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1

Annual Energy Outlook 2001 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Economic Growth World Oil Prices Total Energy Consumption Residential and Commercial Sectors Industrial Sector Transportation Sector Electricity Natural Gas Petroleum Coal Three other organizations—Standard & Poor’s DRI (DRI), the WEFA Group (WEFA), and the Gas Research Institute (GRI) [95]—also produce comprehensive energy projections with a time horizon similar to that of AEO2001. The most recent projections from those organizations (DRI, Spring/Summer 2000; WEFA, 1st Quarter 2000; GRI, January 2000), as well as other forecasts that concentrate on petroleum, natural gas, and international oil markets, are compared here with the AEO2001 projections. Economic Growth Differences in long-run economic forecasts can be traced primarily to

2

Annual Energy Outlook with Projections to 2025-Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2004 with Projections to 2025 Forecast Comparisons Index (click to jump links) Economic Growth World Oil Prices Total Energy Consumption Electricity Natural Gas Petroleum Coal The AEO2004 forecast period extends through 2025. One other organization—Global Insight, Incorporated (GII)—produces a comprehensive energy projection with a similar time horizon. Several others provide forecasts that address one or more aspects of energy markets over different time horizons. Recent projections from GII and others are compared here with the AEO2004 projections. Economic Growth Printer Friendly Version Average annual percentage growth Forecast 2002-2008 2002-2013 2002-2025 AEO2003 3.2 3.3 3.1 AEO2004 Reference 3.3 3.2 3.0

3

Annual Energy Outlook with Projections to 2025 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2005 Forecast Comparisons Table 32. Forecasts of annual average economic growth, 2003-2025 Printer Friendly Version Average annual percentage growth Forecast 2003-2009 2003-2014 2003-2025 AEO2004 3.5 3.2 3.0 AEO2005 Reference 3.4 3.3 3.1 Low growth 2.9 2.8 2.5 High growth 4.1 3.9 3.6 GII 3.4 3.2 3.1 OMB 3.6 NA NA CBO 3.5 3.1 NA OEF 3.5 3.5 NA Only one other organization—Global Insight, Incorporated (GII)—produces a comprehensive energy projection with a time horizon similar to that of AEO2005. Other organizations address one or more aspects of the energy markets. The most recent projection from GII, as well as other forecasts that concentrate on economic growth, international oil prices, energy

4

Annual Energy Outlook 2006 with Projections to 2030 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2006 with Projections to 2030 Only GII produces a comprehensive energy projection with a time horizon similar to that of AEO2006. Other organizations address one or more aspects of the energy markets. The most recent projection from GII, as well as others that concentrate on economic growth, international oil prices, energy consumption, electricity, natural gas, petroleum, and coal, are compared here with the AEO2006 projections. Economic Growth In the AEO2006 reference case, the projected growth in real GDP, based on 2000 chain-weighted dollars, is 3.0 percent per year from 2004 to 2030 (Table 19). For the period from 2004 to 2025, real GDP growth in the AEO2006 reference case is similar to the average annual growth projected in AEO2005. The AEO2006 projections of economic growth are based on the August short-term forecast of GII, extended by EIA through 2030 and modified to reflect EIA’s view on energy prices, demand, and production.

5

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation Analysis Papers > Annual Energy Outlook Forecast Evaluation Release Date: February 2005 Next Release Date: February 2006 Printer-friendly version Annual Energy Outlook Forecast Evaluation* Table 1.Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Printer Friendly Version Average Absolute Percent Error Variable AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 AEO82 to AEO2004 Consumption Total Energy Consumption 1.9 2.0 2.1 2.1 2.1 2.1 Total Petroleum Consumption 2.9 3.0 3.1 3.1 3.0 2.9 Total Natural Gas Consumption 7.3 7.1 7.1 6.7 6.4 6.5 Total Coal Consumption 3.1 3.3 3.5 3.6 3.7 3.8 Total Electricity Sales 1.9 2.0 2.3 2.3 2.3 2.4 Production Crude Oil Production 4.5 4.5 4.5 4.5 4.6 4.7

6

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation by Susan H. Holte In this paper, the Office of Integrated Analysis and Forecasting (OIAF) of the Energy Information Administration (EIA) evaluates the projections published in the Annual Energy Outlook (AEO), (1) by comparing the projections from the Annual Energy Outlook 1982 through the Annual Energy Outlook 2001 with actual historical values. A set of major consumption, production, net import, price, economic, and carbon dioxide emissions variables are included in the evaluation, updating similar papers from previous years. These evaluations also present the reasons and rationales for significant differences. The Office of Integrated Analysis and Forecasting has been providing an

7

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sánchez The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors,

8

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Title of Paper Annual Energy Outlook Forecast Evaluation Title of Paper Annual Energy Outlook Forecast Evaluation by Susan H. Holte OIAF has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Natural gas has generally been the fuel with the least accurate forecasts of consumption, production, and prices. Natural gas was the last fossil fuel to be deregulated following the strong regulation of energy markets in the 1970s and early 1980s. Even after deregulation, the behavior

9

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sanchez by Esmeralda Sanchez Errata -(7/14/04) The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors, expressed as the percentage difference between the Reference Case projection and actual historic value, shown for every AEO and for each year in the forecast horizon (for a given variable). The historical data are typically taken from the Annual Energy Review (AER). The last column of Table 1 provides a summary of the most recent average absolute forecast errors. The calculation of the forecast error is shown in more detail in Tables 2 through 18. Because data for coal prices to electric generating plants were not available from the AER, data from the Monthly Energy Review (MER), July 2003 were used.

10

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: * Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the

11

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Table 2. Total Energy Consumption, Actual vs. Forecasts Table 3. Total Petroleum Consumption, Actual vs. Forecasts Table 4. Total Natural Gas Consumption, Actual vs. Forecasts Table 5. Total Coal Consumption, Actual vs. Forecasts Table 6. Total Electricity Sales, Actual vs. Forecasts Table 7. Crude Oil Production, Actual vs. Forecasts Table 8. Natural Gas Production, Actual vs. Forecasts Table 9. Coal Production, Actual vs. Forecasts Table 10. Net Petroleum Imports, Actual vs. Forecasts Table 11. Net Natural Gas Imports, Actual vs. Forecasts Table 12. Net Coal Exports, Actual vs. Forecasts Table 13. World Oil Prices, Actual vs. Forecasts Table 14. Natural Gas Wellhead Prices, Actual vs. Forecasts Table 15. Coal Prices to Electric Utilities, Actual vs. Forecasts

12

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Evaluation Evaluation Annual Energy Outlook Forecast Evaluation by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the impacts were not incorporated in the AEO projections until their enactment or effective dates in accordance with EIA's requirement to remain policy neutral and include only current laws and regulations in the AEO reference case projections.

13

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Download Adobe Acrobat Reader Printer friendly version on our site are provided in Adobe Acrobat Spreadsheets are provided in Excel Actual vs. Forecasts Formats Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF Table 12. World Oil Prices Excel, PDF Table 13. Natural Gas Wellhead Prices

14

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) MS Excel Viewer PDF (Acrobat Reader required Download Acrobat Reader ) Adobe Acrobat Reader Logo Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF

15

Annual Energy Outlook Forecast Evaluation 2004  

Gasoline and Diesel Fuel Update (EIA)

2004 2004 * The Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) has produced annual evaluations of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and replacing the historical year of data with the most recent. The forecast evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute percent errors for several of the major variables for AEO82 through AEO2004. (There is no report titled Annual Energy Outlook 1988 due to a change in the naming convention of the AEOs.) The average absolute percent error is the simple mean of the absolute values of the percentage difference between the Reference Case projection and the

16

Annual Energy Outlook Forecast Evaluation 2005  

Gasoline and Diesel Fuel Update (EIA)

Forecast Evaluation 2005 Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 * Then Energy Information Administration (EIA) produces projections of energy supply and demand each year in the Annual Energy Outlook (AEO). The projections in the AEO are not statements of what will happen but of what might happen, given the assumptions and methodologies used. The projections are business-as-usual trend projections, given known technology, technological and demographic trends, and current laws and regulations. Thus, they provide a policy-neutral reference case that can be used to analyze policy initiatives. EIA does not propose or advocate future legislative and regulatory changes. All laws are assumed to remain as currently enacted; however, the impacts of emerging regulatory changes, when defined, are reflected.

17

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) PDF (Acrobat Reader required) Table 2. Total Energy Consumption HTML, Excel, PDF Table 3. Total Petroleum Consumption HTML, Excel, PDF Table 4. Total Natural Gas Consumption HTML, Excel, PDF Table 5. Total Coal Consumption HTML, Excel, PDF Table 6. Total Electricity Sales HTML, Excel, PDF Table 7. Crude Oil Production HTML, Excel, PDF Table 8. Natural Gas Production HTML, Excel, PDF Table 9. Coal Production HTML, Excel, PDF Table 10. Net Petroleum Imports HTML, Excel, PDF Table 11. Net Natural Gas Imports HTML, Excel, PDF Table 12. Net Coal Exports HTML, Excel, PDF Table 13. World Oil Prices HTML, Excel, PDF

18

Annual Energy Outlook 1998 Forecasts - Preface  

Gasoline and Diesel Fuel Update (EIA)

1998 With Projections to 2020 1998 With Projections to 2020 Annual Energy Outlook 1999 Report will be Available on December 9, 1998 Preface The Annual Energy Outlook 1998 (AEO98) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA's National Energy Modeling System (NEMS). The report begins with an “Overview” summarizing the AEO98 reference case. The next section, “Legislation and Regulations,” describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. “Issues in Focus” discusses three current energy issues—electricity restructuring, renewable portfolio standards, and carbon emissions. It is followed by the analysis

19

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Average Absolute Percent Error Variable AEO82 to AEO98 AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 Consumption Total Energy Consumption 1.7 1.7 1.8 1.9 1.9 2.1 Total Petroleum Consumption 2.9 2.8 2.9 3.0 2.9 2.9 Total Natural Gas Consumption 5.7 5.6 5.6 5.5 5.5 6.5 Total Coal Consumption 3.0 3.2 3.3 3.5 3.6 3.7 Total Electricity Sales 1.7 1.8 1.9 2.4 2.5 2.4 Production Crude Oil Production 4.3 4.5 4.5 4.5 4.5 4.7 Natural Gas Production 4.8 4.7 4.6 4.6 4.4 4.4 Coal Production 3.6 3.6 3.5 3.7 3.6 3.8 Imports and Exports Net Petroleum Imports 9.5 8.8 8.4 7.9 7.4 7.5 Net Natural Gas Imports 16.7 16.0 15.9 15.8 15.8 15.4

20

Annual Energy Outlook Forecast Evaluation - Table 1. Forecast Evaluations:  

Gasoline and Diesel Fuel Update (EIA)

Average Absolute Percent Errors from AEO Forecast Evaluations: Average Absolute Percent Errors from AEO Forecast Evaluations: 1996 to 2000 Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Variable 1996 Evaluation: AEO82 to AEO93 1997 Evaluation: AEO82 to AEO97 1998 Evaluation: AEO82 to AEO98 1999 Evaluation: AEO82 to AEO99 2000 Evaluation: AEO82 to AEO2000 Consumption Total Energy Consumption 1.8 1.6 1.7 1.7 1.8 Total Petroleum Consumption 3.2 2.8 2.9 2.8 2.9 Total Natural Gas Consumption 6.0 5.8 5.7 5.6 5.6 Total Coal Consumption 2.9 2.7 3.0 3.2 3.3 Total Electricity Sales 1.8 1.6 1.7 1.8 2.0 Production Crude Oil Production 5.1 4.2 4.3 4.5 4.5

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

18 Bureau of Meteorology Annual Report 201314 Hazards, warnings and forecasts  

E-Print Network [OSTI]

and numerical prediction models. #12;19Bureau of Meteorology Annual Report 2013­14 2 Performance Performance programs: · Weather forecasting services; · Flood forecasting and warning services; · Hazard prediction, Warnings and Forecasts portfolio provides a range of forecast and warning services covering weather, ocean

Greenslade, Diana

22

EIA - Annual Energy Outlook 2009 - Comparison with Other Projections  

Gasoline and Diesel Fuel Update (EIA)

Comparison with Other Projections Comparison with Other Projections Annual Energy Outlook 2009 with Projections to 2030 Comparison with Other Projections Only IHS Global Insight (IHSGI) produces a comprehensive energy projection with a time horizon similar to that of AEO2009. Other organizations, however, address one or more aspects of the U.S. energy market. The most recent projection from IHSGI, as well as others that concentrate on economic growth, international oil prices, energy consumption, electricity, natural gas, petroleum, and coal, are compared here with the AEO2009 projections. Economic Growth Projections of the average annual real GDP growth rate for the United States from 2007 through 2010 range from 0.2 percent to 3.1 percent (Table 15). Real GDP grows at an annual rate of 0.6 percent in the AEO2009 reference case over the period, significantly lower than the projections made by the Office of Management and Budget (OMB), the Bureau of Labor Statistics (BLS), and the Social Security Administration (SSA)—although not all of those projections have been updated to take account of the current economic downturn. The AEO2009 projection is slightly lower than the projection by IHSGI and slightly higher than the projection by the Interindustry Forecasting Project at the University of Maryland (INFORUM). In March 2009, the consensus Blue Chip projection was for 2.2-percent average annual growth from 2007 to 2010.

23

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

E-Print Network [OSTI]

of the Department of Energy's Office of Industrial Technologies, EIA extracted energy use infonnation from the Annual Energy Outlook (AEO) - 2000 (8) for each of the seven # The Pacific Northwest National Laboratory is operated by Battelle Memorial Institute...-6, 2000 NEMS The NEMS industrial module is the official forecasting model for EIA and thus the Department of Energy. For this reason, the energy prices and output forecasts used to drive the ITEMS model were taken from EIA's AEO 2000. Understanding...

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

24

Modeling and Analysis Papers - Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Evaluation > Table 1 Evaluation > Table 1 Table 1. Comparison of Absolute Percent Errors for AEO Forecast Evaluation, 1996 to 2002 Average Absolute Percent Error Variable AEO82 to AEO97 AEO82 to AEO98 AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 Consumption Total Energy Consumption 1.6 1.7 1.7 1.8 1.9 1.9 Total Petroleum Consumption 2.8 2.9 2.8 2.9 3.0 2.9 Total Natural Gas Consumption 5.8 5.7 5.6 5.6 5.5 5.5 Total Coal Consumption 2.7 3.0 3.2 3.3 3.5 3.6 Total Electricity Sales 1.6 1.7 1.8 1.9 2.4 2.5 Production Crude Oil Production 4.2 4.3 4.5 4.5 4.5 4.5 Natural Gas Production 5.0 4.8 4.7 4.6 4.6 4.4 Coal Production 3.7 3.6 3.6 3.5 3.7 3.6 Imports and Exports Net Petroleum Imports 10.1 9.5 8.8 8.4 7.9 7.4 Net Natural Gas Imports 17.4 16.7 16.0 15.9 15.8 15.8 Net Coal Exports

25

Annual Energy Outlook 2001-Appendix G: Major Assumptions for the Forecasts  

Gasoline and Diesel Fuel Update (EIA)

Forecasts Forecasts Summary of the AEO2001 Cases/ Scenarios - Appendix Table G1 bullet1.gif (843 bytes) Model Results (Formats - PDF, ZIP) - Appendix Tables - Reference Case - 1998 to 2020 bullet1.gif (843 bytes) Download Report - Entire AEO2001 (PDF) - AEO2001 by Chapters (PDF) bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts Related Links bullet1.gif (843 bytes) Assumptions to the AEO2001 bullet1.gif (843 bytes) Supplemental Data to the AEO2001 (Only available on the Web) - Regional and more detailed AEO 2001 Reference Case Results - 1998, 2000 to 2020 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Forecast Homepage bullet1.gif (843 bytes) EIA Homepage Appendix G Major Assumptions for the Forecasts Component Modules Major Assumptions for the Annual Energy Outlook 2001

26

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

SciTech Connect (OSTI)

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.

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

27

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

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.

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

28

Annual Energy Outlook Forecast Evaluation - Tables 2-18  

Gasoline and Diesel Fuel Update (EIA)

Total Energy Consumption: AEO Forecasts, Actual Values, and Total Energy Consumption: AEO Forecasts, Actual Values, and Absolute and Percent Errors, 1985-1999 Publication 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Average Absolute Error (Quadrillion Btu) AEO82 79.1 79.6 79.9 80.8 82.0 83.3 1.8 AEO83 78.0 79.5 81.0 82.4 83.8 84.6 89.5 1.2 AEO84 78.5 79.4 81.2 83.1 85.0 86.4 93.5 1.5 AEO85 77.6 78.5 79.8 81.2 82.6 83.3 84.2 85.2 85.9 86.7 87.7 1.3 AEO86 77.0 78.8 79.8 80.6 81.5 82.9 84.0 84.8 85.7 86.5 87.9 88.4 87.8 88.7 3.6 AEO87 78.9 80.0 81.9 82.8 83.9 85.3 86.4 87.5 88.4 1.5 AEO89 82.2 83.7 84.5 85.4 86.4 87.3 88.2 89.2 90.8 91.4 90.9 91.7 1.8

29

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

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.

Bolinger, Mark; Wiser, Ryan

2006-12-06T23:59:59.000Z

30

At the 18th annual Health Care Forecast Conference, Norman J. Ornstein, PhD, resident scholar at the American  

E-Print Network [OSTI]

health care in- dustry is ready for reform, but first we have to determine if health care is a right or a privilege." Continued on page 3 ANNUAL HEALTH CARE FORECAST CONFERENCE Health Care Reform ­ When of politics and current health care reform. There, we were pleased to welcome another sellout crowd ­ which

Rose, Michael R.

31

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

E-Print Network [OSTI]

2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA J, The University at Albany, 251 Fuller Rd, Albany, NY 12203, USA 3 University of Oldenburg, Institute of Physics for a half year period (summer 2007) at three different climates in the USA. ECMWF shows the best results

Perez, Richard R.

32

A comparison between a hydro-wind plant and wind speed forecasting using ARIMA models  

Science Journals Connector (OSTI)

In this paper we will present a comparison between two options for harnessing wind power. We will first analyze the behaviour of a wind farm that goes to the electricity market having previously made a forecast of wind speed while accepting the deviation penalties that these may incur. Second we will study the possibility of the wind farm not going to the market individually but as part of a hydro-wind plant.

2014-01-01T23:59:59.000Z

33

Short-term energy outlook annual supplement, 1993  

SciTech Connect (OSTI)

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

NONE

1993-08-06T23:59:59.000Z

34

Energy Department Forecasts Geothermal Achievements in 2015 ...  

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

Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in...

35

A Comparison of Measures-Oriented and Distributions-Oriented Approaches to Forecast Verification  

Science Journals Connector (OSTI)

The authors have carried out verification of 590 1224-h high-temperature forecasts from numerical guidance products and human forecasters for Oklahoma City, Oklahoma, using both a measures-oriented verification scheme and a distributions-...

Harold E. Brooks; Charles A. Doswell III

1996-09-01T23:59:59.000Z

36

Comparison of Airbus, Boeing, Rolls-Royce and AVITAS market forecasts  

Science Journals Connector (OSTI)

Forecasts of future world demand for commercial aircraft are published fairly regularly by Airbus and Boeing. Other players in the aviation business, Rolls Royce and AVITAS, have also published forecasts in the past year. This article analyses and compares the methods used and assumptions made by the several forecasters. It concludes that there are wide areas of similarity in the approaches used and highlights the most significant area of divergence.

Ralph Anker

2000-01-01T23:59:59.000Z

37

Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe  

Science Journals Connector (OSTI)

Abstract This article combines and discusses three independent validations of global horizontal irradiance (GHI) multi-day forecast models that were conducted in the US, Canada and Europe. All forecast models are based directly or indirectly on numerical weather prediction (NWP). Two models are common to the three validation efforts the ECMWF global model and the GFS-driven WRF mesoscale model and allow general observations: (1) the GFS-based WRF- model forecasts do not perform as well as global forecast-based approaches such as ECMWF and (2) the simple averaging of models output tends to perform better than individual models.

Richard Perez; Elke Lorenz; Sophie Pelland; Mark Beauharnois; Glenn Van Knowe; Karl Hemker Jr.; Detlev Heinemann; Jan Remund; Stefan C. Mller; Wolfgang Traunmller; Gerald Steinmauer; David Pozo; Jose A. Ruiz-Arias; Vicente Lara-Fanego; Lourdes Ramirez-Santigosa; Martin Gaston-Romero; Luis M. Pomares

2013-01-01T23:59:59.000Z

38

Testing Competing Precipitation Forecasts Accurately and Efficiently: The Spatial Prediction Comparison Test  

Science Journals Connector (OSTI)

Which model is best? Many challenges exist when testing competing forecast models, especially for those with high spatial resolution. Spatial correlation, double penalties, and small-scale errors are just a few such challenges. Many new methods ...

Eric Gilleland

2013-01-01T23:59:59.000Z

39

Comparison of longterm forecasting of JuneAugust rainfall over changjianghuaihe valley  

Science Journals Connector (OSTI)

In terms of an Artificial Neural Network (ANN) established is a long-term prediction model for JuneAugust flood/drought in the Changjiang-Huaihe Basins and a regression forecasting expression is formulated wi...

Jin Long; Luo Ying; Lin Zhenshan

1997-01-01T23:59:59.000Z

40

Annual Energy Outlook with Projections to 2025-Model Results  

Gasoline and Diesel Fuel Update (EIA)

Model Results Model Results (To view or print in PDF format, Adobe Acrobat Reader 5.0 is required Download Acrobat Reader Now.) Adobe Acrobat Logo AEO2003 Appendix Tables XLS format A - Reference Case Forecast - PDF (728KB) Reference Case Forecast, Annual 2000-2025 - PDF (1115KB), HTML, XLS B - Economic Growth Case Comparisons - PDF (190KB) High Economic Case, Annual 2000-2025 - PDF (2482KB), XLS Low Economic Case, Annual 2000-2025 - PDF (3937KB), XLS C - Oil Price Case Comparisons - PDF (186KB) High Oil Price Case, Annual 2000-2025 - PDF (2533KB), XLS Low Oil Price Case, Annual 2000-2025 - PDF (2344KB), XLS D - Crude Oil Equivalence Summary - PDF (32KB) E - Household Expenditures - PDF (30KB) F - Results from Side Cases - PDF (89KB) G - Major Assumptions for the Forecast - PDF (160KB), HTML

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

A COMPARISON OF CLOUD MICROPHYSICAL QUANTITIES WITH FORECASTS FROM CLOUD PREDICTION MODELS  

E-Print Network [OSTI]

of the Atmospheric System Research (ASR) Program, Bethesda, MD March 15-19, 2010 Environmental Sciences Department/Atmospheric Plains (SGP) site. Cloud forecasts generated by the models are compared with cloud microphysical and radiosonde) are used to derive the cloud microphysical quantities: ice water content, liquid water content

42

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

E-Print Network [OSTI]

for Boundary dominated flow (BDF) wells but it has been observed in shale reservoirs the predominant flow regime is transient flow. Therefore it was imperative to develop newer models to match and forecast transient flow regimes. The SEDM/SEPD, the Duong model...

Joshi, Krunal Jaykant

2012-10-19T23:59:59.000Z

43

Forecasting with Historical Data or Process Knowledge under Misspecification: A Comparison  

E-Print Network [OSTI]

States' ban on nuclear testing, a nuclear engineer is faced with lack of data, and hence must rely of nuclear stockpiles, or the climate next century, forecasting on all scales has become a crucial part engineer may use historical traffic volume data to predict upcoming flow; a nuclear scientist may use

Steinwart, Ingo

44

Forecast Prices  

Gasoline and Diesel Fuel Update (EIA)

Notes: Notes: Prices have already recovered from the spike, but are expected to remain elevated over year-ago levels because of the higher crude oil prices. There is a lot of uncertainty in the market as to where crude oil prices will be next winter, but our current forecast has them declining about $2.50 per barrel (6 cents per gallon) from today's levels by next October. U.S. average residential heating oil prices peaked at almost $1.50 as a result of the problems in the Northeast this past winter. The current forecast has them peaking at $1.08 next winter, but we will be revisiting the outlook in more detail next fall and presenting our findings at the annual Winter Fuels Conference. Similarly, diesel prices are also expected to fall. The current outlook projects retail diesel prices dropping about 14 cents per gallon

45

Forecasting wireless communication technologies  

Science Journals Connector (OSTI)

The purpose of the paper is to present a formal comparison of a variety of multiple regression models in technology forecasting for wireless communication. We compare results obtained from multiple regression models to determine whether they provide a superior fitting and forecasting performance. Both techniques predict the year of wireless communication technology introduction from the first (1G) to fourth (4G) generations. This paper intends to identify the key parameters impacting the growth of wireless communications. The comparison of technology forecasting approaches benefits future researchers and practitioners when developing a prediction of future wireless communication technologies. The items of focus will be to understand the relationship between variable selection and model fit. Because the forecasting error was successfully reduced from previous approaches, the quadratic regression methodology is applied to the forecasting of future technology commercialisation. In this study, the data will show that the quadratic regression forecasting technique provides a better fit to the curve.

Sabrina Patino; Jisun Kim; Tugrul U. Daim

2010-01-01T23:59:59.000Z

46

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

E-Print Network [OSTI]

typical of an advanced combined cycle gas turbine), the $comparison between a combined cycle gas turbine and a fixed-

Bolinger, Mark

2008-01-01T23:59:59.000Z

47

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

E-Print Network [OSTI]

typical of an advanced combined cycle gas turbine), the $comparison between a combined cycle gas turbine and a fixed-

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

48

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

E-Print Network [OSTI]

comparison between a combined cycle gas turbine and a fixed-typical of an advanced combined cycle gas turbine), the $

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

49

A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles  

E-Print Network [OSTI]

-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 Centre for Medium-Range Weather Forecasts (ECMWF; Molteni et al. 1996) Ensemble Prediction System

Xue, Ming

50

RACORO Forecasting  

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

Daniel Hartsock CIMMS, University of Oklahoma ARM AAF Wiki page Weather Briefings Observed Weather Cloud forecasting models BUFKIT forecast soundings + guidance...

51

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

SciTech Connect (OSTI)

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

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

1989-12-01T23:59:59.000Z

52

A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles  

E-Print Network [OSTI]

Submitted to Weather and Forecasting in October 2008, Accepted in January 2009 * Corresponding author) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central-convection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic

Droegemeier, Kelvin K.

53

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

SciTech Connect (OSTI)

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

Poyer, D.A.; Allison, T.

1998-03-01T23:59:59.000Z

54

EIA - Annual Energy Outlook 2007 with Projections to 2030 - Comparison with  

Gasoline and Diesel Fuel Update (EIA)

Comparison with Other Projections Comparison with Other Projections Annual Energy Outlook 2007 with Projections to 2030 Comparison with Other Projections Only Global Insights, Inc. (GII) produces a comprehensive energy projection with a time horizon similar to that of AEO2007. Other organizations, however, address one or more aspects of the energy markets. The most recent projection from GII, as well as others that concentrate on economic growth, international oil prices, energy consumption, electricity, natural gas, petroleum, and coal, are compared here with the AEO2007 projections. Economic Growth In the AEO2007 reference case, the projected growth in real GDP, based on 2000 chain-weighted dollars, is 2.9 percent per year from 2005 to 2030. The AEO2007 projections for economic growth are based on the August short-term projection of GII, extended by EIA through 2030 and modified to reflect EIA’s view on energy prices, demand, and production.

55

Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework  

Science Journals Connector (OSTI)

Abstract Forecasting aggregate demand represents a crucial aspect in all industrial sectors. In this paper, we provide the analytical prediction properties of top-down (TD) and bottom-up (BU) approaches when forecasting the aggregate demand using a multivariate exponential smoothing as demand planning framework. We extend and generalize the results achieved by Widiarta et al. (2009) by employing an unrestricted multivariate framework allowing for interdependency between its variables. Moreover, we establish the necessary and sufficient condition for the equality of mean squared errors (MSEs) of the two approaches. We show that the condition for the equality of \\{MSEs\\} holds even when the moving average parameters of the individual components are not identical. In addition, we show that the relative forecasting accuracy of TD and BU depends on the parametric structure of the underlying framework. Simulation results confirm our theoretical findings. Indeed, the ranking of TD and BU forecasts is led by the parametric structure of the underlying data generation process, regardless of possible misspecification issues.

Giacomo Sbrana; Andrea Silvestrini

2013-01-01T23:59:59.000Z

56

Internal Dose Magnitude Estimation Using Annual Limits on Intake (ALI) Comparisons  

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

Internal and External Dose Estimation (initial version: 08/2008, current version: 07/2013) Internal and External Dose Estimation (initial version: 08/2008, current version: 07/2013) Rapid Internal and External Dose Magnitude Estimation The Radiation Emergency Assistance Center/Training Site REAC/TS PO Box 117, MS-39 Oak Ridge, TN 37831 (865)576-3131 www.orise.orau.gov/reacts prepared by: Stephen L. (Steve) Sugarman, MS, CHP, CHCM Health Physics Project Manager Cytogenetic Biodosimetry Laboratory Coordinator Early Internal and External Dose Estimation (initial version: 08/2008, current version: 07/2013) Internal Dose Magnitude Estimation Using Annual Limits on Intake (ALI) Comparisons and Derived Reference Levels (DRLs) Assessing the radiological condition of injured personnel is an important part of the health physicist's job, although hopefully, one that is not done very often. There are many things to be

57

Testing Competing High-Resolution Precipitation Forecasts  

E-Print Network [OSTI]

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

Gilleland, Eric

58

Annual  

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

19 19 th Annual Triple "E" Seminar Presented by U.S. Department of Energy National Energy Technology Laboratory and Spectroscopy Society of Pittsburgh Thursday, January 20, 2011 8:00 a.m. Registration & Breakfast 8:30 a.m. Opening Remarks/Welcome Michael Nowak, Senior Management & Technical Advisor National Energy Technology Laboratory 8:35 a.m. Overview of Energy Issues Michael Nowak, Senior Management & Technical Advisor National Energy Technology Laboratory 8:45 a.m. Introduction of Presenters McMahan Gray National Energy Technology Laboratory 8:50 a.m. Jane Konrad, Pgh Regional Center for Science Teachers "Green - What Does it Mean" 9:45 a.m. Break 10:00 a.m. John Varine, Spectroscopy Society of Pittsburgh

59

Annual Energy Outlook 2002 with Projections to 2020 - Model Results  

Gasoline and Diesel Fuel Update (EIA)

Model Results To view PDF Files, Download Free Copy of Adobe Reader Get Acrobat Reader Logo AEO2002 Report Available Formats Entire AEO Report as Printed (PDF, 2,292KB) Preface (PDF, 52KB) Overview (PDF, 117KB) Legislation and Regulations (PDF, 119KB) Issues in Focus (PDF, 172KB) Market Trends Macroeconomic & International Oil Market (PDF, 99KB) Energy Demand (PDF, 99KB) Electricity (PDF, 99KB) Oil and Gas (PDF, 99KB) Coal & Carbon Emissions (PDF, 99KB) Forecast Comparisons (PDF, 83KB) List of Acronyms (PDF, 99KB) Notes and Sources (PDF, 99KB) AEO2002 Appendix Tables XLS format A - Reference Case Forecast PDF (243KB) Reference Case Forecast, Annual 1999-2020 PDF (345KB), HTML, XLS B - Economic Growth Case Comparisons PDF (277KB)

60

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

H Tables H Tables Appendix H Comparisons With Other Forecasts, and Performance of Past IEO Forecasts for 1990, 1995, and 2000 Forecast Comparisons Three organizations provide forecasts comparable with those in the International Energy Outlook 2005 (IEO2005). The International Energy Agency (IEA) provides “business as usual” projections to the year 2030 in its World Energy Outlook 2004; Petroleum Economics, Ltd. (PEL) publishes world energy forecasts to 2025; and Petroleum Industry Research Associates (PIRA) provides projections to 2015. For this comparison, 2002 is used as the base year for all the forecasts, and the comparisons extend to 2025. Although IEA’s forecast extends to 2030, it does not publish a projection for 2025. In addition to forecasts from other organizations, the IEO2005 projections are also compared with those in last year’s report (IEO2004). Because 2002 data were not available when IEO2004 forecasts were prepared, the growth rates from IEO2004 are computed from 2001.

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Technical and economical system comparison of photovoltaic and concentrating solar thermal power systems depending on annual global irradiation  

Science Journals Connector (OSTI)

Concentrating solar thermal power and photovoltaics are two major technologies for converting sunlight to electricity. Variations of the annual solar irradiation depending on the site influence their annual efficiency, specific output and electricity generation cost. Detailed technical and economical analyses performed with computer simulations point out differences of solar thermal parabolic trough power plants, non-tracked and two-axis-tracked PV systems. Therefore, 61 sites in Europe and North Africa covering a global annual irradiation range from 923 to 2438 kWh/m2a have been examined. Simulation results are usable irradiation by the systems, specific annual system output and levelled electricity cost. Cost assumptions are made for today's cost and expected cost in 10 years considering different progress ratios. This will lead to a cost reduction by 50% for PV systems and by 40% for solar thermal power plants. The simulation results show where are optimal regions for installing solar thermal trough and tracked PV systems in comparison to non-tracked PV. For low irradiation values the annual output of solar thermal systems is much lower than of PV systems. On the other hand, for high irradiations solar thermal systems provide the best-cost solution even when considering higher cost reduction factors for PV in the next decade. Electricity generation cost much below 10 Eurocents per kWh for solar thermal systems and about 12 Eurocents/kWh for PV can be expected in 10 years in North Africa.

Volker Quaschning

2004-01-01T23:59:59.000Z

62

Energy Information Administration (EIA) - Annual Energy Outlook with  

Gasoline and Diesel Fuel Update (EIA)

Evaluation, 2005 Evaluation, 2005 Annual Energy Outlook Evaluation, 2005 Each year since 1996, EIA's Office of Integrated Analysis and Forecasting has produced a comparison between realized energy outcomes and the projections included in previous editions of the AEO. Each year, the comparison adds the projections from the most recent AEO and updates the historical data to the most recently available. The comparison summarizes the relationship of the AEO reference case projections since 1982 to realized outcomes by calculating the average absolute percent differences for several of the major variables for AEO82 through AEO2005. Annual Energy Outlook Evaluation, 2005 Report Annual Energy Outlook Evaluation, 2005 Report. Need help, contact the National Energy Information Center at 202-586-8800.

63

EIA-Annual Energy Outlook Retrospective Review: Evaluation of Projections  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Annual Energy Outlook Retrospective Review: Evaluation of Projections in Past Editions (1982-2008) Each year since 1996, EIA's Office of Integrated Analysis and Forecasting has produced a comparison between realized energy outcomes and the projections included in previous editions of the AEO. Each year, the comparison adds the projections from the most recent AEO and updates the historical data to the most recently available. The comparison summarizes the relationship of the AEO reference case projections since 1982 to realized outcomes by calculating the average absolute percent differences for several of the major variables for AEO82 through AEO2008. Annual Energy Outlook Restrospective Review, 2008 Report Revisions to Gross Domestic Product and Implications for the Comparisons

64

EIA-Annual Energy Outlook Retrospective Review: Evaluation of Projections  

Gasoline and Diesel Fuel Update (EIA)

9) 9) Annual Energy Outlook Retrospective Review: Evaluation of Projections in Past Editions (1982-2009) Each year since 1996, EIA's Office of Integrated Analysis and Forecasting has produced a comparison between realized energy outcomes and the projections included in previous editions of the AEO. Each year, the comparison adds the projections from the most recent AEO and updates the historical data to the most recently available. The comparison summarizes the relationship of the AEO reference case projections since 1982 to realized outcomes by calculating the average absolute percent differences for several of the major variables for AEO82 through AEO2009. Annual Energy Outlook Restrospective Review, 2009 Report pdf images Table 1. Comparison of Absolute Percent Difference between AEO Reference Case Projections

65

Review/Verify Strategic Skills Needs/Forecasts/Future Mission...  

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

ReviewVerify Strategic Skills NeedsForecastsFuture Mission Shifts Annual Lab Plan (1-10 yrs) Fermilab Strategic Agenda (2-5 yrs) Sector program Execution Plans (1-3...

66

EIA-Annual Energy Outlook Retrospective Review: Evaluation of Projections  

Gasoline and Diesel Fuel Update (EIA)

Retrospective Review: Evaluation of Projections in Past Editions (1982-2006) Retrospective Review: Evaluation of Projections in Past Editions (1982-2006) Annual Energy Outlook Retrospective Review: Evaluation of Projections in Past Editions (1982-2006) Each year since 1996, EIA's Office of Integrated Analysis and Forecasting has produced a comparison between realized energy outcomes and the projections included in previous editions of the AEO. Each year, the comparison adds the projections from the most recent AEO and updates the historical data to the most recently available. The comparison summarizes the relationship of the AEO reference case projections since 1982 to realized outcomes by calculating the average absolute percent differences for several of the major variables for AEO82 through AEO2006. Annual Energy Outlook Retrospective Review, 2006 Report

67

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

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

of the Department of Energy's 2007, 2008, & 2009 Annual Employee Survey Results of the Department of Energy's 2007, 2008, & 2009 Annual Employee Survey Results Item # Personal Work Experiences Year Favorable Neutral Unfavorable DNK/NBJ 2009 87% 7% 6% 0% 2008 86% 8% 6% 0% 1 The people I work with cooperate to get the job done. 2007 78% 13% 9% 0% 2009 68% 17% 15% 0% 2008 66% 17% 17% 0% 2 I am given a real opportunity to improve my skills in my organization. 2007 57% 25% 19% 0% 2009 76% 13% 11% 0% 2008 72% 14% 14% 0% 3 My work gives me a feeling of personal accomplishment. 2007 74% 14% 12% 0% 2009 84% 11% 5% 0% 2008 82% 11% 7% 0% 4 I like the kind of work I do. 2007 85% 10% 5% 0% 1 2009 68% 15% 17% 0% 2008 66% 17% 17% 0% 5 I have trust and confidence in my supervisor. 2007 66% 18% 16% 0% 2009 53% 20% 28% 0% 2008 68% 19% 13% 0% 6

68

Annual Energy Outlook 2012  

Gasoline and Diesel Fuel Update (EIA)

-- -- -- -- not reported. See notes at end of table. (continued on next page) U.S. Energy Information Administration | Annual Energy Outlook 2012 116 Comparison with other...

69

ANNUAL ENERGY  

Gasoline and Diesel Fuel Update (EIA)

(93) (93) ANNUAL ENERGY OUTLOOK 1993 With Projections to 2010 EIk Energy Information Administration January 1993 For Further Information ... The Annual Energy Outlook (AEO) is prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (202/586-2222). General questions concerning energy demand or energy markets may be addressed to Mark E. Rodekohr (202/586-1130), Director of the Energy Demand and Integration Division. General questions regarding energy supply and conversion activities may be addressed to Mary J. Hutzler (202/586-2222), Acting Director of the Energy Supply and Conversion Division. Detailed questions may be addressed to the following EIA analysts: Framing the 1993 Energy Outlook ............. Susan H. Shaw (202/586-4838)

70

Wind Power Forecasting  

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

Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email List Self Supplied Balancing Reserves Dynamic...

71

Solar forecasting review  

E-Print Network [OSTI]

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

Inman, Richard Headen

2012-01-01T23:59:59.000Z

72

Wind Power Forecasting  

Science Journals Connector (OSTI)

The National Center for Atmospheric Research (NCAR) has configured a Wind Power Forecasting System for Xcel Energy that integrates high resolution and ensemble...

Sue Ellen Haupt; William P. Mahoney; Keith Parks

2014-01-01T23:59:59.000Z

73

Energy Demand Forecasting  

Science Journals Connector (OSTI)

This chapter presents alternative approaches used in forecasting energy demand and discusses their pros and cons. It... Chaps. 3 and 4 ...

S. C. Bhattacharyya

2011-01-01T23:59:59.000Z

74

Improving Inventory Control Using Forecasting  

E-Print Network [OSTI]

This project studied and analyzed Electronic Controls, Inc.s forecasting process for three high-demand products. In addition, alternative forecasting methods were developed to compare to the current forecast method. The ...

Balandran, Juan

2005-12-16T23:59:59.000Z

75

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

E-Print Network [OSTI]

pipes, etc. Annual Electricity Consumption Comparison OtherFig. 7. Annual electricity consumption comparison of case-the total annual electricity consumption, Buildings A and B

Xia Ph.D., Jianjun

2014-01-01T23:59:59.000Z

76

Technology Forecasting Scenario Development  

E-Print Network [OSTI]

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

77

CAPP 2010 Forecast.indd  

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

Forecast, Markets & Pipelines 1 Crude Oil Forecast, Markets & Pipelines June 2010 2 CANADIAN ASSOCIATION OF PETROLEUM PRODUCERS Disclaimer: This publication was prepared by the...

78

Exponential smoothing model selection for forecasting  

Science Journals Connector (OSTI)

Applications of exponential smoothing to forecasting time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to selecting the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected exponential smoothing: for sub-annual series it is the seasonal adaptation of trend corrected exponential smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with exponential smoothing to estimate the smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approaches provide the best basis for automated method selection, the Akaike information criteria having a slight edge over its information criteria counterparts.

Baki Billah; Maxwell L. King; Ralph D. Snyder; Anne B. Koehler

2006-01-01T23:59:59.000Z

79

Valuing Climate Forecast Information  

Science Journals Connector (OSTI)

The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. ...

Steven T. Sonka; James W. Mjelde; Peter J. Lamb; Steven E. Hollinger; Bruce L. Dixon

1987-09-01T23:59:59.000Z

80

Comparing Forecast Skill  

Science Journals Connector (OSTI)

A basic question in forecasting is whether one prediction system is more skillful than another. Some commonly used statistical significance tests cannot answer this question correctly if the skills are computed on a common period or using a common ...

Timothy DelSole; Michael K. Tippett

2014-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Annual Energy Outlook 1999  

Gasoline and Diesel Fuel Update (EIA)

9) 9) Annual Energy Outlook 1999 With Projections to 2020 December 1998 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the U.S. Department of Energy. The information contained herein should be attributed to the Energy Information Administration and should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. For Further Information . . . The Annual Energy Outlook 1999 (AEO99) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@eia.doe.gov, 202/586-2222).

82

Annual Energy Outlook 1998  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Distribution Category UC-950 Annual Energy Outlook 1998 With Projections to 2020 December 1997 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the U.S. Department of Energy. The information contained herein should be attributed to the Energy Information Administra- tion and should not be construed as advocating or reflecting any policy position of the Department of Energy or any other or- ganization. The Annual Energy Outlook 1998 (AEO98) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Informa- tion Administration (EIA). The projections are based on results from EIA's National Energy Modeling

83

Annual Energy Outlook 2002  

Gasoline and Diesel Fuel Update (EIA)

2) 2) December 2001 Annual Energy Outlook 2002 With Projections to 2020 December 2001 For Further Information . . . The Annual Energy Outlook 2002 (AEO2002) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@ eia.doe.gov, 202/586-2222), Director, Office of Integrated Analysis and Forecasting; Scott Sitzer (ssitzer@ eia.doe.gov, 202/586-2308), Director, Coal and Electric Power Division; Susan H. Holte (sholte@eia.doe.gov, 202/586-4838), Director, Demand and Integration Division; James M. Kendell (jkendell@eia.doe.gov, 202/586-9646), Director, Oil and Gas Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor. For ordering information and questions on other energy statistics available from EIA, please contact EIA's National

84

Annual Energy Outlook 1996  

Gasoline and Diesel Fuel Update (EIA)

96) 96) Distribution Category UC-950 Annual Energy Outlook 1996 With Projections to 2015 January 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the Department of Energy. The information contained herein should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. For Further Information . . . The Annual Energy Outlook (AEO) is prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@eia.doe.gov, 202/586-2222). General questions may be addressed to Arthur T. Andersen (aanderse@eia.doe.gov, 202/ 586-1130),

85

Annual Energy Outlook 1997  

Gasoline and Diesel Fuel Update (EIA)

7) 7) Distribution Category UC-950 Annual Energy Outlook 1997 With Projections to 2015 December 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the Department of Energy. The information contained herein should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. For Further Information . . . The Annual Energy Outlook 1997 (AEO97) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@eia.doe.gov, 202/586-2222). General questions may be addressed to Arthur T. Andersen (aanderse@eia.doe.gov, 202/586-1441),

86

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Highlights Highlights World energy consumption is projected to increase by 57 percent from 2002 to 2025. Much of the growth in worldwide energy use in the IEO2005 reference case forecast is expected in the countries with emerging economies. Figure 1. World Marketed Energy Consumptiion by Region, 1970-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data In the International Energy Outlook 2005 (IEO2005) reference case, world marketed energy consumption is projected to increase on average by 2.0 percent per year over the 23-year forecast horizon from 2002 to 2025—slightly lower than the 2.2-percent average annual growth rate from 1970 to 2002. Worldwide, total energy use is projected to grow from 412 quadrillion British thermal units (Btu) in 2002 to 553 quadrillion Btu in

87

EIA - AEO2010 - Comparison With Other Projections  

Gasoline and Diesel Fuel Update (EIA)

Comparison With Other Projections Comparison With Other Projections Annual Energy Outlook 2010 with Projections to 2035 Comparison With Other Projections Only IHS Global Insights, Inc. (IHSGI) produces a comprehensive energy projection with a time horizon similar to that of AEO2010. Other organizations, however, address one or more aspects of the U.S. energy market. The most recent projection from IHSGI, as well as others that concentrate on economic growth, international oil prices, energy consumption, electricity, natural gas, petroleum, and coal, are compared here with the AEO2010 projections. Economic growth Projections of the average annual growth rate of real GDP in the United States from 2008 to 2018 range from 2.1 percent to 2.8 percent (Table 9). In the AEO2010 Reference case, real GDP grows by an average of 2.2 percent per year over the period, lower than projected by the Office of Management and Budget (OMB), the Congressional Budget Office (CBO), the Social Security Administration (SSA), and the Bureau of Labor Statistics (BLS)—although none of those projections has been updated since August 2009. The AEO2010 projection is similar to the IHSGI projection and slightly higher than projections by the Interindustry Forecasting Project at the University of Maryland (INFORUM). In March 2009, the consensus Blue Chip projection was for 2.2-percent average annual growth from 2008 to 2018.

88

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

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.

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

2005-07-01T23:59:59.000Z

89

American Solar Energy Society Proc. ASES Annual Conference, Raleigh, NC, EVALUATION OF NUMERICAL WEATHER PREDICTION  

E-Print Network [OSTI]

© American Solar Energy Society ­ Proc. ASES Annual Conference, Raleigh, NC, EVALUATION;© American Solar Energy Society ­ Proc. ASES Annual Conference, Raleigh, NC, irradiance forecasts over OF NUMERICAL WEATHER PREDICTION SOLAR IRRADIANCE FORECASTS IN THE US Richard Perez ASRC, Albany, NY, Perez

Perez, Richard R.

90

Annual Reports  

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

Occupational Radiation Exposure Occupational Radiation Exposure Home Welcome What's New Register Dose History Request Data File Submittal REMS Data Selection HSS Logo Annual Reports User Survey on the Annual Report Please take the time to complete a survey on the Annual Report. Your input is important to us! The 2012 Annual Report View or print the annual report in PDF format The 2011 Annual Report View or print the annual report in PDF format The 2010 Annual Report View or print the annual report in PDF format The 2009 Annual Report View or print the annual report in PDF format The 2008 Annual Report View or print the annual report in PDF format The 2007 Annual Report View or print the annual report in PDF format The 2006 Annual Report View or print the annual report in PDF format The 2005 Annual Report

91

Sandia National Laboratories: solar forecasting  

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

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

92

Consensus Coal Production Forecast for  

E-Print Network [OSTI]

Rate Forecasts 19 5. EIA Forecast: Regional Coal Production 22 6. Wood Mackenzie Forecast: W.V. Steam to data currently published by the Energy Information Administration (EIA), coal production in the state in this report calls for state production to decline by 11.3 percent in 2009 to 140.2 million tons. During

Mohaghegh, Shahab

93

Performance model and annual yield comparison of parabolic-trough solar thermal power plants with either nitrogen or synthetic oil as heat transfer fluid  

Science Journals Connector (OSTI)

Abstract The majority of commercial parabolic-trough plants in the world operate with synthetic oil as heat transfer fluid in the solar field. However, the synthetic oils that are available at affordable cost present some challenges such as their flammability, environmental toxicity and a temperature limitation of around 400C. As alternative, this work proposes the use of pressurized nitrogen as heat transfer fluid. In order to analyze the feasibility of this technology, a comparison between a plant with nitrogen and a conventional plant with synthetic oil has been carried out. In both cases, 50MWe parabolic-trough plants with 6h of thermal storage are used as reference. A performance model including the solar field, the thermal storage system and the power block has been developed for each plant in the TRNSYS simulation software. This paper also describes the specifications, design and sizing of the solar field and explains the basic operation strategy applied in each model. Both annual simulations have been performed considering the same location, Almera (Spain), and meteorological data. In summary, the results show that similar net annual electricity productions can be attained for parabolic-trough plants with the same collection area using either nitrogen or synthetic oil as heat transfer fluid.

Mario Biencinto; Lourdes Gonzlez; Eduardo Zarza; Luis E. Dez; Javier Muoz-Antn

2014-01-01T23:59:59.000Z

94

Annual Reports  

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

Annual Reports science-innovationassetsimagesicon-science.jpg Annual Reports x Strategic Plan Annual Report - 2011 (pdf) Advancing Science for National Security See more Los...

95

Annual Energy Outlook 1999  

Gasoline and Diesel Fuel Update (EIA)

(Errata as of 9/9/1999) (Errata as of 9/9/1999) arrow1.gif (850 bytes) Preface bullet1.gif (843 bytes) Administrator's Message bullet1.gif (843 bytes) Overview bullet1.gif (843 bytes) Legislation & Regulations bullet1.gif (843 bytes) Issues in Focus bullet1.gif (843 bytes) Market Trends bullet1.gif (843 bytes) Forecast Comparisons bullet1.gif (843 bytes) Major Assumptions for the Forecasts bullet1.gif (843 bytes) Model Results (Appendix Tables) bullet1.gif (843 bytes) Download Report bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Supplemental Data to the AEO99 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage

96

On Sequential Probability Forecasting  

E-Print Network [OSTI]

at the same time. [Probability, Statistics and Truth, MacMillan 1957. page 11] ... the collective "denotes a collective wherein the attribute of the single event is the number of points thrown. [Probability, StatisticsOn Sequential Probability Forecasting David A. Bessler 1 David A. Bessler Texas A&M University

McCarl, Bruce A.

97

Annual Coal Report 2012  

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

Annual Coal Report 2012 Annual Coal Report 2012 December 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. iii U.S. Energy Information Administration | Annual Coal Report 2012 Contacts This publication was prepared by the U.S. Energy Information Administration (EIA). General information about the data in this report can be obtained from:

98

Voluntary Green Power Market Forecast through 2015  

SciTech Connect (OSTI)

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.

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

2010-05-01T23:59:59.000Z

99

Annual Energy Outlook 2001 - Issues in Focus  

Gasoline and Diesel Fuel Update (EIA)

Issues in Focus Issues in Focus Macroeconomic Forecasting with the Revised National Income and Product Accounts (NIPA) Phasing Out MTBE in Gasoline World Oil Demand and Prices Distributed Electricity Generation Resources Natural Gas Supply Availability Restructuring of State Retail Markets for Electricity Carbon Dioxide Emissions in AEO2001 Macroeconomic Forecasting with the Revised National Income and Product Accounts (NIPA) The NIPA Comprehensive Revision Economic activity is a key determinant of growth in U.S. energy supply and demand. The derivation of the forecast of economic activity is therefore a critical step in developing the energy forecast presented in the Annual Energy Outlook 2001 (AEO2001). In turn, the forecast of economic activity is rooted fundamentally in the historical data series maintained by a

100

An assessment of electrical load forecasting using artificial neural network  

Science Journals Connector (OSTI)

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using artificial neural networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. For annual forecasts, there should be 10 to 12 years of historical monthly data available for each electrical system or electrical buss. A case study is performed by using the proposed method of peak load data of a state electricity board of India which maintain high quality, reliable, historical data providing the best possible results. Model's quality is directly dependent upon data integrity.

V. Shrivastava; R.B. Misra; R.C. Bansal

2012-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Price forecasting for notebook computers.  

E-Print Network [OSTI]

??This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a (more)

Rutherford, Derek Paul

2012-01-01T23:59:59.000Z

102

Ensemble Forecasts and their Verification  

E-Print Network [OSTI]

· Ensemble forecast verification ­ Performance metrics: Brier Score, CRPSS · New concepts and developments of weather Sources: Insufficient spatial resolution, truncation errors in the dynamical equations

Maryland at College Park, University of

103

Probabilistic manpower forecasting  

E-Print Network [OSTI]

- ing E. Results- Probabilistic Forecasting . 26 27 Z8 29 31 35 36 38 39 IV. CONCLUSIONS. V. GLOSSARY 42 44 APPENDICES REFERENCES 50 70 LIST OF TABLES Table Page Outline of Job-Probability Matrix Job-Probability Matrix. Possible... Outcomes of Job A Possible Outcomes of Jobs A and B 10 Possible Outcomes of Jobs A, B and C II LIST GF FIGURES Figure Page Binary Representation of Numbers 0 Through 7 12 First Cumulative Probability Table 14 3. Graph of Cumulative Probability vs...

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

104

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Journals Connector (OSTI)

This paper reports on the development of a diagnostic approach that can be used to examine the sources of numerical model forecast error that contribute to degraded tropical cyclone (TC) motion forecasts. Tropical cyclone motion forecasts depend ...

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

105

2012 Uranium Marketing Annual Report  

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

Uranium Marketing Annual Uranium Marketing Annual Report May 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 May 2013 U.S. Energy Information Administration | 2012 Uranium Marketing Annual Report i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. May 2013 U.S. Energy Information Administration | 2012 Uranium Marketing Annual Report ii

106

Annual Coal Distribution Report 2012  

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

December 2013 December 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 Annual Coal Distribution Report 2012 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. iii U.S. Energy Information Administration | Annual Coal Distribution Report 2012 Overview of Annual Coal Distribution Tables, 2012 Introduction The Annual Coal Distribution Report (ACDR) provides detailed information on domestic coal distribution by origin state,

107

Uranium Marketing Annual Report  

Gasoline and Diesel Fuel Update (EIA)

Uranium Marketing Uranium Marketing Annual Report May 2011 www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. U.S. Energy Information Administration | 2010 Uranium Marketing Annual Report ii Contacts This report was prepared by the staff of the Renewables and Uranium Statistics Team, Office of Electricity, Renewables, and Uranium Statistics. Questions about the preparation and content of this report may be directed to Michele Simmons, Team Leader,

108

Annual Energy Outlook 2001  

Gasoline and Diesel Fuel Update (EIA)

Homepage Homepage Annual Energy Outlook 2001 With Projections to 2020 Preface The Annual Energy Outlook 2001 (AEO2001) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA’s National Energy Modeling System (NEMS). The report begins with an “Overview” summarizing the AEO2001 reference case. The next section, “Legislation and Regulations,” discusses evolving legislative and regulatory issues. “Issues in Focus” discusses the macroeconomic projections, world oil and natural gas markets, oxygenates in gasoline, distributed electricity generation, electricity industry restructuring, and carbon dioxide emissions. It is followed by the analysis of energy market trends.

109

Annual Energy Outlook 2012  

Gasoline and Diesel Fuel Update (EIA)

2 2 Source: U.S. Energy Information Administration, Office of Energy Analysis. U.S. Energy Information Administration / Annual Energy Outlook 2010 213 Appendix F Regional Maps Figure F1. United States Census Divisions Pacific East South Central South Atlantic Middle Atlantic New England West South Central West North Central East North Central Mountain AK WA MT WY ID NV UT CO AZ NM TX OK IA KS MO IL IN KY TN MS AL FL GA SC NC WV PA NJ MD DE NY CT VT ME RI MA NH VA WI MI OH NE SD MN ND AR LA OR CA HI Middle Atlantic New England East North Central West North Central Pacific West South Central East South Central South Atlantic Mountain Source: U.S. Energy Information Administration, Office of Integrated Analysis and Forecasting. Appendix F Regional Maps Figure F1. United States Census Divisions U.S. Energy Information Administration | Annual Energy Outlook 2012

110

Where can I find free economic forecasts? Economic forecasts have become an integral part of business and individual investment decisions. Economic  

E-Print Network [OSTI]

, the Conference Board provides short term (quarterly and annual) forecasts for real GDP, real consumer spending include (among others): GDP and real GDP, price indices for GDP and consumer spending, unemployment are projections of economic activity including GDP growth. These reports can be found on-line at: http

Johnson, Eric E.

111

Project Profile: Forecasting and Influencing Technological Progress...  

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

Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Logos of the University of...

112

Forecasting with adaptive extended exponential smoothing  

Science Journals Connector (OSTI)

Much of product level forecasting is based upon time series techniques. However, traditional time series forecasting techniques have offered either smoothing constant adaptability or consideration of various t...

John T. Mentzer Ph.D.

113

Electricity price forecasting in a grid environment.  

E-Print Network [OSTI]

??Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate (more)

Li, Guang, 1974-

2007-01-01T23:59:59.000Z

114

Annual Energy Outlook Retrospective Review  

Reports and Publications (EIA)

The Annual Energy Outlook Retrospective Review provides a yearly comparison between realized energy outcomes and the Reference case projections included in previous Annual Energy Outlooks (AEO) beginning with 1982. This edition of the report adds the AEO 2012 projections and updates the historical data to incorporate the latest data revisions.

2014-01-01T23:59:59.000Z

115

Correcting and combining time series forecasters  

Science Journals Connector (OSTI)

Combined forecasters have been in the vanguard of stochastic time series modeling. In this way it has been usual to suppose that each single model generates a residual or prediction error like a white noise. However, mostly because of disturbances not ... Keywords: Artificial neural networks hybrid systems, Linear combination of forecasts, Maximum likelihood estimation, Time series forecasters, Unbiased forecasters

Paulo Renato A. Firmino; Paulo S. G. De Mattos Neto; Tiago A. E. Ferreira

2014-02-01T23:59:59.000Z

116

NOAA Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps  

E-Print Network [OSTI]

Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12;Bay Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12 N Collier N Charlotte S Charlotte NOAA Harmful Algal Bloom Operational Forecast System Southwest

117

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

Forecast Energy Forecast Energy Jump to: navigation, search Name Forecast Energy Address 2320 Marinship Way, Suite 300 Place Sausalito, California Zip 94965 Sector Services Product Intelligent Monitoring and Forecasting Services Year founded 2010 Number of employees 11-50 Company Type For profit Website http://www.forecastenergy.net Coordinates 37.865647°, -122.496315° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.865647,"lon":-122.496315,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

118

Price forecasting for notebook computers  

E-Print Network [OSTI]

This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a series of time periods, and the rates of change in the influence...

Rutherford, Derek Paul

2012-06-07T23:59:59.000Z

119

Forecasting phenology under global warming  

Science Journals Connector (OSTI)

...Forrest Forecasting phenology under global warming Ines Ibanez 1 * Richard B. Primack...and site-specific responses to global warming. We found that for most species...climate change|East Asia, global warming|growing season, hierarchical...

2010-01-01T23:59:59.000Z

120

Demand Forecasting of New Products  

E-Print Network [OSTI]

Keeping Unit or SKU) employing attribute analysis techniques. The objective of this thesis is to improve Abstract This thesis is a study into the demand forecasting of new products (also referred to as Stock

Sun, Yu

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico | Open  

Open Energy Info (EERE)

Weather Research and Forecasting (WRF)-Chem Modeling in Mexico Weather Research and Forecasting (WRF)-Chem Modeling in Mexico Jump to: navigation, search Name PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico Agency/Company /Organization Pacific Northwest National Laboratory Sector Energy Topics Co-benefits assessment, - Environmental and Biodiversity, - Health, Background analysis Resource Type Publications Website http://www.pnl.gov/atmospheric Country Mexico UN Region Latin America and the Caribbean References PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico[1] PNNL Publications on WRF-Chem modeling in Mexico include: Fast JD, M Shrivastava, RA Zaveri, and JC. Barnard. 2010. "Modeling particulates and direct radiative forcing from urban to synoptic scales downwind of Mexico City." Annual European Geosciences Union Assembly,

122

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

E-Print Network [OSTI]

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 ...

Goto, Susumu

2007-01-01T23:59:59.000Z

123

A new improved forecasting method integrated fuzzy time series with the exponential smoothing method  

Science Journals Connector (OSTI)

This paper presents a new method of integrated fuzzy time series with the exponential smoothing method to forecast university enrolments. The data of historical enrolments of the University of Alabama shown in Liu et al. (2011) are adopted to illustrate the forecasting process of the proposed method. A comparison has been made with five previous fuzzy time series models. Meanwhile, the mean squared error has also been calculated as the evaluation criterion to illustrate the performance of the proposed method. The empirical analysis shows that the proposed model reflects the fluctuations in fuzzy time series better and provides better overall forecasting results than the five listed previous models.

Peng Ge; Jun Wang; Peiyu Ren; Huafeng Gao; Yuyan Luo

2013-01-01T23:59:59.000Z

124

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

Office of Environmental Management (EM)

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

125

Applying Bayesian Forecasting to Predict New Customers' Heating Oil Demand.  

E-Print Network [OSTI]

??This thesis presents a new forecasting technique that estimates energy demand by applying a Bayesian approach to forecasting. We introduce our Bayesian Heating Oil Forecaster (more)

Sakauchi, Tsuginosuke

2011-01-01T23:59:59.000Z

126

2007 Annual Peer Review  

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

2007 Annual Peer Review 2007 Annual Peer Review September 27, 2007 San Francisco, California Welcoming Remarks Imre Gyuk US Dept. of Energy DOE / ESS Program Overview (View .pdf) John Boyes Sandia National Laboratories PRESENTATIONS\ ECONOMICS - BENEFIT STUDIES Evaluating Value Propositions for Four Modular Electricity Storage Demonstrations in California (View .pdf) Jim Eyer (Distributed Utility Assoc.) Update on Benefit and Cost Comparison of Modular Energy Storage Technologies for Four Viable Value Propositions (View .pdf) Susan Schoenung (Longitude 122 West, Inc.) ECONOMICS - ENVIRONMENT BENEFITS STUDIES Emissions from Traditional & Flywheel Plants for Regulation Services (View .pdf) Rick Fioravanti (KEMA, Inc.) UTILITY & COMMERCIAL APPLICATIONS OF ADVANCED ENERGY STORAGE SYSTEMS

127

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

Solar Energy Market Forecast Solar Energy Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast Agency/Company /Organization: United States Department of Energy Sector: Energy Focus Area: Solar Topics: Market analysis, Technology characterizations Resource Type: Publications Website: giffords.house.gov/DOE%20Perspective%20on%20Solar%20Market%20Evolution References: Solar Energy Market Forecast[1] Summary " Energy markets / forecasts DOE Solar America Initiative overview Capital market investments in solar Solar photovoltaic (PV) sector overview PV prices and costs PV market evolution Market evolution considerations Balance of system costs Silicon 'normalization' Solar system value drivers Solar market forecast Additional resources"

128

Data transforms with exponential smoothing methods of forecasting  

Science Journals Connector (OSTI)

Abstract In this paper, transforms are used with exponential smoothing, in the quest for better forecasts. Two types of transforms are explored: those which are applied to a time series directly, and those which are applied indirectly to the prediction errors. The various transforms are tested on a large number of time series from the M3 competition, and ANOVA is applied to the results. We find that the non-transformed time series is significantly worse than some transforms on the monthly data, and on a distribution-based performance measure for both annual and quarterly data.

Adrian N. Beaumont

2014-01-01T23:59:59.000Z

129

Annual Energy Outlook 1999 - Contact  

Gasoline and Diesel Fuel Update (EIA)

contact.gif (4492 bytes) contact.gif (4492 bytes) The Annual Energy Outlook 1999 (AEO99) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@eia.doe.gov, 202/586-2222). General questions may be addressed to Arthur T. Andersen (aanderse@eia.doe.gov, 202/586-1441), Director of the International, Economic, and Greenhouse Gas Division; Susan H. Holte (sholte@eia.doe.gov, 202/586-4838), Director of the Demand and Integration Division; James M. Kendell (jkendell@eia.doe.gov, 202/586-9646), Director of the Oil and Gas Division; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Director of the Coal and Electric Power Division; or Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Modeling Analyst. Detailed questions about the forecasts and related model components may be addressed to the following analysts:

130

Annual Energy Outlook 2000 - Contact  

Gasoline and Diesel Fuel Update (EIA)

Homepage Homepage For Further Information... The Annual Energy Outlook 2001 (AEO2001) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@eia.doe.gov, 202/586-2222), Director, Office of Integrated Analysis and Forecasting; Susan H. Holte (sholte@eia.doe.gov, 202/586-4838), Director of the Demand and Integration Division; James M. Kendell (jkendell@eia.doe.gov, 202/586-9646), Director of the Oil and Gas Division; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Director of the Coal and Electric Power Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Modeling Analyst. For ordering information and questions on other energy statistics available from EIA, please contact EIA’s National Energy Information Center. Addresses, telephone numbers, and hours are as follows:

131

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Journals Connector (OSTI)

Ensemble prediction systems produce forecasts that represent the probability distribution of a continuous forecast variable. Most often, the verification problem is simplified by transforming the ensemble forecast into probability forecasts for ...

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

132

Aggregate vehicle travel forecasting model  

SciTech Connect (OSTI)

This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.

Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)

1995-05-01T23:59:59.000Z

133

Annual Energy Outlook 2000  

Gasoline and Diesel Fuel Update (EIA)

Homepage Homepage Preface The Annual Energy Outlook 2000 (AEO2000) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA’s National Energy Modeling System (NEMS). The report begins with an “Overview” summarizing the AEO2000 reference case. The next section, “Legislation and Regulations,” describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. “Issues in Focus” discusses current energy issues—appliance standards, gasoline and diesel fuel standards, natural gas industry expansion, competitive electricity pricing, renewable portfolio standards, and carbon emissions. It is followed by the analysis of energy market trends.

134

Communication of uncertainty in temperature forecasts  

Science Journals Connector (OSTI)

We used experimental economics to test whether undergraduate students presented with a temperature forecast with uncertainty information in a table and bar graph format were able to use the extra information to interpret a given forecast. ...

Pricilla Marimo; Todd R. Kaplan; Ken Mylne; Martin Sharpe

135

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

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

Sathaye, Jayant

2013-01-01T23:59:59.000Z

136

Massachusetts state airport system plan forecasts.  

E-Print Network [OSTI]

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 ...

Mathaisel, Dennis F. X.

137

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Journals Connector (OSTI)

For over 30 years, weather forecasting for the Antarctic continent and adjacent Southern Ocean has relied on weather satellites. Significant advancements in forecasting skill have come via the weather satellite. The advent of the high-resolution ...

Matthew A. Lazzara; Linda M. Keller; Charles R. Stearns; Jonathan E. Thom; George A. Weidner

2003-02-01T23:59:59.000Z

138

Forecasting Water Use in Texas Cities  

E-Print Network [OSTI]

In this research project, a methodology for automating the forecasting of municipal daily water use is developed and implemented in a microcomputer program called WATCAL. An automated forecast system is devised by modifying the previously...

Shaw, Douglas T.; Maidment, David R.

139

Energy demand forecasting: industry practices and challenges  

Science Journals Connector (OSTI)

Accurate forecasting of energy demand plays a key role for utility companies, network operators, producers and suppliers of energy. Demand forecasts are utilized for unit commitment, market bidding, network operation and maintenance, integration of renewable ... Keywords: analytics, energy demand forecasting, machine learning, renewable energy sources, smart grids, smart meters

Mathieu Sinn

2014-06-01T23:59:59.000Z

140

Consensus Coal Production And Price Forecast For  

E-Print Network [OSTI]

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

Mohaghegh, Shahab

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Supplement to the annual energy outlook 1994  

SciTech Connect (OSTI)

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

NONE

1994-03-01T23:59:59.000Z

142

Load Forecasting of Supermarket Refrigeration  

E-Print Network [OSTI]

energy system. Observed refrigeration load and local ambient temperature from a Danish su- permarket renewable energy, is increasing, therefore a flexible energy system is needed. In the present ThesisLoad Forecasting of Supermarket Refrigeration Lisa Buth Rasmussen Kongens Lyngby 2013 M.Sc.-2013

143

Essays on macroeconomics and forecasting  

E-Print Network [OSTI]

explanatory variables. Compared to Stock and Watson (2002)â??s models, the models proposed in this chapter can further allow me to select the factors structurally for each variable to be forecasted. I find advantages to using the structural dynamic factor...

Liu, Dandan

2006-10-30T23:59:59.000Z

144

Forecasting-based SKU classification  

Science Journals Connector (OSTI)

Different spare parts are associated with different underlying demand patterns, which in turn require different forecasting methods. Consequently, there is a need to categorise stock keeping units (SKUs) and apply the most appropriate methods in each category. For intermittent demands, Croston's method (CRO) is currently regarded as the standard method used in industry to forecast the relevant inventory requirements; this is despite the bias associated with Croston's estimates. A bias adjusted modification to CRO (SyntetosBoylan Approximation, SBA) has been shown in a number of empirical studies to perform very well and be associated with a very robust behaviour. In a 2005 article, entitled On the categorisation of demand patterns published by the Journal of the Operational Research Society, Syntetos et al. (2005) suggested a categorisation scheme, which establishes regions of superior forecasting performance between CRO and SBA. The results led to the development of an approximate rule that is expressed in terms of fixed cut-off values for the following two classification criteria: the squared coefficient of variation of the demand sizes and the average inter-demand interval. Kostenko and Hyndman (2006) revisited this issue and suggested an alternative scheme to distinguish between CRO and SBA in order to improve overall forecasting accuracy. Claims were made in terms of the superiority of the proposed approach to the original solution but this issue has never been assessed empirically. This constitutes the main objective of our work. In this paper the above discussed classification solutions are compared by means of experimentation on more than 10,000 \\{SKUs\\} from three different industries. The results enable insights to be gained into the comparative benefits of these approaches. The trade-offs between forecast accuracy and other implementation related considerations are also addressed.

G. Heinecke; A.A. Syntetos; W. Wang

2013-01-01T23:59:59.000Z

145

Supplemental Tables to the Annual Energy Outlook 2003  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2003 Annual Energy Outlook 2003 Assumptions to the AEO2003 Nattional Energy Modeling System/Annual Energy Outlook 2003 Conference E-Mail Subscription Lists Forecasts Home Page Supplement Tables to the Annual Energy Outlook 2003 AEO Supplement Reference Case Forecast (2000-2025) - (HTML) Table 1. Energy Consumption by Source and Sector (New England) Table 2. Energy Consumption by Source and Sector (Middle Atlantic) Table 3. Energy Consumption by Source and Sector (East North Central) Table 4. Energy Consumption by Source and Sector (West North Central) Table 5. Energy Consumption by Source and Sector (South Atlantic) Table 6. Energy Consumption by Source and Sector (East South Central) Table 7. Energy Consumption by Source and Sector (West South Central)

146

Assumptions to the Annual Energy Outlook 2013  

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

Assumptions to the Annual Assumptions to the Annual Energy Outlook 2013 May 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Table of Contents Introduction .................................................................................................................................................. 3

147

Annual Energy Outlook 96 Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for for the Annual Energy Outlook 1996 January 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 1996 (AEO96). In this context, assumptions include general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports listed in the Appendix. 1 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview. The National Energy Modeling System The projections

148

Forecasting wind speed financial return  

E-Print Network [OSTI]

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.

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

149

Joint Polar Satellite System Science Seminar Annual Digest  

E-Print Network [OSTI]

iii Joint Polar Satellite System Science Seminar Annual Digest 2013 #12;#12;Joint Polar Satellite Polar Satellite System (JPSS) Program Science, it is my pleasure to present this digest, which services, such as forecasting of severe weather events and environmental monitoring of land, ocean

150

Improved one day-ahead price forecasting using combined time series and artificial neural network models for the electricity market  

Science Journals Connector (OSTI)

The price forecasts embody crucial information for generators when planning bidding strategies to maximise profits. Therefore, generation companies need accurate price forecasting tools. Comparison of neural network and auto regressive integrated moving average (ARIMA) models to forecast commodity prices in previous researches showed that the artificial neural network (ANN) forecasts were considerably more accurate than traditional ARIMA models. This paper provides an accurate and efficient tool for short-term price forecasting based on the combination of ANN and ARIMA. Firstly, input variables for ANN are determined by time series analysis. This model relates the current prices to the values of past prices. Secondly, ANN is used for one day-ahead price forecasting. A three-layered feed-forward neural network algorithm is used for forecasting next-day electricity prices. The ANN model is then trained and tested using data from electricity market of Iran. According to previous studies, in the case of neural networks and ARIMA models, historical demand data do not significantly improve predictions. The results show that the combined ANN??ARIMA forecasts prices with high accuracy for short-term periods. Also, it is shown that policy-making strategies would be enhanced due to increased precision and reliability.

Ali Azadeh; Seyed Farid Ghaderi; Behnaz Pourvalikhan Nokhandan; Shima Nassiri

2011-01-01T23:59:59.000Z

151

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

Lewis Poulin Implementation and Operational Services Section Canadian Meteorological Centre, Dorval, Qc National Prediction Operations Division ICEBO 2013, Montreal, Qc October 10 2013 Version 2013-09-27 Weather Forecast Data An Important... and weather information ? Numerical weather forecast production 101 ? From deterministic to probabilistic forecasts ? Some MSC weather forecast (NWP) datasets ? Finding the appropriate data for the appropriate forecast ? Preparing for probabilistic...

Poulin, L.

2013-01-01T23:59:59.000Z

152

BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts  

Science Journals Connector (OSTI)

Bayesian model averaging (BMA) probability quantitative precipitation forecast (PQPF) models were established by calibrating their parameters using 17-day ensemble forecasts of 24-h accumulated precipitation, and observations from 43 ...

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

153

Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts  

Science Journals Connector (OSTI)

The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (...

Felix Fundel; Andre Walser; Mark A. Liniger; Christoph Frei; Christof Appenzeller

2010-01-01T23:59:59.000Z

154

Funding Opportunity Announcement for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

155

Upcoming Funding Opportunity for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

156

Huge market forecast for linear LDPE  

Science Journals Connector (OSTI)

Huge market forecast for linear LDPE ... It now appears that the success of the new technology, which rests largely on energy and equipment cost savings, could be overwhelming. ...

1980-08-25T23:59:59.000Z

157

Impact of vegetation fraction from Indian geostationary satellite on short-range weather forecast  

Science Journals Connector (OSTI)

Indian economy is largely depending upon the agricultural productivity and thus influences the trade among the SAARC countries. High-resolution and good-quality regional weather forecasts are necessary for planners, resource managers, insurers and national agro-advisory services. In this study, high resolution updated land-surface state in terms of vegetation fraction (VF) from operational vegetation index products of Indian geostationary satellite (INSAT 3A) sensor (CCD) was utilized in numerical weather prediction (NWP) model (e.g. WRF) to investigate its impact on short-range weather forecast over the control run. Results showed that the updated vegetation fraction from INSAT 3A CCD improved the low-level 24h temperature (?18%) and moisture (?10%) forecast in comparison to control run. The 24h rainfall forecast was also improved (more than 5%) over central and southern India with the use of updated vegetation fraction compared to control experiment. INSAT 3A VF based experiment also showed a net improvement of 27% in surface sensible heat fluxes from WRF in comparison to control experiment when both were compared with area-averaged measurements from Large Aperture Scintillometer (LAS). This triggers the need of more and more use of realistic and updated land surface states through satellite remote sensing data as well as in situ micrometeorological measurements to improve the forecast quality, skill and consistency.

Prashant Kumar; Bimal K. Bhattacharya; P.K. Pal

2013-01-01T23:59:59.000Z

158

NOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future)  

E-Print Network [OSTI]

conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through are generated every 6 hours and you can step backward in hourly increments to view conditions over the previousNOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future) and Nowcasts

159

Optimal combined wind power forecasts using exogeneous variables  

E-Print Network [OSTI]

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

160

Ensemble typhoon quantitative precipitation forecasts model in Taiwan  

Science Journals Connector (OSTI)

In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened at a ...

Jing-Shan Hong; Chin-Tzu Fong; Ling-Feng Hsiao; Yi-Chiang Yu; Chian-You Tzeng

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Forecast of geothermal drilling activity  

SciTech Connect (OSTI)

The numbers of each type of geothermal well expected to be drilled in the United States for each 5-year period to 2000 AD are specified. Forecasts of the growth of geothermally supplied electric power and direct heat uses are presented. The different types of geothermal wells needed to support the forecasted capacity are quantified, including differentiation of the number of wells to be drilled at each major geothermal resource for electric power production. The rate of growth of electric capacity at geothermal resource areas is expected to be 15 to 25% per year (after an initial critical size is reached) until natural or economic limits are approached. Five resource areas in the United States should grow to significant capacity by the end of the century (The Geysers; Imperial Valley; Valles Caldera, NM; Roosevelt Hot Springs, UT; and northern Nevada). About 3800 geothermal wells are expected to be drilled in support of all electric power projects in the United States between 1981 and 2000 AD. Half of the wells are expected to be drilled in the Imperial Valley. The Geysers area is expected to retain most of the drilling activity for the next 5 years. By the 1990's, the Imperial Valley is expected to contain most of the drilling activity.

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

1981-10-01T23:59:59.000Z

162

New Concepts in Wind Power Forecasting Models  

E-Print Network [OSTI]

New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, João Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind for more accurate short term wind power forecasting models has led to solid and impressive development

Kemner, Ken

163

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near-surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

164

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near­surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

165

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network [OSTI]

: Technology Forecast, Laws of Technical systems evolution, Analysis of Contradictions. 1. Introduction Let us: If technology forecasting practice remains at the present level, it is necessary to significantly improve to new demands (like Green House Gases - GHG Effect reduction or covering exploded nuclear reactor

Paris-Sud XI, Université de

166

UHERO FORECAST PROJECT DECEMBER 5, 2014  

E-Print Network [OSTI]

deficits. After solid 3% growth this year, real GDP growth will recede a bit for the next two years. New household spending. Real GDP will firm above 3% in 2015. · The pace of growth in China has continuedUHERO FORECAST PROJECT DECEMBER 5, 2014 Asia-Pacific Forecast: Press Version: Embargoed Until 2

167

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

. Satellite images and Numerical Weather Prediction (NWP) models are used together with the synoptic surfaceAmending Numerical Weather Prediction forecasts using GPS Integrated Water Vapour: a case study to validate the amounts of humidity in Numerical Weather Prediction (NWP) model forecasts. This paper presents

Stoffelen, Ad

168

A Forecasting Support System Based on Exponential Smoothing  

Science Journals Connector (OSTI)

This chapter presents a forecasting support system based on the exponential smoothing scheme to forecast time-series data. Exponential smoothing methods are simple to apply, which facilitates...

Ana Corbern-Vallet; Jos D. Bermdez; Jos V. Segura

2010-01-01T23:59:59.000Z

169

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

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

principal investigator for the project. For wind power point forecasting, ARGUS PRIMA trains a neural network using data from weather forecasts, observations, and actual wind...

170

Improved Prediction of Runway Usage for Noise Forecast :.  

E-Print Network [OSTI]

??The research deals with improved prediction of runway usage for noise forecast. Since the accuracy of the noise forecast depends on the robustness of runway (more)

Dhanasekaran, D.

2014-01-01T23:59:59.000Z

171

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

Energy Savers [EERE]

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

172

PBL FY 2002 Third Quarter Review Forecast of Generation Accumulated...  

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

Power Business Line Generation Accumulated Net Revenues Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) FY 2002 Third Quarter Review Forecast in Millions...

173

Annual Report  

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

09 09 THROUGH 09/30/2010 The following Annual Freedom of Information Act report covers the Period 10/01/2009, through 09/30/2010, as required by 5 U.S.C. 552. I. BASIC INFORMATION REGARDING REPORT 1. Kevin T. Hagerty, Director Office of Information Resources, MA-90 U.S. Department of Energy 1000 Independence Ave., SW Washington, DC 20585 202-586-5955 Alexander Morris, FOIA Officer Sheila Jeter, FOIA/Privacy Act Specialist FOIA Office, MA-90 Office of Information Resources U.S. Department of Energy 1000 Independence Ave., SW Washington, DC 20585 202-586-5955 2. An electronic copy of the Freedom of Information Act (FOIA) report can be obtained at http://management.energy.gov/documents/annual_reports.htm. The report can then be accessed by clicking FOIA Annual Reports.

174

Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030  

Science Journals Connector (OSTI)

Natural gas is the primary source for electricity production in Turkey. However, Turkey does not have indigenous resources and imports more than 98.0% of the natural gas it consumes. In 2011, more than 20.0% of Turkey's annual trade deficit was due to imported natural gas, estimated at US$ 20.0 billion. Turkish government has very ambitious targets for the country's energy sector in the next decade according to the Vision 2023 agenda. Previously, we have estimated that Turkey's annual electricity demand would be 530,000GWh at the year 2023. Considering current energy market dynamics it is almost evident that a substantial amount of this demand would be supplied from natural gas. However, meticulous analysis of the Vision 2023 goals clearly showed that the information about the natural gas sector is scarce. Most importantly there is no demand forecast for natural gas in the Vision 2023 agenda. Therefore, in this study the aim was to generate accurate forecasts for Turkey's natural gas demand between 2013 and 2030. For this purpose, two semi-empirical models based on econometrics, gross domestic product (GDP) at purchasing power parity (PPP) per capita, and demographics, population change, were developed. The logistic equation, which can be used for long term natural gas demand forecasting, and the linear equation, which can be used for medium term demand forecasting, fitted to the timeline series almost seamlessly. In addition, these two models provided reasonable fits according to the mean absolute percentage error, MAPE %, criteria. Turkey's natural gas demand at the year 2030 was calculated as 76.8 billion m3 using the linear model and 83.8 billion m3 based on the logistic model. Consequently, found to be in better agreement with the official Turkish petroleum pipeline corporation (BOTAS) forecast, 76.4 billion m3, than results published in the literature.

Mehmet Melikoglu

2013-01-01T23:59:59.000Z

175

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

SciTech Connect (OSTI)

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

United States. Bonneville Power Administration.

1994-02-01T23:59:59.000Z

176

C:\ANNUAL\VENTCHAP.V8\NGA.VP  

Gasoline and Diesel Fuel Update (EIA)

Energy Energy Information Administration / Natural Gas Annual 1997 243 Selected Natural Gas and Related Reports Recurring Natural Gas Reports · Natural Gas Monthly, DOE/EIA-0130. Published monthly. Other Reports Covering Natural Gas, Natural Gas Liquids, and Other Energy Sources · Monthly Energy Review, DOE/EIA-0035. Published monthly. Provides national aggregate data for natural gas, natural gas liquids, and other energy sources. · Short-Term Energy Outlook, DOE/EIA-0202. Published quarterly. Provides forecasts for next six quarters for nat- ural gas and other energy sources. · U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Reserves -1997 Annual Report, DOE/EIA-0216(97)/Ad- vance Summary, September 1998. · Annual Energy Review 1997, DOE/ EIA-0384(96), July 1998. Published annually. · Annual Report to Congress 1997, DOE/ EIA-0173(97), July 1998. Published

177

United States Annual Energy Outlook 2012 (Early Release) | Open Energy  

Open Energy Info (EERE)

Annual Energy Outlook 2012 (Early Release) Annual Energy Outlook 2012 (Early Release) Jump to: navigation, search Tool Summary LAUNCH TOOL Name: United States Annual Energy Outlook 2012 (Early Release) Focus Area: Other Renewable Electricity Topics: Market Analysis Website: www.eia.gov/forecasts/aeo/er/ Equivalent URI: cleanenergysolutions.org/content/united-states-annual-energy-outlook-2 Language: English Policies: "Deployment Programs,Regulations" is not in the list of possible values (Deployment Programs, Financial Incentives, Regulations) for this property. DeploymentPrograms: Technical Assistance Regulations: Utility/Electricity Service Costs The Annual Energy Outlook 2012 (AEO2012) reference case overview evaluates a wide range of trends and issues that could have major implications for

178

Annual Training Plan Template  

Broader source: Energy.gov [DOE]

The Annual Training Plan Template is used by an organization's training POC to draft their organization's annual training plan.

179

Assumptions to the Annual Energy Outlook - Household Expenditures Module  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module Assumption to the Annual Energy Outlook Household Expenditures Module Figure 5. United States Census Divisions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division (see

180

Uranium industry annual 1994  

SciTech Connect (OSTI)

The Uranium Industry Annual 1994 (UIA 1994) provides current statistical data on the US uranium industry`s activities relating to uranium raw materials and uranium marketing during that survey year. The UIA 1994 is prepared for use by the Congress, Federal and State agencies, the uranium and nuclear electric utility industries, and the public. It contains data for the 10-year period 1985 through 1994 as collected on the Form EIA-858, ``Uranium Industry Annual Survey.`` Data collected on the ``Uranium Industry Annual Survey`` (UIAS) provide a comprehensive statistical characterization of the industry`s activities for the survey year and also include some information about industry`s plans and commitments for the near-term future. Where aggregate data are presented in the UIA 1994, care has been taken to protect the confidentiality of company-specific information while still conveying accurate and complete statistical data. A feature article, ``Comparison of Uranium Mill Tailings Reclamation in the United States and Canada,`` is included in the UIA 1994. Data on uranium raw materials activities including exploration activities and expenditures, EIA-estimated resources and reserves, mine production of uranium, production of uranium concentrate, and industry employment are presented in Chapter 1. Data on uranium marketing activities, including purchases of uranium and enrichment services, and uranium inventories, enrichment feed deliveries (actual and projected), and unfilled market requirements are shown in Chapter 2.

NONE

1995-07-05T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Annual Energy Outlook with Projections to 2025  

Gasoline and Diesel Fuel Update (EIA)

4 with Projections to 2025 4 with Projections to 2025 Report #: DOE/EIA-0383(2004) Release date: January 2004 Next release date: January 2005 Errata August 25, 2004 The Annual Energy Outlook presents a midterm forecast and analysis of US energy supply, demand, and prices through 2025 Table of Contents Summary Tables Adobe Acrobat Logo Yearly Tables MS Excel Viewer Regional and other detailed tables (Supplemental) MS Excel Viewer Overview Market Drivers Trends in Economic Activity Economic Growth Cases International Oil Markets Energy Demand Projections Residential Sector Commercial Sector Industrial Sector Transportation Sector Alternative Technology Cases Electricity Forecast Electricity Sales Electricity Generating Capacity Electricity Fuel Costs and Prices Electricity from Nuclear Power

182

1993 Solid Waste Reference Forecast Summary  

SciTech Connect (OSTI)

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.

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

1993-08-01T23:59:59.000Z

183

Annual Energy Outlook 2000 - Model Results & Report  

Gasoline and Diesel Fuel Update (EIA)

Homepage Homepage AEO2000 Report Available Formats Entire AEO Report as Printed (PDF, 2.2MB) Overview (PDF, 102KB) Legislation and Regulations (PDF, 63KB) Issues in Focus (PDF, 274KB) Market Trends Macroeconomic & International Oil Markets (PDF, 92KB) Energy Demand (PDF, 120KB) Electricity (PDF, 129KB) Oil and Gas (PDF, 134KB) Coal & Carbon Emissions (PDF, 115KB) Forecast Comparisons (PDF, 78KB) AEO2000 Appendix Tables (1997-2020) XLS files A - Reference Case Forecast PDF (314KB), HTML, XLS B - High Economic Growth Case Comparisons PDF (315KB), XLS B - Low Economic Growth Case Comparisons PDF (313KB), XLS C - High Oil Price Case Comparisons PDF (315KB), XLS C - Low Oil Price Case Comparisons PDF (314KB), XLS D - Crude Oil Equivalence Summary PDF (297KB)

184

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

SciTech Connect (OSTI)

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

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

2013-10-01T23:59:59.000Z

185

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

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

186

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

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 study. Key words : wind power forecast, ramps, phase er- rors, forecasts ensemble. 1 Introduction Most

Boyer, Edmond

187

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

and number of countries withforeign subsidiaries) are significantly positively related to more optimistic management earnings forecasts....

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

188

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

SciTech Connect (OSTI)

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

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

2011-10-01T23:59:59.000Z

189

Annual Capital Expenditures Survey | Data.gov  

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

Annual Capital Expenditures Survey Annual Capital Expenditures Survey BusinessUSA Data/Tools Apps Challenges Let's Talk BusinessUSA You are here Data.gov » Communities » BusinessUSA » Data Annual Capital Expenditures Survey Dataset Summary Description Provides national estimates of investment in new and used buildings and other structures, machinery, and equipment by U.S. nonfarm businesses with and without employees. Data are published by industry for companies with employees for NAICS 3-digit and selected 4-digit industries. Data on the amount of business expenditures for new plant and equipment and measures of the stock of existing facilities are critical to evaluate productivity growth, the ability of U.S. business to compete with foreign business, changes in industrial capacity, and measures of overall economic performance. In addition, ACES data provide industry analysts with capital expenditure data for market analysis, economic forecasting, identifying business opportunities and developing new and strategic plans. The ACES is an integral part of the Federal Government's effort to improve and supplement ongoing statistical programs. Private companies and organizations,, educators and students, and economic researchers use the survey results for analyzing and conducting impact evaluations on past and current economic performance, short-term economic forecasts, productivity, long-term economic growth, tax policy, capacity utilization, business fixed capital stocks and capital formation, domestic and international competitiveness trade policy, market research, and financial analysis.

190

Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables  

Science Journals Connector (OSTI)

The stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in previous literature, different scenarios were developed by either assigning arbitrary values or assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and inputted to the scenario set. This article focuses on the long-term forecasting of electricity demand using autoregressive, simple linear and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario's electricity demand as a case study, the annual energy, peak load and base load demand were forecasted up to the year 2025. In order to generate different scenarios, different ranges in the economic, demographic and climatic variables were used. [Received 16 October 2007; Revised 31 May 2008; Revised 25 October 2008; Accepted 1 November 2008

F. Chui; A. Elkamel; R. Surit; E. Croiset; P.L. Douglas

2009-01-01T23:59:59.000Z

191

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

E-Print Network [OSTI]

J.B. , 2004: Probabilistic wind power forecasts using localforecast intervals for wind power output using NWP-predictedsources such as wind and solar power. Integration of this

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

192

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

E-Print Network [OSTI]

United States California Solar Initiative Coastally Trappedparticipants in the California Solar Initiative (CSI)on location. In California, solar irradiance forecasts near

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

193

Annual Report  

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

1 1 2011 Annual Report to the Oak Ridge Community Annual Report to the Oak Ridge Community DOE/ORO/2399 Progress Cleanup P Progress Cleanup P 2 This report was produced by URS | CH2M Oak Ridge LLC, DOE's Environmental Management contractor for the Oak Ridge Reservation. About the Cover After recontouring and revegetation, the P1 Pond at East Tennessee Technology Park is flourishing. The contaminated pond was drained, recontoured, and restocked with fish that would not disturb the pond sediment. 1 Message from the Acting Manager Department of Energy Oak Ridge Office To the Oak Ridge Community: Fiscal Year (FY) 2011 marked many accomplishments in Oak Ridge. Our Environmental Management (EM) program completed a majority of its American Recovery and Reinvestment Act (ARRA)-funded projects,

194

Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

11) | April 2011 11) | April 2011 with Projections to 2035 Annual Energy Outlook 2011 For further information . . . The Annual Energy Outlook 2011 was prepared by the U.S. Energy Information Administration (EIA), under the direction of John J. Conti (john.conti@eia.gov, 202-586-2222), Assistant Administrator of Energy Analysis; Paul D. Holtberg (paul.holtberg@eia.gov, 202/586-1284), Co-Acting Director, Office of Integrated and International Energy Analysis, and Team Leader, Analysis Integration Team; Joseph A. Beamon (joseph.beamon@eia.gov, 202/586-2025), Director, Office of Electricity, Coal, Nuclear, and Renewables Analysis; A. Michael Schaal (michael.schaal@eia.gov, 202/586-5590), Director, Office of Petroleum, Gas, and Biofuel Analysis;

195

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

energy data + forecasting energy data + forecasting Home FRED Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in formulating policies and energy plans based on easy to use forecasting tools, visualizations, sankey diagrams, and open data. The platform will live on OpenEI and this community was established to initiate discussion around continuous development of this tool, integrating it with new datasets, and connecting with the community of users who will want to contribute data to the tool and use the tool for planning purposes. Links: FRED beta demo energy data + forecasting Syndicate content 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2084382122

196

Wind Speed Forecasting for Power System Operation  

E-Print Network [OSTI]

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...

Zhu, Xinxin

2013-07-22T23:59:59.000Z

197

Evaluation of hierarchical forecasting for substitutable products  

Science Journals Connector (OSTI)

This paper addresses hierarchical forecasting in a production planning environment. Specifically, we examine the relative effectiveness of Top-Down (TD) and Bottom-Up (BU) strategies for forecasting the demand for a substitutable product (which belongs to a family) as well as the demand for the product family under different types of family demand processes. Through a simulation study, it is revealed that the TD strategy consistently outperforms the BU strategy for forecasting product family demand. The relative superiority of the TD strategy further improves by as much as 52% as the product demand variability increases and the degree of substitutability between the products decreases. This phenomenon, however, is not always true for forecasting the demand for the products within the family. In this case, it is found that there are a few situations wherein the BU strategy marginally outperforms the TD strategy, especially when the product demand variability is high and the degree of product substitutability is low.

S. Viswanathan; Handik Widiarta; R. Piplani

2008-01-01T23:59:59.000Z

198

Forecasting Capital Expenditure with Plan Data  

Science Journals Connector (OSTI)

The short-term forecasting of capital expenditure presents one of the most difficult problems ... reason is that year-to-year fluctuations in capital expenditure are extremely wide. Some simple methods which...

W. Gerstenberger

1977-01-01T23:59:59.000Z

199

Forecasting Agriculturally Driven Global Environmental Change  

Science Journals Connector (OSTI)

...of each variable on GDP (13, 17), combined with global GDP projections (14...population, and per capita GDP, combined with projected...measure of agricultural demand for water, is forecast...Just as demand for energy is the major cause...

David Tilman; Joseph Fargione; Brian Wolff; Carla D'Antonio; Andrew Dobson; Robert Howarth; David Schindler; William H. Schlesinger; Daniel Simberloff; Deborah Swackhamer

2001-04-13T23:59:59.000Z

200

Medium- and Long-Range Forecasting  

Science Journals Connector (OSTI)

In contrast to short and extended range forecasts, predictions for periods beyond 5 days use time-averaged, midtropospheric height fields as their primary guidance. As time ranges are increased to 3O- and 90-day outlooks, guidance increasingly ...

A. James Wagner

1989-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

Updated Satellite Technique to Forecast Heavy Snow  

Science Journals Connector (OSTI)

Certain satellite interpretation techniques have proven quite useful in the heavy snow forecast process. Those considered best are briefly reviewed, and another technique is introduced. This new technique was found to be most valuable in cyclonic ...

Edward C. Johnston

1995-06-01T23:59:59.000Z

202

Forecasting energy markets using support vector machines  

Science Journals Connector (OSTI)

Abstract In this paper we investigate the efficiency of a support vector machine (SVM)-based forecasting model for the next-day directional change of electricity prices. We first adjust the best autoregressive SVM model and then we enhance it with various related variables. The system is tested on the daily Phelix index of the German and Austrian control area of the European Energy Exchange (???) wholesale electricity market. The forecast accuracy we achieved is 76.12% over a 200day period.

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

203

ORIGINAL PAPER Comparison of point forecast accuracy of model averaging  

E-Print Network [OSTI]

applications Cees G. H. Diks · Jasper A. Vrugt ? The Author(s) 2010. This article is published with open access Laboratory, Mail Stop B258, Los Alamos, NM 87545, USA e-mail: jasper@uci.edu J. A. Vrugt Institute

Vrugt, Jasper A.

204

LNG Annual Report - 2005 | Department of Energy  

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

5 LNG Annual Report - 2005 LNG Annual Report - 2005 LNG Annual Report - 2005 More Documents & Publications LNG Annual Report - 2004 LNG Annual Report - 2006 LNG Annual Report -...

205

LNG Annual Report - 2006 | Department of Energy  

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

6 LNG Annual Report - 2006 LNG Annual Report - 2006 LNG Annual Report - 2006 More Documents & Publications LNG Annual Report - 2007 LNG Annual Report - 2005 LNG Annual Report -...

206

LNG Annual Report - 2004 | Department of Energy  

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

4 LNG Annual Report - 2004 LNG Annual Report - 2004 LNG Annual Report - 2004 More Documents & Publications LNG Annual Report - 2005 LNG Annual Report - 2007 LNG Annual Report -...

207

2010 ANNUAL RESEARCH REPORT 2010 ANNUAL RESEARCH REPORT 2010 ANNUAL RESEARCH REPORT RESEARCH REPORT2010ANNUAL RESEARCH REPORT2010ANNUAL RESEARCH REPORT  

E-Print Network [OSTI]

2010 ANNUAL RESEARCH REPORT 2010 ANNUAL RESEARCH REPORT 2010 ANNUAL RESEARCH REPORT RESEARCH REPORT2010ANNUAL RESEARCH REPORT2010ANNUAL RESEARCH REPORT 2010 ANNUAL RESEARCH REPORT 2010 ANNUAL RESEARCH REPORT 2010 ANNUAL RESEARCH REPORT RESEARCH REPORT2010ANNUAL RESEARCH REPORT2010ANNUAL RESEARCH REPORT

Jawitz, James W.

208

Annual Reports | Department of Energy  

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

Documents Documents » Annual Reports Annual Reports Note: Some of the following documents are in PDF and will require Adobe Reader for viewing. Freedom of Information Act Annual Reports Annual Report for 2012 Annual Report for 2011 Annual Report for 2010 Annual Report for 2009 Annual Report for 2008 (pdf) Annual Report for 2007 (pdf) Annual Report for 2006 (pdf) Annual Report for 2005 (pdf) Annual Report for 2004 (pdf) Annual Report for 2003 (pdf) Annual Report for 2002 (pdf) (Revised 11/03/03) Annual Report for 2001 (pdf) Annual Report for 2000 (pdf) Annual Report for 1999 (pdf) Annual Report for 1998 (pdf) Annual Report for 1997 (pdf) Annual Report for 1996 (pdf) Annual Report for 1995 (pdf) Annual Report for 1994 (pdf) Chief FOIA Officers Reports Aviation Management Green Leases

209

Petroleum supply annual 1993. Volume 1  

SciTech Connect (OSTI)

The Petroleum Supply Annual (PSA) contains information on the supply and disposition of crude oil and petroleum products. The publication reflects data that were collected from the petroleum industry during 1993 through annual and monthly surveys. The PSA is divided into two volumes. This first volume contains four sections: Summary Statistics, Detailed Statistics, Refinery Capacity, and Oxygenate Capacity each with final annual data. The second volume contains final statistics for each month of 1993, and replaces data previously published in the Petroleum Supply Monthly (PSM). The tables in Volumes 1 and 2 are similarly numbered to facilitate comparison between them. Below is a description of each section in Volume 1 of the PSA.

Not Available

1994-06-01T23:59:59.000Z

210

Annual Energy Outlook 2003 With Projections to 2025  

Gasoline and Diesel Fuel Update (EIA)

3) 3) Annual Energy Outlook 2003 With Projections to 2025 January 2003 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the U.S. Department of Energy. The information contained herein should be attributed to the Energy Information Administration and should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. For Further Information . . . The Annual Energy Outlook 2003 (AEO2003) was prepared by the Energy Information Administration (EIA), under the direction of Mary J. Hutzler (mhutzler@ eia.doe.gov, 202/586-6351), Director, Integrated Analysis and Forecasting; Scott Sitzer (ssitzer@ eia.doe.gov, 202/586-2308),

211

Energy Information Administration (EIA) - Annual Energy Outlook with  

Gasoline and Diesel Fuel Update (EIA)

2006 with Projections to 2030 2006 with Projections to 2030 Annual Energy Outlook 2006 with Projections to 2030 The Annual Energy Outlook 2006 presents a forecast and analysis of US energy supply, demand, and prices through 2030. The projections are based on results from the Energy Information Administration's National Energy Modeling System. The AEO2006 includes the reference case, additional cases examining energy markets, and complete documentation. Forecast Data Tables Reference Case Tables (links to individual excel and PDF files) High Macroeconomic Growth Case Tables (links to individual excel files) Low Macroeconomic Growth Case Tables (links to individual excel files) High Price Case Tables (links to individual excel files) Low Price Case Tables (links to individual excel files)

212

DOE/EIA-0383(2001) Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

1) 1) Annual Energy Outlook 2001 With Projections to 2020 December 2000 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the U.S. Department of Energy. The information contained herein should be attributed to the Energy Information Administration and should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. This publication is on the WEB at: www.eia.doe.gov/oiaf/aeo/ For Further Information . . . The Annual Energy Outlook 2001 (AEO2001) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@ eia.doe.gov,

213

DOE/EIA-0383(2000) Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

0) 0) Annual Energy Outlook 2000 With Projections to 2020 December 1999 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the U.S. Department of Energy. The information contained herein should be attributed to the Energy Information Administration and should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. This publication is on the WEB at: www.eia.doe.gov/oiaf/aeo/index.html. For Further Information . . . The Annual Energy Outlook 2000 (AEO2000) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@

214

Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting  

Science Journals Connector (OSTI)

......optimum value through a grid-search algorithm...method outperformed TD for estimating the aggregate data series...variable, there is no benefit of forecasting each subaggregate...forecasting strategies in estimating the `component'-level...WILLEMAIN, T. R., SMART, C. N., SHOCKOR......

S. Viswanathan; Handik Widiarta; Rajesh Piplani

2008-07-01T23:59:59.000Z

215

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

SciTech Connect (OSTI)

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.

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

2014-05-01T23:59:59.000Z

216

EIA - Annual Energy Outlook 2009 - Contacts  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts Annual Energy Outlook 2009 with Projections to 2030 For Further Information . . . The Annual Energy Outlook 2009 was prepared by the Energy Information Administration, under the direction of John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Integrated Analysis and Forecasting; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Director, Coal and Electric Power Division; A. Michael Schaal (michael.schaal@eia.doe.gov, 202/586-5590), Director, Oil and Gas Division; Glen E. Sweetnam (glen.sweetnam@eia.doe.gov, 202/586-2188), Director, International, Economic, and Greenhouse Gases Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor.

217

Waste generation forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1995-FY 2002, September 1994 revision  

SciTech Connect (OSTI)

A comprehensive waste-forecasting task was initiated in FY 1991 to provide a consistent, documented estimate of the volumes of waste expected to be generated as a result of U.S. Department of Energy-Oak Ridge Operations (DOE-ORO) Environmental Restoration (ER) OR-1 Project activities. Continual changes in the scope and schedules for remedial action (RA) and decontamination and decommissioning (D&D) activities have required that an integrated data base system be developed that can be easily revised to keep pace with changes and provide appropriate tabular and graphical output. The output can then be analyzed and used to drive planning assumptions for treatment, storage, and disposal (TSD) facilities. The results of this forecasting effort and a description of the data base developed to support it are provided herein. The initial waste-generation forecast results were compiled in November 1991. Since the initial forecast report, the forecast data have been revised annually. This report reflects revisions as of September 1994.

Not Available

1994-12-01T23:59:59.000Z

218

ARM - Annual Reports  

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

Annual Reports Program Fact Sheets Campaign Backgrounders Education and Outreach Posters Brochures Research Highlights Summaries Annual Reports For proper viewing, the ARM...

219

LNG Annual Report - 2009 | Department of Energy  

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

9 LNG Annual Report - 2009 LNG Annual Report - 2009 LNG Annual Report - 2009 More Documents & Publications LNG Annual Report - 2008...

220

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

E-Print Network [OSTI]

Lightning Forecasts..........................................................................................45 2.7 First Flash Forecasts and Lead Times.....................................................................47 vii... Cell Number ? 25 August 2000..............................................68 3.4 First Flash Forecast Time........................................................................................70 3.5 Lightning Forecasting Algorithm (LFA) Development...

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

21st Annual Transportation  

E-Print Network [OSTI]

would cost more than $40 billion over next 20 years ·! If used alone, state gas tax would need more than levels #12;Benefits of new approach ·! Recognizes uncertainties of transportation revenue forecasts arise ·! Provides greatest regional benefit if current revenue forecasts prove true #12;MHSIS Maps

Minnesota, University of

222

Analysis of Precipitation Using Satellite Observations and Comparisons with Global Climate Models  

E-Print Network [OSTI]

is investigated by comparisons with satellite observa- iv tions. Speci cally, six-year long (2000-2005) simulations are performed using a high- resolution (36-km) Weather Research Forecast (WRF) model and the Community Atmosphere Model (CAM) at T85 spatial... . . . . . . . . . . . . . . . . 31 B. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 1. Satellite data . . . . . . . . . . . . . . . . . . . . . . . 33 2. Weather research and forecast model simulations . . . 34 3. Community atmosphere model simulations...

Murthi, Aditya

2011-08-08T23:59:59.000Z

223

12-32021E2_Forecast  

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

FORECAST OF VACANCIES FORECAST OF VACANCIES Until end of 2014 (Issue No. 20) Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff at the P4-P5 levels: * Advanced university degree (or equivalent postgraduate degree); * 7 or 10 years, respectively, of experience in a field of relevance to the post; * Resource management experience; * Strong analytical skills; * Computer skills: standard Microsoft Office software; * Languages: Fluency in English. Working knowledge of other official languages (Arabic, Chinese, French, Russian, Spanish) advantageous; * Ability to work effectively in multidisciplinary and multicultural teams; * Ability to communicate effectively. Professional staff at the P1-P3 levels:

224

Building Energy Software Tools Directory: Degree Day Forecasts  

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

Forecasts Forecasts Degree Day Forecasts example chart Quick and easy web-based tool that provides free 14-day ahead degree day forecasts for 1,200 stations in the U.S. and Canada. Degree Day Forecasts charts show this year, last year and three-year average. Historical degree day charts and energy usage forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700. Expertise Required No special expertise required. Simple to use. Users Over 1,000 weekly users. Audience Anyone who needs degree day forecasts (next 14 days) for the U.S. and Canada. Input Select a weather station (1,200 available) and balance point temperature. Output Charts show (1) degree day (heating and cooling) forecasts for the next 14

225

Building Energy Software Tools Directory: Energy Usage Forecasts  

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

Energy Usage Forecasts Energy Usage Forecasts Energy Usage Forecasts Quick and easy web-based tool that provides free 14-day ahead energy usage forecasts based on the degree day forecasts for 1,200 stations in the U.S. and Canada. The user enters the daily non-weather base load and the usage per degree day weather factor; the tool applies the degree day forecast and displays the total energy usage forecast. Helpful FAQs explain the process and describe various options for the calculation of the base load and weather factor. Historical degree day reports and 14-day ahead degree day forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature, load calculation, energy simulation Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700.

226

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc The marketing team of a new telecommunications company is usually tasked with producing forecasts for diverse three decades of experience working with telecommunications operators around the world we seek

McBurney, Peter

227

River Forecast Application for Water Management: Oil and Water?  

Science Journals Connector (OSTI)

Managing water resources generally and managing reservoir operations specifically have been touted as opportunities for applying forecasts to improve decision making. Previous studies have shown that the application of forecasts into water ...

Kevin Werner; Kristen Averyt; Gigi Owen

2013-07-01T23:59:59.000Z

228

Data Mining in Load Forecasting of Power System  

Science Journals Connector (OSTI)

This project applies Data Mining technology to the prediction of electric power system load forecast. It proposes a mining program of electric power load forecasting data based on the similarity of time series .....

Guang Yu Zhao; Yan Yan; Chun Zhou Zhao

2013-01-01T23:59:59.000Z

229

Operational Rainfall and Flow Forecasting for the Panama Canal Watershed  

Science Journals Connector (OSTI)

An integrated hydrometeorological system was designed for the utilization of data from various sensors in the 3300 km2 Panama Canal Watershed for the purpose of producing ... forecasts. These forecasts are used b...

Konstantine P. Georgakakos; Jason A. Sperfslage

2005-01-01T23:59:59.000Z

230

Power System Load Forecasting Based on EEMD and ANN  

Science Journals Connector (OSTI)

In order to fully mine the characteristics of load data and improve the accuracy of power system load forecasting, a load forecasting model based on Ensemble Empirical Mode ... is proposed in this paper. Firstly,...

Wanlu Sun; Zhigang Liu; Wenfan Li

2011-01-01T23:59:59.000Z

231

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network [OSTI]

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

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

2005-01-01T23:59:59.000Z

232

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

Energy Savers [EERE]

Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar...

233

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

This paper presents the National Energy Boards long term energy demand forecasting model in its present state of ... results of recent research at the NEB. Energy demand forecasts developed with the aid of this....

R. A. Preece; L. B. Harsanyi; H. M. Webster

1980-01-01T23:59:59.000Z

234

Forecasting Energy Demand Using Fuzzy Seasonal Time Series  

Science Journals Connector (OSTI)

Demand side energy management has become an important issue for energy management. In order to support energy planning and policy decisions forecasting the future demand is very important. Thus, forecasting the f...

?Irem Ual Sar?; Basar ztaysi

2012-01-01T23:59:59.000Z

235

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

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.

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

2010-04-01T23:59:59.000Z

236

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

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)

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

2010-04-15T23:59:59.000Z

237

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

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

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

238

PRI Annual Report 2003  

E-Print Network [OSTI]

The Annual Report highlights the activities and people that make PRI a multidisciplinary research center.

Maynard-Moody, Steven

2004-10-29T23:59:59.000Z

239

Application of a Combination Forecasting Model in Logistics Parks' Demand  

Science Journals Connector (OSTI)

Logistics parks demand is an important basis of establishing the development policy of logistics industry and logistics infrastructure for planning. In order to improve the forecast accuracy of logistics parks demand, a combination forecasting ... Keywords: Logistics parks' demand, combine, simulated annealing algorithm, grey forecast model, exponential smoothing method

Chen Qin; Qi Ming

2010-05-01T23:59:59.000Z

240

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION. The very first results show an improvement brought by this approach. 1. INTRODUCTION Solar radiation

Boyer, Edmond

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

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

242

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

· advantage: no NWP data necessary ­ very actual shortest term forecasts possible · wind power inputAccuracy 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

Heinemann, Detlev

243

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE - APRIL 2014 Anil Puri, Ph.D. -- Director, Center for Economic Analysis and Forecasting -- Dean, Mihaylo College of Business and Economics Mira Farka, Ph.D. -- Co-Director, Center for Economic Analysis and Forecasting -- Associate Professor

de Lijser, Peter

244

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

Forecasting wave height probabilities with numerical weather prediction models Mark S. Roulstona; Numerical weather prediction 1. Introduction Wave forecasting is now an integral part of operational weather methods for generating such forecasts from numerical model output from the European Centre for Medium

Stevenson, Paul

245

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data in California and for climate zones within those areas. The staff California Energy Demand 2008-2018 forecast

246

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

E-Print Network [OSTI]

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

Povinelli, Richard J.

247

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect (OSTI)

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To generate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations, auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to simulate forecast error curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and present some experimental results by generating new error forecasts together with their statistics.

Makarov, Yuri V.; Reyes Spindola, Jorge F.; Samaan, Nader A.; Diao, Ruisheng; Hafen, Ryan P.

2010-11-02T23:59:59.000Z

248

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

E-Print Network [OSTI]

An important determinant of our energy future is the rate at which energy conservation technologies, once developed, are put into use. At Synergic Resources Corporation, we have adapted and applied a methodology to forecast the use of conservation...

Lang, K.

1982-01-01T23:59:59.000Z

249

Forecasting the Locational Dynamics of Transnational Terrorism  

E-Print Network [OSTI]

Forecasting the Locational Dynamics of Transnational Terrorism: A Network Analytic Approach Bruce A-0406 Fax: (919) 962-0432 Email: skyler@unc.edu Abstract--Efforts to combat and prevent transnational terror of terrorism. We construct the network of transnational terrorist attacks, in which source (sender) and target

Massachusetts at Amherst, University of

250

Do quantitative decadal forecasts from GCMs provide  

E-Print Network [OSTI]

' · Empirical models quantify our ability to predict without knowing the laws of physics · Climatology skill' model? 2. Dynamic climatology (DC) is a more appropriate benchmark for near- term (initialised) climate forecasts · A conditional climatology, initialised at launch and built from the historical archive

Stevenson, Paul

251

Sunny outlook for space weather forecasters  

Science Journals Connector (OSTI)

... For decades, companies have tailored public weather data for private customers from farmers to airlines. On Wednesday, a group of businesses said that they ... utilities and satellite operators. But Terry Onsager, a physicist at the SWPC, says that private forecasting firms are starting to realize that they can add value to these predictions. ...

Eric Hand

2012-04-27T23:59:59.000Z

252

Modeling of Uncertainty in Wind Energy Forecast  

E-Print Network [OSTI]

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

253

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

Prediction versus Projection: How weather forecasting and climate models differ. Aaron B. Wilson Context: Global http://data.giss.nasa.gov/ #12;Numerical Weather Prediction Collect Observations alters associated weather patterns. Models used to predict weather depend on the current observed state

Howat, Ian M.

254

Customized forecasting tool improves reserves estimation  

SciTech Connect (OSTI)

Unique producing characteristics of the Teapot sandstone formation, Powder River basin, Wyoming, necessitated the creation of individualized production forecasting methods for wells producing from this reservoir. The development and use of a set of production type curves and correlations for Teapot wells are described herein.

Mian, M.A.

1986-04-01T23:59:59.000Z

255

Storm-in-a-Box Forecasting  

Science Journals Connector (OSTI)

...But the WRF has no immediate...being tuned to local conditions...temperatures and winds with altitude...resulting WRF forecasts...captured the local sea-breeze winds better...spread the local operation of mesoscale...to be the WRF model now...

Richard A. Kerr

2004-05-14T23:59:59.000Z

256

FORECAST OF VACANCIES Until end of 2016  

E-Print Network [OSTI]

#12;FORECAST OF VACANCIES Until end of 2016 (Issue No. 22) #12;Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff the team of professionals. Second half 2015 VACANCY GRADE REQUIREMENTS / ROLE EXPECTED DATE OF VACANCY

257

Online short-term solar power forecasting  

SciTech Connect (OSTI)

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)

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)

2009-10-15T23:59:59.000Z

258

Annual Energy Outlook 2012 - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2012 Annual Energy Outlook 2012 Release Date: June 25, 2012 | Next Early Release Date: December 5, 2012 | Report Number: DOE/EIA-0383(2012) Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Executive Summary Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Annual Energy Outlook 2012 presents yearly projections and analysis of energy topics Download the complete June 2012 published report. Executive summary The projections in the U.S. Energy Information Administration's (EIA's) Annual Energy Outlook 2012 (AEO2012) focus on the factors that shape the

259

Annual Energy Outlook 2013 - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2013 Annual Energy Outlook 2013 Release Dates: April 15 - May 2, 2013 | Next Early Release Date: December 2013 (See release cycle changes) | correction | full report Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Annual Energy Outlook 2013 presents yearly projections and analysis of energy topics Download the full report. The projections in the U.S. Energy Information Administration's (EIA's) Annual Energy Outlook 2013 (AEO2013) focus on the factors that shape the

260

Annual Energy Outlook 2013 - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2013 Annual Energy Outlook 2013 Release Dates: April 15 - May 2, 2013 | Next Early Release Date: December 2013 (See release cycle changes) | correction | full report Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Annual Energy Outlook 2013 presents yearly projections and analysis of energy topics Download the full report. The projections in the U.S. Energy Information Administration's (EIA's) Annual Energy Outlook 2013 (AEO2013) focus on the factors that shape the

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

LNG Annual Report - 2011 | Department of Energy  

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

1 LNG Annual Report - 2011 LNG Annual Report - 2011 (Revised 3152012) LNG Annual Report 2011 More Documents & Publications LNG Annual Report - 2012 LNG Annual Report - 2013 LNG...

262

LNG Annual Report - 2007 | Department of Energy  

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

7 LNG Annual Report - 2007 LNG Annual Report - 2007 (Revised 10102008) LNG Annual Report - 2007 More Documents & Publications LNG Annual Report - 2008 LNG Annual Report - 2006...

263

LNG Annual Report - 2010 | Department of Energy  

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

10 LNG Annual Report - 2010 LNG Annual Report - 2010 LNG Annual Report - 2010 More Documents & Publications LNG Annual Report - 2009 LNG Annual Report - 2008...

264

LNG Annual Report - 2012 | Department of Energy  

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

2 LNG Annual Report - 2012 LNG Annual Report - 2012 (Revised 3212013) LNG Annual Report - 2012 More Documents & Publications LNG Annual Report - 2013 LNG Annual Report - 2011 LNG...

265

Draft 2014 Annual Plan | Department of Energy  

Office of Environmental Management (EM)

Draft 2014 Annual Plan Draft 2014 Annual Plan Section 999: Draft 2014 Annual Plan Section 999 - Draft 2014 Annual Plan More Documents & Publications 2011 Annual Plan 2013...

266

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect (OSTI)

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.

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

267

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect (OSTI)

We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour forecasts to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.

Werth, D.; Garrett, A.

2009-04-15T23:59:59.000Z

268

A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China  

SciTech Connect (OSTI)

Highlights: ? We propose a hybrid model that combines seasonal SARIMA model and grey system theory. ? The model is robust at multiple time scales with the anticipated accuracy. ? At month-scale, the SARIMA model shows good representation for monthly MSW generation. ? At medium-term time scale, grey relational analysis could yield the MSW generation. ? At long-term time scale, GM (1, 1) provides a basic scenario of MSW generation. - Abstract: Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term.

Xu, Lilai, E-mail: llxu@iue.ac.cn [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021 (China); Xiamen Key Lab of Urban Metabolism, Xiamen 361021 (China); Gao, Peiqing, E-mail: peiqing15@yahoo.com.cn [Xiamen City Appearance and Environmental Sanitation Management Office, 51 Hexiangxi Road, Xiamen 361004 (China); Cui, Shenghui, E-mail: shcui@iue.ac.cn [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021 (China); Xiamen Key Lab of Urban Metabolism, Xiamen 361021 (China); Liu, Chun, E-mail: xmhwlc@yahoo.com.cn [Xiamen City Appearance and Environmental Sanitation Management Office, 51 Hexiangxi Road, Xiamen 361004 (China)

2013-06-15T23:59:59.000Z

269

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect (OSTI)

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.

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

270

Annual Energy Outlook with Projections to 2025-Homepage  

Gasoline and Diesel Fuel Update (EIA)

Legislation & Regulations Overview Issues in Focus Economic Market Trends Energy Demand Market Trends Electricity and Renewable Market Trends Oil and Natural Gas Market Trends Coal Market Trnds Forecast Comparisons Emissions Market Trends Additional Links Preface Major Assumptions for the Forecasts Summary of the AEO2003 Cases Acronyms The projections in AEO2002 are not statements of what will happen but of what might happen, given the assumptions and methodologies used. The projections are business-as-usual trend forecasts, given known technology, technological and demographic trends, and current laws and regulations. Thus, they provide a policy-neutral reference case that can be used to analyze policy initiatives. EIA does not propose, advocate, or speculate on

271

HEURISTIC APPROACH FOR OPTIMAL PARAMETER ESTIMATION OF ELECTRIC LOAD FORECAST MODEL  

Science Journals Connector (OSTI)

Load forecasting is a crucial aspect of electric power system planning and operation. This paper presents a heuristic approach for optimal parameter estimation of long term load forecast models. The problem is viewed as an optimization one in which the goal is to minimize the total estimation error by properly adjusting the model coefficients. A particle swarm optimization algorithm is developed to minimize the error associated with the estimated model parameters. Real data of Egyptian network is used to perform this study. Results are reported and compared to those obtained using the well known least error squares estimation technique. Comparison results are in favor of the proposed approach which signifies its potential as a promising estimation tool.

M. R. AlRashidi; K. M. EL?Naggar

2009-01-01T23:59:59.000Z

272

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

ANL Wind Power Forecasting and Electricity Markets ANL Wind Power Forecasting and Electricity Markets Jump to: navigation, search Logo: Wind Power Forecasting and Electricity Markets Name Wind Power Forecasting and Electricity Markets Agency/Company /Organization Argonne National Laboratory Partner Institute for Systems and Computer Engineering of Porto (INESC Porto) in Portugal, Midwest Independent System Operator and Horizon Wind Energy LLC, funded by U.S. Department of Energy Sector Energy Focus Area Wind Topics Pathways analysis, Technology characterizations Resource Type Software/modeling tools Website http://www.dis.anl.gov/project References Argonne National Laboratory: Wind Power Forecasting and Electricity Markets[1] Abstract To improve wind power forecasting and its use in power system and electricity market operations Argonne National Laboratory has assembled a team of experts in wind power forecasting, electricity market modeling, wind farm development, and power system operations.

273

U.S. Energy Information Administration | Natural Gas Annual  

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

U.S. Energy Information Administration | Natural Gas Annual U.S. Energy Information Administration | Natural Gas Annual Office of Oil, Gas, and Coal Supply Statistics www.eia.gov Natural Gas Annual 2012 U.S. Department of Energy Washington, DC 20585 2012 U.S. Energy Information Administration | Natural Gas Monthly ii This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or

274

Short-Term World Oil Price Forecast  

Gasoline and Diesel Fuel Update (EIA)

4 4 Notes: This graph shows monthly average spot West Texas Intermediate crude oil prices. Spot WTI crude oil prices peaked last fall as anticipated boosts to world supply from OPEC and other sources did not show up in actual stocks data. So where do we see crude oil prices going from here? Crude oil prices are expected to be about $28-$30 per barrel for the rest of this year, but note the uncertainty bands on this projection. They give an indication of how difficult it is to know what these prices are going to do. Also, EIA does not forecast volatility. This relatively flat forecast could be correct on average, with wide swings around the base line. Let's explore why we think prices will likely remain high, by looking at an important market barometer - inventories - which measures the

275

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

FRED FRED http://en.openei.org/community/group/fred Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in formulating policies and energy plans based on easy to use forecasting tools, visualizations, sankey diagrams, and open data. The platform will live on OpenEI and this community was established to initiate discussion around continuous development of this tool, integrating it with new datasets, and connecting with the community of users who will want to contribute data to the tool and use the tool for planning purposes. energy data + forecasting Fri, 22 Jun 2012 15:30:20 +0000 Dbrodt 34

276

Voluntary Green Power Market Forecast through 2015  

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

158 158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 * www.nrel.gov NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Operated by the Alliance for Sustainable Energy, LLC Contract No. DE-AC36-08-GO28308 Technical Report NREL/TP-6A2-48158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory

277

FORSITE: a geothermal site development forecasting system  

SciTech Connect (OSTI)

The Geothermal Site Development Forecasting System (FORSITE) is a computer-based system being developed to assist DOE geothermal program managers in monitoring the progress of multiple geothermal electric exploration and construction projects. The system will combine conceptual development schedules with site-specific status data to predict a time-phased sequence of development likely to occur at specific geothermal sites. Forecasting includes estimation of industry costs and federal manpower requirements across sites on a year-by-year basis. The main advantage of the system, which relies on reporting of major, easily detectable industry activities, is its ability to use relatively sparse data to achieve a representation of status and future development.

Entingh, D.J.; Gerstein, R.E.; Kenkeremath, L.D.; Ko, S.M.

1981-10-01T23:59:59.000Z

278

2006 TEPP Annual Report  

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

Emergency Emergency Preparedness Program 2006 Annual Report US Department of Energy - Offi ce of Environmental Management Transportation Emergency Preparedness Program 2006 Annual Report 2 2 Table of Contents Executive Summary.......................................................................................................................4 I. Transportation Emergency Preparedness Program Purpose.......................................6

279

Library Annual Report Library Annual Report  

E-Print Network [OSTI]

Library Annual Report 2007 Library Annual Report 2007 #12;www.library.uwa.edu.au Our mission: By delivering excellent information resources and services the Library is integral to the University's mission of advancing, transmitting and sustaining knowledge. Our vision: The Library will continue to be at the heart

Tobar, Michael

280

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

2014-12-30T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Annual Report 2008.doc  

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

Cold War FY08 Annual Summary Report Cold War FY08 Annual Summary Report Page 1 of 14 Savannah River Site (SRS) Cold War Built Environment Historic Preservation Annual Summary Report Fiscal Year (FY) 2008 October 2008 Prepared by: The U. S. Department of Energy (DOE) Savannah River Operations Office (SR) SRS Cold War FY08 Annual Summary Report Page 2 of 14 TABLE OF CONTENTS Page BASIS.............................................................................................3

282

ANNUAL REVIEW FACTSHEET WORKFLOW  

E-Print Network [OSTI]

that the PhD has updated his T&SP (15. Second annual review). The PhD and the Promotor have to plan a date for the second annual review and the PhD has to enter that date in ProDoc (20 Date second annual review plannedANNUAL REVIEW FACTSHEET WORKFLOW PhD Candidate / Promotor / Dean / TGS / Doctorate Board / Pro

Twente, Universiteit

283

Annual Energy Review 2002  

Gasoline and Diesel Fuel Update (EIA)

Information Administration Annual Energy Review 2002 125 a Unfinished oils, motor gasoline blending components, aviation gasoline blending components, and other...

284

Solar Wind Forecasting with Coronal Holes  

E-Print Network [OSTI]

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

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

2007-01-09T23:59:59.000Z

285

Annual Report and Accounts  

E-Print Network [OSTI]

Annual Report and Accounts 2013�2014 The Research Agency of the Forestry CommissionHC 2 #12;Forest Research Annual Report and Accounts 2013�2014 Presented to the House of Commons pursuant to Section 7 Annual Report and Accounts 2013�2014 Forest Research 1 #12;� Crown Copyright 2014 You may re

286

Annual Growth Bands in Hymenaea courbaril  

SciTech Connect (OSTI)

One significant source of annual temperature and precipitation data arises from the regular annual secondary growth rings of trees. Several tropical tree species are observed to form regular growth bands that may or may not form annually. Such growth was observed in one stem disk of the tropical legume Hymenaea courbaril near the area of David, Panama. In comparison to annual reference {Delta}{sup 14}C values from wood and air, the {Delta}{sup 14}C values from the secondary growth rings formed by H. courbaril were determined to be annual in nature in this one stem disk specimen. During this study, H. courbaril was also observed to translocate recently produced photosynthate into older growth rings as sapwood is converted to heartwood. This process alters the overall {Delta}{sup 14}C values of these transitional growth rings as cellulose with a higher {Delta}{sup 14}C content is translocated into growth rings with a relatively lower {Delta}{sup 14}C content. Once the annual nature of these growth rings is established, further stable isotope analyses on H. courbaril material in other studies may help to complete gaps in the understanding of short and of long term global climate patterns.

Westbrook, J A; Guilderson, T P; Colinvaux, P A

2004-02-09T23:59:59.000Z

287

Annual Energy Outlook with Projections to 2025- Preface  

Gasoline and Diesel Fuel Update (EIA)

Preface Preface Preface The Annual Energy Outlook 2004 (AEO2004) presents midterm forecasts of energy supply, demand, and prices through 2025 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA's National Energy Modeling System (NEMS). The report begins with an "Overview" summarizing the AEO2004 reference case. The next section, "Legislation and Regulations," discusses evolving legislation and regulatory issues. "Issues in Focus" includes discussions of future labor productivity growth; lower 48 natural gas depletion and productive capacity; natural gas supply options, with a focus on liquefied natural gas; natural gas demand for Canadian oil sands production; National Petroleum Council forecasts for natural gas; natural gas consumption in the industrial and electric power sectors; nuclear power plant construction costs; renewable electricity tax credits; and U.S. greenhouse gas intensity. It is followed by a discussion of "Energy Market Trends."

288

Annual Energy Outlook 2002 with Projections to 2020 - Contact  

Gasoline and Diesel Fuel Update (EIA)

For Further Information..... For Further Information..... The Annual Energy Outlook 2002 (AEO2002) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@eia.doe.gov, 202/586-2222), Acting Administrator, EIA; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Acting Director, Office of Integrated Analysis and Forecasting; Susan H. Holte (sholte@eia.doe.gov, 202/586-4838), Director of the Demand and Integration Division; James M. Kendell (jkendell@eia.doe.gov, 202/586-9646), Director of the Oil and Gas Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor. For ordering information and questions on other energy statistics available from EIA, please contact EIA’s National Energy Information Center.

289

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS27 data.

290

Assumptions to the Annual Energy Outlook 2001 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Completed Copy in PDF Format Completed Copy in PDF Format Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Household Expenditures Module Key Assumptions The historical input data used to develop the HEM version for the AEO2001 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2001 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and

291

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

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

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

292

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

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

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

293

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.

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

2013-10-01T23:59:59.000Z

294

Assumptions to the Annual Energy Outlook 2013  

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

Energy Module Energy Module This page inTenTionally lefT blank 21 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 International Energy Module The LFMM International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the LFMM IEM computes BRENT and WTI prices, provides a supply curve of world crude-like liquids, and generates a worldwide oil supply- demand balance with regional detail. The IEM also provides, for each year of the projection period, endogenous and

295

Annual Energy Outlook with Projections to 2025  

Gasoline and Diesel Fuel Update (EIA)

5 with Projections to 2025 5 with Projections to 2025 Report #: DOE/EIA-0383(2005) Release date full report: January 2005 Next release date full report: January 2006 Early Release Reference Case date: December 2005 The Annual Energy Outlook presents a midterm forecast and analysis of US energy supply, demand, and prices through 2025. The projections are based on results from the Energy Information Administration's National Energy Modeling System. AEO2005 includes a reference case and over 30 sensitivities. Data Tables Summary Tables Adobe Acrobat Logo Yearly Tables Excel logo Regional and other detailed tables Excel logo (Supplemental) Contents Overview Market Drivers Trends in Economic Activity Economic Growth Cases International Oil Markets Energy Demand Projections Buildings Sector

296

China's Present Situation of Coal Consumption and Future Coal Demand Forecast  

Science Journals Connector (OSTI)

This article analyzes China's coal consumption changes since 1991 and proportion change of coal consumption to total energy consumption. It is argued that power, iron and steel, construction material, and chemical industries are the four major coal consumption industries, which account for 85% of total coal consumption in 2005. Considering energy consumption composition characteristics of these four industries, major coal demand determinants, potentials of future energy efficiency improvement, and structural changes, etc., this article makes a forecast of 2010s and 2020s domestic coal demand in these four industries. In addition, considering such relevant factors as our country's future economic growth rate and energy saving target, it forecasts future energy demands, using per unit GDP energy consumption method and energy elasticity coefficient method as well. Then it uses other institution's results about future primary energy demand, excluding primary coal demand, for reference, and forecasts coal demands in 2010 and 2020 indirectly. After results comparison between these two methods, it is believed that coal demands in 2010 might be 26202850 million tons and in 2020 might be 30903490 million tons, in which, coal used in power generation is still the driven force of coal demand growth.

Wang Yan; Li Jingwen

2008-01-01T23:59:59.000Z

297

Electric Grid - Forecasting system licensed | ornl.gov  

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

Electric Grid - Forecasting system licensed Location Based Technologies has signed an agreement to integrate and market an Oak Ridge National Laboratory technology that provides...

298

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network [OSTI]

??Electricity generated from wind power is both variable and uncertain. Wind forecasts provide valuable information for wind farm management, but they are not perfect. Chapter (more)

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

299

Forecasting supply/demand and price of ethylene feedstocks  

SciTech Connect (OSTI)

The history of the petrochemical industry over the past ten years clearly shows that forecasting in a turbulent world is like trying to predict tomorrow's headlines.

Struth, B.W.

1984-08-01T23:59:59.000Z

300

PBL FY 2003 Third Quarter Review Forecast of Generation Accumulated...  

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

for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net Cost Recovery Adjustment Clause (SN CRAC) FY 2003 Third Quarter Review Forecast in Millions...

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

FY 2004 Second Quarter Review Forecast of Generation Accumulated...  

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

for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net Cost Recovery Adjustment Clause (SN CRAC) FY 2004 Second Quarter Review Forecast In Millions...

302

Integrating agricultural pest biocontrol into forecasts of energy biomass production  

E-Print Network [OSTI]

Analysis Integrating agricultural pest biocontrol into forecasts of energy biomass production T pollution, greenhouse gas emissions, and soil erosion (Nash, 2007; Searchinger et al., 2008). On the other

Gratton, Claudio

303

FY 1996 solid waste integrated life-cycle forecast characteristics summary. Volumes 1 and 2  

SciTech Connect (OSTI)

For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the physical waste forms, hazardous waste constituents, and radionuclides of the waste expected to be shipped to the CWC from 1996 through the remaining life cycle of the Hanford Site (assumed to extend to 2070). In previous years, forecast data has been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to two previous reports: the more detailed report on waste volumes, WHC-EP-0900, FY1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary and the report on expected containers, WHC-EP-0903, FY1996 Solid Waste Integrated Life-Cycle Forecast Container Summary. All three documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on two main characteristics: the physical waste forms and hazardous waste constituents of low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major generators for each waste category and waste characteristic are also discussed. The characteristics of low-level waste (LLW) are described in Appendix A. In addition, information on radionuclides present in the waste is provided in Appendix B. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste is expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters. The range is primarily due to uncertainties associated with the Tank Waste Remediation System (TWRS) program, including uncertainties regarding retrieval of long-length equipment, scheduling, and tank retrieval technologies.

Templeton, K.J.

1996-05-23T23:59:59.000Z

304

EIA - Annual Energy Outlook 2011 - overview  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2011 Annual Energy Outlook 2011 Release Date: April 26, 2011 | Next Early Release Date: January 23, 2012 | Report Number: DOE/EIA-0383(2011) Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Changes from Previous AEO Executive Summary Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Annual Energy Outlook 2011 presents yearly projections and analysis of energy topics Download the complete April 2011 published report. Changes from previous AEO2010 Significant update of the technically recoverable U.S. shale gas

305

EIA - Annual Energy Outlook 2011 - overview  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2011 Annual Energy Outlook 2011 Release Date: April 26, 2011 | Next Early Release Date: January 23, 2012 | Report Number: DOE/EIA-0383(2011) Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Changes from Previous AEO Executive Summary Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Annual Energy Outlook 2011 presents yearly projections and analysis of energy topics Download the complete April 2011 published report. Changes from previous AEO2010 Significant update of the technically recoverable U.S. shale gas

306

Wind power forecast error smoothing within a wind farm  

Science Journals Connector (OSTI)

Smoothing of wind power forecast errors is well-known for large areas. Comparable effects within a wind farm are investigated in this paper. A Neural Network was taken to predict the power output of a wind farm in north-western Germany comprising 17 turbines. A comparison was done between an algorithm that fits mean wind and mean power data of the wind farm and a second algorithm that fits wind and power data individually for each turbine. The evaluation of root mean square errors (RMSE) shows that relative small smoothing effects occur. However, it can be shown for this wind farm that individual calculations have the advantage that only a few turbines are needed to give better results than the use of mean data. Furthermore different results occurred if predicted wind speeds are directly fitted to observed wind power or if predicted wind speeds are first fitted to observed wind speeds and then applied to a power curve. The first approach gives slightly better RMSE values, the bias improves considerably.

Nadja Saleck; Lueder von Bremen

2007-01-01T23:59:59.000Z

307

Forecasting for inventory control with exponential smoothing  

Science Journals Connector (OSTI)

Exponential smoothing, often used in sales forecasting for inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains appropriate under more general conditions, where the variance is allowed to grow or contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular, the problem of finding the predictive distribution of aggregate lead-time demand, for use in inventory control calculations, is considered using a bootstrap approach. A method for establishing order-up-to levels directly from the simulated predictive distribution is also explored.

Ralph D. Snyder; Anne B. Koehler; J.Keith Ord

2002-01-01T23:59:59.000Z

308

Probabilistic Verification of Global and Mesoscale Ensemble Forecasts of Tropical Cyclogenesis  

Science Journals Connector (OSTI)

Probabilistic forecasts of tropical cyclogenesis have been evaluated for two samples: a near-homogeneous sample of ECMWF and Weather Research and Forecasting (WRF) Modelensemble Kalman filter (EnKF) ensemble forecasts during the National Science ...

Sharanya J. Majumdar; Ryan D. Torn

2014-10-01T23:59:59.000Z

309

LNG Annual Report - 2013 | Department of Energy  

Energy Savers [EERE]

Annual Report - 2013 LNG Annual Report - 2013 LNG Annual Report - 2013 LNG 2013.pdf More Documents & Publications LNG Annual Report - 2012 LNG Monthly Report - August 2014...

310

LNG Annual Report - 2008 | Department of Energy  

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

8 LNG Annual Report - 2008 LNG Annual Report - 2008 (Revised 10142009) LNG Annual Report - 2008 More Documents & Publications LNG Annual Report - 2009...

311

LM Annual Post Competition Accountability Reports | Department...  

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

LM Annual Post Competition Accountability Reports LM Annual Post Competition Accountability Reports Third Annual Post Competition Accountability Report Second Annual Post...

312

Annual Energy Outlook with Projections to 2025-Appendix A Reference Case  

Gasoline and Diesel Fuel Update (EIA)

A Reference Case Forecast Tables A Reference Case Forecast Tables Annual Energy Outlook 2004 with Projections to 2025 Appendix A Reference Case Forecast (2001-2025) Tables Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF format. MS Excel Viewer Spreadsheets are provided in excel Table Title Formats Summary Tables PDF Year by Year Tables PDF Table 1. Total Energy Supply and Disposition Summary Excel PDF Table 2. Energy Consumption by Sector and Source Excel PDF Table 3. Energy Prices by Sector and Source Excel PDF Table 4. Residential Sector Key Indicators and Consumption Excel PDF Table 5. Commercial Sector Indicators and Consumption Excel PDF Table 6. Industrial Key Indicators and Consumption Excel PDF Table 7. Transportation Sector Key Indicators and Delivered Energy Indicators

313

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

States. States. OGSM encompasses domestic crude oil and natural gas supply by both conventional and nonconventional recovery techniques. Nonconventional recovery includes unconventional gas recovery from low permeability formations of sandstone and shale, and coalbeds. Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 93 Figure 7. Oil and Gas Supply Model Regions Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Report #:DOE/EIA-0554(2007) Release date: April 2007 Next release date: March 2008 Primary inputs for the module are varied. One set of key assumptions concerns estimates of domestic technically recoverable oil and gas resources. Other factors affecting the projection include the assumed

314

Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumption to the Annual Energy Outlook Petroleum Market Module Figure 8. Petroleum Administration for Defense Districts. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohols, ethers, and bioesters natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of U.S. refining

315

Random switching exponential smoothing and inventory forecasting  

Science Journals Connector (OSTI)

Abstract Exponential smoothing models represent an important prediction tool both in business and in macroeconomics. This paper provides the analytical forecasting properties of the random coefficient exponential smoothing model in the multiple source of error framework. The random coefficient state-space representation allows for switching between simple exponential smoothing and local linear trend. Therefore it enables controlling, in a flexible manner, the random changing dynamic behavior of the time series. The paper establishes the algebraic mapping between the state-space parameters and the implied reduced form ARIMA parameters. In addition, it shows that the parametric mapping allows overcoming the difficulties that are likely to emerge in estimating directly the random coefficient state-space model. Finally, it presents an empirical application comparing the forecast accuracy of the suggested model vis--vis other benchmark models, both in the ARIMA and in the exponential smoothing class. Using time series relative to wholesalers inventories in the USA, the out-of-sample results show that the reduced form of the random coefficient exponential smoothing model tends to be superior to its competitors.

Giacomo Sbrana; Andrea Silvestrini

2014-01-01T23:59:59.000Z

316

Expert Panel: Forecast Future Demand for Medical Isotopes  

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

Expert Panel: Expert Panel: Forecast Future Demand for Medical Isotopes March 1999 Expert Panel: Forecast Future Demand for Medical Isotopes September 25-26, 1998 Arlington, Virginia The Expert Panel ............................................................................................. Page 1 Charge To The Expert Panel........................................................................... Page 2 Executive Summary......................................................................................... Page 3 Introduction ...................................................................................................... Page 4 Rationale.......................................................................................................... Page 6 Economic Analysis...........................................................................................

317

A robust automatic phase-adjustment method for financial forecasting  

Science Journals Connector (OSTI)

In this work we present the robust automatic phase-adjustment (RAA) method to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid model composed of a qubit multilayer perceptron (QuMLP) with a quantum-inspired ... Keywords: Financial forecasting, Hybrid models, Quantum-inspired evolutionary algorithm, Qubit multilayer perceptron, Random walk dilemma

Ricardo de A. Arajo

2012-03-01T23:59:59.000Z

318

Short term forecasting of solar radiation based on satellite data  

E-Print Network [OSTI]

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

Heinemann, Detlev

319

Developing electricity forecast web tool for Kosovo market  

Science Journals Connector (OSTI)

In this paper is presented a web tool for electricity forecast for Kosovo market for the upcoming ten years. The input data i.e. electricity generation capacities, demand and consume are taken from the document "Kosovo Energy Strategy 2009-2018" compiled ... Keywords: .NET, database, electricity forecast, internet, simulation, web

Blerim Rexha; Arben Ahmeti; Lule Ahmedi; Vjollca Komoni

2011-02-01T23:59:59.000Z

320

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

resources resulting in water stress. Effective water management ­ a solution Supply side management Demand side management #12;Developing a regression equation based on cluster analysis for forecasting waterFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil

Keller, Arturo A.

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network [OSTI]

production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size. On the other hand if forecasted high production events do not occur, the cost of de- optimisation Energies and Energy Systems Sophia Antipolis, France andrea.michiorri@mines-paristech.fr Abstract

Paris-Sud XI, Université de

322

Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa  

E-Print Network [OSTI]

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

323

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network [OSTI]

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

Washington at Seattle, University of

324

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network [OSTI]

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size Andrew. R.Lawrence@ecmwf.int #12;Abstract An ensemble-based data assimilation approach is used to transform old en- semble. The impact of the transformations are propagated for- ward in time over the ensemble's forecast period

Hansens, Jim

325

Improving baseline forecasts in a 500-industry dynamic CGE model of the USA.  

E-Print Network [OSTI]

??MONASH-style CGE models have been used to generate baseline forecasts illustrating how an economy is likely to evolve through time. One application of such forecasts (more)

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

326

E-Print Network 3.0 - africa conditional forecasts Sample Search...  

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

Search Powered by Explorit Topic List Advanced Search Sample search results for: africa conditional forecasts Page: << < 1 2 3 4 5 > >> 1 COLORADO STATE UNIVERSITY FORECAST...

327

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect (OSTI)

In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of forecasts. We use automatically coupled wavelet transform and autoregressive integrated moving-average (ARIMA) forecasting to reflect multi-scale variability of forecast errors. The proposed analysis reveals slow-changing quasi-deterministic components of forecast errors. This helps improve forecasts produced by other means, e.g., using weather-based models, and reduce forecast errors prediction intervals.

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

2014-10-27T23:59:59.000Z

328

Historical Natural Gas Annual  

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

Annual . 1996 Published October 1997 1997 Published October 1998 1998 Published October 1999 1999 Published October 2000 2000 Published December 2001...

329

Annual Report Generator.  

E-Print Network [OSTI]

?? This report analyzes the needs to build an annual report generator which has the properties of Modularity, Simplicity in use and Maintainability based on (more)

Lin, Yingwei

2006-01-01T23:59:59.000Z

330

Historical Natural Gas Annual  

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

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

331

Historical Natural Gas Annual  

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

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

332

Historical Natural Gas Annual  

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

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

333

BPA 2002 Annual Report  

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

STATEMENTS 2002 Annual Report of the Bonneville Power Administration Cover photo BPA fish biologist Andy Thoms (upper right) works with students from H.B. Lee Middle School...

334

Annual Energy Outlook 2012  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

and may differ slightly from official EIA data reports. Sources: 2010 data based on: Oak Ridge National Laboratory, Transportation Energy Data Book: Edition 28 and Annual (Oak...

335

Annual Energy Outlook 2012  

Gasoline and Diesel Fuel Update (EIA)

Federal Highway Administration, Highway Statistics 2009 (Washington, DC, April 2011); Oak Ridge National Laboratory, Transportation Energy Data Book: Edition 30 and Annual (Oak...

336

2007 Annual Report  

SciTech Connect (OSTI)

This annual report includes: a brief overview of Western; FY 2007 highlights; FY 2007 Integrated Resource Planning, or IRP, survey; and financial data.

none,

2007-01-01T23:59:59.000Z

337

Annual Performance Report  

Office of Legacy Management (LM)

left blank U.S. Department of Energy Annual Performance Report, Shiprock, New Mexico October 2014 Doc. No. S12021 Page i Contents Abbreviations ......

338

NLC Annual Conference  

Broader source: Energy.gov [DOE]

The National League of Cities (NLC) is hosting its annual Congressional City Conference in Washington, D.C., from March 7-11, 2015.

339

Annual Energy Outlook 2012  

Gasoline and Diesel Fuel Update (EIA)

4 Regional maps Figure F3. Petroleum Administration for Defense Districts 216 U.S. Energy Information Administration Annual Energy Outlook 2010 Figure F3. Petroleum...

340

Annual Energy Outlook 2012  

Gasoline and Diesel Fuel Update (EIA)

36 Reference case Energy Information Administration Annual Energy Outlook 2012 6 Table A3. Energy prices by sector and source (2010 dollars per million Btu, unless otherwise...

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Annual Energy Outlook 2012  

Gasoline and Diesel Fuel Update (EIA)

9 U.S. Energy Information Administration | Annual Energy Outlook 2012 Table G1. Heat rates Fuel Units Approximate heat content Coal 1 Production . . . . . . . . . . . . . . . . . ....

342

SFU LIBRARY ANNUAL REPORT  

E-Print Network [OSTI]

SFU LIBRARY ANNUAL REPORT 2006/07 #12;22 TABLE OF CONTENTS Message from the University Librarian................................................... ....................................... 7 WAC Bennett Library.................................................................. ....................................... 8 Samuel and Frances Belzberg Library

343

Annual Coal Distribution Report  

Gasoline and Diesel Fuel Update (EIA)

Distribution Report Release Date: December 19, 2013 | Next Release Date: December 12, 2014 | full report | RevisionCorrection Revision to the Annual Coal Distribution Report...

344

EMSL 2009 Annual Report  

SciTech Connect (OSTI)

The EMSL 2009 Annual Report describes the science conducted at EMSL during 2009 as well as outreach activities and awards and honors received by users and staff.

Showalter, Mary Ann; Kathmann, Loel E.; Manke, Kristin L.; Wiley, Julie G.; Reed, Jennifer R.

2010-02-26T23:59:59.000Z

345

SPEER Third Annual Summit  

Broader source: Energy.gov [DOE]

The South-Central Partnership for Energy Efficiency as a Resource (SPEER) is hosting their 3rd Annual Summit in Dallas, Texas.

346

OPSI Annual Meeting  

Broader source: Energy.gov [DOE]

The Organization of PJM States, Inc. (OPSI) is hosting its annual meeting in Chicago, IL, on October 13-14, 2014.

347

DOE'EIA-0"38!3-(S Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

DOE'EIA-0"38!3-(S DOE'EIA-0"38!3-(S Annual Energy With Proectjions to 20 For Further Information ... The Annual Energy Outlook (AEO) is prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (202/586-2222). General questions concerning energy demand or energy markets may be addressed to Mark E. Rodekohr (202/586-1130), Director of the Energy Demand and Integration Division. General questions concerning energy supply and conversion activities may be addressed to Scott Sitzer (202/586-2308), Director of the Energy Supply and Conversion Division. Detailed questions about the forecasts and related model components may be addressed to the following analysts: Highlights ..............................

348

DOE/EIA-0131(10) Natural Gas Annual 2010 Publication Date:  

Gasoline and Diesel Fuel Update (EIA)

10) 10) Natural Gas Annual 2010 Publication Date: December 2011 Energy Information Administration Office of Oil, Gas, and Coal Supply Statistics U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. ii Energy Information Administration/Natural Gas Annual 2010

349

Annual Report School of Engineering  

E-Print Network [OSTI]

Annual Report 1999-2000 School of Engineering University of Connecticut #12;#12;University of Connecticut School of Engineering Annual Report 1999-2000 Table of Contents School of Engineering Annual Report Summary................................................................................... 3

Alpay, S. Pamir

350

University of Lethbridge Annual Report  

E-Print Network [OSTI]

University of Lethbridge Annual Report 2011/12 #12; i University of Lethbridge 2011/12 Annual Report ACCOUNTABILITY STATEMENT The University of Lethbridge Annual Report for the year ended March 31, 2012 was prepared

Seldin, Jonathan P.

351

Annual Energy Outlook 2006 with Projections to 2030 - Contact Information  

Gasoline and Diesel Fuel Update (EIA)

For Further Information For Further Information Annual Energy Outlook 2006 with Projections to 2030 For Further Information . . . The Annual Energy Outlook 2006 (AEO2006) was prepared by the Energy Information Administration (EIA), under the direction of John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Integrated Analysis and Forecasting; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Director, Coal and Electric Power Division; Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Acting Director, Oil and Gas Division and Senior Technical Advisor; and Glen E. Sweetnam (glen.sweetnam@eia.doe.gov, 202/586-2188), Director, International, Economic, and Greenhouse Gases Division.

352

Annual Energy Outlook with Projections to 2025-Overview  

Gasoline and Diesel Fuel Update (EIA)

Overview Overview Annual Energy Outlook 2004 with Projections to 2025 Overview Index (click to jump links) Key Energy Issues to 2025 Economic Growth Energy Prices Energy Consumption Energy Intensity Electricity Generation Energy Production and Imports Carbon Dioxide Emissions Key Energy Issues to 2025 For almost 4 years, natural gas prices have remained at levels substantially higher than those of the 1990s. This has led to a reevaluation of expectations about future trends in natural gas markets, the economics of exploration and production, and the size of the natural gas resource. The Annual Energy Outlook 2004 (AEO2004) forecast reflects such revised expectations, projecting greater dependence on more costly alternative supplies of natural gas, such as imports of liquefied natural gas (LNG), with expansion of existing terminals and development of new facilities, and remote resources from Alaska and from the Mackenzie Delta in Canada, with completion of the Alaska Natural Gas Transportation System and the Mackenzie Delta pipeline.

353

Annual Energy Outlook with Projections to 2025 - Market Trends- Energy  

Gasoline and Diesel Fuel Update (EIA)

Energy Demand Energy Demand Annual Energy Outlook 2005 Market Trends - Energy Demand Figure 42. Energy use per capita and per dollar of gross domestic product, 1970-2025 (index, 1970 = 1). Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure data Average Energy Use per Person Increases in the Forecast Energy intensity, as measured by energy use per 2000 dollar of GDP, is projected to decline at an average annual rate of 1.6 percent, with efficiency gains and structural shifts in the economy offsetting growth in demand for energy services (Figure 42). The projected rate of decline falls between the average rate of 2.3 percent from 1970 through 1986, when energy prices increased in real terms, and the 0.7-percent rate from 1986 through

354

Annual Energy Outlook 2007: With Projections to 2030  

Gasoline and Diesel Fuel Update (EIA)

8) 8) June 2008 Annual Energy Outlook 2008 With Projections to 2030 DRAFT -- 6/24/2008 ***** NOT FOR RELEASE ***** For Further Information . . . The Annual Energy Outlook 2008 was prepared by the Energy Information Administration, under the direc- tion of John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Integrated Analysis and Forecasting; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Director, Coal and Electric Power Division; A. Michael Schaal (michael.schaal@eia.doe.gov, 202/586-5590), Director, Oil and Gas Division; Glen E. Sweetnam (glen.sweetnam@eia.doe.gov, 202/586-2188), Director, International, Economic, and Greenhouse Gases Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor.

355

Energy Information Administration (EIA) - Annual Energy Outlook with  

Gasoline and Diesel Fuel Update (EIA)

with Projections to 2030 with Projections to 2030 Annual Energy Outlook 2007 with Projections to 2030 The Annual Energy Outlook 2007 presents a projection and analysis of US energy supply, demand, and prices through 2030. The projections are based on results from the Energy Information Administration's National Energy Modeling System. The AEO2007 includes the reference case, additional cases examining energy markets, and complete documentation. The report is also released in print. Errata as of 10/15/07 Forecast Data Tables Reference Case Tables (links to individual excel and PDF files) High Economic Growth Case Tables (links to individual excel and PDF files) Low Economic Growth Case Tables (links to individual excel and PDF files) High Price Case Tables (links to individual excel and PDF files)

356

Annual Energy Outlook 2006 with Projections to 2030  

Gasoline and Diesel Fuel Update (EIA)

6) 6) February 2006 Annual Energy Outlook 2006 With Projections to 2030 February 2006 For Further Information . . . The Annual Energy Outlook 2006 (AEO2006) was prepared by the Energy Information Administration (EIA), under the direction of John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Integrated Analysis and Forecasting; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Director, Coal and Electric Power Division; Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Acting Director, Oil and Gas Division and Senior Technical Advisor; and Glen E. Sweetnam (glen.sweetnam@eia.doe.gov, 202/586-2188), Director, International, Economic, and Greenhouse Gases Division. For ordering information and questions on other energy statistics available

357

Annual Energy Outlook 2006 with Projections to 2030 - Market Trends -  

Gasoline and Diesel Fuel Update (EIA)

Market Trends - Market Drivers Market Trends - Market Drivers Annual Energy Outlook 2006 with Projections to 2030 Strong Economic Growth Is Expected To Continue Through 2030 Figure 24. Average annual growth rates of real GDP, labor frce, and productivity in three cases, 2004-2030 (percent per year). Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure data AEO2006 presents three views of economic growth for the forecast period from 2004 through 2030. Although probabilities are not assigned, the reference case reflects the most likely view of how the economy will unfold over the period. In the reference case, the Nation’s economic growth, measured in terms of real GDP based on 2000 chain-weighted dollars, is projected to average 3.0 percent per year (Figure 24). The labor force is

358

Assumptions to the Annual Energy Outlook 2001 - Footnotes  

Gasoline and Diesel Fuel Update (EIA)

Feedback Feedback Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage FOOTNOTES [1] Energy Information Administration, Annual Energy Outlook 2001 (AEO2001), DOE/EIA-0383(2001), (Washington, DC, December 2000). [2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986. [3] Energy Information Administration, The National Energy Modeling System: An Overview 2000, DOE/EIA-0581(2000), (Washington, DC, March 2000). [4] The underlying macroeconomic growth cases use Standard and Poor’s DRI February 2000 T250200 and February TO250299 and TP250299.

359

Annual Energy Outlook 2009: With Projections to 2030  

Gasoline and Diesel Fuel Update (EIA)

9) 9) March 2009 Annual Energy Outlook 2009 With Projections to 2030 For Further Information . . . The Annual Energy Outlook 2009 was prepared by the Energy Information Administration, under the direc- tion of John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Integrated Analysis and Forecasting; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Director, Coal and Electric Power Division; A. Michael Schaal (michael.schaal@eia.doe.gov, 202/586-5590), Director, Oil and Gas Division; Glen E. Sweetnam (glen.sweetnam@eia.doe.gov, 202/586-2188), Director, International, Economic, and Greenhouse Gases Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor. For ordering information and questions on other

360

Energy Information Administration (EIA) - Annual Energy Outlook 2007 -  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts Annual Energy Outlook 2007 with Projections to 2030 For Further Information . . . The Annual Energy Outlook 2007 (AEO2007) was prepared by the Energy Information Administration (EIA), under the direction of John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Integrated Analysis and Forecasting; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Director, Coal and Electric Power Division; A. Michael Schaal (michael.schaal@eia.doe.gov, 202/586-5590), Director, Oil and Gas Division; Glen E. Sweetnam (glen.sweetnam@eia.doe.gov, 202/586-2188), Director, International, Economic, and Greenhouse Gases Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor.

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Assumptions to the Annual Energy Outlook - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumption to the Annual Energy Outlook Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20041 (AEO2004), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every two years. The National Energy Modeling System The projections in the AEO2004 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers in the U.S. Congress, the Administration, including DOE Program Offices, and other government agencies.

362

2005 TEPP Annual Report  

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

Transportation Emergency Transportation Emergency Preparedness Program 2005 Annual Report Special thanks to participants in the Haralson County, Georgia and Leigh Valley International Airport, Pennsylvania exercises who are featured on the front cover of this report. Transportation Emergency Preparedness Program 2005 Annual Report Table of Contents Executive Summary ..................................................................................................1 I. Transportation Emergency Preparedness Program Purpose ......................3 II. Training ............................................................................................................3 III. TEPP Central Operations .................................................................................5

363

2004 TEPP Annual Report  

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

Transportation Transportation Transportation Emergency Preparedness Program 2004 Annual Report United States Department of Energy Transportation Emergency Preparedness Program (TEPP) 2004 Annual Report Table of Contents Executive Summary..................................................................................... 1 I. Transportation Emergency Preparedness Program Purpose ...... 3 II. Training.............................................................................................. 3 III. Outreach and Conferences ............................................................... 5 IV. Go-Kits ............................................................................................... 5 V. TEPP Exercise and Tabletop Activities ..........................................

364

Annual Fire Safety Report  

E-Print Network [OSTI]

2010 Annual Fire Safety Report University of California, Irvine HIGHER EDUCATION OPPORTUNITY to the Fire Safety in Student Housing Buildings of current or perspective students and employees be reported publish an annual fire safety report, keep a fire log, and report fire statistics to the Secretary

Loudon, Catherine

365

Information Annual Report  

E-Print Network [OSTI]

Information Technology Services 2012­13 Annual Report #12;#12;Contents Administrative Information ______117 Telecommunications and Networking Services __151 #12;#12;5 ITS 2012-13 Administrative Information Services INFORMATION TECHNOLOGY SERVICES Administrative Information Services 2012­13 Annual Report

Maroncelli, Mark

366

Annual Energy Review, 2008  

SciTech Connect (OSTI)

The Annual Energy Review (AER) is the Energy Information Administration's (EIA) primary report of annual historical energy statistics. For many series, data begin with the year 1949. Included are statistics on total energy production, consumption, trade, and energy prices; overviews of petroleum, natural gas, coal, electricity, nuclear energy, renewable energy, and international energy; financial and environment indicators; and data unit conversions.

None

2009-06-01T23:59:59.000Z

367

ANNUAL SECURITY FIRE SAFETY REPORT  

E-Print Network [OSTI]

ANNUAL SECURITY AND FIRE SAFETY REPORT OCTOBER 1, 2013 DARTMOUTH COLLEGE http................................................................................................................................................................... 7 ANNUAL SECURITY REPORT........................................................................................................................9 PREPARATION OF THE REPORT AND DISCLOSURE OF CRIME STATISTICS

368

2013 Uranium Marketing Annual Report  

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

Industry Annual, Tables 10, 11 and 16. 2003-2013-Form EIA-858, "Uranium Marketing Annual Survey". million pounds U 3 O 8 equivalent 1 Includes purchases between...

369

Historical Natural Gas Annual  

Gasoline and Diesel Fuel Update (EIA)

6 6 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at selected points in the flow of gas from the wellhead to the burner-tip. Data include production, transmission within the United States, imports and exports of natural gas, underground storage activities, and deliveries to consumers. The publication presents historical data at the national level for 1930-1996 and detailed annual historical information by State for 1967-1996. The Historical Natural Gas Annual tables are available as self-extracting executable files in ASCII TXT or CDF file formats. Tables 1-3 present annual historical data at the national level for 1930-1996. The remaining tables contain detailed annual historical information, by State, for 1967-1996. Please read the file entitled READMEV2 for a description and documentation of information included in this file.

370

Historical Natural Gas Annual  

Gasoline and Diesel Fuel Update (EIA)

7 7 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at selected points in the flow of gas from the wellhead to the burner-tip. Data include production, transmission within the United States, imports and exports of natural gas, underground storage activities, and deliveries to consumers. The publication presents historical data at the national level for 1930-1997 and detailed annual historical information by State for 1967-1997. The Historical Natural Gas Annual tables are available as self-extracting executable files in ASCII TXT or CDF file formats. Tables 1-3 present annual historical data at the national level for 1930-1997. The remaining tables contain detailed annual historical information, by State, for 1967-1997. Please read the file entitled READMEV2 for a description and documentation of information included in this file.

371

Historical Natural Gas Annual  

Gasoline and Diesel Fuel Update (EIA)

8 8 The Historical Natural Gas Annual contains historical information on supply and disposition of natural gas at the national, regional, and State level as well as prices at selected points in the flow of gas from the wellhead to the burner-tip. Data include production, transmission within the United States, imports and exports of natural gas, underground storage activities, and deliveries to consumers. The publication presents historical data at the national level for 1930-1998 and detailed annual historical information by State for 1967-1998. The Historical Natural Gas Annual tables are available as self-extracting executable files in ASCII TXT or CDF file formats. Tables 1-3 present annual historical data at the national level for 1930-1998. The remaining tables contain detailed annual historical information, by State, for 1967-1998. Please read the file entitled READMEV2 for a description and documentation of information included in this file.

372

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Electricity Electricity Electricity consumption nearly doubles in the IEO2005 projection period. The emerging economies of Asia are expected to lead the increase in world electricity use. Figure 58. World Net Electricity Consumption, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 59. World Net Electricity Consumption by Region, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data The International Energy Outlook 2005 (IEO2005) reference case projects that world net electricity consumption will nearly double over the next two decades.10 Over the forecast period, world electricity demand is projected to grow at an average rate of 2.6 percent per year, from 14,275 billion

373

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts The International Energy Outlook is prepared by the Energy Information Administration (EIA). General questions concerning the contents of the report should be referred to John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Office of Integrated Analysis and Forecasting. Specific questions about the report should be referred to Linda E. Doman (202/586-1041) or the following analysts: World Energy and Economic Outlook Linda Doman (linda.doman@eia.doe.gov, 202-586-1041) Macroeconomic Assumptions Nasir Khilji (nasir.khilji@eia.doe.gov, 202-586-1294) Energy Consumption by End-Use Sector Residential Energy Use John Cymbalsky (john.cymbalsky@eia.doe.gov, 202-586-4815) Commercial Energy Use Erin Boedecker (erin.boedecker@eia.doe.gov, 202-586-4791)

374

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Natural Gas Natural gas is the fastest growing primary energy source in the IEO2005 forecast. Consumption of natural gas is projected to increase by nearly 70 percent between 2002 and 2025, with the most robust growth in demand expected among the emerging economies. Figure 34. World Natural Gas Consumption, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center on 202-586-8800. Figure Data Figure 35. Natural Gas Consumption by Region, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 36. Increase in Natural Gas Consumption by Region and Country, 2002-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data

375

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption by End-Use Sector Energy Consumption by End-Use Sector In the IEO2005 projections, end-use energy consumption in the residential, commercial, industrial, and transportation sectors varies widely among regions and from country to country. One way of looking at the future of world energy markets is to consider trends in energy consumption at the end-use sector level. With the exception of the transportation sector, which is almost universally dominated by petroleum products at present, the mix of energy use in the residential, commercial, and industrial sectors can vary widely from country to country, depending on a combination of regional factors, such as the availability of energy resources, the level of economic development, and political, social, and demographic factors. This chapter outlines the International Energy Outlook 2005 (IEO2005) forecast for regional energy consumption by end-use sector.

376

Volatility forecasting with smooth transition exponential smoothing  

Science Journals Connector (OSTI)

Adaptive exponential smoothing methods allow smoothing parameters to change over time, in order to adapt to changes in the characteristics of the time series. This paper presents a new adaptive method for predicting the volatility in financial returns. It enables the smoothing parameter to vary as a logistic function of user-specified variables. The approach is analogous to that used to model time-varying parameters in smooth transition generalised autoregressive conditional heteroskedastic (GARCH) models. These non-linear models allow the dynamics of the conditional variance model to be influenced by the sign and size of past shocks. These factors can also be used as transition variables in the new smooth transition exponential smoothing (STES) approach. Parameters are estimated for the method by minimising the sum of squared deviations between realised and forecast volatility. Using stock index data, the new method gave encouraging results when compared to fixed parameter exponential smoothing and a variety of GARCH models.

James W. Taylor

2004-01-01T23:59:59.000Z

377

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect (OSTI)

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

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

2014-07-09T23:59:59.000Z

378

Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumption to the Annual Energy Outlook Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

379

Annual energy outlook 1997 with projections to 2015  

SciTech Connect (OSTI)

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

NONE

1996-12-01T23:59:59.000Z

380

Coal production forecast and low carbon policies in China  

Science Journals Connector (OSTI)

With rapid economic growth and industrial expansion, China consumes more coal than any other nation. Therefore, it is particularly crucial to forecast China's coal production to help managers make strategic decisions concerning China's policies intended to reduce carbon emissions and concerning the country's future needs for domestic and imported coal. Such decisions, which must consider results from forecasts, will have important national and international effects. This article proposes three improved forecasting models based on grey systems theory: the Discrete Grey Model (DGM), the Rolling DGM (RDGM), and the p value RDGM. We use the statistical data of coal production in China from 1949 to 2005 to validate the effectiveness of these improved models to forecast the data from 2006 to 2010. The performance of the models demonstrates that the p value RDGM has the best forecasting behaviour over this historical time period. Furthermore, this paper forecasts coal production from 2011 to 2015 and suggests some policies for reducing carbon and other emissions that accompany the rise in forecasted coal production.

Jianzhou Wang; Yao Dong; Jie Wu; Ren Mu; He Jiang

2011-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

EIA - International Energy Outlook 2009-Appendix I. Comparisons With  

Gasoline and Diesel Fuel Update (EIA)

I. Comparisons With International Energy Agency and IEO2008 Projections I. Comparisons With International Energy Agency and IEO2008 Projections International Energy Outlook 2009 Appendix I. Comparisons With International Energy Agency and IEO2008 Projections Table I1. Comparison of IEO2009 and IEA World Energy Consumption Growth Rates by Region, 2006-2015 (Average Annual Percent Growth). Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table I2. Comparison of IEO2009 and IEA World Energy Consumption Growth Rates by Region, 2015-2030 (Average Annual Percent Growth). Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table I3. Comparison of IEO2009 and IEA World Energy Consumption Growth Rates by Fuel, 2006-2015 (Average Annual Percent Growth). Need help, contact the National Energy Information Center at 202-586-8800.

382

FY 1996 solid waste integrated life-cycle forecast container summary volume 1 and 2  

SciTech Connect (OSTI)

For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the containers expected to be used for these waste shipments from 1996 through the remaining life cycle of the Hanford Site. In previous years, forecast data have been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to the more detailed report on waste volumes: WHC-EP0900, FY 1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary. Both of these documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on the types of containers that will be used for packaging low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major waste generators for each waste category and container type are also discussed. Containers used for low-level waste (LLW) are described in Appendix A, since LLW requires minimal treatment and storage prior to onsite disposal in the LLW burial grounds. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste are expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters.

Valero, O.J.

1996-04-23T23:59:59.000Z

383

Forecasting the daily outbreak of topic-level political risk from social media using hidden Markov model-based techniques  

Science Journals Connector (OSTI)

Abstract Nowadays, as an arena of politics, social media ignites political protests, so analyzing topics discussed negatively in the social media has increased in importance for detecting a nation's political risk. In this context, this paper designs and examines an automatic approach to forecast the daily outbreak of political risk from social media at a topic level. It evaluates the forecasting performances of topic features, investigated among the previous works that analyze social media data for politics, hidden Markov model (HMM)-based techniques, widely used for the anomaly detection with time-series data, and detection models, into which the topic features and the detection techniques are combined. When applied to South Korea's Web forum, Daum Agora, statistical comparisons with the constraints of false positive rate of political risk, and eventually the predictive governance benefits the people.

Jong Hwan Suh

2014-01-01T23:59:59.000Z

384

Measuring the forecasting accuracy of models: evidence from industrialised countries  

Science Journals Connector (OSTI)

This paper uses the approach suggested by Akrigay (1989), Tse and Tung (1992) and Dimson and Marsh (1990) to examine the forecasting accuracy of stock price index models for industrialised markets. The focus of this paper is to compare the Mean Absolute Percentage Error (MAPE) of three models, that is, the Random Walk model, the Single Exponential Smoothing model and the Conditional Heteroskedastic model with the MAPE of the benchmark Naive Forecast 1 case. We do not evidence that a single model to provide better forecasting accuracy results compared to other models.

Athanasios Koulakiotis; Apostolos Dasilas

2009-01-01T23:59:59.000Z

385

Annual Energy Outlook with Projections to 2025 - Preface  

Gasoline and Diesel Fuel Update (EIA)

Preface Preface Annual Energy Outlook 2005 Preface The Annual Energy Outlook 2005 (AEO2005) presents midterm forecasts of energy supply, demand, and prices through 2025 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA’s National Energy Modeling System (NEMS). The report begins with an “Overview” summarizing the AEO2005 reference case. The next section, “Legislation and Regulations,” discusses evolving legislation and regulatory issues, including legislation and regulations that have been enacted and some that are proposed. Next, the “Issues in Focus” section discusses key energy market issues and examines their potential impacts. In particular, it includes a discussion of the world oil price assumptions used in the reference case and four alternative world oil price cases examined in AEO2005. “Issues in Focus” is followed by “Market Trends,” which provides a summary of energy market trends in the AEO2005 forecast.

386

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

E-Print Network [OSTI]

Solar irradiance data . . . . . . . . . . . . .Accuracy . . . . . . . . . . . . . . . . . Solar Resourcev Uncertainty In Solar Resource: Forecasting

Marquez, Ricardo

2012-01-01T23:59:59.000Z

387

Natural Gas Annual 2006  

Gasoline and Diesel Fuel Update (EIA)

6 6 Released: October 31, 2007 The Natural Gas Annual 2006 Summary Highlights provides an overview of the supply and disposition of natural gas in 2006 and is intended as a supplement to the Natural Gas Annual 2006. The Natural Gas Annual 2006 Summary Highlights provides an overview of the supply and disposition of natural gas in 2006 and is intended as a supplement to the Natural Gas Annual 2006. Natural Gas Annual --- Full report in PDF (5 MB) Special Files --- All CSV files contained in a self-extracting executable file. Respondent/Company Level Natural Gas Data Files Annual Natural and Supplemental Gas Supply and Disposition Company level data (1996 to 2007) as reported on Form EIA-176 are provided in the EIA-176 Query System and selected data files. EIA-191A Field Level Underground Natural Gas Storage Data: Detailed annual data (2006 and 2007) of storage field capacity, field type, and maximum deliverability as of December 31st of the report year, as reported by operators of all U.S. underground natural gas storage fields.

388

U.S. Energy Information Administration | Annual Energy Outlook 2013  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2013 Annual Energy Outlook 2013 Source: U.S. Energy Information Administration, Office of Energy Analysis. U.S. Energy Information Administration / Annual Energy Outlook 2010 213 Appendix F Regional Maps Figure F1. United States Census Divisions Pacific East South Central South Atlantic Middle Atlantic New England West South Central West North Central East North Central Mountain AK WA MT WY ID NV UT CO AZ NM TX OK IA KS MO IL IN KY TN MS AL FL GA SC NC WV PA NJ MD DE NY CT VT ME RI MA NH VA WI MI OH NE SD MN ND AR LA OR CA HI Middle Atlantic New England East North Central West North Central Pacific West South Central East South Central South Atlantic Mountain Source: U.S. Energy Information Administration, Office of Integrated Analysis and Forecasting. Appendix F Regional Maps Figure F1. United States Census Divisions U.S. Energy Information Administration | Annual Energy Outlook 2013

389

School of Engineering Annual Report  

E-Print Network [OSTI]

School of Engineering 1 9 9 9 Annual Report #12;University of ConnecticutUniversity of Connecticut School of EngineeringSchool of Engineering Annual ReportAnnual Report 1998-19991998-1999 TTable of Contentsable of Contents SCHOOL OF ENGINEERING Annual Report Summary

Alpay, S. Pamir

390

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

Open Energy Info (EERE)

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Jump to: navigation, search LEDSGP green logo.png FIND MORE DIA TOOLS This tool is part of the Development Impacts Assessment (DIA) Toolkit from the LEDS Global Partnership. Tool Summary LAUNCH TOOL Name: Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Agency/Company /Organization: Energy Sector Management Assistance Program of the World Bank Sector: Energy Focus Area: Non-renewable Energy Topics: Baseline projection, Co-benefits assessment, GHG inventory Resource Type: Software/modeling tools User Interface: Spreadsheet Complexity/Ease of Use: Simple Website: www.esmap.org/esmap/EFFECT Cost: Free Equivalent URI: www.esmap.org/esmap/EFFECT Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Screenshot

391

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory  

Gasoline and Diesel Fuel Update (EIA)

Forecasting Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels MICHAEL YE, ∗ JOHN ZYREN, ∗∗ AND JOANNE SHORE ∗∗ Abstract This paper presents a short-term monthly forecasting model of West Texas Intermedi- ate crude oil spot price using OECD petroleum inventory levels. Theoretically, petroleum inventory levels are a measure of the balance, or imbalance, between petroleum production and demand, and thus provide a good market barometer of crude oil price change. Based on an understanding of petroleum market fundamentals and observed market behavior during the post-Gulf War period, the model was developed with the objectives of being both simple and practical, with required data readily available. As a result, the model is useful to industry and government decision-makers in forecasting price and investigat- ing the impacts of changes on price, should inventories,

392

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network [OSTI]

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 ...

Choi, Han-Lim

2009-01-01T23:59:59.000Z

393

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network [OSTI]

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting Seasonal Prediction · POAMA · Issues for future Outline #12;Pacific Adaptation Strategy Assistance Program Major source Adaptation Strategy Assistance Program El Nino Mean State · Easterlies westward surface current upwelling

Lim, Eun-pa

394

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network [OSTI]

Forecasting volatility has held the attention of academics and practitioners all over the world. The objective for this master's thesis is to predict the volatility in stock market by using generalized autoregressive ...

Yang, Xiaorong

2008-01-01T23:59:59.000Z

395

Exponential smoothing with covariates applied to electricity demand forecast  

Science Journals Connector (OSTI)

Exponential smoothing methods are widely used as forecasting techniques in industry and business. Their usual formulation, however, does not allow covariates to be used for introducing extra information into the forecasting process. In this paper, we analyse an extension of the exponential smoothing formulation that allows the use of covariates and the joint estimation of all the unknowns in the model, which improves the forecasting results. The whole procedure is detailed with a real example on forecasting the daily demand for electricity in Spain. The time series of daily electricity demand contains two seasonal patterns: here the within-week seasonal cycle is modelled as usual in exponential smoothing, while the within-year cycle is modelled using covariates, specifically two harmonic explanatory variables. Calendar effects, such as national and local holidays and vacation periods, are also introduced using covariates. [Received 28 September 2010; Revised 6 March 2011, 2 October 2011; Accepted 16 October 2011

José D. Bermúdez

2013-01-01T23:59:59.000Z

396

Initial conditions estimation for improving forecast accuracy in exponential smoothing  

Science Journals Connector (OSTI)

In this paper we analyze the importance of initial conditions in exponential smoothing models on forecast errors and prediction intervals. We work with certain exponential smoothing models, namely Holts additive...

E. Vercher; A. Corbern-Vallet; J. V. Segura; J. D. Bermdez

2012-07-01T23:59:59.000Z

397

A Bayesian approach to forecast intermittent demand for seasonal products  

Science Journals Connector (OSTI)

This paper investigates the forecasting of a large fluctuating seasonal demand prior to peak sale season using a practical time series, collected from the US Census Bureau. Due to the extreme natural events (e.g. excessive snow fall and calamities), sales may not occur, inventory may not replenish and demand may set off unrecorded during the peak sale season. This characterises a seasonal time series to an intermittent category. A seasonal autoregressive integrated moving average (SARIMA), a multiplicative exponential smoothing (M-ES) and an effective modelling approach using Bayesian computational process are analysed in the context of seasonal and intermittent forecast. Several forecast error indicators and a cost factor are used to compare the models. In cost factor analysis, cost is measured optimally using dynamic programming model under periodic review policy. Experimental results demonstrate that Bayesian model performance is much superior to SARIMA and M-ES models, and efficient to forecast seasonal and intermittent demand.

Mohammad Anwar Rahman; Bhaba R. Sarker

2012-01-01T23:59:59.000Z

398

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones  

Science Journals Connector (OSTI)

The authors develop a statistical guidance product, the tropical cyclone tornado parameter (TCTP), for forecasting the probability of one or more tornadoes during a 6-h period that are associated with landfalling tropical cyclones affecting the ...

Matthew J. Onderlinde; Henry E. Fuelberg

2014-10-01T23:59:59.000Z

399

Wind Power Forecasting: State-of-the-Art 2009  

E-Print Network [OSTI]

Wind Power Forecasting: State-of-the-Art 2009 ANL/DIS-10-1 Decision and Information Sciences about Argonne and its pioneering science and technology programs, see www.anl.gov. #12;Wind Power

Kemner, Ken

400

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network [OSTI]

storms 17 4. Genesis Forecasts 17 5. Summary and Concluding Remarks 18 a. Atlantic Summary 18 statistical models, provided the best intensity guidance at each time period. The 2007 season marked the first

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Recently released EIA report presents international forecasting data  

SciTech Connect (OSTI)

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

NONE

1995-05-01T23:59:59.000Z

402

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

403

Information-Based Skill Scores for Probabilistic Forecasts  

Science Journals Connector (OSTI)

The information content, that is, the predictive capability, of a forecast system is often quantified with skill scores. This paper introduces two ranked mutual information skill (RMIS) scores, RMISO and RMISY, for the evaluation of probabilistic ...

Bodo Ahrens; Andr Walser

2008-01-01T23:59:59.000Z

404

A methodology for forecasting carbon dioxide flooding performance  

E-Print Network [OSTI]

A methodology was developed for forecasting carbon dioxide (CO2) flooding performance quickly and reliably. The feasibility of carbon dioxide flooding in the Dollarhide Clearfork "AB" Unit was evaluated using the methodology. This technique is very...

Marroquin Cabrera, Juan Carlos

2012-06-07T23:59:59.000Z

405

Evolutionary Optimization of an Ice Accretion Forecasting System  

Science Journals Connector (OSTI)

The ability to model and forecast accretion of ice on structures is very important for many industrial sectors. For example, studies conducted by the power transmission industry indicate that the majority of failures are caused by icing on ...

Pawel Pytlak; Petr Musilek; Edward Lozowski; Dan Arnold

2010-07-01T23:59:59.000Z

406

Diagnosing the Origin of Extended-Range Forecast Errors  

Science Journals Connector (OSTI)

Experiments with the ECMWF model are carried out to study the influence that a correct representation of the lower boundary conditions, the tropical atmosphere, and the Northern Hemisphere stratosphere would have on extended-range forecast skill ...

T. Jung; M. J. Miller; T. N. Palmer

2010-06-01T23:59:59.000Z

407

Application of an Improved SVM Algorithm for Wind Speed Forecasting  

Science Journals Connector (OSTI)

An improved Support Vector Machine (SVM) algorithm is used to forecast wind in Doubly Fed Induction Generator (DFIG) wind power system without aerodromometer. The ... Validation (CV) method. Finally, 3.6MW DFIG w...

Huaqiang Zhang; Xinsheng Wang; Yinxiao Wu

2011-01-01T23:59:59.000Z

408

Research on Development Trends of Power Load Forecasting Methods  

Science Journals Connector (OSTI)

In practical problem, number of samples is often limited, for complex issues such as power load forecasting, generally available historical data and information of impact factor are very ... support vector mechan...

Litong Dong; Jun Xu; Haibo Liu; Ying Guo

2014-01-01T23:59:59.000Z

409

Representing Forecast Error in a Convection-Permitting Ensemble System  

Science Journals Connector (OSTI)

Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have ...

Glen S. Romine; Craig S. Schwartz; Judith Berner; Kathryn R. Fossell; Chris Snyder; Jeff L. Anderson; Morris L. Weisman

2014-12-01T23:59:59.000Z

410

Weather Research and Forecasting Model 2.2 Documentation  

E-Print Network [OSTI]

................................................................................................. 20 3.1.2 Integrate's Flow of ControlWeather Research and Forecasting Model 2.2 Documentation: A Step-by-step guide of a Model Run .......................................................................................................................... 19 3.1 The Integrate Subroutine

Sadjadi, S. Masoud

411

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect (OSTI)

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

Yoo, Wucherl; Sim, Alex

2014-07-07T23:59:59.000Z

412

Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach  

Science Journals Connector (OSTI)

The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good ...

Juan J. Flores; Roberto Loaeza; Hctor Rodrguez; Erasmo Cadenas

2009-11-01T23:59:59.000Z

413

Annual Reports | Department of Energy  

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

Services » Annual Reports Services » Annual Reports Annual Reports Annual Reports December 28, 2012 Southeastern Power Administration 2012 Annual Report This report reflects our agency's programs,accomplishments, operational, and financial activities for the 12-month period beginning October 1, 2011, and ending September 30, 2012. December 31, 2011 Southeastern Power Administration 2011 Annual Report This report reflects our agency's programs, accomplishments, operational, and financial activities for the 12-month period beginning October 1, 2010, and ending September 31, 2011. December 27, 2010 Southeastern Power Administration 2010 Annual Report This report reflects our agency's programs, accomplishments, operational, and financial activities for the 12-month period beginning October 1, 2009,

414

Annual Reports | Department of Energy  

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

Annual Reports Annual Reports Annual Reports OHA Annual Reports Available for Download January 1, 2013 OHA 2012 ANNUAL REPORT Report on the FY 2011 operations of the Office of Hearings and Appeals (OHA). Here are highlights for the past year: September 30, 2011 OHA 2011 ANNUAL REPORT Report on the FY 2011 operations of the Office of Hearings and Appeals (OHA) September 30, 2010 OHA 2010 ANNUAL REPORT Report on the FY 2010 operations of the Office of Hearings and Appeals (OHA) September 8, 2009 OHA 2009 ANNUAL REPORT Report on the FY 2009 operations of the Office of Hearings and Appeals (OHA) September 30, 2008 OHA 2008 ANNUAL REPORT Report on the FY 2008 operations of the Office of Hearings and Appeals (OHA) September 30, 2007 OHA 2007 ANNUAL REPORT Report on the FY 2007 operations of the Office of Hearings and Appeals

415

A model for short term electric load forecasting  

E-Print Network [OSTI]

A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE, III Submitted to the Graduate College of Texas ASM University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE May 1975 Major... Subject: Electrical Engineering A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE& III Approved as to style and content by: (Chairman of Committee) (Head Depart t) (Member) ;(Me r (Member) (Member) May 1975 ABSTRACT...

Tigue, John Robert

1975-01-01T23:59:59.000Z

416

Radiation fog forecasting using a 1-dimensional model  

E-Print Network [OSTI]

measuring site (Molly Caren), the soil moisture measuring site (Wilmington), and (b) location of the forecast site (Ohio River Basin near Cincinnati including Lunken airport) . . 23 3 An example of a COBEL configuration file for 25 August 1996, showing... measuring site (Molly Caren), the soil moisture measuring site (Wilmington), and (b) location of the forecast site (Ohio River Basin near Cincinnati including Lunken airport) . . 23 3 An example of a COBEL configuration file for 25 August 1996, showing...

Peyraud, Lionel

2012-06-07T23:59:59.000Z

417

Natural Gas Annual, 2004  

Gasoline and Diesel Fuel Update (EIA)

4 4 EIA Home > Natural Gas > Natural Gas Data Publications Natural Gas Annual, 2004 Natural Gas Annual 2004 Release date: December 19, 2005 Next release date: January 2007 The Natural Gas Annual, 2004 provides information on the supply and disposition of natural gas in the United States. Production, transmission, storage, deliveries, and price data are published by State for 2004. Summary data are presented for each State for 2000 to 2004. The data that appear in the tables of the Natural Gas Annual, 2004 is available as self-extracting executable file or CSV file format. This volume emphasizes information for 2004, although some tables show a five-year history. Please read the file entitled README.V1 for a description and documentation of information included in this file.

418

Petroleum Marketing Annual  

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

5 Entire . The entire report as a single file. PDF 2.9MB . . Front Matter . Petroleum Marketing Annual Cover Page, Contacts, Preface, and Table of Contents PDF . . Highlights ....

419

Petroleum Marketing Annual 2007  

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

7 Released: August 29, 2008 Petroleum Marketing Annual --- Full report in PDF (1.2 MB) Summary Statistics Summary Statistics Tables PDF 1 Crude Oil Prices PDF TXT 1A Refiner...

420

Petroleum Marketing Annual  

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

9 Entire . The entire report as a single file. PDF 1.2MB . Front Matter . Petroleum Marketing Annual Cover Page, Preface, and Table of Contents PDF . Highlights . Petroleum...

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

Petroleum Marketing Annual 1997  

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

7 Entire . The entire report as a single file. PDF 1.2MB . . Front Matter . Petroleum Marketing Annual Cover Page, Contacts, Preface, and Table of Contents PDF . . Highlights ....

422

Petroleum Marketing Annual 2008  

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

8 Released: August 27, 2009 Petroleum Marketing Annual --- Full report in PDF (1.2 MB) Summary Statistics Summary Statistics Tables PDF 1 Crude Oil Prices PDF TXT 1A Refiner...

423

Petroleum Marketing Annual  

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

6 Entire . The entire report as a single file. PDF 2.9MB . . Front Matter . Petroleum Marketing Annual Cover Page, Contacts, Preface, and Table of Contents PDF . . Highlights ....

424

Petroleum Marketing Annual  

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

8 Entire . The entire report as a single file. PDF 1.2MB . Front Matter . Petroleum Marketing Annual Cover Page, Preface, and Table of Contents PDF . Highlights . Petroleum...

425

2009 Annual Report  

SciTech Connect (OSTI)

This annual report includes: a brief overview of Western; some of our major achievements in FY 2009; FY 2009 customer Integrated Resource Planning, or IRP, survey; and financial data.

none,

2009-01-01T23:59:59.000Z

426

2010 Annual Report  

SciTech Connect (OSTI)

This annual report includes: an overview of Western; approaches for future hydropower and transmission service; major achievements in FY 2010; FY 2010 customer Integrated Resource Planning, or IRP, survey; and financial data.

none,

2010-01-01T23:59:59.000Z

427

RAS ANNUAL REPORT 2002:  

Science Journals Connector (OSTI)

......ESO; the PPARC Science Committee funding...replacement. 11. Education Committee The Mary...video tapes, posters or optical equipment...Association for Science Education's annual meeting...good support. Science Year 2001. The......

The RAS Annual General Meeting

2002-10-01T23:59:59.000Z

428

Annual Energy Outlook 2013  

Gasoline and Diesel Fuel Update (EIA)

Mayer Brown Annual Global Energy Conference May 15, 2014 | Houston, TX By Adam Sieminski, EIA Administrator The U.S. has experienced a rapid increase in natural gas and oil...

429

Annual Report Directory2009  

E-Print Network [OSTI]

09 Annual Report #12;Directory2009 Chancellor Rex Williams, BE(Hons) Pro-Chancellor L John Wood-Chancellor Ian Town, MBChB(Otago), DM(Soton) University Registrar Jeff Field, JP, MA, DipJ, Dip

Hickman, Mark

430

Annual Reports - Hanford Site  

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

the 200 East Area April 2014 0 SGW-54165 2012 Groundwater Annual Report August 2013 0 DOERL-2013-22 The Regulatory Basis and Implementation of a Graded Approach to Evaluation...

431

NARUC Annual Meeting  

Broader source: Energy.gov [DOE]

The National Association of Regulatory Utility Commissioneers (NARUC) is hosting its annual meeting in San Fransisco, CA, from Nov. 16-19, 2014. Registration and housing begins Aug. 27.

432

Natural gas annual 1994  

SciTech Connect (OSTI)

The Natural Gas Annual provides information on the supply and disposition of natural gas to a wide audience including industry, consumers, Federal and State agencies, and educational institutions. The 1994 data are presented in a sequence that follows natural gas (including supplemental supplies) from its production to its end use. This is followed by tables summarizing natural gas supply and disposition from 1990 to 1994 for each Census Division and each State. Annual historical data are shown at the national level.

NONE

1995-11-17T23:59:59.000Z

433

Natural gas annual 1995  

SciTech Connect (OSTI)

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

NONE

1996-11-01T23:59:59.000Z

434

Weather-based forecasts of California crop yields  

SciTech Connect (OSTI)

Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over the 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.

Lobell, D B; Cahill, K N; Field, C B

2005-09-26T23:59:59.000Z

435

Wave height forecasting in Dayyer, the Persian Gulf  

Science Journals Connector (OSTI)

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.

B. Kamranzad; A. Etemad-Shahidi; M.H. Kazeminezhad

2011-01-01T23:59:59.000Z

436

Natural Gas Annual 2007  

Gasoline and Diesel Fuel Update (EIA)

7 7 Released: January 28, 2009 The Natural Gas Annual 2007 provides information on the supply and disposition of natural gas in the United States. Production, transmission, storage, deliveries, and price data are published by State for 2007. Summary data are presented for each State for 2003 to 2007. The Natural Gas Annual 2007 Summary Highlights provides an overview of the supply and disposition of natural gas in 2007 and is intended as a supplement to the Natural Gas Annual 2007. Natural Gas Annual --- Full report in PDF (5 MB) Special Files --- All CSV files contained in a self-extracting executable file. Respondent/Company Level Natural Gas Data Files Annual Natural and Supplemental Gas Supply and Disposition Company level data (1996 to 2007) as reported on Form EIA-176 are provided in the EIA-176 Query System and selected data files. EIA-191A Field Level Underground Natural Gas Storage Data: Detailed annual data (2005 to 2007) of storage field capacity, field type, and maximum deliverability as of December 31st of the report year, as reported by operators of all U.S. underground natural gas storage fields.

437

Natural Gas Annual 2009  

Gasoline and Diesel Fuel Update (EIA)

9 9 Released: December 28, 2010 The Natural Gas Annual 2009 provides information on the supply and disposition of natural gas in the United States. Production, transmission, storage, deliveries, and price data are published by State for 2009. Summary data are presented for each State for 2005 to 2009. The Natural Gas Annual 2009 Summary Highlights provides an overview of the supply and disposition of natural gas in 2009 and is intended as a supplement to the Natural Gas Annual 2009. Natural Gas Annual --- Full report in PDF (5 MB) Special Files --- All CSV files contained in a self-extracting executable file. Respondent/Company Level Natural Gas Data Files Annual Natural and Supplemental Gas Supply and Disposition Company level data (1996 to 2009) as reported on Form EIA-176 are provided in the EIA-176 Query System and selected data files. EIA-191A Field Level Underground Natural Gas Storage Data: Detailed annual data (2005 to 2009) of storage field capacity, field type, and maximum deliverability as of December 31st of the report year, as reported by operators of all U.S. underground natural gas storage fields.

438

Natural Gas Annual 2008  

Gasoline and Diesel Fuel Update (EIA)

8 8 Released: March 2, 2010 The Natural Gas Annual 2008 provides information on the supply and disposition of natural gas in the United States. Production, transmission, storage, deliveries, and price data are published by State for 2008. Summary data are presented for each State for 2004 to 2008. The Natural Gas Annual 2008 Summary Highlights provides an overview of the supply and disposition of natural gas in 2008 and is intended as a supplement to the Natural Gas Annual 2008. Natural Gas Annual --- Full report in PDF (5 MB) Special Files --- All CSV files contained in a self-extracting executable file. Respondent/Company Level Natural Gas Data Files Annual Natural and Supplemental Gas Supply and Disposition Company level data (1996 to 2008) as reported on Form EIA-176 are provided in the EIA-176 Query System and selected data files. EIA-191A Field Level Underground Natural Gas Storage Data: Detailed annual data (2005 to 2008) of storage field capacity, field type, and maximum deliverability as of December 31st of the report year, as reported by operators of all U.S. underground natural gas storage fields.

439

annual maximum extent: Topics by E-print Network  

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

Report 2010Annual Report Engineering Websites Summary: 2010Annual Report 2010Annual Report 2010Annual Report 2010 Annual Report Technology Transfer Office Assistant Vice...

440

EIA - Annual Energy Outlook 2013 Early Release  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2013 Annual Energy Outlook 2013 Release Dates: April 15 - May 2, 2013 | Next Early Release Date: December 16, 2013 (See release cycle changes) | correction | full report Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Table Title Formats Summary Reference Case Tables Year-by-Year Reference Case Tables Table 1. Total Energy Supply and Disposition Summary Table 2. Energy Consumption by Sector and Source Table 3. Energy Prices by Sector and Source Table 4. Residential Sector Key Indicators and Consumption

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

EIA - Annual Energy Outlook 2013 Early Release  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2013 Annual Energy Outlook 2013 Release Dates: April 15 - May 2, 2013 | Next Early Release Date: December 2013 (See release cycle changes) | correction | full report Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Table Title Formats Summary Reference Case Tables Year-by-Year Reference Case Tables Table 1. Total Energy Supply and Disposition Summary Table 2. Energy Consumption by Sector and Source Table 3. Energy Prices by Sector and Source Table 4. Residential Sector Key Indicators and Consumption

442

Petroleum supply annual 1995: Volume 2  

SciTech Connect (OSTI)

The Petroleum Supply Annual (PSA) contains information on the supply and disposition of crude oil and petroleum products. The publication reflects data that were collected from the petroleum industry during 1995 through monthly surveys. The PSA is divided into two volumes. This first volume contains three sections: Summary Statistics, Detailed Statistics, and selected Refinery Statistics each with final annual data. The second volume contains final statistics for each month of 1995, and replaces data previously published in the Petroleum Supply Monthly (PSM). The tables in Volumes 1 and 2 are similarly numbered to facilitate comparison between them. Explanatory Notes, located at the end of this publication, present information describing data collection, sources, estimation methodology, data quality control procedures, modifications to reporting requirements and interpretation of tables. Industry terminology and product definitions are listed alphabetically in the Glossary.

NONE

1996-06-01T23:59:59.000Z

443

Petroleum supply annual 1996: Volume 2  

SciTech Connect (OSTI)

The Petroleum Supply Annual (PSA) contains information on the supply and disposition of crude oil and petroleum products. The publication reflects data that were collected from the petroleum industry during 1996 through monthly surveys. The PSA is divided into two volumes. The first volume contains three sections: Summary Statistics, Detailed Statistics, and Refinery Capacity; each with final annual data. The second volume contains final statistics for each month of 1996, and replaces data previously published in the Petroleum Supply Monthly (PSM). The tables in Volumes 1 and 2 are similarly numbered to facilitate comparison between them. Explanatory Notes, located at the end of this publication, present information describing data collection, sources, estimation methodology, data quality control procedures, modifications to reporting requirements and interpretation of tables. Industry terminology and product definitions are listed alphabetically in the Glossary. 35 tabs.

NONE

1997-06-01T23:59:59.000Z

444

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

The International Energy Outlook 2005 (IEO2005) presents an assessment by the Energy Information Administration (EIA) of the outlook for international energy markets through 2025. U.S. projections appearing in IEO2005 are consistent with those published in EIA's Annual Energy Outlook 2005 (AEO2005), which was prepared using the National Energy Modeling System (NEMS). The International Energy Outlook 2005 (IEO2005) presents an assessment by the Energy Information Administration (EIA) of the outlook for international energy markets through 2025. U.S. projections appearing in IEO2005 are consistent with those published in EIA's Annual Energy Outlook 2005 (AEO2005), which was prepared using the National Energy Modeling System (NEMS). Table of Contents Projection Tables Reference Case High Economic Growth Case Low Economic Growth Case Reference Case Projections by End-Use Sector and Region Projections of Oil Production Capacity and Oil Production in Three Cases Projections of Nuclear Generating Capacity Highlights World Energy and Economic Outlook Outlook for World Energy Consumption World Economic Outlook Alternative Growth Cases

445

Solid waste integrated forecast technical (SWIFT) report: FY1997 to FY 2070, Revision 1  

SciTech Connect (OSTI)

This web site provides an up-to-date report on the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons with previous forecasts and with other national data sources. This web site does not include: liquid waste (current or future generation); waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); or waste that has been received by the WM Project to date (i.e., inventory waste). The focus of this web site is on low-level mixed waste (LLMW), and transuranic waste (both non-mixed and mixed) (TRU(M)). Some details on low-level waste and hazardous waste are also provided. Currently, this web site is reporting data th at was requested on 10/14/96 and submitted on 10/25/96. The data represent a life cycle forecast covering all reported activities from FY97 through the end of each program's life cycle. Therefore, these data represent revisions from the previous FY97.0 Data Version, due primarily to revised estimates from PNNL. There is some useful information about the structure of this report in the SWIFT Report Web Site Overview.

Valero, O.J.; Templeton, K.J.; Morgan, J.

1997-01-07T23:59:59.000Z

446

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

SciTech Connect (OSTI)

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

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

1993-05-01T23:59:59.000Z

447

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

E-Print Network [OSTI]

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

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

2008-09-17T23:59:59.000Z

448

Annual DOE Occupational Radiation Exposure Reports | Department...  

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

Annual DOE Occupational Radiation Exposure Reports Annual DOE Occupational Radiation Exposure Reports September 24, 2013 Annual DOE Occupational Radiation Exposure | 2012 Report...

449

Annual Progress Reports | Department of Energy  

Energy Savers [EERE]

Annual Progress Reports Annual Progress Reports This page contains annual progress reports for the Fuel Cell Technologies Office and the Transportation Fuel Cell Power...

450

Geothermal Technologies Office Annual Report 2012 | Department...  

Office of Environmental Management (EM)

Geothermal Technologies Office Annual Report 2012 Geothermal Technologies Office Annual Report 2012 This annual report for the U.S. Department of Energys Geothermal Technologies...

451

Stanford Geothermal Workshop 2012 Annual Meeting | Department...  

Energy Savers [EERE]

2012 Annual Meeting Stanford Geothermal Workshop 2012 Annual Meeting Presentation slides for the Stanford Geothermal Workshop Annual Meeting presentation by Doug Hollett,...

452

Waste Processing Annual Technology Development Report 2007 |...  

Office of Environmental Management (EM)

Waste Processing Annual Technology Development Report 2007 Waste Processing Annual Technology Development Report 2007 Waste Processing Annual Technology Development Report 2007...

453

Combined Fiscal Year (FY) 2014 Annual Performance Results and FYs 2015 and 2016 Annual Performance Plan  

Broader source: Energy.gov [DOE]

Combined Fiscal Year (FY) 2014 Annual Performance Results and FYs 2015 and 2016 Annual Performance Plan

454

DOE/EIA-0131(09) Natural Gas Annual 2009 Publication Date:  

Gasoline and Diesel Fuel Update (EIA)

09) 09) Natural Gas Annual 2009 Publication Date: December 2010 Energy Information Administration Office of Oil, Gas, and Coal Supply Statistics U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Energy Information Administration/Natural Gas Annual 2009 ii Contacts The Natural Gas Annual 2009 is prepared by the Energy

455

A U.S. Energy Information Administration | Annual Energy Outlook 2011  

Gasoline and Diesel Fuel Update (EIA)

U.S. Energy Information Administration | Annual Energy Outlook 2011 U.S. Energy Information Administration | Annual Energy Outlook 2011 Annual Energy Outlook 2011 Assumptions to the July 2011 www.eia.gov U.S. Depa rtment of Energy W ashington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Contents Introduction ......................................................................................................................................................................................................................3

456

DOE/EIA-0131(08) Natural Gas Annual 2008 Publication Date:  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Natural Gas Annual 2008 Publication Date: March 2010 Energy Information Administration Office of Oil and Gas U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Energy Information Administration/Natural Gas Annual 2008 ii Contacts The Natural Gas Annual 2008 is prepared by the Energy

457

A suite of metrics for assessing the performance of solar power forecasting  

Science Journals Connector (OSTI)

Abstract Forecasting solar energy generation is a challenging task because of the variety of solar power systems and weather regimes encountered. Inaccurate forecasts can result in substantial economic losses and power system reliability issues. One of the key challenges is the unavailability of a consistent and robust set of metrics to measure the accuracy of a solar forecast. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, and applications) that were developed as part of the U.S. Department of Energy SunShot Initiatives efforts to improve the accuracy of solar forecasting. In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design-of-experiments methodology in conjunction with response surface, sensitivity analysis, and nonparametric statistical testing methods. The three types of forecasting improvements are (i) uniform forecasting improvements when there is not a ramp, (ii) ramp forecasting magnitude improvements, and (iii) ramp forecasting threshold changes. Day-ahead and 1-hour-ahead forecasts for both simulated and actual solar power plants are analyzed. The results show that the proposed metrics can efficiently evaluate the quality of solar forecasts and assess the economic and reliability impacts of improved solar forecasting. Sensitivity analysis results show that (i) all proposed metrics are suitable to show the changes in the accuracy of solar forecasts with uniform forecasting improvements, and (ii) the metrics of skewness, kurtosis, and Rnyi entropy are specifically suitable to show the changes in the accuracy of solar forecasts with ramp forecasting improvements and a ramp forecasting threshold.

Jie Zhang; Anthony Florita; Bri-Mathias Hodge; Siyuan Lu; Hendrik F. Hamann; Venkat Banunarayanan; Anna M. Brockway

2015-01-01T23:59:59.000Z

458

Oil prices Brownian motion or mean reversion? A study using a one year ahead density forecast criterion  

Science Journals Connector (OSTI)

For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMAGARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion.

Nigel Meade

2010-01-01T23:59:59.000Z

459

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Oil Markets Oil Markets IEO2005 projects that world crude oil prices in real 2003 dollars will decline from their current level by 2010, then rise gradually through 2025. In the International Energy Outlook 2005 (IEO2005) reference case, world demand for crude oil grows from 78 million barrels per day in 2002 to 103 million barrels per day in 2015 and to just over 119 million barrels per day in 2025. Much of the growth in oil consumption is projected for the emerging Asian nations, where strong economic growth results in a robust increase in oil demand. Emerging Asia (including China and India) accounts for 45 percent of the total world increase in oil use over the forecast period in the IEO2005 reference case. The projected increase in world oil demand would require an increment to world production capability of more than 42 million barrels per day relative to the 2002 crude oil production capacity of 80.0 million barrels per day. Producers in the Organization of Petroleum Exporting Countries (OPEC) are expected to be the major source of production increases. In addition, non-OPEC supply is expected to remain highly competitive, with major increments to supply coming from offshore resources, especially in the Caspian Basin, Latin America, and deepwater West Africa. The estimates of incremental production are based on current proved reserves and a country-by-country assessment of ultimately recoverable petroleum. In the IEO2005 oil price cases, the substantial investment capital required to produce the incremental volumes is assumed to exist, and the investors are expected to receive at least a 10-percent return on investment.

460

Natural Gas Annual, 2003  

Gasoline and Diesel Fuel Update (EIA)

3 3 EIA Home > Natural Gas > Natural Gas Data Publications Natural Gas Annual, 2003 Natural Gas Annual 2003 Release date: December 22, 2004 Next release date: January 2006 The Natural Gas Annual, 2003 provides information on the supply and disposition of natural gas in the United States. Production, transmission, storage, deliveries, and price data are published by State for 2003. Summary data are presented for each State for 1999 to 2003. “The Natural Gas Industry and Markets in 2003” is a special report that provides an overview of the supply and disposition of natural gas in 2003 and is intended as a supplement to the Natural Gas Annual 2003. The data that appear in the tables of the Natural Gas Annual, 2003 is available as self-extracting executable file or CSV file format. This volume emphasizes information for 2003, although some tables show a five-year history. Please read the file entitled README.V1 for a description and documentation of information included in this file.

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

Natural Gas Annual, 2002  

Gasoline and Diesel Fuel Update (EIA)

2 2 EIA Home > Natural Gas > Natural Gas Data Publications Natural Gas Annual, 2002 Natural Gas Annual 2002 Release date: January 29, 2004 Next release date: January 2005 The Natural Gas Annual, 2002 provides information on the supply and disposition of natural gas in the United States. Production, transmission, storage, deliveries, and price data are published by State for 2002. Summary data are presented for each State for 1998 to 2002. “The Natural Gas Industry and Markets in 2002” is a special report that provides an overview of the supply and disposition of natural gas in 2002 and is intended as a supplement to the Natural Gas Annual 2002. Changes to data sources for this Natural Gas Annual, as a result of ongoing data quality efforts, have resulted in revisions to several data series. Production volumes have been revised for the Federal offshore and several States. Several data series based on the Form EIA-176, including deliveries to end-users in several States, were also revised. Additionally, revisions have been made to include updates to the electric power and vehicle fuel end-use sectors.

462

Variational Assimilation for Xenon Dynamical Forecasts in Neutronic using Advanced Background Error Covariance Matrix  

E-Print Network [OSTI]

Data assimilation method consists in combining all available pieces of information about a system to obtain optimal estimates of initial states. The different sources of information are weighted according to their accuracy by the means of error covariance matrices. Our purpose here is to evaluate the efficiency of variational data assimilation for the xenon induced oscillations forecasts in nuclear cores. In this paper we focus on the comparison between 3DVAR schemes with optimised background error covariance matrix B and a 4DVAR scheme. Tests were made in twin experiments using a simulation code which implements a mono-dimensional coupled model of xenon dynamics, thermal, and thermal-hydraulic processes. We enlighten the very good efficiency of the 4DVAR scheme as well as good results with the 3DVAR one using a careful multivariate modelling of B.

Ponot, Anglique; Bouriquet, Bertrand; Erhard, Patrick; Gratton, Serge; Thual, Olivier

2013-01-01T23:59:59.000Z

463

Variational assimilation for xenon dynamical forecasts in neutronic using advanced background error covariance matrix modelling  

Science Journals Connector (OSTI)

Abstract Data assimilation method consists in combining all available pieces of information about a system to obtain optimal estimates of initial states. The different sources of information are weighted according to their accuracy by the means of error covariance matrices. Our purpose here is to evaluate the efficiency of variational data assimilation for the xenon induced oscillations forecasts in nuclear cores. In this paper we focus on the comparison between 3DVAR schemes with optimised background error covariance matrix B and a 4DVAR scheme. Tests were made in twin experiments using a simulation code which implements a mono-dimensional coupled model of xenon dynamics, thermal, and thermalhydraulic processes. We enlighten the very good efficiency of the 4DVAR scheme as well as good results with the 3DVAR one using a careful multivariate modelling of B.

Anglique Ponot; Jean-Philippe Argaud; Bertrand Bouriquet; Patrick Erhard; Serge Gratton; Olivier Thual

2013-01-01T23:59:59.000Z

464

STFC Annual Delivery Plan Report 200STFC Annual Delivery Plan Report 200STFC Annual Delivery Plan Report 200STFC Annual Delivery Plan Report 2009/109/109/109/10 Annual Delivery Report  

E-Print Network [OSTI]

STFC Annual Delivery Plan Report 200STFC Annual Delivery Plan Report 200STFC Annual Delivery Plan Report 200STFC Annual Delivery Plan Report 2009/109/109/109/10 0 Annual Delivery Report 2009/10 August 2010 #12;STFC Annual Delivery Plan Report 200STFC Annual Delivery Plan Report 200STFC Annual Delivery

465

Annual Report Curtin University of Technology Annual Report 2006  

E-Print Network [OSTI]

Annual Report 2006 #12;#12;Curtin University of Technology Annual Report 2006 Chancellor's Foreword ........................................................................................................................ 2 Vice-Chancellor's Report ................................................................................................... 25 Report on Operations 28 Governance of the University

466

Annual Report GreenTouch 20102011 Annual Report  

E-Print Network [OSTI]

2010­2011 Annual Report #12;1 GreenTouch 2010­2011 Annual Report Contents Chairman's Letter............................................................ 30 Service Energy Aware Sustainable Optical Networks (SEASON............................................................................................ 43 Beyond Cellular Green Generation (BCG2

Lefèvre, Laurent

467

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

SciTech Connect (OSTI)

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

Hirst, E.

1992-06-01T23:59:59.000Z

468

Survey of Variable Generation Forecasting in the West: August 2011 - June 2012  

SciTech Connect (OSTI)

This report surveyed Western Interconnection Balancing Authorities regarding their implementation of variable generation forecasting, the lessons learned to date, and recommendations they would offer to other Balancing Authorities who are considering variable generation forecasting. Our survey found that variable generation forecasting is at an early implementation stage in the West. Eight of the eleven Balancing Authorities interviewed began forecasting in 2008 or later. It also appears that less than one-half of the Balancing Authorities in the West are currently utilizing variable generation forecasting, suggesting that more Balancing Authorities in the West will engage in variable generation forecasting should more variable generation capacity be added.

Porter, K.; Rogers, J.

2012-04-01T23:59:59.000Z

469

EIS-0380: Annual Mitigation Action Plan Annual Report  

Broader source: Energy.gov [DOE]

Los Alamos National Laboratory Site-Wide Environmental Impact Statement Fiscal Year 2013 Mitigation Action Plan Annual Report

470

Natural Gas Annual, 2001  

Gasoline and Diesel Fuel Update (EIA)

1 1 EIA Home > Natural Gas > Natural Gas Data Publications Natural Gas Annual, 2001 The Natural Gas Annual, 2001 provides information on the supply and disposition of natural gas in the United States. Production, transmission, storage, deliveries, and price data are published by State for 2001. Summary data are presented for each State for 1997 to 2001. The data that appear in the tables of the Natural Gas Annual, 2001 are available as self-extracting executable files in ASCII TXT or CSV file format. This volume emphasizes information for 2001, although some tables show a five-year history. Please read the file entitled README.V1 for a description and documentation of information included in this file. Also available are files containing the following data: Summary Statistics - Natural Gas in the United States, 1997-2001 (Table 1) ASCII TXT, and Natural Gas Supply and Disposition by State, 2001 (Table 2) ASCII TXT.

471

Annual2.vp  

Gasoline and Diesel Fuel Update (EIA)

9 9 October 2000 Energy Information Administration Office of Oil and Gas U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the Department of Energy. The information contained herein should not be construed as advocating or reflecting any policy of the Department of Energy or any other organization. DOE/EIA-E-0110(99) Distribution Category/UC-960 ii Energy Information Administration / Historical Natural Gas Annual 1930 Through 1999 Contacts The Historical Natural Gas Annual is prepared by the Energy Information Administration, Office of Oil and Gas, Natural Gas Division, under the direction of Joan E. Heinkel. General questions and comments concerning the contents of the Historical Natural Gas Annual may be obtained from the National Energy Information

472

Annual Energy Review 1995  

Gasoline and Diesel Fuel Update (EIA)

4(95) 4(95) Distribution Category UC-950 Annual Energy Review 1995 July 1996 Energy Information Administration Office of Energy Markets and End Use U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the Department of Energy. The information contained herein should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. Annual Energy Review 1995 The Annual Energy Review (AER) presents the Energy Information Ad- ministration's historical energy statistics. For most series, statistics are given for every year from 1949 through 1995. The statistics, expressed in either physical units or British thermal units, cover all major energy activities, including consumption, production, trade,

473

Annual2.vp  

Gasoline and Diesel Fuel Update (EIA)

8 8 November 1999 Energy Information Administration Office of Oil and Gas U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the Department of Energy. The information contained herein should not be construed as advocating or reflecting any policy of the Department of Energy or any other organization. DOE/EIA-E-0110(98) Distribution Category/UC-960 ii Energy Information Administration / Historical Natural Gas Annual 1930 Through 1998 Contacts The Historical Natural Gas Annual is prepared by the En- ergy Information Administration, Office of Oil and Gas, Natural Gas Division, under the direction of Joan E. Heinkel. General questions and comments concerning the contents of the Historical Natural Gas Annual may be obtained from the National Energy Information

474

Petroleum Marketing Annual 2009  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Marketing Annual 2009 Petroleum Marketing Annual 2009 Released: August 6, 2010 Monthly price and volume statistics on crude oil and petroleum products at a national, regional and state level. Notice: Changes to EIA Petroleum Data Program Petroleum Marketing Annual --- Full report in PDF (1.2 MB) Previous Issues --- Previous reports are available on the historical page. Summary Statistics Summary Statistics Tables PDF 1 Crude Oil Prices PDF TXT 1A Refiner Acquisition Cost of Crude Oil by PAD Districts HTML PDF TXT 2 U.S. Refiner Prices of Petroleum Products to End Users HTML PDF TXT 3 U.S. Refiner Volumes of Petroleum Products to End Users PDF TXT Motor Gasoline to End Users HTML Residual Fuel Oil and No. 4 Fuel to End Users HTML Other Petroleum Products to End Users HTML

475

ANNUAL FUNDING NOTICE for  

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

HRD-BEN-2012-0038 HRD-BEN-2012-0038 Date: April 30, 2012 To: All Plan Participants From: SRNS Benefits Administration Re: Savannah River Nuclear Solutions, LLC Multiple Employer Pension Plan Funding Notices Attached are three pension plan notices of which the wording is very closely regulated by the federal government. Therefore, we are providing a quick overview of the notices here. 2011 Annual Funding Notice: The government previously required the distribution of an abbreviated form of this information and called it the Summary Annual Report (SAR). You may have seen SARs from previous years posted on the SRS Intranet "InSite". The enhanced version of that SAR is called the Annual Funding Notice. This particular notice covers the plan year 2011 and is issued after the

476

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))

1992-02-01T23:59:59.000Z

477

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

1992-02-01T23:59:59.000Z

478

ANNUAL FUNDING NOTICE for  

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

HRD-BEN-2013-0029 HRD-BEN-2013-0029 Date: April 30, 2013 To: All Plan Participants From: SRNS Benefits Administration Re: Savannah River Nuclear Solutions, LLC Multiple Employer Pension Plan Funding Notices Attached is the pension plan notice of which the wording is very closely regulated by the federal government. Therefore, we are providing a quick overview of the notice here. 2012 Annual Funding Notice: The government previously required the distribution of an abbreviated form of this information and called it the Summary Annual Report (SAR). You may have seen SARs from previous years posted on the SRS Intranet "InSite". The enhanced version of that

479

Natural gas annual 1997  

SciTech Connect (OSTI)

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

NONE

1998-10-01T23:59:59.000Z

480

Vehicle Technologies Office: Annual Progress Reports  

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

Annual Progress Reports Annual Progress Reports to someone by E-mail Share Vehicle Technologies Office: Annual Progress Reports on Facebook Tweet about Vehicle Technologies Office: Annual Progress Reports on Twitter Bookmark Vehicle Technologies Office: Annual Progress Reports on Google Bookmark Vehicle Technologies Office: Annual Progress Reports on Delicious Rank Vehicle Technologies Office: Annual Progress Reports on Digg Find More places to share Vehicle Technologies Office: Annual Progress Reports on AddThis.com... Publications Key Publications Plans & Roadmaps Partnership Documents Annual Progress Reports Success Stories Conferences Proceedings Newsletters Analysis Software Tools Awards & Patents Glossary Annual Progress Reports 2013 DOE Vehicle Technologies Office Annual Merit Review

Note: This page contains sample records for the topic "forecast comparisons annual" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Numerical Simulation of 2010 Pakistan Flood in the Kabul River Basin by Using Lagged Ensemble Rainfall Forecasting  

Science Journals Connector (OSTI)

Lagged ensemble forecasting of rainfall and rainfallrunoffinundation (RRI) forecasting were applied to the devastating flood in the Kabul River basin, the first strike of the 2010 Pakistan flood. The forecasts were performed using the Global ...

Tomoki Ushiyama; Takahiro Sayama; Yuya Tatebe; Susumu Fujioka; Kazuhiko Fukami

2014-02-01T23:59:59.000Z

482

2013 Uranium Marketing Annual Report  

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

Industry Annual, Tables 28, 29, 30 and 31. 2003-13-Form EIA-858, "Uranium Marketing Annual Survey". Notes: Totals may not equal sum of components because of independent...

483

2013 Uranium Marketing Annual Report  

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

Industry Annual, Tables 10, 11 and 16. 2003-13-Form EIA-858, "Uranium Marketing Annual Survey". dollars per pound U 3 O 8 equivalent dollars per pound U 3 O 8...

484

2013 Uranium Marketing Annual Report  

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

Industry Annual, Tables 28, 29, 30 and 31. 2003-13-Form EIA-858, "Uranium Marketing Annual Survey". million pounds U 3 O 8 equivalent million pounds U 3 O 8 equivalent...

485

2013 Uranium Marketing Annual Report  

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

Industry Annual, Tables 22, 23, 25, and 27. 2003-13-Form EIA-858, "Uranium Marketing Annual Survey". - No data reported. 0 10 20 30 40 50 60 70 1994 1995 1996 1997...

486

EIA - Assumptions to the Annual Energy Outlook 2010 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2010 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2010 [1] (AEO2010), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in the AEO2010 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

487

EIA - Assumptions to the Annual Energy Outlook 2009 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2009 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2009 (AEO2009),1 including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2009 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

488

2011 Quality Council Annual Report  

Broader source: Energy.gov [DOE]

DEPARTMENT OF ENERGY QUALITY COUNCIL ANNUAL REPORT For Calendar Year 2011 Office of Health Safety and Security

489

NCAI 71st Annual Convention  

Broader source: Energy.gov [DOE]

Save the date for the National Congress of American Indians (NCAI) 71st Annual Convention at the Hyatt Regency Atlanta.

490

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20031 (AEO2003), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2003 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of Energy’s Office of Policy and International Affairs, other DOE offices, and other government agencies.

491

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

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

Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes The Expert Panel has concluded that the Department of Energy and National Institutes of Health must develop the capability to produce a diverse supply of radioisotopes for medical use in quantities sufficient to support research and clinical activities. Such a capability would prevent shortages of isotopes, reduce American dependence on foreign radionuclide sources and stimulate biomedical research. The expert panel recommends that the U.S. government build this capability around either a reactor, an accelerator or a combination of both technologies as long as isotopes for clinical and research applications can be supplied reliably, with diversity in adequate

492

Forecasting correlated time series with exponential smoothing models  

Science Journals Connector (OSTI)

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection criterion is introduced into the forecasting scheme for selecting the most adequate multivariate model for describing the behaviour of the time series under study. The forecasting performance of this procedure is tested using some real examples.

Ana Corbern-Vallet; Jos D. Bermdez; Enriqueta Vercher

2011-01-01T23:59:59.000Z

493

Application of GIS on forecasting water disaster in coal mines  

SciTech Connect (OSTI)

In many coal mines of China, water disasters occur very frequently. It is the most important problem that water gets inrush into drifts and coal faces, locally known as water gush, during extraction and excavation. Its occurrence is controlled by many factors such as geological, hydrogeological and mining technical conditions, and very difficult to be predicted and prevented by traditional methods. By making use of overlay analysis of Geographic Information System, a multi-factor model can be built to forecast the potential of water gush. This paper introduced the method of establishment of the water disaster forecasting system and forecasting model and two practical successful cases of application in Jiaozuo and Yinzhuang coal mines. The GIS proved helpful for ensuring the safety of coal mines.

Sun Yajun; Jiang Dong; Ji Jingxian [China Univ. of Mining and Technology, Jiangshy (China)] [and others

1996-08-01T23:59:59.000Z

494

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

SciTech Connect (OSTI)

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

Bolinger, Mark; Wiser, Ryan; Golove, William

2003-08-13T23:59:59.000Z

495

OHA 2012 ANNUAL REPORT DRAFT....  

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

Annual Report Annual Report O H A ffice of earin s ppeals & g Doe/hg-0024 U.S. DEPARTMENT OF ENERGY Office of Hearings Appeals & FY 2012 Annual Report Message from the Director . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Areas of JURISDICTION II. Working with Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. General Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 3 5 A. Personnel Security B. Whistleblower C. 11 D. 14 17 18 I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

496

Annual Report CMS Spring Assembly  

E-Print Network [OSTI]

Annual Report 2007-2008 CMS Spring Assembly & Length of Service Awards March 9, 2012 #12;Annual Report 2007-2008 News & Events: Alumni David Mearns (CMS MS `86) Selected as co-recipient of USF's Distinguished Alumni Award, Fall 2011 #12;Annual Report 2007-2008 News & Events: Faculty Dr. Robert Byrne

Meyers, Steven D.

497

Oak Ridge Reservation Annual Site  

E-Print Network [OSTI]

Oak Ridge Reservation Annual Site Environmental Report DOE/ORO/2445 2012 #12;Cover Image Jeff Riggs Logistical Services Design Creative Media Communications Oak Ridge National Laboratory Oak Ridge Reservation Annual Site Environmental Report 2012 #12;DOE/ORO/2445 Oak Ridge Reservation Annual Site Environmental

Pennycook, Steve

498

Oak Ridge Reservation Annual Site  

E-Print Network [OSTI]

Oak Ridge Reservation Annual Site Environmental Report DOE/ORO-2473 2013 #12;Cover Image & Design Creative Media Communications Oak Ridge National Laboratory Oak Ridge Reservation Annual Site Environmental Report 2013 #12;DOE/ORO/2473 Oak Ridge Reservation Annual Site Environmental Report for 2013 on the World

Pennycook, Steve

499

Ethics Center Annual Report III  

E-Print Network [OSTI]

#12;Ethics Center Annual Report III 2012-2013 TEXAS TECH UNIVERSITY #12;#12;TEXAS TECH UNIVERSITY August 31, 2013 Prepared by the TTU Ethics Center Annual Report III 2012-2013 #12;1 AnnualReportIII|8 proposed a university ethics center to provide the campus with ethics education resources. Since its

Rock, Chris

500

Annual Report University of Lethbridge  

E-Print Network [OSTI]

Annual Report 2012-13 University of Lethbridge #12;i University of Lethbridge 2012-13 Annual Report ACCOUNTABILITY STATEMENT The University of Lethbridge Annual Report for the year ended March 31, 2013, or fiscal implications of which we are aware have been considered in preparing this report. Original signed

Morris, Joy