Powered by Deep Web Technologies
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.


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-Table 1  

Annual Energy Outlook 2012 (EIA)

Annual Energy Outlook Forecast Evaluation > Table 1 Annual Energy Outlook Forecast Evaluation Table 1. Comparison of Absolute Percent Errors for AEO Forecast Evaluation, 1996 to...

6

ELECTRICITY DEMAND FORECAST COMPARISON REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005 ..............................................................................3 Residential Forecast Comparison ..............................................................................................5 Nonresidential Forecast Comparisons

7

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

8

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

9

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,

10

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

11

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.

12

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

13

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

14

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.

15

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

16

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

17

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

18

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.

19

Annual Energy Outlook 1998 Forecasts  

Gasoline and Diesel Fuel Update (EIA)

EIA Administrator's Press Briefing on the Annual Energy Outlook 1998 (AEO98) Annual Energy Outlook 1998 - Errata as of 3698 Data from the AEO98 Assumptions to the AEO98 (Nat'Gas...

20

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

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

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

22

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

23

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

24

Wind Power Forecasting Error Distributions: An International Comparison; Preprint  

DOE Green Energy (OSTI)

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

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

2012-09-01T23:59:59.000Z

25

Industrial production index forecast: Methods comparison  

Science Conference Proceedings (OSTI)

The purpose of this work is to investigate the suitability of different methods as short term forecast tools. It is studied and compared the application of the Kalman filter method with other forecasting methods when applied to a set of qualitative and quantitative information. The work data set is made of qualitative surveys of conjunture and the industrial production index (IPI). The objective is the attainment of short term forecast models for the Portuguese IPI of the transforming industry. After the previous treatment of the data

M. Filomena Teodoro

2012-01-01T23:59:59.000Z

26

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

27

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

E-Print Network (OSTI)

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

Bolinger, Mark

2008-01-01T23:59:59.000Z

28

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

29

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

30

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

DOE Green Energy (OSTI)

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

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

2012-07-01T23:59:59.000Z

31

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

32

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

33

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

34

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

35

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.

36

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

37

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

38

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

E-Print Network (OSTI)

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

Bolinger, Mark

2008-01-01T23:59:59.000Z

39

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

40

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

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.


41

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

42

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

E-Print Network (OSTI)

Forecasting with Historical Data or Process Knowledge under Misspecification: A Comparison Luke, University of Stuttgart May 16, 2012 Abstract When faced with the task of forecasting a dynamic system intuition dictates that perfect knowledge of the system should in theory yield perfect forecasting, often

Steinwart, Ingo

43

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

44

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

45

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

DOE Green Energy (OSTI)

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

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

2013-10-01T23:59:59.000Z

46

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect

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

47

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

SciTech Connect

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

48

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

DOE Green Energy (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

49

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

DOE Green Energy (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

50

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty  

SciTech Connect

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2013-12-18T23:59:59.000Z

51

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

E-Print Network (OSTI)

Comparisons are made of energy forecasts using results from the Industrial module of the National Energy Modeling System (NEMS) and an industrial economic-engineering model called the Industrial Technology and Energy Modeling System (ITEMS), a model developed for industrial energy analysis at the Pacific Northwest National Laboratory. Although the results are mixed, generally ITEMS show greater penetration of energy efficient technologies and thus lower energy use, even though the business as usual forecasts for ITEMS uses a higher discount rate than NEMS uses.

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

2000-04-01T23:59:59.000Z

52

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

Science Conference Proceedings (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

53

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

E-Print Network (OSTI)

to the EIAs natural gas price forecasts in AEO 2004 and AEOcost comparisons of fixed-price renewable generationwith variable price gas-fired generation that are based

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

54

forecasts  

U.S. Energy Information Administration (EIA)

Sheet3 Sheet2 Sheet1 Figure 106. Average annual minemouth coal prices by region, 1990-2040 (2011 dollars per million Btu) Appalachia Interior West US Average

55

Comparison of Energy Information Administration and Bonneville Power Administration load forecasts  

SciTech Connect

Comparisons of the modeling methodologies underlying the project Independence Evaluation System (PIES) and the Bonneville Power Administration forecasts are discussed in this paper. This Technical Memorandum is presented in order to reconcile apparent inconsistencies between the forecasts. These represent different purposes for the modeling effort as well as different forecasts. Nonetheless, both are appropriate within the context that they are intended. The BPA forecasts are site-specific, detailed, micro-level, yearly forecasts of the demand for electricity. PIES develops regional, macro forecasts and does not contain estimates of the timing of the completion of plants within the period of the forecast. The BPA forecast is intended to be utilized in analyzing a sub-regional capacity expansion program. PIES is a regional energy market-clearing, non-normative model which allows different scenarios to be compared by changing input variables. Clearly, both forecasts are dependent upon the accuracy of the assumptions and input variables included. However, the differing levels of aggregation and objectives require different types of input variables.

Reed, H.J.

1978-06-01T23:59:59.000Z

56

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty  

Science Conference Proceedings (OSTI)

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2013-07-25T23:59:59.000Z

57

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

Science Conference Proceedings (OSTI)

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

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

2005-02-09T23:59:59.000Z

58

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

E-Print Network (OSTI)

A Comparison of Precipitation Forecast Skill between Small Convection- Allowing and Large Submitted to Weather and Forecasting in October 2008, Accepted in January 2009 * Corresponding author precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20

Droegemeier, Kelvin K.

59

Comparison of Model Forecast Skill of Sea-Level Pressure Along the East and West Coasts of the United States  

E-Print Network (OSTI)

1 Comparison of Model Forecast Skill of Sea-Level Pressure Along the East and West Coasts, University of Washington, Seattle, Washington Submitted to: Weather and Forecasting May 2008 Revised recent advances in numerical weather prediction, major errors in short-range forecasts still occur

Mass, Clifford F.

60

A.: Modeling and forecasting electricity loads: A comparison  

E-Print Network (OSTI)

In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components a deterministic (representing seasonalities) and a stochastic (representing noise). They differ in the choice of the seasonality reduction method. Model A utilizes differencing, while Model B uses a recently developed seasonal volatility technique. In both models the stochastic component is described by an ARMA time series. Models are tested on a time series of system-wide loads from the California power market and compared with the official forecast of the California System Operator (CAISO). 1.

Rafa? Weron

2004-01-01T23:59:59.000Z

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.


61

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

SciTech Connect

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

Bolinger, Mark A.; Wiser, Ryan H.

2010-01-04T23:59:59.000Z

62

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

SciTech Connect

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

Bolinger, Mark; Wiser, Ryan

2009-01-28T23:59:59.000Z

63

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

Science Conference Proceedings (OSTI)

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

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

2008-01-07T23:59:59.000Z

64

Base Resource Forecasts - Power Marketing - Sierra Nevada Region...  

NLE Websites -- All DOE Office Websites (Extended Search)

Marketing > Base Resource Forecasts Base Resource Forecasts Note: Annual, rolling (monthly for 12 months), base resource forecasts are posted when they become available. Annual...

65

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

E-Print Network (OSTI)

forecasts (or any other forecast, for that matter) in makingcase natural gas price forecast, but to also examine a wideAEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

66

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

E-Print Network (OSTI)

the accuracy of two methods to forecast natural gas prices:forecasting models along with the AEO forecast. Appendix ATable 1. Forecast Year AEO Predicted Price from 1996-2003

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

2005-01-01T23:59:59.000Z

67

Short-term energy outlook annual supplement, 1993  

SciTech Connect

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

68

Short-term energy outlook, annual supplement 1994  

SciTech Connect

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.

Not Available

1994-08-01T23:59:59.000Z

69

Historical Developments Leading to Current Forecast Models of Annual Atlantic Hurricane Activity  

Science Conference Proceedings (OSTI)

There is considerable interest in forecasting interannual hurricane activity for the Atlantic basin. Various predictors representing different components of the tropical Atlantic climate have been suggested. The choice of predictors is based on ...

J. C. Hess; J. B. Elsner

1994-09-01T23:59:59.000Z

70

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

E-Print Network (OSTI)

AEO 2009 Natural Gas Price Forecast to NYMEX Futures Priceslong-term natural gas price forecasts from the AEO series toAEO reference-case gas price forecast compares to the NYMEX

Bolinger, Mark

2009-01-01T23:59:59.000Z

71

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

E-Print Network (OSTI)

longer-term market-based forecasts that can be used to more-AEO 2008 Natural Gas Price Forecast to NYMEX Futures Priceslong-term natural gas price forecasts from the AEO series to

Bolinger, Mark

2008-01-01T23:59:59.000Z

72

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

E-Print Network (OSTI)

a portion of the gas price forecast through 2010 can beAEO 2006 reference case forecast to conduct a 25-yearAEO 2006 Natural Gas Price Forecast to NYMEX Futures Prices

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

73

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

E-Print Network (OSTI)

9: Two Alternative Price Forecasts (denoted by open circlesAEO 2007 Natural Gas Price Forecast to NYMEX Futures Priceslong-term natural gas price forecasts from the AEO series to

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

74

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

E-Print Network (OSTI)

to the EIAs natural gas price forecasts in AEO 2004 and AEOon the AEO 2005 natural gas price forecasts will likely onceof AEO 2005 Natural Gas Price Forecast to NYMEX Futures

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

75

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

E-Print Network (OSTI)

the base-case natural gas price forecast, but to alsoof AEO 2010 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEO

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

76

Annual Cycle Integration of the NMC Medium-Range Forecasting (MRF) Model  

Science Conference Proceedings (OSTI)

The NMC Global Spectral Model was integrated for one year. The model used is the same as the 1989 operational medium range forecast model except that the horizontal resolution was reduced from T80 to T40. Overall, the model was very successful in ...

M. Kanamitsu; K. C. Mo; E. Kalnay

1990-12-01T23:59:59.000Z

77

forecast | OpenEI  

Open Energy Info (EERE)

Browse Upload data GDR Community Login | Sign Up Search Facebook icon Twitter icon forecast Dataset Summary Description The EIA's annual energy outlook (AEO) contains yearly...

78

Forecasting Annual Discharge of River Murray, Australia, from a Geophysical Model of ENSO  

Science Conference Proceedings (OSTI)

Annual discharge (Q) in the largest river system in Australia, the River Murray (including the extensive tributary network of the Darling River), is often inversely related to sea surface temperature (SST) anomalies in the eastern equatorial ...

H. J. Simpson; M. A. Cane; S. K. Lin; S. E. Zebiak; A. L. Herczeg

1993-02-01T23:59:59.000Z

79

Southern Hemisphere Medium-Range Forecast Skill and Predictability: A Comparison of Two Operational Models  

Science Conference Proceedings (OSTI)

The skill of two global numerical weather prediction models, the National Centers for Environmental Prediction (NCEP) medium-range forecast model and the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model, has been ...

James A. Renwick; Craig S. Thompson

2001-09-01T23:59:59.000Z

80

A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes  

Science Conference Proceedings (OSTI)

The ensemble transform Kalman filter (ETKF) ensemble forecast scheme is introduced and compared with both a simple and a masked breeding scheme. Instead of directly multiplying each forecast perturbation with a constant or regional rescaling ...

Xuguang Wang; Craig H. Bishop

2003-05-01T23:59:59.000Z

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

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

E-Print Network (OSTI)

AEO 2005 reference case oil price forecast and NYMEX oi lthan the reference case oil price forecast for that year. Inoil futures case where oil prices are based on the NYMEX

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

82

Aerosols in forecasts of the UV index: A comparison of different approaches  

Science Conference Proceedings (OSTI)

The DWD provides forecasts of the UV Index as a public service to raise awareness for the negative influence of UV radiation on human health. Revising the current forecast algorithm

2013-01-01T23:59:59.000Z

83

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

Science Conference Proceedings (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

84

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

E-Print Network (OSTI)

this hybrid NYMEX-EIA gas price projection still does notonly a portion of the gas price forecast through 2010 of AEO 2006 Natural Gas Price Forecast to NYMEX Futures

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

85

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

E-Print Network (OSTI)

of AEO 2008 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEOto contemporaneous natural gas prices that can be locked in

Bolinger, Mark

2008-01-01T23:59:59.000Z

86

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

E-Print Network (OSTI)

of AEO 2007 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEOto contemporaneous natural gas prices that can be locked in

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

87

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

E-Print Network (OSTI)

of AEO 2009 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEOto contemporaneous natural gas prices that can be locked in

Bolinger, Mark

2009-01-01T23:59:59.000Z

88

EIA - Annual Energy Outlook 2011 - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Home > Forecasts & Analysis > Annual Energy Outlook 2011 : Annual Energy Outlook 2011 with Projections to 2035

89

Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model  

E-Print Network (OSTI)

1 1 Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model 2007 Federal prices are being inputted into the model 2 Sales forecast Select yearsSales forecast Select years --Draft 0.53% Irrigation 2.76% Annual Growth Rates Preliminary Electricity ForecastAnnual Growth Rates

90

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

91

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

E-Print Network (OSTI)

this hybrid NYMEX-EIA gas price projection still does notcomparison with fixed- price renewable generation (becauseonly a portion of the gas price forecast through 2010

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

92

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

93

A comparison of cloud microphysical quantities with forecasts from cloud prediction models  

SciTech Connect

Numerical weather prediction models (ECMWF, NCEP) are evaluated using ARM observational data collected at the Southern Great Plains (SGP) site. Cloud forecasts generated by the models are compared with cloud microphysical quantities, retrieved using a variety of parameterizations. Information gained from this comparison will be utilized during the FASTER project, as models are evaluated for their ability to reproduce fast physical processes detected in the observations. Here the model performance is quantified against the observations through a statistical analysis. Observations from remote sensing instruments (radar, lidar, radiometer and radiosonde) are used to derive the cloud microphysical quantities: ice water content, liquid water content, ice effective radius and liquid effective radius. Unfortunately, discrepancies in the derived quantities arise when different retrieval schemes are applied to the observations. The uncertainty inherent in retrieving the microphysical quantities using various retrievals is estimated from the range of output microphysical values. ARM microphysical retrieval schemes (Microbase, Mace) are examined along with the CloudNet retrieval processing of data from the ARM sites for this purpose. Through the interfacing of CloudNet and ARM processing schemes an ARMNET product is produced and employed as accepted observations in the assessment of cloud model predictions.

Dunn, M.; Jensen, M.; Hogan, R.; OConnor, E.; Huang, D.

2010-03-15T23:59:59.000Z

94

A Comparison of Divergent Winds from the National Meteorological Center and the European Centre for Medium Range Weather Forecasts Global Analyses for 19801986  

Science Conference Proceedings (OSTI)

A comparison is made of the divergent wind analyses of the National Meteorological Center (NMC) and those of the ECMWF/WMO dataset produced by the European Centre for Medium Range Weather Forecasts (ECMWF). Using a reliability criterion based on ...

Steven J. Lambert

1989-05-01T23:59:59.000Z

95

Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison (Presentation)  

Science Conference Proceedings (OSTI)

This presentation summarizes the work to investigate the uncertainty in wind forecasting at different times of year and compare wind forecast errors in different power systems using large-scale wind power prediction data from six countries: the United States, Finland, Spain, Denmark, Norway, and Germany.

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

2013-10-01T23:59:59.000Z

96

Comparison of post-processing methods for the calibration of 100 m wind ensemble forecasts at off- and onshore sites  

Science Conference Proceedings (OSTI)

Ensemble forecasts are a valuable addition to deterministic wind forecasts since they allow the quantification of forecast uncertainties. To remove common deficiencies of ensemble forecasts such as biases and ensemble spread deficits, various post-...

Constantin Junk; Lueder von Bremen; Martin Khn; Stephan Spth; Detlev Heinemann

97

A Comparison of the Noah and OSU Land Surface Models in the ECPC Seasonal Forecast Model  

Science Conference Proceedings (OSTI)

The Noah land surface model (LSM) has recently been implemented into the Experimental Climate Prediction Centers (ECPCs) global Seasonal Forecast Model (SFM). Its performance is compared to the older ECPC SFM with the Oregon State University (...

Laurel L. De Haan; Masao Kanamitsu; Cheng-Hsuan Lu; John O. Roads

2007-10-01T23:59:59.000Z

98

A Comparison of Analysis and Forecast Correction Techniques:Impact of Negative Dissipation  

Science Conference Proceedings (OSTI)

The impact of negative dissipation on posttime analysis and forecast correction techniques is examined in a simplified context. The experiments are conducted using a three-level quasigeostrophic model (with a nonsingular tangent propagator matrix)...

Carolyn A. Reynolds

1999-11-01T23:59:59.000Z

99

A Comparison of Exhaust Condensation Trail Forecast Algorithms at Low Relative Humidity  

Science Conference Proceedings (OSTI)

The Schrader and Schumann contrail forecast algorithms and a third algorithm are evaluated under low relative humidity conditions using a dataset of asynoptic atmospheric soundings and 318 coincident ground-based aircraft and contrail ...

Michael K. Walters; Jeffrey D. Shull; Robert P. Asbury III

2000-01-01T23:59:59.000Z

100

Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America  

Science Conference Proceedings (OSTI)

In this work, the quality of several probabilistic quantitative precipitation forecasts (PQPFs) is examined. The analysis is focused over South America during a 2-month period in the warm season. Several ways of generating and calibrating the ...

Juan Ruiz; Celeste Saulo; Eugenia Kalnay

2009-02-01T23:59:59.000Z

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

Comparison of Objective and Subjective Precipitation Probability Forecasts: The Sufficiency Relation  

Science Conference Proceedings (OSTI)

In this paper the sufficiency relation is used to compare objective and subjective probability of precipitation (PoP) forecasts. The theoretical significance of the sufficiency relation in comparative evaluation arises from the fact that if it ...

Allan H. Murphy; Qian Ye

1990-09-01T23:59:59.000Z

102

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

Science Conference Proceedings (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

103

A Comparison of Probabilistic Forecasts from Bred, Singular-Vector, and Perturbed Observation Ensembles  

Science Conference Proceedings (OSTI)

The statistical properties of analysis and forecast errors from commonly used ensemble perturbation methodologies are explored. A quasigeostrophic channel model is used, coupled with a 3D-variational data assimilation scheme. A perfect model is ...

Thomas M. Hamill; Chris Snyder; Rebecca E. Morss

2000-06-01T23:59:59.000Z

104

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

E-Print Network (OSTI)

There is a huge demand in the industry to forecast production in shale gas reservoirs accurately. There are many methods including volumetric, Decline Curve Analysis (DCA), analytical simulation and numerical simulation. Each one of these methods has its advantages and disadvantages, but only the DCA technique can use readily available production data to forecast rapidly and to an extent accurately. The DCA methods in use in the industry such as the Arps method had originally been developed 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 and the Arps with a minimum decline rate are models that have the ability to match and forecast wells with transient flow followed by boundary flow. I have revised the Duong model to forecast better than the original model. I have also observed a certain variation of the Duong model proves to be a robust model for most of the well cases and flow regimes. The modified Duong has been shown to work best compared to other deterministic models in most cases. For grouped datasets the SPED & Duong models forecast accurately while the Modified Arps does a poor job.

Joshi, Krunal Jaykant

2012-08-01T23:59:59.000Z

105

A Comparison of Climates Simulated by a General Circulation Model when Run in the Annual Cycle and Perpetual Modes  

Science Conference Proceedings (OSTI)

A comparison is made between the climate simulated by the Canadian Climate Centre (CCC) General Circulation Model when run its usual annual cyclemode, in which the solar declination angle varies annually, and the climate which is simulated when ...

F. W. Zwiers; G. J. Boer

1987-11-01T23:59:59.000Z

106

Comparisons of Short Term Load Forecasting using Artificial Neural Network and Regression Method  

E-Print Network (OSTI)

In power systems the next days power generation must be scheduled every day, day ahead short-term load forecasting (STLF) is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and in turn, increases the operating cost by using expensive peaking units. On the other hand, over prediction of STLF leads to the unnecessarily large reserve capacity, which is also related to high operating cost. the research work in this area is still a challenge to the electrical engineering scholars because of its high complexity. How to estimate the future load with the historical data has remained a difficulty up to now, especially for the load forecasting of holidays, days with extreme weather and other anomalous days. With the recent development of new mathematical, data mining and artificial intelligence tools, it is potentially possible to improve the forecasting result. This paper presents a new neural network based approach for short-term load forecasting that uses the most correlated weather data for training, validating and testing the neural network. Correlation analysis of weather data determines the input parameters of the neural networks. And its results compare to regression method. Index terms Load Forecasting, artificial neural network, short term

Mr. Rajesh Deshmukh; Dr. Amita Mahor

2011-01-01T23:59:59.000Z

107

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

108

A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export  

Science Conference Proceedings (OSTI)

This study compares the application of two forecasting methods on the amount of Taiwan export, the ARIMA time series method and the fuzzy time series method. Models discussed for the fuzzy time series method include the Factor models, the Heuristic models, ... Keywords: ARIMA model, Fuzzy time series, Taiwan export

Chi-Chen Wang

2011-08-01T23:59:59.000Z

109

Comparison of Ensemble Kalman FilterBased Forecasts to Traditional Ensemble and Deterministic Forecasts for a Case Study of Banded Snow  

Science Conference Proceedings (OSTI)

The ensemble Kalman filter (EnKF) technique is compared to other modeling approaches for a case study of banded snow. The forecasts include a 12- and 3-km grid-spaced deterministic forecast (D12 and D3), a 12-km 30-member ensemble (E12), and a 12-...

Astrid Suarez; Heather Dawn Reeves; Dustan Wheatley; Michael Coniglio

2012-02-01T23:59:59.000Z

110

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

111

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

112

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

113

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

114

A Comparison of the Annual Cycle of Two Sea Surface Temperature Climatologies of the World Ocean  

Science Conference Proceedings (OSTI)

A comparison of the annual cycle of two monthly sea surface temperature climatologies for the world ocean is presented. One set of the climatological fields used consist of one-degree objectively analyzed monthly means, based on approximately 1.5 ...

Sydney Levitus

1987-02-01T23:59:59.000Z

115

Forecast Correlation Coefficient Matrix of Stock Returns in Portfolio Analysis  

E-Print Network (OSTI)

Unadjusted Forecasts . . . . . . . . . . . . . . . .Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . .Unadjusted Forecasts . . . . . . . . . . . . . . . . . . .

Zhao, Feng

2013-01-01T23:59:59.000Z

116

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network (OSTI)

the entire forecast period, primarily because both weather-adjusted peak and electricity consumption were forecast. Keywords Electricity demand, electricity consumption, demand forecast, weather normalization, annual peak demand, natural gas demand, self-generation, conservation, California Solar Initiative. #12

117

Comparison of Model Forecast Skill of Sea Level Pressure along the East and West Coasts of the United States  

Science Conference Proceedings (OSTI)

Despite recent advances in numerical weather prediction, major errors in short-range forecasts still occur. To gain insight into the origin and nature of model forecast errors, error frequencies and magnitudes need to be documented for different ...

Garrett B. Wedam; Lynn A. McMurdie; Clifford F. Mass

2009-06-01T23:59:59.000Z

118

Accuracy of RUC-1 and RUC-2 Wind and Aircraft Trajectory Forecasts by Comparison with ACARS Observations  

Science Conference Proceedings (OSTI)

As part of an investigation into terminal airspace productivity sponsored by the NASA Ames Research Center, a study was performed at the Forecast Systems Laboratory to investigate sources of wind forecast error and to assess differences in wind ...

Barry E. Schwartz; Stanley G. Benjamin; Steven M. Green; Matthew R. Jardin

2000-06-01T23:59:59.000Z

119

2013 Midyear Economic Forecast Sponsorship Opportunity  

E-Print Network (OSTI)

2013 Midyear Economic Forecast Sponsorship Opportunity Thursday, April 18, 2013, ­ Hyatt Regency Irvine 11:30 a.m. ­ 1:30 p.m. Dr. Anil Puri presents his annual Midyear Economic Forecast addressing

de Lijser, Peter

120

Forecast Technical Document Forecast Types  

E-Print Network (OSTI)

Forecast Technical Document Forecast Types A document describing how different forecast types are implemented in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Forecast Types Background Different `types' of forecast are possible for a specified area

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

Comparisons between Model Forecast and Observed Boundary Layer Profiles and Related Comments on Cloud Prediction  

Science Conference Proceedings (OSTI)

In this study comparisons are made between Met Office mesoscale model boundary layer profiles, and radiosonde data collected in the central United Kingdom during three intensive boundary layer cloud experiments. Significant differences between ...

J. D. Price; M. R. Bush

2004-12-01T23:59:59.000Z

122

Analysis and comparison of active solar desiccant and absorption cooling systems. Part 2; Annual simulation results  

DOE Green Energy (OSTI)

A comparative analysis has been performed to compare the cooling and dehumidification performance of future ventilation mode desiccant systems, proposed advanced absorption systems, and conventional vapor compression systems. A common framework has been developed for direct comparison of these different cooling technologies; this method is described in a companion paper. This paper presents the application of this method to annual simulations of cooling system performance in five cities.

Warren, M.L. (ASI Controls, San Ramon, CA (US)); Wahlig, M. (Lawrence Berkeley Lab., CA (USA). Applied Science Div.)

1991-02-01T23:59:59.000Z

123

RACORO Forecasting  

NLE Websites -- All DOE Office Websites (Extended Search)

Weather Briefings Observed Weather Cloud forecasting models BUFKIT forecast soundings + guidance from Norman NWS enhanced pages and discussions NAM-WRF updated...

124

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

Science Conference Proceedings (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

125

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.

126

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

Science Conference Proceedings (OSTI)

An experiment has been designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar ...

Adam J. Clark; William A. Gallus Jr.; Ming Xue; Fanyou Kong

2009-08-01T23:59:59.000Z

127

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

SciTech Connect

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

128

Forecasting Forecast Skill  

Science Conference Proceedings (OSTI)

We have shown that it is possible to predict the skill of numerical weather forecastsa quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average ...

Eugenia Kalnay; Amnon Dalcher

1987-02-01T23:59:59.000Z

129

Comparison of Impacts of WRF Dynamic Core, Physics Package, and Initial Conditions on Warm Season Rainfall Forecasts  

Science Conference Proceedings (OSTI)

A series of simulations for 15 events occurring during August 2002 were performed using the Weather Research and Forecasting (WRF) model over a domain encompassing most of the central United States to compare the sensitivity of warm season ...

William A. Gallus Jr.; James F. Bresch

2006-09-01T23:59:59.000Z

130

Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5  

Science Conference Proceedings (OSTI)

This study presents two support vector machine (SVM) based models for forecasting hourly precipitation during tropical cyclone (typhoon) events. The two SVM-based models are the traditional Gaussian kernel SVMs (GSVMs) and the advanced wavelet ...

Chih-Chiang Wei

2012-04-01T23:59:59.000Z

131

An Objective Comparison of Model Output Statistics and Perfect Prog Systems in Producing Numerical Weather Element Forecasts  

Science Conference Proceedings (OSTI)

The perfect prog (PP) and model output statistics (MOS) approaches were used to develop multiple linear regression equations to forecast probabilities of more than a trace of precipitation over 6-h periods, probabilities of precipitation ...

N. Brunet; R. Verret; N. Yacowar

1988-12-01T23:59:59.000Z

132

A Statistical Comparison of the Forecasts Produced by the NGM and LFM for the 1987/88 Cool Season  

Science Conference Proceedings (OSTI)

Forecasts from the National Meteorological Center's (NMC) nested grid model (NGM) and limited-area fine-mesh model (LFM) were compared objectively for the 1987/88 cool season. Mean values of various predicted variables were computed for each ...

John S. Jensenius Jr.

1990-03-01T23:59:59.000Z

133

Forecast Combinations  

E-Print Network (OSTI)

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this chapter we analyze theoretically the factors that determine the advantages from combining forecasts (for example, the degree of correlation between forecast errors and the relative size of the individual models forecast error variances). Although the reasons for the success of simple combination schemes are poorly understood, we discuss several possibilities related to model misspecification, instability (non-stationarities) and estimation error in situations where thenumbersofmodelsislargerelativetothe available sample size. We discuss the role of combinations under asymmetric loss and consider combinations of point, interval and probability forecasts. Key words: Forecast combinations; pooling and trimming; shrinkage methods; model misspecification, diversification gains

Allan Timmermann; Jel Codes C

2006-01-01T23:59:59.000Z

134

Forecasting overview  

E-Print Network (OSTI)

Forecasting is required in many situations: deciding whether to build another power generation plant in the next five years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volume; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. Some things are easier to forecast than others. The time of the sunrise tomorrow morning can be forecast very precisely. On the other hand, currency exchange rates are very difficult to forecast with any accuracy. The predictability of an event or a quantity depends on how well we understand the factors that contribute to it, and how much unexplained variability is involved. Forecasting situations vary widely in their time horizons, factors determining actual outcomes, types of data patterns, and many other aspects. Forecasting methods can be very simple such as using the most recent observation as a forecast (which is called the nave method), or highly complex such as neural nets and econometric systems of simultaneous equations. The

Rob J Hyndman

2009-01-01T23:59:59.000Z

135

Numerical Forecasting of Radiation Fog. Part II: A Comparison of Model Simulation with Several Observed Fog Events  

Science Conference Proceedings (OSTI)

A 1D model adapted for forecasting the formation and development of fog, and forced with mesoscale parameters derived from a 3D limited-area model, was used to simulate three fog event observations made during the Lille 88 campaign. The model ...

Daniel Guedalia; Thierry Bergot

1994-06-01T23:59:59.000Z

136

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

NLE Websites -- All DOE Office Websites (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

137

Annual  

NLE Websites -- All DOE Office Websites (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

138

Wavelets and Field Forecast Verification  

Science Conference Proceedings (OSTI)

Current field forecast verification measures are inadequate, primarily because they compress the comparison between two complex spatial field processes into one number. Discrete wavelet transforms (DWTs) applied to analysis and contemporaneous ...

William M. Briggs; Richard A. Levine

1997-06-01T23:59:59.000Z

139

Downscaling Extended Weather Forecasts for Hydrologic Prediction  

SciTech Connect

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

Leung, Lai-Yung R.; Qian, Yun

2005-03-01T23:59:59.000Z

140

Short-term streamflow forecasting: ARIMA vs neural networks  

Science Conference Proceedings (OSTI)

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of ... Keywords: artificial neural networks, auto regressive integrated moving average, forecasting, streamflow

Juan Frausto-Solis; Esmeralda Pita; Javier Lagunas

2008-03-01T23:59:59.000Z

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.


141

Assumptions to the Annual Energy Outlook - Table 41  

Annual Energy Outlook 2012 (EIA)

> Forecasts >Assumptions to the Annual Energy Outlook> Download Report Assumption to the Annual Energy Outlook Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF...

142

Comparison of 10-m Wind Forecasts from a Regional Area Model and QuikSCAT Scatterometer Wind Observations over the Mediterranean Sea  

Science Conference Proceedings (OSTI)

Surface wind forecasts from a limited-area model [the Quadrics Bologna Limited-Area Model (QBOLAM)] covering the entire Mediterranean area at 0.1 grid spacing are verified against Quick Scatterometer (QuikSCAT) wind observations. Only forecasts ...

Christophe Accadia; Stefano Zecchetto; Alfredo Lavagnini; Antonio Speranza

2007-05-01T23:59:59.000Z

143

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)

144

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

SciTech Connect

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

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

1983-07-01T23:59:59.000Z

145

Solar forecasting review  

E-Print Network (OSTI)

of Solar Forecasting . . . . . . . . . 2.4.1 Solarbudget at the foundation of satellite based forecastingWeather Research and Forecasting (WRF) Model 7.1 Global

Inman, Richard Headen

2012-01-01T23:59:59.000Z

146

Comparative Evaluation of Weather Forecasting Systems: Sufficiency, Quality, and Accuracy  

Science Conference Proceedings (OSTI)

The concept of sufficiency, originally introduced in the context of the comparison of statistical experiments, has recently been shown to provide a coherent basis for comparative evaluation of forecasting systems. Specifically, forecasting system ...

Martin Ehrendorfer; Allan H. Murphy

1988-09-01T23:59:59.000Z

147

Annual energy outlook 1995, with projections to 2010  

Science Conference Proceedings (OSTI)

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

NONE

1995-01-01T23:59:59.000Z

148

NFI Forecasts Methodology NFI Forecasts Methodology  

E-Print Network (OSTI)

NFI Forecasts Methodology NFI Forecasts Methodology Overview Issued by: National Forest Inventory.brewer@forestry.gsi.gov.uk Website: www.forestry.gov.uk/inventory 1 NFI Softwood Forecasts Methodology Overview #12;NFI Forecasts ........................................................................................................4 Rationale behind the new approach to the GB Private sector production forecast ........4 Volume

149

Forecast Technical Document Restocking in the Forecast  

E-Print Network (OSTI)

Forecast Technical Document Restocking in the Forecast A document describing how restocking of felled areas is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the forecast Background During the period of a production forecast it is assumed that, as forest sub

150

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS FORECAST IMPROVEMENTS  

E-Print Network (OSTI)

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS BRISBANE FORECAST IMPROVEMENTS The Bureau of Meteorology is progressively upgrading its forecast system to provide more detailed forecasts across Australia. From October 2013 new and improved 7 day forecasts will be introduced for Brisbane, Gold Coast

Greenslade, Diana

151

Annual Energy Outlook 2002 with Projections to 2020 - Table of...  

Gasoline and Diesel Fuel Update (EIA)

Issues in Focus Market Trends Energy Demand Electricity Oil and Natural Gas Coal Emissions Forecast Comparisons Major Assumptions for the Forecasts Summary of the AEO2002...

152

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.

153

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.

154

Issues in midterm analysis and forecasting, 1996  

SciTech Connect

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

NONE

1996-08-01T23:59:59.000Z

155

Another Approach to Forecasting Forecast Skill  

Science Conference Proceedings (OSTI)

The skill of a medium-range numerical forecast can fluctuate widely from day to day. Providing an a priori estimate of the skill of the forecast is therefore important. Existing approaches include Monte Carlo Forecasting and Lagged Average ...

W. Y. Chen

1989-02-01T23:59:59.000Z

156

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

157

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

158

From: Mark Bolinger and Ryan Wiser, Berkeley Lab (LBNL) Subject: Comparison of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices  

E-Print Network (OSTI)

of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices Date: January 4, 2010 1. Introduction, 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

159

The Forecast Gap: Linking Forwards and Forecasts  

Science Conference Proceedings (OSTI)

This report addresses a common problem in price forecasting: What to do when confronted with a persistent gap between results obtained from a structural forecast model and actual forward or spot prices? The report examines examples taken from natural gas and electric power forecasts and presents a novel approach to closing this forecast gap. Inspection reveals that the ratio of actual prices to forecast prices often exhibits stochastic movements that resemble those of commodity price movements. By usin...

2008-12-15T23:59:59.000Z

160

Annual energy outlook 1994: With projections to 2010  

Science Conference Proceedings (OSTI)

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

Not Available

1994-01-01T23:59:59.000Z

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.


161

Airborne Volcanic Ash Forecast Area Reliability  

Science Conference Proceedings (OSTI)

In support of aircraft flight safety operations, daily comparisons between modeled, hypothetical, volcanic ash plumes calculated with meteorological forecasts and analyses were made over a 1.5-yr period. The Hybrid Single-Particle Lagrangian ...

Barbara J. B. Stunder; Jerome L. Heffter; Roland R. Draxler

2007-10-01T23:59:59.000Z

162

Sensitivity of Global Ensemble Forecasts to the Initial Ensemble Mean and Perturbations: Comparison of EnKF, Singular Vector, and 4D-Var Approaches  

Science Conference Proceedings (OSTI)

This study examines the sensitivity of global ensemble forecasts to the use of different approaches for specifying both the initial ensemble mean and perturbations. The current operational ensemble prediction system of the Meteorological Service ...

Mark Buehner; Ahmed Mahidjiba

2010-10-01T23:59:59.000Z

163

Forecasting in Meteorology  

Science Conference Proceedings (OSTI)

Public weather forecasting heralded the beginning of modern meteorology less than 150 years ago. Since then, meteorology has been largely a forecasting discipline. Thus, forecasting could have easily been used to test and develop hypotheses, ...

C. S. Ramage

1993-10-01T23:59:59.000Z

164

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

165

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

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

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

166

Annual Logging Symposium, May 25-28, 2008 COMPARISON OF WIRELINE FORMATION-TESTER SAMPLING  

E-Print Network (OSTI)

or an underbalanced drilling environment, there is no difference between the performances of focused or conventional-implicit compositional reservoir simulator is used to model both invasion and filtrate-cleanup processes. Comparison to a variety of circumstances, including the drilling environment, formation properties, and radial extent

Torres-Verdín, Carlos

167

Climate Forecasts for Corn Producer Decision-Making  

Science Conference Proceedings (OSTI)

Corn is the most widely grown crop in the Americas, with annual production in the US of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision-tools for corn producers based on these improved ...

Eugene S. Takle; Christopher J. Anderson; Jeffrey Andresen; James Angel; Roger W. Elmore; Benjamin M. Gramig; Patrick Guinan; Steven Hilberg; Doug Kluck; Raymond Massey; Dev Niyogi; Jeanne M. Schneider; Martha D. Shulski; Dennis Todey; Melissa Widhalm

168

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

NLE Websites -- All DOE Office Websites (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...

169

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST  

E-Print Network (OSTI)

Policy Report, over the entire forecast period, primarily because both weather-adjusted peak and commercial sectors. Keywords Electricity demand, electricity consumption, demand forecast, weather normalization, annual peak demand, natural gas demand, self-generation, California Solar Initiative. #12;ii #12

170

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

E-Print Network (OSTI)

Appendix A: Fuel Price Forecast Introduction................................................................................................................................. 3 Price Forecasts............................................................................................................................... 12 Oil Price Forecast Range

171

Air-Conditioning Effect Estimation for Mid-Term Forecasts of Tunisian Electricity Consumption  

E-Print Network (OSTI)

: Engineering-industry, secondary: Econometrics. 1 Introduction The electric power mid-term loads forecasting: Estimated annual temperature sensitive electricity load components 3 Mid-term load forecasting StatisticalAir-Conditioning Effect Estimation for Mid-Term Forecasts of Tunisian Electricity Consumption

Paris-Sud XI, Université de

172

Simulations of Precipitation Using NRCM and Comparisons with Satellite Observations and CAM: Annual Cycle  

SciTech Connect

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36 km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000-2005) from within the Tropics (30"S-30"N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the longtime mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36 km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations.

Murthi, Aditya; Bowman, Kenneth P.; Leung, Lai-Yung R.

2011-04-14T23:59:59.000Z

173

The Influence of Variations in Surface Treatment on 24-Hour Forecasts with a Limited Area Model, Including a Comparison of Modeled and Satellite-Measured Surface Temperatures  

Science Conference Proceedings (OSTI)

The effect of variations in surface parameters on 24-hour limited area forecasts has been examined on a day in July 1981. The vehicle for the study is a ten-level primitive equation model covering most of the continental United States. Variations ...

George Diak; Stacey Heikkinen; John Rates

1986-01-01T23:59:59.000Z

174

A Comparison of Skill between Two Versions of the NCEP Climate Forecast System (CFS) and CPCs Operational Short-Lead Seasonal Outlooks  

Science Conference Proceedings (OSTI)

Analyses of the relative prediction skills of NOAAs Climate Forecast System versions 1 and 2 (CFSv1 and CFSv2, respectively), and the NOAA/Climate Prediction Centers (CPC) operational seasonal outlook, are conducted over the 15-yr common period ...

Peitao Peng; Anthony G. Barnston; Arun Kumar

2013-04-01T23:59:59.000Z

175

Comparison of a 51-Member Low-Resolution (TL399L62) Ensemble with a 6-Member High-Resolution (TL799L91) Lagged-Forecast Ensemble  

Science Conference Proceedings (OSTI)

The 51-member TL399L62 ECMWF ensemble prediction system (EPS51) is compared with a lagged ensemble system based on the six most recent ECMWF TL799L91 forecasts (LAG6). The EPS51 and LAG6 systems are compared to two 6-member ensembles with a ...

Roberto Buizza

2008-09-01T23:59:59.000Z

176

EIA - Annual Energy Outlook (AEO) 2013 Data Tables  

U.S. Energy Information Administration (EIA)

Monthly and yearly energy forecasts, analysis of energy topics, financial analysis, ... Annual Energy Outlook 2013. Release Dates: April 15 - May 2, 2013 ...

177

Annual Energy Outlook 2013 - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Monthly and yearly energy forecasts, analysis of energy topics, financial analysis, ... Annual Energy Outlook 2013. Release Dates: April 15 - May 2, 2013 ...

178

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)

179

Forecasts, Meteorology Services, Environmental Sciences Department  

NLE Websites -- All DOE Office Websites (Extended Search)

Forecasts Short Term Forecast Suffolk County Northern Nassau Southern Nassau Area Forecast Discussion - OKX Area Forecast Discussion - NYS Area Forecast Discussion Mount Holly Area...

180

Evaluating Forecasters' Rules of Thumb: A Study of d(prog)/dt  

Science Conference Proceedings (OSTI)

Forecasters often develop rules of thumb for adjusting model guidance. Ideally, before use, these rules of thumb should be validated through a careful comparison of model forecasts and observations over a large sample. Practically, such ...

Thomas M. Hamill

2003-10-01T23:59:59.000Z

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.


181

Verifying Forecasts Spatially  

Science Conference Proceedings (OSTI)

Numerous new methods have been proposed for using spatial information to better quantify and diagnose forecast performance when forecasts and observations are both available on the same grid. The majority of the new spatial verification methods can be ...

Eric Gilleland; David A. Ahijevych; Barbara G. Brown; Elizabeth E. Ebert

2010-10-01T23:59:59.000Z

182

Forecasting of Supercooled Clouds  

Science Conference Proceedings (OSTI)

Using parameterizations of cloud microphysics, a technique to forecast supercooled cloud events is suggested. This technique can be coupled on the mesoscale with a prognostic equation for cloud water to improve aircraft icing forecasts. The ...

Andr Tremblay; Anna Glazer; Wanda Szyrmer; George Isaac; Isztar Zawadzki

1995-07-01T23:59:59.000Z

183

Time Series and Forecasting  

Science Conference Proceedings (OSTI)

Time Series and Forecasting. Leigh, Stefan and Perlman, S. (1991). "An Index for Comovement of Time Sequences With ...

184

Forecast Technical Document Volume Increment  

E-Print Network (OSTI)

Forecast Technical Document Volume Increment Forecasts A document describing how volume increment is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Volume increment forecasts Background A volume increment forecast is a fundamental output of the forecast

185

The Strategy of Professional Forecasting  

E-Print Network (OSTI)

This paper develops and compares two theories of strategic behavior of professional forecasters. The first theory posits that forecasters compete in a forecasting contest with pre-specified rules. In equilibrium of a winner-take-all contest, forecasts are excessively differentiated. According to the alternative reputational cheap talk theory, forecasters aim at convincing the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts and the realized state. If the market expects forecaster honesty, forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded.

Marco Ottaviani; Peter Norman Srensen

2003-01-01T23:59:59.000Z

186

Business forecasting methods  

E-Print Network (OSTI)

Forecasting is a common statistical task in business, where it helps inform decisions about scheduling of production, transportation and personnel, and provides a guide to long-term strategic planning. However, business forecasting is often done poorly and is frequently confused with planning and goals. They are three different things. Forecasting is about predicting the future as accurately as possible, given all the information available including historical data and knowledge of any future events that might impact the forecasts. Goals are what you would like to happen. Goals should be linked to forecasts and plans, but this does not always occur. Too often, goals are set without any plan for how to achieve them, and no forecasts for whether they are realistic. Planning is a response to forecasts and goals. Planning involves determining the appropriate actions that are required to make your forecasts match your goals. Forecasting should be an integral part of the decision-making activities of management, as it can play an important role in many areas of a company. Modern organizations require short-, medium- and long-term forecasts, depending on the specific application.

Rob J Hyndman

2009-01-01T23:59:59.000Z

187

ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL  

E-Print Network (OSTI)

5.5 ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL USING RETROSPECTIVE FORECASTS, Colorado 1. INTRODUCTION Improving weather forecasts is a primary goal of the U.S. National Oceanic predictions has been to improve the accuracy of the numerical forecast models. Much effort has been expended

Hamill, Tom

188

Bayesian Correlation Score: A Utilitarian Measure of Forecast Skill  

Science Conference Proceedings (OSTI)

From the theory of sufficient comparisons of experiments, a measure of skill is derived for categorical forecasts of continuous predictands. Called Bayesian correlation wore (BCS), the measure is specified in terms of three parameters of a normal-...

Roman Krzysztofowicz

1992-01-01T23:59:59.000Z

189

Evaluation of Prototypical Climate Forecasts: The Sufficiency Relation  

Science Conference Proceedings (OSTI)

The sufficiency relation, originally developed in the context of the comparison of statistical experiments, provides a sound basis for the comparative evaluation of forecasting systems. The importance of this relation resides in the fact that if ...

Martin Ehrendorfer; Allan H. Murphy

1992-08-01T23:59:59.000Z

190

Issues in midterm analysis and forecasting 1998  

SciTech Connect

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

NONE

1998-07-01T23:59:59.000Z

191

ORNL integrated forecasting system  

SciTech Connect

This paper describes the integrated system for forecasting electric energy and load. In the system, service area models of electrical energy (kWh) and load distribution (minimum and maximum loads and load duration curve) are linked to a state-level model of electrical energy (kWh). Thus, the service area forecasts are conditional upon the state-level forecasts. Such a linkage reduces considerably the data requirements for modeling service area electricity demand.

Rizy, C.G.

1983-01-01T23:59:59.000Z

192

Solar forecasting review  

E-Print Network (OSTI)

Online 24-h solar power forecasting based on weather typeweather observations at blue hill massachusetts, Solarof weather patterns on the intensity of solar irradiance;

Inman, Richard Headen

2012-01-01T23:59:59.000Z

193

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

194

Seasonal tropical cyclone forecasts  

E-Print Network (OSTI)

Seasonal forecasts of tropical cyclone activity in various regions have been developed since the first attempts in the early 1980s by Neville

Suzana J. Camargo; Anthony G. Barnston; Philip J. Klotzbach; Christopher W. Landsea

2007-01-01T23:59:59.000Z

195

Probabilistic Forecasts from the National Digital Forecast Database  

Science Conference Proceedings (OSTI)

The Bayesian processor of forecast (BPF) is developed for a continuous predictand. Its purpose is to process a deterministic forecast (a point estimate of the predictand) into a probabilistic forecast (a distribution function, a density function, ...

Roman Krzysztofowicz; W. Britt Evans

2008-04-01T23:59:59.000Z

196

Annual Energy Outlook 2013 - Energy Information Administration  

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

Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Market Trends Issues in Focus Legislation & Regulations Comparison Appendices Annual Energy...

197

Global and Local Skill Forecasts  

Science Conference Proceedings (OSTI)

A skill forecast gives the probability distribution for the error in a forecast. Statistically, Well-founded skill forecasting methods have so far only been applied within the context of simple models. In this paper, the growth of analysis errors ...

P. L. Houtekamer

1993-06-01T23:59:59.000Z

198

Distortion Representation of Forecast Errors  

Science Conference Proceedings (OSTI)

Forecast error is decomposed into three components, termed displacement error, amplitude error, mid residual error, respectively. Displacement error measures how much of the forecast error can be accounted for by moving the forecast to best fit ...

Ross N. Hoffman; Zheng Liu; Jean-Francois Louis; Christopher Grassoti

1995-09-01T23:59:59.000Z

199

Composite forecasting in commodity systems  

E-Print Network (OSTI)

Paper No. COMPOSI1E FORECASTING IN CO/Yt.flDITI SYSTfu\\1S1980 .i CfIAPTER COMPOSITE FORECASTING IN COMMOOITY SYSTEMS*to utilizeeconometric .modelsfor forecasting ! ,urposes. The

Johnson, Stanley R; Rausser, Gordon C.

1980-01-01T23:59:59.000Z

200

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network (OSTI)

Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN; the former with primary contributions in the areas of climate and hydrologic forecasting and the latter Service (NWS) California Nevada River Forecast Center (CNRFC), the California Department of Water

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

CONSULTANT REPORT DEMAND FORECAST EXPERT  

E-Print Network (OSTI)

CONSULTANT REPORT DEMAND FORECAST EXPERT PANEL INITIAL forecast, end-use demand modeling, econometric modeling, hybrid demand modeling, energyMahon, Carl Linvill 2012. Demand Forecast Expert Panel Initial Assessment. California Energy

202

Does the term structure forecast  

E-Print Network (OSTI)

provides more accurate forecasts of real consumption growth14. Harvey, C.R. (1989): \\Forecasts of economic growth fromC.R. (1993): \\Term structure forecasts economic growth", Fi-

Berardi, Andrea; Torous, Walter

2002-01-01T23:59:59.000Z

203

Coefficients for Debiasing Forecasts  

Science Conference Proceedings (OSTI)

Skill-score decompositions can be used to analyze the effects of bias on forecasting skill. However, since bias terms are typically squared, and bias is measured in skill-score units rather than in units of the forecasts, such decompositions only ...

Thomas R. Stewart; Patricia Reagan-Cirincione

1991-08-01T23:59:59.000Z

204

Evaluating Point Forecasts  

E-Print Network (OSTI)

Typically, point forecasting methods are compared and assessed by means of an error measure or scoring function, such as the absolute error or the squared error. The individual scores are then averaged over forecast cases, to result in a summary measure of the predictive performance, such as the mean absolute error or the (root) mean squared error. I demonstrate that this common practice can lead to grossly misguided inferences, unless the scoring function and the forecasting task are carefully matched. Effective point forecasting requires that the scoring function be specified ex ante, or that the forecaster receives a directive in the form of a statistical functional, such as the mean or a quantile of the predictive distribution. If the scoring function is specified ex ante, the forecaster can issue the optimal point forecast, namely, the Bayes rule. If the forecaster receives a directive in the form of a functional, it is critical that the scoring function be consistent for it, in the sense that the expect...

Gneiting, Tilmann

2009-01-01T23:59:59.000Z

205

Forecasters Objectives and Strategies ?  

E-Print Network (OSTI)

This chapter develops a unified modeling framework for analyzing the strategic behavior of forecasters. The theoretical model encompasses reputational objectives, competition for the best accuracy, and bias. Also drawing from the extensive literature on analysts, we review the empirical evidence on strategic forecasting and illustrate how our model can be structurally estimated.

Ivn Marinovic; Marco Ottaviani; Peter Norman Srensen

2011-01-01T23:59:59.000Z

206

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).

207

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

208

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),

209

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),

210

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

211

A New Verification Score for Public Forecasts  

Science Conference Proceedings (OSTI)

CREF, a new verification score for public forecasts, is introduced. This verification score rewards a forecaster who forecasts a rare event accurately. CREF was used to verify local forecasts at the Weather Service Forecast Office (WSFO) in ...

Dean P. Gulezian

1981-02-01T23:59:59.000Z

212

An Empirical Benchmark for Decadal Forecasts of Global Surface Temperature Anomalies  

Science Conference Proceedings (OSTI)

The suitability of a linear inverse model (LIM) as a benchmark for decadal surface temperature forecast skill is demonstrated. Constructed from the observed simultaneous and 1-yr lag covariability statistics of annually averaged sea surface ...

Matthew Newman

2013-07-01T23:59:59.000Z

213

An Overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment  

Science Conference Proceedings (OSTI)

The NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis ...

Adam J. Clark; Steven J. Weiss; John S. Kain; Israel L. Jirak; Michael Coniglio; Christopher J. Melick; Christopher Siewert; Ryan A. Sobash; Patrick T. Marsh; Andrew R. Dean; Ming Xue; Fanyou Kong; Kevin W. Thomas; Yunheng Wang; Keith Brewster; Jidong Gao; Xuguang Wang; Jun Du; David R. Novak; Faye E. Barthold; Michael J. Bodner; Jason J. Levit; C. Bruce Entwistle; Tara L. Jensen; James Correia Jr.

2012-01-01T23:59:59.000Z

214

Time Series Models Adoptable for Forecasting Nile Floods and Ethiopian Rainfalls  

Science Conference Proceedings (OSTI)

Long-term rainfall forecasting is used in making economic and agricultural decisions in many countries. It may also be a tool in minimizing the devastation resulting from recurrent droughts. To be able to forecast the total annual rainfall or the ...

M. G. El-Fandy; S. M. M. Taiel; Z. H. Ashour

1994-01-01T23:59:59.000Z

215

NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS  

E-Print Network (OSTI)

· NATIONAL AND GLOBAL FORECASTS · WEST VIRGINIA PROFILES AND FORECASTS · ENERGY · HEALTHCARE Industry Insight: West Virginia Fiscal Forecast 34 CHAPTER 4: WEST ViRGiNiA'S 35 COUNTiES AND MSAs West Forecast Summary 2 CHAPTER 1: THE UNiTED STATES ECONOMY Figure 1.1: United States Real GDP Growth 3 Figure

Mohaghegh, Shahab

216

APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY  

E-Print Network (OSTI)

1 APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY Rick Katz.isse.ucar.edu/HP_rick/dmuu.pdf #12;2 QUOTES ON USE OF PROBABILITY FORECASTS · Lao Tzu (Chinese Philosopher) "He who knows does and Value of Probability Forecasts (4) Cost-Loss Decision-Making Model (5) Simulation Example (6) Economic

Katz, Richard

217

Why are survey forecasts superior to model forecasts?  

E-Print Network (OSTI)

We investigate two characteristics of survey forecasts that are shown to contribute to their superiority over purely model-based forecasts. These are that the consensus forecasts incorporate the effects of perceived changes in the long-run outlook, as well as embodying departures from the path toward the long-run expectation. Both characteristics on average tend to enhance forecast accuracy. At the level of the individual forecasts, there is scant evidence that the second characteristic enhances forecast accuracy, and the average accuracy of the individual forecasts can be improved by applying a mechanical correction.

Michael P. Clements; Michael P. Clements

2010-01-01T23:59:59.000Z

218

Annual Reports  

NLE Websites -- All DOE Office Websites (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

219

LOAD FORECASTING Eugene A. Feinberg  

E-Print Network (OSTI)

's electricity price forecasting model, produces forecast of gas demand consistent with electric load. #12Gas demand Council's Market Price of Electricity Forecast Natural GasDemand Electric Load Aggregating Natural between the natural gas and electricity and new uses of natural gas emerge. T natural gas forecasts

Feinberg, Eugene A.

220

Demand Forecast INTRODUCTION AND SUMMARY  

E-Print Network (OSTI)

Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required of any forecast of electricity demand and developing ways to reduce the risk of planning errors that could arise from this and other uncertainties in the planning process. Electricity demand is forecast

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

Factors Driving Prices & Forecast  

Gasoline and Diesel Fuel Update (EIA)

This spread is a function of the balance between demand and fresh supply (production and net imports). Finally I will discuss the current forecast for distillate prices this winter...

222

Modeling and Forecasting Aurora  

Science Conference Proceedings (OSTI)

Modeling the physical processes needed for forecasting space-weather events requires multiscale modeling. This article discusses several modelsresearchers use to treat the various auroral processes that influence space weather.

Dirk Lummerzheim

2007-01-01T23:59:59.000Z

223

Valuing Climate Forecast Information  

Science Conference Proceedings (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

224

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.

225

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

226

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

227

Forecasting in the Presence of Level Shifts  

E-Print Network (OSTI)

accuracy. Journal of Forecasting 19 : 537-560. Hamilton JD.430. Harvey AC. 1989. Forecasting, structural time seriesMH, Timmermann A. 1994. Forecasting stock returns: An

Smith, Aaron

2004-01-01T23:59:59.000Z

228

Multivariate Forecast Evaluation And Rationality Testing  

E-Print Network (OSTI)

10621088. MULTIVARIATE FORECASTS Chaudhuri, P. (1996): OnKingdom. MULTIVARIATE FORECASTS Kirchgssner, G. , and U. K.2005): Estimation and Testing of Forecast Rationality under

Komunjer, Ivana; OWYANG, MICHAEL

2007-01-01T23:59:59.000Z

229

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:

230

FROM ANALYSTS ' EARNINGS FORECASTS  

E-Print Network (OSTI)

We examine the accuracy and bias of intrinsic equity prices estimated from three accounting-based valuation models using analysts earnings forecasts over a four-year horizon. The models are: (a) the earnings capitalization model, (b) the residual income model without a terminal value, and (c) the residual income model with a terminal value that assumes residual income will grow beyond the horizon at a constant rate determined from the expected residual income growth rate over the forecast horizon. Our analysis is based on valuation errors that are calculated by comparing estimated prices to actual prices. We contribute to the literature by examining whether: (i) the analysts earnings forecasts convey information about value beyond that conveyed by current earnings, book value and dividends, (ii) the use of firm specific growth rates in terminal value calculations results in more unbiased and accurate valuations than the use of constant growth rates, and (iii) different models perform better under different ex-ante conditions. We find that analysts earnings forecasts convey information about value beyond that conveyed by current earnings, book values and dividends. Each of the models that we used has valuation errors that decline monotonically as the horizon increases implying that earnings forecasts at each horizon convey new value relevant information. We cannot find a clear advantage to using firm specific growth rates instead of a constant rate of 4 % across all sample

Theodore Sougiannis; Takashi Yaekura

2000-01-01T23:59:59.000Z

231

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect

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

232

Density Forecasting for Long-Term Peak Electricity Demand  

E-Print Network (OSTI)

Long-term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation. In a long-term context, planners must adopt a probabilistic view of potential peak demand levels. Therefore density forecasts (providing estimates of the full probability distributions of the possible future values of the demand) are more helpful than point forecasts, and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty. This paper proposes a new methodology to forecast the density of long-term peak electricity demand. Peak electricity demand in a given season is subject to a range of uncertainties, including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. A comprehensive forecasting solution is described in this paper. First, semi-parametric additive models are used to estimate the relationships between demand and the driver variables, including temperatures, calendar effects and some demographic and economic variables. Then the demand distributions are forecasted by using a mixture of temperature simulation, assumed future economic scenarios, and residual bootstrapping. The temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks. The proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for South Australia since 2007. The performance of the methodology is evaluated by comparing the forecast results with the actual demand of the summer 20072008.

Rob J. Hyndman; Shu Fan

2009-01-01T23:59:59.000Z

233

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Robert P. Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare the industrial forecast

234

Earnings Management Pressure on Audit Clients: Auditor Response to Analyst Forecast Signals  

E-Print Network (OSTI)

This study investigates whether auditors respond to earnings management pressure created by analyst forecasts. Analyst forecasts create an important earnings target for management, and professional standards direct auditors to consider how this pressure could affect their clients. Using annual analyst forecasts available during the planning phase of the audit, I examine whether this form of earnings management pressure affects clients financial statement misstatements. Next, I investigate whether auditors respond to earnings forecast pressure through audit fees and reporting delay. I find that higher levels of analyst forecast pressure increase the likelihood of client restatement. I also find that auditors charge higher audit fees and delay the issuance of the audit report in response to pressure from analyst expectations. Finally, I find that when audit clients are subject to high analyst forecast pressure, a high audit fee response by auditors mitigates the likelihood of client misstatements.

Newton, Nathan J.

2013-08-01T23:59:59.000Z

235

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

236

Consensus Coal Production Forecast for  

E-Print Network (OSTI)

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

Mohaghegh, Shahab

237

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400 .......................................................................................................................................1-1 ENERGY DEMAND FORECASTING AT THE CALIFORNIA ENERGY COMMISSION: AN OVERVIEW

238

Forecast Technical Document Technical Glossary  

E-Print Network (OSTI)

Forecast Technical Document Technical Glossary A document defining some of the terms used in the 2011 Production Forecast technical documentation. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the Forecast documentation. In some cases, the terms and the descriptions are "industry standard", in others

239

Forecast Technical Document Tree Species  

E-Print Network (OSTI)

Forecast Technical Document Tree Species A document listing the tree species included in the 2011 Production Forecast Tom Jenkins Justin Gilbert Ewan Mackie Robert Matthews #12;PF2011 ­ List of tree species The following is the list of species used within the Forecast System. Species are ordered alphabetically

240

3, 21452173, 2006 Probabilistic forecast  

E-Print Network (OSTI)

HESSD 3, 2145­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page for probabilistic forecasts of continuous hydrological variables F. Laio and S. Tamea DITIC ­ Department­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page Abstract Introduction

Paris-Sud XI, Université de

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

4, 189212, 2007 Forecast and  

E-Print Network (OSTI)

OSD 4, 189­212, 2007 Forecast and analysis assessment through skill scores M. Tonani et al. Title Science Forecast and analysis assessment through skill scores M. Tonani 1 , N. Pinardi 2 , C. Fratianni 1 Forecast and analysis assessment through skill scores M. Tonani et al. Title Page Abstract Introduction

Paris-Sud XI, Université de

242

FINANCIAL FORECASTING USING GENETIC ALGORITHMS  

E-Print Network (OSTI)

predecessors to forecast stock prices and manage portfolios for approximately 3 years.) We examineFINANCIAL FORECASTING USING GENETIC ALGORITHMS SAM MAHFOUD and GANESH MANI LBS Capital Management entitled Genetic Algorithms for Inductive Learning). Time-series forecasting is a special type

Boetticher, Gary D.

243

Forecast of auroral activity  

Science Conference Proceedings (OSTI)

A new technique is developed to predict auroral activity based on a sample of over 9000 auroral sites identified in global auroral images obtained by an ultraviolet imager on the NASA Polar satellite during a 6-month period. Four attributes of auroral activity sites are utilized in forecasting

A. T. Y. Lui

2004-01-01T23:59:59.000Z

244

ANNUAL HEATING AND COOLING REQUIREMENTS AND DESIGN DAY PERFORMANCE FOR A RESIDENTIAL MODEL IN SIX CLIMATES: A COMPARISON OF NBSLD, BLAST 2, AND DOE-2.1  

E-Print Network (OSTI)

I-' O'l Annual Heating Requirements NBSLD BLAST DOE-2 (SWF)Cooling Requirements (10 6 Btu) Btu) I'" I NBSLD III DOE-2 (DOE-2.1 predictions of annual heating and cooling requirements

Carroll, William L.

2011-01-01T23:59:59.000Z

245

Voluntary Green Power Market Forecast through 2015  

SciTech Connect

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

246

Voluntary Green Power Market Forecast through 2015  

SciTech Connect

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

247

MSSM Forecast for the LHC  

E-Print Network (OSTI)

We perform a forecast of the MSSM with universal soft terms (CMSSM) for the LHC, based on an improved Bayesian analysis. We do not incorporate ad hoc measures of the fine-tuning to penalize unnatural possibilities: such penalization arises from the Bayesian analysis itself when the experimental value of $M_Z$ is considered. This allows to scan the whole parameter space, allowing arbitrarily large soft terms. Still the low-energy region is statistically favoured (even before including dark matter or g-2 constraints). Contrary to other studies, the results are almost unaffected by changing the upper limits taken for the soft terms. The results are also remarkable stable when using flat or logarithmic priors, a fact that arises from the larger statistical weight of the low-energy region in both cases. Then we incorporate all the important experimental constrains to the analysis, obtaining a map of the probability density of the MSSM parameter space, i.e. the forecast of the MSSM. Since not all the experimental information is equally robust, we perform separate analyses depending on the group of observables used. When only the most robust ones are used, the favoured region of the parameter space contains a significant portion outside the LHC reach. This effect gets reinforced if the Higgs mass is not close to its present experimental limit and persits when dark matter constraints are included. Only when the g-2 constraint (based on $e^+e^-$ data) is considered, the preferred region (for $\\mu>0$) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-$\\mu$ possibilities.

Maria Eugenia Cabrera; Alberto Casas; Roberto Ruiz de Austri

2009-11-24T23:59:59.000Z

248

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,

249

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.

250

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

251

Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

Forecasting Dangerous Inmate Misconduct: An Applications ofidentify with useful forecasting skill the very few inmatescontribute substantially to forecasting skill necessarily

Berk, Richard; Kriegler, Brian; Baek, Jong-Ho

2005-01-01T23:59:59.000Z

252

Information and Inference in Econometrics: Estimation, Testing and Forecasting  

E-Print Network (OSTI)

Application: Forecasting Equity Premium . . . . . . . . . .2.6.1 Forecasting4 Forecasting Using Supervised Factor Models 4.1

Tu, Yundong

2012-01-01T23:59:59.000Z

253

Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

Forecasting Dangerous Inmate Misconduct: An Applications ofidentify with useful forecasting skill the very few inmatescontribute substantially to forecasting skill necessarily

Richard A. Berk; Brian Kriegler; Jong-Ho Baek

2011-01-01T23:59:59.000Z

254

Load transfer coupling regression curve fitting for distribution load forecasting  

SciTech Connect

The planning of distribution facilities requires forecasts of future substation and feeder loads. Extrapolation based on a curve fit to past annual peak loads is currently the most popular manner of accomplishing this forecast. Curve fitting suffers badly from data shifts caused by switching as loads are routinely moved from one substation to another during the course of utility operations. This switching contaminates the data, reducing forecast accuracy. A new regression application reduces error due to these transfers by over an order of magnitude. A key to the usefulness of this method is that the amount of the transfer, and its direction (whether it was to or from a substation), is not a required input. The new technique, aspects of computer implementation of it, and a series of tests showing its advantage over normal multiple regression methods are given.

Willis, H.L.; Powell, R.W.

1984-05-01T23:59:59.000Z

255

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,

256

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

257

Management Earnings Forecasts and Value of Analyst Forecast Revisions  

E-Print Network (OSTI)

Prior studies evaluate the relative importance of the sources of value that financial analysts bring to the market based on the price impact of forecast revisions over the event time. We find that management earnings forecasts influence the timing and precision of analyst forecasts. More importantly, evidence suggests that prior studies finding of weaker (stronger) stock-price responses to forecast revisions in the period immediately after (before) the prior-quarter earnings announcement is likely to be the artifact of a temporal pattern of management earnings forecasts over the event time. To the extent that management earnings forecasts are public disclosures, our results suggest that the relative importance of analysts ' information discovery role documented in prior studies is likely to be overstated.

Yongtae Kim; Minsup Song

2013-01-01T23:59:59.000Z

258

A Verification of Numerical Model Forecasts for Sounding-Derived Indices above Udine, Northeast Italy  

Science Conference Proceedings (OSTI)

In this work, 40 different indices derived from real soundings and the corresponding ECMWF model forecasts for the same location (near Udine, northeast Italy) are compared. This comparison is repeated for more than 500 days, from June 2004 to ...

Agostino Manzato

2008-06-01T23:59:59.000Z

259

Chapter 11 Forecasting breaks and forecasting during breaks  

E-Print Network (OSTI)

Success in accurately forecasting breaks requires that they are predictable from relevant information available at the forecast origin using an appropriate model form, which can be selected and estimated before the break. To clarify the roles of these six necessary conditions, we distinguish between the information set for normal forces and the one for break drivers, then outline sources of potential information. Relevant non-linear, dynamic models facing multiple breaks can have more candidate variables than observations, so we discuss automatic model selection. As a failure to accurately forecast breaks remains likely, we augment our strategy by modelling breaks during their progress, and consider robust forecasting devices.

Jennifer L. Castle; Nicholas W. P. Fawcett; David F. Hendry

2011-01-01T23:59:59.000Z

260

United States energy supply and demand forecasts 1979-1995  

SciTech Connect

Forecasts of U.S. energy supply and demand by fuel type and economic sector, as well as historical background information, are presented. Discussion and results pertaining to the development of current and projected marginal energy costs, and their comparison with market prices, are also presented.

Walton, H.L.

1979-01-01T23:59:59.000Z

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.


261

Sensitivity of Tropical Storm Forecast to Radiative Destabilization  

Science Conference Proceedings (OSTI)

This paper examines the medium-range forecast of a typhoon using a global model. The focus of this study is on a comparison of two longwave radiative transfer calculations, one is based on an emissivity formulation while the other utilizes a band ...

T. N. Krishnamurti; K. S. Yap; D. K. Oosterhof

1991-09-01T23:59:59.000Z

262

Forecasting Uncertain Hotel Room Demand  

E-Print Network (OSTI)

Economic systems are characterized by increasing uncertainty in their dynamics. This increasing uncertainty is likely to incur bad decisions that can be costly in financial terms. This makes forecasting of uncertain economic variables an instrumental activity in any organization. This paper takes the hotel industry as a practical application of forecasting using the Holt-Winters method. The problem here is to forecast the uncertain demand for rooms at a hotel for each arrival day. Forecasting is part of hotel revenue management system whose objective is to maximize the revenue by making decisions regarding when to make rooms available for customers and at what price. The forecast approach discussed in this paper is based on quantitative models and does not incorporate management expertise. Even though, forecast results are found to be satisfactory for certain days, this is not the case for other arrival days. It is believed that human judgment is important when dealing with ...

Mihir Rajopadhye Mounir; Mounir Ben Ghaliay; Paul P. Wang; Timothy Baker; Craig V. Eister

2001-01-01T23:59:59.000Z

263

Forecast Technical Document Growing Stock Volume  

E-Print Network (OSTI)

Forecast Technical Document Growing Stock Volume Forecasts A document describing how growing stock (`standing') volume is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Growing stock volume forecasts Background A forecast of standing volume (or

264

Aviation forecasting and systems analyses  

SciTech Connect

The 9 papers in this report deal with the following areas: method of allocating airport runway slots; method for forecasting general aviation activity; air traffic control network-planning model based on second-order Markov chains; analyzing ticket-choice decisions of air travelers; assessing the safety and risk of air traffic control systems: risk estimation from rare events; forecasts of aviation fuel consumption in Virginia; estimating the market share of international air carriers; forecasts of passenger and air-cargo activity at Logan International Airport; and forecasting method for general aviation aircraft and their activity.

Geisinger, K.E.; Brander, J.R.G.; Wilson, F.R.; Kohn, H.M.; Polhemus, N.W.

1980-01-01T23:59:59.000Z

265

Studies of inflation and forecasting.  

E-Print Network (OSTI)

??This dissertation contains five research papers in the area of applied econometrics. The two broad themes of the research are inflation and forecasting. The first (more)

Bermingham, Colin

2011-01-01T23:59:59.000Z

266

UWIG Forecasting Workshop -- Albany (Presentation)  

SciTech Connect

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

Lew, D.

2011-04-01T23:59:59.000Z

267

Subhourly wind forecasting techniques for wind turbine operations  

DOE Green Energy (OSTI)

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

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

1984-08-01T23:59:59.000Z

268

Evaluating Density Forecasts: Forecast Combinations, Model Mixtures, Calibration and Sharpness  

E-Print Network (OSTI)

In a recent article Gneiting, Balabdaoui and Raftery (JRSSB, 2007) propose the criterion of sharpness for the evaluation of predictive distributions or density forecasts. They motivate their proposal by an example in which standard evaluation procedures based on probability integral transforms cannot distinguish between the ideal forecast and several competing forecasts. In this paper we show that their example has some unrealistic features from the perspective of the time-series forecasting literature, hence it is an insecure foundation for their argument that existing calibration procedures are inadequate in practice. We present an alternative, more realistic example in which relevant statistical methods, including information-based methods, provide the required discrimination between competing forecasts. We conclude that there is no need for a subsidiary criterion of sharpness.

James Mitchell; Kenneth F. Wallis

2008-01-01T23:59:59.000Z

269

On the Prediction of Forecast Skill  

Science Conference Proceedings (OSTI)

Using 10-day forecast 500 mb height data from the last 7 yr, the potential to predict the skill of numerical weather forecasts is discussed. Four possible predictor sets are described. The first, giving the consistency between adjacent forecasts, ...

T. N. Palmer; S. Tibaldi

1988-12-01T23:59:59.000Z

270

Equitable Skill Scores for Categorical Forecasts  

Science Conference Proceedings (OSTI)

Many skill scores used to evaluate categorical forecasts of discrete variables are inequitable, in the sense that constant forecasts of some events lead to better scores than constant forecasts of other events. Inequitable skill scores may ...

Lev S. Gandin; Allan H. Murphy

1992-02-01T23:59:59.000Z

271

Whither the Weather Analysis and Forecasting Process?  

Science Conference Proceedings (OSTI)

An argument is made that if human forecasters are to continue to maintain a skill advantage over steadily improving model and guidance forecasts, then ways have to be found to prevent the deterioration of forecaster skills through disuse. The ...

Lance F. Bosart

2003-06-01T23:59:59.000Z

272

Lagged Ensembles, Forecast Configuration, and Seasonal Predictions  

Science Conference Proceedings (OSTI)

An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) is presented. The focus of the analysis is on the construction of lagged ensemble forecasts ...

Mingyue Chen; Wanqiu Wang; Arun Kumar

273

Improving Forecast Communication: Linguistic and Cultural Considerations  

Science Conference Proceedings (OSTI)

One goal of weather and climate forecasting is to inform decision making. Effective communication of forecasts to various sectors of the public is essential for meeting that goal, yet studies repeatedly show that forecasts are not well understood ...

Karen Pennesi

2007-07-01T23:59:59.000Z

274

Lagged Ensembles, Forecast Configuration, and Seasonal Predictions  

Science Conference Proceedings (OSTI)

An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts ...

Mingyue Chen; Wanqiu Wang; Arun Kumar

2013-10-01T23:59:59.000Z

275

Evaluation of errors in national energy forecasts.  

E-Print Network (OSTI)

??Energy forecasts are widely used by the U.S. government, politicians, think tanks, and utility companies. While short-term forecasts were reasonably accurate, medium and long-range forecasts (more)

Sakva, Denys

2005-01-01T23:59:59.000Z

276

What Is the True Value of Forecasts?  

Science Conference Proceedings (OSTI)

Understanding the economic value of weather and climate forecasts is of tremendous practical importance. Traditional models that have attempted to gauge forecast value have focused on a best-case scenario, in which forecast users are assumed to ...

Antony Millner

2009-10-01T23:59:59.000Z

277

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Conference Proceedings (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

278

Forecasting Electric Vehicle Costs with Experience Curves  

E-Print Network (OSTI)

April, 5. R 2~1. Dino. "Forecasting the Price Evolution of 1ElectromcProducts," Ioumal of Forecasting, oL4, No I, 1985.costs and a set of forecasting tools that can be refined as

Lipman, Timonthy E.; Sperling, Daniel

2001-01-01T23:59:59.000Z

279

Probabilistic Verification of Monthly Temperature Forecasts  

Science Conference Proceedings (OSTI)

Monthly forecasting bridges the gap between medium-range weather forecasting and seasonal predictions. While such forecasts in the prediction range of 14 weeks are vital to many applications in the context of weather and climate risk management, ...

Andreas P. Weigel; Daniel Baggenstos; Mark A. Liniger; Frdric Vitart; Christof Appenzeller

2008-12-01T23:59:59.000Z

280

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand.Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

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

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand The demand forecast is the combined product of the hard work and expertise of numerous California Energy previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

282

A Forecast for the California Labor Market  

E-Print Network (OSTI)

issue for the state. A Forecast for the California Laborto Go? The UCLA Anderson Forecast for the Nation andAngeles: UCLA Anderson Forecast: Nation 1.1 1.9. Dhawan,

Mitchell, Daniel J. B.

2001-01-01T23:59:59.000Z

283

STAFF FORECAST OF 2007 PEAK STAFFREPORT  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION STAFF FORECAST OF 2007 PEAK DEMAND STAFFREPORT June 2006 CEC-400.................................................................................. 9 Sources of Forecast Error....................................................................... .................11 Tables Table 1: Revised versus September 2005 Peak Demand Forecast ......................... 2

284

Operational Forecaster Uncertainty Needs and Future Roles  

Science Conference Proceedings (OSTI)

Key results of a comprehensive survey of U.S. National Weather Service operational forecast managers concerning the assessment and communication of forecast uncertainty are presented and discussed. The survey results revealed that forecasters are ...

David R. Novak; David R. Bright; Michael J. Brennan

2008-12-01T23:59:59.000Z

285

Calibration of Probabilistic Forecasts of Binary Events  

Science Conference Proceedings (OSTI)

Probabilistic forecasts of atmospheric variables are often given as relative frequencies obtained from ensembles of deterministic forecasts. The detrimental effects of imperfect models and initial conditions on the quality of such forecasts can ...

Cristina Primo; Christopher A. T. Ferro; Ian T. Jolliffe; David B. Stephenson

2009-03-01T23:59:59.000Z

286

CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST  

E-Print Network (OSTI)

1 CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST QUALITY: EVIDENCE FROM FRENCH IPOS Anis attributes, ownership retained, auditor quality, and underwriter reputation and management earnings forecast quality measured by management earnings forecast accuracy and bias. Using 117 French IPOs, we find

Paris-Sud XI, Université de

287

Forecasting women's apparel sales using mathematical  

E-Print Network (OSTI)

Forecasting women's apparel sales using mathematical modeling Celia Frank and Ashish Garg, USA Les Sztandera Philadelphia University, Philadelphia, PA, USA Keywords Apparel, Forecasting average (MA), auto- regression (AR), or combinations of them are used for forecasting sales. Since

Raheja, Amar

288

Calibration of Probabilistic Quantitative Precipitation Forecasts  

Science Conference Proceedings (OSTI)

From 1 August 1990 to 31 July 1995, the Weather Service Forecast Office in Pittsburgh prepared 6159 probabilistic quantitative precipitation forecasts. Forecasts were made twice a day for 24-h periods beginning at 0000 and 1200 UTC for two river ...

Roman Krzysztofowicz; Ashley A. Sigrest

1999-06-01T23:59:59.000Z

289

Evaluating Probabilistic Forecasts Using Information Theory  

Science Conference Proceedings (OSTI)

The problem of assessing the quality of an operational forecasting system that produces probabilistic forecasts is addressed using information theory. A measure of the quality of the forecasting scheme, based on the amount of a data compression ...

Mark S. Roulston; Leonard A. Smith

2002-06-01T23:59:59.000Z

290

Virtual Floe Ice Drift Forecast Model Intercomparison  

Science Conference Proceedings (OSTI)

Both sea ice forecast models and methods to measure their skill are needed for operational sea ice forecasting. Two simple sea ice models are described and tested here. Four different measures of skill are also tested. The forecasts from the ...

Robert W. Grumbine

1998-09-01T23:59:59.000Z

291

Ensemble Cloud Model Applications to Forecasting Thunderstorms  

Science Conference Proceedings (OSTI)

A cloud model ensemble forecasting approach is developed to create forecasts that describe the range and distribution of thunderstorm lifetimes that may be expected to occur on a particular day. Such forecasts are crucial for anticipating severe ...

Kimberly L. Elmore; David J. Stensrud; Kenneth C. Crawford

2002-04-01T23:59:59.000Z

292

The evolution of consensus in macroeconomic forecasting  

E-Print Network (OSTI)

When professional forecasters repeatedly forecast macroeconomic variables, their forecasts may converge over time towards a consensus. The evolution of consensus is analyzed with Blue Chip data under a parametric polynomial decay function that permits flexibility in the decay path. For the most part, this specification fits the data. We test whether forecast differences decay to zero at the same point in time for a panel of forecasters, and discuss possible explanations for this, along with its implications for studies using panels of forecasters.

Allan W. Gregory; James Yetman; Jel Codes C E; Robert Eggert; Fred Joutz

2004-01-01T23:59:59.000Z

293

Background pollution forecast over bulgaria  

Science Conference Proceedings (OSTI)

Both, the current level of air pollution studies and social needs in the country, are in a stage mature enough for creating Bulgarian Chemical Weather Forecasting and Information System The system is foreseen to provide in real time forecast of the spatial/temporal ...

D. Syrakov; K. Ganev; M. Prodanova; N. Miloshev; G. Jordanov; E. Katragkou; D. Melas; A. Poupkou; K. Markakis

2009-06-01T23:59:59.000Z

294

Frequency Dependence in Forecast Skill  

Science Conference Proceedings (OSTI)

A method is proposed to calculate measures of forecast skill for high, medium and low temporal frequency variations in the atmosphere. This method is applied to a series of 128 consecutive 1 to 10-day forecasts produced at NMC with their ...

H. M. van Den Dool; Suranjana Saha

1990-01-01T23:59:59.000Z

295

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

296

Improving Forecasting: A plea for historical retrospectives  

NLE Websites -- All DOE Office Websites (Extended Search)

Improving Forecasting: A plea for historical retrospectives Title Improving Forecasting: A plea for historical retrospectives Publication Type Journal Article Year of Publication...

297

A comparison of the carbon dioxide fluxes of two annual cropping systems and a perennial hay field in southern Manitoba over 30 months.  

E-Print Network (OSTI)

??The eddy-covariance method was used to measure net ecosystem productivity over three adjacent fields from 2009 to 2011: two annual cropping systems (oat-canola-oat and hay-oat-fallow) (more)

Taylor, Amanda M.

2013-01-01T23:59:59.000Z

298

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect

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

299

2007 Annual Peer Review  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

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

300

Sixth Northwest Conservation and Electric Power Plan Appendix D: Wholesale Electricity Price Forecast  

E-Print Network (OSTI)

Forecast Introduction................................................................... 16 The Base Case Forecast..................................................................... 16 Base Case Price Forecast

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

Quantitative Precipitation Forecast Techniques for Use in Hydrologic Forecasting  

Science Conference Proceedings (OSTI)

Quantitative hydrologic forecasting usually requires knowledge of the spatial and temporal distribution of precipitation. First, it is important to accurately measure the precipitation falling over a particular watershed of interest. Second, ...

Konstantine P. Georgakakos; Michael D. Hudlow

1984-11-01T23:59:59.000Z

302

Load Forecast For use in Resource Adequacy  

E-Print Network (OSTI)

Load Forecast 2019 For use in Resource Adequacy Massoud Jourabchi #12;In today's presentation d l­ Load forecast methodology ­ Drivers of the forecast f i­ Treatment of conservation ­ Incorporating impact of weather ­ Forecast for 2019 #12;Regional Loads (MWA and MW)Regional Loads (MWA and MW

303

Forecast Technical Document Felling and Removals  

E-Print Network (OSTI)

Forecast Technical Document Felling and Removals Forecasts A document describing how volume fellings and removals are handled in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Felling and removals forecasts Background A fellings and removals

304

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity forecast is the combined product of the hard work and expertise of numerous staff members in the Demand the commercial sector forecast. Mehrzad Soltani Nia helped prepare the industrial forecast. Miguel Garcia

305

Combining forecast weights: Why and how?  

Science Conference Proceedings (OSTI)

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions

Yip Chee Yin; Ng Kok-Haur; Lim Hock-Eam

2012-01-01T23:59:59.000Z

306

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network (OSTI)

1 PROBLEMS OF FORECAST1 Dmitry KUCHARAVY dmitry.kucharavy@insa-strasbourg.fr Roland DE GUIO roland for the purpose of Innovative Design. First, a brief analysis of problems for existing forecasting methods of the forecast errors. Second, using a contradiction analysis, a set of problems related to technology forecast

Paris-Sud XI, Université de

307

Using reforecasts for probabilistic forecast calibration  

E-Print Network (OSTI)

1 Using reforecasts for probabilistic forecast calibration Tom Hamill NOAA Earth System Research that is currently operational. #12;3 Why compute reforecasts? · For many forecast problems, such as long-lead forecasts or high-precipitation events, a few past forecasts may be insufficient for calibrating

Hamill, Tom

308

Assessing Forecast Accuracy Measures Department of Economics  

E-Print Network (OSTI)

Assessing Forecast Accuracy Measures Zhuo Chen Department of Economics Heady Hall 260 Iowa State forecast accuracy measures. In the theoretical direction, for comparing two forecasters, only when the errors are stochastically ordered, the ranking of the forecasts is basically independent of the form

309

Feasibility study to update annualized cost of leaving (ACOL) procedures at the Navy Personnel Research and Development Center (NPRDC)  

Science Conference Proceedings (OSTI)

Accurate forecasts of officer retention rates are required in order to shape correctly the size and internal structure of the Navy manpower force through accession, promotion, and related policies. This study, conducted in 1987 for the Navy Personnel Research and Development Center (NPRDC), reviews existing forecasting and simulation methodologies and suggests new methods to implement in the future in order to improve forecasts of naval officer retention rates. The study also considers alternative sources of data to capture civilian earnings opportunities in the models. Two major types of models -- Annualized Cost of Leaving (ACOL) and Dynamic Retention (DR) -- are discussed in detail with respect to the ability to model and evaluate manpower policies of interest to NPRDC staff. A variety of other techniques which should be considered during the estimation stage are also discussed. The general study approach involved researching the subject area, the current data, the current models, and current estimation procedures. Available data and methodologies were then compared with the NPRDC problem in order to recommend potential solutions. This study did not include data collection or data analysis. This report is organized in eight sections. The Background Section discusses the history of officer retention models, the scope of officer manpower analysis at NPRDC, and NPRDC's history of officer loss-rate forecasting. Section 3 discusses the approach to model selection, which includes addition to a thorough discussion of the Dynamic Retention Model (DRM) and a comparison of the DRM and ACOL model. Section 5 presents alternative modeling directions for forecasting and a summary of compensation policy issues. The summary and conclusions appear in Section 6, and recommendations are in Section 7. References are in Section 8.2. 30 refs., 1 tab. (JF)

Trumble, D.; Flanagan, D.M.

1990-12-01T23:59:59.000Z

310

Can earnings forecasts be improved by taking into account the forecast bias?  

E-Print Network (OSTI)

Can earnings forecasts be improved by taking into account the forecast bias? François DOSSOU allow the calculation of earnings adjusted forecasts, for horizons from 1 to 24 months. We explain variables. From the forecast evaluation statistics viewpoints, the adjusted forecasts make it possible quasi

Paris-Sud XI, Université de

311

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

E-Print Network (OSTI)

OF NUMERICAL WEATHER PREDICTION SOLAR IRRADIANCE FORECASTS IN THE US Richard Perez ASRC, Albany, NY, Perez to solar radiation forecasting include (1) numerical weather prediction (NWP) models that infer local cloud© American Solar Energy Society ­ Proc. ASES Annual Conference, Raleigh, NC, EVALUATION

Perez, Richard R.

312

Short-term energy outlook: Annual supplement, 1987  

SciTech Connect

The Energy Information Administration (EIA) publishes forecasts of short-term energy supply, demand, and prices in the Short-Term Energy Outlook (Outlook). This volume, Short-Term Energy Outlook, Annual Supplement, (Supplement) discusses major changes in the forecasting methodology, analyzes previous forecast errors, and examines current issues that affect EIA's short-term energy forecasts. The principal users of the Supplement are managers and energy analysts in private industry and government. Chapter 2 evaluates the accuracy of previous short-term energy forecasts and the major assumptions underlying these forecasts published in the last 13 issues of the Outlook. Chapter 3 compares the EIA's present energy projections with past projections and with recent projections made by other forecasting groups. Chapter 4 analyzes the 1986 increase in residual fuel oil demand after 8 consecutive years of decline. Sectoral analysis shows where and why this increase occurred. Chapter 5 discusses the methodology, estimation, and forecasts of fossil fuel shares used in the generation of electricity. Chapter 6 presents an update of the methodology used to forecast natural gas demand, with an emphasis on sectoral disaggregation. Chapter 7 compares the current use of generation data as a representation of short-term electricity demand with proposed total and sectoral sales equations. 8 refs., 7 figs., 63 tabs.

1987-12-11T23:59:59.000Z

313

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":""}]}

314

Value of Wind Power Forecasting  

DOE Green Energy (OSTI)

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

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

2011-04-01T23:59:59.000Z

315

Fuzzy forecasting with DNA computing  

Science Conference Proceedings (OSTI)

There are many forecasting techniques including: exponential smoothing, ARIMA model, GARCH model, neural networks and genetic algorithm, etc. Since financial time series may be influenced by many factors, conventional model based techniques and hard ...

Don Jyh-Fu Jeng; Junzo Watada; Berlin Wu; Jui-Yu Wu

2006-06-01T23:59:59.000Z

316

Sampling Errors in Seasonal Forecasting  

Science Conference Proceedings (OSTI)

The limited numbers of start dates and ensemble sizes in seasonal forecasts lead to sampling errors in predictions. Defining the magnitude of these sampling errors would be useful for end users as well as informing decisions on resource ...

Stephen Cusack; Alberto Arribas

2009-03-01T23:59:59.000Z

317

Scoring Rules for Forecast Verification  

Science Conference Proceedings (OSTI)

The problem of probabilistic forecast verification is approached from a theoretical point of view starting from three basic desiderata: additivity, exclusive dependence on physical observations (locality), and strictly proper behavior. By ...

Riccardo Benedetti

2010-01-01T23:59:59.000Z

318

Richardson's Barotropic Forecast: A Reappraisal  

Science Conference Proceedings (OSTI)

To elucidate his numerical technique and to examine the effectiveness of geostrophic initial winds, Lewis Fry Richardson carried out an idealized forecast using the linear shallow-water equations and simple analytical pressure and velocity ...

Peter Lynch

1992-01-01T23:59:59.000Z

319

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.

320

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:

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

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:

322

Light truck forecasts  

SciTech Connect

The recent dramatic increase in the number of light trucks (109% between 1963 and 1974) has prompted concern about the energy consequences of the growing popularity of the light truck. An estimate of the future number of light trucks is considered to be a reasonable first step in assessing the energy impact of these vehicles. The monograph contains forecasts based on two models and six scenarios. The coefficients for the models have been derived by ordinary least squares regression of national level time series data. The first model is a two stage model. The first stage estimates the number of light trucks and cars (together), and the second stage applies a share's submodel to determine the number of light trucks. The second model is a simultaneous equation model. The two models track one another remarkably well, within about 2%. The scenarios were chosen to be consistent with those used in the Lindsey-Kaufman study Projection of Light Truck Population to Year 2025. Except in the case of the most dismal economic scenario, the number of light trucks is expected to increase from the 1974 level of 0.09 light truck per person to about 0.12 light truck per person in 1995.

Liepins, G.E.

1979-09-01T23:59:59.000Z

323

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,

324

The Potential Impact of Using Persistence as a Reference Forecast on Perceived Forecast Skill  

Science Conference Proceedings (OSTI)

Skill is defined as actual forecast performance relative to the performance of a reference forecast. It is shown that the choice of reference (e.g., random or persistence) can affect the perceived performance of the forecast system. Two scores, ...

Marion P. Mittermaier

2008-10-01T23:59:59.000Z

325

Evaluation of Wave Forecasts Consistent with Tropical Cyclone Warning Center Wind Forecasts  

Science Conference Proceedings (OSTI)

An algorithm to generate wave fields consistent with forecasts from the official U.S. tropical cyclone forecast centers has been made available in nearreal time to forecasters since summer 2007. The algorithm removes the tropical cyclone from ...

Charles R. Sampson; Paul A. Wittmann; Efren A. Serra; Hendrik L. Tolman; Jessica Schauer; Timothy Marchok

2013-02-01T23:59:59.000Z

326

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

327

The Complex Relationship between Forecast Skill and Forecast Value: A Real-World Analysis  

Science Conference Proceedings (OSTI)

For routine forecasts of temperature and precipitation, the relative skill advantage of human forecasters with respect to the numericalstatistical guidance is small (and diminishing). Since the relationship between forecast skill and the value ...

Paul J. Roebber; Lance F. Bosart

1996-12-01T23:59:59.000Z

328

Quantification of Uncertainity in Fire-Weather Forecasts: Some Results of Operational and Experimental Forecasting Programs  

Science Conference Proceedings (OSTI)

Fire-weather forecasts (FWFs) prepared by National Weather Service (NWS) forecasters on an operational basis are traditionally expressed in categorical terms. However, to make rational and optimal use of such forecasts, fire managers need ...

Barbara G. Brown; Allan H. Murphy

1987-09-01T23:59:59.000Z

329

Ability to Forecast Regional Soil Moisture with a Distributed Hydrological Model Using ECMWF Rainfall Forecasts  

Science Conference Proceedings (OSTI)

This study mimics an online forecast system to provide nine day-ahead forecasts of regional soil moisture. It uses modified ensemble rainfall forecasts from the numerical weather prediction model of the European Centre for Medium-Range Weather ...

J. M. Schuurmans; M. F. P. Bierkens

2009-04-01T23:59:59.000Z

330

Spatial Structure, Forecast Errors, and Predictability of the South Asian Monsoon in CFS Monthly Retrospective Forecasts  

Science Conference Proceedings (OSTI)

The spatial structure of the boreal summer South Asian monsoon in the ensemble mean of monthly retrospective forecasts by the Climate Forecast System of the National Centers for Environmental Prediction is examined. The forecast errors and ...

Hae-Kyung Lee Drbohlav; V. Krishnamurthy

2010-09-01T23:59:59.000Z

331

What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting  

Science Conference Proceedings (OSTI)

Differences of opinion exist among forecastersand between forecasters and usersregarding the meaning of the phrase good (bad) weather forecasts. These differences of opinion are fueled by a lack of clarity and/or understanding concerning the ...

Allan H. Murphy

1993-06-01T23:59:59.000Z

332

Forecasting new product penetration with flexible substitution patterns  

E-Print Network (OSTI)

choice model for forecasting demand for alternative-fuel7511, Urban Travel Demand Forecasting Project, Institute of89 (1999) 109129 Forecasting new product penetration with ?

Brownstone, David; Train, Kenneth

1999-01-01T23:59:59.000Z

333

Overestimation Reduction in Forecasting Telecommuting as a TDM Policy  

E-Print Network (OSTI)

M. , Ethics and advocacy in forecasting for public policy.change and social forecasting: the case of telecommuting asOverestimation Reduction in Forecasting Telecommuting as a

Tal, Gil

2008-01-01T23:59:59.000Z

334

Forecasting US CO2 Emissions Using State-Level Data  

E-Print Network (OSTI)

F. Hendry (eds), Economic Forecasting, Blackwell Publishing,W. : 2002, Macroeconomic forecasting using di?usion indexes,2003, Macroeconomic forecasting in the euro area: Country

Steinhauser, Ralf; Auffhammer, Maximilian

2005-01-01T23:59:59.000Z

335

NoVaS Transformations: Flexible Inference for Volatility Forecasting  

E-Print Network (OSTI)

and Correlation Forecasting in G. Elliott, C.W.J.Handbook of Economic Forecasting, Amsterdam: North-Holland,Transformations, forthcoming in Forecasting in the Presence

Politis, Dimitris N; Thomakos, Dimitrios D

2008-01-01T23:59:59.000Z

336

Forecasting new product penetration with flexible substitution patterns  

E-Print Network (OSTI)

7511, Urban Travel Demand Forecasting Project, Institute ofchoice model for forecasting demand for alternative-fuel89 (1999) 109129 Forecasting new product penetration with

Brownstone, David; Train, Kenneth

1999-01-01T23:59:59.000Z

337

Earthquake Forecasting in Diverse Tectonic Zones of the Globe  

E-Print Network (OSTI)

Long-term probabilistic forecasting of earthquakes, J.2000), Probabilistic forecasting of earthquakes, Geophys. J.F.F. (2006), The EEPAS forecasting model and the probability

Kagan, Y. Y.; Jackson, D. D.

2010-01-01T23:59:59.000Z

338

Ensemble-based methods for forecasting census in hospital units  

E-Print Network (OSTI)

P, Fitzgerald G: Regression forecasting of patient admissionapproach to modeling and forecasting demand in the emergencySJ, Haug PJ, Snow GL: Forecasting daily patient volumes in

Koestler, Devin C; Ombao, Hernando; Bender, Jesse

2013-01-01T23:59:59.000Z

339

Forecasting Danerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

costs could alter forecasting skill and the predictors thatForecasting Dangerous Inmate Misconduct: An Applications ofOn-Line Working Paper Series Forecasting Dangerous Inmate

Berk, Richard A.; Kriegler, Brian; Baek, John-Ho

2005-01-01T23:59:59.000Z

340

Developing a Practical Forecasting Screener for Domestic Violence Incidents  

E-Print Network (OSTI)

Developing a Practical Forecasting Screener for Domesticcomplicated did not improve forecasting skill. Taking thethe local costs of forecasting errors. It is also feasible

Richard A. Berk; Susan B. Sorenson; Yan He

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.


341

Forecasting with Dynamic Microsimulation: Design, Implementation, and Demonstration  

E-Print Network (OSTI)

Goulias Page 84 Forecasting with Dynamic Microsimulation:Goulias Page 80 Forecasting with Dynamic Microsimulation:L. Demographic Forecasting with a Dynamic Stochastic

Ravulaparthy, Srinath; Goulias, Konstadinos G.

2011-01-01T23:59:59.000Z

342

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007 INTEGRATED Table of Contents General Instructions for Demand Forecast Submittals.............................................................................. 4 Protocols for Submitted Demand Forecasts

343

CALIFORNIA ENERGY COMMISSION0 Annual Update to the Forecasted  

E-Print Network (OSTI)

CHP (onsite and wholesale) in California ­ accounts for the AB 32 mandates, RPS, and CPUC CHP Existing Renewable Generation In-State Renewable Energy · For all RPS-eligible generators, staff averaged the 2006-2011 QFER reported generation · In-state RPS-eligible with COD after 1/1/2011 and prior to 12

344

Highlights of the Annual Energy Outlook 1998 Forecasts  

Annual Energy Outlook 2012 (EIA)

is designed to work with a frames capable browser. It may not work as well without the frames capability. Go on to the Table of Contents...

345

Supplement to the annual energy outlook 1994  

Science Conference Proceedings (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

346

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"

347

Table 28. Comparison of coal projections, 2015, 2025, 2030, and ...  

U.S. Energy Information Administration (EIA)

U.S. Energy Information Administration Annual Energy Outlook 2012 115 Comparison with other projections Table 28. Comparison of coal projections, 2015, 2025, 2030 ...

348

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

349

Supplement to the Annual Energy Outlook 1993  

Science Conference Proceedings (OSTI)

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

Not Available

1993-02-17T23:59:59.000Z

350

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)

351

A Review of Numerical Forecast Guidance for Hurricane Hugo  

Science Conference Proceedings (OSTI)

Numerical forecast guidance for Hurricane Hugo from the National Meteorological Center is examined, as well as forecasts from the European Center for Medium Range Forecasting and the United Kingdom Meteorological Office. No one forecast product ...

John H. Ward

1990-09-01T23:59:59.000Z

352

Short-Range Ensemble Forecasts of Precipitation Type  

Science Conference Proceedings (OSTI)

Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type ...

Matthew S. Wandishin; Michael E. Baldwin; Steven L. Mullen; John V. Cortinas Jr.

2005-08-01T23:59:59.000Z

353

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Conference Proceedings (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

354

Using Customers' Reported Forecasts to Predict Future Sales  

E-Print Network (OSTI)

Using Customers' Reported Forecasts to Predict Future Sales Nihat Altintas , Alan Montgomery orders using forecasts provided by their customers. Our goal is to improve the supplier's operations through a better un- derstanding of the customers's forecast behavior. Unfortunately, customer forecasts

Murphy, Robert F.

355

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

356

The Automated Tropical Cyclone Forecasting System (ATCF)  

Science Conference Proceedings (OSTI)

The U.S. Navy Automated Tropical Cyclone Forecasting System (ATCF) is an IBM-AT compatible software package developed for the Joint Typhoon Warning Center (JTWC), Guam. ATCF is designed to assist forecasters with the process of making tropical ...

Ronald J. Miller; Ann J. Schrader; Charles R. Sampson; Ted L. Tsui

1990-12-01T23:59:59.000Z

357

Evaluation of LFM-2 Quantitative Precipitation Forecasts  

Science Conference Proceedings (OSTI)

The results of a near real time experiment designed to assess the state of the art of quantitative precipitation forecasting skill of the operational NMC LFM-2 are described. All available LFM-2 quantitative precipitation forecasts were verified ...

Lance F. Bosart

1980-08-01T23:59:59.000Z

358

Forecaster Workstation Design: Concepts and Issues  

Science Conference Proceedings (OSTI)

Some basic ideas about designing a meteorological workstation for operational weather forecasting are presented, in part as a complement to the recently published discussion of workstation design by R. R. Hoffman. Scientific weather forecasting ...

Charles A. Doswell III

1992-06-01T23:59:59.000Z

359

Performance of Recent Multimodel ENSO Forecasts  

Science Conference Proceedings (OSTI)

The performance of the International Research Institute for Climate and Society ENSO forecast plume during the 200211 period is evaluated using deterministic and probabilistic verification measures. The plume includes multiple model forecasts ...

Michael K. Tippett; Anthony G. Barnston; Shuhua Li

2012-03-01T23:59:59.000Z

360

Local Forecast Communication In The Altiplano  

Science Conference Proceedings (OSTI)

Forecasts play an important role in planting decisions for Andean peasant producers. Predictions of the upcoming cropping season determine when, where, and what farmers will plant. This research looks at the sources of forecast information used ...

Jere L. Gilles; Corinne Valdivia

2009-01-01T23:59:59.000Z

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.


361

Bayesian Model Verification of NWP Ensemble Forecasts  

Science Conference Proceedings (OSTI)

Forecasts of convective precipitation have large uncertainties. To consider the forecast uncertainties of convection-permitting models, a convection-permitting ensemble prediction system (EPS) based on the Consortium for Small-scale Modeling (...

Andreas Rpnack; Andreas Hense; Christoph Gebhardt; Detlev Majewski

2013-01-01T23:59:59.000Z

362

Economic and Statistical Measures of Forecast Accuracy  

E-Print Network (OSTI)

This paper argues in favour of a closer link between decision and forecast evaluation problems. Although the idea of using decision theory for forecast evaluation appears early in the dynamic stochastic programming literature, and has continued...

Granger, Clive W J; Pesaran, M Hashem

2004-06-16T23:59:59.000Z

363

Forecasting consumer products using prediction markets  

E-Print Network (OSTI)

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

Trepte, Kai

2009-01-01T23:59:59.000Z

364

Probabilistic Visibility Forecasting Using Bayesian Model Averaging  

Science Conference Proceedings (OSTI)

Bayesian model averaging (BMA) is a statistical postprocessing technique that has been used in probabilistic weather forecasting to calibrate forecast ensembles and generate predictive probability density functions (PDFs) for weather quantities. ...

Richard M. Chmielecki; Adrian E. Raftery

2011-05-01T23:59:59.000Z

365

Intercomparison of Spatial Forecast Verification Methods  

Science Conference Proceedings (OSTI)

Advancements in weather forecast models and their enhanced resolution have led to substantially improved and more realistic-appearing forecasts for some variables. However, traditional verification scores often indicate poor performance because ...

Eric Gilleland; David Ahijevych; Barbara G. Brown; Barbara Casati; Elizabeth E. Ebert

2009-10-01T23:59:59.000Z

366

Forecasting with Reference to a Specific Climatology  

Science Conference Proceedings (OSTI)

Seasonal forecasts are most commonly issued as anomalies with respect to some multiyear reference period. However, different seasonal forecasting centers use different reference periods. This paper shows that for near-surface temperature, ...

Emily Wallace; Alberto Arribas

2012-11-01T23:59:59.000Z

367

Probabilistic Quantitative Precipitation Forecasts for River Basins  

Science Conference Proceedings (OSTI)

A methodology has been formulated to aid a field forecaster in preparing probabilistic quantitative precipitation forecasts (QPFs) for river basins. The format of probabilistic QPF is designed to meet three requirements: (i) it is compatible with ...

Roman Krzysztofowicz; William J. Drzal; Theresa Rossi Drake; James C. Weyman; Louis A. Giordano

1993-12-01T23:59:59.000Z

368

A General Framework for Forecast Verification  

Science Conference Proceedings (OSTI)

A general framework for forecast verification based on the joint distribution of forecasts and observations is described. For further elaboration of the framework, two factorizations of the joint distribution are investigated: 1) the calibration-...

Allan H. Murphy; Robert L. Winkler

1987-07-01T23:59:59.000Z

369

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Conference Proceedings (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

370

Value from Ambiguity in Ensemble Forecasts  

Science Conference Proceedings (OSTI)

This study explores the objective application of ambiguity information, that is, the uncertainty in forecast probability derived from an ensemble. One application approach, called uncertainty folding, merges ambiguity with forecast uncertainty ...

Mark S. Allen; F. Anthony Eckel

2012-02-01T23:59:59.000Z

371

Management of supply chain: an alternative modelling technique for forecasting  

E-Print Network (OSTI)

Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view

Datta, Shoumen

2007-05-23T23:59:59.000Z

372

Improving week two forecasts with multi-model re-forecast ensembles  

E-Print Network (OSTI)

Improving week two forecasts with multi-model re-forecast ensembles Jeffrey S. Whitaker and Xue Wei NOAA-CIRES Climate Diagnostics Center, Boulder, CO Fr´ed´eric Vitart Seasonal Forecasting Group, ECMWF dataset of ensemble 're-forecasts' from a single model can significantly improve the skill

Whitaker, Jeffrey S.

373

Forecasting for energy and chemical decision analysis  

SciTech Connect

This paper focuses on uncertainty and bias in forecasts used for major energy and chemical investment decisions. Probability methods for characterizing uncertainty in the forecast are reviewed. Sources of forecasting bias are classified based on the results of relevant psychology research. Examples are drawn from the energy and chemical industry to illustrate the value of explicit characterization of uncertainty and reduction of bias in forecasts.

Cazalet, E.G.

1984-08-01T23:59:59.000Z

374

A Rank Approach to Equity Forecast Construction  

E-Print Network (OSTI)

that are fit for their purpose; for example, returningaggregate county and sector forecasts that are consistent by construction....

Satchell, Stephen E; Wright, Stephen M

2006-03-14T23:59:59.000Z

375

Annual ENSO  

Science Conference Proceedings (OSTI)

Using various observational data, the seasonal cycle of the tropical Pacific is investigated, suggesting the existence of an annual El NioSouthern Oscillation (ENSO). A positive sea surface temperature anomaly (SSTA) appearing off Peru in ...

Tomoki Tozuka; Toshio Yamagata

2003-08-01T23:59:59.000Z

376

Forecasting Techniques Utilized by the Forecast Branch of the National Meteorological Center During a Major Convective Rainfall Event  

Science Conference Proceedings (OSTI)

Meteorologists within the Forecast Branch (FB) of the National Meteorological Center (NMC) produce operational quantitative precipitation forecasts (QPFs). These manual forecasts are prepared utilizing various forecasting techniques, which are ...

Theodore W. Funk

1991-12-01T23:59:59.000Z

377

Application of Forecast Verification Science to Operational River Forecasting in the U.S. National Weather Service  

Science Conference Proceedings (OSTI)

Forecast verification in operational hydrology has been very limited to date, mainly due to the complexity of verifying both forcing input forecasts and hydrologic forecasts on multiple spacetime scales. However, forecast verification needs to ...

Julie Demargne; Mary Mullusky; Kevin Werner; Thomas Adams; Scott Lindsey; Noreen Schwein; William Marosi; Edwin Welles

2009-06-01T23:59:59.000Z

378

Load Forecasting for Modern Distribution Systems  

Science Conference Proceedings (OSTI)

Load forecasting is a fundamental activity for numerous organizations and activities within a utility, including planning, operations, and control. Transmission and Distribution (T&D) planning and design engineers use the load forecast to determine whether any changes and additions are needed to the electric system to satisfy the anticipated load. Other load forecast users include system operations, financial ...

2013-03-08T23:59:59.000Z

379

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

380

Modeling and Forecasting Electric Daily Peak Loads  

E-Print Network (OSTI)

Forecast Update As part of the Mid Term Assessment, staff is preparing a long term wholesale electricity 29, 2012 Preliminary Results of the Electricity Price Forecast Update As part of the Mid Term Assessment, staff is preparing a long term wholesale electricity market price forecast. A summary of the work

Abdel-Aal, Radwan E.

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

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work to the contributing authors listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad

382

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

383

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work and expertise of numerous, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped prepare

384

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work listed previously, Mohsen Abrishami prepared the commercial sector forecast. Mehrzad Soltani Nia helped

385

Load forecast and treatment of conservation  

E-Print Network (OSTI)

Load forecast and treatment of conservation July 28th 2010 Resource Adequacy Technical Committee conservation is implicitly incorporated in the short-term demand forecast? #12;3 Incorporating conservation in the short-term model Our short-term model is an econometric model which can not explicitly forecast

386

FINAL STAFF FORECAST OF 2008 PEAK DEMAND  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION FINAL STAFF FORECAST OF 2008 PEAK DEMAND STAFFREPORT June 2007 CEC-200 of the information in this paper. #12;Abstract This document describes staff's final forecast of 2008 peak demand demand forecasts for the respective territories of the state's three investor-owned utilities (IOUs

387

STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES 2005 TO 2018 report, Staff Forecast: Retail Electricity Prices, 2005 to 2018, was prepared with contributions from the technical assistance provided by Greg Broeking of R.W. Beck, Inc. in preparing retail price forecasts

388

Blue Chip Consensus US GDP Forecast  

E-Print Network (OSTI)

and metro area from Moodys Economy.com Equivalent to US-level Gross Domestic Product ? The GMP forecasts have a large impact on the peak load forecasts Rule of thumb: 1 % growth in RTO GMP ? approx. 1,000 MW growth in forecast RTO peak load

James F. Wilson

2007-01-01T23:59:59.000Z

389

5, 183218, 2008 A rainfall forecast  

E-Print Network (OSTI)

HESSD 5, 183­218, 2008 A rainfall forecast model using Artificial Neural Network N. Q. Hung et al An artificial neural network model for rainfall forecasting in Bangkok, Thailand N. Q. Hung, M. S. Babel, S Geosciences Union. 183 #12;HESSD 5, 183­218, 2008 A rainfall forecast model using Artificial Neural Network N

Paris-Sud XI, Université de

390

System Demonstration Multilingual Weather Forecast Generation System  

E-Print Network (OSTI)

System Demonstration Multilingual Weather Forecast Generation System Tianfang Yao DongmoZhang Qian (Multilingual Weather Forecasts Assistant) system will be demonstrated. It is developed to generate the multilingual text of the weather forecasts automatically. The raw data from the weather observation can be used

391

(1) Ensemble forecast calibration & (2) using reforecasts  

E-Print Network (OSTI)

1 (1) Ensemble forecast calibration & (2) using reforecasts Tom Hamill NOAA Earth System Research · Calibration: ; the statistical adjustment of the (ensemble) forecast ­ Rationale 1: Infer large-sample probabilities from small ensemble. ­ Rationale 2: Remove bias, increase forecast reliability while preserving

Hamill, Tom

392

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 of individual features are estimated. A time series analysis is used to forecast and can be used, for example, to forecast (1) notebook computer price at introduction, and (2) rate of price erosion for a notebook's life cycle. Results indicate that this approach can forecast the price of a notebook computer up to four months in advance of its introduction with an average error of under 10% and the rate of price erosion to within 10% of the price for seven months after introduction-the length of the typical life cycle of a notebook. Since all data are publicly available, this approach can be used to assist managerial decision making in the notebook computer industry, for example, in determining when and how to upgrade a model and when to introduce a new model.

Rutherford, Derek Paul

1997-01-01T23:59:59.000Z

393

Space-Time Wind Speed Forecasting for Improved Power System Dispatch  

E-Print Network (OSTI)

In order to support large scale integration of wind power, state-of-the-art wind speed forecasting methods should provide accurate and adequate information to enable efficient scheduling of wind power in electric energy systems. In this article, space-time wind forecasts are incorporated into power system economic dispatch models. First, we proposed a new space-time wind forecasting model, which generalizes and improves upon a so-called regime-switching space-time model by allowing the forecast regimes to vary with the dominant wind direction and with the seasons. Then, results from the new wind forecasting model are implemented into a power system economic dispatch model, which takes into account both spatial and temporal wind speed correlations. This, in turn, leads to an overall more cost-effective scheduling of system-wide wind generation portfolio. The potential economic benefits arise in the system-wide generation cost savings and in the ancillary service cost savings. This is illustrated in a test system in the northwest region of the U.S. Compared with persistent and autoregressive models, our proposed method could lead to annual integration cost savings on the scale of tens of millions of dollars in regions with high wind penetration, such as Texas and the Northwest. Key words: Power system economic dispatch; Power system operation; Space-time statistical model; Wind data; Wind speed forecasting.

Xinxin Zhu; Marc G. Genton; Yingzhong Gu; Le Xie

2012-01-01T23:59:59.000Z

394

How Do You Like Your Weather?: Using Weather Forecast Data to Improve Short-Term Load Forecasts  

Science Conference Proceedings (OSTI)

This document provides a quick overview of weather forecasts as a data issue in the development of electricity demand forecasts. These are three sections in this Brief: o reasons behind the rise in interest in using weather forecasts in electricity forecasting models, o an overview of what some utilities are doing to evaluate weather forecasts, and o a resource list of weather forecast providers.

2001-09-28T23:59:59.000Z

395

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

DOE Green Energy (OSTI)

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

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

2013-01-01T23:59:59.000Z

396

NERSC Annual Reports  

NLE Websites -- All DOE Office Websites (Extended Search)

NERSC Annual Reports NERSC Annual Reports Sort by: Default | Name anrep2000.png NERSC Annual Report 2000 Download Image: anrep2000.png | png | 203 KB Download File:...

397

Essays on macroeconomics and forecasting  

E-Print Network (OSTI)

This dissertation consists of three essays. Chapter II uses the method of structural factor analysis to study the effects of monetary policy on key macroeconomic variables in a data rich environment. I propose two structural factor models. One is the structural factor augmented vector autoregressive (SFAVAR) model and the other is the structural factor vector autoregressive (SFVAR) model. Compared to the traditional vector autogression (VAR) model, both models incorporate far more information from hundreds of data series, series that can be and are monitored by the Central Bank. Moreover, the factors used are structurally meaningful, a feature that adds to the understanding of the â??black boxâ? of the monetary transmission mechanism. Both models generate qualitatively reasonable impulse response functions. Using the SFVAR model, both the â??price puzzleâ? and the â??liquidity puzzleâ? are eliminated. Chapter III employs the method of structural factor analysis to conduct a forecasting exercise in a data rich environment. I simulate out-of-sample real time forecasting using a structural dynamic factor forecasting model and its variations. I use several structural factors to summarize the information from a large set of candidate 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 forecasting models compared to alternatives that include univariate autoregression (AR) model, the VAR model and Stock and Watsonâ??s (2002) models, especially when forecasting real variables. In chapter IV, we measure U.S. technology shocks by implementing a dual approach, which is based on more reliable price data instead of aggregate quantity data. By doing so, we find the relative volatility of technology shocks and the correlation between output fluctuation and technology shocks to be much smaller than those revealed in most real-business-cycle (RBC) studies. Our results support the findings of Burnside, Eichenbaum and Rebelo (1996), who showed that the correlation between technology shocks and output is exaggerated in the RBC literature. This suggests that one should examine other sources of fluctuations for a better understanding of the business cycle phenomena.

Liu, Dandan

2005-08-01T23:59:59.000Z

398

Annual Report  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

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.

399

Critical Operating Constraint Forecasting (COCF)  

Science Conference Proceedings (OSTI)

This document represents the progress report and Task 1 letter report of the California Institute for Energy and Environment (CIEE) contract funded by the California Energy Commission (CEC), Critical Operating Constraint Forecasting (COCF) for California Independent System Operator (CAISO) Planning Phase. Task 1 was to accomplish the following items: Collect data from CAISO to set up the WECC power flow base case representing the CAISO system in the summer of 2006 Run TRACE for maximizing California Impo...

2006-06-30T23:59:59.000Z

400

Forecasting Techniques The Use of Hourly Model-Generated Soundings to Forecast Mesoscale Phenomena. Part I: Initial Assessment in Forecasting Warm-Season Phenomena  

Science Conference Proceedings (OSTI)

Since late 1995, NCEP has made available to forecasters hourly model guidance at selected sites in the form of vertical profiles of various forecast fields. These profiles provide forecasters with increased temporal resolution and greater ...

Robert E. Hart; Gregory S. Forbes; Richard H. Grumm

1998-12-01T23:59:59.000Z

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.


401

Meese-Rogoff redux: Micro-based exchange-rate forecasting  

E-Print Network (OSTI)

Johnatban. "Exchange Rate Forecasting: The Errors We'veBased Exchange-Rate Forecasting By MARTIN D . D . EVANS ANDon longer-horizon forecasting, we examine forecasting over

Evans, MDD; Lyons, Richard K.

2005-01-01T23:59:59.000Z

402

The Past as Prologue? Business Cycles and Forecasting since the 1960s  

E-Print Network (OSTI)

Forecasters, Journal of Forecasting, Vol. 28, No. 2, Mar,of Macroeconomic Forecasting Journal of Macroeconomics,of Federal Reserve Forecasting, Journal of Macroeconomics,

Bardhan, Ashok Deo; Hicks, Daniel; Kroll, Cynthia A.; Yu, Tiffany

2010-01-01T23:59:59.000Z

403

Material World: Forecasting Household Appliance Ownership in a Growing Global Economy  

E-Print Network (OSTI)

and V. Letschert (2005). Forecasting Electricity Demand in8364 Material World: Forecasting Household ApplianceMcNeil, 2008). Forecasting Diffusion Forecasting Variables

Letschert, Virginie

2010-01-01T23:59:59.000Z

404

Highly improved predictive skill in the forecasting of the East Asian summer monsoon  

E-Print Network (OSTI)

Highly improved predictive skill in the forecasting of the East Asian summer monsoon Eungul Lee,1 4 August 2008; published 29 October 2008. [1] The East Asian summer monsoon greatly influences of the monsoon is very low in comparison with that of the Indian summer monsoon because of the complexity

Wisconsin at Madison, University of

405

Uranium industry annual 1994  

SciTech Connect

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

406

Mathematical and computer modelling reports: Modeling and forecasting energy markets with the intermediate future forecasting system  

Science Conference Proceedings (OSTI)

This paper describes the Intermediate Future Forecasting System (IFFS), which is the model used to forecast integrated energy markets by the U.S. Energy Information Administration. The model contains representations of supply and demand for all of the ...

Frederic H. Murphy; John J. Conti; Susan H. Shaw; Reginald Sanders

1989-09-01T23:59:59.000Z

407

BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multi-model Ensemble Forecasts  

Science Conference Proceedings (OSTI)

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

Jianguo Liu; Zhenghui Xie

408

Using National Air Quality Forecast Guidance to Develop Local Air Quality Index Forecasts  

Science Conference Proceedings (OSTI)

The National Air Quality Forecast Capability (NAQFC) currently provides next-day forecasts of ozone concentrations over the contiguous United States. It was developed collaboratively by NOAA and Environmental Protection Agency (EPA) in order to ...

Brian Eder; Daiwen Kang; S. Trivikrama Rao; Rohit Mathur; Shaocai Yu; Tanya Otte; Ken Schere; Richard Wayland; Scott Jackson; Paula Davidson; Jeff McQueen; George Bridgers

2010-03-01T23:59:59.000Z

409

Role of Retrospective Forecasts of GCMs Forced with Persisted SST Anomalies in Operational Streamflow Forecasts Development  

Science Conference Proceedings (OSTI)

Seasonal streamflow forecasts contingent on climate information are essential for water resources planning and management as well as for setting up contingency measures during extreme years. In this study, operational streamflow forecasts are ...

A. Sankarasubramanian; Upmanu Lall; Susan Espinueva

2008-04-01T23:59:59.000Z

410

Evaluation of MJO Forecast Skill from Several Statistical and Dynamical Forecast Models  

Science Conference Proceedings (OSTI)

This work examines the performance of MaddenJulian oscillation (MJO) forecasts from NCEPs coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in nearreal time. ...

Kyong-Hwan Seo; Wanqiu Wang; Jon Gottschalck; Qin Zhang; Jae-Kyung E. Schemm; Wayne R. Higgins; Arun Kumar

2009-05-01T23:59:59.000Z

411

A Probabilistic Forecast Contest and the Difficulty in Assessing Short-Range Forecast Uncertainty  

Science Conference Proceedings (OSTI)

Results are presented from a probability-based weather forecast contest. Rather than evaluating the absolute errors of nonprobabilistic temperature and precipitation forecasts, as is common in other contests, this contest evaluated the skill of ...

Thomas M. Hamill; Daniel S. Wilks

1995-09-01T23:59:59.000Z

412

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

Science Conference Proceedings (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

413

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting  

Science Conference Proceedings (OSTI)

Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal ...

Giuseppe Mascaro; Enrique R. Vivoni; Roberto Deidda

2010-02-01T23:59:59.000Z

414

Evaluation of Probabilistic Precipitation Forecasts Determined from Eta and AVN Forecasted Amounts  

Science Conference Proceedings (OSTI)

This note examines the connection between the probability of precipitation and forecasted amounts from the NCEP Eta (now known as the North American Mesoscale model) and Aviation (AVN; now known as the Global Forecast System) models run over a 2-...

William A. Gallus Jr.; Michael E. Baldwin; Kimberly L. Elmore

2007-02-01T23:59:59.000Z

415

Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System  

Science Conference Proceedings (OSTI)

A simple binning technique is developed to produce reliable 3-h probabilistic quantitative precipitation forecasts (PQPFs) from the National Centers for Environmental Prediction (NCEP) multimodel short-range ensemble forecasting system obtained ...

David J. Stensrud; Nusrat Yussouf

2007-02-01T23:59:59.000Z

416

The Impact of Writing Area Forecast Discussions on Student Forecaster Performance  

Science Conference Proceedings (OSTI)

A brief study is provided on the forecast performance of students who write a mock area forecast discussion (AFD) on a weekly basis. Student performance was tracked for one semester (11 weeks) during the University of MissouriColumbia's local ...

Patrick S. Market

2006-02-01T23:59:59.000Z

417

An Operational Model for Forecasting Probability of Precipitation and Yes/No Forecast  

Science Conference Proceedings (OSTI)

An operational system for forecasting probability of precipitation (PoP) and yes/no forecast over 10 stations during monsoon season is developed. A perfect prog method (PPM) approach is followed for statistical interpretation of numerical weather ...

Ashok Kumar; Parvinder Maini; S. V. Singh

1999-02-01T23:59:59.000Z

418

Evaluation of Probabilistic Medium-Range Temperature Forecasts from the North American Ensemble Forecast System  

Science Conference Proceedings (OSTI)

Ensemble temperature forecasts from the North American Ensemble Forecast System were assessed for quality against observations for 10 cities in western North America, for a 7-month period beginning in February 2007. Medium-range probabilistic ...

Doug McCollor; Roland Stull

2009-02-01T23:59:59.000Z

419

Further Evaluation of the National Meterological Center's Medium-Range Forecast Model Precpitation Forecasts  

Science Conference Proceedings (OSTI)

Precipitation forecasts made by the National Meteorological Center's medium-range forecast (MRF) model are evaluated for the period, 1 March 1987 to 31 March 1989. As shown by Roads and Maisel, the MRF model wet bias was substantially alleviated ...

John O. Roads; T. Norman Maisal; Jordan Alpert

1991-12-01T23:59:59.000Z

420

R/ECON July 2000 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON July 2000 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF JULY 2000 NEW JERSEY of growth will decelerate over the forecast period. The R/ECON TM forecast for New Jersey in 2000 looks to decelerate over the course of the forecast. These forces will combine to push the unemployment rate to more

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

Increasing NOAA's computational capacity to improve global forecast modeling  

E-Print Network (OSTI)

1 Increasing NOAA's computational capacity to improve global forecast modeling A NOAA of the NWS's forecast products, even its regional forecast products, are constrained by the limitations of NOAA's global forecast model. Unfortunately, our global forecasts are less accurate than those from

Hamill, Tom

422

Forecasting Random Walks Under Drift Instability  

E-Print Network (OSTI)

Forecasting Random Walks Under Drift Instability? M. Hashem Pesaran University of Cambridge, CIMF, and USC Andreas Pick University of Cambridge, CIMF March 11, 2008 Abstract This paper considers forecast averaging when the same model is used... but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to using forecasts based on a single...

Pesaran, M Hashem; Pick, Andreas

423

Trends in Severe Local Storm Watch Verification at the National Severe Storms Forecast Center  

Science Conference Proceedings (OSTI)

Trends of tornado and severe thunderstorm watch verification for the period 19671990 are presented. Over the past 10 years the annual number of reported severe thunderstorm events has increased substantially. In comparison, the number of tornado ...

Richard W. Anthony; Preston W. Leftwich Jr.

1992-12-01T23:59:59.000Z

424

Solar future: 1978. [Market forecast to 1992  

SciTech Connect

The growth in sales of solar heating equipment is discussed. Some forecasts are made for the continued market growth of collectors, pool systems, and photovoltaics. (MOW)

Butt, S.H.

1978-03-01T23:59:59.000Z

425

Energy conservation and official UK energy forecasts  

SciTech Connect

Behind the latest United Kingdom (UK) official forecasts of energy demand are implicit assumptions about future energy-price elasticities. Mr. Pearce examines the basis of the forecasts and finds that the long-term energy-price elasticities that they imply are two or three times too low. The official forecasts substantially understate the responsiveness of demand to energy price rises. If more-realistic price elasticities were assumed, the official forecasts would imply a zero primary energy-demand growth to 2000. This raises the interesting possibility of a low energy future being brought about entirely by market forces. 15 references, 3 tables.

Pearce, D.

1980-09-01T23:59:59.000Z

426

Geothermal wells: a forecast of drilling activity  

DOE Green Energy (OSTI)

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

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

1981-07-01T23:59:59.000Z

427

Time Series Prediction Forecasting the Future and ...  

Science Conference Proceedings (OSTI)

Time Series Prediction Forecasting the Future and Understanding the Past Santa Fe Institute Proceedings on the Studies in the Sciences of ...

2012-10-01T23:59:59.000Z

428

Promotional forecasting in the grocery retail business  

E-Print Network (OSTI)

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

Koottatep, Pakawkul

2006-01-01T23:59:59.000Z

429

Rolling 12 Month Forecast November-2008.xls  

NLE Websites -- All DOE Office Websites (Extended Search)

Month Exceedence Level: 90% (Dry) First Preference CVP Generation Project Use November 2008 October 2009 November 2008 Twelve-Month Forecast of CVP Generation and Base Resource...

430

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;

431

Annual Report  

NLE Websites -- All DOE Office Websites (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,

432

Specification, estimation, and forecasts of industrial demand and price of electricity  

Science Conference Proceedings (OSTI)

This paper discusses the specification of electricity-demand and price equations for manufacturing industries and presents empirical results based on the data for 16 Standard Industrial Classification (SIC) three-digit industries from 1959 to 1976. Performances of estimated equations are evaluated by sample-period simulation tests. The estimated coefficients are then used to forecast electricity demand by industry. Results show that most of the estimated coefficients have expected signs and are statistically significant. The estimated equations perform well in terms of sample-period simulation tests, registering small mean absolute percentage errors and mean square percentage errors for most of the industries studied. Forecasted results indicate that total electricity demand by manufacturing industries would grow at an average annual rate of 3.53% according to the baseline forecast, 2.39% in the high-price scenario, and 4.76% in the low-price scenario. The forecasted growth rates vary substantially among industries. The results also indicate that the price of electricity would continue to grow at a faster rate than the general price level in the forecasted period 1977 to 1990. 19 references, 6 tables.

Chang, H.S. (Univ. of Tennessee, Knoxville); Chern, W.S.

1981-01-01T23:59:59.000Z

433

Forecasting Prices andForecasting Prices and Congestion forCongestion for  

E-Print Network (OSTI)

Abstract--In deregulated electricity markets, short-term load forecasting is important for reliable power322 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 25, NO. 1, FEBRUARY 2010 Short-Term Load Forecasting presents a similar day-based wavelet neural network method to forecast tomorrow's load. The idea

Tesfatsion, Leigh

434

Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations  

E-Print Network (OSTI)

Power Forecasting in Five U.S. Electricity Markets MISO NYISO PJM ERCOT CAISO Peak load 109,157 MW (7 ........................................................................................... 18 4 WIND POWER FORECASTING AND ELECTRICITY MARKET OPERATIONS............................................................ 18 4-1 Market Operation and Wind Power Forecasting in Five U.S. Electricity Markets .......... 21 #12

Kemner, Ken

435

A Statistical Solar Flare Forecast Method  

E-Print Network (OSTI)

A Bayesian approach to solar flare prediction has been developed, which uses only the event statistics of flares already observed. The method is simple, objective, and makes few ad hoc assumptions. It is argued that this approach should be used to provide a baseline prediction for certain space weather purposes, upon which other methods, incorporating additional information, can improve. A practical implementation of the method for whole-Sun prediction of Geostationary Observational Environment Satellite (GOES) events is described in detail, and is demonstrated for 4 November 2003, the day of the largest recorded GOES flare. A test of the method is described based on the historical record of GOES events (1975-2003), and a detailed comparison is made with US National Oceanic and Atmospheric Administration (NOAA) predictions for 1987-2003. Although the NOAA forecasts incorporate a variety of other information, the present method out-performs the NOAA method in predicting mean numbers of event days, for both M-X and X events. Skill scores and other measures show that the present method is slightly less accurate at predicting M-X events than the NOAA method, but substantially more accurate at predicting X events, which are important contributors to space weather.

M. S. Wheatland

2005-05-14T23:59:59.000Z

436

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

437

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

438

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

439

Forecasting during the Lake-ICE/SNOWBANDS Field Experiments  

Science Conference Proceedings (OSTI)

Despite improvements in numerical weather prediction models, statistical models, forecast decision trees, and forecasting rules of thumb, human interpretation of meteorological information for a particular forecast situation can still yield a ...

Peter J. Sousounis; Greg E. Mann; George S. Young; Richard B. Wagenmaker; Bradley D. Hoggatt; William J. Badini

1999-12-01T23:59:59.000Z

440

Experiments in Temperature and Precipitation Forecasting for Illinois  

Science Conference Proceedings (OSTI)

Six years of daily temperature and precipitation forecasting are studied for Urbana, Illinois. Minimum temperature forecast skills, measured against a climatological control, are 57%, 48%, 34% and 20% for the respective forecast ranges of one, ...

John R. Gyakum

1986-06-01T23:59:59.000Z

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.


441

The Economic Value Of Ensemble-Based Weather Forecasts  

Science Conference Proceedings (OSTI)

The potential economic benefit associated with the use of an ensemble of forecasts versus anequivalent or higher-resolution control forecast is discussed. Neither forecast systems are post-processed,except a simple calibration that is applied to ...

Yuejian Zhu; Zoltan Toth; Richard Wobus; David Richardson; Kenneth Mylne

2002-01-01T23:59:59.000Z

442

A Probabilistic Forecast Approach for Daily Precipitation Totals  

Science Conference Proceedings (OSTI)

Commonly, postprocessing techniques are employed to calibrate a model forecast. Here, a probabilistic postprocessor is presented that provides calibrated probability and quantile forecasts of precipitation on the local scale. The forecasts are ...

Petra Friederichs; Andreas Hense

2008-08-01T23:59:59.000Z

443

Precipitation and Temperature Forecast Performance at the Weather Prediction Center  

Science Conference Proceedings (OSTI)

The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multi-year verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from ...

David R. Novak; Christopher Bailey; Keith Brill; Patrick Burke; Wallace Hogsett; Robert Rausch; Michael Schichtel

444

Uses and Applications of Climate Forecasts for Power Utilities  

Science Conference Proceedings (OSTI)

The uses and potential applications of climate forecasts for electric and gas utilities were assessed 1) to discern needs for improving climate forecasts and guiding future research, and 2) to assist utilities in making wise use of forecasts. In-...

Stanley A. Changnon; Joyce M. Changnon; David Changnon

1995-05-01T23:59:59.000Z

445

Forecasting and Verifying in a Field Research Project: DOPLIGHT '87  

Science Conference Proceedings (OSTI)

Verification of forecasts during research field experiments is discussed and exemplified using the DOPLIGHT '87 experiment. We stress the importance of forecast verification if forecasting is to be a serious component of the research. A direct ...

Charles A. Doswell III; John A. Flueck

1989-06-01T23:59:59.000Z

446

Diversity in Interpretations of Probability: Implications for Weather Forecasting  

Science Conference Proceedings (OSTI)

Over the last years, probability weather forecasts have become increasingly popular due in part to the development of ensemble forecast systems. Despite its widespread use in atmospheric sciences, probability forecasting remains a subtle and ...

Ramn de Ela; Ren Laprise

2005-05-01T23:59:59.000Z

447

An Alternative Tropical Cyclone Intensity Forecast Verification Technique  

Science Conference Proceedings (OSTI)

The National Hurricane Center (NHC) does not verify official or model forecasts if those forecasts call for a tropical cyclone to dissipate or if the real tropical cyclone dissipates. A new technique in which these forecasts are included in a ...

Sim D. Aberson

2008-12-01T23:59:59.000Z

448

On the Reliability and Calibration of Ensemble Forecasts  

Science Conference Proceedings (OSTI)

An important aspect of ensemble forecasting is that the resulting probabilities are reliable (i.e., the forecast probabilities match the observed frequencies). In the medium-range forecasting context, the literature tends to focus on the ...

Christine Johnson; Neill Bowler

2009-05-01T23:59:59.000Z

449

Scoring Probabilistic Forecasts: The Importance of Being Proper  

Science Conference Proceedings (OSTI)

Questions remain regarding how the skill of operational probabilistic forecasts is most usefully evaluated or compared, even though probability forecasts have been a long-standing aim in meteorological forecasting. This paper explains the ...

Jochen Brcker; Leonard A. Smith

2007-04-01T23:59:59.000Z

450

Prediction of Consensus Tropical Cyclone Track Forecast Error  

Science Conference Proceedings (OSTI)

The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, ...

James S. Goerss

2007-05-01T23:59:59.000Z

451

An Experiment in Mesoscale Weather Forecasting in the Michigan Area  

Science Conference Proceedings (OSTI)

During an experiment in mesoscale weather forecasting in the Michigan area, consensus improved over NWS guidance in maximum/minimum temperature and probability of precipitation forecasts out to 24 hours. Forecasts were generally best in the ...

Dennis G. Baker

1986-12-01T23:59:59.000Z

452

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

453

Price and Load Forecasting in Volatile Energy Markets  

Science Conference Proceedings (OSTI)

With daily news stories about wildly fluctuating electricity prices and soaring natural gas prices, forecasters' responsibilities are expanding, visibility is increasing, and pressure exists to produce more frequent forecasts and more kinds of forecasts. The proceedings of EPRI's 13th Forecasting Symposium, held November 13-15 in Nashville, Tennessee, address current forecasting issues and developments, as well as explain the role that forecasters have played in recent events in energy markets.

2001-12-05T23:59:59.000Z

454

Annual Capital Expenditures Survey | Data.gov  

NLE Websites -- All DOE Office Websites (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.

455

Short-term energy outlook: Annual supplement 1989  

SciTech Connect

This Supplement is published once a year as a complement to the Short-Term Energy Outlook, Quarterly Projections (Outlook). The purpose is to review the accuracy of the forecasts presented in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts. A brief description of the content of each chapter follows below: Chapter 2 evaluates the accuracy of the short-term energy forecasts published in the last 6 issues of the Outlook, for 1988/1989. Chapter 3 discusses the economics of the petrochemical feedstock market, and describes a new model which more fully captures the determinants of feedstock demand. Chapter 4 examines present and proposed new methods of forecasting short-term natural gas prices at the wellhead and spot prices. Chapter 5 discusses the modeling of natural demand in the short term. Chapter 6 discusses regional trends in the demand for fuel by electric utilities. Chapter 7 focuses on industrial coal use trends in recent years. Chapter 8 compares EIA's base case energy projections as published in the Outlook (89/2Q) with recent projections made by three other major forecasting groups. The chapter focuses on macroeconomic assumptions, primary energy demand, and primary energy supply, showing the differences and similarities in the four forecasts.

1989-10-31T23:59:59.000Z

456

Blasting Vibration Forecast Base on Neural Network  

Science Conference Proceedings (OSTI)

The influence of blasting vibration to surroundings around the blasting area can not be ignored, in order to guarantee the safety of surroundings around blasting area, blasting vibration forecasting model based on neural network is established by improved ... Keywords: Blasting vibration, Neural network, Forecast

Haiwang Ye; Fang Liu; Jian Chang; Lin Feng; Yang Wang; Peng Yao; Kai Wu

2010-10-01T23:59:59.000Z

457

Evaluating the Skill of Categorical Forecasts  

Science Conference Proceedings (OSTI)

A generalized skill score is presented for evaluating forecasts in any number of categories. Each forecast in a sample is given a mark; the skill score for the sample is just the average mark. Each mark has an expected value of zero for an ...

Neil D. Gordon

1982-07-01T23:59:59.000Z

458

Forecasting demand of commodities after natural disasters  

Science Conference Proceedings (OSTI)

Demand forecasting after natural disasters is especially important in emergency management. However, since the time series of commodities demand after natural disasters usually has a great deal of nonlinearity and irregularity, it has poor prediction ... Keywords: ARIMA, Demand forecasting, EMD, Emergency management, Natural disaster

Xiaoyan Xu; Yuqing Qi; Zhongsheng Hua

2010-06-01T23:59:59.000Z

459

Time series forecasting with Qubit Neural Networks  

Science Conference Proceedings (OSTI)

This paper proposes a quantum learning scheme approach for time series forecasting, through the application of the new non-standard Qubit Neural Network (QNN) model. The QNN description was adapted in this work in order to resemble classical Artificial ... Keywords: artificial intelligence, artificial neural networks, quantum computing, qubit neural networks, time series forecasting

Carlos R. B. Azevedo; Tiago A. E. Ferreira

2007-08-01T23:59:59.000Z

460

Making Forecasts and Weather Normalization Work Together  

Science Conference Proceedings (OSTI)

Electric utility industry restructuring has changed the consistency between weather-normalized sales and energy forecasts. This Technology Review discusses the feasibility of integrating weather normalization and forecasting processes, and addresses whether the conflicting goal of obtaining greater consistency and accuracy with fewer staff resources can be met with more integrated approaches.

2000-09-11T23:59:59.000Z

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.


461

A spatially distributed flash flood forecasting model  

Science Conference Proceedings (OSTI)

This paper presents a distributed model that is in operational use for forecasting flash floods in northern Austria. The main challenge in developing the model was parameter identification which was addressed by a modelling strategy that involved a model ... Keywords: Distributed modelling, Dominant processes concept, Floods, Forecasting, Kalman Filter, Model accuracy, Parameter identification, Stream routing

Gnter Blschl; Christian Reszler; Jrgen Komma

2008-04-01T23:59:59.000Z

462

The NCEP Climate Forecast System Version 2  

Science Conference Proceedings (OSTI)

The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled Reanalysis ...

Suranjana Saha; Shrinivas Moorthi; Xingren Wu; Jiande Wang; Sudhir Nadiga; Patrick Tripp; David Behringer; Yu-Tai Hou; Hui-ya Chuang; Mark Iredell; Michael Ek; Jesse Meng; Rongqian Yang; Malaquas Pea Mendez; Huug van den Dool; Qin Zhang; Wanqiu Wang; Mingyue Chen; Emily Becker

463

Forecast of geothermal-drilling activity  

DOE Green Energy (OSTI)

The number of geothermal wells that will be drilled to support electric power production in the United States through 2000 A.D. are forecasted. Results of the forecast are presented by 5-year periods for the five most significant geothermal resources.

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

1982-07-01T23:59:59.000Z

464

Preemptive Forecasts Using an Ensemble Kalman Filter  

Science Conference Proceedings (OSTI)

An ensemble Kalman filter (EnKF) estimates the error statistics of a model forecast using an ensemble of model forecasts. One use of an EnKF is data assimilation, resulting in the creation of an increment to the first-guess field at the ...

Brian J. Etherton

2007-10-01T23:59:59.000Z

465

Incentives for Retailer Forecasting: Rebates vs. Returns  

Science Conference Proceedings (OSTI)

This paper studies a manufacturer that sells to a newsvendor retailer who can improve the quality of her demand information by exerting costly forecasting effort. In such a setting, contracts play two roles: providing incentives to influence the retailer's ... Keywords: endogenous adverse selection, forecasting, rebates, returns, supply chain contracting

Terry A. Taylor; Wenqiang Xiao

2009-10-01T23:59:59.000Z

466

Efficient forecasting for hierarchical time series  

Science Conference Proceedings (OSTI)

Forecasting is used as the basis for business planning in many application areas such as energy, sales and traffic management. Time series data used in these areas is often hierarchically organized and thus, aggregated along the hierarchy levels based ... Keywords: forecasting, hierarchies, optimization, time series

Lars Dannecker; Robert Lorenz; Philipp Rsch; Wolfgang Lehner; Gregor Hackenbroich

2013-10-01T23:59:59.000Z

467

Possibility of Skill Forecast Based on the Finite-Time Dominant Linear Solutions for a Primitive Equation Regional Forecast Model  

Science Conference Proceedings (OSTI)

The possibility of using forecast errors originating from the finite-time dominant linear modes for the prediction of forecast skill for a primitive equation regional forecast model is studied. This is similar to the method for skill prediction ...

Tomislava Vuki?evi?

1993-06-01T23:59:59.000Z

468

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)

469

Evaluating the Cloud Cover Forecast of NCEP Global Forecast System with Satellite Observation  

E-Print Network (OSTI)

To assess the quality of daily cloud cover forecast generated by the operational global numeric model, the NCEP Global Forecast System (GFS), we compose a large sample with outputs from GFS model and satellite observations from the International Satellite Cloud Climatology Project (ISCCP) in the period of July 2004 to June 2008, to conduct a quantitative and systematic assessment of the performance of a cloud model that covers a relatively long range of time, basic cloud types, and in a global view. The evaluation has revealed the goodness of the model forecast, which further illustrates our completeness on understanding cloud generation mechanism. To quantity the result, we found a remarkably high correlation between the model forecasts and the satellite observations over the entire globe, with mean forecast error less than 15% in most areas. Considering a forecast within 30% difference to the observation to be a "good" one, we find that the probability for the GFS model to make good forecasts varies between...

Ye, Quanzhi

2011-01-01T23:59:59.000Z

470

SunShot Initiative: Forecasting and Influencing Technological...  

NLE Websites -- All DOE Office Websites (Extended Search)

Forecasting and Influencing Technological Progress in Solar Energy to someone by E-mail Share SunShot Initiative: Forecasting and Influencing Technological Progress in Solar Energy...

471

New Climate Research Centers Forecast Changes and Challenges...  

NLE Websites -- All DOE Office Websites (Extended Search)

Climate Research Centers Forecast Changes and Challenges New Climate Research Centers Forecast Changes and Challenges October 25, 2013 - 12:24pm Addthis This artist's rendering...

472

Exploiting Domain Knowledge to Forecast Heating Oil Consumption  

Science Conference Proceedings (OSTI)

The GasDay laboratory at Marquette University provides forecasts of energy consumption. One such service is the Heating Oil Forecaster

George F. Corliss; Tsuginosuke Sakauchi; Steven R. Vitullo; Ronald H. Brown

2011-01-01T23:59:59.000Z

473

Building Energy Software Tools Directory: Energy Usage Forecasts  

NLE Websites -- All DOE Office Websites (Extended Search)

Alphabetically Tools by Platform PC Mac UNIX Internet Tools by Country Related Links Energy Usage Forecasts Energy Usage Forecasts Quick and easy web-based tool that provides...

474

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

Science Conference Proceedings (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

475

A Short-Range Forecasting Experiment Conducted during the Canadian Atlantic Storms Program  

Science Conference Proceedings (OSTI)

During the Canadian Atlantic Storms Program (CASP), a dedicated forecast center conducted experiments in mesoscale forecasting. Several forecast products, including a marine forecast and a site-specific public forecast, were written every 3 h. ...

K. A. Macdonald; M. Danks; J. D. Abraham

1988-06-01T23:59:59.000Z

476

Annual Reports | Department of Energy  

NLE Websites -- All DOE Office Websites (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

477

NERSC Annual Reports  

NLE Websites -- All DOE Office Websites (Extended Search)

Annual Reports NERSC Annual Reports Sort by: Default | Name annrep2011.png NERSC Annual Report 2011 Download Image: annrep2011.png | png | 2.7 MB Download File: annrep2011.pdf |...

478

Decision support for financial forecasting  

SciTech Connect

A primary mission of the Budget Management Division of the Air Force is fiscal analysis. This involves formulating, justifying, and tracking financial data during budget preparation and execution. An essential requirement of this process is the ready availability and easy manipulation of past and current budget data. This necessitates the decentralization of the data. A prototypical system, BAFS (Budget Analysis and Forecasting System), that provides such a capability is presented. In its current state, the system is designed to be a decision support tool. A brief report of the budget decisions and activities is presented. The system structure and its major components are discussed. An insight into the implementation strategies and the tool used is provided. The paper concludes with a discussion of future enhancements and the system's evolution into an expert system. 4 refs., 3 figs.

Jairam, B.N.; Morris, J.D.; Emrich, M.L.; Hardee, H.K.

1988-10-01T23:59:59.000Z

479

Construction Safety Forecast for ITER  

SciTech Connect

The International Thermonuclear Experimental Reactor (ITER) project is poised to begin its construction activity. This paper gives an estimate of construction safety as if the experiment was being built in the United States. This estimate of construction injuries and potential fatalities serves as a useful forecast of what can be expected for construction of such a major facility in any country. These data should be considered by the ITER International Team as it plans for safety during the construction phase. Based on average U.S. construction rates, ITER may expect a lost workday case rate of < 4.0 and a fatality count of 0.5 to 0.9 persons per year.

cadwallader, lee charles

2006-11-01T23:59:59.000Z

480

Earthquake Forecast via Neutrino Tomography  

E-Print Network (OSTI)

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

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

2010-01-17T23:59:59.000Z

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.


481

Petroleum supply annual 1993. Volume 1  

Science Conference Proceedings (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

482

Petroleum supply annual 1998: Volume 1  

SciTech Connect

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 1998 through annual and monthly surveys. The PSA is divided into two volumes. This first volume contains three sections: Summary Statistics, Detailed Statistics, and Refinery Statistics; each with final annual data. The second volume contains final statistics for each month of 1998, and replaces data previously published in the PSA. The tables in Volumes 1 and 2 are similarly numbered to facilitate comparison between them. 16 figs., 59 tabs.

NONE

1999-06-01T23:59:59.000Z

483

Petroleum supply annual 1994. Volume 1  

SciTech Connect

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 1994 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 1994, 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.

NONE

1995-05-22T23:59:59.000Z

484

Forecasting future volatility from option prices, Working  

E-Print Network (OSTI)

Weisbach are gratefully acknowledged. I bear full responsibility for all remaining errors. Forecasting Future Volatility from Option Prices Evidence exists that option prices produce biased forecasts of future volatility across a wide variety of options markets. This paper presents two main results. First, approximately half of the forecasting bias in the S&P 500 index (SPX) options market is eliminated by constructing measures of realized volatility from five minute observations on SPX futures rather than from daily closing SPX levels. Second, much of the remaining forecasting bias is eliminated by employing an option pricing model that permits a non-zero market price of volatility risk. It is widely believed that option prices provide the best forecasts of the future volatility of the assets which underlie them. One reason for this belief is that option prices have the ability to impound all publicly available information including all information contained in the history of past prices about the future volatility of the underlying assets. A second related reason is that option pricing theory maintains that if an option prices fails to embody optimal forecasts of the future volatility of the underlying asset, a profitable trading strategy should be available whose implementation would push the option price to the level that reflects the best possible forecast of future volatility.

Allen M. Poteshman

2000-01-01T23:59:59.000Z

485

Annual Energy Outlook 2012  

Annual Energy Outlook 2012 (EIA)

U.S. Energy Information Administration | Annual Energy Outlook 2012 Energy Information Administration Annual Energy Outlook 2012 - DRAFT - June 12, 2012 1 Table B1. Total energy...

486

Annual Coal Distribution Report  

Gasoline and Diesel Fuel Update (EIA)

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

487

Annual Energy Outlook  

Annual Energy Outlook 2012 (EIA)

4) January 2004 Annual Energy Outlook 2004 With Projections to 2025 January 2004 For Further Information . . . The Annual Energy Outlook 2004 (AEO2004) was prepared by the Energy...

488

2007 TEPP Annual Report  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Annual Report United States Department of Energy Transportation Emergency Preparedness Program 1 Transportation Emergency Preparedness Program 2007 Annual Report US Department of...

489

1993 Solid Waste Reference Forecast Summary  

SciTech Connect

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

490

Forecast Bias Correction: A Second Order Method  

E-Print Network (OSTI)

The difference between a model forecast and actual observations is called forecast bias. This bias is due to either incomplete model assumptions and/or poorly known parameter values and initial/boundary conditions. In this paper we discuss a method for estimating corrections to parameters and initial conditions that would account for the forecast bias. A set of simple experiments with the logistic ordinary differential equation is performed using an iterative version of a first order version of our method to compare with the second order version of the method.

Crowell, Sean

2010-01-01T23:59:59.000Z

491

Optimal Updating of Forecasts for the Timing of Future Events  

Science Conference Proceedings (OSTI)

A major problem in forecasting is estimating the time of some future event. traditionally, forecasts are designed to minimize an error cost function that is evaluated once, possibly when the event occurs and forecast accuracy can be determined. However, ... Keywords: Air Transportation, Dynamic Programming Applications, Forecasting

Juhwen Hwang; Medini R. Singh; W. J. Hurley; Robert A. Shumsky

1998-03-01T23:59:59.000Z

492

Customization and Marketing of Monsoon Forecasts A CSIRCMMACS Synergy  

E-Print Network (OSTI)

Customization and Marketing of Monsoon Forecasts A CSIRCMMACS Synergy Criteria for Technical forecasts of monsoon can significantly aid many sectors like agriculture, power and production industries to the operational forecast, to develop and deliver customized monsoon forecasts based on user need is required

Swathi, P S

493

Update On The Wholesale Electricity Price Forecast & Modeling Results  

E-Print Network (OSTI)

Forecast Base Case includes § Medium Demand Forecast § Medium Natural Gas Price Forecast § Federal CO2 Rathdrum Power LLC-ID 4) CO2 Emissions - 2009 Selected Natural Gas Plants Plant level, emission percentage § Significantly lower electricity prices than 6th Plan Forecast, due to lower demand, lower gas prices, deferred

494

RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN  

E-Print Network (OSTI)

or over predicting electricity demand due to poor weather forecasts is several hundred million dollars outages that many in the area experienced. Deep Thunder can also improve generation-side load forecasting by providing high-resolution weather forecast data for use in electricity demand forecast models. Integrating

Manry, Michael

495

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST forecast is the combined product of the hard work and expertise of numerous staff members in the Demand prepared the residential sector forecast. Mohsen Abrishami prepared the commercial sector forecast. Lynn

496

R/ECON December 1999 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON December 1999 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF DECEMBER 1999 NEW and wage growth slow later in the forecast, income growth will average 5% a year between 2000 and 2004. Over the forecast period, population growth will average 0.5% a year. The population will rise from 8

497

R/ECON July 2001 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON July 2001 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF JULY 2001 NEW JERSEY each year. The R/ECONTM forecast for New Jersey looks for growth in real output of 2.6 percent years. Over the forecast period, both the construction and manufacturing sectors will lose jobs

498

R/ECON April 2001 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON April 2001 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF APRIL 2001 NEW JERSEY and 2005, and by an average of 43,000 thereafter (from 2005 to 2020). The R/ECONTM forecast for New Jersey.6 percent a year over the rest of the forecast period. Personal income will rise 5.6 percent this year, down

499

R/ECON October 1999 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON October 1999 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF OCTOBER 1999 NEW JERSEY the rate of inflation should remain under 3% a year. (See Table 1.) #12;Throughout the forecast period and wage growth slow later in the forecast period, income growth will average 4.8% a year between 2000

500

Does increasing model stratospheric resolution improve extended range forecast skill?  

E-Print Network (OSTI)

Does increasing model stratospheric resolution improve extended range forecast skill? Greg Roff,1 forecast skill at high Southern latitudes is explored. Ensemble forecasts are made for two model configurations that differ only in vertical resolution above 100 hPa. An ensemble of twelve 30day forecasts