Sample records for forecast comparisons energy

  1. 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

    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...

  2. ELECTRICITY DEMAND FORECAST COMPARISON REPORT

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005.................................................................................................................................3 PACIFIC GAS & ELECTRIC PLANNING AREA ........................................................................................9 Commercial Sector

  3. 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

  4. 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

    2000-01-01T23:59:59.000Z

    of the Department of Energy's Office of Industrial Technologies, EIA extracted energy use infonnation from the Annual Energy Outlook (AEO) - 2000 (8) for each of the seven # The Pacific Northwest National Laboratory is operated by Battelle Memorial Institute... Energy Technology Conference, Houston, Texas, pp.115-124. 7. U.S. Department of Energy. 1994. Manufacturing Consumption ofEnergy, J99J. DOEIEIA-0512(91). Washington, D. C. 8. U.S. Department of Energy. 1999. Annual Energy Outlook. 2000...

  5. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400. Hall Deputy Director Energy Efficiency and Demand Analysis Division Scott W. Matthews Acting Executive

  6. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluating A PotentialJumpGermanFife Energy Park atFisiaFlorida:Forecast Energy Jump to:

  7. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    Energy Commission's final forecasts for 2012­2022 electricity consumption, peak, and natural gas demand Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand

  8. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    the California Energy Commission staff's revised forecasts for 2012­2022 electricity consumption, peak Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand

  9. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    Energy Commission staff's revised forecasts for 2012­2022 electricity consumption, peak, and natural Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility

  10. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

    has developed longterm forecasts of transportation energy demand as well as projected ranges of transportation fuel and crude oil import requirements. The transportation energy demand forecasts makeCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY

  11. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand The California Energy Demand 2014 ­ 2024 Revised Forecast, Volume 2: Electricity Demand by Utility Planning Area Energy Policy Report. The forecast includes three full scenarios: a high energy demand case, a low

  12. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand, EndUser Natural Gas Demand, and Energy Efficiency SEPTEMBER 2013 CEC2002013004SDV1REV CALIFORNIA The California Energy Demand 2014 ­ 2024 Revised Forecast, Volume 1: Statewide Electricity Demand and Methods

  13. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

    requirements. The transportation energy demand forecasts make assumptions about fuel price forecastsCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY ENERGY COMMISSION Gordon Schremp, Jim Page, and Malachi Weng-Gutierrez Principal Authors Jim Page Project

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

    SciTech Connect (OSTI)

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

    2012-09-01T23:59:59.000Z

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

  15. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    supervised data preparation. Steven Mac and Keith O'Brien prepared the historical energy consumption data. Nahid Movassagh forecasted consumption for the agriculture and water pumping sectors. Cynthia Rogers generation, conservation, energy efficiency, climate zone, investorowned, public, utilities, additional

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

    SciTech Connect (OSTI)

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

    2012-07-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01T23:59:59.000Z

    revisions to the EIAs natural gas price forecasts in AEOsolely on the AEO 2005 natural gas price forecasts willComparison of AEO 2005 Natural Gas Price Forecast to NYMEX

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

    to estimate the base-case natural gas price forecast, but toComparison of AEO 2010 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

  19. CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Manager Kae Lewis Acting Manager Demand Analysis Office Valerie T. Hall Deputy Director Energy Efficiency Demand Forecast report is the product of the efforts of many current and former California Energy

  20. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    and water pumping sectors. Mark Ciminelli forecasted energy for transportation, communication and utilities. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data at the California Public Utilities Commission. This forecast was produced with the Energy Commission demand forecast

  1. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand, EndUser Natural Gas Demand, and Energy Efficiency DECEMBER 2013 CEC2002013004SFV1 CALIFORNIA and expertise of numerous California Energy Commission staff members in the Demand Analysis Office. In addition

  2. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST

    E-Print Network [OSTI]

    procurement process at the California Public Utilities Commission. This forecast was produced with the Energy Commission demand forecast models. Both the staff draft energy consumption and peak forecasts are slightly and commercial sectors. Keywords Electricity demand, electricity consumption, demand forecast, weather

  3. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    , Gary Occhiuzzo, and Keith O'Brien prepared the historical energy consumption data. Nahid Movassagh forecasted consumption for the agriculture and water pumping sectors. Don Schultz and Doug Kemmer developed. California Energy Commission, Electricity Supply Analysis Division. Publication Number: CEC2002012001CMFVI

  4. 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

  5. Modeling of Uncertainty in Wind Energy Forecast

    E-Print Network [OSTI]

    regression and splines are combined to model the prediction error from Tunø Knob wind power plant. This data of the thesis is quantile regression and splines in the context of wind power modeling. Lyngby, February 2006Modeling of Uncertainty in Wind Energy Forecast Jan Kloppenborg Møller Kongens Lyngby 2006 IMM-2006

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01T23:59:59.000Z

    late January 2008, extend its natural gas futures strip anComparison of AEO 2008 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

  7. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01T23:59:59.000Z

    Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

  8. Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01T23:59:59.000Z

    Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

  10. Sandia National Laboratories: Solar Energy Forecasting and Resource...

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

    & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource Assessment, provides an authoritative voice on the...

  11. 2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA

    E-Print Network [OSTI]

    Perez, Richard R.

    2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA J models 1 INTRODUCTION Solar radiation and PV production forecasts are becoming increasingly important/) three teams of experts are benchmarking their solar radiation forecast against ground truth data

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    2 2. Annual Energy Outlook (Administrations Annual Energy Outlook forecasted price (of Energy, Annual Energy Outlook 2004 with Projections to

  13. 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 dedicated models to forecast the 12 individual months directly. Results indicate better performance is superior to naïve forecasts based on persistence and seasonality, and is better than results quoted

  14. PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022

    E-Print Network [OSTI]

    PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022 AUGUST 2011 CEC-200-2011-011-SD CALIFORNIA for electric vehicles. #12;ii #12;iii ABSTRACT The Preliminary California Energy Demand Forecast 2012 includes three full scenarios: a high energy demand case, a low energy demand case, and a mid energy demand

  15. Solar Forecasting | 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 DataDepartment of Energy Your Density Isn'tOriginEducationVideo »UsageSecretary ofSmallConfidential,2 Solar BackgroundWakePowersSystems

  16. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST, and utilities. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption STAFFFINALREPORT NOVEMBER 2007 CEC-200-2007-015-SF2 Arnold Schwarzenegger, Governor #12;CALIFORNIA ENERGY

  17. analytical energy forecasting: Topics by E-print Network

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

    COMMISSION Tom Gorin Lynn Marshall Principal Author Tom Gorin Project 11 Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Computer Technologies and...

  18. Test application of a semi-objective approach to wind forecasting for wind energy applications

    SciTech Connect (OSTI)

    Wegley, H.L.; Formica, W.J.

    1983-07-01T23:59:59.000Z

    The test application of the semi-objective (S-O) wind forecasting technique at three locations is described. The forecasting sites are described as well as site-specific forecasting procedures. Verification of the S-O wind forecasts is presented, and the observed verification results are interpreted. Comparisons are made between S-O wind forecasting accuracy and that of two previous forecasting efforts that used subjective wind forecasts and model output statistics. (LEW)

  19. 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 meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts acknowledges the support of PPM Energy, Inc. The data used in this work were obtained from Oregon State

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

    SciTech Connect (OSTI)

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

    2013-10-01T23:59:59.000Z

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

  1. 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

    Page 1 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 take a few moments to answer this short survey

  2. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ |RippeyInformationSodaAtlas (PACA RegionEnergyMarket

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13T23:59:59.000Z

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2005-12-19T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Lantz, E.; Hand, M.

    2010-05-01T23:59:59.000Z

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

  6. 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.girodo@uni-oldenburg.de ABSTRACT Solar energy is expected to contribute major shares of the future global energy supply. Due to its and solar energy conversion processes has to account for this behaviour in respective operating strategies

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06T23:59:59.000Z

    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.

  8. 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

  9. 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

  10. 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

  11. TRANSPORTATION ENERGY FORECASTS AND ANALYSES FOR THE 2009

    E-Print Network [OSTI]

    Page Manager FOSSIL FUELS OFFICE Mike Smith Deputy Director FUELS AND TRANSPORTATION DIVISION Melissa, Weights and Measurements/Gary Castro, Allan Morrison, John Mough, Ed Williams Clean Energy FuelsCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS AND ANALYSES FOR THE 2009 INTEGRATED

  12. Integrating agricultural pest biocontrol into forecasts of energy biomass production

    E-Print Network [OSTI]

    Gratton, Claudio

    Analysis Integrating agricultural pest biocontrol into forecasts of energy biomass production T), University of Lome, 114 Rue Agbalepedogan, BP: 20679, Lome, Togo e Center for Agricultural & Energy Policy model of potential biomass supply that incorporates the effect of biological control on crop choice

  13. Exploiting weather forecasts for sizing photovoltaic energy bids

    E-Print Network [OSTI]

    Giannitrapani, Antonello

    1 Exploiting weather forecasts for sizing photovoltaic energy bids Antonio Giannitrapani, Simone for a photovoltaic (PV) power producer taking part into a competitive electricity market characterized by financial set from an Italian PV plant. Index Terms--Energy market, bidding strategy, photovoltaic power

  14. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01T23:59:59.000Z

    the Energy Information Administrations (EIA) web site. Wein the past, compared the EIAs reference case long-termgas price forecasts from the EIA. As such, we have concluded

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

    SciTech Connect (OSTI)

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

    2005-02-09T23:59:59.000Z

    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. Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids

    E-Print Network [OSTI]

    Prasanna, Viktor K.

    1 Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids Yogesh Simmhan, prasanna}@usc.edu I. INTRODUCTION Smart Power Grids exemplify an emerging class of Cyber Physical-on paradigm to support operational needs. Smart Grids are an outcome of instrumentation, such as Phasor

  17. 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

    2011-03-28T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    2009-03-01T23:59:59.000Z

    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. Solar Forecast Improvement Project | 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 onYouTube YouTube Note: Since the.pdfBreakingMayDepartment of Energy Ready,SmartEnergyEnergy Resource LibrarySolar

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

    SciTech Connect (OSTI)

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04T23:59:59.000Z

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

  1. Wind Forecasting Improvement Project | 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn AprilA group currentBradley NickellApril 16, 2008 TBD-0075 -In theWide

  2. energy data + forecasting | OpenEI Community

    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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty Edit withTianlinPapersWindey Wind Home Rmckeel's Home Kyoung's picture Submitted

  3. OpenEI Community - energy data + forecasting

    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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRoseConcernsCompany Oil and GasOff<div class="form-item">

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

    E-Print Network [OSTI]

    Kolter, J. Zico

    -Gaussian case using the copula transform. On a wind power forecasting task, we show that this probabilisticLarge-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random high-dimensional conditional Gaussian distributions to forecasting wind power and extend it to the non

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01T23:59:59.000Z

    gas price forecasts with contemporaneous natural gas pricesreference-case natural gas price forecast, and that have notof AEO 2009 Natural Gas Price Forecast to NYMEX Futures

  6. Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    Gas Price Forecast W ith natural gas prices significantlyof AEO 2006 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEO

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01T23:59:59.000Z

    gas price forecasts with contemporaneous natural gas pricesreference-case natural gas price forecast, and that have notof AEO 2008 Natural Gas Price Forecast to NYMEX Futures

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

    the base-case natural gas price forecast, but to alsogas price forecasts with contemporaneous natural gas pricesof AEO 2010 Natural Gas Price Forecast to NYMEX Futures

  9. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01T23:59:59.000Z

    Natural Gas Price Forecast Although natural gas prices areof AEO 2007 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEO

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2009-01-28T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2008-01-07T23:59:59.000Z

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

  12. LED Lighting Forecast | 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 DataDepartment of Energy Your Density Isn't YourTransport(FactDepartment ofLetter Report:40PM toLED Lighting Facts LED Lighting Facts

  13. Probabilistic wind power forecasting -European Wind Energy Conference -Milan, Italy, 7-10 May 2007 Probabilistic short-term wind power forecasting

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    Probabilistic wind power forecasting - European Wind Energy Conference - Milan, Italy, 7-10 May 2007 Probabilistic short-term wind power forecasting based on kernel density estimators Jeremie Juban jeremie.juban@ensmp.fr; georges.kariniotakis@ensmp.fr Abstract Short-term wind power forecasting tools

  14. Long-term Industrial Energy Forecasting (LIEF) model (18-sector version)

    SciTech Connect (OSTI)

    Ross, M.H. [Univ. of Michigan, Ann Arbor, MI (US). Dept. of Physics; Thimmapuram, P.; Fisher, R.E.; Maciorowski, W. [Argonne National Lab., IL (US)

    1993-05-01T23:59:59.000Z

    The new 18-sector Long-term Industrial Energy Forecasting (LIEF) model is designed for convenient study of future industrial energy consumption, taking into account the composition of production, energy prices, and certain kinds of policy initiatives. Electricity and aggregate fossil fuels are modeled. Changes in energy intensity in each sector are driven by autonomous technological improvement (price-independent trend), the opportunity for energy-price-sensitive improvements, energy price expectations, and investment behavior. Although this decision-making framework involves more variables than the simplest econometric models, it enables direct comparison of an econometric approach with conservation supply curves from detailed engineering analysis. It also permits explicit consideration of a variety of policy approaches other than price manipulation. The model is tested in terms of historical data for nine manufacturing sectors, and parameters are determined for forecasting purposes. Relatively uniform and satisfactory parameters are obtained from this analysis. In this report, LIEF is also applied to create base-case and demand-side management scenarios to briefly illustrate modeling procedures and outputs.

  15. Draft forecast of the final report for the comparison to 40 CFR Part 191, Subpart B, for the Waste Isolation Pilot Plant

    SciTech Connect (OSTI)

    Bertram-Howery, S.G.; Marietta, M.G.; Anderson, D.R.; Gomez, L.S.; Rechard, R.P. (Sandia National Labs., Albuquerque, NM (USA)); Brinster, K.F.; Guzowski, R.V. (Science Applications International Corp., Albuquerque, NM (USA))

    1989-12-01T23:59:59.000Z

    The United States Department of Energy is planning to dispose of transuranic wastes, which have been generated by defense programs, at the Waste Isolation Pilot Plant. The WIPP Project will assess compliance with the requirements of the United States Environmental Protection Agency. This report forecasts the planned 1992 document, Comparison to 40 CFR, Part 191, Subpart B, for the Waste Isolation Pilot Plant (WIPP). 130 refs., 36 figs., 11 tabs.

  16. 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

  17. Forecasting the Market Penetration of Energy Conservation Technologies: The Decision Criteria for Choosing a Forecasting Model

    E-Print Network [OSTI]

    Lang, K.

    1982-01-01T23:59:59.000Z

    capital requirements and research and development programs in the alum inum industry. : CONCLUSIONS Forecasting the use of conservation techndlo gies with a market penetration model provides la more accountable method of projecting aggrega...

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

    E-Print Network [OSTI]

    Cañizares, Claudio A.

    1 Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines H. In this paper, the MARS technique is applied to forecast the hourly Ontario energy price (HOEP). The MARS models values of the latest pre- dispatch price and demand information, made available by the Ontario

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    of two methods to forecast natural gas prices: using theof two methods to forecast natural gas prices is performed:accurate average forecast of natural gas prices than the

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01T23:59:59.000Z

    Gas Price Forecast With natural gas prices significantlyto the EIAs natural gas price forecasts in AEO 2004 and AEOon the AEO 2005 natural gas price forecasts will likely once

  1. NCAR WRF-based data assimilation and forecasting systems for wind energy applications power

    E-Print Network [OSTI]

    Kim, Guebuem

    NCAR WRF-based data assimilation and forecasting systems for wind energy applications power Yuewei of these modeling technologies w.r.t. wind energy applications. Then I'll discuss wind farm

  2. 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.

    2011-10-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

    Hnilo, J J

    2006-03-17T23:59:59.000Z

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

  4. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

    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:

  5. The Incremental Benefits of the Nearest Neighbor Forecast of U.S. Energy Commodity Prices

    E-Print Network [OSTI]

    Kudoyan, Olga

    2012-02-14T23:59:59.000Z

    This thesis compares the simple Autoregressive (AR) model against the k- Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil...

  6. The Incremental Benefits of the Nearest Neighbor Forecast of U.S. Energy Commodity Prices

    E-Print Network [OSTI]

    Kudoyan, Olga

    2012-02-14T23:59:59.000Z

    This thesis compares the simple Autoregressive (AR) model against the k- Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil...

  7. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Saleshttp://www.fnal.gov/directorate/nalcal/nalcal02_07_05_files/nalcal.gifNRELPower SystemsDebbie Brodt-Giles PhotoElla

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

    SciTech Connect (OSTI)

    Stoffel, T.

    2012-06-01T23:59:59.000Z

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

  9. 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 define an extremely low power output gradient and demonstrate how decentralized energy storage conservative bids on the power market. Energy storage strikes the major problems of wind power and joining

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit 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

  11. Forecasting the Dark Energy Measurement with Baryon Acoustic Oscillations: Prospects for the LAMOST surveys

    E-Print Network [OSTI]

    Xin Wang; Xuelei Chen; Zheng Zheng; Fengquan Wu; Pengjie Zhang; Yongheng Zhao

    2009-01-09T23:59:59.000Z

    The Large Area Multi-Object Spectroscopic Telescope (LAMOST) is a dedicated spectroscopic survey telescope being built in China, with an effective aperture of 4 meters and equiped with 4000 fibers. Using the LAMOST telescope, one could make redshift survey of the large scale structure (LSS). The baryon acoustic oscillation (BAO) features in the LSS power spectrum provide standard rulers for measuring dark energy and other cosmological parameters. In this paper we investigate the meaurement precision achievable for a few possible surveys: (1) a magnitude limited survey of all galaxies, (2) a survey of color selected red luminous galaxies (LRG), and (3) a magnitude limited, high density survey of zforecast the error on the dark energy equation of state and other cosmological parameters for different survey parameters. In a few cases we also use the Markov Chain Monte Carlo (MCMC) method to make the same forecast as a comparison. The fiber time required for each of these surveys is also estimated. These results would be useful in designing the surveys for LAMOST.

  12. 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.

    2013-10-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Prasanna, Viktor K.

    and institutional campuses can significantly contribute to energy conservation. The rollout of smart grids of occupants, and is a micro-grid test-bed for the DoE sponsored Los Angeles Smart Grid Demonstration ProjectImproving Energy Use Forecast for Campus Micro-grids using Indirect Indicators Saima Aman

  14. Comparison of Various Deterministic Forecasting Techniques in Shale Gas Reservoirs with Emphasis on the Duong Method

    E-Print Network [OSTI]

    Joshi, Krunal Jaykant

    2012-10-19T23:59:59.000Z

    There is a huge demand in the industry to forecast production in shale gas reservoirs accurately. There are many methods including volumetric, Decline Curve Analysis (DCA), analytical simulation and numerical simulation. Each one of these methods...

  15. 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)

    2005-10-31T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit 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

  17. Comparison of Various Deterministic Forecasting Techniques in Shale Gas Reservoirs with Emphasis on the Duong Method

    E-Print Network [OSTI]

    Joshi, Krunal Jaykant

    2012-10-19T23:59:59.000Z

    variation of the Duong model proves to be a robust model for most of the well cases and flow regimes. The modified Duong has been shown to work best compared to other deterministic models in most cases. For grouped datasets the SPED & Duong models forecast...

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

    SciTech Connect (OSTI)

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

    2005-07-01T23:59:59.000Z

    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.

  19. Implementation of a Corporate Energy Accounting and Forecasting Model

    E-Print Network [OSTI]

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

    1981-01-01T23:59:59.000Z

    The development and implementation of a Frito-Lay computer based energy consumption reporting and modeling program is discussed. The system has been designed to relate actual plant energy consumption to a standard consumption which incorporates...

  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 Northern Study Area.

    SciTech Connect (OSTI)

    Finley, Cathy [WindLogics

    2014-04-30T23:59:59.000Z

    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. A comprehensive analysis of wind energy forecast errors for the various model-based power forecasts was presented for a suite of wind energy ramp definitions. The results compiled over the year-long study period showed that the power forecasts based on the research models (ESRL_RAP, HRRR) more accurately predict wind energy ramp events than the current operational forecast models, both at the system aggregate level and at the local wind plant level. At the system level, the ESRL_RAP-based forecasts most accurately predict both the total number of ramp events and the occurrence of the events themselves, but the HRRR-based forecasts more accurately predict the ramp rate. At the individual site level, the HRRR-based forecasts most accurately predicted the actual ramp occurrence, the total number of ramps and the ramp rates (40-60% improvement in ramp rates over the coarser resolution forecast

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

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

    of Solid-State Lighting in General Illumination Applications compares the annual lighting energy consumption in the U.S. with and without further market penetration of LED...

  2. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOEHazel Crest,EnergySerranopolis Jump to:Econ IncIRENADelhiFocus IncEnergy

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

    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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual Siteof Energy 2,AUDITCaliforniaWeifangwiki Home Jweers'sAIRMaster+

  4. 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 FuelsNovember 13,Statement | DepartmentBlog2013 | DepartmentProjectsEnergy Energy

  5. ANL Software Improves Wind Power Forecasting | 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742Energy China 2015ofDepartmentDepartment of2 of 5) ALARA TrainingANDREW W. TUNNELL t: (205)This is

  6. 2013DvorakandSailor'sEnergy Model Forecasting Accuracy Along

    E-Print Network [OSTI]

    Firestone, Jeremy

    available buoy and tower data hourly from 2006-2010 NOAA National Data Buoy Center buoys (23) and tall. #12;2013DvorakandSailor'sEnergy What Time is Offshore Wind Power Most Useful? Analyzed hourly a REpower 5M, 5 MW power curve, determined capacity factor out to 200-m depth Incredibly strong resource

  7. 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.

    2000-01-07T23:59:59.000Z

    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.

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

    Office of Environmental Management (EM)

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 1112011AT&T,Office of Policy, OAPM | DepartmentIOffshore Wind Economic Impacts Model,Metrics,

  9. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del SolStrengthening a solidSynthesisAppliances Tips: ShoppingAdministrationToday's

  10. ANL Software Improves Wind Power Forecasting | 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 DataDepartment of Energy Your Density Isn't Your Destiny: The Future of1 A Strategic Framework for8.pdfAL2008-07.pdf2ProgramAMWTPANDREW

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

    SciTech Connect (OSTI)

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

    2014-04-30T23:59:59.000Z

    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.

  12. Short-term energy outlook, annual supplement 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-01T23:59:59.000Z

    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.

  13. Short-term energy outlook annual supplement, 1993

    SciTech Connect (OSTI)

    NONE

    1993-08-06T23:59:59.000Z

    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.

  14. Steam System Forecasting and Management

    E-Print Network [OSTI]

    Mongrue, D. M.; Wittke, D. O.

    1982-01-01T23:59:59.000Z

    '. This and the complex and integrated nature of the plants energy balance makes steam system forecasting and management essential for optimum use of the plant's energy. This paper discusses the method used by Union carbide to accomplish effective forecasting...

  15. A Comparison of Parallel Programming Paradigms and Data Distributions for a Limited Area Numerical Weather Forecast Routine

    E-Print Network [OSTI]

    van Engelen, Robert A.

    . Published in proceedings of the 9 th ACM International Conference on Supercomputing, July 1995, Barcelona for producing routine weather forecasts at several European meteorological institutes. Results are shown

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01T23:59:59.000Z

    the Energy Information Administrations (EIA) web site. Wein the past, compared the EIAs reference-case long-termfuel price projection from the EIA or some other long-term

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01T23:59:59.000Z

    the Energy Information Administrations (EIA) web site. Wein the past, compared the EIAs reference-case long-termfuel price projection from the EIA or some other long-term

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

    the Energy Information Administrations (EIA) web site. Wein the past, compared the EIAs reference-case long-termfuel price projection from the EIA or some other long-term

  19. PRIOR FLARING AS A COMPLEMENT TO FREE MAGNETIC ENERGY FOR FORECASTING SOLAR ERUPTIONS

    SciTech Connect (OSTI)

    Falconer, David A.; Moore, Ronald L.; Barghouty, Abdulnasser F. [ZP13 MSFC/NASA, Huntsville, AL 35812 (United States); Khazanov, Igor [CSPAR, Cramer Hall/NSSTC, The University of Alabama in Huntsville, Huntsville, AL 35899 (United States)

    2012-09-20T23:59:59.000Z

    From a large database of (1) 40,000 SOHO/MDI line-of-sight magnetograms covering the passage of 1300 sunspot active regions across the 30 Degree-Sign radius central disk of the Sun, (2) a proxy of each active region's free magnetic energy measured from each of the active region's central-disk-passage magnetograms, and (3) each active region's full-disk-passage history of production of major flares and fast coronal mass ejections (CMEs), we find new statistical evidence that (1) there are aspects of an active region's magnetic field other than the free energy that are strong determinants of the active region's productivity of major flares and fast CMEs in the coming few days; (2) an active region's recent productivity of major flares, in addition to reflecting the amount of free energy in the active region, also reflects these other determinants of coming productivity of major eruptions; and (3) consequently, the knowledge of whether an active region has recently had a major flare, used in combination with the active region's free-energy proxy measured from a magnetogram, can greatly alter the forecast chance that the active region will have a major eruption in the next few days after the time of the magnetogram. The active-region magnetic conditions that, in addition to the free energy, are reflected by recent major flaring are presumably the complexity and evolution of the field.

  20. INTERNATIONAL COMPARISON OF RESIDENTIAL ENERGY USE: INDICATORS OF RESIDENTIAL ENERGY USE AND EFFICIENCY PART ONE: THE DATA BASE

    E-Print Network [OSTI]

    Schipper, L.

    2013-01-01T23:59:59.000Z

    Forecasting Commission) 1974. Energi Prognos Utredningen80) (To be pub- lished in "Energi paa 80 Thet" (Energy in~SUMMARY RESIDENTIAL ENERGI~ YEAR J8 I Househo1ds,106 SFD,%

  1. Comparison of Building Energy Modeling Programs: Building Loads

    E-Print Network [OSTI]

    LBNL-6034E Comparison of Building Energy Modeling Programs: Building Loads Dandan Zhu1 , Tianzhen Energy, the U.S.-China Clean Energy Research Center for Building Energy Efficiency, of the U;Comparison of Building Energy Modeling Programs: Building Loads A joint effort between Lawrence Berkeley

  2. Comparison Groups on Bills: Automated, Personalized Energy Information

    E-Print Network [OSTI]

    individualized energy information for a mass audience-the entire residential customer base of an electric or gas or gas utility. Keywords: Innovative billing, energy feedback, energy efficiency Running head: ComparisonComparison Groups on Bills: Automated, Personalized Energy Information Maithili Iyer Lawrence

  3. 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.

    2010-01-01T23:59:59.000Z

    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.

  4. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

    Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN Manager Joseph O' Hagan Project Manager Kelly Birkinshaw Program Area Manager ENERGY-RELATED ENVIRONMENTAL

  5. Conservation The Northwest ForecastThe Northwest Forecast

    E-Print Network [OSTI]

    & Resources Creating Mr. Toad's Wild Ride for the PNW's Energy Efficiency InCreating Mr. Toad's Wild RideNorthwest Power and Conservation Council The Northwest ForecastThe Northwest Forecast Energy EfficiencyEnergy Efficiency Dominates ResourceDominates Resource DevelopmentDevelopment Tom EckmanTom Eckman

  6. Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty

    Reports and Publications (EIA)

    2009-01-01T23:59:59.000Z

    It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energy-related financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the marketclearing process for risk transfer can be used to generate "price bands" around observed futures prices for crude oil, natural gas, and other commodities.

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

    SciTech Connect (OSTI)

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

    2010-09-01T23:59:59.000Z

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system breaking points, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.

  8. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOEHazel Crest,EnergySerranopolis Jump to:Econ IncIRENADelhiFocus Inc

  9. 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...

  10. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

    Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN: California Energy Commission Energy-Related Environmental Research Joseph O' Hagan Contract Manager Joseph O' Hagan Project Manager Kelly Birkinshaw Program Area Manager ENERGY-RELATED ENVIRONMENTAL RESEARCH Martha

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

    E-Print Network [OSTI]

    Hong, Tianzhen

    2009-01-01T23:59:59.000Z

    ABORATORY Comparisons of HVAC Simulations between EnergyPlusemployer. Comparisons of HVAC Simulations between EnergyPlusThis paper compares HVAC simulations between EnergyPlus and

  12. NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    · NATIONAL AND GLOBAL FORECASTS · WEST VIRGINIA PROFILES AND FORECASTS · ENERGY · HEALTHCARE Research West Virginia University College of Business and Economics P.O. Box 6527, Morgantown, WV 26506 EXPERT OPINION PROVIDED BY Keith Burdette Cabinet Secretary West Virginia Department of Commerce

  13. The Value of Wind Power Forecasting

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

    Wind Power Forecasting Preprint Debra Lew and Michael Milligan National Renewable Energy Laboratory Gary Jordan and Richard Piwko GE Energy Presented at the 91 st American...

  14. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

    Quantifying PV power output variability, Solar Energy, vol.each solar sen at node i, P(t) the total power output of theSolar Forecasting Historically, traditional power generation technologies such as fossil and nu- clear power which were designed to run in stable output

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

    SciTech Connect (OSTI)

    Reno Harnish

    2011-08-16T23:59:59.000Z

    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.

  16. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

    Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN with primary contributions in the area of decision support for reservoir planning and management Commission Energy-Related Environmental Research Joseph O' Hagan Contract Manager Joseph O' Hagan Project

  17. 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

    2008-01-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    2013-01-01T23:59:59.000Z

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

  19. Comparison of Building Energy Modeling Programs: HVAC Systems

    E-Print Network [OSTI]

    LBNL-6432E Comparison of Building Energy Modeling Programs: HVAC Systems Xin Zhou1 , Tianzhen Hong2 programs (BEMPs) for HVAC calculations: EnergyPlus, DeST, and DOE-2.1E. This is a joint effort between purposes, BEMPs can be divided into load modules and HVAC system modules. This technical report

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

    SciTech Connect (OSTI)

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

    2012-08-01T23:59:59.000Z

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

  1. A Comparison of Methods for Sizing Energy Storage Devices in Renewable Energy Systems Thomas Bailey

    E-Print Network [OSTI]

    Victoria, University of

    A Comparison of Methods for Sizing Energy Storage Devices in Renewable Energy Systems by Thomas of Methods for Sizing Energy Storage Devices in Renewable Energy Systems by Thomas Bailey B.Eng, University, storage technologies are proposed as a means to increase the penetration of renewable energy, to minimize

  2. INTERNATIONAL COMPARISON OF RESIDENTIAL ENERGY USE: INDICATORS OF RESIDENTIAL ENERGY USE AND EFFICIENCY PART ONE: THE DATA BASE

    E-Print Network [OSTI]

    Schipper, L.

    2013-01-01T23:59:59.000Z

    and Analysis of Swedish Residential Energy Use Data 1960-80.1980. International Residential Energy Use and ConservationInternational Comparison of Residential Energy ! Js~. Report

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

    E-Print Network [OSTI]

    Hong, Tainzhen

    2010-01-01T23:59:59.000Z

    Comparison of energy efficiency between variable refrigeranttheir superior energy efficiency. The variable refrigerantfew studies reporting the energy efficiency of VRF systems

  4. Comparison of Fuel Cell Technologies | Department of Energy

    Office of Environmental Management (EM)

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 1112011AT&T, Inc.'sEnergyTexas1.Space DataEnergy Superior EnergyReport 2009Comparison

  5. Integrating climate change into energy demand forecasts: A commercial sector analysis

    SciTech Connect (OSTI)

    Scott, M.J.; Belzer, D.B.; Hadley, D.L.; Wrench, L.E.

    1993-10-01T23:59:59.000Z

    This study examines the effects of global climate change on commercial building energy use. The methodology used included estimating balance points and degree-day response coefficients, estimating cross-section regressions to extrapolate to a full sample, extrapolating the building sample to the year 2030, and estimating the energy consumption in the year 2030 under different temperature regimes. Results show that total primary energy consumption in U.S. commercial buildings will rise although the absolute increase in consumption may not be large, given offsetting heating benefits. Nonetheless, the effect on electric utilities may be severe.

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

    E-Print Network [OSTI]

    Robinson, J.

    2010-01-01T23:59:59.000Z

    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 ...

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

    E-Print Network [OSTI]

    Robinson, J.

    2010-01-01T23:59:59.000Z

    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...

  8. A comparison of two energy efficient motors

    SciTech Connect (OSTI)

    Kueck, J.D.; Casada, D.A.; Otaduy, P.J. [Oak Ridge National Lab., TN (United States)

    1998-06-01T23:59:59.000Z

    Limited testing provides evidence that a particular energy efficient motor (EEM) appears to have a unique susceptibility to reduced negative sequence impedance. The effect of the reduction in negative sequence impedance is to allow a higher negative sequence current flow under conditions of voltage unbalance or negative sequence harmonic distortion in the bus voltage. This higher current flow results in additional heating of the rotor and 120 Hz vibration, and may combine with other conditions to cause premature failure. In addition, energy efficient motors operate at slightly higher speeds, and may sometimes cause the driven loads to require more horsepower from the motor causing further overheating. Existing industry guidance for the application of three phase motors in poor power quality environments may be inadequate because this guidance does not consider the combined effect of such conditions as voltage unbalance, harmonic distortion and over voltage. A specific application of an EEM is studied in this paper; the motor may be failing due to a combination of over voltage, voltage unbalance, harmonic distortion and loading the motor to full rated load, while the motor has a Service Factor of 1.0.

  9. Beyond "Partly Sunny": A Better Solar Forecast | 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 DataDepartment of Energy Your Density Isn't Your Destiny: The FutureCommentsEnergyand Sustained CoordinationWater PilotBeverly

  10. 3TIER Environmental Forecast Group Inc 3TIER | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty Edit withTianlinPapersWindey Wind6:00-06:00 U.S. National SoftwareE Place:

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

    Office of Environmental Management (EM)

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33Frequently Asked QuestionsDepartment ofDepartment of Energy NewPollution,Prudent

  12. Forecasting the Dark Energy Measurement with Baryon Acoustic Oscillations: Prospects for the LAMOST surveys

    E-Print Network [OSTI]

    Wang, Xin; Zheng, Zheng; Wu, Fengquan; Zhang, Pengjie; Zhao, Yongheng

    2008-01-01T23:59:59.000Z

    The Large Area Multi-Object Spectroscopic Telescope (LAMOST) is a dedicated spectroscopic survey telescope being built in China, with an effective aperture of 4 meters and equiped with 4000 fibers. Using the LAMOST telescope, one could make redshift survey of the large scale structure (LSS). The baryon acoustic oscillation (BAO) features in the LSS power spectrum provide standard rulers for measuring dark energy and other cosmological parameters. In this paper we investigate the meaurement precision achievable for a few possible surveys: (1) a magnitude limited survey of all galaxies, (2) a survey of color selected red luminous galaxies (LRG), and (3) a magnitude limited, high density survey of zforecast the error on the dark energy equation of state and other cosmological parameters for different survey parameters. In a few cases we also use the...

  13. Energy Department Announces $2.5 Million to Improve Wind 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 FuelsNovember 13,Statement | DepartmentBlog Energy BlogDeployment |Geothermal

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

    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 DataDepartment of Energy Your Density Isn't Your Destiny: Theof"WaveInteractions and Policy

  15. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33Frequently20,000 Russian NuclearandJune 17,Agenda AgendaDepartment ofBen Solution CenterBetterThe

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

    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 DataDepartment of Energy Your Density Isn't Your Destiny:RevisedAdvisoryStandard |in STEMEnergyI.ofTrack(CHP)Saving GiftApplications |

  17. Comparison of Building Energy Modeling Programs: HVAC Systems

    E-Print Network [OSTI]

    Zhou, Xin

    2014-01-01T23:59:59.000Z

    and Mehry Yazdanian. Comparisons of HVAC Simulations betweeninformation Comparison of HVAC System Simulations inCLT (kW) Comparison of HVAC System Simulations in Different

  18. 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.

    1998-03-01T23:59:59.000Z

    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.

  19. Methodologies for Estimating Building Energy Savings Uncertainty: Review and Comparison

    E-Print Network [OSTI]

    Baltazar, J.C.; Sun, Y.; Claridge, D.

    2014-01-01T23:59:59.000Z

    for the right case is given. ESL-IC-14-09-11 Proceedings of the 14th International Conference for Enhanced Building Operations, Beijing, China, September 14-17, 2014 ...p. 1 INTERNATIONAL CONFERENCE FOR ENHANCED BUILDING OPERATIONS TSINGHUA UNIVERSITY BEIJING, CHINA SEPTEMBER 14 -17, 2014 Methodologies for Estimating Building Energy Savings Uncertainty: Review and Comparison Juan-Carlos Baltazar PhD, PE, Yifu...

  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. Comparison of Fuel Cell Technologies: Fact Sheet | 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 onYouTube YouTube Note: Since the YouTube platformBuildingCoal CombustionSmartEnergy Superior Energy(AprilComparison

  2. Comparison

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power Administration would like submit the followingth Lomonosov1CompactComparison of ion

  3. Solid low-level waste forecasting guide

    SciTech Connect (OSTI)

    Templeton, K.J.; Dirks, L.L.

    1995-03-01T23:59:59.000Z

    Guidance for forecasting solid low-level waste (LLW) on a site-wide basis is described in this document. Forecasting is defined as an approach for collecting information about future waste receipts. The forecasting approach discussed in this document is based solely on hanford`s experience within the last six years. Hanford`s forecasting technique is not a statistical forecast based upon past receipts. Due to waste generator mission changes, startup of new facilities, and waste generator uncertainties, statistical methods have proven to be inadequate for the site. It is recommended that an approach similar to Hanford`s annual forecasting strategy be implemented at each US Department of Energy (DOE) installation to ensure that forecast data are collected in a consistent manner across the DOE complex. Hanford`s forecasting strategy consists of a forecast cycle that can take 12 to 30 months to complete. The duration of the cycle depends on the number of LLW generators and staff experience; however, the duration has been reduced with each new cycle. Several uncertainties are associated with collecting data about future waste receipts. Volume, shipping schedule, and characterization data are often reported as estimates with some level of uncertainty. At Hanford, several methods have been implemented to capture the level of uncertainty. Collection of a maximum and minimum volume range has been implemented as well as questionnaires to assess the relative certainty in the requested data.

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

    SciTech Connect (OSTI)

    New, Joshua Ryan [ORNL; Miller, William A [ORNL; Huang, Yu (Joe) [White Box Technologies; Levinson, Ronnen [Lawrence Berkeley National Laboratory (LBNL)

    2014-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01T23:59:59.000Z

    andvalidation. SolarEnergy. 73:5,307? Perez,R. ,forecastdatabase. SolarEnergy. 81:6,809?812. forecastsintheUS. SolarEnergy. 84:12,2161?2172.

  6. NREL: Transmission Grid Integration - 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's Possible for Renewable Energy: Grid IntegrationReport AvailableForecasting NREL researchers use

  7. 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)

    1993-08-01T23:59:59.000Z

    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.

  8. Wind Speed Forecasting for Power System Operation

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

    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...

  9. 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

  10. Wind Speed Forecasting for Power System Operation

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

    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...

  11. RACORO 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's Possible forPortsmouth/Paducah47,193.70COMMUNITYResponses: QuestionDaniel Hartsock CIMMS,

  12. Forecasted Opportunities

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power AdministrationField8,Dist.New MexicoFinancingProofWorkingEnergyGo modelP eForForAForecasted

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

    SciTech Connect (OSTI)

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

    2013-11-01T23:59:59.000Z

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

  14. CALIFORNIA ENERGY CALIFORNIA ENERGY DEMAND 2010-2020

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2010-2020 ADOPTED FORECAST for this report: Kavalec, Chris and Tom Gorin, 2009. California Energy Demand 20102020, Adopted Forecast. California Energy Commission. CEC2002009012CMF #12; i Acknowledgments The demand forecast

  15. 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

    2012-01-01T23:59:59.000Z

    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) ...

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

    E-Print Network [OSTI]

    2011-01-01T23:59:59.000Z

    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,

  17. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    2011-01-01T23:59:59.000Z

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

  19. Recently released EIA report presents international forecasting data

    SciTech Connect (OSTI)

    NONE

    1995-05-01T23:59:59.000Z

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

  20. The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss

    E-Print Network [OSTI]

    Auffhammer, Maximilian

    2005-01-01T23:59:59.000Z

    Agency: 1982-2005a, Annual Energy Outlook, EIA, Washington,Agency: 2004, Annual Energy Outlook Forecast Evaluation,Agency: 2005b, Annual Energy Outlook, EIA, Washington, D.C.

  1. The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss

    E-Print Network [OSTI]

    Auffhammer, Maximilian

    2005-01-01T23:59:59.000Z

    2005a, Annual Energy Outlook, EIA, Washington, D.C. Energy2005b, Annual Energy Outlook, EIA, Washington, D.C. Granger,Paper ???? The Rationality of EIA Forecasts under Symmetric

  2. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    NONE

    1996-08-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    2011-01-01T23:59:59.000Z

    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. ,

  4. Short term forecasting of solar radiation based on satellite data

    E-Print Network [OSTI]

    Heinemann, Detlev

    Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer University, D-26111 Oldenburg Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources into existing energy supply structures. Fluctuations of solar irradiance

  5. 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

  6. Impact of PV forecasts uncertainty in batteries management in microgrids

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    -- Photovoltaic systems, Batteries, Forecasting I. INTRODUCTION This paper presents first results of a study Energies and Energy Systems Sophia Antipolis, France andrea.michiorri@mines-paristech.fr Abstract production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size

  7. Technology Forecasting Scenario Development

    E-Print Network [OSTI]

    Technology Forecasting and Scenario Development Newsletter No. 2 October 1998 Systems Analysis was initiated on the establishment of a new research programme entitled Technology Forecasting and Scenario and commercial applica- tion of new technology. An international Scientific Advisory Panel has been set up

  8. Rainfall-River Forecasting

    E-Print Network [OSTI]

    US Army Corps of Engineers

    ;2Rainfall-River Forecasting Joint Summit II NOAA Integrated Water Forecasting Program · Minimize losses due management and enhance America's coastal assets · Expand information for managing America's Water Resources, Precipitation and Water Quality Observations · USACE Reservoir Operation Information, Streamflow, Snowpack

  9. Bull. Astr. Soc. India (2010) 38, 147163 Comparison of energies between eruptive phenomena and

    E-Print Network [OSTI]

    Howard, Tim

    2010-01-01T23:59:59.000Z

    Bull. Astr. Soc. India (2010) 38, 147163 Comparison of energies between eruptive phenomena the energy carried away by a coronal mass ejection (CME) and the radiative energy loss in associated flare plasma, with the decrease in magnetic free energy during a release in active region NOAA 10930

  10. Annual energy outlook 1995, with projections to 2010

    SciTech Connect (OSTI)

    NONE

    1995-01-01T23:59:59.000Z

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

  11. Weather Forecast Data an Important Input into Building Management Systems

    E-Print Network [OSTI]

    Poulin, L.

    2013-01-01T23:59:59.000Z

    it can generate as much or more energy that it needs ? Building activities need N kWhrs per day (solar panels, heating, etc) ? Harvested from solar panels & passive solar. Amount depends on weather ? NWP models forecast DSWRF @ surface (MJ/m2...://collaboration.cmc.ec.gc.ca/cmc/cmoi/SolarScribe/SolarScribe/ CMC NWP datasets for Day 2 Forecasts ? Regional Deterministic Prediction System (RDPS) ? RDPS raw model data ? 10 km resolution, North America, 000-054 forecasts ? Data at: http...

  12. The Energy Return on Energy Investment (EROI) of Photovoltaics: Methodology and Comparisons with Fossil Fuel Life Cycles

    E-Print Network [OSTI]

    , 08003 Barcelona, Spain 2 Center for Life Cycle Analysis, Columbia University, New York, NY 10027, USA 3 of that energy (or its equivalent from some other source) is required to extract, grow, etc., a new unit1 The Energy Return on Energy Investment (EROI) of Photovoltaics: Methodology and Comparisons

  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.

    2013-10-01T23:59:59.000Z

    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. Towards Green Cryptography: a Comparison of Lightweight Ciphers from the Energy Viewpoint

    E-Print Network [OSTI]

    Nesterov, Yurii

    different technologies. A recent initiative can also be mentioned for soft- ware implementations [6Towards Green Cryptography: a Comparison of Lightweight Ciphers from the Energy Viewpoint St de Louvain, Institute of Information and Communication Technologies, Electronics and Applied

  15. Comparison of Two Autonomous AC-DC Converters for Piezoelectric Energy Scavenging Systems

    E-Print Network [OSTI]

    Boyer, Edmond

    - 1 - Comparison of Two Autonomous AC-DC Converters for Piezoelectric Energy Scavenging Systems E Cornaredo, Milan, Italy Abstract - Piezoelectric Energy Scavenging Systems (PESS) are used to convert of the output voltage of these transducers is needed. Since the output power level of the energy transducer can

  16. Performance Comparison of Hybrid Vehicle Energy Management Controllers on Real-World Drive Cycle Data

    E-Print Network [OSTI]

    Grizzle, Jessy W.

    Performance Comparison of Hybrid Vehicle Energy Management Controllers on Real-World Drive Cycle. Hybrid vehicles are characterized by multiple energy sources; the strategy to control the energy flow Data Daniel F. Opila, Xiaoyong Wang, Ryan McGee, Jeffrey A. Cook, and J.W. Grizzle Abstract-- Hybrid

  17. Comparison of House and Senate Clean Energy Deployment Administration (CEDA) provisions

    E-Print Network [OSTI]

    Laughlin, Robert B.

    Comparison of House and Senate Clean Energy Deployment Administration (CEDA) provisions What is CEDA? Both the American Clean Energy Leadership Act of 2009 (ACELA) in the Senate and Subtitle J- Nuclear and Advanced Technologies of the American Clean Energy and Security Act (H.R. 2454) in the House

  18. Comparison of Allocation Schemes for Virtual Machines in Energy-aware

    E-Print Network [OSTI]

    Do, Tien Van

    Comparison of Allocation Schemes for Virtual Machines in Energy-aware Server Farms TIEN VAN DO. In this paper, a simple energy-aware policy incorporating allocation schemes of virtual servers is proposed in a server farm. Keywords: green computing; virtualization; virtual machine; performance model; energy-aware

  19. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    NONE

    1998-07-01T23:59:59.000Z

    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.

  20. Probabilistic manpower forecasting

    E-Print Network [OSTI]

    Koonce, James Fitzhugh

    1966-01-01T23:59:59.000Z

    PROBABILISTIC MANPOWER FORECASTING A Thesis JAMES FITZHUGH KOONCE Submitted to the Graduate College of the Texas ASSAM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May, 1966 Major Subject...: Computer Science and Statistics PROBABILISTIC MANPOWER FORECASTING A Thesis By JAMES FITZHUGH KOONCE Submitted to the Graduate College of the Texas A@M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May...

  1. Forecasting Building Occupancy Using Sensor Network James Howard

    E-Print Network [OSTI]

    Hoff, William A.

    of the forecasting algorithm for the different conditions. 1. INTRODUCTION According to the U.S. Department of Energy could take advantage of times when electricity cost is lower, to chill a cold water storage tankForecasting Building Occupancy Using Sensor Network Data James Howard Colorado School of Mines

  2. UPF Forecast | Y-12 National Security Complex

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

    Uranium Processing Facility UPF Forecast UPF Forecast UPF Procurement provides the following forecast of subcontracting opportunities. Keep in mind that these requirements may be...

  3. Long Term Forecast ofLong Term Forecast of TsunamisTsunamis

    E-Print Network [OSTI]

    : ImproveImprove NOAANOAA''ss understandingunderstanding and forecast capabilityand forecast capability inin

  4. NatioNal aNd Global Forecasts West VirGiNia ProFiles aNd Forecasts

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    · NatioNal aNd Global Forecasts · West VirGiNia ProFiles aNd Forecasts · eNerGy · Healt Global Insight, paid for by the West Virginia Department of Revenue. 2013 WEST VIRGINIA ECONOMIC OUTLOOKWest Virginia Economic Outlook 2013 is published by: Bureau of Business & Economic Research West

  5. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 15 SEPTEMBER 28, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  6. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 27 OCTOBER 10, 2013

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  7. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 16 AUGUST 29, 2013

    E-Print Network [OSTI]

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  8. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 3 AUGUST 16, 2012

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  9. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 12 OCTOBER 25, 2012

    E-Print Network [OSTI]

    Gray, William

    to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined This is the fourth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for individual event parameters such as named storms and hurricanes. We issue forecasts for ACE using three

  10. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 2012

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  11. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 13 SEPTEMBER 26, 2013

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  12. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 18 AUGUST 31, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  13. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 11 OCTOBER 24, 2013

    E-Print Network [OSTI]

    Gray, William

    to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for individual event parameters such as named storms and hurricanes. We issue forecasts for ACE using three

  14. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 2 AUGUST 15, 2013

    E-Print Network [OSTI]

    Gray, William

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  15. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2012

    E-Print Network [OSTI]

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  16. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 17 AUGUST 30, 2012

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  17. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 4 AUGUST 17, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  18. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 29 OCTOBER 12, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  19. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 11 SEPTEMBER 24, 2014

    E-Print Network [OSTI]

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  20. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 30 SEPTEMBER 12, 2013

    E-Print Network [OSTI]

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  1. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2011

    E-Print Network [OSTI]

    Birner, Thomas

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the third year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  2. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 28 SEPTEMBER 10, 2014

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  3. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 13 OCTOBER 26, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

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

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over Our Instagram Secretary Moniz9 SeptemberSettingUncertainties in the Short-Term

  5. Annual energy outlook 1994: With projections to 2010

    SciTech Connect (OSTI)

    Not Available

    1994-01-01T23:59:59.000Z

    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.

  6. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    2005-07-01T23:59:59.000Z

    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.

  8. Comparison of closed and open thermochemical processes, for long-term thermal energy storage applications

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 Comparison of closed and open thermochemical processes, for long-term thermal energy storage-term thermal storage, second law analysis * Corresponding author: E-mail: mazet@univ-perp.fr Nomenclature c Energy Tecnosud, Rambla de la thermodynamique, 66100 Perpignan, France b Université de Perpignan Via

  9. Experimental Comparison of Algorithms for Energy-Efficient Multicasting in Ad Hoc

    E-Print Network [OSTI]

    Caragiannis, Ioannis

    Experimental Comparison of Algorithms for Energy-Efficient Multicasting in Ad Hoc Networks Stavros Technology Institute and Dept. of Computer Engineering and Informatics University of Patras, 26500 Rio, Greece Abstract. Energy is a scarce resource in ad hoc wireless networks and it is of paramount

  10. Master thesis Solar Energy Meteorology Comparison of different methods to estimate cloud height for solar

    E-Print Network [OSTI]

    Peinke, Joachim

    Master thesis ­ Solar Energy Meteorology Comparison of different methods to estimate cloud height: · Interest in meteorology and solar energy · Experiences with data handling and analysis · Good programming for solar irradiance calculations In order to derive incoming solar irradiance at the earths surface

  11. Solar Energy 74 (2003) 157173 Comparison between ray-tracing simulations and bi-directional

    E-Print Network [OSTI]

    Solar Energy 74 (2003) 157­173 Comparison between ray-tracing simulations and bi-Louis Scartezzini a Solar Energy and Building Physics Laboratory LESO-PB, Swiss Federal Institute of Technology EPFL Cyclotron Road, MS 2-300, Berkeley, CA 94720-8134, USA Abstract Evaluation of solar heat gain and daylight

  12. Final Map Draft Comparison Report WIND ENERGY RESOURCE MODELING AND MEASUREMENT PROJECT

    E-Print Network [OSTI]

    SOLUTIONS, LLC (now AWS Truewind LLC) 255 FULLER ROAD, SUITE 274 ALBANY, NEW YORK Michael Brower PrincipalII Final Map Draft Comparison Report #12;WIND ENERGY RESOURCE MODELING AND MEASUREMENT PROJECT Tel: 978-749-9591 Fax: 978-749-9713 mbrower@awstruewind.com August 10, 2004 #12;2 WIND ENERGY RESOURCE

  13. The International Urban Energy Balance Models Comparison Project: First Results from Phase 1

    E-Print Network [OSTI]

    Ribes, Aurélien

    The International Urban Energy Balance Models Comparison Project: First Results from Phase 1 C. S and Kapodistrian University of Athens, Athens, Greece h University of Lo´ dz´, Lo´ dz´, Poland i Royal) ABSTRACT A large number of urban surface energy balance models now exist with different assumptions about

  14. The water consumption of energy production: an international comparison

    E-Print Network [OSTI]

    Marks, David H.

    Producing energy resources requires significant quantities of fresh water. As an energy sector changes or expands, the mix of technologies deployed to produce fuels and electricity determines the associated burden on ...

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

    Open Energy Info (EERE)

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

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18T23:59:59.000Z

    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.

  17. Energy efficiency and the cost of GHG abatement: A comparison of bottom-up and hybrid models for the US

    E-Print Network [OSTI]

    Energy efficiency and the cost of GHG abatement: A comparison of bottom-up and hybrid models energyeconomy models. Using the CIMS hybrid model, we conducted simulations for comparison with the Mc February 2011 Accepted 16 August 2011 Available online 17 September 2011 Keywords: Energy efficiency

  18. Distributed Wind Policy Comparison Tool | 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 onYouTube YouTube Note: Since the YouTube| DepartmentStatementDepartmentDiggingDistributed Wind Policy Comparison Tool

  19. 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

  20. Improving Inventory Control Using Forecasting

    E-Print Network [OSTI]

    Balandran, Juan

    2005-12-16T23:59:59.000Z

    EMGT 835 FIELD PROJECT: Improving Inventory Control Using Forecasting By Juan Mario Balandran jmbg@hotmail.com Master of Science The University of Kansas Fall Semester, 2005 An EMGT Field Project report submitted...............................................................................................................................................10 Current Inventory Forecast Process ...........................................................................................10 Development of Alternative Forecast Process...

  1. timber quality Modelling and forecasting

    E-Print Network [OSTI]

    Forest and timber quality in Europe Modelling and forecasting yield and quality in Europe Forest and timber quality in Europe Modelling and forecasting yield and quality in Europe M E F Y Q U E #12;Valuing and the UK are working closely together to develop a model to help forecast timber growth, yield, quality

  2. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

    electricity demand forecast means that the region's electricity needs would grow by 5,343 average megawattsDemand 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

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

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

    energy performance of appliances and equipment as it compares with models and test procedures. The study looked to determine whether DOE and industry test procedures...

  4. OCTOBER-NOVEMBER FORECAST FOR 2014 CARIBBEAN BASIN HURRICANE ACTIVITY

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

    and hurricanes, but instead predicts both hurricane days and Accumulated Cyclone Energy (ACE). Typically, while) tropical cyclone (TC) activity. We have decided to issue this forecast, because Klotzbach (2011) has

  5. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    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

  6. 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 because oil, coal, and natural gas are potential fuels for electricity generation. Natural gas

  7. Deep Energy Retrofit Performance Metric Comparison: Eight California Case Studies

    SciTech Connect (OSTI)

    Walker, Iain; Fisher, Jeremy; Less, Brennan

    2014-06-01T23:59:59.000Z

    In this paper we will present the results of monitored annual energy use data from eight residential Deep Energy Retrofit (DER) case studies using a variety of performance metrics. For each home, the details of the retrofits were analyzed, diagnostic tests to characterize the home were performed and the homes were monitored for total and individual end-use energy consumption for approximately one year. Annual performance in site and source energy, as well as carbon dioxide equivalent (CO{sub 2}e) emissions were determined on a per house, per person and per square foot basis to examine the sensitivity to these different metrics. All eight DERs showed consistent success in achieving substantial site energy and CO{sub 2}e reductions, but some projects achieved very little, if any source energy reduction. This problem emerged in those homes that switched from natural gas to electricity for heating and hot water, resulting in energy consumption dominated by electricity use. This demonstrates the crucial importance of selecting an appropriate metric to be used in guiding retrofit decisions. Also, due to the dynamic nature of DERs, with changes in occupancy, size, layout, and comfort, several performance metrics might be necessary to understand a projects success.

  8. Comparison of CHEERS energy use predictions with actual utility bills

    SciTech Connect (OSTI)

    Wilcox, B.A.; Hunt, M.B.

    1998-07-01T23:59:59.000Z

    The usefulness of Home Energy Rating Systems (HERS) is primarily derived from the accurate analysis of the present energy efficiency of a home and the cost effectiveness of the measures that are recommended for improving its efficiency. The Energy Efficient Mortgage is predicated on the concept that the mortgage money spent to improve the efficiency of a home will cost less per months to finance that the utility bill savings that are generated. Computer simulation programs are used to estimate the annual energy used for heating, cooling and domestic hot water. A large sample of rated homes in San Jose California was analyzed to compare predicted energy sue with actual bills. The HERS predictions for both heating and cooling were found to significantly overestimate the energy use of low rated homes compared to efficient homes. Cooling energy use of low rated homes with air conditioning was actually lower than for efficient homes with air conditioning. Significant correlation between family characteristics and home efficiency are thought to be part of the reason for this dilemma. A number of areas are proposed for further work to improve the HERS estimates.

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

    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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directed off Energy.gov. Are you sure you want toworldPower 2010 1AAcquisitionDevelopmentChooseCoal

  10. Distributed Wind Policy Comparison Tool Website | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489 No revision| Open Energy Information At1986)DistributedTool

  11. Distributed Wind Policy Comparison Tool | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489 No revision| Open Energy Information

  12. Verification of hourly forecasts of wind turbine power output

    SciTech Connect (OSTI)

    Wegley, H.L.

    1984-08-01T23:59:59.000Z

    A verification of hourly average wind speed forecasts in terms of hourly average power output of a MOD-2 was performed for four sites. Site-specific probabilistic transformation models were developed to transform the forecast and observed hourly average speeds to the percent probability of exceedance of an hourly average power output. (This transformation model also appears to have value in predicting annual energy production for use in wind energy feasibility studies.) The transformed forecasts were verified in a deterministic sense (i.e., as continuous values) and in a probabilistic sense (based upon the probability of power output falling in a specified category). Since the smoothing effects of time averaging are very pronounced, the 90% probability of exceedance was built into the transformation models. Semiobjective and objective (model output statistics) forecasts were made compared for the four sites. The verification results indicate that the correct category can be forecast an average of 75% of the time over a 24-hour period. Accuracy generally decreases with projection time out to approx. 18 hours and then may increase due to the fairly regular diurnal wind patterns that occur at many sites. The ability to forecast the correct power output category increases with increasing power output because occurrences of high hourly average power output (near rated) are relatively rare and are generally not forecast. The semiobjective forecasts proved superior to model output statistics in forecasting high values of power output and in the shorter time frames (1 to 6 hours). However, model output statistics were slightly more accurate at other power output levels and times. Noticeable differences were observed between deterministic and probabilistic (categorical) forecast verification results.

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01T23:59:59.000Z

    Forecasting and Resource Assessment, 1 st Edition, Editors:Forecasting and Resource Assessment, 1 st Edition, Editors:Forecasting and Resource Assessment, 1 st Ed.. Editor: Jan

  14. Forecasting the Standard & Poor's 500 stock index futures price: interest rates, dividend yields, and cointegration

    E-Print Network [OSTI]

    Fritsch, Roger Erwin

    1997-01-01T23:59:59.000Z

    forward price series is constructed using interest rate and dividend yield data. Out-of-sample forecasts from error correction models are compared to those from vector autoregressions (VAR) fit to levels and VARs fit to first differences. This comparison...

  15. Comparison of Fuel Cell Technologies | 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 onYouTube YouTube Note: Since the YouTube platformBuildingCoal CombustionSmartEnergy Superior Energy(April

  16. Comparison of Energy Efficiency Incentive Programs: Rebates and White

    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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:PowerCER.png ElColumbia, NorthCommunitySouth Jump

  17. An Energy Evolution:Alternative Fueled Vehicle Comparisons | 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 DataDepartment of Energy Your Density Isn't Your Destiny: The Future of1Albuquerque, NMPerformance | Department of Energy| Department

  18. Wind for Schools Portal Comparison | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty Edit withTianlinPapers Home Kyoung's pictureWind Power Energia JumpMaps.jpgWindWind

  19. Property:Data Comparison to Computational Models | 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal PwerPerkins County, Nebraska:PrecourtOid Jump to: navigation, searchto Computational Models Jump

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

    SciTech Connect (OSTI)

    Hong, Tainzhen; Liu, Xaiobing

    2009-11-01T23:59:59.000Z

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

  1. Forecasting oilfield economic performance

    SciTech Connect (OSTI)

    Bradley, M.E. (Univ. of Chicago, IL (United States)); Wood, A.R.O. (BP Exploration, Anchorage, AK (United States))

    1994-11-01T23:59:59.000Z

    This paper presents a general method for forecasting oilfield economic performance that integrates cost data with operational, reservoir, and financial information. Practices are developed for determining economic limits for an oil field and its components. The economic limits of marginal wells and the role of underground competition receive special attention. Also examined is the influence of oil prices on operating costs. Examples illustrate application of these concepts. Categorization of costs for historical tracking and projections is recommended.

  2. LCA can help determine environmental burdens from"cradle to grave"and facilitate more consistent comparisons of energy

    E-Print Network [OSTI]

    power (CSP), biopower, geothermal, ocean energy, hydropower, nuclear, natural gas, and coal technologies comparisons of energy technologies. Figure 1. Generalized life cycle stages for energy technologies Source and renewable energy technologies, the majority of GHG emissions occur upstream of operation. LCA of Energy

  3. INTERNATIONAL COMPARISON OF RESIDENTIAL ENERGY USE: INDICATORS OF RESIDENTIAL ENERGY USE AND EFFICIENCY PART ONE: THE DATA BASE

    SciTech Connect (OSTI)

    Schipper, L.; Ketoff, A.; Meyers, S.

    1981-05-01T23:59:59.000Z

    This summary report presents information on the end-uses of energy in the residential sector of seven major OECD countries over the period 1960-1978. Much of the information contained herein has never been published before. We present data on energy consumption by energy type and end-use for three to five different years for each country. Each year table is complemented by a set of indicators, which are assembled for the entire 20-year period at the end of each country listing. Finally, a set of key indicators from each country is displayed together in a table, allowing comparison for three periods: early (1960-63), pre-embargo (1970-73), and recent (1975-78). Analysis of these results, smoothing and interpolation of the data, addition of further data, and analytical comparison of in-country and cross-country trends will follow in the next phase of our work.

  4. A COMPARISON OF THE INTENSITIES AND ENERGIES OF GRADUAL SOLAR ENERGETIC PARTICLE EVENTS WITH THE DYNAMICAL PROPERTIES OF ASSOCIATED CORONAL MASS EJECTIONS

    SciTech Connect (OSTI)

    Kahler, S. W. [Air Force Research Laboratory, Space Vehicles Directorate, 3550 Aberdeen Ave., Kirtland AFB, NM 87117 (United States); Vourlidas, A., E-mail: AFRL.RVB.PA@kirtland.af.mil [Space Sciences Division, Naval Research Laboratory, Washington, DC 20375 (United States)

    2013-06-01T23:59:59.000Z

    Gradual solar energetic particle (SEP) events observed at 1 AU are produced by shocks driven by coronal mass ejections (CMEs). Characterizations of the remotely imaged CMEs and of their associated SEP events observed in situ can be used to increase our ability to forecast SEP events and to understand better the physical connections between the two phenomena. We carry out a statistical comparison of the peak intensities Ip20, of 120 western-hemisphere 20 MeV SEP events with those of their associated CMEs observed by the Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph over the past solar cycle. For a subset of 96 events observed with the EPACT instrument on the Wind spacecraft we also compare the SEP 2 MeV peak intensities Ip2, power-law energy spectral exponents {gamma}, total SEP energies Esep, and 2 MeV nuc{sup -1} H/He ratios with CME properties. New analyses of white-light CME images enable us to improve calculations of the CME masses and potential energies and then to determine two values of their kinetic energies based on frontal V (fr) and center-of-mass V (cm) speeds. Despite considerable scatter in the SEP and CME data, the large dynamical ranges of both the SEP and CME parameters allow us to determine statistical trends in the comparisons of the logs of the parameters. Ip2, Ip20, and Esep are significantly correlated with CME kinetic energies, masses, and speeds, while {gamma} trends lower (harder). Those correlations are higher with V (fr) than with V (cm) parameters, indicating a less significant role for the body of the CME than for the CME front in SEP production. The high ratios ({>=}10%) of Esep to CME energies found by Mewaldt et al. are confirmed, and the fits are consistent with a linear relationship between the two energies. The 2 MeV nuc{sup -1} H/He ratios decrease with increasing CME speeds, which may be an effect of shock geometry. We discuss several factors that limit the estimates of both the SEP and CME energies.

  5. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27T23:59:59.000Z

    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.

  6. Cost and Performance Comparison Baseline for Fossil Energy Power Plants

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power Administration would likeConstitution AndControllingCoolCorrective Action1, CostCostBaseline

  7. Cost and Performance Comparison Baseline for Fossil Energy Power Plants

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power Administration would likeConstitution AndControllingCoolCorrective Action1,

  8. Cost and Performance Comparison Baseline for Fossil Energy Power Plants

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power Administration would likeConstitution AndControllingCoolCorrective Action1,a, September 2013

  9. Subhourly wind forecasting techniques for wind turbine operations

    SciTech Connect (OSTI)

    Wegley, H.L.; Kosorok, M.R.; Formica, W.J.

    1984-08-01T23:59:59.000Z

    Three models for making automated forecasts of subhourly wind and wind power fluctuations were examined to determine the models' appropriateness, accuracy, and reliability in wind forecasting for wind turbine operation. Such automated forecasts appear to have value not only in wind turbine control and operating strategies, but also in improving individual wind turbine control and operating strategies, but also in improving individual wind turbine operating strategies (such as determining when to attempt startup). A simple persistence model, an autoregressive model, and a generalized equivalent Markhov (GEM) model were developed and tested using spring season data from the WKY television tower located near Oklahoma City, Oklahoma. The three models represent a pure measurement approach, a pure statistical method and a statistical-dynamical model, respectively. Forecasting models of wind speed means and measures of deviations about the mean were developed and tested for all three forecasting techniques for the 45-meter level and for the 10-, 30- and 60-minute time intervals. The results of this exploratory study indicate that a persistence-based approach, using onsite measurements, will probably be superior in the 10-minute time frame. The GEM model appears to have the most potential in 30-minute and longer time frames, particularly when forecasting wind speed fluctuations. However, several improvements to the GEM model are suggested. In comparison to the other models, the autoregressive model performed poorly at all time frames; but, it is recommended that this model be upgraded to an autoregressive moving average (ARMA or ARIMA) model. The primary constraint in adapting the forecasting models to the production of wind turbine cluster power output forecasts is the lack of either actual data, or suitable models, for simulating wind turbine cluster performance.

  10. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    2013-10-30T23:59:59.000Z

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

  12. Performance comparison of U.K. low-energy cooling systems by energy simulation

    E-Print Network [OSTI]

    Olsen, Erik L. (Erik Lee), 1979-

    2002-01-01T23:59:59.000Z

    Building energy simulation is an important tool for evaluating the energy consumption of a building and can provide guidance in the design of a building and its mechanical systems. EnergyPlus is a new energy simulation ...

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01T23:59:59.000Z

    andvalidation. SolarEnergy. 73:5,307? Perez,R. ,irradianceforecastsforsolarenergyapplicationsbasedonusingsatellitedata. SolarEnergy67:1?3,139?150.

  14. Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts

    E-Print Network [OSTI]

    Giannitrapani, Antonello

    bid is computed by exploiting the forecast energy price for the day ahead market, the historical wind renewable energy resources, such as wind and photovoltaic, has grown rapidly. It is well known the problem of optimizing energy bids for an independent Wind Power Producer (WPP) taking part

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

    E-Print Network [OSTI]

    energy-using devices in the average U.S. household that used over 4,700 kWh of electricity, natural gas-using devices to energy price, household income, and the cost of these devices. This analysis findsTHE DESIRE TO ACQUIRE: FORECASTING THE EVOLUTION OF HOUSEHOLD ENERGY SERVICES by Steven Groves BASc

  16. Renewable Forecast Min-Max2020.xls

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiation Protection Technical s o Freiberge s 3 c/)RenewableRenewable EnergyForecast of

  17. Mathematical Forecasting Donald I. Good

    E-Print Network [OSTI]

    Boyer, Robert Stephen

    Mathematical Forecasting Donald I. Good Technical Report 47 September 1989 Computational Logic Inc the physical behavior of computer programs can reduce these risks for software engineering in the same way that it does for aerospace and other fields of engineering. Present forecasting capabilities for computer

  18. Regional-seasonal weather forecasting

    SciTech Connect (OSTI)

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01T23:59:59.000Z

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  19. Energy and Security in Northeast Asia: Supply and Demand, Conflict and

    E-Print Network [OSTI]

    Fesharaki, Fereidun; Banaszak, Sarah; WU, Kang; Valencia, Mark J.; Dorian, James P.

    1998-01-01T23:59:59.000Z

    scenario, China's primary energy consumption is forecast toChina and Period Historical GDP Oil Coal Gas Fossil Energy Total Forecast:China Period Historical GDP Oil Coal Gas Nuclear Power Hydroelectricity Primary Energy Total Forecast:

  20. 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.

    2012-09-01T23:59:59.000Z

    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.

  1. Short-term planning and forecasting for petroleum. Master's thesis

    SciTech Connect (OSTI)

    Elkins, R.D.

    1988-06-01T23:59:59.000Z

    The Defense Fuel Supply Center (DFSC) has, in recent past, been unable to adequately forecast for short-term petroleum requirements. This has resulted in inaccurate replenishment quantities and required short-notice corrections, which interrupted planned resupply methods. The relationship between the annual CINCLANTFLT DFM budget and sales from the the Norfolk Defense Fuel Support Point (DFSP) is developed and the past sales data from the Norfolk DFSP is used to construct seasonality indices. Finally, the budget/sales relationship is combined with the seasonality indices to provide a new forecasting model. The model is then compared with the current one for FY-88 monthly forecasts. The comparison suggests that the new model can provide accurate, timely requirements data and improve resupply of the Norfolk Defense Fuel Support Point.

  2. Paper presented at EWEC 2008, Brussels, Belgium (31 March-03 April) Uncertainty Estimation of Wind Power Forecasts

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    -Antipolis, France Abstract--Short-term wind power forecasting tools providing "single-valued" (spot) predictions associated to the future wind power produc- tion for performing more efficiently functions such as reserves and modelling architec- tures for probabilistic wind power forecasting. Then, a comparison is carried out

  3. An international comparison of government expenditures for energy conservation research and development:

    SciTech Connect (OSTI)

    McDonald, S.C.

    1988-03-01T23:59:59.000Z

    This study provides a comparison of US and foreign government spending for energy conservation research and development (R and D). The countries included in this analysis are: the United States, United Kingdom, France, Sweden, West Germany, and Japan. The approach of this paper was to compare the research program of each country at a high level of aggregation with the US Department of Energy (DOE) program structure. This paper does not allow for differences in the way each country defines or accounts for research.

  4. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-02-23T23:59:59.000Z

    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.

  5. 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

    2003-08-13T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    1993-07-01T23:59:59.000Z

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

  7. Skill forecasting from different wind power ensemble prediction methods This article has been downloaded from IOPscience. Please scroll down to see the full text article.

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    Skill forecasting from different wind power ensemble prediction methods This article has been Contact us My IOPscience #12;Skill forecasting from different wind power ensemble prediction methods uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation

  8. Comparison Between Air and Helium for Use as Working Fluids in the Energy-Conversion Cycle of the MPBR

    E-Print Network [OSTI]

    Galen, T. A.

    A comparison between air and helium for use as working fluids in the energy-conversion cycle of the MPBR is presented. To date, helium has been selected in the MPBR indirect-cycle working reference design. Air open- and ...

  9. Comparison of Energy Efficiency in PSTN and VoIP Florin Bota, Faheem Khuhawar, Marco Mellia, Michela Meo

    E-Print Network [OSTI]

    Comparison of Energy Efficiency in PSTN and VoIP Systems Florin Bota, Faheem Khuhawar, Marco Mellia.lastname@polito.it ABSTRACT The importance of deploying energy efficient networks has vastly increased due to the rapidly to existing networks that could prove to be energy efficient. In this paper, two telephone net- works namely

  10. Comparison of a Clean Energy Standard and other Mandates with a Carbon Tax Kemal Sarica and Wallace E. Tyner

    E-Print Network [OSTI]

    Ginzel, Matthew

    according to Annual Energy Outlook 2010 and is further enhanced with data generated from a global generalComparison of a Clean Energy Standard and other Mandates with a Carbon Tax Kemal Sarica and Wallace E. Tyner Purdue University Abstract To deal with Greenhouse Gas (GHG) emissions and other energy

  11. Comparison of Software Models for Energy Savings from Cool Roofs Joshua New, Oak Ridge National Laboratory (United States)

    E-Print Network [OSTI]

    Tennessee, University of

    , multiple substrate types, and insulation levels. A base case and energy-efficient alternative canComparison of Software Models for Energy Savings from Cool Roofs Joshua New, Oak Ridge National Department of Energy as an industry-consensus tool to help building owners, manufacturers, distributors

  12. Anal. Chem. 1981, 53, 1241-1244 1241 Comparison of Mass Spectra Obtained with Low-Energy Ion

    E-Print Network [OSTI]

    Chait, Brian T.

    and High-Energy Californium-252 Fission Fragment Bombardment Werner Ens and K. G. Standing Physics mass spectrometry (1,2)and low-energy ion bombardment mass spectrometry (3-5). In these techniquesAnal. Chem. 1981, 53, 1241-1244 1241 Comparison of Mass Spectra Obtained with Low-Energy Ion

  13. Urban Form Energy Use and Emissions in China: Preliminary Findings and Model Proof of Concept

    E-Print Network [OSTI]

    Aden, Nathaniel

    2011-01-01T23:59:59.000Z

    China's building sector--A review of energy and climate models forecast,China's building sector--A review of energy and climate models forecast,

  14. Lifecycle Assessment of Beijing-Area Building Energy Use and Emissions: Summary Findings and Policy Applications

    E-Print Network [OSTI]

    Aden, Nathaniel

    2010-01-01T23:59:59.000Z

    China's building sector--A review of energy and climate models forecast,China's building sector--A review of energy and climate models forecast,

  15. The Revised Austin Energy Code and Comparisons with the Texas State Energy Standard

    E-Print Network [OSTI]

    Crow, G.

    For the past two years the City of Austin Energy Code has been under review using the State Energy Standard and ASHRAE 90.2P as models for the revised Austin Energy Code. The major changes to these documents are presented in this paper....

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

    SciTech Connect (OSTI)

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

    2011-06-15T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Hodge, B.

    2013-12-01T23:59:59.000Z

    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. Short-term energy outlook, Annual supplement 1995

    SciTech Connect (OSTI)

    NONE

    1995-07-25T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Mellit, Adel [Department of Electronics, Faculty of Sciences and Technology, LAMEL, Jijel University, Ouled-aissa, P.O. Box 98, Jijel 18000 (Algeria); Pavan, Alessandro Massi [Department of Materials and Natural Resources, University of Trieste Via A. Valerio, 2 - 34127 Trieste (Italy)

    2010-05-15T23:59:59.000Z

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

  20. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    2012-10-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Goto, Susumu

    2007-01-01T23:59:59.000Z

    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 ...

  3. Potential Global Benefits of Improved Ceiling Fan Energy Efficiency

    E-Print Network [OSTI]

    Sathaye, Nakul

    2014-01-01T23:59:59.000Z

    energy savings forecasts Annual Electricity Savings (TWh) Cumulative Electricity Savings (TWh) Year Australia Brazil Canada China

  4. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 2011

    E-Print Network [OSTI]

    Gray, William

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone (TC) activity starting in early August. We have decided to discontinue our individual for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  5. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 1 SEPTEMBER 14, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all are not developing any new tropical cyclones after Earl and Fiona. We expect Earl to generate large amounts of ACE This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting

  6. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2011

    E-Print Network [OSTI]

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  7. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2012

    E-Print Network [OSTI]

    Gray, William

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  8. Development and Demonstration of a Relocatable Ocean OSSE System: Optimizing Ocean Observations for Hurricane Forecast

    E-Print Network [OSTI]

    forecasts for individual storms and improved seasonal forecast of the ocean thermal energy availableDevelopment and Demonstration of a Relocatable Ocean OSSE System: Optimizing Ocean Observations in the Gulf of Mexico is being extended to provide NOAA the ability to evaluate new ocean observing systems

  9. EnergyPlusDeST DOE-2.1E Building energy modeling programs comparison

    E-Print Network [OSTI]

    ST #12; 1 IEA BESTest ASHRAE Standard 140[1] [2] 1 EnergyPlus, DeST, DOE-2, ESP, BLAST TRNSYS 1 ASHRAE RP865[1] 2ANSI/ASHRAE Standard 140 [4] 3 IEA SHC BESTest / [5] EnergyPlus [6] [7

  10. Optimal combined wind power forecasts using exogeneous variables

    E-Print Network [OSTI]

    Optimal combined wind power forecasts using exogeneous variables Fannar Orn Thordarson Kongens of the thesis is combined wind power forecasts using informations from meteorological forecasts. Lyngby, January

  11. Technology Forecasting Scenario Development

    E-Print Network [OSTI]

    analysis (LCA). - Per Dannemand Andersen (*) : M.Sc. (Mech. Eng.), B.Com. (Org.), Ph.D. (Management/science interaction, wind energy economics and implementing policies, decision sup- port to the Danish Energy Agency on wind energy issues, Danish executive committee member of IEA's wind energy agreement. - Dominic Idier

  12. Energy, cost, and CO2 emission comparison between radiant wall panel1 systems and radiator systems2

    E-Print Network [OSTI]

    Boyer, Edmond

    Energy, cost, and CO2 emission comparison between radiant wall panel1 systems and radiator systems2 of Engineering Science, University of Kragujevac, 34000 Kragujevac, Serbia5 2 Department PIMENT Lab., University15 by software EnergyPlus. The investigation shows that the PH-WI gives the best results. The RH-16

  13. International Comparison of Energy Labeling and Standards for Energy Efficient and Green Buildings

    E-Print Network [OSTI]

    Hennicke, P.; Shrestha, S.; Schleicher, T.

    2011-01-01T23:59:59.000Z

    This paper discusses the approaches of the European Union, Germany and India to reduce GHG- emissions and mitigate climate change impacts from buildings through the establishment of energy performance standards and green building...

  14. International Comparison of Energy Labeling and Standards for Energy Efficient and Green Buildings

    E-Print Network [OSTI]

    Hennicke, P.; Shrestha, S.; Schleicher, T.

    2011-01-01T23:59:59.000Z

    This paper discusses the approaches of the European Union, Germany and India to reduce GHG- emissions and mitigate climate change impacts from buildings through the establishment of energy performance standards and green building...

  15. Energy Performance Comparison of Heating and Air Conditioning Systems for Multi-Family Residential Buildings

    SciTech Connect (OSTI)

    Wang, Weimin; Zhang, Jian; Jiang, Wei; Liu, Bing

    2011-07-31T23:59:59.000Z

    The type of heating, ventilation and air conditioning (HVAC) system has a large impact on the heating and cooling energy consumption in multifamily residential buildings. This paper compares the energy performance of three HVAC systems: a direct expansion (DX) split system, a split air source heat pump (ASHP) system, and a closed-loop water source heat pump (WSHP) system with a boiler and an evaporative fluid cooler as the central heating and cooling source. All three systems use gas furnace for heating or heating backup. The comparison is made in a number of scenarios including different climate conditions, system operation schemes and applicable building codes. It is found that with the minimum code-compliant equipment efficiency, ASHP performs the best among all scenarios except in extremely code climates. WSHP tends to perform better than the split DX system in cold climates but worse in hot climates.

  16. Forecast and Funding Arrangements - Hanford Site

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC) Environmental Assessments (EA)Budget(DANCE) Target 1Annual Waste Forecast and Funding

  17. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01T23:59:59.000Z

    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.

  18. Comparison of domestic olivine and European magnesite for electrically charged thermal energy storage

    SciTech Connect (OSTI)

    Laster, W.R.; Gay, B.M.; Palmour, H.; Schoenhals, R.J.

    1982-01-01T23:59:59.000Z

    Electrically charged thermal energy storage (TES) heaters employing high heat capacity ceramic refractories for sensible heat storage have been in use in Europe for several years. With these devices, low cost off-peak electrical energy is stored by heating a storage core composed of ceramic material to approximately 800/sup 0/C. During the peak period, no electrical energy is used as the building heating needs are supplied by extracting the stored energy from the core by forced air circulation. The recent increase in use of off-peak TES units in the U.S. has led to the search for a domestic supply of high heat capacity ceramic refractory material. North Carolina's extensive but underutilized supply of refractory grade olivine has been proposed as a source of storage material for these units. In this paper the suitability of North Carolina olivine for heat storage applications is assessed by comparing its thermal performance with that of European materials. Using the method of ASHRAE Standard 94.2, the thermal performance of two commercially available room-size TES units was determined experimentally with two different storage materials, North Carolina olivine and German magnesite. Comparisons are made and conclusions are drawn.

  19. Inter-Machine Comparison of the Termination Phase and Energy Conversion in Tokamak Disruptions with Runaway Current Plateau Formation and Implications for ITER

    E-Print Network [OSTI]

    Inter-Machine Comparison of the Termination Phase and Energy Conversion in Tokamak Disruptions with Runaway Current Plateau Formation and Implications for ITER

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2003-01-01T23:59:59.000Z

    from the EIAs Annual Energy Outlook 2001 and 2002,forecast contained in Annual Energy Outlook 2003 a seven-forecast contained in Annual Energy Outlook 2003. the six-

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

    SciTech Connect (OSTI)

    NONE

    1996-02-01T23:59:59.000Z

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

  2. Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract

    E-Print Network [OSTI]

    Dalang, Robert C.

    Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract] Nicolas Gast EPFL, IC generation. The use of energy storage compensates to some extent these negative effects; it plays a buffer role between demand and production. We revisit a model of real storage proposed by Bejan et al.[1]. We

  3. Development and Deployment of an Advanced Wind Forecasting Technique

    E-Print Network [OSTI]

    Kemner, Ken

    findings. Part 2 addresses how operators of wind power plants and power systems can incorporate advanced the output of advanced wind energy forecasts into decision support models for wind power plant and power and applications of power market simulation models around the world. Argonne's software tools are used extensively

  4. CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014

    E-Print Network [OSTI]

    de Lijser, Peter

    CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE - APRIL 2014 Anil Puri, Ph.D. -- Director-year increase in the debt ceiling -- both of which proceeded without the usual drama. Second, the private sector, corporate coffers are flush with cash, and low US energy prices have dramatically improved the global

  5. TV Energy Consumption Trends and Energy-Efficiency Improvement Options

    E-Print Network [OSTI]

    Park, Won Young

    2011-01-01T23:59:59.000Z

    China Estimates of global and country-specific energy saving potentials will be based on the above TV market forecast

  6. Comparison of Activity Determination of Radium 226 in FUSRAP Soil using Various Energy Lines - 12299

    SciTech Connect (OSTI)

    Tucker, Brian [Shaw Environmental and Infrastructure, Stoughton, MA 02072 (United States); Donakowski, Jough [Unites States Army Corps of Engineers, Kansas City, MO 64106 (United States); Hays, David [United States Army Corps of Engineers, Maywood, NJ 07607 (United States)

    2012-07-01T23:59:59.000Z

    Gamma spectroscopy is used at the Formerly Utilized Sites Remedial Action Program (FUSRAP) Maywood Superfund Site as the primary radioanalytical tool for quantization of activities of the radionuclides of concern in site soil. When selecting energy lines in gamma spectroscopy, a number of factors are considered including assumptions concerning secondary equilibrium, interferences, and the strength of the lines. The case of the Maywood radionuclide of concern radium-226 (Ra-226) is considered in this paper. At the FUSRAP Maywood Superfund Site, one of the daughters produced from radioactive decay of Ra-226, lead-214 (Pb- 214), is used to quantitate Ra-226. Another Ra-226 daughter, bismuth-214 (Bi-214), also may be used to quantitate Ra-226. In this paper, a comparison of Ra-226 to Pb-214 activities and Ra-226 to Bi-214 activities, obtained using gamma spectrometry for a large number of soil samples, was performed. The Pb-214, Bi-214, and Ra-226 activities were quantitated using the 352 kilo electron volt (keV), 609 keV, and 186 keV lines, respectively. The comparisons were made after correcting the Ra-226 activities by a factor of 0.571 and both ignoring and accounting for the contribution of a U-235 interfering line to the Ra-226 line. For the Pb-214 and Bi-214 activities, a mean in-growth factor was employed. The gamma spectrometer was calibrated for efficiency and energy using a mixed gamma standard and an energy range of 59 keV to 1830 keV. The authors expect other sites with Ra-226 contamination in soil may benefit from the discussions and points in this paper. Proper use of correction factors and comparison of the data from three different gamma-emitting radionuclides revealed agreement with expectations and provided confidence that using such correction factors generates quality data. The results indicate that if contamination is low level and due to NORM, the Ra-226 can be measured directly if corrected to subtract the contribution from U-235. If there is any indication that technologically enhanced uranium may be present, the preferred measurement approach for quantitation of Ra-226 activity is detection of one of the Ra-226 daughters, Pb-214 or Bi-214, using a correction factor obtained from an in-growth curve. The results also show that the adjusted Ra-226 results compare very well with both the Pb-214 and Bi-214 results obtained using an in-growth curve correction factor. (authors)

  7. Forecasting consumer products using prediction markets

    E-Print Network [OSTI]

    Trepte, Kai

    2009-01-01T23:59:59.000Z

    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 ...

  8. 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 ...

  9. 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 ...

  10. CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY FORECAST Volume 1: Statewide Electricity Demand, End-User Natural Gas Demand, and Energy Efficiency The California Energy Demand 2014-2024 Preliminary Forecast, Volume 1: Statewide Electricity Demand

  11. Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations

    E-Print Network [OSTI]

    Kemner, Ken

    forecasting methods and better integration of advanced wind power forecasts into system and plant operations and wind power plants) ­ Review and assess current practices Propose and test new and improved approachesWind Power Forecasting andWind Power Forecasting and Electricity Market Operations Audun Botterud

  12. EWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology The Anemos Wind Power Forecasting Platform Technology -

    E-Print Network [OSTI]

    Boyer, Edmond

    the fluctuating output from wind farms into power plant dispatching and energy trading, wind power predictionsEWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology 1 The Anemos Wind Power a professional, flexible platform for operating wind power prediction models, laying the main focus on state

  13. 1995 shipment review & five year forecast

    SciTech Connect (OSTI)

    Fetherolf, D.J. Jr. [East Penn Manufacturing Co., Inc., Lyon Station, PA (United States)

    1996-01-01T23:59:59.000Z

    This report describes the 1995 battery shipment review and five year forecast for the battery market. Historical data is discussed.

  14. Subscriber Services Complete Forecast

    E-Print Network [OSTI]

    funded solar energy, then cut funding off, heavily funded electric vehicles, then cut funding off. There is a better understanding of the elements that are essential to operating a fusion power plant. Other fusion power plant in the United States. Fusion is, of course, just one of the energy technologies

  15. Electric Load Forecasting

    E-Print Network [OSTI]

    -commitment, coordination, and interchange evaluation. In addition, the liberalization of electric energy markets worldwide has led to the development of energy exchanges where consumers, 1066-033X/07/$25.002007IEEE OCTOBER/GODDARD SPACE FLIGHT CENTER SCIENTIFIC VISUALIZATION STUDIO Authorized licensed use limited to: Katholieke

  16. 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

  17. 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

  18. Calculator simplifies field production forecasting

    SciTech Connect (OSTI)

    Bixler, B.

    1982-05-01T23:59:59.000Z

    A method of forecasting future field production from an assumed average well production schedule and drilling schedule has been programmed for the HP-41C hand-held programmable computer. No longer must tedious row summations be made by hand for staggered well production schedules. Details of the program are provided.

  19. Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings

    E-Print Network [OSTI]

    Fridley, David G.

    2008-01-01T23:59:59.000Z

    forecasts of operational energy till 2020 based on differing assumptions of technology penetration and efficiency. China

  20. 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. (Decision and Information Sciences); (INESC Porto)

    2011-11-29T23:59:59.000Z

    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.

  1. Yield Comparison for Domestic Photovoltaic Installation across the UK Scottish Institute for Solar Energy Research, May 2014

    E-Print Network [OSTI]

    Painter, Kevin

    Yield Comparison for Domestic Photovoltaic Installation across the UK Scottish Institute for Solar Energy Research, May 2014 This study has been carried out in order to assess the available electricity to simulate the potential yield for a range of solar technologies. The software is preinstalled with global

  2. Wind Power Forecasting Data

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron SpinPrincetonUsingWhat is abig world of tinyWind Industry

  3. Characterizing emerging industrial technologies in energy models

    E-Print Network [OSTI]

    Laitner, John A. Skip; Worrell, Ernst; Galitsky, Christina; Hanson, Donald A.

    2003-01-01T23:59:59.000Z

    EIA), 2001. Annual Energy Outlook 2002, Energy Informationas forecasted in the Annual Energy Outlook 2002, we estimateQuads based on the Annual Energy Outlook 2002 (AEO 2002) (

  4. Radar-Derived Forecasts of Cloud-to-Ground Lightning Over Houston, Texas

    E-Print Network [OSTI]

    Mosier, Richard Matthew

    2011-02-22T23:59:59.000Z

    .1.6 Comparison to Previous Studies......................................................................59 3.2 VII Forecast Method...............................................................................................61 3.2.1 Percentile Value...) percentile values for the entire dataset (1997-2006) when considering only cells with a minimum track count of 2.......................................................................... 117 3.5 Same as Figure 3.4 for the POD values...

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    of Energy, Energy Information Administration (U.S. DOE/EIA).of Energy, Energy Information Administration (U.S. DOE/EIA).of Energy, Energy Information Administration ( U.S. DOE/

  6. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del SolStrengtheningWildfires may contribute more to global

  7. A moist "available enthalpy" norm: definition and comparison with existing "energy" norms

    E-Print Network [OSTI]

    Marquet, Pascal

    2015-01-01T23:59:59.000Z

    Moist-air norms and inner-products are currently used in atmospheric science for computing dry or moist singular vectors and for determining forecast errors or sensitivity to observations based on tangent linear and adjoint models. A new moist-air norm is defined starting from old results published in Marquet (QJRMS 1993) and based on the "Available Enthalpy" approach, namely one of the Exergy function defined in general thermodynamics. Some interesting and promising impacts of this new "Available Enthalpy" norm are described in this brief version of a paper to be submitted to the QJRMS.

  8. Navy Mobility Fuels Forecasting System. Phase I report

    SciTech Connect (OSTI)

    Davis, R.M.; Hadder, G.R.; Singh, S.P.N.; Whittle, C.

    1985-07-01T23:59:59.000Z

    The Department of the Navy (DON) requires an improved capability to forecast mobility fuel availability and quality. The changing patterns in fuel availability and quality are important in planning the Navy's Mobility Fuels R and D Program. These changes come about primarily because of the decline in the quality of crude oil entering world markets as well as the shifts in refinery capabilities domestically and worldwide. The DON requested ORNL's assistance in assembling and testing a methodology for forecasting mobility fuel trends. ORNL reviewed and analyzed domestic and world oil reserve estimates, production and price trends, and recent refinery trends. Three publicly available models developed by the Department of Energy were selected as the basis of the Navy Mobility Fuels Forecasting System. The system was used to analyze the availability and quality of jet fuel (JP-5) that could be produced on the West Coast of the United States under an illustrative business-as-usual and a world oil disruption scenario in 1990. Various strategies were investigated for replacing the lost JP-5 production. This exercise, which was strictly a test case for the forecasting system, suggested that full recovery of lost fuel production could be achieved by relaxing the smoke point specifications or by increasing the refiners' gate price for the jet fuel. A more complete analysis of military mobility fuel trends is currently under way.

  9. Fostering a Renewable Energy Technology Industry: An International Comparison of Wind Industry Policy Support Mechanisms

    E-Print Network [OSTI]

    Lewis, Joanna; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    and Renewable Energy, Wind & Hydropower Technologiesand Renewable Energy, Wind & Hydropower Technologies2004. International Wind Energy Development, World Market

  10. Learning Energy Demand Domain Knowledge via Feature Transformation

    E-Print Network [OSTI]

    Povinelli, Richard J.

    Learning Energy Demand Domain Knowledge via Feature Transformation Sanzad Siddique Department -- 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

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

    E-Print Network [OSTI]

    Dudley, Junqiao Han

    2010-01-01T23:59:59.000Z

    water supplied by thermal energy storage in the centralchilled water thermal energy storage (TES) tank provides

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

    E-Print Network [OSTI]

    Dudley, Junqiao Han

    2010-01-01T23:59:59.000Z

    We have studied a low energy building on a campus of theEnergyPlus Model in a Low Energy Campus Building Junqiao HanEnergyPlus Model in a Low Energy Campus Building Junqiao Han

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

    SciTech Connect (OSTI)

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

    2013-05-01T23:59:59.000Z

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

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

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

    collects data on a variety of physical processes that impact the wind forecasts used by wind farms, system operators and other industry professionals. By having access to...

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

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

    collects data on a variety of physical processes that impact the wind forecasts used by wind farms, system operators and other industry professionals. By having access to...

  16. Geothermal wells: a forecast of drilling activity

    SciTech Connect (OSTI)

    Brown, G.L.; Mansure, A.J.; Miewald, J.N.

    1981-07-01T23:59:59.000Z

    Numbers and problems for geothermal wells expected to be drilled in the United States between 1981 and 2000 AD are forecasted. The 3800 wells forecasted for major electric power projects (totaling 6 GWe of capacity) are categorized by type (production, etc.), and by location (The Geysers, etc.). 6000 wells are forecasted for direct heat projects (totaling 0.02 Quads per year). Equations are developed for forecasting the number of wells, and data is presented. Drilling and completion problems in The Geysers, The Imperial Valley, Roosevelt Hot Springs, the Valles Caldera, northern Nevada, Klamath Falls, Reno, Alaska, and Pagosa Springs are discussed. Likely areas for near term direct heat projects are identified.

  17. Online Forecast Combination for Dependent Heterogeneous Data

    E-Print Network [OSTI]

    Sancetta, Alessio

    the single individual forecasts. Several studies have shown that combining forecasts can be a useful hedge against structural breaks, and forecast combinations are often more stable than single forecasts (e.g. Hendry and Clements, 2004, Stock and Watson, 2004... in expectations. Hence, we have the following. Corollary 4 Suppose maxt?T kl (Yt, hwt,Xti)kr ? A taking expectation on the left hand side, adding 2A ? T and setting ? = 0 in mT (?), i.e. TX t=1 E [lt (wt)? lt (ut...

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

    E-Print Network [OSTI]

    Hong, Tainzhen

    2010-01-01T23:59:59.000Z

    movement toward net zero energy buildings, many technologiesmovement towards net zero energy buildings brings tremendous

  19. U-M Construction Forecast December 15, 2011 U-M Construction Forecast

    E-Print Network [OSTI]

    Kamat, Vineet R.

    U-M Construction Forecast December 15, 2011 U-M Construction Forecast Spring Fall 2012 As of December 15, 2011 Prepared by AEC Preliminary & Advisory #12;U-M Construction Forecast December 15, 2011 Overview Campus by campus Snapshot in time Not all projects Construction coordination efforts

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

    SciTech Connect (OSTI)

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

    2005-06-30T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    sources of total energy consumption data for Chinas ironprovide national energy consumption data up to 2003. Thecollection after 2005 Energy consumption data by process in

  2. Data:277f9ce5-df68-45d9-bc9e-4bd12653f00b | Open Energy Information

    Open Energy Info (EERE)

    charge calculated as follows: forecast capacity rate (1.87) + reactive supply (0.046) + regulation and frequency response (0.1897) Energy charge calculated as follows: forecast...

  3. Data:7146d5fb-ec7c-48ac-98a8-118f52ea32e2 | Open Energy Information

    Open Energy Info (EERE)

    charge calculated as follows: forecast capacity rate (1.87) + reactive supply (0.046) + regulation and frequency response (0.1897) Energy charge calculated as follows: forecast...

  4. Dynamic Filtering and Mining Triggers in Mesoscale Meteorology Forecasting

    E-Print Network [OSTI]

    Plale, Beth

    Dynamic Filtering and Mining Triggers in Mesoscale Meteorology Forecasting Nithya N. Vijayakumar {rramachandran, xli}@itsc.uah.edu Abstract-- Mesoscale meteorology forecasting as a data driven application Triggers, Data Mining, Stream Processing, Meteorology Forecasting I. INTRODUCTION Mesoscale meteorologists

  5. INTERNATIONAL COMPARISON OF RESIDENTIAL ENERGY USE: INDICATORS OF RESIDENTIAL ENERGY USE AND EFFICIENCY PART ONE: THE DATA BASE

    E-Print Network [OSTI]

    Schipper, L.

    2013-01-01T23:59:59.000Z

    the housing and energy consumption data is a detailed studyConventions 6 All energy consumption data refer energy,Italian residential energy consumption data by end use. Unt

  6. 1992 five year battery forecast

    SciTech Connect (OSTI)

    Amistadi, D.

    1992-12-01T23:59:59.000Z

    Five-year trends for automotive and industrial batteries are projected. Topic covered include: SLI shipments; lead consumption; automotive batteries (5-year annual growth rates); industrial batteries (standby power and motive power); estimated average battery life by area/country for 1989; US motor vehicle registrations; replacement battery shipments; potential lead consumption in electric vehicles; BCI recycling rates for lead-acid batteries; US average car/light truck battery life; channels of distribution; replacement battery inventory end July; 2nd US battery shipment forecast.

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

    SciTech Connect (OSTI)

    Tanguay, Jesse; Kim, Ho Kyung; Cunningham, Ian A. [Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, London, Ontario N6A 5K8 (Canada) and Department of Medical Biophysics, University of Western Ontario, London, Ontario N6A 5C1 (Canada); School of Mechanical Engineering, Pusan National University, Jangjeon-dong, Geumjeong-gu, Busan 609-735 (Korea, Republic of); Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, London, Ontario N6A 5K8 (Canada); Department of Medical Biophysics, University of Western Ontario, London, Ontario N6A 5C1 (Canada); Lawson Health Research Institute, London, Ontario N6A 4V2 (Canada) and London Health Sciences Centre, London, Ontario N6A 5A5 (Canada)

    2012-01-15T23:59:59.000Z

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

  8. Fostering a Renewable Energy Technology Industry: An International Comparison of Wind Industry Policy Support Mechanisms

    E-Print Network [OSTI]

    Lewis, Joanna; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    2004. International Wind Energy Development, World Market2005. International Wind Energy Development, World Market2004, March 2005. Canadian Wind Energy Association (CanWEA),

  9. A COMPARISON OF THE CONDUCTOR REQUIREMENTS FOR ENERGY STORAGE DEVICES MADE WITH IDEAL COIL GEOMETRIES

    E-Print Network [OSTI]

    Hassenzahl, W.

    2011-01-01T23:59:59.000Z

    Superconducting Magnetic Energy Storage Program," Los AlamosWisconsin Superconductive Energy Storage Project. Y2!.l,J. J. Stekly, "Magnetic Energy Storage Using Superconducting

  10. Fostering a Renewable Energy Technology Industry: An International Comparison of Wind Industry Policy Support Mechanisms

    E-Print Network [OSTI]

    Lewis, Joanna; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    Wind Energy Development in China: Institutional Dynamics and Policyand Candles: Wind Power in China. Energy Policy, 28, 271-wind power development in the United States. Energy Policy.

  11. Fostering a Renewable Energy Technology Industry: An International Comparison of Wind Industry Policy Support Mechanisms

    E-Print Network [OSTI]

    Lewis, Joanna; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    Policy and Renewable Energy Technology. Proceedings of theDiffusion of Renewable Energy Technologies: Wind Power inFostering a Renewable Energy Technology Industry: An

  12. Energy Efficiency Design Options for Residential Water Heaters: Economic Impacts on Consumers

    E-Print Network [OSTI]

    Lekov, Alex

    2011-01-01T23:59:59.000Z

    Administration. 2010. Annual Energy Outlook 2010 withthe price forecasts in EIAs Annual Energy Outlook 2010. The

  13. Forecasting of Solar Radiation Detlev Heinemann, Elke Lorenz, Marco Girodo

    E-Print Network [OSTI]

    Heinemann, Detlev

    Forecasting of Solar Radiation Detlev Heinemann, Elke Lorenz, Marco Girodo Oldenburg University have been presented more than twenty years ago (Jensenius, 1981), when daily solar radiation forecasts

  14. Alternative methods for forecasting GDP Dominique Gugan

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    analysis. Better forecast performance for macroeconomic variables will lead to Paris School of Economics the speed of computers that can develop search algorithms from appropriate selection criteria, Devroye. 1 Introduction Forecasting macroeconomic variables such as GDP and inflation play an important role

  15. A NEW APPROACH FOR EVALUATING ECONOMIC FORECASTS

    E-Print Network [OSTI]

    Vertes, Akos

    APPROACH FOR EVALUATING ECONOMIC FORECASTS Tara M. Sinclair , H.O. Stekler, and Warren Carnow Department of Economics The George Washington University Monroe Hall #340 2115 G Street NW Washington, DC 20052 JEL Codes, Mahalanobis Distance Abstract This paper presents a new approach to evaluating multiple economic forecasts

  16. 2013 Midyear Economic Forecast Sponsorship Opportunity

    E-Print Network [OSTI]

    de Lijser, Peter

    2013 Midyear Economic Forecast Sponsorship Opportunity Thursday, April 18, 2013, ­ Hyatt Regency Irvine 11:30 a.m. ­ 1:30 p.m. Dr. Anil Puri presents his annual Midyear Economic Forecast addressing and Economics at California State University, Fullerton, the largest accredited business school in California

  17. Dynamic Algorithm for Space Weather Forecasting System

    E-Print Network [OSTI]

    Fischer, Luke D.

    2011-08-08T23:59:59.000Z

    /effective forecasts, and we have performed preliminary benchmarks on this algorithm. The preliminary benchmarks yield surprisingly effective results thus far?forecasts have been made 8-16 hours into the future with significant magnitude and trend accuracy, which is a...

  18. Nonparametric models for electricity load forecasting

    E-Print Network [OSTI]

    Genève, Université de

    Electricity consumption is constantly evolving due to changes in people habits, technological innovations1 Nonparametric models for electricity load forecasting JANUARY 23, 2015 Yannig Goude, Vincent at University Paris-Sud 11 Orsay. His research interests are electricity load forecasting, more generally time

  19. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01T23:59:59.000Z

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

  20. Review and comparison of web- and disk-based tools for residential energy analysis

    E-Print Network [OSTI]

    Mills, Evan

    2002-01-01T23:59:59.000Z

    Schools KCPL Electricity Calculator KUA Electricity Calculator My Home On-Line Energy Profile On-Line Home Energy Audit

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

    SciTech Connect (OSTI)

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

    2013-11-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    and CO2 Emissions. Paris: IEA. http://www.iea.org/w/id=298 International Energy Agency (IEA). 2008a. Energy2008 Edition. Paris: IEA. International Energy Agency (IEA).

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    kg (30.451 MJ/kg) cleaned coal, energy consumption is 97.32As a result, the overall coal energy use in China is reducedAs a result, the overall coal energy use in China is reduced

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    energy use for the pellet production is fuel, so this valueof energy used for the production of pellets, the followingthe energy use for the production of pellet, lime, coke, and

  5. Three Essays on Energy Economics and Forecasting

    E-Print Network [OSTI]

    Shin, Yoon Sung

    2012-02-14T23:59:59.000Z

    rarely respond to changes of crude oil prices for the first five days. Based on collusive behaviors of retailers, this price asymmetry in Korea diesel market is explained. The second essay aims to evaluate the new incentive system for biodiesel...

  6. INTERNATIONAL COMPARISON OF RESIDENTIAL ENERGY USE: INDICATORS OF RESIDENTIAL ENERGY USE AND EFFICIENCY PART ONE: THE DATA BASE

    E-Print Network [OSTI]

    Schipper, L.

    2013-01-01T23:59:59.000Z

    Statistics, 1969-79, I 22. ETSU, Energy Technology Supportthe Department of Energy" (ETSU). Unfortunately, there arebeen recently calculated by ETSU (22). We have combined the

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

    E-Print Network [OSTI]

    Zhou, Nan

    2011-01-01T23:59:59.000Z

    forecast of energy demand underlying both scenarios does not take into consideration resource constraints which, in the case of ChinaChinas oil reserve forecast and analysis based on peak oil models, Energy

  8. Determinants of energy intensity in industrialized countries : a comparison of China and India

    E-Print Network [OSTI]

    Huang, Feiya

    2006-01-01T23:59:59.000Z

    The amount of final energy per unit of economic output (usually in terms of gross domestic product, or GDP), known as energy intensity, is often used to measure the effectiveness of energy use and the consumption patterns ...

  9. Alternative energy systems for Puerto Rico : analysis and comparison of anaerobic waste digestion

    E-Print Network [OSTI]

    Cuevas, Emil A. (Emil Andr Cuevas Melndez)

    2006-01-01T23:59:59.000Z

    Energy prices in Puerto Rico are increasing constantly, making evident the need for alternative energy sources. Several methods to produce power have been developed as alternatives to burning petroleum, such as solar energy ...

  10. Efficient Energy Transfer in Light-Harvesting Systems, II: Quantum-Classical Comparison, Flux Network, and Robustness Analysis

    E-Print Network [OSTI]

    Jianlan Wu; Fan Liu; Jian Ma; Robert J. Silbey; Jianshu Cao

    2012-09-05T23:59:59.000Z

    Following the calculation of optimal energy transfer in thermal environment in our first paper (Wu et al., New J. Phys., 2010, 12, 105012), full quantum dynamics and leading-order `classical' hopping kinetics are compared in the seven-site Fenna-Matthews-Olson (FMO) protein complex. The difference between these two dynamic descriptions is due to higher-order quantum corrections. Two thermal bath models, classical white noise (the Haken-Strobl-Reineker model) and quantum Debye model, are considered. In the seven-site FMO model, we observe that higher-order corrections lead to negligible changes in the trapping time or in energy transfer efficiency around the optimal and physiological conditions (2% in the HSR model and 0.1% in the quantum Debye model for the initial site at BChl 1). However, using the concept of integrated flux, we can identify significant differences in branching probabilities of the energy transfer network between hopping kinetics and quantum dynamics (26% in the HSR model and 32% in the quantum Debye model for the initial site at BChl 1). This observation indicates that the quantum coherence can significantly change the distribution of energy transfer pathways in the flux network with the efficiency nearly the same. The quantum-classical comparison of the average trapping time with the removal of the bottleneck site, BChl 4, demonstrates the robustness of the efficient energy transfer by the mechanism of multi-site quantum coherence. To reconcile with the latest eight-site FMO model, the quantum-classical comparison with the flux network analysis is summarized in the appendix. The eight-site FMO model yields similar trapping time and network structure as the seven-site FMO model but leads to a more disperse distribution of energy transfer pathways.

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

    SciTech Connect (OSTI)

    Stoffel, T.; Reda, I.

    2013-05-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2011-10-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2013-10-01T23:59:59.000Z

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

  14. PSO (FU 2101) Ensemble-forecasts for wind power

    E-Print Network [OSTI]

    PSO (FU 2101) Ensemble-forecasts for wind power Analysis of the Results of an On-line Wind Power Ensemble- forecasts for wind power (FU2101) a demo-application producing quantile forecasts of wind power correct) quantile forecasts of the wind power production are generated by the application. However

  15. An adaptive neural network approach to one-week ahead load forecasting

    SciTech Connect (OSTI)

    Peng, T.M. (Pacific Gas and Electric Co., San Francisco, CA (United States)); Hubele, N.F.; Karady, G.G. (Arizona State Univ., Tempe, AZ (United States))

    1993-08-01T23:59:59.000Z

    A new neural network approach is applied to one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called Adaline.'' An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. Load decomposition is made for the load sequence using digital filters with different passband frequencies. After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized Least Mean Square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4 percent are derived from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts.

  16. Gaussian Processes for Short-Horizon Wind Power Forecasting Joseph Bockhorst, Chris Barber

    E-Print Network [OSTI]

    Bockhorst, Joseph

    on this task, and attention has shifted to statistical and machine learning approaches. Among the challenges of wind energy into electrical trans- mission systems. The importance of wind forecasts for wind energy throughout a power system must be nearly in balance at all times, 2) because it depends strongly on wind

  17. Hybrid methodology for hourly global radiation forecasting in Mediterranean area

    E-Print Network [OSTI]

    Voyant, Cyril; Paoli, Christophe; Nivet, Marie Laure

    2012-01-01T23:59:59.000Z

    The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, ARMA techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use ARMA for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and ARMA for each location. In t...

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

    E-Print Network [OSTI]

    Dudley, Junqiao Han

    2010-01-01T23:59:59.000Z

    of Automated Demand Response in a Large Office Building, inBuilding Control Strategies and Techniques for Demand Response.Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building

  19. Energy Prices and California's Economic

    E-Print Network [OSTI]

    Sadoulet, Elisabeth

    1 Energy Prices and California's Economic Security David RolandHolst October, 2009 on Energy Prices, Renewables, Efficiency, and Economic Growth: Scenarios and Forecasts, financial support drivers, the course of fossil fuel energy prices, energy efficiency trends, and renewable energy

  20. Potential Economic Value of Seasonal Hurricane Forecasts

    E-Print Network [OSTI]

    Emanuel, Kerry Andrew

    This paper explores the potential utility of seasonal Atlantic hurricane forecasts to a hypothetical property insurance firm whose insured properties are broadly distributed along the U.S. Gulf and East Coasts. Using a ...

  1. Essays in International Macroeconomics and Forecasting

    E-Print Network [OSTI]

    Bejarano Rojas, Jesus Antonio

    2012-10-19T23:59:59.000Z

    This dissertation contains three essays in international macroeconomics and financial time series forecasting. In the first essay, I show, numerically, that a two-country New-Keynesian Sticky Prices model, driven by monetary and productivity shocks...

  2. IMPLICATIONS OF INTERNATIONAL COMPARISONS OF ENERGY USE: THE SWEDISH/AMERICAN CASE REVIEWED

    E-Print Network [OSTI]

    Schipper, Lee

    2013-01-01T23:59:59.000Z

    Swedish experience 1s district heating, by which blocks (orfrom central plants. district heating save energy? How does

  3. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01T23:59:59.000Z

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

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

    Reports and Publications (EIA)

    1998-01-01T23:59:59.000Z

    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.

  5. TOWARD RELIABLE BENCHMARKING OF SOLAR FLARE FORECASTING METHODS

    SciTech Connect (OSTI)

    Bloomfield, D. Shaun; Higgins, Paul A.; Gallagher, Peter T. [Astrophysics Research Group, School of Physics, Trinity College Dublin, College Green, Dublin 2 (Ireland); McAteer, R. T. James, E-mail: shaun.bloomfield@tcd.ie [Department of Astronomy, New Mexico State University, Las Cruces, NM 88003-8001 (United States)

    2012-03-10T23:59:59.000Z

    Solar flares occur in complex sunspot groups, but it remains unclear how the probability of producing a flare of a given magnitude relates to the characteristics of the sunspot group. Here, we use Geostationary Operational Environmental Satellite X-ray flares and McIntosh group classifications from solar cycles 21 and 22 to calculate average flare rates for each McIntosh class and use these to determine Poisson probabilities for different flare magnitudes. Forecast verification measures are studied to find optimum thresholds to convert Poisson flare probabilities into yes/no predictions of cycle 23 flares. A case is presented to adopt the true skill statistic (TSS) as a standard for forecast comparison over the commonly used Heidke skill score (HSS). In predicting flares over 24 hr, the maximum values of TSS achieved are 0.44 (C-class), 0.53 (M-class), 0.74 (X-class), 0.54 ({>=}M1.0), and 0.46 ({>=}C1.0). The maximum values of HSS are 0.38 (C-class), 0.27 (M-class), 0.14 (X-class), 0.28 ({>=}M1.0), and 0.41 ({>=}C1.0). These show that Poisson probabilities perform comparably to some more complex prediction systems, but the overall inaccuracy highlights the problem with using average values to represent flaring rate distributions.

  6. Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    to accurately forecast natural gas prices. Many policyseek alternative methods to forecast natural gas prices. Thethe accuracy of forecasts for natural gas prices as reported

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

    SciTech Connect (OSTI)

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

    1994-05-01T23:59:59.000Z

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

  8. Energy and growth, a comparison of 13 industrial and developing countries

    SciTech Connect (OSTI)

    Leach, G.; Jarass, L.; Obermair, G.M.; Hoffmann, L.

    1986-01-01T23:59:59.000Z

    The traditional approach to national energy issues and planning has been strongly supply oriented-planning has focused on aggregate quantities like fuel production, imports and exports, and how to meet growing demand by adding to supplies. The most serious flaw in this approach is that it dehumanizes energy planning by ignoring the major determinants and opportunities for change which lie at the level of individual people and their needs. This book demonstrates that sound energy planning must be rooted in a detailed understanding of energy demand and the social and economic activities which not only consume energy but also make up the pattern of life in a country. Its approach combines analysis of energy use with coverage of a wide sample of countries. The countries studied are UK, France, West Germany, USA, Japan, Philippines, Brazil, Portugal, Chile, India, Pakistan, Sri Lanka and Kenya.

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

    SciTech Connect (OSTI)

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

    2008-08-13T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2014-05-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Hong, Tainzhen

    2010-01-01T23:59:59.000Z

    dual compressor available on the market Compared with the selected building, a more energy efficient building will have lower space cooling and heating

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

    E-Print Network [OSTI]

    Hong, Tainzhen

    2010-01-01T23:59:59.000Z

    heat pump, and the energy consumption of the whole GSHP system given the accurate information of the building, GSHP system, weather data,

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

    Office of Environmental Management (EM)

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 1112011AT&T, Inc.'sEnergyTexas1.Space DataEnergy Superior EnergyReport

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

    Office of Environmental Management (EM)

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 1112011AT&T, Inc.'sEnergyTexas1.Space DataEnergy Superior EnergyReportof the

  15. Exploring and mapping: A comparison of the IEA-MARKAL and CEC-EFOM technical energy system models and the ANL electric utility model

    SciTech Connect (OSTI)

    Wene, Clas-Otto.

    1989-11-01T23:59:59.000Z

    The purpose of the study is to conduct technical and conceptual inter-comparisons of three quite different, state-of-the-art energy-emissions models that can select least-cost measures for controlling emissions, calculate future emissions, and analyze energy technology options. The goal is to provide an improved basis for selecting and applying models to address complex energy/environmental issues. 49 refs., 9 figs., 5 tabs.

  16. Efficient energy transfer in light-harvesting systems: Quantum-classical comparison, flux network, and robustness analysis

    SciTech Connect (OSTI)

    Wu Jianlan [Physics Department, Zhejiang University, 38 ZheDa Road, Hangzhou, Zhejiang 310027 (China); Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Liu Fan; Silbey, Robert J.; Cao Jianshu [Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Ma Jian [Physics Department, Zhejiang University, 38 ZheDa Road, Hangzhou, Zhejiang 310027 (China)

    2012-11-07T23:59:59.000Z

    Following the calculation of optimal energy transfer in thermal environment in our first paper [J. L. Wu, F. Liu, Y. Shen, J. S. Cao, and R. J. Silbey, New J. Phys. 12, 105012 (2010)], full quantum dynamics and leading-order 'classical' hopping kinetics are compared in the seven-site Fenna-Matthews-Olson (FMO) protein complex. The difference between these two dynamic descriptions is due to higher-order quantum corrections. Two thermal bath models, classical white noise (the Haken-Strobl-Reineker (HSR) model) and quantum Debye model, are considered. In the seven-site FMO model, we observe that higher-order corrections lead to negligible changes in the trapping time or in energy transfer efficiency around the optimal and physiological conditions (2% in the HSR model and 0.1% in the quantum Debye model for the initial site at BChl 1). However, using the concept of integrated flux, we can identify significant differences in branching probabilities of the energy transfer network between hopping kinetics and quantum dynamics (26% in the HSR model and 32% in the quantum Debye model for the initial site at BChl 1). This observation indicates that the quantum coherence can significantly change the distribution of energy transfer pathways in the flux network with the efficiency nearly the same. The quantum-classical comparison of the average trapping time with the removal of the bottleneck site, BChl 4, demonstrates the robustness of the efficient energy transfer by the mechanism of multi-site quantum coherence. To reconcile with the latest eight-site FMO model which is also investigated in the third paper [J. Moix, J. L. Wu, P. F. Huo, D. F. Coker, and J. S. Cao, J. Phys. Chem. Lett. 2, 3045 (2011)], the quantum-classical comparison with the flux network analysis is summarized in Appendix C. The eight-site FMO model yields similar trapping time and network structure as the seven-site FMO model but leads to a more disperse distribution of energy transfer pathways.

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    Administration (U.S. DOE/EIA). 2007. Annual Energy ReviewFactors. Washington, DC: EIA. http://www.eia.doe.gov/aer/Administration (U.S. DOE/EIA). 2008. United States

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

    E-Print Network [OSTI]

    Dudley, Junqiao Han

    2010-01-01T23:59:59.000Z

    which combines the two cooling supply fans to just one asquantitatively the two cooling supply fans in the EnergyPlussupply fan has a capacity of 25,000 cfm. Model uses district heating and cooling

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    DOE/EIA), 2010e. Manufacturing Energy Consumption Survey (Survey (MECS)-2006 (Table 1.1). Washington, DC: EIA. http://Survey (MECS)-2006 (Table 3.1). Washington, DC: EIA. http://

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

    E-Print Network [OSTI]

    Hasanbeigi, Ali

    2012-01-01T23:59:59.000Z

    flow models for the US steel industry, Energy 26, no. 2 (pdf Association for Iron and Steel Technology (AIST). 2010a.American BOF Roundup. Iron & Steel Technology. November.

  1. Comparison of the 1984 DOE/EIA annual energy outlook and the 1984 GRI baseline projection

    SciTech Connect (OSTI)

    Ashby, A.; Holtberg, P.; Woods, T.

    1985-01-01T23:59:59.000Z

    A comparative analysis of the Gas Research Institute (GRI) Baseline Projection of US Energy Supply and Demand with the DOE/EIA 1984 Annual Energy Outlook shows many similar assumptions, but many cases of widening differences between the projections of primary energy consumption and sector-specific energy consumption. The DOE/EIA expects a faster and more significant decline in the electricity to natural gas price ratio, lower sector-specific end-use prices of refined petroleum products, and a faster growth in industrial raw material energy demand. In contrast to the GRI report, it also omits an estimate of industrial cogeneration and does not retire any exisiting generating capacity. The report examines the basic assumptions and results of both projections using five scenarios. 17 tables.

  2. Comparison of 2006 IECC and 2009 IECC Commercial Energy Code Requirements for Kansas City, MO

    SciTech Connect (OSTI)

    Huang, Yunzhi; Gowri, Krishnan

    2011-03-22T23:59:59.000Z

    This report summarizes code requirements and energy savings of commercial buildings in climate zone 4 built to the 2009 IECC when compared to the 2006 IECC. In general, the 2009 IECC has higher insulation requirements for exterior walls, roof, and windows and have higher efficiency requirements for HVAC equipment (HVAC equipment efficiency requirements are governed by National Appliance Conversion Act of 1987 (NAECA), and are applicable irrespective of the IECC version adopted). The energy analysis results show that residential and nonresidential commercial buildings meeting the 2009 IECC requirements save between 6.1% and 9.0% site energy, and between 6.4% and 7.7% energy cost when compared to 2006 IECC. Analysis also shows that semiheated buildings have energy and cost savings of 3.9% and 5.6%.

  3. Washington State energy use profile 1960 to 1980

    SciTech Connect (OSTI)

    Hinman G.; Alguire, F.; Devlin, T.; Hanson, J.; Horton, D.; Olsen, D.

    1980-12-01T23:59:59.000Z

    A comprehensive energy data base for the state of Washington is presented to provide energy suppliers, consumers, and policy makers with the most current energy data and information possible so that energy planning and policy decisions may be made on an informed basis. The first section provides an overview of demographic and economic factors, energy use, energy resources, and prices. The second section provides greater detail on the uses, supplies, and prices of the principal energy resources used in the state. The third section focuses on electricity and describes uses, supplies, and prices for this intermediate energy form. The fourth section disaggregates energy consumption by users and provides additional detail on use in the residential, commercial, industrial, agricultural, and transportation sectors. The fifth section shows some comparisons of actual figures with those appearing in some recent forecasts. (MCW)

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

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742Energy China U.S. DepartmentEnergy This partAs theFebruary 24,

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

    SciTech Connect (OSTI)

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

    2010-05-14T23:59:59.000Z

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

  6. Analysis of PG E's residential end-use metered data to improve electricity demand forecasts

    SciTech Connect (OSTI)

    Eto, J.H.; Moezzi, M.M.

    1992-06-01T23:59:59.000Z

    It is generally acknowledged that improvements to end-use load shape and peak demand forecasts for electricity are limited primarily by the absence of reliable end-use data. In this report we analyze recent end-use metered data collected by the Pacific Gas and Electric Company from more than 700 residential customers to develop new inputs for the load shape and peak demand electricity forecasting models used by the Pacific Gas and Electric Company and the California Energy Commission. Hourly load shapes are normalized to facilitate separate accounting (by the models) of annual energy use and the distribution of that energy use over the hours of the day. Cooling electricity consumption by central air-conditioning is represented analytically as a function of climate. Limited analysis of annual energy use, including unit energy consumption (UEC), and of the allocation of energy use to seasons and system peak days, is also presented.

  7. Comparison of simplified models of urban climate for improved prediction of building energy use in cities

    E-Print Network [OSTI]

    Street, Michael A. (Michael Anthony)

    2013-01-01T23:59:59.000Z

    Thermal simulation of buildings is a requisite tool in the design of low-energy buildings, yet, definition of weather boundary conditions during simulation of urban buildings suffers from a lack of data that accounts for ...

  8. A Field Comparison of Performance Based Energy Efficient and Conventionally Constructed Homes in South Texas

    E-Print Network [OSTI]

    Schertz, S.; Stracener, J.

    1986-01-01T23:59:59.000Z

    . The program, originally developed at Gulf Power Co., is an energy efficiency designation and implied the inclusion of some or all of ten recommended construction features. Central Power and Light Company's criteria for qualification as a "Good Cents Home...

  9. IMPLICATIONS OF INTERNATIONAL COMPARISONS OF ENERGY USE: THE SWEDISH/AMERICAN CASE REVIEWED

    E-Print Network [OSTI]

    Schipper, Lee

    2013-01-01T23:59:59.000Z

    Swedish experience 1s district heating, by which blocks (orfrom central plants. district heating save energy? How doesdistrict heat is produced with electricity depends on the charac- teristics of the heating

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

    E-Print Network [OSTI]

    Hong, Tianzhen

    2009-01-01T23:59:59.000Z

    total cooling capacity, sensible heat ratio, and supply airair economizer cooling hours, but the supply fan energy willsupply fan has a draw-through configuration (downstream of the DX cooling

  11. Optimisation and comparison of integrated models of direct-drive linear machines for wave energy conversion

    E-Print Network [OSTI]

    Crozier, Richard Carson

    2014-06-30T23:59:59.000Z

    Combined electrical and structural models of five types of permanent magnet linear electrical machines suitable for direct-drive power take-off on wave energy applications are presented. Electromagnetic models were ...

  12. Improved Building Energy Performance Modelling through Comparison of Measured Data with Simulated Results

    E-Print Network [OSTI]

    Bambrook, S.; Jacob, D.

    2008-01-01T23:59:59.000Z

    -ICE for the same building. The dataset collected at the demonstration building consists of total energy consumption, weather data, indoor conditions and HVAC system parameters, measured in at least hourly time steps. This paper presents the first steps in modelling...

  13. Improved Building Energy Performance Modelling through Comparison of Measured Data with Simulated Results

    E-Print Network [OSTI]

    Bambrook, S.; Jacob, D.

    -ICE for the same building. The dataset collected at the demonstration building consists of total energy consumption, weather data, indoor conditions and HVAC system parameters, measured in at least hourly time steps. This paper presents the first steps in modelling...

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

    Broader source: Energy.gov [DOE]

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

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

    E-Print Network [OSTI]

    Darling, Robert M.

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

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

    SciTech Connect (OSTI)

    NONE

    1996-02-26T23:59:59.000Z

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

  17. Impact of GPS Zenith Tropospheric Delay data on precipitation forecasts in Mediterranean France and Spain

    E-Print Network [OSTI]

    Haase, Jennifer

    implies that the GPS data has good potential for influencing numerical models in rapidly developing, high for the forecasting of rainfall. Water vapor plays an important role in energy transfer and in the formation of clouds. 1992]. Rocken et al. (1993)] demonstrated agreement between water vapor radiometers and GPS derived

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

    E-Print Network [OSTI]

    Heinemann, Detlev

    QUALIFIED FORECAST OF ENSEMBLE POWER PRODUCTION BY SPATIALLY DISPERSED GRID- CONNECTED PV SYSTEMS: The contribution of power production by Photovoltaic (PV) systems to the electricity supply is constantly of the electricity grids and for energy trading. This paper presents an approach to predict regional PV power output

  19. Skill forecasting from ensemble predictions of wind power P. Pinson,a

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    Skill forecasting from ensemble predictions of wind power P. Pinson,a , H.Aa. Nielsena , H. Madsena with the commonly provided short-term wind power point predictions. Alternative approaches for the use uncertainty (and potential energy imbalances). Wind power ensemble predictions are derived from the conversion

  20. Powering Up With Space-Time Wind Forecasting Amanda S. HERING and Marc G. GENTON

    E-Print Network [OSTI]

    Genton, Marc G.

    Powering Up With Space-Time Wind Forecasting Amanda S. HERING and Marc G. GENTON The technology to harvest electricity from wind energy is now advanced enough to make entire cities powered by it a reality be more realistically assessed with a loss measure that depends upon the power curve relating wind speed

  1. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (TO APPEAR, 2014) 1 Electricity Market Forecasting

    E-Print Network [OSTI]

    Giannakis, Georgios

    to spatially-varying energy prices, known as locational marginal prices (LMPs) [24], [17]. Schemes inference. Day-ahead price forecast- ing is cast as a low-rank kernel learning problem. Uniquely exploiting- temporally varying prices. Through a novel nuclear norm-based regularization, kernels across pricing nodes

  2. Term Structure of Commodities Futures. Forecasting and Pricing. Marcos Escobar, Nicols Hernndez, Luis Seco

    E-Print Network [OSTI]

    Seco, Luis A.

    1 Term Structure of Commodities Futures. Forecasting and Pricing. Marcos Escobar, Nicolás Hernández that often exhibit sudden changes from backwardation into contango (such as energy, agricultural products generation purposes. It also provides the "risk neutral" processes needed for derivatives pricing, answering

  3. PHYSICAL REVIEW E 85, 056220 (2012) Forecasting synchronizability of complex networks from data

    E-Print Network [OSTI]

    Lai, Ying-Cheng

    2012-01-01T23:59:59.000Z

    PHYSICAL REVIEW E 85, 056220 (2012) Forecasting synchronizability of complex networks from data Ri-Qi Su,1 Xuan Ni,1 Wen-Xu Wang,1,2 and Ying-Cheng Lai1,3 1 School of Electrical, Computer, and Energy of Management and Center for Complexity Research, Beijing Normal University, Beijing 100875, China 3 Department

  4. Comparisons on thin and thick neutron target for low energy proton beam

    SciTech Connect (OSTI)

    Zhong, B.; Yu, G.; Wang, X.; Wang, K. [Dept. of Engineering Physics, Tsinghua Univ., Beijing 100084 (China)

    2012-07-01T23:59:59.000Z

    As the progress on accelerator physics and neutronics, the compact neutron sources driven by low energy and high intensity beam are becoming extensively developed and researched all around the world. The neutron target of an accelerator driven neutron source is one of the key components, and the stability of the neutron target affect the operation and performance of the neutron facility. When a low energy proton is projected to the beryllium target, the main reaction is the inelastic scattering between the proton and extra-nuclear electrons. As the decreasing of proton energy, the rate of elastic scattering between proton and target nucleus begins to increase. When the energy of proton is very low, the pickup charge reaction begins to appear. Focus on the problems brought by high intensity proton beam such as proton implantation, radiation damages, heat deposition and gas production, we performed sufficient numerical simulations for both thin and thick target determined by proton range. The results show that the critical problem for thick target is the proton implantation, causing the forming of bubbles and beryllium flaked in vacuum. The thin target sacrifices a little neutron yield, but avoid the proton stopped in target, and decrease the radiation damage and energy deposition. (authors)

  5. Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast

    E-Print Network [OSTI]

    Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast Role of the Economic Forecast..................................................................................................................................... 2 Economic Growth Assumptions

  6. WP2 IEA Wind Task 26:The Past and Future Cost of Wind Energy

    E-Print Network [OSTI]

    Lantz, Eric

    2014-01-01T23:59:59.000Z

    Chinas activities alone could nearly double global capacity by 2020. The Global Wind Energy Council deployment forecast

  7. Viability, Development, and Reliability Assessment of Coupled Coastal Forecasting Systems

    E-Print Network [OSTI]

    Singhal, Gaurav

    2012-10-19T23:59:59.000Z

    disaster, Cook Inlet (CI) and Prince William Sound (PWS) are regions that suffer from a lack of accurate wave forecast information. This dissertation develops high- resolution integrated wave forecasting schemes for these regions in order to meet...

  8. Potential to Improve Forecasting Accuracy: Advances in Supply Chain Management

    E-Print Network [OSTI]

    Datta, Shoumen

    2008-07-31T23:59:59.000Z

    Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view of the great strides made by research and the increasing abundance of data made possible by automatic ...

  9. Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output Perturbation

    E-Print Network [OSTI]

    Washington at Seattle, University of

    Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output. This is typically not feasible for mesoscale weather prediction carried out locally by organizations without by simulating realizations of the geostatistical model. The method is applied to 48-hour mesoscale forecasts

  10. The effect of multinationality on management earnings forecasts

    E-Print Network [OSTI]

    Runyan, Bruce Wayne

    2005-08-29T23:59:59.000Z

    This study examines the relationship between a firm??s degree of multinationality and its managers?? earnings forecasts. Firms with a high degree of multinationality are subject to greater uncertainty regarding earnings forecasts due...

  11. Navy Mobility Fuels Forecasting System Phase 4 report

    SciTech Connect (OSTI)

    Das, S.; Hadder, G.R.; Leiby, P.N.; Lee, R.; Davis, R.M.

    1988-09-01T23:59:59.000Z

    The Department of Navy's Maritime Strategy is designed to maintain military readiness and the ability to operate in all major theaters of the world. Mobility fuels required for sea, air, and land operations are vital components of the Navy's peacetime and wartime strategies. The purpose of the Navy's Mobility Fuels Technology Program is to understand fuel supply and fuel property impacts on Navy equipment performance and fleet readiness and operations. Oak Ridge National Laboratory (ORNL) has assisted the Department of Navy in developing and testing a methodology for forecasting mobility fuel availability, quality, and relative price, as well as evaluating options to increase fuel supplies during world oil supply disruptions. Publicly available models developed by the Energy Information Administration of the Department of Energy were selected as the foundation of the Navy Mobility Fuels Forecasting System (NMFFS). The NMFFS was enhanced as ORNL reviewed data on world oil reserves, production and prices, trends in crude oil and refined product quality, and changes in refinery process technology. The system was used to analyze the availability, quality, and relative price of military fuels that could be produced in several domestic and foreign refining regions under Business-As-Usual (BAU) and two hypothetical world crude oil disruption scenarios in the year 1995. 25 refs., 11 figs., 29 tabs.

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

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01T23:59:59.000Z

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

  13. Comparison of screened hybrid density functional theory to diffusion Monte Carlo in calculations of total energies of silicon phases and defects

    E-Print Network [OSTI]

    Wilkins, John

    Comparison of screened hybrid density functional theory to diffusion Monte Carlo in calculations of total energies of silicon phases and defects Enrique R. Batista,1, * Jochen Heyd,2 Richard G. Hennig,3 for the prediction of defect properties using the Heyd-Scuseria-Ernzerhof HSE screened-exchange hybrid functional

  14. Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1

    E-Print Network [OSTI]

    1 Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1 1 Great Lakes forecasts in operational hydrology builds a sample of possibilities for the future, of climate series from-parametric method can be extended into a new weighted parametric hydrological forecasting technique to allow

  15. A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION

    E-Print Network [OSTI]

    Boyer, Edmond

    1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving. The very first results show an improvement brought by this approach. 1. INTRODUCTION Solar radiation

  16. FORECASTING SOLAR RADIATION PRELIMINARY EVALUATION OF AN APPROACH

    E-Print Network [OSTI]

    Perez, Richard R.

    FORECASTING SOLAR RADIATION -- PRELIMINARY EVALUATION OF AN APPROACH BASED UPON THE NATIONAL, and undertake a preliminary evaluation of, a simple solar radiation forecast model using sky cover predictions forecasts is 0.05o in latitude and longitude. Solar Radiation model: The model presented in this paper

  17. PSO (FU 2101) Ensemble-forecasts for wind power

    E-Print Network [OSTI]

    PSO (FU 2101) Ensemble-forecasts for wind power Wind Power Ensemble Forecasting Using Wind Speed the problems of (i) transforming the meteorological ensembles to wind power ensembles and, (ii) correcting) data. However, quite often the actual wind power production is outside the range of ensemble forecast

  18. How Can China Lighten Up? Urbanization, Industrialization and Energy Demand Scenarios

    E-Print Network [OSTI]

    Aden, Nathaniel T.

    2010-01-01T23:59:59.000Z

    An LANL report on Chinas energy forecast to 2015 predictedE.Iain McCreary, Chinas Energy A forecast to 2015, LANL,forecasts from the Chinese Energy Research Institute (ERI) and the Institute of Technical Information for the Building Materials Industry of China (

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

    SciTech Connect (OSTI)

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

    2011-01-01T23:59:59.000Z

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

  20. Hybrid Vehicle Comparison Testing Using Ultracapacitor vs. Battery Energy Storage (Presentation)

    SciTech Connect (OSTI)

    Gonder, J.; Pesaran, A.; Lustbader, J.; Tataria, H.

    2010-02-01T23:59:59.000Z

    With support from General Motors, NREL researchers converted and tested a hybrid electric vehicle (HEV) with three energy storage configurations: a nickel metal-hydride battery and two ultracapacitor (Ucap) modules. They found that the HEV equipped with one Ucap module performed as well as or better than the HEV with a stock NiMH battery configuration. Thus, Ucaps could increase the market penetration and fuel savings of HEVs.

  1. Energy-efficiency comparison of advanced ammonia heat-exchanger types

    SciTech Connect (OSTI)

    Panchal, C.; Rabas, T.

    1990-01-01T23:59:59.000Z

    Ammonia is the most cost-effective working fluid for many Rankine power cycles and is widely utilized in industrial refrigeration applications. For example, it was selected as the most advantageous working fluid for the comprehensive closed-cycle Ocean Thermal Energy Conversion investigations where the heat source and sink are the warm, surface seawater and the cold, deep seawater, respectively. An essential part of this investigation was to measure the performance of many advanced heat-exchanger types using ammonia as the working fluid and to compare these results with those for conventional shell-and-tube designs. This paper presents an overview of these experiments and their potential significance for improved energy efficiency for industrial refrigeration applications. The heat exchangers used for industrial refrigeration systems account for about 50% of the equipment cost. However, current practice is to use state-of-the-art designs -- the shell-and-tube type without enhanced tubes. Substantial energy savings are possible through the use of advanced ammonia evaporator and condenser heat-exchanger types. 31 refs., 10 figs., 6 tabs.

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

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01T23:59:59.000Z

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

  3. Knowledge fusion: Comparison of fuzzy curve smoothers to statistically motivated curve smoothers

    SciTech Connect (OSTI)

    Burr, T.; Strittmatter, R.B.

    1996-03-01T23:59:59.000Z

    This report describes work during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security (NN) Knowledge Fusion (KF) Project. The project team selected satellite sensor data to use as the one main example to which its analysis algorithms would be applied. The specific sensor-fusion problem has many generic features, which make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series that define a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. This report gives a detailed comparison of two of the forecasting methods (fuzzy forecaster and statistically motivated curve smoothers as forecasters). The two methods are compared on five simulated and five real data sets. One of the five real data sets is satellite sensor data. The conclusion is the statistically motivated curve smoother is superior on simulated data of the type we studied. The statistically motivated method is also superior on most real data. In defense of the fuzzy-logic motivated methods, we point out that fuzzy-logic methods were never intended to compete with statistical methods on numeric data. Fuzzy logic was developed to handle real-world situations where either real data was not available or was supplemented with either ``expert opinion`` or some sort of linguistic information.

  4. Facebook IPO updated valuation and user forecasting

    E-Print Network [OSTI]

    Facebook IPO updated valuation and user forecasting Based on: Amendment No. 6 to Form S-1 (May 9. Peter Cauwels and Didier Sornette, Quis pendit ipsa pretia: facebook valuation and diagnostic Extreme Growth JPMPaper Cauwels and Sornette 840 1110 1820 S1- filing- May 9 2012 1006 1105 1371 Facebook

  5. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Sripada, Yaji

    for generating textual summaries. Our algorithm has been implemented in a weather forecast generation system. 1 presentation, aid human understanding of the underlying data sets. SUMTIME is a research project aiming turbines. In the domain of meteorology, time series data produced by numerical weather prediction (NWP

  6. Forecasting sudden changes in environmental pollution patterns

    E-Print Network [OSTI]

    Olascoaga, Maria Josefina

    Forecasting sudden changes in environmental pollution patterns María J. Olascoagaa,1 and George of Mexico in 2010. We present a methodology to predict major short-term changes in en- vironmental River's mouth in the Gulf of Mexico. The resulting fire could not be extinguished and the drilling rig

  7. New Concepts in Wind Power Forecasting Models

    E-Print Network [OSTI]

    Kemner, Ken

    New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, Joo Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. Renyi's Entropy is combined

  8. SIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL

    E-Print Network [OSTI]

    Gambardella, Luca Maria

    SIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL Luca Maria Gambardella1 , Gianluca@idsia.ch 2 LCST, La Spezia Container Terminal, La Spezia (IT) 3 DSP, Data System & Planning sa, Manno (CH working in intermodal container terminals. INTRODUCTION The amount of work a container terminal deals

  9. Forecast Technical Document Felling and Removals

    E-Print Network [OSTI]

    of local investment and business planning. Timber volume production will be estimated at sub. Planning of operations. Control of the growing stock. Wider reporting (under UKWAS). The calculation fellings and removals are handled in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan

  10. Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price forecast of the Fifth Northwest Power

    E-Print Network [OSTI]

    Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price as traded on the wholesale, short-term (spot) market at the Mid-Columbia trading hub. This price represents noted. BASE CASE FORECAST The base case wholesale electricity price forecast uses the Council's medium

  11. Monthly energy review

    SciTech Connect (OSTI)

    Not Available

    1988-03-01T23:59:59.000Z

    The U.S. energy market for the first quarter of 1988 is discussed. Production, energy consumption, imports, price adjustments, and forecasts for the rest of the year are given.

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

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade Year-0E (2001)gasoline prices4Consumption TheX I A OCompany

  13. Comparison of Clean Diesel Buses to CNG Buses | 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 DataDepartment of Energy Your Density Isn't Your Destiny: Theof"Wave the WhiteNational| DepartmentCommunitiesDepartmentReport

  14. Max Tech Appliance Design: Potential for Maximizing U.S. Energy Savings through Standards

    E-Print Network [OSTI]

    Garbesi, Karina

    2011-01-01T23:59:59.000Z

    Administration, Annual Energy Outlook. Can be downloaded at:forecasts in its Annual Energy Outlook (AEO) [2], based onaeo/overview/). In its Annual Energy Outlook (AEO) (http://

  15. New Method and Reporting of Uncertainty in LBNL National Energy Modeling System Runs

    E-Print Network [OSTI]

    Gumerman, Etan Z.; LaCommare, Kristina Hamachi; Marnay, Chris

    2002-01-01T23:59:59.000Z

    PV Quad SO 2 TWh Annual Energy Outlook combined GPRA caseshows the most recent Annual Energy Outlook (AEO) value, theradical. The Annual Energy Outlook forecasts unrestricted

  16. How Can China Lighten Up? Urbanization, Industrialization and Energy Demand Scenarios

    E-Print Network [OSTI]

    Aden, Nathaniel T.

    2010-01-01T23:59:59.000Z

    on the forecast of total energy demand. Based on this, weIndustrialization and Energy Demand Scenarios Nathaniel T.adjustment spurred energy demand for construction of new

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

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

    ED2, September. CEC (2005b) Energy demand forecast methodsCalifornia Baseline Energy Demands to 2050 for Advancedof a baseline scenario for energy demand in California for a

  19. Forecasting Water Quality & Biodiversity

    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 DataDepartment of Energy Your Density Isn't YourTransport inEnergy0.pdf Flash2010-60.pdf2 DOE Hydrogen andMeetingonup onFood7,2

  20. Using Wikipedia to forecast disease

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron SpinPrincetonUsing Maps to Predict Solar Final Report to

  1. Stellar Astrophysics Requirements NERSC Forecast

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over Our Instagram Secretary900Steep Slope Calculator Estimates Cooling andRequirements

  2. Supply Forecast and Analysis (SFA)

    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 DataDepartment of Energy Your Density Isn'tOriginEducationVideoStrategic Safety GoalsEnergyCompliance with OrdertheMatthew Langholtz

  3. 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.

    2012-09-01T23:59:59.000Z

    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.

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

    SciTech Connect (OSTI)

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

    2006-12-01T23:59:59.000Z

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

  5. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01T23:59:59.000Z

    California Energy Demand Scenario Projections to 2050 RyanCEC (2003a) California energy demand 2003-2013 forecast.CEC (2005a) California energy demand 2006-2016: Staff energy

  6. More Issues of Building Energy Simulation

    E-Print Network [OSTI]

    Kang, Z.; Zhao, J.

    2006-01-01T23:59:59.000Z

    The paper investigates the development of building energy simulation software. It is shown that such applications can be used for energy forecasting, system design and operations, and energy evaluation. Several energy simulation methods are analyzed...

  7. Using Wikipedia to forecast diseases

    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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del SolStrengthening aTurbulence mayUndergraduateAboutUserHadoop SCP/SFTP

  8. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

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

    2014-12-30T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Mendes, Goncalo

    2014-01-01T23:59:59.000Z

    Power ($/kW) TOU Demand Charge Energy Charge Non-CoincidentPower ($/kW) TOU Demand Charge Energy Charge Non-CoincidentEnergy ($/kWh) Energy Charge TOU Demand Charge Energy Charge

  10. Comparison of ASHRAE Standard 90.1, 189.1 and IECC Codes for Large Office Building in Texas), Energy Systems Laboratory, Texas A&M University.

    E-Print Network [OSTI]

    Mukhopadhyay, J.; Baltazar, J.C.; Kim,H.; Haberl, J.

    2011-01-01T23:59:59.000Z

    herein is necessarily error-free. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the Energy.... The comparison is carried out using the simulation model for a large office building initially developed by Ahmad et al. (2005) and Kim et al. (2009) using DOE-2.1e simulation program. The model has been updated and modified as per the requirements...

  11. Navy mobility fuels forecasting system. Phase II, report

    SciTech Connect (OSTI)

    Hadder, G.R.; Vineyard, T.A.; Das, S.; Lee, R.

    1986-06-01T23:59:59.000Z

    The Department of the Navy (DON) requires an improved capability to forecast mobility fuel availability and quality. The changing patterns in fuel availability and quality are important in planning the Navy's fuel use strategy and the Mobility Fuels Technology Program. Until recently, there has been a long-term decline in the quality of crude oil entering world markets and a shift in refinery capacities domestically and worldwide. Three publicly available models developed by the Energy Information Administration of the Department of Energy were selected as the basis of the Navy Mobility Fuels Forecasting System (NMFFS). The system was used to analyze the availability and quality of jet fuel (JP-5) and diesel fuel (F-76) that could be produced in several domestic and foreign refinery regions under business-as-usual and four hypothetical world crude oil disruption scenarios in 1990. The results from the study indicate that jet fuel availability could be reduced in some refinery regions under the disruptions studied. Various strategies were investigated for increasing JP-5 production during the disruptions. In general, the availability of JP-5 was more limited than F-76 under the disruption cases studied; however, in all cases one or more strategies were identified to increase refinery output of JP-5 in all study regions. Based on the four hypothetical disruption scenarios studied, the analysis suggested tat JP-5 production could be increased by relaxing the smoke point, freezing point, flash point, and by increasing the refiners' gate price for the jet fuel in the study regions. A more complete analysis of navy mobility fuel trends, as well as several changes in the models to make them easier to use in fuel planning and forecastig studies, are planned as part of the third phase of this program.

  12. Lessons learned -- a comparison of the proposed on-site waste management facilities at the various Department of Energy sites

    SciTech Connect (OSTI)

    Ciocco, J. [Dept. of Energy, Germantown, MD (United States); Singh, D. [Booz Allen and Hamilton, Germantown, MD (United States); Survochak, S. [DOE RFETS, Golden, CO (United States); Elo, M. [Burns and Roe, Germantown, MD (United States)

    1996-12-31T23:59:59.000Z

    The Department of Energy Sites (DOE) are faced with the challenge of managing several categories of waste generated from past or future cleanup activities, such as 11(e)2 byproduct material, low-level radioactive (LL), low-level radioactive mixed (LLM), transuranic (TRU), high level radioactive (HL), and hazardous waste (HW). DOE must ensure safe and efficient management of these wastes while complying with all applicable federal and state laws. Proposed waste management strategies for the EM-40 Environmental Restoration (ER) program at these sites indicate that on-site disposal is becoming a viable option. For purposes of this paper, on-site disposal cells managed by the EM-40 program at Hanford, Weldon Spring, Fernald Environmental Management Project (FEMP) and Rocky Flats were compared. Programmatic aspects and design features were evaluated to determine what comparisons can be made, and to identify benefits lessons learned that may be applicable to other sites. Based on comparative analysis, it can be concluded that the DOE EM-40 disposal cells are very unique. Stakeholders played a major role in the decision to locate the various DOE on-site disposal facilities. The disposal cells will be used to manage 11(e)2 by-product materials, LL, LLM, and/or HLW. The analysis further suggests that the design criteria are comparable. Lessons learned relative to the public involvement activities at Weldon Spring, and the design approach at Hanford should be considered when planning future on-site disposal facilities at DOE sites. Further, a detailed analysis of progress made at Hanford should be evaluated for application at sites such as Rocky Flats that are currently planning on-site disposal facilities.

  13. A sensitivity analysis of the treatment of wind energy in the AEO99 version of NEMS

    E-Print Network [OSTI]

    Osborn, Julie G.; Wood, Frances; Richey, Cooper; Sanders, Sandy; Short, Walter; Koomey, Jonathan

    2001-01-01T23:59:59.000Z

    Administration. 1998. Annual Energy Outlook 1999: WithDepartment of Energys Annual Energy Outlook (AEO) forecastDepartment of Energys Annual Energy Outlook 1999 (AEO99)

  14. Forecasting model of the PEPCO service area economy. Volume 3

    SciTech Connect (OSTI)

    Not Available

    1984-03-01T23:59:59.000Z

    Volume III describes and documents the regional economic model of the PEPCO service area which was relied upon to develop many of the assumptions of future values of economic and demographic variables used in the forecast. The PEPCO area model is mathematically linked to the Wharton long-term forecast of the U.S. Volume III contains a technical discussion of the structure of the regional model and presents the regional economic forecast.

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

    SciTech Connect (OSTI)

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

    2009-11-20T23:59:59.000Z

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

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

    Office of Environmental Management (EM)

    Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report.pdf More Documents & Publications Computational Advances in Applied...

  17. Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures

    E-Print Network [OSTI]

    Richard A. Berk; Brian Kriegler; Jong-Ho Baek

    2011-01-01T23:59:59.000Z

    Forecasting Dangerous Inmate Misconduct: An Applications ofof Term Length more dangerous than other inmates servingIV beds or moving less dangerous Level IV inmates to Level

  18. Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures

    E-Print Network [OSTI]

    Berk, Richard; Kriegler, Brian; Baek, Jong-Ho

    2005-01-01T23:59:59.000Z

    Forecasting Dangerous Inmate Misconduct: An Applications ofof Term Length more dangerous than other inmates servingIV beds or moving less dangerous Level IV inmates to Level

  19. Forecasting the underlying potential governing climatic time series

    E-Print Network [OSTI]

    Livina, V N; Mudelsee, M; Lenton, T M

    2012-01-01T23:59:59.000Z

    We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for `potential analysis' of climatic tipping points which altogether serves anticipating, detecting and forecasting climate transitions and bifurcations using several independent techniques of time series analysis.

  20. Econometric model and futures markets commodity price forecasting

    E-Print Network [OSTI]

    Just, Richard E.; Rausser, Gordon C.

    1979-01-01T23:59:59.000Z

    Versus CCll1rnercial Econometric M:ldels." Uni- versity ofWorking Paper No. 72 ECONOMETRIC ! 'econometric forecasts with the futures