaped the U.S. energy outlook in 2010. More information is available on the Office of Electricity and...

  19. ENERGY STAR PortfolioManager Baseline Year Instructions

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum Based|DepartmentStatementof EnergyQuality'Lean' System09ENERGY STAR Guide

  20. ENERGY STAR PortfolioManager Current Year Instructions

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum Based|DepartmentStatementof EnergyQuality'Lean' System09ENERGY STAR

  1. 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.

  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 2005), we once again find that the AEO 2006 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. In fact, the NYMEX-AEO 2006 reference case comparison yields by far the largest premium--$2.3/MMBtu levelized over five years--that we have seen over the last six years. In other words, on average, one would have had to pay $2.3/MMBtu more than the AEO 2006 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

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

  4. First Year Analysis of Industrial Energy Conservation in Texas A&M's Energy Analysis and Diagnostic Center Program 

    E-Print Network [OSTI]

    Grubb, M. K.; Heffington, W. M.

    1988-01-01

    Laboratory, Department of Mechanical Engineering, Texas A&M University Texas Governor's Energy Management Center Louisiana Department of Natural Resources Center for Energy and Mineral Resources, Texas A&M University FIRST YEAR ANALYSIS OF INDUSTRIAL ENERGY... audits, an average of seven ECOs per audit. The 109 recommendations are divided into four groups: Electrical Energy ECOs, Natural Gas ECOs, Non-Energy Saving ECOs, and Opportunity ECOs. This paper will briefly discuss the EADC method of energy auditing...

  5. Wind Power Forecasting

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power AdministrationRobust,Field-effectWorking WithTelecentricNCubicthe FOIA?ResourceMeasurement BuoyForecasting Sign

  6. Department of Energy's Fiscal Year 2014 Consolidated Financial Statements

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-in electricLaboratoryof Energy ElevenLG Refrigerator-Freezer Consolidated

  7. Energy Infrastructure Events and Expansions Year-in-Review 2010 |

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-inPPLfor Innovative Solar PowerTribesDepartment of EnergyDepartment of

  8. FY2012 Three Year Rolling Timeline | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-inPPLforLDRD Report to Congress MoreHyd rog enOffice of Nuclear EnergyFY 12

  9. Wind Farms through the Years | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Financing ToolInternationalReport FY2014 -Energy Costs by IncreasingWholeWind Energy WindFarms

  10. Fiscal Year 2010 Agency Financial Report | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i nA Guide to Tapping into Funding for EnergyOctoberProgram |

  11. Three-year Rolling Timeline | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsofProgram: Report15 MeetingDevelopmentDepartmentofThermostatsThisThree-year

  12. Utility-Scale Solar through the Years | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsofProgram: Report1538-1950 TimelineUtility-Scale Solar through the Years

  13. Urban Consortium Energy Task Force - Year 21 Final Report

    SciTech Connect (OSTI)

    2003-04-01

    The Urban Consortium Energy Task Force (UCETF), comprised of representatives of large cities and counties in the United States, is a subgroup of the Urban Consortium, an organization of the nation's largest cities and counties joined together to identify, develop and deploy innovative approaches and technological solutions to pressing urban issues.

  14. U.S. Energy Production Through the Years | Department of Energy

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

    about how much energy it produces Pick an energy source Total Energy Produced Coal Crude Oil Natural Gas Total Renewable Energy Non-Biofuel Renewable Energy Biofuels Nuclear...

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

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

  16. Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01

    and validation.   Solar Energy.   73:5, 307? Perez, R. , irradiance forecasts for solar energy applications based on using satellite data.   Solar Energy 67:1?3, 139?150.  

  17. Natural Gas Year-in-Review - Energy Information Administration

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet)DecadeYear Jan Feb Marthrough 1996) in155 13,348 47,873165,360 165,928

  18. Figure 2. Energy Consumption of Vehicles, Selected Survey Years

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet)DecadeYear Jan Feb Mar Apr May Jun20032,485,331Gas ProvedDec.12 13Cubic2 Figure

  19. Property:RenewableFuelStandard/Year | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EA EIS Report Url Jump to:ProgrammableYear Jump to: navigation, search This

  20. Six-Year Review of Covered Products | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LIST OF APPLICABLE DIRECTIVES Pursuant toPower WindDepartment ofSix-Year Review of

  1. Property:Building/YearConstruction2 | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceIIInformationEnergy InformationInformation Total

  2. Property:Buildings/PublicationYear | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceIIInformationEnergy

  3. Lasers, Electron Beams and New Years Resolutions | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankADVANCED MANUFACTURING OFFICE INDUSTRIALU.S.Leadership on Clean Energy |Department of

  4. FTCP Annual Report - Calendar Year 2008 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14, 2011 CX-006821:forEnergyADVANCEDTO:2

  5. New Year, New Certification Opportunities for Home Energy Workers |

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on DeliciousMathematicsEnergyInterested Parties -DepartmentAvailable for PublicDepartmentTindall HomesNew

  6. Fiscal Year 2013 President's Budget Request | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof Energy Services » ProgramPolicy

  7. Fiscal Year 2013 President's Budget Request | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof Energy Services » ProgramPolicySenate Appropriations Committee

  8. Price forecasting for notebook computers 

    E-Print Network [OSTI]

    Rutherford, Derek Paul

    1997-01-01

    of individual features are estimated. A time series analysis is used to forecast and can be used, for example, to forecast (1) notebook computer price at introduction, and (2) rate of price erosion for a notebook's life cycle. Results indicate that this approach...

  9. Multivariate Forecast Evaluation And Rationality Testing

    E-Print Network [OSTI]

    Komunjer, Ivana; OWYANG, MICHAEL

    2007-01-01

    Economy, 95(5), 1062—1088. MULTIVARIATE FORECASTS Chaudhuri,Notion of Quantiles for Multivariate Data,” Journal of thePress, United Kingdom. MULTIVARIATE FORECASTS Kirchgässner,

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2003-12-01

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

  11. Learning Energy Demand Domain Knowledge via Feature Transformation

    E-Print Network [OSTI]

    Povinelli, Richard J.

    -- Domain knowledge is an essential factor for forecasting energy demand. This paper introduces a method knowledge substantially improves energy demand forecasting accuracy. However, domain knowledge may differ. The first stage automatically captures energy demand forecasting domain knowledge through nonlinear

  12. Behavioral Aspects in Simulating the Future US Building Energy Demand

    E-Print Network [OSTI]

    Stadler, Michael

    2011-01-01

    Floor-space forecast to 2050 Gross demand for energy Macro-Floor-space forecast to 2050 Gross demand for energy Macro-Floor-space forecast to 2050 Gross demand for energy Macro-

  13. Energy Materials Coordinating Committee (EMaCC). Fiscal year 1994

    SciTech Connect (OSTI)

    1995-07-31

    The committee serves primarily to enhance coordination among the Department`s materials programs and to further effective use of materials expertise within the Department. This is accomplished through the exchange of budgetary and planning information among program managers and through technical meetings/workshops involving DOE and major contractors. The program descriptions consist of a funding summary for each Assistant Secretary office and the Office of Energy Research, and detailed project summaries with project goals and accomplishments. A FY 1994 budget summary table for each program is included. A directory and a keyword index is included at the end of this document.

  14. Energy Materials Coordinating Committee, fiscal year 1997. Annual technical report

    SciTech Connect (OSTI)

    1998-07-31

    The DOE Energy Materials Coordinating Committee (EMaCC) serves primarily to enhance coordination among the Department`s materials programs and to further effective use of materials expertise within the Department. These functions are accomplished through the exchange of budgetary and planning information among program managers and through technical meetings/workshops on selected topics involving both DOE and major contractors. In addition, EMaCC assists in obtaining materials-related inputs for both intra- and interagency compilations. This report summarizes EMaCC activities for FY 1997 and describes the materials research programs of various offices and divisions within the Department.

  15. WIPP Marks 12 Years of Operations | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal Gas &SCE-Sessions | DepartmentResidential Savings| Department ofof Energy do

  16. Webtrends Archives by Fiscal Year - News | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal Gas &SCE-Sessions | DepartmentResidentialJeannieEnergyofFrom the EERE Web

  17. Webtrends Archives by Fiscal Year - States | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal Gas &SCE-Sessions | DepartmentResidentialJeannieEnergyofFrom the EERE WebFrom

  18. Webtrends Archives by Fiscal Year - Tribal Energy Program | Department of

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal Gas &SCE-Sessions | DepartmentResidentialJeannieEnergyofFrom the EEREEnergy

  19. FTCP Annual Plan - Fiscal Year 2005 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-inPPLfor InnovativeProcessing22,673,list7.pdfFORD FORD FORD FORDHowElectricis

  20. Facility Representative of the Year Award | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-inPPLforLDRD Report to Congress MoreHyd rog enOffice of NuclearRepresentative

  1. Fiscal Year 2013 - Conference Reporting Activities | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-inPPLforLDRD Report to CongressAprilDepartment of

  2. NREL's Record-Setting Year Highlights Clean Energy Innovation and

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy AEnergy Managing SwimmingMicrosoft The

  3. Property:NrelPartnerYear | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop Inc Jump to:Newberg,Energy LLCALLETE Inc dEA EIS Report JumpNrelPartnerType

  4. New Year, New Certification Opportunities for Home Energy Workers |

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i nAand DOEDepartmentNew Jersey is homeAdvancedIncreasesFounded inDepartment

  5. Fiscal Year 2015 Annual Work Plan Update | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i nA Guide to Tapping into Funding for

  6. Fiscal Year 2017 Project Prioritization | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i nA Guide to Tapping into Funding forFY'17 Projects for the NNMCB to

  7. Thoughts on a Two-Year Race | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyThe U.S.Laclede GasEfficiency MaineAutoSecuritythomas.wheeler@hq.doe.gov

  8. 10 Years after the 2003 Northeast Blackout | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i n c i p a l De p u t y A s s i s t a n t S eOF 1121Dave Schmitz |Patricia A.

  9. Property:Building/YearConstruction | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceIIInformationEnergy InformationInformation Total Jump

  10. Property:Buildings/ModelYear | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceIIInformationEnergy InformationInformationModelXmlFile Jump

  11. FTCP Annual Plan - Fiscal Year 2002 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14, 2011 CX-006821:forEnergyADVANCEDTO:2 FTCP Annual

  12. FTCP Annual Plan - Fiscal Year 2003 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14, 2011 CX-006821:forEnergyADVANCEDTO:2 FTCP Annual3

  13. FTCP Annual Plan - Fiscal Year 2004 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14, 2011 CX-006821:forEnergyADVANCEDTO:2 FTCP

  14. FTCP Annual Report - Calendar Year 2007 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14, 2011 CX-006821:forEnergyADVANCEDTO:2 FTCPCalendar

  15. FTCP Annual Report - Fiscal Year 2002 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14, 2011 CX-006821:forEnergyADVANCEDTO:22 FTCP Annual

  16. FTCP Annual Report - Fiscal Year 2004 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14, 2011 CX-006821:forEnergyADVANCEDTO:22 FTCP Annual4

  17. Home Performance with ENERGY STAR -- 10 Years of Continued Growth! |

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12,ExecutiveFinancingR Walls - Buildingof Energy Score: ProgramDepartment of

  18. Fiscal Year 2012 ASCEM Annual Report | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14,EnergyFinancingWIPP |DepartmentOpening in Texas |2

  19. Fiscal Year 2013 ASCEM Annual Report | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14,EnergyFinancingWIPP |DepartmentOpening in Texas |23

  20. Fiscal Year 2014 ASCEM Status Report | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:FinancingPetroleum12, 2015Executive Order14,EnergyFinancingWIPP |DepartmentOpening in Texas

  1. Webtrends Archives by Fiscal Year - Kids Site | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LIST OFAMERICA'S FUTURE. regulatorsEnergy Information Center Webtrends Archives

  2. West Valley Accomplishments: Year in Review | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LIST OFAMERICA'S FUTURE. regulatorsEnergy InformationWest Coast Port

  3. Are You Attending Solar Decathlon This Year? | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment of Energy Facilities By E-mail: YouAbsorption heat FindFOIAAmanda

  4. Black Friday Savings All Year 'Round | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment of EnergyResearchers atDayWhenBethanyOn Friday, shoppers across the

  5. FY15 Year in Review | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof Energy Services » Program ManagementAct4 DOE/CF-0074 Volume 4FY

  6. Fiscal Year 2013 Budget Request Briefing | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof Energy Services » ProgramPolicy andResearchDataDepartmentFindings72Fiscal

  7. Wind Farm Growth Through the Years | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsofProgram: Report1538-1950DepartmentWave EnergyElectricityRateWind CareerWindWind

  8. Classification CommuniQué - Year: 2012 | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-in electric vehicle (PEV) charging stationConstructionPolicy

  9. Best of 2014: Our Year in Review | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuels DataEnergy Webinar:I DueBETO Quiz -Technologies for Hybrid BestBest of 2014:

  10. Book Review ~ The pioneering years of solar energy research at The Australian National

    E-Print Network [OSTI]

    Following the sun Book Review ~ The pioneering years of solar energy research at The Australian research and development project in solar energy in a location described as "diabolical" sounds daunting. There was widespread interest in solar energy during the early 1970s with an oil crisis affecting the Western world

  11. Wave energy potential in the Eastern Mediterranean Levantine Basin. An integrated 10-year study

    E-Print Network [OSTI]

    Georgiou, Georgios

    that remains to be covered before wave energy science and technology reach the maturity level of its windData bank Wave energy potential in the Eastern Mediterranean Levantine Basin. An integrated 10-year Article history: Received 30 July 2013 Accepted 25 March 2014 Available online Keywords: Wave energy

  12. Program, Course or Contract Title: Energy Systems & Climate Change Quarter and Academic Year: Fall 2009

    E-Print Network [OSTI]

    2009 In fall quarter, we studied the science of conventional energy sources, their costs and benefits alternative and renewable energy sources less formally, and will focus on these in winter quarter. We learnedProgram, Course or Contract Title: Energy Systems & Climate Change Quarter and Academic Year: Fall

  13. Over the Energy Edge: Results from a Seven Year New Commercial Buildings Research and Demonstration Project

    E-Print Network [OSTI]

    Diamond, Richard

    Over the Energy Edge: Results from a Seven Year New Commercial Buildings Research and Demonstration is that the actual, installed energy-efficiency measures and building characteristics changed from the design practice rather than assumptions based on the regional building code. For example, the Energy Edge small

  14. Wave energy potential in the Eastern Mediterranean Levantine Basin. An integrated 10-year study

    E-Print Network [OSTI]

    Georgiou, Georgios

    Data bank Wave energy potential in the Eastern Mediterranean Levantine Basin. An integrated 10-year Article history: Received 30 July 2013 Accepted 25 March 2014 Available online Keywords: Wave energy Numerical atmospheric Wave modeling a b s t r a c t The main characteristics of wave energy potential over

  15. 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.

  16. 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.

  17. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    the cloud index,” Solar Energy, vol. 81, no. 2, pp. 280 –Cover Indices,” ASME Journal of Solar Energy Engineering (inHorizontal Irradiance,” submitted to Solar Energy, 2012.

  18. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    the role of energy storage in the smart grid,” in Power andof energy storage, and the balance between grid flexibility

  19. Solar Wind Forecasting with Coronal Holes

    E-Print Network [OSTI]

    S. Robbins; C. J. Henney; J. W. Harvey

    2007-01-09

    An empirical model for forecasting solar wind speed related geomagnetic events is presented here. The model is based on the estimated location and size of solar coronal holes. This method differs from models that are based on photospheric magnetograms (e.g., Wang-Sheeley model) to estimate the open field line configuration. Rather than requiring the use of a full magnetic synoptic map, the method presented here can be used to forecast solar wind velocities and magnetic polarity from a single coronal hole image, along with a single magnetic full-disk image. The coronal hole parameters used in this study are estimated with Kitt Peak Vacuum Telescope He I 1083 nm spectrograms and photospheric magnetograms. Solar wind and coronal hole data for the period between May 1992 and September 2003 are investigated. The new model is found to be accurate to within 10% of observed solar wind measurements for its best one-month periods, and it has a linear correlation coefficient of ~0.38 for the full 11 years studied. Using a single estimated coronal hole map, the model can forecast the Earth directed solar wind velocity up to 8.5 days in advance. In addition, this method can be used with any source of coronal hole area and location data.

  20. 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).

  1. 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).

  2. Energy Prices and California's Economic

    E-Print Network [OSTI]

    Sadoulet, Elisabeth

    months, with national unemployment at 25 year highs, retail U.S. gasoline prices have risen 40 percent1 Energy Prices and California's Economic Security David RolandHolst October, 2009 on Energy Prices, Renewables, Efficiency, and Economic Growth: Scenarios and Forecasts, financial support

  3. Annual report to Congress on Federal Government Energy Management and Conservation Programs, Fiscal Year 1997

    SciTech Connect (OSTI)

    1999-08-13

    In fulfillment of statutory requirements, this report provides information on energy consumption in Federal buildings and operations and also documents activities conducted by Federal agencies in fulfilling those requirements during Fiscal Year 1997.

  4. The Chandra High Energy Transmission Grating: Design, Fabrication, Ground Calibration and Five Years in Flight

    E-Print Network [OSTI]

    Canizares, Claude R.

    Details of the design, fabrication, and ground and flight calibration of the High Energy Transmission Grating (HETG) on the Chandra X?Ray Observatory are presented after 5 years of flight experience. Specifics include the ...

  5. Annual report to Congress on Federal Government Energy Management and Conservation Programs, Fiscal Year 1998

    SciTech Connect (OSTI)

    2000-03-20

    In fulfillment of statutory requirements, this report provides information on energy consumption in Federal buildings and operations and also documents activities conducted by Federal agencies in fulfilling those requirements during Fiscal Year 1998.

  6. Statement from Secretary of Energy Samuel W. Bodman Regarding...

    Energy Savers [EERE]

    Energy Outlook which forecasts to 2030: "EIA's updated forecast projecting higher oil prices and increased demand reinforces the Department of Energy's commitment to the...

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

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

    Real Time Energy Management, Solar Forecasting Metrics, and More DOE Announces Webinars on Real Time Energy Management, Solar Forecasting Metrics, and More January 31, 2014 -...

  8. ESTIMATING RISK TO CALIFORNIA ENERGY INFRASTRUCTURE FROM PROJECTED CLIMATE CHANGE

    E-Print Network [OSTI]

    Sathaye, Jayant

    2011-01-01

    California Energy Demand 2010-2020: Adopted Forecast. CEC-energy infrastructure. Franco and Sanstad (2006) provide an overview and a methodology for demand forecasts

  9. First Year Analysis of Industrial Energy Conservation in Texas A&M's Energy Analysis and Diagnostic Center Program 

    E-Print Network [OSTI]

    Grubb, M. K.; Heffington, W. M.

    1988-01-01

    conservation opportunities (ECOs) in 15 energy audits, an average of seven ECOs per audit. The 109 recommendations are divided into four groups: Electrical Energy ECOs, Natural Gas ECOs, Non-Energy Saving ECOs, and Opportunity ECOs. This paper... and Diagnostic Center (EADC). The EADC program is funded by the Office of Industrial Programs, United States Department of Energy and monitored by the University City Science Center in Philadelphia, Pennsylvania. This paper covers the first year of operation...

  10. Power Forecasting for Plug-in Electric Vehicles

    E-Print Network [OSTI]

    Lavaei, Javad

    Power Forecasting for Plug-in Electric Vehicles with Statistic Simulations Guangbin Li (gl2423) #12 of the most heated-discussed issues. Energy shortage and environment pollution are the main bottleneck the tradeoff between energy supply and environment pollution. As the international oil price was continuously

  11. 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.

  12. 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.

  13. A Ten-Year, $7 Million Energy Initiative Marching on: Texas A&M University Campus Energy Systems CC 

    E-Print Network [OSTI]

    Deng, S.; Claridge, D. E.; Turner, W. D.; Bruner, H. L.; Williams, L.; Riley, J. G.

    2006-01-01

    The $35 million in measured savings for the ten-year, $7 million continuous commissioning (CC) program at the Texas A&M University (TAMU) makes the decision to continue easy. In today's energy environment and with the ...

  14. A Twelve Year, $10 Million Energy Initiative Marching On: the Texas A&M University Campus Energy Systems CC® 

    E-Print Network [OSTI]

    Deng, S.; Claridge, D. E.; Turner, W. D.; Riley, J. G.; Williams, L.; Bruner, H. L.

    2008-01-01

    The $58.5 million in measured savings for the twelve-year, $10 million continuous commissioning® (CC®) program at the Texas A&M University (TAMU) makes the decision to continue easy. In today's energy environment and with ...

  15. Downscaling Extended Weather Forecasts for Hydrologic Prediction

    SciTech Connect (OSTI)

    Leung, Lai-Yung R.; Qian, Yun

    2005-03-01

    Weather and climate forecasts are critical inputs to hydrologic forecasting systems. The National Center for Environmental Prediction (NCEP) issues 8-15 days outlook daily for the U.S. based on the Medium Range Forecast (MRF) model, which is a global model applied at about 2? spatial resolution. Because of the relatively coarse spatial resolution, weather forecasts produced by the MRF model cannot be applied directly to hydrologic forecasting models that require high spatial resolution to represent land surface hydrology. A mesoscale atmospheric model was used to dynamically downscale the 1-8 day extended global weather forecasts to test the feasibility of hydrologic forecasting through this model nesting approach. Atmospheric conditions of each 8-day forecast during the period 1990-2000 were used to provide initial and boundary conditions for the mesoscale model to produce an 8-day atmospheric forecast for the western U.S. at 30 km spatial resolution. To examine the impact of initialization of the land surface state on forecast skill, two sets of simulations were performed with the land surface state initialized based on the global forecasts versus land surface conditions from a continuous mesoscale simulation driven by the NCEP reanalysis. Comparison of the skill of the global and downscaled precipitation forecasts in the western U.S. showed higher skill for the downscaled forecasts at all precipitation thresholds and increasingly larger differences at the larger thresholds. Analyses of the surface temperature forecasts show that the mesoscale forecasts generally reduced the root-mean-square error by about 1.5 C compared to the global forecasts, because of the much better resolved topography at 30 km spatial resolution. In addition, initialization of the land surface states has large impacts on the temperature forecasts, but not the precipitation forecasts. The improvements in forecast skill using downscaling could be potentially significant for improving hydrologic forecasts for managing river basins.

  16. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    of solar- radiation data,” Solar Energy, vol. 19, no. 6, pp.16 independent data banks,” Solar Energy, vol. 80, no. 4,data,” Final Report of International Energy Agency Solar

  17. 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.

  18. Solid waste 30-year volume summary

    SciTech Connect (OSTI)

    Valero, O.J.; Armacost, L.L.; DeForest, T.J.; Templeton, K.J.; Williams, N.C.

    1994-06-01

    A 30-year forecast of the solid waste volumes to be generated or received at the US Department of Energy Hanford Site is described in this report. The volumes described are low-level mixed waste (LLMW) and transuranic/transuranic mixed (TRU/TRUM) waste that will require treatment, storage, and disposal at Hanford`s Solid Waste Operations Complex (SWOC) during the 30-year period from FY 1994 through FY 2023. The data used to complete this document were collected from onsite and offsite waste generators who currently, or are planning to, ship solid wastes to the Hanford Site. An analysis of the data suggests that over 300,000 m{sup 3} of LLMW and TRU/TRUM waste will be managed at Hanford`s SWOC over the next 30 years. An extensive effort was made this year to collect this information. The 1993 solid waste forecast was used as a starting point, which identified approximately 100,000 m{sup 3} of LLMW and TRU/TRUM waste to be sent to the SWOC. After analyzing the forecast waste volume, it was determined that additional waste was expected from the tank waste remediation system (TWRS), onsite decontamination and decommissioning (D&D) activities, and onsite remedial action (RA) activities. Data presented in this report establish a starting point for solid waste management planning. It is recognized that forecast estimates will vary (typically increasing) as facility planning and missions continue to change and become better defined, but the information presented still provides useful insight into Hanford`s future solid waste management requirements.

  19. 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.

  20. QUALIFIED FORECAST OF ENSEMBLE POWER PRODUCTION BY SPATIALLY DISPERSED GRID-CONNECTED PV SYSTEMS

    E-Print Network [OSTI]

    Heinemann, Detlev

    , Energy Meteorology Group, D-26111 Oldenburg, Germany, elke.lorenz@uni-oldenburg.de + UniversityQUALIFIED FORECAST OF ENSEMBLE POWER PRODUCTION BY SPATIALLY DISPERSED GRID- CONNECTED PV SYSTEMS Schneider° * University of Oldenburg, Institute of Physics, Energy and Semiconductor Research Laboratory

  1. The Electrochemical Society Interface Summer 2010 37 n recent years, energy research has

    E-Print Network [OSTI]

    Subramanian, Venkat

    the development and implementation of cost effective solar-based technologies and energy-storage technologiesThe Electrochemical Society Interface · Summer 2010 37 I n recent years, energy research has had. The development of next-generation bio- fuels from plant-based sources will require a systems approach to account

  2. LESSONS FROM FIVE YEARS OF EXPERIENCE IN ENERGY CONTRACT AUCTIONS IN SOUTH AMERICA

    E-Print Network [OSTI]

    Rudnick, Hugh

    and experiences from the energy auctions implemented in South America, namely in Brazil, Chile, Peru and Colombia1 LESSONS FROM FIVE YEARS OF EXPERIENCE IN ENERGY CONTRACT AUCTIONS IN SOUTH AMERICA Rodrigo Morenod a Imperial College London, UK b PSR, Brazil c Pontificia Universidad Catolica de Chile d Systep

  3. Received 17 Oct 2014 | Accepted 6 Mar 2015 | Published 21 Apr 2015 Skilful multi-year predictions of tropical trans-basin

    E-Print Network [OSTI]

    Chikamoto, Yoshimitsu

    ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show and energy sectors worldwide. Climate predictions may exhibit enhanced skill on timescales of years-year predictions of tropical trans-basin climate variability Yoshimitsu Chikamoto1, Axel Timmermann1,2, Jing

  4. Weather forecasting : the next generation : the potential use and implementation of ensemble forecasting

    E-Print Network [OSTI]

    Goto, Susumu

    2007-01-01

    This thesis discusses ensemble forecasting, a promising new weather forecasting technique, from various viewpoints relating not only to its meteorological aspects but also to its user and policy aspects. Ensemble forecasting ...

  5. Putting downward pressure on natural gas prices: The impact of renewable energy and energy efficiency

    E-Print Network [OSTI]

    Wiser, Ryan; Bolinger, Mark; St. Clair, Matthew

    2004-01-01

    using forecasts of U.S. coal minemouth prices and total U.S.price forecasts of recent years, however, suggest that RE and EE may increasingly displace coalprice forecasts (e.g. , UCS 2003, 2004) generally find greater coal

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

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

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

  7. Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01

    weather prediction solar irradiance forecasts in the US.2013: Review of solar irradiance forecasting methods and asatellite-derived irradiances: Description and validation.

  8. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    and operation of solar power plants and the model- ing offor application to solar ther- mal power plants energy

  9. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01

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

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-08-13

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

  11. FY 2009 National Renewable Energy Laboratory (NREL) Annual Report: A Year of Energy Transformation

    SciTech Connect (OSTI)

    Not Available

    2010-01-01

    This FY2009 Annual Report surveys the National Renewable Energy Laboratory's (NREL) accomplishments in renewable energy and energy efficiency research and development, commercialization and deployment of technologies, and strategic energy analysis. It offers NREL's vision and progress in building a clean, sustainable research campus and reports on community involvement.

  12. Easing the natural gas crisis: Reducing natural gas prices through increased deployment of renewable energy and energy efficiency

    E-Print Network [OSTI]

    Wiser, Ryan; Bolinger, Mark; St. Clair, Matt

    2004-01-01

    gas-price forecasts of recent years suggest that coal mayprice forecasts (e.g. , UCS 2004a, 2004b), generally find less gas displacement (and greater coal

  13. Japan's Long-term Energy Demand and Supply Scenario to 2050 - Estimation for the Potential of Massive CO2 Mitigation

    E-Print Network [OSTI]

    Komiyama, Ryoichi

    2010-01-01

    Framework Energy supply/demand forecasts change greatlyThis analysis makes energy supply/demand forecasts for theEnergy Demand (Reference Scenario) In millions of tons oil equivalent (Mtoe) I l f Results* •Forecasts *

  14. Energy Efficiency in Western Utility Resource Plans: Impacts on Regional Resources Assessment and Support for WGA Policies

    E-Print Network [OSTI]

    Hopper, Nicole; Goldman, Charles; Schlegal, Jeff

    2006-01-01

    2006-2016 Staff Energy Demand Forecast: Revised Septemberforecast. 2004–05 energy and peak demand forecast data were016-0: “Actual and Forecast Demands, Net Energy for Load,

  15. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    2.1.2 European Solar Radiation Atlas (ESRA)synthetic hourly radiation,” Solar Energy, vol. 49, pp. 67–for supplementing solar radiation network data,” Final

  16. Utilize cloud computing to support dust storm forecasting Qunying Huanga

    E-Print Network [OSTI]

    Chen, Songqing

    storm forecasting operational system should support a disruptive fashion by scaling up to enable high to save energy and costs. With the capability of providing a large, elastic, and virtualized pool and property damages since 1995 (Figure 1). Deaths and injuries are usually caused by car accidents, because

  17. Massachusetts state airport system plan forecasts.

    E-Print Network [OSTI]

    Mathaisel, Dennis F. X.

    This report is a first step toward updating the forecasts contained in the 1973 Massachusetts State System Plan. It begins with a presentation of the forecasting techniques currently available; it surveys and appraises the ...

  18. Management Forecast Quality and Capital Investment Decisions

    E-Print Network [OSTI]

    Goodman, Theodore H.

    Corporate investment decisions require managers to forecast expected future cash flows from potential investments. Although these forecasts are a critical component of successful investing, they are not directly observable ...

  19. Forecasting consumer products using prediction markets

    E-Print Network [OSTI]

    Trepte, Kai

    2009-01-01

    Prediction Markets hold the promise of improving the forecasting process. Research has shown that Prediction Markets can develop more accurate forecasts than polls or experts. Our research concentrated on analyzing Prediction ...

  20. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

    FORECASTING THE ROLE OF RENEWABLES IN HAWAII Jayant SathayeFORECASTING THE ROLF OF RENEWABLES IN HAWAII J Sa and Henrythe Conservation Role of Renewables November 18, 1980 Page 2

  1. NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    income 7 Figure 1.14: United States inflation Rate 8 Figure 1.15: Select United States interest Rates 8 2014 TABLE OF CONTENTS EXECUTiVE SUMMARY 1 CHAPTER 1: THE UNiTED STATES ECONOMY 3 Recent Trends Forecast Summary 2 CHAPTER 1: THE UNiTED STATES ECONOMY Figure 1.1: United States Real GDP Growth 3 Figure

  2. www.postersession.com In recent years, energy efficiency has become one of the

    E-Print Network [OSTI]

    Hutcheon, James M.

    in the U.S. market due to its cost of $0.76 per square foot. The analysis of Polyisocyanurate insulation to determine the effect of polyiscocyanurate insulation on a typical 2560 square foot, single story, singleprinted by www.postersession.com In recent years, energy efficiency has become one of the most

  3. Northwest Power and Conservation Council Kennecott Energy comments on 5 year plan

    E-Print Network [OSTI]

    % of the nations coal used for electricity generation. As a fuel provider, Kennecott Energy is committed in helping includes a ten-year history of helping generating companies in the US dramatically reduce emissions from coal-fired plants while at the same time increasing production. Sustainable development is one of our

  4. Consensus Coal Production And Price Forecast For

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    Consensus Coal Production And Price Forecast For West Virginia: 2011 Update Prepared for the West December 2011 © Copyright 2011 WVU Research Corporation #12;#12;W.Va. Consensus Coal Forecast Update 2011 i Table of Contents Executive Summary 1 Recent Developments 3 Consensus Coal Production And Price Forecast

  5. Forecasting phenology under global warming

    E-Print Network [OSTI]

    Silander Jr., John A.

    Forecasting phenology under global warming Ine´s Iba´n~ez1,*, Richard B. Primack2, Abraham J in phenology. Keywords: climate change; East Asia, global warming; growing season, hierarchical Bayes; plant is shifting, and these shifts have been linked to recent global warming (Parmesan & Yohe 2003; Root et al

  6. LOAD FORECASTING Eugene A. Feinberg

    E-Print Network [OSTI]

    Feinberg, Eugene A.

    , regression, artificial intelligence. 1. Introduction Accurate models for electric power load forecasting to make important decisions including decisions on pur- chasing and generating electric power, load for different operations within a utility company. The natures 269 #12;270 APPLIED MATHEMATICS FOR POWER SYSTEMS

  7. Solar Energy With an average of over 300 sunny days a year, Israel is an ideal labo-

    E-Print Network [OSTI]

    Maoz, Shahar

    35 Solar Energy With an average of over 300 sunny days a year, Israel is an ideal labo- ratory for testing one particularly promising alternative to fossil fuels: solar energy. In contrast to fossil fuels as much energy strikes the earth in the form of solar radiation as is used in a whole year throughout

  8. Target Allocation Methodology for China's Provinces: Energy Intensity in the 12th FIve-Year Plan

    SciTech Connect (OSTI)

    Ohshita, Stephanie; Price, Lynn

    2011-03-21

    Experience with China's 20% energy intensity improvement target during the 11th Five-Year Plan (FYP) (2006-2010) has shown the challenges of rapidly setting targets and implementing measures to meet them. For the 12th FYP (2011-2015), there is an urgent need for a more scientific methodology to allocate targets among the provinces and to track physical and economic indicators of energy and carbon saving progress. This report provides a sectoral methodology for allocating a national energy intensity target - expressed as percent change in energy per unit gross domestic product (GDP) - among China's provinces in the 12th FYP. Drawing on international experience - especially the European Union (EU) Triptych approach for allocating Kyoto carbon targets among EU member states - the methodology here makes important modifications to the EU approach to address an energy intensity rather than a CO{sub 2} emissions target, and for the wider variation in provincial energy and economic structure in China. The methodology combines top-down national target projections and bottom-up provincial and sectoral projections of energy and GDP to determine target allocation of energy intensity targets. Total primary energy consumption is separated into three end-use sectors - industrial, residential, and other energy. Sectoral indicators are used to differentiate the potential for energy saving among the provinces. This sectoral methodology is utilized to allocate provincial-level targets for a national target of 20% energy intensity improvement during the 12th FYP; the official target is determined by the National Development and Reform Commission. Energy and GDP projections used in the allocations were compared with other models, and several allocation scenarios were run to test sensitivity. The resulting allocations for the 12th FYP offer insight on past performance and offer somewhat different distributions of provincial targets compared to the 11th FYP. Recommendations for reporting and monitoring progress on the targets, and methodology improvements, are included.

  9. A Buildings Module for the Stochastic Energy Deployment System

    E-Print Network [OSTI]

    Marnay, Chris

    2008-01-01

    forecast is used, thereby eliminating any discrepancies. The final energy demandenergy results as well as the AEO- 07 forecasts for total natural gas demand

  10. 1995 solid waste 30-year container volume summary

    SciTech Connect (OSTI)

    Templeton, K.J.; DeForest, T.J.; Patridge, M.D. [Pacific Northwest Lab., Richland, WA (United States)

    1995-07-01

    This report describes a 30-year forecast of the solid waste volumes by container category. The volumes described are low-level mixed waste (LLMW) and transuranic/transuranic mixed (TRU-TRUM) waste. These volumes and their associated container categories 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 1995 through FY 2024. The data presented in this report establish a baseline for solid waste management both in the present and future. With knowledge of the volumes by container type, decisions on the facility handling and storage requirements can be adequately made. It is recognized that the forecast estimates will vary as facility planning and missions continue to change and become better defined; however, the data presented in this report still provide useful insight into Hanford`s future solid waste management requirements.

  11. Audit of the US Department of Energy`s consolidated financial statements for Fiscal Year 1996

    SciTech Connect (OSTI)

    1997-02-24

    The Office of Inspector General audited the Department`s Consolidated Statement of Financial position as of September 30, 1996, and the related Statement of Operations and Changes in Net Position for the year ended. Results are described.

  12. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

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

    2011-11-29

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

  13. Subscriber Services Complete Forecast

    E-Print Network [OSTI]

    funded solar energy, then cut funding off, heavily funded electric vehicles, then cut funding off produce no long-lived radioactive waste. In the 1980s, scientists planned to build a major fusion countries have now taken the lead in fusion research. France will build the ITER with Japan as a major

  14. Estimation of Energy Savings Resulting From the BestPractices Program, Fiscal Year 2002

    SciTech Connect (OSTI)

    Truett, LF

    2003-09-24

    Within the U.S. Department of Energy (DOE), the Office of Energy Efficiency and Renewable Energy (EERE) has a vision of a future with clean, abundant, reliable, and affordable energy. Within EERE, the Industrial Technologies Program (ITP), formerly the Office of Industrial Technologies, works in partnership with industry to increase energy efficiency, improve environmental performance, and boost productivity. The BestPractices (BP) Program, within ITP, works directly with industries to encourage energy efficiency. The purpose of the BP Program is to improve energy utilization and management practices in the industrial sector. The program targets distinct technology areas, including pumps, process heating, steam, compressed air, motors, and insulation. This targeting is accomplished with a variety of delivery channels, such as computer software, printed publications, Internet-based resources, technical training, technical assessments, and other technical assistance. A team of program evaluators from Oak Ridge National Laboratory (ORNL) was tasked to evaluate the fiscal year 2002 (FY02) energy savings of the program. The ORNL assessment enumerates levels of program activity for technology areas across delivery channels. In addition, several mechanisms that target multiple technology areas--e.g., Plant-wide Assessments (PWAs), the ''Energy Matters'' newsletter, and special events--are also evaluated for their impacts. When possible, the assessment relies on published reports and the Industrial Assessment Center (IAC) database for estimates of energy savings that result from particular actions. Data were also provided by ORNL, Lawrence Berkeley National Laboratory (LBNL) and Project Performance Corporation (PPC), the ITP Clearinghouse at Washington State University, the National Renewable Energy Laboratory (NREL), Energetics Inc., and the Industrial Technologies Program Office. The estimated energy savings in FY02 resulting from activities of the BP Program are almost 81.9 trillion Btu (0.0819 Quad), which is about 0.25% of the 32.5 Quads of energy consumed during FY02 by the industrial sector in the United States. The technology area with the largest estimated savings is steam, with 32% of the total energy savings. The delivery mechanism with the largest savings is that of software systems distribution, encompassing 44% of the total savings. Training results in an energy savings of 33%. Energy savings from PWAs and PWA replications equal 10%. Sources of overestimation of energy savings might derive from (1) a possible overlap of energy savings resulting from separate events (delivery channels) occurring in conjunction with one another (e.g., a training event and CTA at the same plant), and (2) a possible issue with the use of the average CTA value to assess savings for training and software distribution. Any overestimation attributable to these sources probably is outweighed by underestimations caused by the exclusion of savings resulting from general awareness workshops, data not submitted to the ITP Tracking Database, omission of savings attributable to web downloads of publications, use of BP products by participants over multiple years, and the continued utilization of equipment installed or replaced in previous years. Next steps in improving these energy savings estimates include continuing to enhance the design of the ITP Tracking Database and to improve reporting of program activities for the distribution of products and services; obtaining more detailed information on implementation rates and savings estimates for software training, tools, and assessments; continuing attempts to quantify savings based on Qualified Specialist activities; defining a methodology for assessing savings based on web downloads of publications; establishing a protocol for evaluating savings from other BP-sponsored events and activities; and continuing to refine the estimation methodology and reduction factors.

  15. 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.

  16. Target Allocation Methodology for China's Provinces: Energy Intensity in the 12th FIve-Year Plan

    E-Print Network [OSTI]

    Ohshita, Stephanie

    2011-01-01

    Targets Residential Energy Other Energy Energy saving EnergyEnergy saving Energy growth rates goals based on growthEcon Trend Analysis & Equal Savings Energy End- Use Sectors

  17. Predicting yearly energy savings using BIN weather data with heat-pipe heat exchangers

    SciTech Connect (OSTI)

    Mathur, G.D. [Zexel USA Corp., Decatur, IL (United States)

    1997-12-31

    In an earlier paper, the author had investigated the impact that a heat pipe heat exchanger (HPHE) has on the energy consumption and the peak demand on an existing air conditioning system. A detailed performance investigation was carried out for a number of cities for year round operation of the HVAC system with HPHE. Heating degree days and cooling hours were used for predicting the energy savings with the HPHE. In order to calculate the true energy savings, a more realistic approach is to use the BIN weather data. The author has developed a simulation program that can predict the energy savings by using the BIN weather data. The investigation has been carried out for 33 US cities with widely different climactic conditions and the results are presented in this paper. Economic analysis reveals that a simple retrofit on an existing HVAC system can pay for itself in less than a year. Based on this investigation, it is recommended that electric utilities use this technology as a demand side management strategy for reducing energy and peak demand.

  18. Energy Materials Coordinating Committee (EMaCC). Annual Technical Report, Fiscal Year 2000

    SciTech Connect (OSTI)

    none,

    2001-07-31

    The Energy Materials Coordinating Committee Annual Report (attached, DOE/SC-0040) provides an annual summary of non-classified materials-related research programs supported by various elements within the Department of Energy. The EMaCC Annual Report is a useful working tool for project managers who want to know what is happening in other divisions, and it provides a guide for persons in industry and academia to the materials program within the Department. The major task of EMaCC this year was to make the Annual Report a more user-friendly document by removing redundant program information and shortening the project summaries.

  19. EcoCAR 3: Year 1 Finals Power Up University Teams | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment ofOffice ofof EnergyPlants" NowDepartment of EnergyYear1 of

  20. Options in the Eleventh Year for Interim Standard Offer Number Four Contracts

    SciTech Connect (OSTI)

    Hinrichs, Thomas C.

    1992-03-24

    The Interim Standard Offer Number Four Contracts (ISM), under which most of the geothermal industry is selling power (outside of The Geysers), has an initial ten year period of known fixed energy payments. In the eleventh year, the price goes to the Avoided Cost of the buying utility. The specific contract language is ''Seller will be paid at a rate equal to the utilities' published avoided cost of energy as updated and authorized by the Commission (CPUC)''. The first geothermal contract will reach the end of the initial 10 year period in early 1994, a few will end in 1995 and 1996, and the majority will end in the 1997-2000 period. This is beginning to be focused upon by the utilities, lenders and, of course, the operators themselves. The prime reason for focusing on the issue is that avoided costs of the utilities directly track the delivered cost of the natural gas, and most forecasts are showing that the price of gas in the eleventh year of the contracts will be significantly lower than the last year of the fixed period of energy payments. There are many forums in which the predication of natural gas prices are discussed. In the State of California, the agency responsible for the official forecast is the California Energy Commission. Every two years, the CEC holds hearings for input into its biennial Fuels Report (FR) which establishes the forecast of natural gas prices in addition to other parameters which are used in the planning process. The attached Exhibit I is an excerpt out of the 1991 Fuels Report (FR91). Figure 1 compares the forecast of FR89 and FR91 for the Utility Electric Generation (UEG) in PG&E's service area, and Figure 2, the forecast in the SOCAL service area. The FR91 SOCAL service area forecast indicates a bottoming of the gas price in 1994 at $2.50/mmbtu. Recent prices in 1992 are already at these levels. Converting this to an avoided energy cost brings about a price of 2 to 2-1/2 Cents/kWh. The 1992 energy price in the IS04 contract is 9.3 Cents/kWh.

  1. What Energy-Saving Gifts Are You Giving this Year? | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LIST OFAMERICA'S FUTURE. regulatorsEnergyDepartment of Energy WhatWhat

  2. Improving Inventory Control Using Forecasting

    E-Print Network [OSTI]

    Balandran, Juan

    2005-12-16

    and encouragement. I am very grateful to Lucille and Michael Hobbs for their friendship, understanding and financial support. Finally, thank you to Tom Decker, Pat Jackson and Brian Zellar for all their contributions and hard work on this project... below: 1. Na?ve 2. Linear Regression 3. Moving Average 4. Exponential 5. Double exponential The na?ve forecasting method assumes that more recent data values are the best predictors of future values. The model is ? t+1 = Y t . Where ? t...

  3. Energy-water analysis of the 10-year WECC transmission planning study cases.

    SciTech Connect (OSTI)

    Tidwell, Vincent Carroll; Passell, Howard David; Castillo, Cesar; Moreland, Barbara

    2011-11-01

    In 2011 the Department of Energy's Office of Electricity embarked on a comprehensive program to assist our Nation's three primary electric interconnections with long term transmission planning. Given the growing concern over water resources in the western U.S. the Western Electricity Coordinating Council (WECC) requested assistance with integrating water resource considerations into their broader electric transmission planning. The result is a project with three overarching objectives: (1) Develop an integrated Energy-Water Decision Support System (DSS) that will enable planners in the Western Interconnection to analyze the potential implications of water stress for transmission and resource planning. (2) Pursue the formulation and development of the Energy-Water DSS through a strongly collaborative process between the Western Electricity Coordinating Council (WECC), Western Governors Association (WGA), the Western States Water Council (WSWC) and their associated stakeholder teams. (3) Exercise the Energy-Water DSS to investigate water stress implications of the transmission planning scenarios put forward by WECC, WGA, and WSWC. The foundation for the Energy-Water DSS is Sandia National Laboratories Energy-Power-Water Simulation (EPWSim) model (Tidwell et al. 2009). The modeling framework targets the shared needs of energy and water producers, resource managers, regulators, and decision makers at the federal, state and local levels. This framework provides an interactive environment to explore trade-offs, and 'best' alternatives among a broad list of energy/water options and objectives. The decision support framework is formulated in a modular architecture, facilitating tailored analyses over different geographical regions and scales (e.g., state, county, watershed, interconnection). An interactive interface allows direct control of the model and access to real-time results displayed as charts, graphs and maps. The framework currently supports modules for calculating water withdrawal and consumption for current and planned electric power generation; projected water demand from competing use sectors; and, surface and groundwater availability. WECC's long range planning is organized according to two target planning horizons, a 10-year and a 20-year. This study supports WECC in the 10-year planning endeavor. In this case the water implications associated with four of WECC's alternative future study cases (described below) are calculated and reported. In future phases of planning we will work with WECC to craft study cases that aim to reduce the thermoelectric footprint of the interconnection and/or limit production in the most water stressed regions of the West.

  4. An integrated heat pump storage system for year-round off-peak electrical energy use

    SciTech Connect (OSTI)

    Russell, L.D.; Forbes, R.E.; Hilson, D.W.

    1982-05-01

    An integrated system that provides for primarily off-peak electrical energy use and heat storage during the heating season, cooling storage during the cooling season, and hot water heating during both seasons was evaluated in this project. Results indicate that such a system is feasible and can be employed to shift electrical energy use from peak to off-peak periods. Further, this shifting of energy use can be done with whatever timing is desired year-round to assist with load-leveling for the utility. Results indicate potential for both residential and commercial applications. It is concluded that widespread use of such systems could lead to significant benefits for electrical power utilities and their customers.

  5. Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof EnergyApril 2014Department ofWindOPENOccurrence

  6. Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof EnergyApril 2014Department ofWindOPENOccurrenceRollout - Renewable

  7. Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative Fuelsof EnergyApril 2014Department ofWindOPENOccurrenceRollout - RenewableRollout

  8. SUMMARY OF 2013 ATLANTIC TROPICAL CYCLONE ACTIVITY AND VERIFICATION OF AUTHORS' SEASONAL AND TWO-WEEK FORECASTS

    E-Print Network [OSTI]

    Connors, Daniel A.

    -WEEK FORECASTS The 2013 Atlantic hurricane season was much quieter than predicted in our seasonal outlooks. While as past forecasts and verifications are available via the World Wide Web at http Cyclone Energy (ACE) (92) 165 165 142 30 32% Net Tropical Cyclone Activity (NTC) (103%) 175 175 150 43 42

  9. Optimal combined wind power forecasts using exogeneous variables

    E-Print Network [OSTI]

    Optimal combined wind power forecasts using exogeneous variables Fannar ¨Orn Thordarson Kongens to the Klim wind farm using three WPPT forecasts based on different weather forecasting systems. It is shown of the thesis is combined wind power forecasts using informations from meteorological forecasts. Lyngby, January

  10. Weather Forecasts are for Wimps: Why Water Resource Managers Do Not Use Climate Forecasts

    E-Print Network [OSTI]

    Rayner, Steve; Lach, Denise; Ingram, Helen

    2005-01-01

    and Winter, S. G. : 1960, Weather Information and EconomicThe ENSO Signal 7, 4–6. WEATHER FORECASTS ARE FOR WIMPSWEATHER FORECASTS ARE FOR WIMPS ? : WHY WATER RESOURCE

  11. Resolve to Save Energy in the New Year | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i nAandSummary From: v2.7| DepartmentMultifamily ResidentialLooking for ways

  12. Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget Rollout Â… Renewable Electricity Generation

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Financing Tool Fits the BillDepartmentSitesUMTRCA3Energy Office ofApril 30, 2013 Office of

  13. Office of Energy Efficiency and Renewable Energy Fiscal Year 2014 Budget Rollout Â… Sustainable Transportation

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Financing Tool Fits the BillDepartmentSitesUMTRCA3Energy Office ofApril 30, 2013 Office ofDr.

  14. Energy Secretary Highlights One-Year Anniversary of the Energy Policy Act

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy A plug-inPPLfor Innovative SolarSavings PerformanceNationwide as PartDepartment ofof

  15. Office of Fossil Energy Kicks Off 19th Year of Mickey Leland Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergy AEnergy Managing853926 NewsORMAT NEVADAEnergyA chart EM Celebrates

  16. Start 2015 with an #EnergyResolution to Save Money and Energy All Year Long

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment of EnergyResearchers at the Energyonecelebrates specialwarm spring5|

  17. Energy Department Announces Five Year Renewal of Funding for First Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirley Ann JacksonDepartment ofOffice ofof EnergyPlants"OEEnergy Practices

  18. U.S. Energy Production Through the Years | Department of Energy

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsofProgram: Report1538-1950 Timeline ofTurkey Near-ZeroEnergyEmployers

  19. Energy projections to the year 2010: a technical report in support of the National Energy Policy Plan

    SciTech Connect (OSTI)

    Not Available

    1983-10-01

    Underlying these energy projections are assumptions and results about key variables - world oil prices, economic growth, energy consumption, and production potential - which are described in this document. The projections are based on information available through June 1983. Projecting US energy supply, demand, and prices through the year 2010 is by nature a highly uncertain process. These projections try to account for uncertainty by providing a variety of scenarios that account for alternative future conditions. Results indicate that although the outlook for future world oil prices is highly uncertain, most analysts now agree that, barring a significant oil supply disruption, world oil prices will most likely fall in real terms until the mid 1980's. From 1985 to 1990, prices will most likely increase in real terms. Beyond 1990, the outlook becomes increasingly uncertain. The oil price increases of 1973 to 1974 and 1979 to 1980 have set into motion powerful energy conservation forces that are likely to continue causing energy (especially oil) to be used more efficiently. Consequently, we need to pay continuing attention to analyzing and evaluating energy conservation trends in world economies. The recent decline in world oil prices has added a new dimension to the uncertainty about future market conditions. Prior to 1983, OPEC had never officially reduced the posted price of oil, but rather used the influence of inflation to allow prices to fall gradually in real terms during periods of excess world supply. Now, investment planners must not only be concerned about the potential for oil price shocks, which can send the oil price very high, but also about future price breaks which could send the price very low. Under all but extreme assumptions, both the US and the rest of the world will remain dependent on liquid fuels, including oil supplies from OPEC, throughout at least the next 20 years. 73 references.

  20. 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.