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Sample records for forecast future demand

  1. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

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

  2. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

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

  3. A model for forecasting future air travel demand on the North Atlantic

    E-Print Network [OSTI]

    Taneja, Nawal K.

    1971-01-01

    Introduction: One of the key problems in the analysis and planning of any transport properties and facilities is estimating the future volume of traffic that may be expected to use these properties and facilities. Estimates ...

  4. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

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

  5. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

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

  6. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

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

  7. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

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

  8. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

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

  9. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

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

  10. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

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

  11. FORECAST COMBINATION IN REVENUE MANAGEMENT DEMAND FORECASTING

    E-Print Network [OSTI]

    Fernandez, Thomas

    Demandness in Rewriting and Narrowing Sergio Antoy1 and Salvador Lucas2 1 Computer Science by a strategy to compute a step. The notion of demandness provides a suitable framework for pre- senting that the notion of demandness is both atomic and fundamental to the study of strategies. 1 Introduction Modern

  12. Forecasting Market Demand for New Telecommunications Services: An Introduction

    E-Print Network [OSTI]

    Parsons, Simon

    Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc in demand forecasting for new communication services. Acknowledgments: The writing of this paper commenced employers or consultancy clients. KEYWORDS: Demand Forecasting, New Product Marketing, Telecommunica- tions

  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 networks, Neural networks, Modeling, Forecasting, Energy demand, Time series forecasting, Power system demand time series based only on data for six years to forecast the demand for the seventh year. Both

  14. 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

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

    SciTech Connect (OSTI)

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

    2005-07-01

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

  16. FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007

    E-Print Network [OSTI]

    ......................................................................... 11 3. Demand Side Management (DSM) Program Impacts................................... 13 4. Demand Sylvia Bender Manager DEMAND ANALYSIS OFFICE Scott W. Matthews Chief Deputy Director B.B. Blevins Forecast Methods and Models ....................................................... 14 5. Demand-Side

  17. Forecasting Market Demand for New Telecommunications Services: An Introduction

    E-Print Network [OSTI]

    McBurney, Peter

    Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc to redress this situation by presenting a discussion of the issues involved in demand forecasting for new or consultancy clients. KEYWORDS: Demand Forecasting, New Product Marketing, Telecommunica­ tions Services. 1 #12

  18. Reducing the demand forecast error due to the bullwhip effect in the computer processor industry

    E-Print Network [OSTI]

    Smith, Emily (Emily C.)

    2010-01-01

    Intel's current demand-forecasting processes rely on customers' demand forecasts. Customers do not revise demand forecasts as demand decreases until the last minute. Intel's current demand models provide little guidance ...

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

    E-Print Network [OSTI]

    Stadler, Michael

    2011-01-01

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

  20. Draft for Public Comment Appendix A. Demand Forecast

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

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

  2. PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022

    E-Print Network [OSTI]

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

  3. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

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

  4. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

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

  5. Risk Management for Video-on-Demand Servers leveraging Demand Forecast

    E-Print Network [OSTI]

    Li, Baochun

    Risk Management for Video-on-Demand Servers leveraging Demand Forecast Di Niu, Hong Xu, Baochun Li}@eecg.toronto.edu Shuqiao Zhao Multimedia Development Group UUSee, Inc. shuqiao.zhao@gmail.com ABSTRACT Video-on-demand (VoD) servers are usually over-provisioned for peak demands, incurring a low average resource effi- ciency

  6. Operationalizing demand forecasts in the warehouse

    E-Print Network [OSTI]

    Li, Dan, Ph. D. University of Rochester

    2015-01-01

    Demand planning affects the subsequent business activities including distribution center operational planning and management. Today's competitive environment requires distribution centers to rapidly respond to changes in ...

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

    SciTech Connect (OSTI)

    Lemont, S.

    1980-01-01

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

  8. Transportation Energy: Supply, Demand and the Future

    E-Print Network [OSTI]

    Saldin, Dilano

    trends in China, India, Eastern Europe and other developing areas. China oil demand +104% by 2030, India 2000 2020 2040 2060 Supply demand Energy UWM-CUTS 14 U.S. DOE viewpoint, source:http://tonto.eia.doe.gov/FTPROOT/features/longterm.pdf#search='oilTransportation Energy: Supply, Demand and the Future http://www.uwm.edu/Dept/CUTS//2050/energy05

  9. ArizonaArizona''s Electricity Future:s Electricity Future: The Demand for WaterThe Demand for Water

    E-Print Network [OSTI]

    Keller, Arturo A.

    ArizonaArizona''s Electricity Future:s Electricity Future: The Demand for WaterThe Demand for Water'' projected energy demandprojected energy demand 317 1,281 257 511 5,506 1,989 0 1,000 2,000 3,000 4,000 5

  10. Transportation Energy Futures Series: Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future

    SciTech Connect (OSTI)

    Grenzeback, L. R.; Brown, A.; Fischer, M. J.; Hutson, N.; Lamm, C. R.; Pei, Y. L.; Vimmerstedt, L.; Vyas, A. D.; Winebrake, J. J.

    2013-03-01

    Freight transportation demand is projected to grow to 27.5 billion tons in 2040, and to nearly 30.2 billion tons in 2050. This report describes the current and future demand for freight transportation in terms of tons and ton-miles of commodities moved by truck, rail, water, pipeline, and air freight carriers. It outlines the economic, logistics, transportation, and policy and regulatory factors that shape freight demand, the trends and 2050 outlook for these factors, and their anticipated effect on freight demand. After describing federal policy actions that could influence future freight demand, the report then summarizes the capabilities of available analytical models for forecasting freight demand. This is one in a series of reports produced as a result of the Transportation Energy Futures project, a Department of Energy-sponsored multi-agency effort to pinpoint underexplored strategies for reducing GHGs and petroleum dependence related to transportation.

  11. New directions for forecasting air travel passenger demand

    E-Print Network [OSTI]

    Garvett, Donald Stephen

    1974-01-01

    While few will disagree that sound forecasts are an essential prerequisite to rational transportation planning and analysis, the making of these forecasts has become a complex problem with the broadening of the scope and ...

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

    E-Print Network [OSTI]

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

    2005-01-01

    Forecasts Using NEMS and GIS National Climatic Data Center.with Changing Boundaries." Use of GIS to Understand Socio-Forecasts Using NEMS and GIS Appendix A. Map Results Gallery

  13. Demand Response: Lessons Learned with an Eye to the Future |...

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

    Demand Response: Lessons Learned with an Eye to the Future Demand Response: Lessons Learned with an Eye to the Future July 11, 2013 - 11:56am Addthis Patricia A. Hoffman Patricia...

  14. Freight Transportation Demand: Energy-Efficient Scenarios for a Low-Carbon Future

    Broader source: Energy.gov [DOE]

    Freight transportation demand is projected to grow to 27.5 billion tons in 2040, and by extrapolation, to nearly 30.2 billion tons in 2050, requiring ever-greater amounts of energy. This report describes the current and future demand for freight transportation in terms of tons and ton-miles of commodities moved by truck, rail, water, pipeline, and air freight carriers. It outlines the economic, logistics, transportation, and policy and regulatory factors that shape freight demand; the possible trends and 2050 outlook for these factors, and their anticipated effect on freight demand and related energy use.After describing federal policy actions that could influence freight demand, the report then summarizes the available analytical models for forecasting freight demand, and identifies possible areas for future action.

  15. Sixth Northwest Conservation and Electric Power Plan Chapter 3: Electricity Demand Forecast

    E-Print Network [OSTI]

    Sixth Northwest Conservation and Electric Power Plan Chapter 3: Electricity Demand Forecast Summary.............................................................................................. 11 Demand From Plug-in Hybrid Electric Vehicles (PHEV megawatt-hours of electricity in 2007. That demand is expected to grow to 25,000 average megawatts by 2030

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

    E-Print Network [OSTI]

    Beigl, Michael

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

  17. Developing a framework for dependable demand forecasts in the consumer packaged goods industry

    E-Print Network [OSTI]

    Uriarte, Daniel Antonio

    2010-01-01

    As a consumer packaged goods company, "Company X" manufactures products "make-to-stock"; therefore, having reliable demand forecasts is fundamental for successful planning and execution. Not isolated to "Company X" or to ...

  18. FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS

    E-Print Network [OSTI]

    Keller, Arturo A.

    Winter (November - April) water demand Developed by Limaye et al. 1993 Residential water demand ­ f {PPHFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil resources resulting in water stress. Effective water management ­ a solution Supply side management Demand

  19. Residential Electricity Demand in China -- Can Efficiency Reverse the Growth?

    E-Print Network [OSTI]

    Letschert, Virginie

    2010-01-01

    with Residential Electricity Demand in India's Future - How2008). The Boom of Electricity Demand in the residential2005). Forecasting Electricity Demand in Developing

  20. Draft Fourth Northwest Conservation and Electric Power Plan, Appendix D ECONOMIC AND DEMAND FORECASTS

    E-Print Network [OSTI]

    , and high) based on different assumptions about the key determinants of electricity demand. Much economy is the dominant determinant of electricity demand both now and in the future. The demand of alternative energy forms, such as natural gas, are also important determinants of electricity demand. Demand

  1. Electricity Demand Forecasting using Gaussian Processes Manuel Blum and Martin Riedmiller

    E-Print Network [OSTI]

    Teschner, Matthias

    that are normally much less attractive than the prices in the wholesale market. The electricity demand is mainlyElectricity Demand Forecasting using Gaussian Processes Manuel Blum and Martin Riedmiller University of Freiburg, Department of Computer Science Georges-Koehler Allee 079 79110 Freiburg, Germany

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

    SciTech Connect (OSTI)

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

    2015-01-01

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

  3. Global GPS Phones Market Size, Segmentation, Demand Forecast...

    Open Energy Info (EERE)

    we deeply analyzed the world's main region market conditions that including the product price, profit, capacity, production, capacity utilization, supply, demand and industry...

  4. Patterns of crude demand: Future patterns of demand for crude oil as a func-

    E-Print Network [OSTI]

    Langendoen, Koen

    #12;2 #12;Patterns of crude demand: Future patterns of demand for crude oil as a func- tion;5 Summary The crude oil market is actually experiencing dramatic changes on a world wide scale. Most schemes, and/or change quality of the feedstock (crude). Demand for crude oil is growing, especially

  5. How USDA Forecasts Production and Supply/Demand 

    E-Print Network [OSTI]

    Anderson, David P.; O'Brien, Daniel; Welch, Mark

    2009-06-01

    USDA publishes crop supply and demand estimates for each month. Producers, merchandisers, processors, traders and other market participants rely on this information when making their buying and selling decisions. This leaflet explains how USDA makes...

  6. Electrical ship demand modeling for future generation warships

    E-Print Network [OSTI]

    Sievenpiper, Bartholomew J. (Bartholomew Jay)

    2013-01-01

    The design of future warships will require increased reliance on accurate prediction of electrical demand as the shipboard consumption continues to rise. Current US Navy policy, codified in design standards, dictates methods ...

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01

    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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01

    the AEO 2005 reference case oil price forecast and NYMEX oibasis-adjusted NYMEX crude oil futures con tracts fo r 2010more than the reference case oil price forecast for that

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01

    Figure 9: Two Alternative Price Forecasts (denoted by openComparison of AEO 2007 Natural Gas Price Forecast toNYMEX Futures Prices Date: December 6, 2006 Introduction On

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

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

    18F-FDG PET has been studied for detecting and staging recurrent ovarian cancer. Potential savings were estimated at 8500 per patient with PET (J Nucl Med...

  11. Expert Panel: Forecast Future Demand for Medical Isotopes

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

    One last perceived cause of these delivery problems has been the lack of a hard and fast commitment to honor delivery schedules and timetables. In commercial contracts 21 there...

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

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

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

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01

    range of different plausible price projections, using eitherthat renewables can provide price certainty over even longerof AEO 2009 Natural Gas Price Forecast to NYMEX Futures

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

    E-Print Network [OSTI]

    Stadler, Michael

    2011-01-01

    forecast to 2050 On-site generation cost & performance (e.g.forecast to 2050 On-site generation cost & performance (e.g.forecast to 2050 On-site generation cost & performance (e.g.

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

    Reports and Publications (EIA)

    1998-01-01

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

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

    E-Print Network [OSTI]

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

    2005-01-01

    index.html. Appendix A.1 Natural Gas Price Data for FuturesError STEO Error A.1 Natural Gas Price Data for Futuresof forecasts for natural gas prices as reported by the

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

    E-Print Network [OSTI]

    Bush, Sarah, 1973-

    2003-01-01

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

  18. Water Requirements for Future Energy production in California

    E-Print Network [OSTI]

    Sathaye, J.A.

    2011-01-01

    CALIFORNIA WATER RESOURCES. Water Demand Energy Suppon future forecasts of of Water energy predicted energy aunder these PHASE II: WATER ENERGY REQUIREMENTS FOR FUTURE

  19. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

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

  20. Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

    E-Print Network [OSTI]

    Wong, Vincent

    1 Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design Pedram Samadi for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation: Demand side management, VCG mechanism design, energy consumption control, smart grid. I. INTRODUCTION

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

    SciTech Connect (OSTI)

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04

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

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

    E-Print Network [OSTI]

    Mancco, Richard

    2012-01-01

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

  3. Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices

    E-Print Network [OSTI]

    Kulkarni, Siddhivinayak

    2009-01-01

    This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the cr...

  4. Enhanced Oil Recovery to Fuel Future Oil Demands | GE Global...

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

    of the fascinating things of my job is contemplating questions like: What will the future energy mix look like? This is difficult to predict but it is fair to argue that oil will...

  5. Implications for the Future of Treated Wood in Four U.S. Demand Sectors

    E-Print Network [OSTI]

    Implications for the Future of Treated Wood in Four U.S. Demand Sectors Todd F. Shupe Associate are a thousand times less than a lethal dose." Chromated Copper Arsenate-Treated Wood (Pressure Treated Wood

  6. THE FUTURE DEMAND FOR ALTERNATIVE FUEL PASSENGER VEHICLES: A DIFFUSION OF INNOVATION APPROACH

    E-Print Network [OSTI]

    Levinson, David M.

    ..............................................................................................................27 3.1.2 Natural Gas Vehicles ..........................................................................................................26 3.1.1 Liquefied Petroleum Gas VehiclesTHE FUTURE DEMAND FOR ALTERNATIVE FUEL PASSENGER VEHICLES: A DIFFUSION OF INNOVATION APPROACH UC

  7. Exponential Communication Ine ciency of Demand Queries

    E-Print Network [OSTI]

    Sandholm, Tuomas W.

    FORECAST COMBINATION IN REVENUE MANAGEMENT DEMAND FORECASTING SILVIA RIEDEL A thesissubmitted Combination in RevenueManagement Demand Forecasting Abstract The domain of multi level forecastcombination

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

    E-Print Network [OSTI]

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

    2008-01-01

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

  9. Reconstruction of the Past and Forecast of the Future European and British Ice Sheets and Associated Sea–Level Change 

    E-Print Network [OSTI]

    Hagdorn, Magnus K M

    The aim of this project is to improve our understanding of the past European and British ice sheets as a basis for forecasting their future. The behaviour of these ice sheets is investigated by simulating them using a ...

  10. DOE Announces Webinars on the Buildings of the Future Research...

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

    (in Buildings): Towards the Energy System of the Future - Andy Walker, National Renewable Energy Laboratory Forecasting Building Energy Demands from Very Dense Cities - Jorge...

  11. Renewable Electricity Futures Study. Volume 3. End-Use Electricity Demand

    SciTech Connect (OSTI)

    Hostick, Donna; Belzer, David B.; Hadley, Stanton W.; Markel, Tony; Marnay, Chris; Kintner-Meyer, Michael

    2012-06-15

    The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%–90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT). Learn more at the RE Futures website. http://www.nrel.gov/analysis/re_futures/

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

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

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2005-12-19

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

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

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

  15. Renewable Electricity Futures Study. Volume 3: End-Use Electricity Demand

    SciTech Connect (OSTI)

    Hostick, D.; Belzer, D.B.; Hadley, S.W.; Markel, T.; Marnay, C.; Kintner-Meyer, M.

    2012-06-01

    The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01

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

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01

    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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01

    revisions to the EIA’s natural gas price forecasts in AEOon the AEO 2005 natural gas price forecasts will likely onceComparison of AEO 2005 Natural Gas Price Forecast to NYMEX

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01

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

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01

    Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01

    Comparison of AEO 2008 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01

    Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01

    Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

  4. Renewable Electricity Futures Study Volume 3: End-Use Electricity Demand

    Broader source: Energy.gov [DOE]

    This volume details the end-use electricity demand and efficiency assumptions. The projection of electricity demand is an important consideration in determining the extent to which a predominantly renewable electricity future is feasible. Any scenario regarding future electricity use must consider many factors, including technological, sociological, demographic, political, and economic changes (e.g., the introduction of new energy-using devices; gains in energy efficiency and process improvements; changes in energy prices, income, and user behavior; population growth; and the potential for carbon mitigation).

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

    SciTech Connect (OSTI)

    Fournier, W.M.; Hasson, V.

    1980-10-10

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

  6. Forecasting future oil production in Norway and the UK: a general improved methodology

    E-Print Network [OSTI]

    Fievet, Lucas; Cauwels, Peter; Sornette, Didier

    2014-01-01

    We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the standard methodology that tends to underestimate remaining oil reserves, our method gives a better description of future oil production, as validated by our back-tests starting in 2008. Specifically, we predict remaining reserves extractable until 2030 to be 188 +/- 10 million barrels for Norway and 98 +/- 10 million barrels for the UK, which are respectively 45% and 66% above the predictions using the standard methodology.

  7. Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs: FORECASTING EARTH'S SURFACE RESPONSE

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

    Pelletier, Jon D.; Brad Murray, A.; Pierce, Jennifer L.; Bierman, Paul R.; Breshears, David D.; Crosby, Benjamin T.; Ellis, Michael; Foufoula-Georgiou, Efi; Heimsath, Arjun M.; Houser, Chris; et al

    2015-07-14

    In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore »the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

  8. Improved forecasts of extreme weather events by future space borne Doppler wind lidar

    E-Print Network [OSTI]

    Marseille, Gert-Jan

    of forecast failures, in particular those with large socio economic impact. Forecast failures of high- impact on their ability to improve meteorological analyses and subsequently reduce the probability of forecast failures true atmospheric state. This was generated by the European Centre for Medium-Range Weather Forecasts

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01

    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

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01

    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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01

    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

  12. The Future of Food Demand: Understanding Differences in Global Economic Models

    SciTech Connect (OSTI)

    Valin, Hugo; Sands, Ronald; van der Mensbrugghe, Dominique; Nelson, Gerald; Ahammad, Helal; Blanc, Elodie; Bodirsky, Benjamin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Heyhoe, Edwina; Kyle, G. Page; Mason d'Croz, Daniel; Paltsev, S.; Rolinski, Susanne; Tabeau, Andrzej; van Meijl, Hans; von Lampe, Martin; Willenbockel, Dirk

    2014-01-01

    Understanding the capacity of agricultural systems to feed the world population under climate change requires a good prospective vision on the future development of food demand. This paper reviews modeling approaches from ten global economic models participating to the AgMIP project, in particular the demand function chosen and the set of parameters used. We compare food demand projections at the horizon 2050 for various regions and agricultural products under harmonized scenarios. Depending on models, we find for a business as usual scenario (SSP2) an increase in food demand of 59-98% by 2050, slightly higher than FAO projection (54%). The prospective for animal calories is particularly uncertain with a range of 61-144%, whereas FAO anticipates an increase by 76%. The projections reveal more sensitive to socio-economic assumptions than to climate change conditions or bioenergy development. When considering a higher population lower economic growth world (SSP3), consumption per capita drops by 9% for crops and 18% for livestock. Various assumptions on climate change in this exercise do not lead to world calorie losses greater than 6%. Divergences across models are however notable, due to differences in demand system, income elasticities specification, and response to price change in the baseline.

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

    E-Print Network [OSTI]

    Hwang, Kai

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

  14. Supply chain planning decisions under demand uncertainty

    E-Print Network [OSTI]

    Huang, Yanfeng Anna

    2008-01-01

    Sales and operational planning that incorporates unconstrained demand forecasts has been expected to improve long term corporate profitability. Companies are considering such unconstrained demand forecasts in their decisions ...

  15. Proceedings of the Chinese-American symposium on energy markets and the future of energy demand

    SciTech Connect (OSTI)

    Meyers, S. (ed.)

    1988-11-01

    The Symposium was organized by the Energy Research Institute of the State Economic Commission of China, and the Lawrence Berkeley Laboratory and Johns Hopkins University from the United States. It was held at the Johns Hopkins University Nanjing Center in late June 1988. It was attended by about 15 Chinese and an equal number of US experts on various topics related to energy demand and supply. Each presenter is one of the best observers of the energy situation in their field. A Chinese and US speaker presented papers on each topic. In all, about 30 papers were presented over a period of two and one half days. Each paper was translated into English and Chinese. The Chinese papers provide an excellent overview of the emerging energy demand and supply situation in China and the obstacles the Chinese planners face in managing the expected increase in demand for energy. These are matched by papers that discuss the energy situation in the US and worldwide, and the implications of the changes in the world energy situation on both countries. The papers in Part 1 provide historical background and discuss future directions. The papers in Part 2 focus on the historical development of energy planning and policy in each country and the methodologies and tools used for projecting energy demand and supply. The papers in Part 3 examine the pattern of energy demand, the forces driving demand, and opportunities for energy conservation in each of the major sectors in China and the US. The papers in Part 4 deal with the outlook for global and Pacific region energy markets and the development of the oil and natural gas sector in China.

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01

    approach to evaluating price risk would be to use suchthe base-case natural gas price forecast, but to alsorange of different plausible price projections, using either

  17. Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

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

    Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; Bierman, Paul R.; Breshears, David D.; Crosby, Benjamin T.; Ellis, Michael; Foufoula-Georgiou, Efi; Heimsath, Arjun M.; Houser, Chris; et al

    2015-07-14

    In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore »the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2009-01-28

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

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

    SciTech Connect (OSTI)

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

    2008-01-07

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

  20. Fast Automated Demand Response to Enable the Integration of Renewable Resources

    E-Print Network [OSTI]

    Watson, David S.

    2013-01-01

    California Energy Demand 2010 2020 Adopted Forecast presentsEnergy Commission, Demand Analysis Office. Ag and Water Pumping Energy Forecasts (

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

    E-Print Network [OSTI]

    Komiyama, Ryoichi

    2008-01-01

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

  2. Electricity Demand-Side Management for an Energy Efficient Future in China: Technology Options and Policy Priorities

    E-Print Network [OSTI]

    de Weck, Olivier L.

    Neufville Professor of Engineering Systems Chair, ESD Education Committee #12;2 #12;3 Electricity DemandElectricity Demand-Side Management for an Energy Efficient Future in China: Technology Options: ______________________________________________________________ : Stephen R. Connors Director, Analysis Group for Regional Electricity Alternatives Thesis Supervisor

  3. Abstract--In this paper, we present a new approach for very short term electricity load demand forecasting. In particular,

    E-Print Network [OSTI]

    Koprinska, Irena

    . Electricity market operators and participants use load forecasting for many reasons such as to make unit of the national electricity market, as its market operator NEMMCO must issue every 5 minutes the production this information as the basis for any re-bids of the capacity they wish to bring to the market. In this paper, we

  4. Results from the Second Forum on the Future Role of the Human in the Forecast Process. Part II: Cognitive Psychological Aspects of Expert Weather Forecasters

    E-Print Network [OSTI]

    Schultz, David

    : Cognitive Psychological Aspects of Expert Weather Forecasters NEIL A. STUART* NOAA/National Weather Service of Applied Research Associates, Fairborn, Ohio In Preparation for Submission to Forecasters Forum, Weather and Forecasting 30 June 2006 Corresponding author address: Neil A. Stuart, National Weather Service, 10009 General

  5. Quantile Forecasting of Commodity Futures' Returns: Are Implied Volatility Factors Informative? 

    E-Print Network [OSTI]

    Dorta, Miguel

    2012-07-16

    - returns has excess kurtosis or skewness, Gaussian based forecast could overexpose investors to financial risk. GARCH-class models, extensively used for log-returns density forecasting, have a somewhat limited ability to allow higher moments to be time... pricing model, which is based on the assumption of a log- normal density and risk-neutrality, would coincide with the true only if the underlying price process is a Brownian motion. Hence, differences between BS-derived put-IVs versus BS-derived call...

  6. Learning Energy Demand Domain Knowledge via Feature Transformation

    E-Print Network [OSTI]

    Povinelli, Richard J.

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

  7. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01

    3 2.1 Demand-Side Managementbuildings. The demand side management framework is discussedIssues 2.1 Demand-Side Management Framework Forecasting

  8. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01

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

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

    E-Print Network [OSTI]

    Fritsch, Roger Erwin

    1997-01-01

    Daily Standard & Poor's 500 stock index cash and futures prices are studies in a cointegration framework using Johansen's maximum likelihood procedure. To account for the time varying relationship(basis) between the two ...

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

    E-Print Network [OSTI]

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

    2005-01-01

    Update on Petroleum, Natural Gas, Heating Oil and Gasoline.of the Market for Natural Gas Futures. Energy Journal 16 (Modeling Forum. 2003. Natural Gas, Fuel Diversity and North

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

    SciTech Connect (OSTI)

    Not Available

    1992-09-01

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

  12. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1992-04-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  13. International Oil Supplies and Demands

    SciTech Connect (OSTI)

    Not Available

    1991-09-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world's dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group's thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  14. Demand-Side Planning in Texas-Past, Present, and Future 

    E-Print Network [OSTI]

    Biedrzycki, C.

    1986-01-01

    Section 23.22 of the Substantive Rules of the Public Utility Commission of Texas requires that generating utilities and utilities with more than 20,000 customers file energy efficiency plans. The plans identify and evaluate supply-side and demand...

  15. Orphan drugs : future viability of current forecasting models, in light of impending changes to influential market factors

    E-Print Network [OSTI]

    Gottlieb, Joshua

    2011-01-01

    Interviews were conducted to establish a baseline for how orphan drug forecasting is currently undertaken by financial market and industry analysts with the intention of understanding the variables typically accounted for ...

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

    E-Print Network [OSTI]

    Komiyama, Ryoichi

    2010-01-01

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

  17. Energy, Water and Fish: Biodiversity Impacts of Energy-Sector Water Demand in the United States Depend on

    E-Print Network [OSTI]

    Olden, Julian D.

    Energy, Water and Fish: Biodiversity Impacts of Energy- Sector Water Demand in the United States to increase the impact of energy sector water use on freshwater biodiversity. We forecast changes in future: Biodiversity Impacts of Energy-Sector Water Demand in the United States Depend on Efficiency and Policy

  18. International Oil Supplies and Demands. Volume 1

    SciTech Connect (OSTI)

    Not Available

    1991-09-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--90 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  19. International Oil Supplies and Demands. Volume 2

    SciTech Connect (OSTI)

    Not Available

    1992-04-01

    The eleventh Energy Modeling Forum (EMF) working group met four times over the 1989--1990 period to compare alternative perspectives on international oil supplies and demands through 2010 and to discuss how alternative supply and demand trends influence the world`s dependence upon Middle Eastern oil. Proprietors of eleven economic models of the world oil market used their respective models to simulate a dozen scenarios using standardized assumptions. From its inception, the study was not designed to focus on the short-run impacts of disruptions on oil markets. Nor did the working group attempt to provide a forecast or just a single view of the likely future path for oil prices. The model results guided the group`s thinking about many important longer-run market relationships and helped to identify differences of opinion about future oil supplies, demands, and dependence.

  20. CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY

    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 CALIFORNIA ENERGY DEMAND 2014-2024 PRELIMINARY FORECAST Volume 1

  1. Promotional forecasting in the grocery retail business

    E-Print Network [OSTI]

    Koottatep, Pakawkul

    2006-01-01

    Predicting customer demand in the highly competitive grocery retail business has become extremely difficult, especially for promotional items. The difficulty in promotional forecasting has resulted from numerous internal ...

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

    SciTech Connect (OSTI)

    1995-04-01

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

  3. The role of building technologies in reducing and controlling peak electricity demand

    E-Print Network [OSTI]

    Koomey, Jonathan; Brown, Richard E.

    2002-01-01

    AND CONTROLLING PEAK ELECTRICITY DEMAND Jonathan Koomey* andData to Improve Electricity Demand Forecasts–Final Report.further research. Electricity demand varies constantly. At

  4. New product forecasting in volatile markets

    E-Print Network [OSTI]

    Baldwin, Alexander (Alexander Lee)

    2014-01-01

    Forecasting demand for limited-life cycle products is essentially projecting an arc trend of demand growth and decline over a relatively short time horizon. When planning for a new limited-life product, many marketing and ...

  5. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    Forecasts of California transportation energy demand, 2005-alternative transportation energy pathways on California’salternative transportation energy pathways on California’s

  6. 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 ...

  7. Electricity demand-side management for an energy efficient future in China : technology options and policy priorities

    E-Print Network [OSTI]

    Cheng, Chia-Chin

    2005-01-01

    The main objective of this research is to identify robust technology and policy options which achieve substantial reductions in electricity demand in China's Shandong Province. This research utilizes a scenario-based ...

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

    E-Print Network [OSTI]

    to the electricity price forecast. This resource mix is used to forecast the fuel consumption and carbon dioxide (CO2Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price forecast of the Fifth Northwest Power Plan. This forecast is an estimate of the future price of electricity

  9. Future Power Systems 20: The Smart Enterprise, its Objective...

    Energy Savers [EERE]

    0: The Smart Enterprise, its Objective and Forecasting. Future Power Systems 20: The Smart Enterprise, its Objective and Forecasting. Future Power Systems 20: The Smart Enterprise,...

  10. Solar Forecasting

    Broader source: Energy.gov [DOE]

    On December 7, 2012, DOE announced $8 million to fund two solar projects that are helping utilities and grid operators better forecast when, where, and how much solar power will be produced at U.S....

  11. Transportation Energy Futures

    E-Print Network [OSTI]

    Sperling, Daniel

    1989-01-01

    s values, forecasts of future energy prices and politicalYergin, D. , eds. 1979. Energy Future: Report of the Energy02, Sacramento, Calif. ENERGY FUTURES 103. Ullman, T. L. ,

  12. Demand forecasting for aircraft engine aftermarket

    E-Print Network [OSTI]

    Ho, Kien K. (Kine Kit)

    2008-01-01

    In 2006, Pratt and Whitney launched the Global Material Solutions business model aiming to supply spare parts to non-OEM engines with minimum 95% on-time delivery and fill-rate. Lacking essential technical knowledge of the ...

  13. Load forecasting for active distribution networks Simone Paoletti, Member, IEEE, Marco Casini, Member, IEEE, Antonio Giannitrapani, Member, IEEE,

    E-Print Network [OSTI]

    Giannitrapani, Antonello

    forecasting for distribution networks with Active Demand (AD), a new concept in smart-grids introduced within

  14. Nuclear energy acceptance and potential role to meet future energy demand. Which technical/scientific achievements are needed?

    SciTech Connect (OSTI)

    Schenkel, Roland [European Commission, Joint Research Centre, Institute for Transuranium Elements, Hermann-von-Helmholtz-Platz 1,76344 Eggenstein-Leopoldshafen (Germany)

    2012-06-19

    25 years after Chernobyl, the Fukushima disaster has changed the perspectives of nuclear power. The disaster has shed a negative light on the independence, reliability and rigor of the national nuclear regulator and plant operator and the usefulness of the international IAEA guidelines on nuclear safety. It has become clear that, in the light of the most severe earthquake in the history of Japan, the plants at Fukushima Daiichi were not adequately protected against tsunamis. Nuclear acceptance has suffered enormously and has changed the perspectives of nuclear energy dramatically in countries that have a very risk-sensitive population, Germany is an example. The paper analyses the reactions in major countries and the expected impact on future deployment of reactors and on R and D activities. On the positive side, the disaster has demonstrated a remarkable robustness of most of the 14 reactors closest to the epicentre of the Tohoku Seaquake although not designed to an event of level 9.0. Public acceptance can only be regained with a rigorous and worldwide approach towards inherent reactor safety and design objectives that limit the impact of severe accidents to the plant itself (like many of the new Gen III reactors). A widespread release of radioactivity and the evacuation (temporary or permanent) of the population up to 30 km around a facility are simply not acceptable. Several countries have announced to request more stringent international standards for reactor safety. The IAEA should take this move forward and intensify and strengthen the different peer review mission schemes. The safety guidelines and peer reviews should in fact become legally binding for IAEA members. The paper gives examples of the new safety features developed over the last 20 years and which yield much safer reactors with lesser burden to the environment under severe accident conditions. The compatibility of these safety systems with the current concepts for fusion-fission hybrids, which have recently been proposed for energy production, is critically reviewed. There are major challenges remaining that are shortly outlined. Scientific/technical achievements that are required in the light of the Fukushima accident are highlighted.

  15. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

    of a range of world oil prices for future energy demand andTo examine the ef feet of oil prices on energy demand andprojections of world oil prices. Th and demand. determined

  16. Demand Reduction

    Office of Energy Efficiency and Renewable Energy (EERE)

    Grantees may use funds to coordinate with electricity supply companies and utilities to reduce energy demands on their power systems. These demand reduction programs are usually coordinated through...

  17. Detrending Daily Natural Gas Consumption Series to Improve Short-Term Forecasts

    E-Print Network [OSTI]

    Povinelli, Richard J.

    Detrending Daily Natural Gas Consumption Series to Improve Short-Term Forecasts Ronald H. Brown1 that allows long-term natural gas demand signals to be used effect- ively to generate high quality short-term natural gas demand forecasting models. Short data sets in natural gas forecasting inadequately represent

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

    E-Print Network [OSTI]

    Kolter, J. Zico

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

  19. Developing stress-monitoring sites using cross-hole seismology to stress-forecast the times and magnitudes of future earthquakes

    E-Print Network [OSTI]

    Developing stress-monitoring sites using cross-hole seismology to stress-forecast the times 2000 Abstract A new understanding of rockmass deformation suggests that changing stress in the crust almost all rocks in the crust. These stress-aligned micro cracks cause the widely observed splitting

  20. Drivers of Future Energy Demand

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table272/S The National Interim7141. Total3.9Drivers

  1. Future Air Conditioning Energy Consumption in Developing Countries and what can be done about it: The Potential of Efficiency in the Residential Sector

    E-Print Network [OSTI]

    McNeil, Michael A.; Letschert, Virginie E.

    2008-01-01

    Forecasting Electricity Demand in Developing Countries: Adeveloping countries will probably be installed in households where electricity

  2. Multivariate Time Series Forecasting in Incomplete Environments

    E-Print Network [OSTI]

    Roberts, Stephen

    Multivariate Time Series Forecasting in Incomplete Environments Technical Report PARG 08-03 Seung of Oxford December 2008 #12;Seung Min Lee and Stephen J. Roberts Technical Report PARG 08-03 Multivariate missing observations and forecasting future values in incomplete multivariate time series data. We study

  3. Irrigation and the demand for electricity. Progress report

    SciTech Connect (OSTI)

    Maddigan, R. J.; Chern, W. S.; Gallagher, C. A.

    1980-03-01

    In order to anticipate the need for generating capacity, utility planners must estimate the future growth in electricity demand. The need for demand forecasts is no less important for the nation's Rural Electric Cooperatives (RECs) than it is for the investor-owned utilities. The RECs serve an historically agrarian region; therefore, the irrigation sector accounts for a significant portion of the western RECs' total demand. A model is developed of the RECs' demand for electricity used in irrigation. The model is a simultaneous equation system which focuses on both the short-run utilization of electricity in irrigation and the long-run determination of the number of irrigators using electricity. Irrigation demand is described by a set of equations in which the quantity of electricity demanded, the average electricity price, the number of irrigation customers, and the ratio of electricity to total energy used for irrigation are endogenous. The structural equations are estimated using pooled state-level data for the period 1961-1977. In light of the model's results, the impact of changes in relative energy prices on irrigation can be examined.

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

    SciTech Connect (OSTI)

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

    1982-03-31

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

  5. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

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

  6. Forecasting and strategic inventory placement for gas turbine aftermarket spares

    E-Print Network [OSTI]

    Simmons, Joshua T. (Joshua Thomas)

    2007-01-01

    This thesis addresses the problem of forecasting demand for Life Limited Parts (LLPs) in the gas turbine engine aftermarket industry. It is based on work performed at Pratt & Whitney, a major producer of turbine engines. ...

  7. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

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

  9. Testing Electric Vehicle Demand in "Hybrid Households" Using a Reflexive Survey

    E-Print Network [OSTI]

    Kurani, Kenneth S.; Turrentine, Thomas; Sperling, Daniel

    2001-01-01

    the demand electric vehicles’, TransportationResearchA,1994) ~tive NewsCalifornia Electric Vehicle ConsumerStudy.1995) Forecasting Electric Vehicle Ownership Use in the

  10. Urban Studies, Vol. 40, No. 7, 000000, 2003 Induced Demand: A Microscopic Perspective

    E-Print Network [OSTI]

    Levinson, David M.

    . Introduction Transport forecasts often assume limited or no response of demand to changes in supply of the response of demand to supply, also referred to as induced or latent demand. According to the induced demand demand hypoth- esis to date has mostly been carried out at the aggregate level, considering state, metro

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

    E-Print Network [OSTI]

    Tronci, Enrico

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

  12. In the near future, Switzerland is predicted to be affected by climate change, that is bound to impact both water demand and water supply

    E-Print Network [OSTI]

    Lenstra, Arjen K.

    In the near future, Switzerland is predicted to be affected by climate change, that is bound in Switzerland, mandated by the Federal Office for the Environment (FOEN). 4) Climate change and water resources of future water resources in Switzerland. Two possible solutions: -Randomly reduce water availability -Use

  13. Demand models for U.S. domestic air passenger markets

    E-Print Network [OSTI]

    Eriksen, Steven Edward

    1978-01-01

    The airline industry in recent years has suffered from the adverse effects of top level planning decisions based upon inaccurate demand forecasts. The air carriers have recognized the immediate need to develop their ...

  14. CALIFORNIA ENERGY CALIFORNIA ENERGY DEMAND 2010-2020

    E-Print Network [OSTI]

    , and utilities. Ted Dang, Steven Mac, and Libbie Bessman prepared the historical energy consumption data. Miguel CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2010-2020 ADOPTED FORECAST Schwarzenegger, Governor #12; #12; CALIFORNIA ENERGY COMMISSION Chris Kavalec Tom Gorin

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

    E-Print Network [OSTI]

    Hwang, Kai

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

  16. Improving Inventory Control Using Forecasting

    E-Print Network [OSTI]

    Balandran, Juan

    2005-12-16

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

  17. Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1995-02-01

    This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This report serves three purposes. First, it is a reference document providing a detailed description for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports (Public Law 93-275, section 57(b)(1)). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

  18. 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

  19. 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

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

    E-Print Network [OSTI]

    Various, Various,

    2011-01-01

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

  1. ENERGY AND ENVIRONMENT DIVISION ANNUAL REPORT 1978

    E-Print Network [OSTI]

    Cairns, E.L.

    2011-01-01

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

  2. Beyond Renewable Portfolio Standards: An Assessment of Regional Supply and Demand Conditions Affecting the Future of Renewable Energy in the West; Report and Executive Summary

    SciTech Connect (OSTI)

    Hurlbut, D. J.; McLaren, J.; Gelman, R.

    2013-08-01

    This study assesses the outlook for utility-scale renewable energy development in the West once states have met their renewable portfolio standard (RPS) requirements. In the West, the last state RPS culminates in 2025, so the analysis uses 2025 as a transition point on the timeline of RE development. Most western states appear to be on track to meet their final requirements, relying primarily on renewable resources located relatively close to the customers being served. What happens next depends on several factors including trends in the supply and price of natural gas, greenhouse gas and other environmental regulations, consumer preferences, technological breakthroughs, and future public policies and regulations. Changes in any one of these factors could make future renewable energy options more or less attractive.

  3. InDemandInDemandInDemand Energize Your Career

    E-Print Network [OSTI]

    Wolberg, George

    InDemandInDemandInDemand Energize Your Career You can join the next generation of workers who in Energy #12;#12;In Demand | 1 No, this isn't a quiz...but if you answered yes to any or all and Training Administration wants you to have this publication, In Demand: Careers in Energy. It will let you

  4. Electricity Demand and Energy Consumption Management System

    E-Print Network [OSTI]

    Sarmiento, Juan Ojeda

    2008-01-01

    This project describes the electricity demand and energy consumption management system and its application to the Smelter Plant of Southern Peru. It is composted of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks, with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows management the peak demand before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules allow the proper planning because it allows knowing the behavior of the hourly demand and the consumption patterns of the plant, in...

  5. Demand Response Programs, 6. edition

    SciTech Connect (OSTI)

    2007-10-15

    The report provides a look at the past, present, and future state of the market for demand/load response based upon market price signals. It is intended to provide significant value to individuals and companies who are considering participating in demand response programs, energy providers and ISOs interested in offering demand response programs, and consultants and analysts looking for detailed information on demand response technology, applications, and participants. The report offers a look at the current Demand Response environment in the energy industry by: defining what demand response programs are; detailing the evolution of program types over the last 30 years; discussing the key drivers of current initiatives; identifying barriers and keys to success for the programs; discussing the argument against subsidization of demand response; describing the different types of programs that exist including:direct load control, interruptible load, curtailable load, time-of-use, real time pricing, and demand bidding/buyback; providing examples of the different types of programs; examining the enablers of demand response programs; and, providing a look at major demand response programs.

  6. VideoonDemandVideoonDemandVideoonDemand Video on Demand Testbed

    E-Print Network [OSTI]

    Eleftheriadis, Alexandros

    VideoonDemandVideoonDemandVideoonDemand Columbia's Video on Demand Testbed and Interoperability Experiment Columbia's Video on Demand Testbed and Interoperability Experiment S.-F. Chang and A Columbia UniversityColumbia University www.www.ctrctr..columbiacolumbia..eduedu/advent/advent #12;VideoonDemandVideoonDemandVideoonDemand

  7. VideoonDemandVideoonDemandVideoonDemand Video on Demand Testbed

    E-Print Network [OSTI]

    Eleftheriadis, Alexandros

    #12;VideoonDemandVideoonDemandVideoonDemand Columbia's Video on Demand Testbed and Interoperability Experiment Columbia's Video on Demand Testbed and Interoperability Experiment H.H. KalvaKalva, A.www.eeee..columbiacolumbia..eduedu/advent/advent #12;VideoonDemandVideoonDemandVideoonDemand VoD Testbed ArchitectureVoD Testbed Architecture Video

  8. Model documentation report: Industrial sector demand module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1997-01-01

    This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description of the NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects. The NEMS Industrial Demand Model is a dynamic accounting model, bringing together the disparate industries and uses of energy in those industries, and putting them together in an understandable and cohesive framework. The Industrial Model generates mid-term (up to the year 2015) forecasts of industrial sector energy demand as a component of the NEMS integrated forecasting system. From the NEMS system, the Industrial Model receives fuel prices, employment data, and the value of industrial output. Based on the values of these variables, the Industrial Model passes back to the NEMS system estimates of consumption by fuel types.

  9. Demand Response and Open Automated Demand Response

    E-Print Network [OSTI]

    LBNL-3047E Demand Response and Open Automated Demand Response Opportunities for Data Centers G described in this report was coordinated by the Demand Response Research Center and funded by the California. Demand Response and Open Automated Demand Response Opportunities for Data Centers. California Energy

  10. Adaptive sampling and forecasting with mobile sensor networks

    E-Print Network [OSTI]

    Choi, Han-Lim

    2009-01-01

    This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information ...

  11. Wind Power Forecasting Data

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

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

  12. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

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

  13. Forecasting Water Quality & Biodiversity

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

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

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

    SciTech Connect (OSTI)

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

    1995-12-01

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

  15. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power Administration wouldMass map shines lightGeospatial ToolkitSMARTS -BeingFuture forForecasting NREL researchers

  16. High Temperatures & Electricity Demand

    E-Print Network [OSTI]

    High Temperatures & Electricity Demand An Assessment of Supply Adequacy in California Trends.......................................................................................................1 HIGH TEMPERATURES AND ELECTRICITY DEMAND.....................................................................................................................7 SECTION I: HIGH TEMPERATURES AND ELECTRICITY DEMAND ..........................9 BACKGROUND

  17. Projecting Electricity Demand in 2050

    SciTech Connect (OSTI)

    Hostick, Donna J.; Belzer, David B.; Hadley, Stanton W.; Markel, Tony; Marnay, Chris; Kintner-Meyer, Michael CW

    2014-07-01

    This paper describes the development of end-use electricity projections and load curves that were developed for the Renewable Electricity (RE) Futures Study (hereafter RE Futures), which explored the prospect of higher percentages (30% ? 90%) of total electricity generation that could be supplied by renewable sources in the United States. As input to RE Futures, two projections of electricity demand were produced representing reasonable upper and lower bounds of electricity demand out to 2050. The electric sector models used in RE Futures required underlying load profiles, so RE Futures also produced load profile data in two formats: 8760 hourly data for the year 2050 for the GridView model, and in 2-year increments for 17 time slices as input to the Regional Energy Deployment System (ReEDS) model. The process for developing demand projections and load profiles involved three steps: discussion regarding the scenario approach and general assumptions, literature reviews to determine readily available data, and development of the demand curves and load profiles.

  18. Fuel Price Forecasts INTRODUCTION

    E-Print Network [OSTI]

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

  19. Weather Forecasting Spring 2014

    E-Print Network [OSTI]

    Hennon, Christopher C.

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

  20. Advanced Demand Responsive Lighting

    E-Print Network [OSTI]

    Advanced Demand Responsive Lighting Host: Francis Rubinstein Demand Response Research Center demand responsive lighting systems ­ Importance of dimming ­ New wireless controls technologies · Advanced Demand Responsive Lighting (commenced March 2007) #12;Objectives · Provide up-to-date information

  1. Current Trends and Future Challenges in the Freight Railroad Industry: Balancing Private Industry Interests and the Public Welfare 

    E-Print Network [OSTI]

    Allen, Sarah; Kelson, Kendra; Migl, Hayden; Schmidt, Rodney; Shoemaker, David; Thomson, Heather

    2008-01-01

    ?s?improved?position?since?economic?deregulation,?especially?as?it?relates?to?infrastructure? investment?and?safety?performance.?Chapter?3?follows?with?an?evaluation?of?the?current?operating? environment?as?it?relates?to?demand,?capacity,?and?productivity.?In?light?of?demand?forecasts,?we?assess? the...?the?report?with?public?policy?recommendations?designed?to?ensure?the?health,? safety,?and?economic?vitality?of?the?industry,?its?employees?and?customers,?and?the?public.?Based?on? assessments?of?the?industry?s?current?performance?and?predictions?of?future?challenges,?the?following? recommendations...

  2. Negotiating future climates for public policy: a critical assessment of the development of

    E-Print Network [OSTI]

    Hulme, Mike

    ) or of seasonal forecasting (a few months): Earth system models aim to simulate future climatic evolution over

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

    E-Print Network [OSTI]

    Joshi, Krunal Jaykant

    2012-10-19

    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...

  4. The house of the future

    ScienceCinema (OSTI)

    None

    2010-09-01

    Learn what it will take to create tomorrow's net-zero energy home as scientists reveal the secrets of cool roofs, smart windows, and computer-driven energy control systems. The net-zero energy home: Scientists are working to make tomorrow's homes more than just energy efficient -- they want them to be zero energy. Iain Walker, a scientist in the Lab's Energy Performance of Buildings Group, will discuss what it takes to develop net-zero energy houses that generate as much energy as they use through highly aggressive energy efficiency and on-site renewable energy generation. Talking back to the grid: Imagine programming your house to use less energy if the electricity grid is full or price are high. Mary Ann Piette, deputy director of Berkeley Lab's building technology department and director of the Lab's Demand Response Research Center, will discuss how new technologies are enabling buildings to listen to the grid and automatically change their thermostat settings or lighting loads, among other demands, in response to fluctuating electricity prices. The networked (and energy efficient) house: In the future, your home's lights, climate control devices, computers, windows, and appliances could be controlled via a sophisticated digital network. If it's plugged in, it'll be connected. Bruce Nordman, an energy scientist in Berkeley Lab's Energy End-Use Forecasting group, will discuss how he and other scientists are working to ensure these networks help homeowners save energy.

  5. The Role of Demand Response Policy Forum Series

    E-Print Network [OSTI]

    California at Davis, University of

    The Role of Demand Response Policy Forum Series Beyond 33 Percent: California's Renewable Future and Demand Response #12;Historic focus on Seasonal Grid Stress PG&E Demand Bid Test Day 0 2000 4000 6000 8000 Communication Latency #12;Bottom Up Review of End-Use Loads for Demand Response 5 Commercial Residential

  6. Using Belief Functions to Forecast Demand for Mobile Satellite Services

    E-Print Network [OSTI]

    McBurney, Peter

    resources management [9,10,55]; to nuclear power plant control [18]; to information retrieval [48 of their allowance for imprecision of knowledge, and their ability to coherently combine disparate sources in manufacturing processes [108]. To our knowledge, no work has been published which applies the theory

  7. Demand forecast for short life cycle products : Zara case study

    E-Print Network [OSTI]

    Bonnefoi, Tatiana (Bonnefoi Monroy)

    2010-01-01

    The problem of optimally purchasing new products is common to many companies and industries. This thesis describes how this challenge was addressed at Zara, a leading retailer in the "fast fashion" industry. This thesis ...

  8. Device-oriented telecommunications customer call center demand forecasting

    E-Print Network [OSTI]

    Koul, Ashish, 1979-

    2014-01-01

    Verizon Wireless maintains a call center infrastructure employing more than 15,000 customer care representatives across the United States. The current resource management process requires a lead time of several months to ...

  9. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    the natural gas price projections. #12;ii #12;i Table of Contents Executive Summary COMMISSION Lynn Marshall Tom Gorin Principal Authors Lynn Marshall Project Manager Sylvia Bender Manager data. Tom Gorin prepared the demographic projections. Chris Kavalec developed the projections

  10. Solar Forecast Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE)

    For the Solar Forecast Improvement Project (SFIP), the Earth System Research Laboratory (ESRL) is partnering with the National Center for Atmospheric Research (NCAR) and IBM to develop more...

  11. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

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

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

    SciTech Connect (OSTI)

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

    2012-07-01

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

  13. The motor gasoline industry: Past, present, and future. [Contains glossary

    SciTech Connect (OSTI)

    Not Available

    1991-01-01

    Motor gasoline constitutes the largest single component of US demand for petroleum products and is the Nation's most widely used transportation fuel. Because of its importance as a transportation fuel, motor gasoline has been the focus of several regulatory and tax policy initiatives in recent years. Much of the US refining capacity is specifically geared toward maximizing motor gasoline production, and future investments by the petroleum industry in refining infrastructure are likely to be made largely to produce larger volumes of clean motor gasoline. This report addresses major events and developments that have had an impact on motor gasoline supply, distribution, prices, and demand. The report provides historical perspective as well as analyses of important events from the 1970's and 1980's. Long-term forecasts are provided for the period from 1990 to 2010 in an effort to present and analyze possible future motor gasoline trends. Other forecasts examine the near-term impact of the invasion of Kuwait. 18 figs., 10 tabs.

  14. Policy Paper 36: 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.

    1998-01-01

    Kazuya, 1996. "Long-Term Energy Supply/Demand Outlook for19 Energy Supply Security and Infrastructure Issues inseek to project future energy supply and demand for Japan,

  15. Study of long-range electrical demand planning in Maryland. Final report

    SciTech Connect (OSTI)

    Jensen, K.A.; Doane, M.J.; Hartman, R.S.

    1987-01-01

    Arthur D. Little, Inc. was commissioned by the Maryland Power Plant Research Program to undertake a study to perform a critique of current PPSP electricity sales and peak-demand forecasting methodologies; identify a possible set of alternative forecasting methods and models that could provide improved forecasting accuracy; and to recommend to the PPSP methodological improvements that would assist the PPSP in achieving its goals. The report summarizes the study.

  16. Global Energy: Supply, Demand, Consequences, Opportunities

    ScienceCinema (OSTI)

    Majumdar, Arun

    2010-01-08

    July 29, 2008 Berkeley Lab lecture: Arun Majumdar, Director of the Environmental Energy Technologies Division, discusses current and future projections of economic growth, population, and global energy demand and supply, and explores the implications of these trends for the environment.

  17. Cooling Energy Demand Evaluation by Meansof Regression Models Obtained From Dynamic Simulations 

    E-Print Network [OSTI]

    Catalina, T.; Virgone, J.

    2011-01-01

    The forecast of the energy heating/cooling demand would be a good indicator for the choice between different conception solutions according to the building characteristics and the local climate. A previous study (Catalina T. et al 2008...

  18. Neural network based short-term load forecasting using weather compensation

    SciTech Connect (OSTI)

    Chow, T.W.S.; Leung, C.T. [City Univ. of Hong Kong, Kowloon (Hong Kong). Dept. of Electronic Engineering] [City Univ. of Hong Kong, Kowloon (Hong Kong). Dept. of Electronic Engineering

    1996-11-01

    This paper presents a novel technique for electric load forecasting based on neural weather compensation. The proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. The weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

  19. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    of integrating demand response and energy efficiencyand D. Kathan (2009), Demand Response in U.S. ElectricityFRAMEWORKS THAT PROMOTE DEMAND RESPONSE 3.1. Demand Response

  20. Renewable Electricity Futures Study

    E-Print Network [OSTI]

    Renewable Electricity Futures Study End-use Electricity Demand Volume 3 of 4 Volume 2 PDF Volume 3;Renewable Electricity Futures Study Edited By Hand, M.M. National Renewable Energy Laboratory Baldwin, S. U Sandor, D. National Renewable Energy Laboratory Suggested Citations Renewable Electricity Futures Study

  1. Current Status and Future Scenarios of Residential Building Energy Consumption in China

    E-Print Network [OSTI]

    Zhou, Nan

    2010-01-01

    energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecastenergy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast

  2. Natural Gas Infrastructure Implications of Increased Demand from...

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

    examines the potential infrastructure needs of the U.S. interstate natural gas pipeline transmission system across a range of future natural gas demand scenarios that drive...

  3. Improving automotive battery sales forecast

    E-Print Network [OSTI]

    Bulusu, Vinod

    2015-01-01

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

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

    E-Print Network [OSTI]

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

  5. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01

    benefits of Demand Side Management (DSM) are insufficient toefficiency, demand side management (DSM) cost effectivenessResearch Center Demand Side Management Demand Side Resources

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

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

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

  7. 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

  8. Residential Demand Sector Data, Commercial Demand Sector Data, Industrial Demand Sector Data - Annual Energy Outlook 2006

    SciTech Connect (OSTI)

    2009-01-18

    Tables describing consumption and prices by sector and census division for 2006 - includes residential demand, commercial demand, and industrial demand

  9. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    Rutledge, Steven

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

  10. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

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

  11. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01

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

  12. Analysis of recent projections of electric power demand

    SciTech Connect (OSTI)

    Hudson, D.V. Jr.

    1993-08-01

    This report reviews the changes and potential changes in the outlook for electric power demand since the publication of Review and Analysis of Electricity Supply Market Projections (B. Swezey, SERI/MR-360-3322, National Renewable Energy Laboratory). Forecasts of the following organizations were reviewed: DOE/Energy Information Administration, DOE/Policy Office, DRI/McGraw-Hill, North American Electric Reliability Council, and Gas Research Institute. Supply uncertainty was briefly reviewed to place the uncertainties of the demand outlook in perspective. Also discussed were opportunities for modular technologies, such as renewable energy technologies, to fill a potential gap in energy demand and supply.

  13. Demand for Wildlife Hunting in the Southeastern United States

    E-Print Network [OSTI]

    Gray, Matthew

    1 Demand for Wildlife Hunting in the Southeastern United States Presented by: Neelam C. Poudyal... Number of studies scrutinized demand for wildlife hunting (Ziemer et al. 1980; Miller and Hay,1981). Essential to understand what influences hunting demand. Projecting how the future of wildlife hunting

  14. Optimal Demand Response Based on Utility Maximization in Power Networks

    E-Print Network [OSTI]

    Low, Steven H.

    -- Demand side management will be a key component of future smart grid that can help reduce peak load interesting properties of the proposed scheme. I. INTRODUCTION Demand side management will be a key componentOptimal Demand Response Based on Utility Maximization in Power Networks Na Li, Lijun Chen

  15. DEMAND INTERPROCEDURAL PROGRAM ANALYSIS

    E-Print Network [OSTI]

    Reps, Thomas W.

    1 DEMAND INTERPROCEDURAL PROGRAM ANALYSIS USING LOGIC DATABASES Thomas W. Reps Computer Sciences@cs.wisc.edu ABSTRACT This paper describes how algorithms for demand versions of inerprocedural program­ analysis for all elements of the program. This paper concerns the solution of demand versions of interprocedural

  16. Capacity Demand Power (GW)

    E-Print Network [OSTI]

    California at Davis, University of

    Capacity Demand Power (GW) Hour of the Day The "Dip" Electricity Demand in Electricity Demand Every weekday, Japan's electricity use dips about 6 GW at 12 but it also shows that: · Behavior affects naHonal electricity use in unexpected ways

  17. Demand Response Assessment INTRODUCTION

    E-Print Network [OSTI]

    Demand Response Assessment INTRODUCTION This appendix provides more detail on some of the topics raised in Chapter 4, "Demand Response" of the body of the Plan. These topics include 1. The features, advantages and disadvantages of the main options for stimulating demand response (price mechanisms

  18. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    DECC aggregator managed portfolio automated demand responseaggregator designs their own programs, and offers demand responseaggregator is responsible for designing and implementing their own demand response

  19. Price forecasting for U.S. cattle feeders: which technique to apply? 

    E-Print Network [OSTI]

    Hicks, Geoff Cody

    1997-01-01

    the following:1. FAPRI3. AO S5. Univariate Time Series7. Composite 2. WASDE4. Futures Market6. Multivariate Time Series The characteristics of each of the aforementioned forecast techniques are explained within the appropriate chapter. Furthermore, it should...

  20. FireGrid: Forecasting Fire Dynamics to Lead the Emergency Response 

    E-Print Network [OSTI]

    Rein, Guillermo

    2007-06-19

    The predictions of future events has fascinated humanity since the beginning of history. This attraction has permeated into science and engineering, where several disciplines has emerged providing the capability to forecast ...

  1. Uranium 2009 resources, production and demand

    E-Print Network [OSTI]

    Organisation for Economic Cooperation and Development. Paris

    2010-01-01

    With several countries currently building nuclear power plants and planning the construction of more to meet long-term increases in electricity demand, uranium resources, production and demand remain topics of notable interest. In response to the projected growth in demand for uranium and declining inventories, the uranium industry – the first critical link in the fuel supply chain for nuclear reactors – is boosting production and developing plans for further increases in the near future. Strong market conditions will, however, be necessary to trigger the investments required to meet projected demand. The "Red Book", jointly prepared by the OECD Nuclear Energy Agency and the International Atomic Energy Agency, is a recognised world reference on uranium. It is based on information compiled in 40 countries, including those that are major producers and consumers of uranium. This 23rd edition provides a comprehensive review of world uranium supply and demand as of 1 January 2009, as well as data on global ur...

  2. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01

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

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

    E-Print Network [OSTI]

    Garulli, Andrea

    profiles, raise major challenges to wind power integration into the electricity grid. In this work we studyOptimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts Antonio that the inherent variability in wind power generation and the related difficulty in predicting future generation

  4. Journey data based arrival forecasting for bicycle hire schemes

    E-Print Network [OSTI]

    Imperial College, London

    Journey data based arrival forecasting for bicycle hire schemes Marcel C. Guenther and Jeremy T. The global emergence of city bicycle hire schemes has re- cently received a lot of attention of future bicycle migration trends, as these assist service providers to ensure availability of bicycles

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

    SciTech Connect (OSTI)

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

    2013-05-01

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

  6. Automated Demand Response and Commissioning

    E-Print Network [OSTI]

    Piette, Mary Ann; Watson, David S.; Motegi, Naoya; Bourassa, Norman

    2005-01-01

    Fully-Automated Demand Response Test in Large Facilities14in DR systems. Demand Response using HVAC in Commercialof Fully Automated Demand Response in Large Facilities”

  7. Demand Response Spinning Reserve Demonstration

    E-Print Network [OSTI]

    2007-01-01

    F) Enhanced ACP Date RAA ACP Demand Response – SpinningReserve Demonstration Demand Response – Spinning Reservesupply spinning reserve. Demand Response – Spinning Reserve

  8. Demand Response Programs for Oregon

    E-Print Network [OSTI]

    Demand Response Programs for Oregon Utilities Public Utility Commission May 2003 Public Utility ....................................................................................................................... 1 Types of Demand Response Programs............................................................................ 3 Demand Response Programs in Oregon

  9. Wind Power Forecasting

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

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

  10. Exponential Demand Simulation Tool

    E-Print Network [OSTI]

    Reed, Derek D.

    2015-05-15

    Operant behavioral economics investigates the relation between environmental constraint and reinforcer consumption. The standard approach to quantifying this relation is through the use of behavioral economic demand curves. ...

  11. Managing Increased Charging Demand

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

    Managing Increased Charging Demand Carrie Giles ICF International, Supporting the Workplace Charging Challenge Workplace Charging Challenge Do you already own an EV? Are you...

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

    SciTech Connect (OSTI)

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

    2005-02-09

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

  13. FUTURE CLIMATE ANALYSIS

    SciTech Connect (OSTI)

    R.M. Forester

    2000-03-14

    This Analysis/Model Report (AMR) documents an analysis that was performed to estimate climatic variables for the next 10,000 years by forecasting the timing and nature of climate change at Yucca Mountain (YM), Nevada (Figure l), the site of a potential repository for high-level radioactive waste. The future-climate estimates are based on an analysis of past-climate data from analog meteorological stations, and this AMR provides the rationale for the selection of these analog stations. The stations selected provide an upper and a lower climate bound for each future climate, and the data from those sites will provide input to the infiltration model (USGS 2000) and for the total system performance assessment for the Site Recommendation (TSPA-SR) at YM. Forecasting long-term future climates, especially for the next 10,000 years, is highly speculative and rarely attempted. A very limited literature exists concerning the subject, largely from the British radioactive waste disposal effort. The discussion presented here is one method, among many, of establishing upper and lower bounds for future climate estimates. The method used here involves selecting a particular past climate from many past climates, as an analog for future climate. Other studies might develop a different rationale or select other past climates resulting in a different future climate analog.

  14. Price forecasting for notebook computers 

    E-Print Network [OSTI]

    Rutherford, Derek Paul

    1997-01-01

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

  15. Multivariate Forecast Evaluation And Rationality Testing

    E-Print Network [OSTI]

    Komunjer, Ivana; OWYANG, MICHAEL

    2007-01-01

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

  16. Electrical Demand Management 

    E-Print Network [OSTI]

    Fetters, J. L.; Teets, S. J.

    1983-01-01

    The Demand Management Plan set forth in this paper has proven to be a viable action to reduce a 3 million per year electric bill at the Columbus Works location of Western Electric. Measures are outlined which have reduced the peak demand 5% below...

  17. Future Climate Analysis

    SciTech Connect (OSTI)

    C. G. Cambell

    2004-09-03

    This report documents an analysis that was performed to estimate climatic variables for the next 10,000 years by forecasting the timing and nature of climate change at Yucca Mountain, Nevada, the site of a repository for spent nuclear fuel and high-level radioactive waste. The future-climate estimates are based on an analysis of past-climate data from analog meteorological stations, and this report provides the rationale for the selection of these analog stations. The stations selected provide an upper and a lower climate bound for each future climate, and the data from those sites will provide input to the following reports: ''Simulation of Net Infiltration for Present-Day and Potential Future Climates'' (BSC 2004 [DIRS 170007]), ''Total System Performance Assessment (TSPA) Model/Analysis for the License Application'' (BSC 2004 [DIRS 168504]), ''Features, Events, and Processes in UZ Flow and Transport'' (BSC 2004 [DIRS 170012]), and ''Features, Events, and Processes in SZ Flow and Transport'' (BSC 2004 [DIRS 170013]). Forecasting long-term future climates, especially for the next 10,000 years, is highly speculative and rarely attempted. A very limited literature exists concerning the subject, largely from the British radioactive waste disposal effort. The discussion presented here is one available forecasting method for establishing upper and lower bounds for future climate estimates. The selection of different methods is directly dependent on the available evidence used to build a forecasting argument. The method used here involves selecting a particular past climate from many past climates, as an analog for future climate. While alternative analyses are possible for the case presented for Yucca Mountain, the evidence (data) used would be the same and the conclusions would not be expected to drastically change. Other studies might develop a different rationale or select other past climates resulting in a different future climate analog. Other alternative approaches could include simulation of climate over the 10,000-year period; however, this modeling extrapolation is well beyond the bounds of current scientific practice and would not provide results with better confidence. A corroborative alternative approach may be found in ''Future Climate Analysis-10,000 Years to 1,000,000 Years After Present'' (Sharpe 2003 [DIRS 161591]). The current revision of this report is prepared in accordance with ''Technical Work Plan for: Unsaturated Zone Flow Analysis and Model Report Integration'' (BSC 2004 [DIRS 169654]).

  18. Grid Integration of Aggregated Demand Response, Part 2: Modeling Demand Response in a Production Cost Model

    Broader source: Energy.gov [DOE]

    Renewable integration studies have evaluated many challenges associated with deploying large amounts of variable wind and solar generation technologies. These studies can evaluate operational impacts associated with variable generation, benefits of improved wind and solar resource forecasting, and trade-offs between institutional changes, including increasing balancing area cooperation and technical changes such as installing new flexible generation. Demand response (DR) resources present a potentially important source of grid flexibility and can aid in integrating variable generation; however, integration analyses have not yet incorporated these resources explicitly into grid simulation models as part of a standard toolkit for resource planners.

  19. DISTRIBUTED ENERGY SYSTEMS IN CALIFORNIA'S FUTURE: A PRELIMINARY REPORT, VOLUME I

    E-Print Network [OSTI]

    Authors, Various

    2010-01-01

    technologies to satisfy future energy demands. On anotheraffecting the choice of future energy technologies can noabout the character of future energy alternatives (Schwartz,

  20. Residential Electricity Demand in China -- Can Efficiency Reverse the Growth?

    SciTech Connect (OSTI)

    Letschert, Virginie; McNeil, Michael A.; Zhou, Nan

    2009-05-18

    The time when energy-related carbon emissions come overwhelmingly from developed countries is coming to a close. China has already overtaken the United States as the world's leading emitter of greenhouse gas emissions. The economic growth that China has experienced is not expected to slow down significantly in the long term, which implies continued massive growth in energy demand. This paper draws on the extensive expertise from the China Energy Group at LBNL on forecasting energy consumption in China, but adds to it by exploring the dynamics of demand growth for electricity in the residential sector -- and the realistic potential for coping with it through efficiency. This paper forecasts ownership growth of each product using econometric modeling, in combination with historical trends in China. The products considered (refrigerators, air conditioners, fans, washing machines, lighting, standby power, space heaters, and water heating) account for 90percent of household electricity consumption in China. Using this method, we determine the trend and dynamics of demandgrowth and its dependence on macroeconomic drivers at a level of detail not accessible by models of a more aggregate nature. In addition, we present scenarios for reducing residential consumption through efficiency measures defined at the product level. The research takes advantage of an analytical framework developed by LBNL (BUENAS) which integrates end use technology parameters into demand forecasting and stock accounting to produce detailed efficiency scenarios, thus allowing for a technologically realistic assessment of efficiency opportunities specifically in the Chinese context.

  1. California's future `Smart Grid' system will integrate solar, wind, and other renewable electricity generation with energy storage to meet our electricity demands and to support electric transportation. The Sustainable Integrated Grid

    E-Print Network [OSTI]

    California at Riverside, University of

    California's future `Smart Grid' system will integrate solar, wind, and other renewable electricity. The Sustainable Integrated Grid Initiative at UCR combines these elements so that researchers, utility personnel and wind are intermittent in nature and may not be available when needed. Electrical energy stored

  2. The Future of TDM: Technology and Demographic Shifts and Their Implications for

    E-Print Network [OSTI]

    The Future of TDM: Technology and Demographic Shifts and Their Implications for Transportation Demand Management Final report PRC 15-25F #12;2 The Future of TDM: Technology and Demographic Shifts ........................................................................................................... 15 Future Demand and Texas Demographic Trends

  3. Demand Dispatch-Intelligent

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

    such as wind, solar, and electric vehicles as well as dispatchable loads and microgrids. Many of these resources will be "behind-the-meter" (i.e., demand resources) and...

  4. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

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

    2011-11-29

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

  5. Coupling Renewable Energy Supply with Deferrable Demand

    E-Print Network [OSTI]

    Papavasiliou, Anthony

    2011-01-01

    models to simulate and forecast wind speed and wind power.proba- bilistic wind power forecasts. accepted Transactionsload plus 5% of hourly forecast wind power. We set this as

  6. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    World crude oil and natural gas: a demand and supply model.analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.

  7. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    World crude oil and natural gas: a demand and supply model.analysis of the demand for oil in the Middle East. EnergyEstimates elasticity of demand for crude oil, not gasoline.

  8. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    H. , and James M. Gri¢ n. 1983. Gasoline demand in the OECDof dynamic demand for gasoline. Journal of Econometrics 77(An empirical analysis of gasoline demand in Denmark using

  9. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    shift in the short-run price elasticity of gasoline demand.A meta-analysis of the price elasticity of gasoline demand.2007. Consumer demand un- der price uncertainty: Empirical

  10. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01

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

  11. Downscaling Extended Weather Forecasts for Hydrologic Prediction

    SciTech Connect (OSTI)

    Leung, Lai-Yung R.; Qian, Yun

    2005-03-01

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

  12. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    Sterner. 1991. Analysing gasoline demand elasticities: A2011. Measuring global gasoline and diesel price and incomeMutairi. 1995. Demand for gasoline in Kuwait: An empirical

  13. Global Energy: Supply, Demand, Consequences, Opportunities (LBNL Summer Lecture Series)

    ScienceCinema (OSTI)

    Majumdar, Arun

    2011-04-28

    Summer Lecture Series 2009: Arun Majumdar, Director of the Environmental Energy Technologies Division, discusses current and future projections of economic growth, population, and global energy demand and supply, and explores the implications of these trends for the environment.

  14. Stochastic capacity modeling to support demand/capacity gap planning

    E-Print Network [OSTI]

    Niles, Augusta (Augusta L.)

    2014-01-01

    Capacity strategy has established methods of dealing with uncertainty in future demand. This project advances the concept of capacity strategy under conditions of uncertainty in cases where capacity is the primary source ...

  15. Global Energy: Supply, Demand, Consequences, Opportunities (LBNL Summer Lecture Series)

    SciTech Connect (OSTI)

    Majumdar, Arun

    2008-07-29

    Summer Lecture Series 2009: Arun Majumdar, Director of the Environmental Energy Technologies Division, discusses current and future projections of economic growth, population, and global energy demand and supply, and explores the implications of these trends for the environment.

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

    E-Print Network [OSTI]

    Goto, Susumu

    2007-01-01

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

  17. Demand Response Valuation Frameworks Paper

    E-Print Network [OSTI]

    Heffner, Grayson

    2010-01-01

    No. ER06-615-000 CAISO Demand Response Resource User Guide -8 2.1. Demand Response Provides a Range of Benefits to8 2.2. Demand Response Benefits can be Quantified in Several

  18. GENERAL TECHNICAL REPORT PSW-GTR-245 Forecasting Productivity in Forest Fire

    E-Print Network [OSTI]

    GENERAL TECHNICAL REPORT PSW-GTR-245 50 Forecasting Productivity in Forest Fire Suppression in Mediterranean forest ecosystems are demanding serious attention to forest fire conditions. This is particularly a significant proportion of the total budget available for forest fire protection programs. The need to make

  19. Future Climate Analysis

    SciTech Connect (OSTI)

    James Houseworth

    2001-10-12

    This Analysis/Model Report (AMR) documents an analysis that was performed to estimate climatic variables for the next 10,000 years by forecasting the timing and nature of climate change at Yucca Mountain (YM), Nevada (Figure 1), the site of a potential repository for high-level radioactive waste. The future-climate estimates are based on an analysis of past-climate data from analog meteorological stations, and this AMR provides the rationale for the selection of these analog stations. The stations selected provide an upper and a lower climate bound for each future climate, and the data from those sites will provide input to the infiltration model (USGS 2000) and for the total system performance assessment for the Site Recommendation (TSPA-SR) at YM. Forecasting long-term future climates, especially for the next 10,000 years, is highly speculative and rarely attempted. A very limited literature exists concerning the subject, largely from the British radioactive waste disposal effort. The discussion presented here is one method, among many, of establishing upper and lower bounds for future climate estimates. The method used here involves selecting a particular past climate from many past climates, as an analog for future climate. Other studies might develop a different rationale or select other past climates resulting in a different future climate analog. Revision 00 of this AMR was prepared in accordance with the ''Work Direction and Planning Document for Future Climate Analysis'' (Peterman 1999) under Interagency Agreement DE-AI08-97NV12033 with the U.S. Department of Energy (DOE). The planning document for the technical scope, content, and management of ICN 01 of this AMR is the ''Technical Work Plan for Unsaturated Zone (UZ) Flow and Transport Process Model Report'' (BSC 2001a). The scope for the TBV resolution actions in this ICN is described in the ''Technical Work Plan for: Integrated Management of Technical Product Input Department''. (BSC 2001b, Addendum B, Section 4.1).

  20. Optimal Demand Response Libin Jiang

    E-Print Network [OSTI]

    Optimal Demand Response Libin Jiang Steven Low Computing + Math Sciences Electrical Engineering Caltech Oct 2011 #12;Outline Caltech smart grid research Optimal demand response #12;Global trends 1

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

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

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

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01

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

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

    SciTech Connect (OSTI)

    Stoffel, T.

    2012-06-01

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

  4. Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand

    E-Print Network [OSTI]

    Carlini, David

    Estimating a Demand System with Nonnegativity Constraints: Mexican Meat Demand Amos Golan* Jeffrey an almost ideal demand system for five types of meat using cross-sectional data from Mexico, where most households did not buy at least one type of meat during the survey week. The system of demands is shown

  5. Peer-Assisted On-Demand Streaming: Characterizing Demands and

    E-Print Network [OSTI]

    Li, Baochun

    Peer-Assisted On-Demand Streaming: Characterizing Demands and Optimizing Supplies Fangming Liu Abstract--Nowadays, there has been significant deployment of peer-assisted on-demand streaming services over the Internet. Two of the most unique and salient features in a peer-assisted on-demand streaming

  6. Current Status and Future Assumptions INTRODUCTION

    E-Print Network [OSTI]

    of the current and future electricity situation are the demand for electricity, the amount and cost and national energy and environmental policies. Demand defines the need for electricity while generation and its costs. DEMAND FOR ELECTRICITY It has been 20 years since the Council's first power plan in 1983

  7. Energy Demand Staff Scientist

    E-Print Network [OSTI]

    Eisen, Michael

    #12;Sources: China National Bureau of Statistics; U.S. Energy Information Administration, Annual Energy Outlook. Overview:Overview: Energy Use in China and the U.S.Energy Use in China and the U.S. 5 0Energy Demand in China Lynn Price Staff Scientist February 2, 2010 #12;Founded in 1988 Focused

  8. Utility Demand Side Management- DSM Lessons: Experience is the Toughest Teacher 

    E-Print Network [OSTI]

    Gilbert, S. M.

    1990-01-01

    their load management goals, this presentation highlights what has been tried, pros and cons of each approach, lessons learned and trends in demand side management for the future....

  9. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    fraction of residential and commercial demands, leading16 Residential electricity demand endspecific residential electricity demands into electricity

  10. Researcher explores economics of U.S. urban water demand 

    E-Print Network [OSTI]

    Wythe, Kathy

    2009-01-01

    stream_source_info Researcher explores economics of U.S. urban water demand.pdf.txt stream_content_type text/plain stream_size 3811 Content-Encoding ISO-8859-1 stream_name Researcher explores economics of U.S. urban water demand....pdf.txt Content-Type text/plain; charset=ISO-8859-1 Story by Kathy Wythe tx H2O | pg. 24 Researcher explores economics of U.S. urban water demand Photo by: Danielle Supercinski tx H2O | pg. 25 With projected demands for future water supplies...

  11. 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 ...

  12. Forecasting consumer products using prediction markets

    E-Print Network [OSTI]

    Trepte, Kai

    2009-01-01

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

  13. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

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

  14. NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS

    E-Print Network [OSTI]

    Mohaghegh, Shahab

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

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

    SciTech Connect (OSTI)

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

    2009-03-01

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

  16. China's Coal: Demand, Constraints, and Externalities

    SciTech Connect (OSTI)

    Aden, Nathaniel; Fridley, David; Zheng, Nina

    2009-07-01

    This study analyzes China's coal industry by focusing on four related areas. First, data are reviewed to identify the major drivers of historical and future coal demand. Second, resource constraints and transport bottlenecks are analyzed to evaluate demand and growth scenarios. The third area assesses the physical requirements of substituting coal demand growth with other primary energy forms. Finally, the study examines the carbon- and environmental implications of China's past and future coal consumption. There are three sections that address these areas by identifying particular characteristics of China's coal industry, quantifying factors driving demand, and analyzing supply scenarios: (1) reviews the range of Chinese and international estimates of remaining coal reserves and resources as well as key characteristics of China's coal industry including historical production, resource requirements, and prices; (2) quantifies the largest drivers of coal usage to produce a bottom-up reference projection of 2025 coal demand; and (3) analyzes coal supply constraints, substitution options, and environmental externalities. Finally, the last section presents conclusions on the role of coal in China's ongoing energy and economic development. China has been, is, and will continue to be a coal-powered economy. In 2007 Chinese coal production contained more energy than total Middle Eastern oil production. The rapid growth of coal demand after 2001 created supply strains and bottlenecks that raise questions about sustainability. Urbanization, heavy industrial growth, and increasing per-capita income are the primary interrelated drivers of rising coal usage. In 2007, the power sector, iron and steel, and cement production accounted for 66% of coal consumption. Power generation is becoming more efficient, but even extensive roll-out of the highest efficiency units would save only 14% of projected 2025 coal demand for the power sector. A new wedge of future coal consumption is likely to come from the burgeoning coal-liquefaction and chemicals industries. If coal to chemicals capacity reaches 70 million tonnes and coal-to-liquids capacity reaches 60 million tonnes, coal feedstock requirements would add an additional 450 million tonnes by 2025. Even with more efficient growth among these drivers, China's annual coal demand is expected to reach 3.9 to 4.3 billion tonnes by 2025. Central government support for nuclear and renewable energy has not reversed China's growing dependence on coal for primary energy. Substitution is a matter of scale: offsetting one year of recent coal demand growth of 200 million tonnes would require 107 billion cubic meters of natural gas (compared to 2007 growth of 13 BCM), 48 GW of nuclear (compared to 2007 growth of 2 GW), or 86 GW of hydropower capacity (compared to 2007 growth of 16 GW). Ongoing dependence on coal reduces China's ability to mitigate carbon dioxide emissions growth. If coal demand remains on a high growth path, carbon dioxide emissions from coal combustion alone would exceed total US energy-related carbon emissions by 2010. Within China's coal-dominated energy system, domestic transportation has emerged as the largest bottleneck for coal industry growth and is likely to remain a constraint to further expansion. China has a low proportion of high-quality reserves, but is producing its best coal first. Declining quality will further strain production and transport capacity. Furthermore, transporting coal to users has overloaded the train system and dramatically increased truck use, raising transportation oil demand. Growing international imports have helped to offset domestic transport bottlenecks. In the long term, import demand is likely to exceed 200 million tonnes by 2025, significantly impacting regional markets.

  17. Modeling and Forecasting Electric Daily Peak Loads

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

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

  18. 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

  19. Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services

    SciTech Connect (OSTI)

    Kiliccote, Sila; Piette, Mary Ann; Ghatikar, Girish; Koch, Ed; Hennage, Dan; Hernandez, John; Chiu, Albert; Sezgen, Osman; Goodin, John

    2009-11-06

    The Pacific Gas and Electric Company (PG&E) is conducting a pilot program to investigate the technical feasibility of bidding certain demand response (DR) resources into the California Independent System Operator's (CAISO) day-ahead market for ancillary services nonspinning reserve. Three facilities, a retail store, a local government office building, and a bakery, are recruited into the pilot program. For each facility, hourly demand, and load curtailment potential are forecasted two days ahead and submitted to the CAISO the day before the operation as an available resource. These DR resources are optimized against all other generation resources in the CAISO ancillary service. Each facility is equipped with four-second real time telemetry equipment to ensure resource accountability and visibility to CAISO operators. When CAISO requests DR resources, PG&E's OpenADR (Open Automated DR) communications infrastructure is utilized to deliver DR signals to the facilities energy management and control systems (EMCS). The pre-programmed DR strategies are triggered without a human in the loop. This paper describes the automated system architecture and the flow of information to trigger and monitor the performance of the DR events. We outline the DR strategies at each of the participating facilities. At one site a real time electric measurement feedback loop is implemented to assure the delivery of CAISO dispatched demand reductions. Finally, we present results from each of the facilities and discuss findings.

  20. Forecasting Solar Wind Speeds

    E-Print Network [OSTI]

    Takeru K. Suzuki

    2006-02-03

    By explicitly taking into account effects of Alfven waves, I derive from a simple energetics argument a fundamental relation which predicts solar wind (SW) speeds in the vicinity of the earth from physical properties on the sun. Kojima et al. recently found from their observations that a ratio of surface magnetic field strength to an expansion factor of open magnetic flux tubes is a good indicator of the SW speed. I show by using the derived relation that this nice correlation is an evidence of the Alfven wave which accelerates SW in expanding flux tubes. The observations further require that fluctuation amplitudes of magnetic field lines at the surface should be almost universal in different coronal holes, which needs to be tested by future observations.

  1. Forecasting phenology under global warming

    E-Print Network [OSTI]

    Silander Jr., John A.

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

  2. 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

  3. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    retail regulatory authority prohibit such activity. Demand response integration into US wholesale power marketsretail or wholesale level. 17 While demand response began participating at scale in wholesale power markets

  4. Forecasting Future Food Security through Agent Based Modelling 

    E-Print Network [OSTI]

    Georgie, Paul

    2010-11-24

    Regardless of what recognition human involvement has played, the consequences of our changing climate will have a negative effect on both agriculture and human well-being. This is expected to be most exacerbated for ...

  5. (2013) 128 Data Center Demand Response: Avoiding the Coincident Peak via

    E-Print Network [OSTI]

    Wierman, Adam

    2013-01-01

    (2013) 1­28 Data Center Demand Response: Avoiding the Coincident Peak via Workload Shifting.chen@hp.com Abstract Demand response is a crucial aspect of the future smart grid. It has the potential to provide centers' participation in demand response is becoming increasingly important given their high

  6. Demand Dispatch-Intelligent

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power Administration would like submit theCovalent Bonding Low-Cost2DepartmentDelta Dental Claim Form PDF iconDemand

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

    SciTech Connect (OSTI)

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

    2013-11-01

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

  8. Revelation on Demand Nicolas Anciaux

    E-Print Network [OSTI]

    Revelation on Demand Nicolas Anciaux 1 · Mehdi Benzine1,2 · Luc Bouganim1 · Philippe Pucheral1 "revelation on demand". Keywords: Confidentiality and privacy, Secure device, Data warehousing, Indexing model

  9. by popular demand: Addiction II

    E-Print Network [OSTI]

    Niv, Yael

    by popular demand: Addiction II PSY/NEU338:Animal learning and decision making: Psychological, size of other non-drug rewards, and cost (but ultimately the demand is inelastic, or at least

  10. Demand Response: Load Management Programs 

    E-Print Network [OSTI]

    Simon, J.

    2012-01-01

    Management Programs CATEE Conference October, 2012 Agenda Outline I. General Demand Response Definition II. General Demand Response Program Rules III. CenterPoint Commercial Program IV. CenterPoint Residential Programs V. Residential Discussion... Points Demand Response Definition of load management per energy efficiency rule 25.181: ? Load control activities that result in a reduction in peak demand, or a shifting of energy usage from a peak to an off-peak period or from high-price periods...

  11. Call center demand forecasting : improving sales calls prediction accuracy through the combination of statistical methods and judgmental forecast

    E-Print Network [OSTI]

    Boulin, Juan Manuel

    2010-01-01

    Call centers are important for developing and maintaining healthy relationships with customers. At Dell, call centers are also at the core of the company's renowned direct model. For sales call centers in particular, the ...

  12. Chord on Demand Alberto Montresor

    E-Print Network [OSTI]

    Jelasity, Márk

    Chord on Demand Alberto Montresor University of Bologna, Italy montresor@cs.unibo.it M´ark Jelasity to solve a specific task on demand. We introduce T- CHORD, that can build a Chord network efficiently to solve a specific task on demand. Existing join protocols are not designed to handle the massive

  13. Supply Chain Supernetworks Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Supply Chain Supernetworks with Random Demands June Dong and Ding Zhang Department of Marketing of three tiers of decision-makers: the manufacturers, the distributors, and the retailers, with the demands equilibrium model with electronic commerce and with random demands for which modeling, qualitative analysis

  14. Chord on Demand Alberto Montresor

    E-Print Network [OSTI]

    Chord on Demand Alberto Montresor University of Bologna, Italy montresor@cs.unibo.it Mark Jelasity to solve a specific task on demand. We introduce T- CHORD, that can build a Chord network efficiently on demand. Existing join protocols are not designed to handle the massive concurrency involved in a jump

  15. ERCOT Demand Response Paul Wattles

    E-Print Network [OSTI]

    Mohsenian-Rad, Hamed

    ERCOT Demand Response Paul Wattles Senior Analyst, Market Design & Development, ERCOT Whitacre;Definitions of Demand Response · `The short-term adjustment of energy use by consumers in response to price to market or reliability conditions.' (NAESB) #12;Definitions of Demand Response · The common threads

  16. Assessment of Demand Response Resource

    E-Print Network [OSTI]

    Assessment of Demand Response Resource Potentials for PGE and Pacific Power Prepared for: Portland January 15, 2004 K:\\Projects\\2003-53 (PGE,PC) Assess Demand Response\\Report\\Revised Report_011504.doc #12;#12;quantec Assessment of Demand Response Resource Potentials for I-1 PGE and Pacific Power I. Introduction

  17. LPG export growth will exceed demand by 2000

    SciTech Connect (OSTI)

    True, W.R.

    1994-08-08

    LPG supplies for international trade will increase sharply through 2000 and begin to outstrip demand by 1997 or 1998. This outlook depends on several production projects proceeding as planned. Leading the way to increased volumes are projects in Algeria, Nigeria, and Australia, among others. Purvin and Gertz, Dallas, projected this trend earlier this year at an international LPG seminar near Houston. Representatives from LPG-supplying countries also presented information to support this view and subsequently supplied more specifics to OGJ in response to questions. This paper discusses this information. Trends in Africa, Australia, North America, and South America are forecast.

  18. Forecasting wind speed financial return

    E-Print Network [OSTI]

    D'Amico, Guglielmo; Prattico, Flavio

    2013-01-01

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

  19. Acting Globally: Potential Carbon Emissions Mitigation Impacts from an International Standards and Labelling Program

    E-Print Network [OSTI]

    Letschert, Virginie E.

    2010-01-01

    2008). The Boom of Electricity Demand in the residential2005). Forecasting Electricity Demand in Developingwith Residential Electricity Demand in India's Future - How

  20. The state-of-the-art in air transportation demand and systems analysis : a report on the proceedings of a workshop sponsored by the Civil Aeronautics Board, Department of Transportation, and National Aeronautics and Space Administration (June 1975)

    E-Print Network [OSTI]

    Taneja, Nawal K.

    1975-01-01

    Introduction and summary: Forecasting air transportation demand has indeed become a complex and risky business in recent years, especially in view of unpredictable fuel prices, high inflation rates, a declining rate of ...

  1. The imperfect price-reversibility of world oil demand

    SciTech Connect (OSTI)

    Gately, D. [New York Univ., NY (United States)

    1993-12-31

    This paper examines the price-reversibility of world oil demand, using price-decomposition methods employed previously on other energy demand data. We conclude that the reductions in world oil demand following the oil price increases of the 1970s will not be completely reversed by the price cuts of the 1980s. The response to price cuts in the 1980s is perhaps only one-fifth that for price increases in the 1970s. This has dramatic implications for projections of oil demand, especially under low-price assumptions. We also consider the effect on demand of a price recovery (sub-maximum increase) in the 1990s - due either to OPEC or to a carbon tax-specifically whether the effects would be as large as for the price increases of the 1970s or only as large as the smaller demand reversals of the 1980s. On this the results are uncertain, but a tentative conclusion is that the response to a price recovery would lie midway between the small response to price cuts and the larger response to increases in the maximum historical price. Finally, we demonstrate two implications of wrongly assuming that demand is perfectly price-reversible. First, such an assumption will grossly overestimate the demand response to price declines of the 1980s. Secondly, and somewhat surprisingly, it causes an underestimate of the effect of income growth on future demand. 21 refs., 11 figs., 1 tab.

  2. California Energy Futures Study Working Committee

    E-Print Network [OSTI]

    California at Davis, University of

    #12;#12;#12;California Energy Futures Study Working Committee Robert Budnitz, LBNL Linda Cohen, UC Somerville, UC Berkeley H. Youngs ­ EBI, UC Berkeley California's Energy Future, Biofuels #12;Stress tests California's Energy Future, Biofuels #12;#12;#12;Reduced Fuel Demand Scenario H. Youngs ­ EBI, UC Berkeley

  3. 1 Forecasting Greenhouse Gas Emissions from Urban Regions: 2 Microsimulation of Land Use and Transport Patterns in Austin, Texas

    E-Print Network [OSTI]

    Kockelman, Kara M.

    use electricity, natural gas and other energy sources regularly52 for space conditioning and powering1 Forecasting Greenhouse Gas Emissions from Urban Regions: 2 Microsimulation of Land Use 2030 household energy 26 demands and GHG emissions estimates are compared under five different land use

  4. A Framework of Short-Term Activity-Aware Load Forecasting Yong Ding, Martin Neumann and Michael Beigl

    E-Print Network [OSTI]

    Beigl, Michael

    the best use of electric energy and relieve the conflict between supply and demand [Niu et al., 2010]. However, inaccurate load forecasts will lead to not only monetary losses but also grid security losses), such as weather factors, climatic conditions, social activi- ties, and seasonal factors, past usage patterns

  5. Abstract--We present new approaches for 5-minute ahead electricity load forecasting. They were evaluated on data from

    E-Print Network [OSTI]

    Koprinska, Irena

    .e. the cheapest generator is allocated first, the second cheapest next and so on until the electricity demandAbstract--We present new approaches for 5-minute ahead electricity load forecasting. They were evaluated on data from the Australian electricity market operator for 2006-2008. After examining the load

  6. Current Status and Future Scenarios of Residential Building Energy Consumption in China

    SciTech Connect (OSTI)

    Zhou, Nan; Nishida, Masaru; Gao, Weijun

    2008-12-01

    China's rapid economic expansion has propelled it into the ranks of the largest energy consuming nation in the world, with energy demand growth continuing at a pace commensurate with its economic growth. Even though the rapid growth is largely attributable to heavy industry, this in turn is driven by rapid urbanization process, by construction materials and equipment produced for use in buildings. Residential energy is mostly used in urban areas, where rising incomes have allowed acquisition of home appliances, as well as increased use of heating in southern China. The urban population is expected to grow by 20 million every year, accompanied by construction of 2 billion square meters of buildings every year through 2020. Thus residential energy use is very likely to continue its very rapid growth. Understanding the underlying drivers of this growth helps to identify the key areas to analyze energy efficiency potential, appropriate policies to reduce energy use, as well as to understand future energy in the building sector. This paper provides a detailed, bottom-up analysis of residential building energy consumption in China using data from a wide variety of sources and a modeling effort that relies on a very detailed characterization of China's energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast the future outlook for the critical period extending to 2020, based on assumptions of likely patterns of economic activity, availability of energy services, technology improvement and energy intensities.

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

    E-Print Network [OSTI]

    Stadler, Michael

    2011-01-01

    site PV Historical PV price and adoption levels PV ? (kWh/$)in ? - technology adoption decision process. PV ? - lighting

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

    E-Print Network [OSTI]

    Stadler, Michael

    2011-01-01

    increase to parameter Natural gas price Electricity priceparameter GDP Population Natural gas price Electricity pricethe elasticities of Natural gas price this Electricity price

  9. Optimal combined wind power forecasts using exogeneous variables

    E-Print Network [OSTI]

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

  10. Weather Forecasts are for Wimps: Why Water Resource Managers Do Not Use Climate Forecasts

    E-Print Network [OSTI]

    Rayner, Steve; Lach, Denise; Ingram, Helen

    2005-01-01

    and Winter, S. G. : 1960, Weather Information and EconomicThe ENSO Signal 7, 4–6. WEATHER FORECASTS ARE FOR WIMPSWEATHER FORECASTS ARE FOR WIMPS ? : WHY WATER RESOURCE

  11. Generating Demand for Multifamily Building Upgrades | Department...

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

    Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades Better Buildings Residential Network Peer Exchange Call Series: Generating Demand for...

  12. China's Coal: Demand, Constraints, and Externalities

    E-Print Network [OSTI]

    Aden, Nathaniel

    2010-01-01

    raising transportation oil demand. Growing internationalcoal by wire could reduce oil demand by stemming coal roadEastern oil production. The rapid growth of coal demand

  13. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    of Energy demand-side management energy information systemdemand response. Demand-side management (DSM) program goalsa goal for demand-side management (DSM) coordination and

  14. Home Network Technologies and Automating Demand Response

    E-Print Network [OSTI]

    McParland, Charles

    2010-01-01

    LBNL Commercial and Residential Demand Response Overview ofmarket [5]. Residential demand reduction programs have beenin the domain of residential demand response. There are a

  15. Installation and Commissioning Automated Demand Response Systems

    E-Print Network [OSTI]

    Kiliccote, Sila; Global Energy Partners; Pacific Gas and Electric Company

    2008-01-01

    their partnership in demand response automation research andand Techniques for Demand Response. LBNL Report 59975. Mayof Fully Automated Demand Response in Large Facilities.

  16. Coupling Renewable Energy Supply with Deferrable Demand

    E-Print Network [OSTI]

    Papavasiliou, Anthony

    2011-01-01

    8.4 Demand Response Integration . . . . . . . . . . .for each day type for the demand response study - moderatefor each day type for the demand response study - deep

  17. Strategies for Demand Response in Commercial Buildings

    E-Print Network [OSTI]

    Watson, David S.; Kiliccote, Sila; Motegi, Naoya; Piette, Mary Ann

    2006-01-01

    Fully Automated Demand Response Tests in Large Facilities”of Fully Automated Demand Response in Large Facilities”,was coordinated by the Demand Response Research Center and

  18. Demand Responsive Lighting: A Scoping Study

    E-Print Network [OSTI]

    Rubinstein, Francis; Kiliccote, Sila

    2007-01-01

    2 2.0 Demand ResponseFully Automated Demand Response Tests in Large Facilities,was coordinated by the Demand Response Research Center and

  19. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    and D. Kathan (2009). Demand Response in U.S. ElectricityEnergy Financial Group. Demand Response Research Center [2008). Assessment of Demand Response and Advanced Metering.

  20. Hawaiian Electric Company Demand Response Roadmap Project

    E-Print Network [OSTI]

    Levy, Roger

    2014-01-01

    Like HECO actual utility demand response implementations canindustry-wide utility demand response applications tend toobjective. Figure 4. Demand Response Objectives 17  

  1. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01

    23 ii Retail Demand Response in SPP List of Figures and10 Figure 3. Demand Response Resources by11 Figure 4. Existing Demand Response Resources by Type of

  2. Demand Response - Policy | Department of Energy

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

    Coordination of Energy Efficiency and Demand Response Demand Response in U.S. Electricity Markets: Empirical Evidence 2009 Retail Demand Response in Southwest Power Pool (January...

  3. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01

    Barat, and D. Watson. 2007. Demand Response Spinning ReserveKueck, and B. Kirby. 2009. Demand Response Spinning Reserveand B. Kirby. 2012. The Demand Response Spinning Reserve

  4. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    duty fuel demand in alternate scenarios. ..for light-duty fuel demand in alternate scenarios. Minimum52 Heavy-duty vehicle fuel demand for each alternate

  5. The Preservation of Physical Fashion Forecasts

    E-Print Network [OSTI]

    Kosztowny, Alexander John

    2015-01-01

    schools and their libraries, which use trend forecastingin archives and libraries would be that the trend forecastsin a library or archive, not exclusively to trend forecasts.

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

    Energy Savers [EERE]

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

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

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

    that take place in complex terrain, this funding opportunity will improve foundational weather models by developing short-term wind forecasts for use by industry professionals,...

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

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

    processes that take place in complex terrain, this funding would improve foundational weather models by developing short-term wind forecasts for use by industry professionals,...

  9. Making Forecasts for Chaotic Physical Processes Christopher M. Danforth* and James A. Yorke

    E-Print Network [OSTI]

    Maryland at College Park, University of

    Making Forecasts for Chaotic Physical Processes Christopher M. Danforth* and James A. Yorke of years into the future [1], as well as the evolution of galactic clusters [2]. Plasma phys- icists use is followed. Given this limitation, the modeler's goal is that some linear combination of ensemble members

  10. Demand-Side Response from Industrial Loads

    SciTech Connect (OSTI)

    Starke, Michael R; Alkadi, Nasr E; Letto, Daryl; Johnson, Brandon; Dowling, Kevin; George, Raoule; Khan, Saqib

    2013-01-01

    Through a research study funded by the Department of Energy, Smart Grid solutions company ENBALA Power Networks along with the Oak Ridge National Laboratory (ORNL) have geospatially quantified the potential flexibility within industrial loads to leverage their inherent process storage to help support the management of the electricity grid. The study found that there is an excess of 12 GW of demand-side load flexibility available in a select list of top industrial facilities in the United States. Future studies will expand on this quantity of flexibility as more in-depth analysis of different industries is conducted and demonstrations are completed.

  11. 1Bureau of Meteorology | Water Information > INFORMATION SHEET 6 > Flood Forecasting and Warning Services Flood Forecasting

    E-Print Network [OSTI]

    Greenslade, Diana

    SHEET 6 1Bureau of Meteorology | Water Information > INFORMATION SHEET 6 > Flood Forecasting and Warning Services Flood Forecasting and Warning Services The Bureau of Meteorology (the Bureau) is responsible for providing an effective flood forecasting and warning service in each Australian state

  12. Demand Response Technology Roadmap A

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

    meetings and workshops convened to develop content for the Demand Response Technology Roadmap. The project team has developed this companion document in the interest of providing...

  13. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

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

  14. Supply Chain Supernetworks With Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Supply Chain Supernetworks With Random Demands June Dong Ding Zhang School of Business State Field Warehouses: stocking points Customers, demand centers sinks Production/ purchase costs Inventory Customer Demand Customer Demand Retailer OrdersRetailer Orders Distributor OrdersDistributor Orders

  15. 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

  16. Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts

    E-Print Network [OSTI]

    Raftery, Adrian

    Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts VERONICA ensembles that generates calibrated probabilistic forecast products for weather quantities at indi- vidual perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather

  17. The response of world energy and oil demand to income growth and changes in oil prices

    SciTech Connect (OSTI)

    Dargay, J. [Oxford Univ. (United Kingdom). Transport Studies Unit; Gately, D. [New York Univ., NY (United States). Economics Dept.

    1995-11-01

    This paper reviews the path of world oil demand over the past three decades, and the effects of both the oil price increases of the 1970s and the oil price decreases of the 1980s. Compared with demand in the industrialized countries, demand in the Less Developed Countries (LDC) has been more responsive to income growth, less responsive to price increases, and more responsive to price decreases. The LDC has also exhibited much greater heterogeneity in income growth and is effect on demand. The authors expect a smaller demand response to future price increases than to those of the 1970s. The demand response to future income growth will be not substantially smaller than in the past. Finally, given the prospect of growing dependence on OPEC oil, in the event of a major disruption the lessened price-responsiveness of demand could cause dramatic price increases and serious macroeconomic effects.

  18. What Do Consumers Believe About Future Gasoline Soren T. Anderson

    E-Print Network [OSTI]

    Silver, Whendee

    What Do Consumers Believe About Future Gasoline Prices? Soren T. Anderson Michigan State University of consumers about their expectations of future gasoline prices. Overall, we find that consumer beliefs follow a random walk, which we deem a reasonable forecast of gasoline prices, but we find a deviation from

  19. Marketing & Driving Demand Collaborative - Social Media Tools...

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

    & Driving Demand Collaborative - Social Media Tools & Strategies Marketing & Driving Demand Collaborative - Social Media Tools & Strategies Presentation slides from the Better...

  20. Retail Demand Response in Southwest Power Pool

    E-Print Network [OSTI]

    Bharvirkar, Ranjit

    2009-01-01

    Data Collection for Demand-side Management for QualifyingPrepared by Demand-side Management Task Force of the

  1. Honeywell Demonstrates Automated Demand Response Benefits for...

    Office of Environmental Management (EM)

    Honeywell Demonstrates Automated Demand Response Benefits for Utility, Commercial, and Industrial Customers Honeywell Demonstrates Automated Demand Response Benefits for Utility,...

  2. Effects of the drought on California electricity supply and demand

    E-Print Network [OSTI]

    Benenson, P.

    2010-01-01

    Acknowledgments SUMMARY Electricity Demand ElectricityAdverse Impacts ELECTRICITY DEMAND . . . .Demand forElectricity Sales Electricity Demand by Major Utility

  3. Factors shaping the future of Cloud Computing

    E-Print Network [OSTI]

    Francis, Steven (Steven Douglas)

    2011-01-01

    Many different forces are currently shaping the future of the Cloud Computing Market. End user demand and end user investment in existing technology are important drivers. Vendor innovation and competitive strategy are ...

  4. 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

  5. INTELLIGENT HANDLING OF WEATHER FORECASTS Stephan Kerpedjiev

    E-Print Network [OSTI]

    , discourse and semantic. They are based on a conceptual model underlying weather forecasts as well situations represented in the form of texts in NL, weather maps, data tables or combined information objectsINTELLIGENT HANDLING OF WEATHER FORECASTS Stephan Kerpedjiev I n s t i t u t e of Mathematics Acad

  6. Smooth Calibration, Leaky Forecasts, and Finite Recall

    E-Print Network [OSTI]

    Hart, Sergiu

    Smooth Calibration, Leaky Forecasts, and Finite Recall Sergiu Hart October 2015 SERGIU HART c 2015 ­ p. #12;Smooth Calibration, Leaky Forecasts, and Finite Recall Sergiu Hart Center for the Study of Rationality Dept of Mathematics Dept of Economics The Hebrew University of Jerusalem hart@huji.ac.il http://www.ma.huji.ac.il/hart

  7. Weather and Forecasting EARLY ONLINE RELEASE

    E-Print Network [OSTI]

    Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary PDF of the author, Guangzhou 510301, China9 2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological10, China20 21 22 23 24 Submitted to Weather and Forecasting25 2014. 12. 2826 27 Corresponding author: Dr

  8. Weather and Forecasting EARLY ONLINE RELEASE

    E-Print Network [OSTI]

    Johnson, Richard H.

    Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary PDF of the author Fort Collins, Colorado7 October 20128 (submitted to Weather and Forecasting)9 1 Corresponding author address: Rebecca D. Adams-Selin, HQ Air Force Weather Agency 16th Weather Squadron, 101 Nelson Dr., Offutt

  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. Demand Response for Ancillary Services

    SciTech Connect (OSTI)

    Alkadi, Nasr E; Starke, Michael R

    2013-01-01

    Many demand response resources are technically capable of providing ancillary services. In some cases, they can provide superior response to generators, as the curtailment of load is typically much faster than ramping thermal and hydropower plants. Analysis and quantification of demand response resources providing ancillary services is necessary to understand the resources economic value and impact on the power system. Methodologies used to study grid integration of variable generation can be adapted to the study of demand response. In the present work, we describe and illustrate a methodology to construct detailed temporal and spatial representations of the demand response resource and to examine how to incorporate those resources into power system models. In addition, the paper outlines ways to evaluate barriers to implementation. We demonstrate how the combination of these three analyses can be used to translate the technical potential for demand response providing ancillary services into a realizable potential.

  11. Energy Demand Modelling Introduction to the PhD project

    E-Print Network [OSTI]

    Energy Demand Modelling Introduction to the PhD project Erika Zvingilaite Risø DTU System Analysis for optimization of energy systems Environmental effects Global externalities cost of CO2 Future scenarios for the Nordic energy systems 2010, 2020, 2030, 2040, 2050 (energy-production, consumption, emissions, net costs

  12. Demand and Price Volatility: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2011-01-01

    A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

  13. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    A Joint Model of the Global Crude Oil Market and the U.S.Noureddine. 2002. World crude oil and natural gas: a demandelasticity of demand for crude oil, not gasoline. Results

  14. Addressing Energy Demand through Demand Response: International Experiences and Practices

    E-Print Network [OSTI]

    Shen, Bo

    2013-01-01

    electricity. In this manner, demand side management is directly integrated into the wholesale capacity marketcapacity market U.S. Federal Energy Regulatory Commission Florida Reliability Coordinating Council incremental auctions independent electricity

  15. Demand and Price Uncertainty: Rational Habits in International Gasoline Demand

    E-Print Network [OSTI]

    Scott, K. Rebecca

    2013-01-01

    global gasoline and diesel price and income elasticities.shift in the short-run price elasticity of gasoline demand.Habits and Uncertain Relative Prices: Simulating Petrol Con-

  16. MODELING THE DEMAND FOR E85 IN THE UNITED STATES

    SciTech Connect (OSTI)

    Liu, Changzheng; Greene, David L

    2013-10-01

    How demand for E85 might evolve in the future in response to changing economics and policies is an important subject to include in the National Energy Modeling System (NEMS). This report summarizes a study to develop an E85 choice model for NEMS. Using the most recent data from the states of Minnesota, North Dakota, and Iowa, this study estimates a logit model that represents E85 choice as a function of prices of E10 and E85, as well as fuel availability of E85 relative to gasoline. Using more recent data than previous studies allows a better estimation of non-fleet demand and indicates that the price elasticity of E85 choice appears to be higher than previously estimated. Based on the results of the econometric analysis, a model for projecting E85 demand at the regional level is specified. In testing, the model produced plausible predictions of US E85 demand to 2040.

  17. Fitting and forecasting non-linear coupled dark energy

    E-Print Network [OSTI]

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

    2015-01-01

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

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

    E-Print Network [OSTI]

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

    2008-01-01

    demands. Residential and commercial demand has a significantDemand by Sector Residential Peak Demand (MW) Commercialwe convert residential electricity demand based upon climate

  19. Demand Response for Ancillary Services

    Broader source: Energy.gov [DOE]

    Methodologies used to study grid integration of variable generation can be adapted to the study of demand response. In the present work, we describe and implement a methodology to construct detailed temporal and spatial representations of demand response resources and to incorporate those resources into power system models. In addition, the paper outlines ways to evaluate barriers to implementation. We demonstrate how the combination of these three analyses can be used to assess economic value of the realizable potential of demand response for ancillary services.

  20. Physically-based demand modeling 

    E-Print Network [OSTI]

    Calloway, Terry Marshall

    1980-01-01

    nts on the demand. Of course the demand of a real a1r cond1t1oner has lower and upper bounds equal to 0 and 0 , respec- u tively. A constra1ned system can be simulated numerically, but there 1s no explicit system response formula s1m11ar... sect1on. It may now be instruct1ve to relate this model to that of Jones and Bri ce [5] . The average demand pred1 cted by their model is the expected value of the product of a load response factor 0 and a U sw1tching process H(t), which depends...

  1. Earthquake Forecast via Neutrino Tomography

    E-Print Network [OSTI]

    Bin Wang; Ya-Zheng Chen; Xue-Qian Li

    2011-03-29

    We discuss the possibility of forecasting earthquakes by means of (anti)neutrino tomography. Antineutrinos emitted from reactors are used as a probe. As the antineutrinos traverse through a region prone to earthquakes, observable variations in the matter effect on the antineutrino oscillation would provide a tomography of the vicinity of the region. In this preliminary work, we adopt a simplified model for the geometrical profile and matter density in a fault zone. We calculate the survival probability of electron antineutrinos for cases without and with an anomalous accumulation of electrons which can be considered as a clear signal of the coming earthquake, at the geological region with a fault zone, and find that the variation may reach as much as 3% for $\\bar \

  2. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

    Av:l.at:i.on Fuel Total Oil Demand 0:!.1 Demand w:t thoutSince electric:! ty prices oil prices, the demand for will :the ef feet of oil prices on energy demand and supply, \\ve

  3. IEEE TRANSACTIONS ON SMART GRID, VOL. 5, NO. 2, MARCH 2014 861 An Optimal and Distributed Demand Response

    E-Print Network [OSTI]

    Nehorai, Arye

    of demand response management for the future smart grid that integrates plug-in electric vehicles for augmented Lagrangian. I. INTRODUCTION I N THE electricity market, demand response [1] is a mech- anism to manage users' consumption behavior under spe- cific supply conditions. The goal of demand response

  4. Seasonality in air transportation demand

    E-Print Network [OSTI]

    Reichard Megwinoff, H?tor Nicolas

    1988-01-01

    This thesis investigates the seasonality of demand in air transportation. It presents three methods for computing seasonal indices. One of these methods, the Periodic Average Method, is selected as the most appropriate for ...

  5. Demand response enabling technology development

    E-Print Network [OSTI]

    2006-01-01

    Monitoring in an Agent-Based Smart Home, Proceedings of theConference on Smart Homes and Health Telematics, September,Smart Meter Motion sensors Figure 1: Schematic of the Demand Response Electrical Appliance Manager in a Home.

  6. Full Rank Rational Demand Systems

    E-Print Network [OSTI]

    LaFrance, Jeffrey T; Pope, Rulon D.

    2006-01-01

    Dover Publications 1972. Barnett, W.A. and Y.W. Lee. “TheEconometrica 53 (1985): 1421- Barnett, W.A. , Lee, Y.W. ,Laurent demand systems (Barnett and Lee 1985; Barnett, Lee,

  7. Residential Demand Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

  8. Marketing Demand-Side Management 

    E-Print Network [OSTI]

    O'Neill, M. L.

    1988-01-01

    Demand-Side Management is an organizational tool that has proven successful in various realms of the ever changing business world in the past few years. It combines the multi-faceted desires of the customers with the increasingly important...

  9. Coupling Renewable Energy Supply with Deferrable Demand

    E-Print Network [OSTI]

    Papavasiliou, Anthony

    2011-01-01

    forecasting for wind energy: Temperature dependence andlarge amounts of wind energy with a small electric system.Large scale integration of wind energy in the european power

  10. Coupling Renewable Energy Supply with Deferrable Demand

    E-Print Network [OSTI]

    Papavasiliou, Anthony

    2011-01-01

    with transmission line and generator failures, and propose aload forecast errors and generator failures at an acceptablegenerator failure, 2-generator failure and so on) and, to a

  11. Demand Response Spinning Reserve Demonstration

    SciTech Connect (OSTI)

    Eto, Joseph H.; Nelson-Hoffman, Janine; Torres, Carlos; Hirth,Scott; Yinger, Bob; Kueck, John; Kirby, Brendan; Bernier, Clark; Wright,Roger; Barat, A.; Watson, David S.

    2007-05-01

    The Demand Response Spinning Reserve project is a pioneeringdemonstration of how existing utility load-management assets can providean important electricity system reliability resource known as spinningreserve. Using aggregated demand-side resources to provide spinningreserve will give grid operators at the California Independent SystemOperator (CAISO) and Southern California Edison (SCE) a powerful, newtool to improve system reliability, prevent rolling blackouts, and lowersystem operating costs.

  12. Forecasting Random Walks Under Drift Instability

    E-Print Network [OSTI]

    Pesaran, M Hashem; Pick, Andreas

    will yield a biased forecast but will continue to have the least variance. On the other hand a forecast based on the sub-sample {yTi , yTi+1, . . . , yT }, where Ti > 1 is likely to have a lower bias but could be inefficient due to a higher variance... approach considered in Pesaran and Timmermann (2007) is to use different sub-windows to forecast and then average the outcomes, either by means of cross-validated weights or by simply using equal weights. To this end consider the sample {yTi , yTi+1...

  13. 1993 Solid Waste Reference Forecast Summary

    SciTech Connect (OSTI)

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

    1993-08-01

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

  14. Demand Forecast Advisory Committee in Preparation for the Seventh Power Plan

    E-Print Network [OSTI]

    products, electric motors, commercial water heaters, and heating, ventilation, and air conditioning Battery Chargers and External Power Supplies Ceiling Fan Light Kits Residential & Commercial Clothes

  15. Implementing Innovation in Planning Practice: The Case of Travel Demand Forecasting

    E-Print Network [OSTI]

    Newmark, Gregory Louis

    2011-01-01

    Rogers, E. Diffusion of Innovations. , 1995. 12. Mandelbaum,on a Methodological Innovation in Urban Planning. Journal ofthe History of Technological Innovation. American Planning

  16. New product demand forecasting and distribution optimization : a case study at Zara

    E-Print Network [OSTI]

    Garro, Andres

    2011-01-01

    The problem of optimally distributing new products is common to many companies and industries. This thesis describes how this challenge was addressed at Zara, a leading retailer in the "fast fashion" industry. The thesis ...

  17. The Origins of Metropolitan Transportation Planning in Travel Demand Forecasting, 1944-1962

    E-Print Network [OSTI]

    Deutsch, Cheryl

    2013-01-01

    J. (1955). The law of retail gravitation applied to trafficas “Reilly’s Law of Retail Gravitation. ” Concepts like

  18. The Origins of Metropolitan Transportation Planning in Travel Demand Forecasting, 1944-1962

    E-Print Network [OSTI]

    Deutsch, Cheryl

    2013-01-01

    predictions for the Detroit area. In Highway Research Board4: Desire line charts from Detroit, 1942………………………………………………….transportation research in the Detroit Metropolitan Area

  19. OPPORTUNITIES FOR AUTOMATED DEMAND RESPONSE IN CALIFORNIA’S DAIRY PROCESSING INDUSTRY

    SciTech Connect (OSTI)

    Homan, Gregory K.; Aghajanzadeh, Arian; McKane, Aimee

    2015-08-30

    During periods of peak electrical demand on the energy grid or when there is a shortage of supply, the stability of the grid may be compromised or the cost of supplying electricity may rise dramatically, respectively. Demand response programs are designed to mitigate the severity of these problems and improve reliability by reducing the demand on the grid during such critical times. In 2010, the Demand Response Research Center convened a group of industry experts to suggest potential industries that would be good demand response program candidates for further review. The dairy industry was suggested due to the perception that the industry had suitable flexibility and automatic controls in place. The purpose of this report is to provide an initial description of the industry with regard to demand response potential, specifically automated demand response. This report qualitatively describes the potential for participation in demand response and automated demand response by dairy processing facilities in California, as well as barriers to widespread participation. The report first describes the magnitude, timing, location, purpose, and manner of energy use. Typical process equipment and controls are discussed, as well as common impediments to participation in demand response and automated demand response programs. Two case studies of demand response at dairy facilities in California and across the country are reviewed. Finally, recommendations are made for future research that can enhance the understanding of demand response potential in this industry.

  20. Open Automated Demand Response Communications in Demand Response for Wholesale Ancillary Services

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    A. Barat, D. Watson. 2006 Demand Response Spinning ReserveKueck, and B. Kirby 2008. Demand Response Spinning ReserveReport 2009. Open Automated Demand Response Communications

  1. Demand Response and Open Automated Demand Response Opportunities for Data Centers

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    Standardized Automated Demand Response Signals. Presented atand Automated Demand Response in Industrial RefrigeratedActions for Industrial Demand Response in California. LBNL-

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

    SciTech Connect (OSTI)

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

    2011-10-01

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

  3. Wind-Wave Probabilistic Forecasting based on Ensemble

    E-Print Network [OSTI]

    Wind-Wave Probabilistic Forecasting based on Ensemble Predictions Maxime FORTIN Kongens Lyngby 2012.imm.dtu.dk IMM-PhD-2012-86 #12;Summary Wind and wave forecasts are of a crucial importance for a number weather forecasts and do not take any possible correlation into ac- count. Since wind and wave forecasts

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  5. Optimal Demand Response and Power Flow

    E-Print Network [OSTI]

    Willett, Rebecca

    Optimal Demand Response and Power Flow Steven Low Computing + Math Sciences Electrical Engineering #12;Outline Optimal demand response n With L. Chen, L. Jiang, N. Li Optimal power flow n With S. Bose;Optimal demand response Model Results n Uncorrelated demand: distributed alg n Correlated demand

  6. Home Network Technologies and Automating Demand Response

    SciTech Connect (OSTI)

    McParland, Charles

    2009-12-01

    Over the past several years, interest in large-scale control of peak energy demand and total consumption has increased. While motivated by a number of factors, this interest has primarily been spurred on the demand side by the increasing cost of energy and, on the supply side by the limited ability of utilities to build sufficient electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in electricity use through the use of price incentives. DR systems are also be design to shift or curtail energy demand at critical times when the generation, transmission, and distribution systems (i.e. the 'grid') are threatened with instabilities. To be effectively deployed on a large-scale, these proposed DR systems need to be automated. Automation will require robust and efficient data communications infrastructures across geographically dispersed markets. The present availability of widespread Internet connectivity and inexpensive, reliable computing hardware combined with the growing confidence in the capabilities of distributed, application-level communications protocols suggests that now is the time for designing and deploying practical systems. Centralized computer systems that are capable of providing continuous signals to automate customers reduction of power demand, are known as Demand Response Automation Servers (DRAS). The deployment of prototype DRAS systems has already begun - with most initial deployments targeting large commercial and industrial (C & I) customers. An examination of the current overall energy consumption by economic sector shows that the C & I market is responsible for roughly half of all energy consumption in the US. On a per customer basis, large C & I customers clearly have the most to offer - and to gain - by participating in DR programs to reduce peak demand. And, by concentrating on a small number of relatively sophisticated energy consumers, it has been possible to improve the DR 'state of the art' with a manageable commitment of technical resources on both the utility and consumer side. Although numerous C & I DR applications of a DRAS infrastructure are still in either prototype or early production phases, these early attempts at automating DR have been notably successful for both utilities and C & I customers. Several factors have strongly contributed to this success and will be discussed below. These successes have motivated utilities and regulators to look closely at how DR programs can be expanded to encompass the remaining (roughly) half of the state's energy load - the light commercial and, in numerical terms, the more important residential customer market. This survey examines technical issues facing the implementation of automated DR in the residential environment. In particular, we will look at the potential role of home automation networks in implementing wide-scale DR systems that communicate directly to individual residences.

  7. Smart finite state devices: A modeling framework for demand response technologies

    E-Print Network [OSTI]

    Turitsyn, Konstantin

    We introduce and analyze Markov Decision Process (MDP) machines to model individual devices which are expected to participate in future demand-response markets on distribution grids. We differentiate devices into the ...

  8. The process of resort second home development demand quantification : exploration of methodologies and case study application

    E-Print Network [OSTI]

    Wholey, Christopher J. (Christoper John)

    2011-01-01

    Prevalent methodologies utilized by resort second home development professionals to quantify demand for future projects are identified and critiqued. The strengths of each model are synthesized in order to formulate an ...

  9. for the Future The Case for

    E-Print Network [OSTI]

    O'Brien, James F.

    Building for the Future The Case for Green Buildings and Energy Security for the University a contract from the Greenpeace Clean Energy Now! campaign. Building for the Future: The Case for Green and growing demand for renewable energy, energy efficiency, and green building practices from a wide range

  10. 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

  11. 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....

  12. Load Forecast For use in Resource Adequacy

    E-Print Network [OSTI]

    forecast of 4) Calculate Hourly Load Allocation Factor s for each day for 2019 For use in RA analysis as a function ofthe load for electricity in the region as a function of cyclical, weather and economic variables

  13. Wind Speed Forecasting for Power System Operation 

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22

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

  14. Testing Competing High-Resolution Precipitation Forecasts

    E-Print Network [OSTI]

    Gilleland, Eric

    Testing Competing High-Resolution Precipitation Forecasts Eric Gilleland Research Prediction Comparison Test D1 D2 D = D1 ­ D2 copyright NCAR 2013 Loss Differential Field #12;Spatial Prediction Comparison Test Introduced by Hering and Genton

  15. 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 ...

  16. (2013) 128 Data Center Demand Response: Avoiding the Coincident Peak via

    E-Print Network [OSTI]

    Low, Steven H.

    2013-01-01

    significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data has the potential to significantly ease the adoption of renewable energy into the grid. Data centers.chen@hp.com Abstract Demand response is a crucial aspect of the future smart grid. It has the potential to provide

  17. Demand Response as a System Reliability Resource

    E-Print Network [OSTI]

    Joseph, Eto

    2014-01-01

    Barat, and D. Watson. 2007. Demand Response Spinning ReserveKueck, and B. Kirby. 2009. Demand Response Spinning ReserveFormat of 2009-2011 Demand Response Activity Applications.

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

    SciTech Connect (OSTI)

    Edwards, J.D.

    1997-08-01

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

  19. Industrial demand side management: A status report

    SciTech Connect (OSTI)

    Hopkins, M.F.; Conger, R.L.; Foley, T.J.

    1995-05-01

    This report provides an overview of and rationale for industrial demand side management (DSM) programs. Benefits and barriers are described, and data from the Manufacturing Energy Consumption Survey are used to estimate potential energy savings in kilowatt hours. The report presents types and examples of programs and explores elements of successful programs. Two in-depth case studies (from Boise Cascade and Eli Lilly and Company) illustrate two types of effective DSM programs. Interviews with staff from state public utility commissions indicate the current thinking about the status and future of industrial DSM programs. A comprehensive bibliography is included, technical assistance programs are listed and described, and a methodology for evaluating potential or actual savings from projects is delineated.

  20. Traffic congestion forecasting model for the INFORM System. Final report

    SciTech Connect (OSTI)

    Azarm, A.; Mughabghab, S.; Stock, D.

    1995-05-01

    This report describes a computerized traffic forecasting model, developed by Brookhaven National Laboratory (BNL) for a portion of the Long Island INFORM Traffic Corridor. The model has gone through a testing phase, and currently is able to make accurate traffic predictions up to one hour forward in time. The model will eventually take on-line traffic data from the INFORM system roadway sensors and make projections as to future traffic patterns, thus allowing operators at the New York State Department of Transportation (D.O.T.) INFORM Traffic Management Center to more optimally manage traffic. It can also form the basis of a travel information system. The BNL computer model developed for this project is called ATOP for Advanced Traffic Occupancy Prediction. The various modules of the ATOP computer code are currently written in Fortran and run on PC computers (pentium machine) faster than real time for the section of the INFORM corridor under study. The following summarizes the various routines currently contained in the ATOP code: Statistical forecasting of traffic flow and occupancy using historical data for similar days and time (long term knowledge), and the recent information from the past hour (short term knowledge). Estimation of the empirical relationships between traffic flow and occupancy using long and short term information. Mechanistic interpolation using macroscopic traffic models and based on the traffic flow and occupancy forecasted (item-1), and the empirical relationships (item-2) for the specific highway configuration at the time of simulation (construction, lane closure, etc.). Statistical routine for detection and classification of anomalies and their impact on the highway capacity which are fed back to previous items.

  1. Generating Demand for Multifamily Building Upgrades | Department...

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

    Generating Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades Better Buildings Residential Network Peer Exchange Call Series: Generating...

  2. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    demand response: ? Distribution utility ? ISO ? Aggregator (demand response less obstructive and inconvenient for the customer (particularly if DR resources are aggregated by a load aggregator).

  3. California Energy Demand Scenario Projections to 2050

    E-Print Network [OSTI]

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

    2008-01-01

    annual per-capita electricity consumption by demand15 California electricity consumption projections by demandannual per-capita electricity consumption by demand

  4. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    California Long-term Energy Efficiency Strategic Plan. B-2 Coordination of Energy Efficiency and Demand Response> B-4 Coordination of Energy Efficiency and Demand Response

  5. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    Energy Efficiency, Demand Response, and Peak Load Managementdemand response, and load management programs in the Ebefore they undertake load management and demand response

  6. Coordination of Energy Efficiency and Demand Response

    E-Print Network [OSTI]

    Goldman, Charles

    2010-01-01

    > B-2 Coordination of Energy Efficiency and Demand Response> B-4 Coordination of Energy Efficiency and Demand Responseand integration is: Energy efficiency, energy conservation,

  7. Generating Demand for Multifamily Building Upgrades | Department...

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

    Generating Demand for Multifamily Building Upgrades Generating Demand for Multifamily Building Upgrades May 14, 2015 12:30PM to 2:00PM EDT Learn more...

  8. Demand Response Programs Oregon Public Utility Commission

    E-Print Network [OSTI]

    Demand Response Programs Oregon Public Utility Commission January 6, 2005 Mike Koszalka Director;Demand Response Results, 2004 Load Control ­ Cool Keeper ­ ID Irrigation Load Control Price Responsive

  9. Demand for petrochem feedstock to buoy world LPG industry

    SciTech Connect (OSTI)

    Not Available

    1992-05-18

    This paper reports that use of liquefied petroleum gas as petrochemical feedstock will increase worldwide, providing major growth opportunities for LPG producers. World exports of liquefied petroleum gas will increase more slowly than production as producers choose to use LPG locally as chemical feedstock and export in value added forms such as polyethylene. So predicts Poten and Partners Inc., New York. Poten forecasts LPG production in exporting countries will jump to 95 million tons in 2010 from 45 million tons in 1990. However, local and regional demand will climb to 60 million tons/year from 23 million tons/year during the same period. So supplies available for export will rise to 35 million tons in 2010 from 22 million tons in 1990.

  10. Turkey's energy demand and supply

    SciTech Connect (OSTI)

    Balat, M. [Sila Science, Trabzon (Turkey)

    2009-07-01

    The aim of the present article is to investigate Turkey's energy demand and the contribution of domestic energy sources to energy consumption. Turkey, the 17th largest economy in the world, is an emerging country with a buoyant economy challenged by a growing demand for energy. Turkey's energy consumption has grown and will continue to grow along with its economy. Turkey's energy consumption is high, but its domestic primary energy sources are oil and natural gas reserves and their production is low. Total primary energy production met about 27% of the total primary energy demand in 2005. Oil has the biggest share in total primary energy consumption. Lignite has the biggest share in Turkey's primary energy production at 45%. Domestic production should be to be nearly doubled by 2010, mainly in coal (lignite), which, at present, accounts for almost half of the total energy production. The hydropower should also increase two-fold over the same period.

  11. Demand Response and Energy Efficiency 

    E-Print Network [OSTI]

    2009-01-01

    stream_source_info ESL-IC-09-11-05.pdf.txt stream_content_type text/plain stream_size 14615 Content-Encoding ISO-8859-1 stream_name ESL-IC-09-11-05.pdf.txt Content-Type text/plain; charset=ISO-8859-1 Demand Response... 4 An Innovative Solution to Get the Ball Rolling ? Demand Response (DR) ? Monitoring Based Commissioning (MBCx) EnerNOC has a solution involving two complementary offerings. ESL-IC-09-11-05 Proceedings of the Ninth International Conference...

  12. Addressing an Uncertain Future Using Scenario Analysis

    SciTech Connect (OSTI)

    Siddiqui, Afzal S.; Marnay, Chris

    2006-12-15

    The Office of Energy Efficiency and Renewable Energy (EERE) has had a longstanding goal of introducing uncertainty into the analysis it routinely conducts in compliance with the Government Performance and Results Act (GPRA) and for strategic management purposes. The need to introduce some treatment of uncertainty arises both because it would be good general management practice, and because intuitively many of the technologies under development by EERE have a considerable advantage in an uncertain world. For example, an expected kWh output from a wind generator in a future year, which is not exposed to volatile and unpredictable fuel prices, should be truly worth more than an equivalent kWh from an alternative fossil fuel fired technology. Indeed, analysts have attempted to measure this value by comparing the prices observed in fixed-price natural gas contracts compared to ones in which buyers are exposed to market prices (see Bolinger, Wiser, and Golove and (2004)). In addition to the routine reasons for exploring uncertainty given above, the history of energy markets appears to have exhibited infrequent, but troubling, regime shifts, i.e., historic turning points at which the center of gravity or fundamental nature of the system appears to have abruptly shifted. Figure 1 below shows an estimate of how the history of natural gas fired generating costs has evolved over the last three decades. The costs shown incorporate both the well-head gas price and an estimate of how improving generation technology has gradually tended to lower costs. The purpose of this paper is to explore scenario analysis as a method for introducing uncertainty into EERE's forecasting in a manner consistent with the preceding observation. The two questions are how could it be done, and what is its academic basis, if any. Despite the interest in uncertainty methods, applying them poses some major hurdles because of the heavy reliance of EERE on forecasting tools that are deterministic in nature, such as the Energy Information Administration's (EIA's) National Energy Modeling System (NEMS). NEMS is the source of the influential Annual Energy Outlook whose business-as-usual (BAU) case, the Reference Case, forms the baseline for most of the U.S. energy policy discussion. NEMS is an optimizing model because: 1. it iterates to an equilibrium among modules representing the supply, demand, and energy conversion subsectors; and 2. several subsectoral models are individually solved using linear programs (LP). Consequently, it is deeply rooted in the recent past and any effort to simulate the consequences of a major regime shift as depicted in Figure 1 must come by applying an exogenously specified scenario. And, more generally, simulating futures that lie outside of our recent historic experience, even if they do not include regime switches suggest some form of scenario approach. At the same time, the statistical validity of scenarios that deviate significantly outside the ranges of historic inputs should be questioned.

  13. Intra-hour Direct Normal Irradiance solar forecasting using genetic programming

    E-Print Network [OSTI]

    Queener, Benjamin Daniel

    2012-01-01

    guideline for Solar Power Forecasting Performance . . 46 viof forecasting techniques for solar power production with noand A. Pavlovski, “Solar power forecasting performance

  14. A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting

    E-Print Network [OSTI]

    Mathiesen, Patrick; Collier, Craig; Kleissl, Jan

    2013-01-01

    of the WRF model solar irradiance forecasts in Andalusia (Beyer, H. , 2009.    Irradiance forecasting for the power dependent probabilistic irradiance  forecasts for coastal 

  15. Mathematics Of Ice To Aid Global Warming Forecasts Mathematics Of Ice To Aid Global Warming Forecasts

    E-Print Network [OSTI]

    Golden, Kenneth M.

    Mathematics Of Ice To Aid Global Warming Forecasts Mathematics Of Ice To Aid Global Warming forecasts of how global warming will affect polar icepacks. See also: Earth & Climate q Global Warming q the effects of climate warming, and its presence greatly reduces solar heating of the polar oceans." "Sea ice

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

    SciTech Connect (OSTI)

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

    2014-05-01

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

  17. Forecasting Prices andForecasting Prices and Congestion forCongestion for

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    Goal: Design nodal price and grid congestion forecasting tools for market operators and market Traders To facilitate scenario-conditioned planning Price forecasting for Market Participants (MPs) To manage short for portfolio management by power market participants Conclusion #12;Project OverviewProject Overview Project

  18. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01

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

  19. Revelation on Demand Nicolas Anciaux

    E-Print Network [OSTI]

    is willing to reveal the aggregate response (according to his company's policy) to the customer dataRevelation on Demand Nicolas Anciaux 1 · Mehdi Benzine1,2 · Luc Bouganim1 · Philippe Pucheral1 time to support epidemiological studies. In these and many other situations, aggregate data or partial

  20. Demand Response Providing Ancillary Services

    E-Print Network [OSTI]

    1 Demand Response Providing Ancillary Services: A Comparison of Opportunities and Challenges in US to operate (likely price takers) ­ Statistical reliability (property of large aggregations of small resources size based on Mid-Atlantic Reserve Zone #12;Market Rules: Resource Size Min. Size (MW) Aggregation

  1. Water demand management in Kuwait

    E-Print Network [OSTI]

    Milutinovic, Milan, M. Eng. Massachusetts Institute of Technology

    2006-01-01

    Kuwait is an arid country located in the Middle East, with limited access to water resources. Yet water demand per capita is much higher than in other countries in the world, estimated to be around 450 L/capita/day. There ...

  2. On-demand data broadcasting 

    E-Print Network [OSTI]

    Kothandaraman, Kannan

    1998-01-01

    related to on-demand data broadcasting. We look at the problem of data broadcasting in an environment where clients make explicit requests to the server. The server broadcasts requested data items to all the clients, including those who have not requested...

  3. Promising Technology: Demand Control Ventilation

    Broader source: Energy.gov [DOE]

    Demand control ventilation (DCV) measures carbon dioxide concentrations in return air or other strategies to measure occupancy, and accurately matches the ventilation requirement. This system reduces ventilation when spaces are vacant or at lower than peak occupancy. When ventilation is reduced, energy savings are accrued because it is not necessary to heat, cool, or dehumidify as much outside air.

  4. Water supply and demand in an energy supply model

    SciTech Connect (OSTI)

    Abbey, D; Loose, V

    1980-12-01

    This report describes a tool for water and energy-related policy analysis, the development of a water supply and demand sector in a linear programming model of energy supply in the United States. The model allows adjustments in the input mix and plant siting in response to water scarcity. Thus, on the demand side energy conversion facilities can substitute more costly dry cooling systems for conventional evaporative systems. On the supply side groundwater and water purchased from irrigators are available as more costly alternatives to unappropriated surface water. Water supply data is developed for 30 regions in 10 Western states. Preliminary results for a 1990 energy demand scenario suggest that, at this level of spatial analysis, water availability plays a minor role in plant siting. Future policy applications of the modeling system are discussed including the evaluation of alternative patterns of synthetic fuels development.

  5. Effects of the drought on California electricity supply and demand

    E-Print Network [OSTI]

    Benenson, P.

    2010-01-01

    DEMAND . . . .Demand for Electricity and Power PeakDemand . . • . . ELECTRICITY REQUIREMENTS FOR AGRICULTUREResults . . Coriclusions ELECTRICITY SUPPLY Hydroelectric

  6. Automated Demand Response Opportunities in Wastewater Treatment Facilities

    E-Print Network [OSTI]

    Thompson, Lisa

    2008-01-01

    Interoperable Automated Demand Response Infrastructure,study of automated demand response in wastewater treatmentopportunities for demand response control strategies in

  7. Northwest Open Automated Demand Response Technology Demonstration Project

    E-Print Network [OSTI]

    Kiliccote, Sila

    2010-01-01

    Report 2009. Open Automated Demand Response Communicationsand Techniques for Demand Response. California Energyand S. Kiliccote. Estimating Demand Response Load Impacts:

  8. Opportunities, Barriers and Actions for Industrial Demand Response in California

    E-Print Network [OSTI]

    McKane, Aimee T.

    2009-01-01

    and Techniques for Demand Response, report for theand Reliability Demand Response Programs: Final Report.Demand Response

  9. Incorporating Demand Response into Western Interconnection Transmission Planning

    E-Print Network [OSTI]

    Satchwell, Andrew

    2014-01-01

    Aggregator Programs. Demand Response Measurement andIncorporating Demand Response into Western Interconnection13 Demand Response Dispatch

  10. Future Healthcare

    E-Print Network [OSTI]

    Datta, Shoumen

    2010-12-15

    Patients want answers, not numbers. Evidence-based medicine must have numbers to generate answers. Therefore, analysis of numbers to provide answers is the Holy Grail of healthcare professionals and its future systems. ...

  11. Upply Chain Supernetworks with Random Demands

    E-Print Network [OSTI]

    Nagurney, Anna

    Upply Chain Supernetworks with Random Demands June Dong & Ding Zhang School of Business State Warehouses: stocking points Field Warehouses: stocking points Customers, demand centers sinks Production Commerce and Value Chain Management, 1998 Customer Demand Customer Demand Retailer OrdersRetailer Orders

  12. Assessment of Demand Response and Advanced Metering

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    #12;#12;2008 Assessment of Demand Response and Advanced Metering Staff Report Federal Energy metering penetration and potential peak load reduction from demand response have increased since 2006. Significant activity to promote demand response or to remove barriers to demand response occurred at the state

  13. The alchemy of demand response: turning demand into supply

    SciTech Connect (OSTI)

    Rochlin, Cliff

    2009-11-15

    Paying customers to refrain from purchasing products they want seems to run counter to the normal operation of markets. Demand response should be interpreted not as a supply-side resource but as a secondary market that attempts to correct the misallocation of electricity among electric users caused by regulated average rate tariffs. In a world with costless metering, the DR solution results in inefficiency as measured by deadweight losses. (author)

  14. A Unit Commitment Model with Demand Response for the Integration of Renewable Energies

    E-Print Network [OSTI]

    Ikeda, Yuichi; Kataoka, Kazuto; Ogimoto, Kazuhiko

    2011-01-01

    The output of renewable energy fluctuates significantly depending on weather conditions. We develop a unit commitment model to analyze requirements of the forecast output and its error for renewable energies. Our model obtains the time series for the operational state of thermal power plants that would maximize the profits of an electric power utility by taking into account both the forecast of output its error for renewable energies and the demand response of consumers. We consider a power system consisting of thermal power plants, photovoltaic systems (PV), and wind farms and analyze the effect of the forecast error on the operation cost and reserves. We confirm that the operation cost was increases with the forecast error. The effect of a sudden decrease in wind power is also analyzed. More thermal power plants need to be operated to generate power to absorb this sudden decrease in wind power. The increase in the number of operating thermal power plants within a short period does not affect the total opera...

  15. Addressing Energy Demand through Demand Response. International Experiences and Practices

    SciTech Connect (OSTI)

    Shen, Bo; Ghatikar, Girish; Ni, Chun Chun; Dudley, Junqiao; Martin, Phil; Wikler, Greg

    2012-06-01

    Demand response (DR) is a load management tool which provides a cost-effective alternative to traditional supply-side solutions to address the growing demand during times of peak electrical load. According to the US Department of Energy (DOE), demand response reflects “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” 1 The California Energy Commission (CEC) defines DR as “a reduction in customers’ electricity consumption over a given time interval relative to what would otherwise occur in response to a price signal, other financial incentives, or a reliability signal.” 2 This latter definition is perhaps most reflective of how DR is understood and implemented today in countries such as the US, Canada, and Australia where DR is primarily a dispatchable resource responding to signals from utilities, grid operators, and/or load aggregators (or DR providers).

  16. Renewable Electricity Futures Study. Executive Summary

    SciTech Connect (OSTI)

    Mai, T.; Sandor, D.; Wiser, R.; Schneider, T.

    2012-12-01

    The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

  17. Demand Response and Open Automated Demand Response Opportunities for Data Centers

    E-Print Network [OSTI]

    Mares, K.C.

    2010-01-01

    LBNL-1335E. Modius Data Center Infrastructure Manager (and Accenture. 2008. Data Center Energy Forecast. Stanley,Koomey. Four Metrics Define Data Center “Greenness. ” Uptime

  18. A Future for Software Engineering? Leon J. Osterweil

    E-Print Network [OSTI]

    Massachusetts at Amherst, University of

    A Future for Software Engineering? Leon J. Osterweil Laboratory for Advanced Software Engineering the need for a software engineering research community conversation about the future that the community and the more so since we see only greater growth in demands and requirements in the future. The consequences

  19. Renewable Electricity Futures Study. Volume 1. Exploration of High-Penetration Renewable Electricity Futures

    SciTech Connect (OSTI)

    Hand, M. M.; Baldwin, S.; DeMeo, E.; Reilly, J. M.; Mai, T.; Arent, D.; Porro, G.; Meshek, M.; Sandor, D.

    2012-06-15

    The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%–90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT). Learn more at the RE Futures website. http://www.nrel.gov/analysis/re_futures/

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

    E-Print Network [OSTI]

    Raftery, Adrian

    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

  1. Generalized Cost Function Based Forecasting for Periodically Measured Nonstationary Traffic

    E-Print Network [OSTI]

    Zeng, Yong - Department of Mathematics and Statistics, University of Missouri

    1 Generalized Cost Function Based Forecasting for Periodically Measured Nonstationary Traffic true value. However, such a forecast- ing function is not directly applicable for applications potentially result in insufficient allocation of bandwidth leading to short term data loss. To facilitate

  2. The effect of multinationality on management earnings forecasts 

    E-Print Network [OSTI]

    Runyan, Bruce Wayne

    2005-08-29

    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...

  3. Market perceptions of efficiency and news in analyst forecast errors 

    E-Print Network [OSTI]

    Chevis, Gia Marie

    2004-11-15

    Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms...

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

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

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

  5. OPERATIONAL EARTHQUAKE FORECASTING State of Knowledge and Guidelines for Utilization

    E-Print Network [OSTI]

    .................................................................................................................................... 323 II. SCIENCE OF EARTHQUAKE FORECASTING AND PREDICTION 325 A. Definitions and Concepts....................................................................................................................................... 325 B. Research on Earthquake PredictabilityOPERATIONAL EARTHQUAKE FORECASTING State of Knowledge and Guidelines for Utilization Report

  6. China's Coal: Demand, Constraints, and Externalities

    E-Print Network [OSTI]

    Aden, Nathaniel

    2010-01-01

    ??????? ??????????????"); IEA, WEO 2007; IEA Greenhouse Gastonnes exceeded the IEA’s WEO 2000 forecast for 2020 coalCoal Consumption, 1980-2025 WEO 2008 IEO 2008 IEO 2000 NDRC

  7. The role of actinide burning and the Integral Fast Reactor in the future of nuclear power

    SciTech Connect (OSTI)

    Hollaway, W.R.; Lidsky, L.M.; Miller, M.M.

    1990-12-01

    A preliminary assessment is made of the potential role of actinide burning and the Integral Fast Reactor (IFR) in the future of nuclear power. The development of a usable actinide burning strategy could be an important factor in the acceptance and implementation of a next generation of nuclear power. First, the need for nuclear generating capacity is established through the analysis of energy and electricity demand forecasting models which cover the spectrum of bias from anti-nuclear to pro-nuclear. The analyses take into account the issues of global warming and the potential for technological advances in energy efficiency. We conclude, as do many others, that there will almost certainly be a need for substantial nuclear power capacity in the 2000--2030 time frame. We point out also that any reprocessing scheme will open up proliferation-related questions which can only be assessed in very specific contexts. The focus of this report is on the fuel cycle impacts of actinide burning. Scenarios are developed for the deployment of future nuclear generating capacity which exploit the advantages of actinide partitioning and actinide burning. Three alternative reactor designs are utilized in these future scenarios: The Light Water Reactor (LWR); the Modular Gas-Cooled Reactor (MGR); and the Integral Fast Reactor (FR). Each of these alternative reactor designs is described in some detail, with specific emphasis on their spent fuel streams and the back-end of the nuclear fuel cycle. Four separation and partitioning processes are utilized in building the future nuclear power scenarios: Thermal reactor spent fuel preprocessing to reduce the ceramic oxide spent fuel to metallic form, the conventional PUREX process, the TRUEX process, and pyrometallurgical reprocessing.

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

    SciTech Connect (OSTI)

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

    2010-04-01

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

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

    SciTech Connect (OSTI)

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

    2010-04-15

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

  10. Managerial Career Concerns and Earnings Forecasts SARAH SHAIKH

    E-Print Network [OSTI]

    Tipple, Brett

    's aversion to risk, I find that a CEO is less likely to issue an earnings forecast in periods of stricter non is more pronounced for a CEO who has greater concern for his reputation, faces more risk in forecasting the provision of earnings forecasts. Literature has long recognized that the labor market provides distinct

  11. Neural Network forecasts of the tropical Pacific sea surface temperatures

    E-Print Network [OSTI]

    Hsieh, William

    Neural Network forecasts of the tropical Pacific sea surface temperatures Aiming Wu, William W Tang Jet Propulsion Laboratory, Pasadena, CA, USA Neural Networks (in press) December 11, 2005 title: Forecast of sea surface temperature 1 #12;Neural Network forecasts of the tropical Pacific sea

  12. Managing Wind Power Forecast Uncertainty in Electric Brandon Keith Mauch

    E-Print Network [OSTI]

    i Managing Wind Power Forecast Uncertainty in Electric Grids Brandon Keith Mauch Co Paulina Jaramillo Doctor Paul Fischbeck 2012 #12;ii #12;iii Managing Wind Power Forecast Uncertainty generated from wind power is both variable and uncertain. Wind forecasts provide valuable information

  13. Forecasting Uncertainty Related to Ramps of Wind Power Production

    E-Print Network [OSTI]

    Boyer, Edmond

    Forecasting Uncertainty Related to Ramps of Wind Power Production Arthur Bossavy, Robin Girard - The continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power. This paper presents two methods focusing on forecasting large and sharp variations in power output of a wind

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

    E-Print Network [OSTI]

    Heinemann, Detlev

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

  15. Choosing Words in Computer-Generated Weather Forecasts

    E-Print Network [OSTI]

    Reiter, Ehud

    to communicate numeric weather data. A corpus-based analysis of how humans write forecasts showed that there wereTime- Mousam weather-forecast generator to use consistent data-to-word rules, which avoided words which were weather forecast texts from numerical weather pre- diction data (SumTime-Mousam in fact is used

  16. Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging

    E-Print Network [OSTI]

    Raftery, Adrian

    Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging J. MCLEAN 2011, in final form 26 May 2012) ABSTRACT Probabilistic forecasts of wind vectors are becoming critical with univariate quantities, statistical approaches to wind vector forecasting must be based on bivariate

  17. Accuracy of near real time updates in wind power forecasting

    E-Print Network [OSTI]

    Heinemann, Detlev

    Accuracy of near real time updates in wind power forecasting with regard to different weather October 2007 #12;EMS/ECAM 2007 ­ Nadja Saleck Outline · Study site · Wind power forecasting - method #12;EMS/ECAM 2007 ­ Nadja Saleck Wind power forecast data observed wind power input (2004 ­ 2006

  18. Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

    E-Print Network [OSTI]

    Raftery, Adrian

    Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging J. Mc in the context of wind power, where under- forecasting and overforecasting carry different financial penal- ties, calibrated and sharp probabilistic forecasts can help to make wind power a more financially competitive alter

  19. Forecasting Building Occupancy Using Sensor Network James Howard

    E-Print Network [OSTI]

    Hoff, William A.

    Forecasting Building Occupancy Using Sensor Network Data James Howard Colorado School of Mines@mines.edu ABSTRACT Forecasting the occupancy of buildings can lead to signif- icant improvement of smart heating throughout a building, we perform data mining to forecast occupancy a short time (i.e., up to 60 minutes

  20. Weather Forecasting -Predicting Performance for Streaming Video over Wireless LANs

    E-Print Network [OSTI]

    Claypool, Mark

    Weather Forecasting - Predicting Performance for Streaming Video over Wireless LANs Mingzhe Li, "weather forecasts" are created such that selected wireless LAN performance indicators might be used to evaluate the effec- tiveness of individual weather forecasts. The paper evaluates six distinct weather

  1. Weather Forecasting Predicting Performance for Streaming Video over Wireless LANs

    E-Print Network [OSTI]

    Claypool, Mark

    Weather Forecasting ­ Predicting Performance for Streaming Video over Wireless LANs Mingzhe Li, ``weather forecasts'' are created such that selected wireless LAN performance indicators might be used to evaluate the e#ec­ tiveness of individual weather forecasts. The paper evaluates six distinct weather

  2. Preprints, 15th AMS Conference on Weather Analysis and Forecasting

    E-Print Network [OSTI]

    Doswell III, Charles A.

    ) models have substantially improved forecast skill. Recent and planned changes along these lines (e to delivering two kinds of weather products. The first is a day-to-day forecast of weather elements, e by the private sector. Improvements in automated techniques for the forecasting of basic weather elements

  3. Influences of soil moisture and vegetation on convective precipitation forecasts

    E-Print Network [OSTI]

    Robock, Alan

    Influences of soil moisture and vegetation on convective precipitation forecasts over the United and vegetation on 30 h convective precipitation forecasts using the Weather Research and Forecasting model over, the complete removal of vegetation produced substantially less precipitation, while conversion to forest led

  4. Present and Future of Modeling Global Environmental Change: Toward Integrated Modeling, Eds., T. Matsuno and H. Kida, pp. 145172.

    E-Print Network [OSTI]

    Moorcroft, Paul R.

    145 Present and Future of Modeling Global Environmental Change: Toward Integrated Modeling, Eds., T, NH 03824, U.S.A. Abstract--Here we examine the cause, size and future of the U.S. carbon sink.4%, with the remainder due to land use. To forecast the future of the U.S. carbon sink, we used the Ecosystem Demography

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01

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

  6. Online short-term solar power forecasting

    SciTech Connect (OSTI)

    Bacher, Peder; Madsen, Henrik [Informatics and Mathematical Modelling, Richard Pedersens Plads, Technical University of Denmark, Building 321, DK-2800 Lyngby (Denmark); Nielsen, Henrik Aalborg [ENFOR A/S, Lyngsoe Alle 3, DK-2970 Hoersholm (Denmark)

    2009-10-15

    This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model. (author)

  7. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

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

  8. Forecasting Hot Water Consumption in Residential Houses

    E-Print Network [OSTI]

    MacDonald, Mark

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

  9. GENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS

    E-Print Network [OSTI]

    Havlicek, Joebob

    available economic indicators such as Disposable Personal Income and New Housing Starts as independent exhibiting maximal fitness achieved RMS forecast errors below the the average two-week sales figure. 1 (Holland, 1975), (Packard, 1990), (Koza, 1992), (Bäck, et al., 1997), (Mitchell, 1998). For example, Meyer

  10. GOES Aviation Products Aviation Weather Forecasting

    E-Print Network [OSTI]

    Kuligowski, Bob

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

  11. Solar Forecasting System and Irradiance Variability Characterization

    E-Print Network [OSTI]

    solar forecasting system based on numerical weather prediction plus satellite and ground-based data.1 Photovoltaic Systems: Report 3 Development of data base allowing managed access to statewide PV and insolation Based Data 13 Summary 14 References 14 #12;List of Figures Figure Number and Title Page # 1. Topography

  12. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Reiter, Ehud

    summarisation. We found three alternative ways in which we could model data summarisation. One approach is based turbines. In the domain of meteorology, time series data produced by numerical weather prediction (NWP) models is summarised as weather forecast texts. In the domain of gas turbines, sensor data from

  13. "FLIGHT PLAN" FORECASTS SEATTLE/TACOMA AND

    E-Print Network [OSTI]

    ASSESSMENT OF THE "FLIGHT PLAN" FORECASTS FOR SEATTLE/TACOMA AND REGIONAL AIRPORTS TOGETHER 1. Introduction 5 2. Airport Planning Process 7 Traditional Master Planning Application to Seattle/Tacoma. Uncertainty about Capacity 27 A Fuzzy Concept Assessment Factors Application to Seattle/Tacoma 7. Assessment

  14. 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

  15. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27

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

  16. Stochastic Weather Generator Based Ensemble Streamflow Forecasting

    E-Print Network [OSTI]

    Stochastic Weather Generator Based Ensemble Streamflow Forecasting by Nina Marie Caraway B of Civil Engineering 2012 #12;This thesis entitled: Stochastic Weather Generator Based Ensemble Streamflow mentioned discipline. #12;iii Caraway, Nina Marie (M.S., Civil Engineering) Stochastic Weather Generator

  17. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

    basis of data from the Energy Supply Planning Model [9] andeconomic resources will energy supply system. r role in diconstructed an integrated energy supply~demand November 18,

  18. A 110-Day Ensemble Forecasting Scheme for the Major River Basins of Bangladesh: Forecasting Severe Floods of 200307*

    E-Print Network [OSTI]

    Webster, Peter J.

    A 1­10-Day Ensemble Forecasting Scheme for the Major River Basins of Bangladesh: Forecasting Severe of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1­10-day horizons are presented

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

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

  20. Ensuring grid reliability in the future -Capacity and Curtailment Issues on the

    E-Print Network [OSTI]

    California at Davis, University of

    in power purchase agreements to reconcile with RPS priorities · Increase energy storage and demand response ­ Trajectory with CEC higher load (3,324MW) forecast · 40% RPS ­ Trajectory with 40% renewable · Expanded Preferred Resources ­ Trajectory with 40% renewable and additional energy efficiency, customer PV

  1. STEO December 2012 - coal demand

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home RoomPreservation of Fe(II) byMultidayAlumni > The2/01/12 Page 1NEWSSupportcoal demand seen below

  2. Water: The Future’s Fuel

    E-Print Network [OSTI]

    Benavente, Carlos

    2014-01-01

    George W. 1881. The Use of Water as a Fuel. Science, 321-combusted  with  O  Water:  The  Future’s  Fuel   163  Sciences, 3329-3342.  Water:  The  Future’s  Fuel   165  

  3. Scaling Microblogging Services with Divergent Traffic Demands

    E-Print Network [OSTI]

    Fu, Xiaoming

    Scaling Microblogging Services with Divergent Traffic Demands Tianyin Xu, Yang Chen, Lei Jiao, Ben-server architecture has not scaled with user demands, lead- ing to server overload and significant impairment

  4. Michel Meulpolder Managing Supply and Demand of

    E-Print Network [OSTI]

    Michel Meulpolder Managing Supply and Demand of Bandwidth in Peer-to-Peer Communities #12;#12;Managing Supply and Demand of Bandwidth in Peer-to-Peer Communities Proefschrift ter verkrijging van de

  5. Solar in Demand | Department of Energy

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

    Solar in Demand Solar in Demand June 15, 2012 - 10:23am Addthis Kyle Travis, left and Jon Jackson, with Lighthouse Solar, install microcrystalline PV modules on top of Kevin...

  6. Demand Effects in Productivity and Efficiency Analysis 

    E-Print Network [OSTI]

    Lee, Chia-Yen

    2012-07-16

    Demand fluctuations will bias the measurement of productivity and efficiency. This dissertation described three ways to characterize the effect of demand fluctuations. First, a two-dimensional efficiency decomposition (2DED) of profitability...

  7. Industrial Equipment Demand and Duty Factors 

    E-Print Network [OSTI]

    Dooley, E. S.; Heffington, W. M.

    1998-01-01

    Demand and duty factors have been measured for selected equipment (air compressors, electric furnaces, injection molding machines, centrifugal loads, and others) in industrial plants. Demand factors for heavily loaded air ...

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

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01

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

  9. Hawaii demand-side management resource assessment. Final report: DSM opportunity report

    SciTech Connect (OSTI)

    1995-08-01

    The Hawaii Demand-Side Management Resource Assessment was the fourth of seven projects in the Hawaii Energy Strategy (HES) program. HES was designed by the Department of Business, Economic Development, and Tourism (DBEDT) to produce an integrated energy strategy for the State of Hawaii. The purpose of Project 4 was to develop a comprehensive assessment of Hawaii`s demand-side management (DSM) resources. To meet this objective, the project was divided into two phases. The first phase included development of a DSM technology database and the identification of Hawaii commercial building characteristics through on-site audits. These Phase 1 products were then used in Phase 2 to identify expected energy impacts from DSM measures in typical residential and commercial buildings in Hawaii. The building energy simulation model DOE-2.1E was utilized to identify the DSM energy impacts. More detailed information on the typical buildings and the DOE-2.1E modeling effort is available in Reference Volume 1, ``Building Prototype Analysis``. In addition to the DOE-2.1E analysis, estimates of residential and commercial sector gas and electric DSM potential for the four counties of Honolulu, Hawaii, Maui, and Kauai through 2014 were forecasted by the new DBEDT DSM Assessment Model. Results from DBEDTs energy forecasting model, ENERGY 2020, were linked with results from DOE-2.1E building energy simulation runs and estimates of DSM measure impacts, costs, lifetime, and anticipated market penetration rates in the DBEDT DSM Model. Through its algorithms, estimates of DSM potential for each forecast year were developed. Using the load shape information from the DOE-2.1E simulation runs, estimates of electric peak demand impacts were developed. 10 figs., 55 tabs.

  10. Simulated impact of urban expansion on future temperature heatwaves in Sydney

    E-Print Network [OSTI]

    Evans, Jason

    Simulated impact of urban expansion on future temperature heatwaves in Sydney D. Argüesoa,b , J on 2-m temperature are investigated over Greater Sydney using the Weather Research and Forecasting (WRF the expected urban expansion in the future simulation according to local government urbanisation plans

  11. Coordination of Energy Efficiency and Demand Response

    SciTech Connect (OSTI)

    none,

    2010-01-01

    Summarizes existing research and discusses current practices, opportunities, and barriers to coordinating energy efficiency and demand response programs.

  12. Decentralized demand management for water distribution 

    E-Print Network [OSTI]

    Zabolio, Dow Joseph

    1989-01-01

    OF THE DEMAND CURVE 30 31 35 39 Model Development Results 39 45 VI CONTROLLER DESIGN AND COSTS 49 Description of Controller Production and Installation Costs 49 50 VII SYSTEM EVALUATION AND ECONOMICS 53 System Response and Degree of Control... Patterns 9 Typical Winter Diurnal Patterns 10 Trace of Marginal Pump Efficiency and Hourly Demand 11 Original Demand Distribution and Possible Redistributions 33 34 40 41 43 46 12 Typical Nodal Responses to Demand Change 54 ix LIST OF TABLES...

  13. Demand Response Valuation Frameworks Paper

    SciTech Connect (OSTI)

    Heffner, Grayson

    2009-02-01

    While there is general agreement that demand response (DR) is a valued component in a utility resource plan, there is a lack of consensus regarding how to value DR. Establishing the value of DR is a prerequisite to determining how much and what types of DR should be implemented, to which customers DR should be targeted, and a key determinant that drives the development of economically viable DR consumer technology. Most approaches for quantifying the value of DR focus on changes in utility system revenue requirements based on resource plans with and without DR. This ''utility centric'' approach does not assign any value to DR impacts that lower energy and capacity prices, improve reliability, lower system and network operating costs, produce better air quality, and provide improved customer choice and control. Proper valuation of these benefits requires a different basis for monetization. The review concludes that no single methodology today adequately captures the wide range of benefits and value potentially attributed to DR. To provide a more comprehensive valuation approach, current methods such as the Standard Practice Method (SPM) will most likely have to be supplemented with one or more alternative benefit-valuation approaches. This report provides an updated perspective on the DR valuation framework. It includes an introduction and four chapters that address the key elements of demand response valuation, a comprehensive literature review, and specific research recommendations.

  14. Demand Queries with Preprocessing Uriel Feige

    E-Print Network [OSTI]

    Demand Queries with Preprocessing Uriel Feige and Shlomo Jozeph May 1, 2014 )>IJH=?J Given a set of items and a submodular set-function f that determines the value of every subset of items, a demand query, the value of S minus its price. The use of demand queries is well motivated in the context of com

  15. DemandDriven Pointer Analysis Nevin Heintze

    E-Print Network [OSTI]

    Tardieu, Olivier

    Demand­Driven Pointer Analysis Nevin Heintze Research, Agere Systems (formerly Lucent Technologies analysis of a pro­ gram or program component. In this paper we introduce a demand­driven approach for pointer analysis. Specifically, we describe a demand­driven flow­insensitive, subset­based, context

  16. APPLICATION-FORM DEMANDED'ADMISSION

    E-Print Network [OSTI]

    Opportunities and Challenges for Data Center Demand Response Adam Wierman Zhenhua Liu Iris Liu of renewable energy into the grid as well as electric power peak-load shaving: data center demand response. Data center demand response sits at the intersection of two growing fields: energy efficient data

  17. Airline Pilot Demand Projections What this is-

    E-Print Network [OSTI]

    Bustamante, Fabián E.

    60 Mobile applications constantly demand additional memory, and traditional designs increase but also e-mail, Internet access, digital camera features, and video on demand. With feature expansion demanding additional storage and memory in all com- puting devices, DRAM and flash memory densities

  18. Algorithms Demands and Bounds Applications of Flow

    E-Print Network [OSTI]

    Kabanets, Valentine

    2/28/2014 1 Algorithms ­ Demands and Bounds Applications of Flow Networks Design and Analysis of Algorithms Andrei Bulatov Algorithms ­ Demands and Bounds 12-2 Lower Bounds The problem can be generalized) capacities (ii) demands (iii) lower bounds A circulation f is feasible if (Capacity condition) For each e E

  19. Adapton: Composable, Demand-Driven Incremental Computation

    E-Print Network [OSTI]

    Hicks, Michael

    Adapton: Composable, Demand-Driven Incremental Computation CS-TR-5027 -- July 12, 2013 Matthew A demands on the program output; that is, if a program input changes, all depen- dencies will be recomputed. To address these problems, we present cdd ic , a core calculus that applies a demand-driven seman- tics

  20. Pricing Cloud Bandwidth Reservations under Demand Uncertainty

    E-Print Network [OSTI]

    Li, Baochun

    Heap Assumptions on Demand Andreas Podelski1 , Andrey Rybalchenko2 , and Thomas Wies1 1 University analysis produces heap assumptions on demand to eliminate counterexamples, i.e., non-terminating abstract of a non-terminating abstract computation, i.e., it applies shape analysis on demand. The shape analysis