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1

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

2

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

3

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

4

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

5

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

6

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

7

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

8

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.

9

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

10

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,

11

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

12

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

13

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

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 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.

16

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

SciTech Connect

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

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

2013-10-01T23:59:59.000Z

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

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

19

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect

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

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

2013-10-01T23:59:59.000Z

20

Evaluation of hierarchical forecasting for substitutable products  

Science Journals Connector (OSTI)

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

S. Viswanathan; Handik Widiarta; R. Piplani

2008-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast evaluation table" 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

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations during ETEX 2  

E-Print Network (OSTI)

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations an operational numerical weather prediction model to forecast air quality are also investigated. These potential a numerical weather prediction (NWP) model independently of the CTM. The NWP output is typically archived

Dacre, Helen

22

Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis  

E-Print Network (OSTI)

analyses of regional mod- eling with Polar WRF have been performed with results compared to selected localEvaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air.1.1 of the Weather Research and Forecasting model (WRF), a highresolution regional scale model, is used to simulate

Howat, Ian M.

23

Coupling and evaluating gas/particle mass transfer treatments for aerosol simulation and forecast  

E-Print Network (OSTI)

Coupling and evaluating gas/particle mass transfer treatments for aerosol simulation and forecast hindcasting and forecasting. The lack of an efficient yet accurate gas/particle mass transfer treatment December 2007; accepted 21 February 2008; published 12 June 2008. [1] Simulating gas/particle mass transfer

Jacobson, Mark

24

USING SATELLITE OBSERVATIONS AND REANALYSES TO EVALUATE CLIMATE AND WEATHER FORECAST MODELS  

E-Print Network (OSTI)

USING SATELLITE OBSERVATIONS AND REANALYSES TO EVALUATE CLIMATE AND WEATHER FORECAST MODELS Richard Email: rpa@mail.nerc-essc.ac.uk ABSTRACT Satellite observations of water vapour and radiative fluxes are used in combination with reanalyses data to evaluate the Met Office weather and climate prediction

Allan, Richard P.

25

Evaluation of Advanced Wind Power Forecasting Models Results of the Anemos Project  

E-Print Network (OSTI)

1 Evaluation of Advanced Wind Power Forecasting Models ­ Results of the Anemos Project I. Martí1.kariniotakis@ensmp.fr Abstract An outstanding question posed today by end-users like power system operators, wind power producers or traders is what performance can be expected by state-of-the-art wind power prediction models. This paper

Paris-Sud XI, Université de

26

EVALUATION OF PV GENERATION CAPICITY CREDIT FORECAST ON DAY-AHEAD UTILITY MARKETS  

E-Print Network (OSTI)

City, and Sacramento Municipal Utility District, in California 1. BACKGROUND The effective capacity comfortable if capacity could be ascertained operationally by knowing in advance what the output of solar of the NDFD-based solar radiation forecasts for several climatically distinct locations, the evaluation is now

Perez, Richard R.

27

SECTION 51 Table of Contents 51 Lake Rufus Woods Research, Monitoring and Evaluation Plan ................2  

E-Print Network (OSTI)

51-1 SECTION 51 ­ Table of Contents 51 Lake Rufus Woods Research, Monitoring and Evaluation Plan ................2 #12;51-2 51 Lake Rufus Woods Research, Monitoring and Evaluation Plan In light of the various is found in Section 2. #12;51-3 Table 51.1. Rufus Woods Subbasin research, monitoring, and evaluation plan

28

The 1993 atomic mass evaluation: (I) Atomic mass table  

Science Journals Connector (OSTI)

This paper is the first of a series of four. In it, a table is given to replace the 1983 atomic mass table. The differences with the earlier table are briefly discussed and information is given of interest for the users of this table. Part II of this series gives values for several derived quantities (decay-, separation- and reaction energies), part III shows graphs of several of those quantities, and part IV gives a list of input data and full information on the used input data and on the procedures used in deriving the tables in the preceding parts.

G. Audi; A.H. Wapstra

1993-01-01T23:59:59.000Z

29

Evaluating alternative fuels in USA: a proposed forecasting framework using AHP and scenarios  

Science Journals Connector (OSTI)

This paper proposes a forecasting framework that integrates the analytic hierarchy process with scenario analysis techniques to explore the commercialisation of future motor fuel technologies. We analyse the reasons for the uncertainty of oil price and how it affects alternative fuel commercialisation. We propose a set of evaluation criteria including Economic, Cultural, Environmental, Sustainability and Development Time. Finally, we develop four different Scenarios to verify the robustness of each alternative.

M.R. Nava; Tugrul U. Daim

2007-01-01T23:59:59.000Z

30

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

E-Print Network (OSTI)

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

Marquez, Ricardo

2012-01-01T23:59:59.000Z

31

A GIS tool for the evaluation of the precipitation forecasts of a numerical weather prediction model using satellite data  

Science Journals Connector (OSTI)

In this study, the possibility of implementing Geographic Information Systems (GIS) for developing an integrated and automatic operational system for the real-time evaluation of the precipitation forecasts of the numerical weather prediction model BOLAM (BOlogna Limited Area Model) in Greece, is examined. In fact, the precipitation estimates derived by an infrared satellite technique are used for real-time qualitative and quantitative verification of the precipitation forecasts of the model BOLAM through the use of a GIS tool named as precipitation forecasts evaluator (PFE). The application of the developed tool in a case associated with intense precipitation in Greece, suggested that PFE could be a very important support tool for nowcasting and very short-range forecasting of such events.

Haralambos Feidas; Themistoklis Kontos; Nikolaos Soulakellis; Konstantinos Lagouvardos

2007-01-01T23:59:59.000Z

32

MTS Table Top Load frame [Non-Destructive Evaluation (NDE) and Testing  

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

Engineering> Facilities > Non-Destructive Evaluation Engineering> Facilities > Non-Destructive Evaluation (NDE) and Testing Facilities > MTS Table Top Load frame Non-Destructive Evaluation (NDE) and Testing Facilities Overview MTS Table Top Load Frame X-ray Inspection Systems Other Facilities Work with Argonne Contact us For Employees Site Map Help Join us on Facebook Follow us on Twitter NE on Flickr Non-Destructive Evaluation (NDE) and Testing Facilities MTS Table Top Load frame Bookmark and Share PDF version [167KB] The Non-destructive Evaluation group operates an MTS Table Top Load frame for ultimate strength and life cycle testing of various ceramic, ceramic-matrix (FGI), carbon, carbon fiber, cermet (CMC) and metal alloy engineering samples. The load frame is a servo-hydraulic type designed to function in a closed loop configuration under computer control. The system

33

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

E-Print Network (OSTI)

andvalidation. SolarEnergy. 73:5,307? Perez,R. ,irradianceforecastsforsolarenergyapplicationsbasedonforecastdatabase. SolarEnergy. 81:6,809?812.

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

34

An evaluation of market penetration forecasting methodologies for new residential and commercial energy technologies  

SciTech Connect

Forecasting market penetration is an essential step in the development and assessment of new technologies. This report reviews several methodologies that are available for market penetration forecasting. The primary objective of this report is to help entrepreneurs understand these methodologies and aid in the selection of one or more of them for application to a particular new technology. This report also illustrates the application of these methodologies, using examples of new technologies, such as the heat pump, drawn from the residential and commercial sector. The report concludes with a brief discussion of some considerations in selecting a forecasting methodology for a particular situation. It must be emphasized that the objective of this report is not to construct a specific market penetration model for new technologies but only to provide a comparative evaluation of methodologies that would be useful to an entrepreneur who is unfamiliar with the range of techniques available. The specific methodologies considered in this report are as follows: subjective estimation methods, market surveys, historical analogy models, time series models, econometric models, diffusion models, economic cost models, and discrete choice models. In addition to these individual methodologies, which range from the very simple to the very complex, two combination approaches are also briefly discussed: (1) the economic cost model combined with the diffusion model and (2) the discrete choice model combined with the diffusion model. This discussion of combination methodologies is not meant to be exhaustive. Rather, it is intended merely to show that many methodologies often can complement each other. A combination of two or more different approaches may be better than a single methodology alone.

Raju, P.S.; Teotia, A.P.S.

1985-05-01T23:59:59.000Z

35

Communication of uncertainty in temperature forecasts  

Science Journals Connector (OSTI)

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

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

36

Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations  

Gasoline and Diesel Fuel Update (EIA)

AEO82 to AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 AEO82 to AEO2004 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

37

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

38

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

December 22, 2000 (Next Release: December, 2001) Related Links Annual Energy Outlook 2001 Assumptions to the AEO2001 NEMS Conference Contacts Forecast Homepage EIA Homepage AEO Supplement Reference Case Forecast (1999-2020) (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) Table 8. Energy Consumption by Source and Sector (Mountain)

39

An evaluation of forecasting methods for aircraft non-routine maintenance material demand  

Science Journals Connector (OSTI)

Aircraft maintenance can be divided into routine and non-routine activities. Material demand associated with non-routine maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-routine maintenance material demand. Non-routine material consumption is linked to scheduled maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-routine maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-routine maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate maintenance C-check.

Maarten Zorgdrager; Wim J.C. Verhagen; Richard Curran

2014-01-01T23:59:59.000Z

40

An Evaluation of Tropical Cyclone Genesis Forecasts from Global Numerical Models  

Science Journals Connector (OSTI)

Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models ...

Daniel J. Halperin; Henry E. Fuelberg; Robert E. Hart; Joshua H. Cossuth; Philip Sura; Richard J. Pasch

2013-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast evaluation table" 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

An Evaluation of Decadal Probability Forecasts from State-of-the-Art Climate Models  

Science Journals Connector (OSTI)

While state-of-the-art models of Earth's climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the ...

Emma B. Suckling; Leonard A. Smith

2013-12-01T23:59:59.000Z

42

Evaluating the Effects of Underground Nuclear Testing Below the Water Table on Groundwater and Radionuclide Migration in the  

E-Print Network (OSTI)

Evaluating the Effects of Underground Nuclear Testing Below the Water Table on Groundwater, using FEHM, evaluate perturbed groundwater behavior associated with underground nuclear tests to an instantaneous pressurization event caused by a nuclear test when different permeability and porosity

43

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

44

RACORO Forecasting  

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

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

45

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

The AEO Supplementary tables were generated for the reference case of the The AEO Supplementary tables were generated for the reference case of the Annual Energy Outlook 2002 (AEO2002) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 1999 to 2020. Most of the tables were not published in the AEO2002, but contain regional and other more detailed projections underlying the AEO2002 projections. The files containing these tables are in spreadsheet format. A total of one hundred and seven tables is presented. The data for tables 10 and 20 match those published in AEO2002 Appendix tables A2 and A3, respectively. Forecasts for 2000-2002 may differ slightly from values published in the Short Term Energy Outlook, which are the official EIA short-term forecasts and are based on more current

46

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

Homepage Homepage Supplement Tables to the AEO2001 The AEO Supplementary tables were generated for the reference case of the Annual Energy Outlook 2001 (AEO2001) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 1999 to 2020. Most of the tables were not published in the AEO2001, but contain regional and other more detailed projections underlying the AEO2001 projections. The files containing these tables are in spreadsheet format. A total of ninety-five tables is presented. The data for tables 10 and 20 match those published in AEO2001 Appendix tables A2 and A3, respectively. Forecasts for 1999 and 2000 may differ slightly from values published in the Short Term Energy Outlook, which are the official EIA short-term forecasts and are based on more current information than the AEO.

47

Probabilistic manpower forecasting  

E-Print Network (OSTI)

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

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

48

Evaluation and design of ventilation systems for autopsy and surgical examination tables  

E-Print Network (OSTI)

tests were used to determine the control characteristics of the initial LEV design. A proposed design was then evaluated and the results compared to the initial design with respect to fugitive emission control performance. The proposed design was a... grid plate design prevented the majority of air from being exhausted from the center of the table, therefore, creating uniform air distribution and, thus, an increase in fugitive emission control. The results from mannequin exposure monitoring...

Murgash, Mark John

2012-06-07T23:59:59.000Z

49

Evaluation of artificial neural networks as a model for forecasting consumption of wood products  

Science Journals Connector (OSTI)

In specific sciences, such as forest policy, the need for anticipation becomes more urgent because it has to manage valuable natural resources whose protection and sustainable management is rendered essential. In this paper, a modern method has been used, known as artificial neural networks (ANNs). In order to forecast the necessary future volumes of timber in Greece, a neural network has been developed and trained, using a variety of time series derived from the database of the Food and Agriculture Organisation of the United Nations (FAO) (concerning Greece) as external values and as internal value the Consumer Price Index has been used. Comparing the results of this project with linear and non-linear econometric forecasting models, it has been found that neural networks correspond, as confirmed by the econometric indicators MAPE (average absolute percentage error) and RMSE (the square root of the percentage by the average sum of squares differences).

Giorgos Tigas; Panagiotis Lefakis; Konstantinos Ioannou; Athanasios Hasekioglou

2013-01-01T23:59:59.000Z

50

Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models  

SciTech Connect

We examine forecasts of Southeast Pacific stratocumulus at 20S and 85W during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized from ECMWF analyses and each model is run for 3 days to determine the differences with the EPIC field data. Observations during the EPIC cruise show a stable and well-mixed boundary layer under a sharp inversion. The inversion height and the cloud layer have a strong and regular diurnal cycle. A key problem common to the four models is that the forecasted planetary boundary layer (PBL) height is too low when compared to EPIC observations. All the models produce a strong diurnal cycle in the Liquid Water Path (LWP) but there are large differences in the amplitude and the phase compared to the EPIC observations. This, in turn, affects the radiative fluxes at the surface. There is a large spread in the surface energy budget terms amongst the models and large discrepancies with observational estimates. Single Column Model (SCM) experiments with the CAM show that the vertical pressure velocity has a large impact on the PBL height and LWP. Both the amplitude of the vertical pressure velocity field and its vertical structure play a significant role in the collapse or the maintenance of the PBL.

Hannay, C; Williamson, D L; Hack, J J; Kiehl, J T; Olson, J G; Klein, S A; Bretherton, C S; K?hler, M

2008-01-24T23:59:59.000Z

51

Valuing Climate Forecast Information  

Science Journals Connector (OSTI)

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

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

1987-09-01T23:59:59.000Z

52

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

AEO Supplementary tables were generated for the reference case of the Annual Energy Outlook 2000 (AEO2000) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 1998 to 2020. Most of the tables were not published in the AEO2000, but contain regional and other more detailed projections underlying the AEO2000 projections. The files containing these tables are in spreadsheet format. A total of ninety-six tables are presented. AEO Supplementary tables were generated for the reference case of the Annual Energy Outlook 2000 (AEO2000) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 1998 to 2020. Most of the tables were not published in the AEO2000, but contain regional and other more detailed projections underlying the AEO2000 projections. The files containing these tables are in spreadsheet format. A total of ninety-six tables are presented. The data for tables 10 and 20 match those published in AEO200 Appendix tables A2 and A3, respectively. Forecasts for 1998, and 2000 may differ slightly from values published in the Short Term Energy Outlook, Fourth Quarter 1999 or Short Term Energy Outlook, First Quarter 2000, which are the official EIA short-term forecasts and are based on more current information than the AEO.

53

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

Supplemental Tables to the Annual Energy Outlook 2005 Supplemental Tables to the Annual Energy Outlook 2005 EIA Glossary Supplemental Tables to the Annual Energy Outlook 2005 Release date: February 2005 Next release date: February 2006 The AEO Supplemental tables were generated for the reference case of the Annual Energy Outlook 2005 (AEO2005) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 2003 to 2025. Most of the tables were not published in the AEO2005, but contain regional and other more detailed projections underlying the AEO2005 projections. The files containing these tables are in spreadsheet format. A total of one hundred and seventeen tables is presented. The data for tables 10 and 20 match those published in AEO2005 Appendix tables A2 and A3, respectively. Forecasts for 2003-2005 may differ slightly from values published in the Short Term Energy Outlook, which are the official EIA short-term forecasts and are based on more current information than the AEO.

54

The Information Needed to Evaluate the Worth of Uncertain Information, Predictions and Forecasts  

Science Journals Connector (OSTI)

To evaluate the worth of uncertain information one must obtain three types of evaluative information: 1) statistical measures of the uncertainty of the information and of its likely occurrence; 2) the decision rule (how the information is used) ...

Donald R. Davis; Soronadi Nnaji

1982-04-01T23:59:59.000Z

55

Table Search (or Ranking Tables)  

E-Print Network (OSTI)

;Table Search #3 #12;Outline · Goals of table search · Table search #1: Deep Web · Table search #3 search Table search #1: Deep Web · Table search #3: (setup): Fusion Tables · Table search #2: WebTables ­Version 1: modify document search ­Version 2: recover table semantics #12;Searching the Deep Web store

Halevy, Alon

56

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.

57

Space-time forecasting and evaluation of wind speed with statistical tests for comparing accuracy of spatial predictions  

E-Print Network (OSTI)

). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 11 Comparing the predictive distributions for the models when the TDD model produces the best forecast (top panel) and when the BST model produces the best forecast (bottom panel). The small vertical line on the x-axis of each plot represents... of wind to benefit humans is not a new concept. Historically, wind- mills have been used to pump water from wells or to grind grain for centuries. But fast- forwarding into the 21st century, ?windmills? are being used to generate electricity. Wind turbines...

Hering, Amanda S.

2010-10-12T23:59:59.000Z

58

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 1999 Annual Energy Outlook 1999 bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Contacts bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage supplemental.gif (7420 bytes) (Errata as of 9/13/99) The AEO Supplementary tables were generated for the reference case of the Annual Energy Outlook 1999 (AEO99) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 1997 to 2020. Most of the tables were not published in the AEO99, but contain regional and other more detailed projections underlying the AEO99 projections. The files containing these tables are in spreadsheet format. A total of ninety-five tables are presented.

59

The 1977 atomic mass evaluation: in four parts part I. Atomic mass table  

Science Journals Connector (OSTI)

Based on a least-squares fit to experimental data for all nuclides for which data are available and on estimates obtained from systematics for many other nuclides, we present a table of atomic masses, of mass excesses, of total binding energies, and of beta-decay energies, the last three quantities in energy units.

A.H. Wapstra; K. Bos

1977-01-01T23:59:59.000Z

60

Seismic fragility evaluation of a piping system in a nuclear power plant by shaking table test and numerical analysis  

SciTech Connect

In this study, a seismic fragility evaluation of the piping system in a nuclear power plant was performed. For the evaluation of seismic fragility of the piping system, this research was progressed as three steps. At first, several piping element capacity tests were performed. The monotonic and cyclic loading tests were conducted under the same internal pressure level of actual nuclear power plants to evaluate the performance. The cracks and wall thinning were considered as degradation factors of the piping system. Second, a shaking tale test was performed for an evaluation of seismic capacity of a selected piping system. The multi-support seismic excitation was performed for the considering a difference of an elevation of support. Finally, a numerical analysis was performed for the assessment of seismic fragility of piping system. As a result, a seismic fragility for piping system of NPP in Korea by using a shaking table test and numerical analysis. (authors)

Kim, M. K.; Kim, J. H.; Choi, I. K. [Korea Atomic Energy Research Inst., Daedeok-daero 989-111, Yuseong-gu, Daejeon, 305-353 (Korea, Republic of)

2012-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast evaluation table" 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

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

62

Energy Information Administration (EIA) - Supplement Tables - Supplemental  

Gasoline and Diesel Fuel Update (EIA)

6 6 Supplemental Tables to the Annual Energy Outlook 2006 The AEO Supplemental tables were generated for the reference case of the Annual Energy Outlook 2006 (AEO2006) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 2003 to 2030. Most of the tables were not published in the AEO2006, but contain regional and other more detailed projections underlying the AEO2006 projections. The files containing these tables are in spreadsheet format. A total of one hundred and seventeen tables is presented. The data for tables 10 and 20 match those published in AEO2006 Appendix tables A2 and A3, respectively. Forecasts for 2004-2006 may differ slightly from values published in the Short Term Energy Outlook, which are the official EIA short-term forecasts and are based on more current information than the AEO.

63

Probabilistic Verification of Global and Mesoscale Ensemble Forecasts of Tropical Cyclogenesis  

Science Journals Connector (OSTI)

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

Sharanya J. Majumdar; Ryan D. Torn

2014-10-01T23:59:59.000Z

64

FY 2005 Laboratory Table  

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

Congressional Budget Congressional Budget Request Laboratory Tables Preliminary Department of Energy FY 2005 Congressional Budget Request Office of Management, Budget and Evaluation/CFO February 2004 Laboratory Tables Preliminary Department of Energy Department of Energy FY 2005 Congressional Budget FY 2005 Congressional Budget Request Request Office of Management, Budget and Evaluation/CFO February 2004 Laboratory Tables Laboratory Tables Printed with soy ink on recycled paper Preliminary Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. include both the discretionary and mandatory funding in the budget. balances, deferrals, rescissions, or other adjustments appropria ted as offsets to the DOE appropriations by the Congress.

65

Central Wind Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities: Revised Edition  

SciTech Connect

The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.

Rogers, J.; Porter, K.

2011-03-01T23:59:59.000Z

66

Supplement Tables - Contact  

Gasoline and Diesel Fuel Update (EIA)

Supplement Tables to the AEO99 Supplement Tables to the AEO99 bullet1.gif (843 bytes) Annual Energy Outlook 1999 bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage furtherinfo.gif (5474 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:

67

Annual Energy Outlook Retrospective Review: Evaluation of Projections in Past Editions (1982-2006)  

Gasoline and Diesel Fuel Update (EIA)

5 5 Table 1. Summary of Differences between AEO Reference Cases and Realized Outcomes Percent Over- Estimated Average Absolute Percent Difference Percent Over- Estimated Average Absolute Percent Difference Table 3. Gross Domestic Product, Actual vs. Forecasts 34% 5.5% 18% 4.5% Table 4. World Oil Prices, Actual vs. Forecasts 68% 52.9% 36% 20.8% Table 5. Total Petroleum Consumption, Actual vs. Forecasts 31% 2.9% 44% 1.8% Table 6. Domestic Crude Oil Production, Actual vs. Forecasts 51% 4.9% 53% 5.2% Table 7. Petroleum Net Imports, Actual vs. Forecasts 49% 6.4% 51% 3.6% Table 8. Natural Gas Wellhead Prices, Actual vs. Forecasts 61% 63.5% 23% 28.9% Table 9. Total Natural Gas Consumption, Actual vs. Forecasts 38% 6.7% 59% 5.6% Table 10. Natural Gas Production, Actual vs. Forecasts 51% 5.5% 70% 5.8%

68

SECTION 35 Table of Contents 35 Upper Columbia Research, Monitoring and Evaluation Plan.....................2  

E-Print Network (OSTI)

managers to maintain functional ecosystems for resident fish through protection and restoration of in research, monitoring, and evaluation plan AQUATIC Strategy & Objective Strategy Type 1 Monitoring Type 2 Objective 1B: Assess chemical, biological, and physical factors influencing aquatic productivity. (To allow

69

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)

70

Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM) for Mesoscale Weather Forecasting Applications  

E-Print Network (OSTI)

Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange­based surface evapotranspiration

Niyogi, Dev

71

FY 2005 State Table  

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

Office of Management, Budget Office of Management, Budget and Evaluation/CFO February 2004 State Tables State Tables Preliminary Preliminary Department of Energy Department of Energy FY 2005 Congressional Budget FY 2005 Congressional Budget Request Request Office of Management, Budget and Evaluation/CFO February 2004 State Tables State Tables Printed with soy ink on recycled paper Preliminary Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. State Index Page Number

72

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting JASPER A. VRUGT  

E-Print Network (OSTI)

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting JASPER A. VRUGT Earth values must be specified (Table 1). Corresponding author address: Jasper Vrugt, Earth and Envi- ronmental

Vrugt, Jasper A.

73

Forecasting wireless communication technologies  

Science Journals Connector (OSTI)

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

Sabrina Patino; Jisun Kim; Tugrul U. Daim

2010-01-01T23:59:59.000Z

74

Wind Power Forecasting  

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

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

75

Wind Power Forecasting  

Science Journals Connector (OSTI)

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

Sue Ellen Haupt; William P. Mahoney; Keith Parks

2014-01-01T23:59:59.000Z

76

Energy Demand Forecasting  

Science Journals Connector (OSTI)

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

S. C. Bhattacharyya

2011-01-01T23:59:59.000Z

77

A critical evaluation of the upper ocean heat budget in the Climate Forecast System Reanalysis data for the south central equatorial Pacific  

SciTech Connect

Coupled ocean-atmospheric models suffer from the common bias of a spurious rain belt south of the central equatorial Pacific throughout the year. Observational constraints on key processes responsible for this bias are scarce. The recently available reanalysis from a coupled model system for the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data is a potential benchmark for climate models in this region. Its suitability for model evaluation and validation, however, needs to be established. This paper examines the mixed layer heat budget and the ocean surface currents - key factors for the sea surface temperature control in the double Inter-Tropical Convergence Zone in the central Pacific - from 5{sup o}S to 10{sup o}S and 170{sup o}E to 150{sup o}W. Two independent approaches are used. The first approach is through comparison of CFSR data with collocated station observations from field experiments; the second is through the residual analysis of the heat budget of the mixed layer. We show that the CFSR overestimates the net surface flux in this region by 23 W m{sup -2}. The overestimated net surface flux is mainly due to an even larger overestimation of shortwave radiation by 44 W m{sup -2}, which is compensated by a surface latent heat flux overestimated by 14 W m{sup -2}. However, the quality of surface currents and the associated oceanic heat transport in CFSR are not compromised by the surface flux biases, and they agree with the best available estimates. The uncertainties of the observational data from field experiments are also briefly discussed in the present study.

Liu H.; Lin W.; Liu, X.; Zhang, M.

2011-08-26T23:59:59.000Z

78

FY 2006 State Table  

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

State Tables State Tables Preliminary Department of Energy FY 2006 Congressional Budget Request Office of Management, Budget and Evaluation/CFO February 2005 State Tables Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. State Index Page Number FY 2006 Congressional Budget 1/27/2005 Department Of Energy (Dollars In Thousands) 3:32:58PM Page 1 of 2 FY 2004 Comp/Approp FY 2005 Comp/Approp FY 2006 Request State Table

79

Improving Inventory Control Using Forecasting  

E-Print Network (OSTI)

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

Balandran, Juan

2005-12-16T23:59:59.000Z

80

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

SciTech Connect

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

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

2014-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast evaluation table" 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

Technology Forecasting Scenario Development  

E-Print Network (OSTI)

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

82

CAPP 2010 Forecast.indd  

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

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

83

Conversion Tables  

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

Carbon Dioxide Information Analysis Center - Conversion Tables Carbon Dioxide Information Analysis Center - Conversion Tables Contents taken from Glossary: Carbon Dioxide and Climate, 1990. ORNL/CDIAC-39, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. Third Edition. Edited by: Fred O'Hara Jr. 1 - International System of Units (SI) Prefixes 2 - Useful Quantities in CO2 3 - Common Conversion Factors 4 - Common Energy Unit Conversion Factors 5 - Geologic Time Scales 6 - Factors and Units for Calculating Annual CO2 Emissions Using Global Fuel Production Data Table 1. International System of Units (SI) Prefixes Prefix SI Symbol Multiplication Factor exa E 1018 peta P 1015 tera T 1012 giga G 109 mega M 106 kilo k 103 hecto h 102 deka da 10 deci d 10-1 centi c 10-2

84

Comparing Forecast Skill  

Science Journals Connector (OSTI)

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

Timothy DelSole; Michael K. Tippett

2014-12-01T23:59:59.000Z

85

FY 2006 Laboratory Table  

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

Laboratory Tables Laboratory Tables Preliminary Department of Energy FY 2006 Congressional Budget Request Office of Management, Budget and Evaluation/CFO February 2005 Laboratory Tables Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Laboratory / Facility Index FY 2006 Congressional Budget Page 1 of 3 (Dollars In Thousands) 3:43:16PM Department Of Energy 1/27/2005 Page Number FY 2004 Comp/Approp FY 2005 Comp/Approp

86

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect

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

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

2010-11-02T23:59:59.000Z

87

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

5 5 Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF format Excel logo Spreadsheets are provided in excel 1 to117 - Complete set of Supplemental Tables PDF Energy Consumption by Sector (Census Division) Table 1. New England XLS PDF Table 2. Middle Atlantic XLS PDF Table 3. East North Central XLS PDF Table 4. West North Central XLS PDF Table 5. South Atlantic XLS PDF Table 6. East South Central XLS PDF Table 7. West South Central XLS PDF Table 8. Mountain XLS PDF Table 9. Pacific XLS PDF Table 10. Total United States XLS PDF Energy Prices by Sector (Census Division) Table 11. New England XLS PDF Table 12. Middle Atlantic XLS PDF Table 13. East North Central XLS PDF Table 14. West North Central XLS PDF Table 15. South Atlantic XLS PDF Table 16. East South Central

88

GIS DEVELOPMENT GUIDE Table of Contents  

E-Print Network (OSTI)

GIS DEVELOPMENT GUIDE Volume II Table of Contents SURVEY OF AVAILABLE DATA Introduction ...................................................................................13 EVALUATING GIS HARDWARE AND SOFTWARE Introduction ...................................................................................14 Sources of Information About GIS......................................................14 GIS

Ghelli, Giorgio

89

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

Science Journals Connector (OSTI)

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

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

2015-01-01T23:59:59.000Z

90

TABLE OF CONTENTS TABLE OF CONTENTS ...........................................................................................................................................II  

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

i i ii TABLE OF CONTENTS TABLE OF CONTENTS ...........................................................................................................................................II EXECUTIVE SUMMARY ........................................................................................................................................... 3 INTRODUCTION......................................................................................................................................................... 4 COMPLIANCE SUMMARY ....................................................................................................................................... 6 COMPREHENSIVE ENVIRONMENTAL RESPONSE, COMPENSATION, AND LIABILITY ACT (CERCLA) .................... 6

91

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

92

Weather-based forecasts of California crop yields  

SciTech Connect

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

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

2005-09-26T23:59:59.000Z

93

Information-Based Skill Scores for Probabilistic Forecasts  

Science Journals Connector (OSTI)

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

Bodo Ahrens; Andr Walser

2008-01-01T23:59:59.000Z

94

A methodology for forecasting carbon dioxide flooding performance  

E-Print Network (OSTI)

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

Marroquin Cabrera, Juan Carlos

2012-06-07T23:59:59.000Z

95

Sandia National Laboratories: solar forecasting  

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

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

96

Consensus Coal Production Forecast for  

E-Print Network (OSTI)

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

Mohaghegh, Shahab

97

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

98

On Sequential Probability Forecasting  

E-Print Network (OSTI)

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

McCarl, Bruce A.

99

1992 CBECS Detailed Tables  

Gasoline and Diesel Fuel Update (EIA)

Detailed Tables Detailed Tables To download all 1992 detailed tables: Download Acrobat Reader for viewing PDF files. Yellow Arrow Buildings Characteristics Tables (PDF format) (70 tables, 230 pages, file size 1.39 MB) Yellow Arrow Energy Consumption and Expenditures Tables (PDF format) (47 tables, 208 pages, file size 1.28 MB) Yellow Arrow Energy End-Use Tables (PDF format) (6 tables, 6 pages, file size 31.7 KB) Detailed tables for other years: Yellow Arrow 1999 CBECS Yellow Arrow 1995 CBECS Background information on detailed tables: Yellow Arrow Description of Detailed Tables and Categories of Data Yellow Arrow Statistical Significance of Data 1992 Commercial Buildings Energy Consumption Survey (CBECS) Detailed Tables Data from the 1992 Commercial Buildings Energy Consumption Survey (CBECS) are presented in three groups of detailed tables:

100

Table 25  

Gasoline and Diesel Fuel Update (EIA)

89 89 Table 25 Created on: 1/3/2014 3:10:33 PM Table 25. Natural gas home customer-weighted heating degree days, New England Middle Atlantic East North Central West North Central South Atlantic Month/Year/Type of data CT, ME, MA, NH, RI, VT NJ, NY, PA IL, IN, MI, OH, WI IA, KS, MN, MO, ND, NE, SD DE, FL, GA, MD, DC, NC, SC, VA, WV November Normal 702 665 758 841 442 2012 751 738 772 748 527 2013 756 730 823 868 511 % Diff (normal to 2013) 7.7 9.8 8.6 3.2 15.6 % Diff (2012 to 2013) 0.7 -1.1 6.6 16.0 -3.0 November to November Normal 702 665 758 841 442 2012 751 738 772 748 527 2013 756 730 823 868 511 % Diff (normal to 2013) 7.7 9.8 8.6 3.2 15.6 % Diff (2012 to 2013) 0.7 -1.1 6.6 16.0 -3.0

Note: This page contains sample records for the topic "forecast evaluation table" 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

chapter 5. Detailed Tables  

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

5. Detailed Tables 5. Detailed Tables Chapter 5. Detailed Tables The following tables present detailed characteristics of vehicles in the residential sector. Data are from the 1994 Residential Transportation Energy Consumption Survey. Table Organization The "Detailed Tables" section consists of three types of tables: (1) Tables of totals such as number of vehicle-miles traveled (VMT) or gallons consumed; (2) tables of per household statistics such as VMT per household; and (3) tables of per-vehicle statistics, such as vehicle fuel consumption per vehicle. The tables have been grouped together by specific topics such as model-year data or family-income data to facilitate finding related information. The Quick-Reference Guide to the detailed tables indicates major topics of each table.

102

Notices TABLE  

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

7 Federal Register 7 Federal Register / Vol. 76, No. 160 / Thursday, August 18, 2011 / Notices TABLE 2-NET BURDEN CHANGE-Continued 2011-2012 2012-2013 Change % Change Burden disposition Total Applicants .................................... 23,611,500 24,705,864 +1,094,364 +4.63 Net decrease in burden. The increase in applicants is offset by the results of the Department's simplification changes. This has created an over- all decrease in burden of 8.94% or 2,881,475 hours. Total Applicant Burden ......................... 32,239,328 29,357,853 ¥2,881,475 ¥8.94 Total Annual Responses ....................... 32,239,328 46,447,024 +14,207,696 +44.07 Cost for All Applicants .......................... $159,370.20 $234,804.24 $75,434.04 +47.33 The Department is proud that efforts to simplify the FAFSA submission

103

Table 4  

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

4. Mean Annual Electricity Expenditures for Lighting, by Number of 4. Mean Annual Electricity Expenditures for Lighting, by Number of Household Members by Number of Rooms, 1993 (Dollars) Number of Rooms Number of Household Members All Households One to Three Four Five Six Seven Eight or More RSE Column Factors: 0.5 1.8 1.1 0.9 0.9 1.0 1.2 RSE Row Factors All Households................................... 83 49 63 76 87 104 124 2.34 One..................................................... 55 44 51 54 69 78 87 5.33 Two..................................................... 80 56 63 77 82 96 107 3.38 Three.................................................. 92 60 73 82 95 97 131 4.75 Four.................................................... 106 64 78 93 96 124 134 4.53 Five or More....................................... 112 70 83 98 99 117 150 5.89 Notes: -- To obtain the RSE percentage for any table cell, multiply the

104

Evaluation of Mixed-Phase Cloud Parameterizations in Short-Range Weather Forecasts with CAM3 and AM2 for Mixed-Phase Arctic Cloud Experiment  

SciTech Connect

By making use of the in-situ data collected from the recent Atmospheric Radiation Measurement Mixed-Phase Arctic Cloud Experiment, we have tested the mixed-phase cloud parameterizations used in the two major U.S. climate models, the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) and the Geophysical Fluid Dynamics Laboratory climate model (AM2), under both the single-column modeling framework and the U.S. Department of Energy Climate Change Prediction Program-Atmospheric Radiation Measurement Parameterization Testbed. An improved and more physically based cloud microphysical scheme for CAM3 has been also tested. The single-column modeling tests were summarized in the second quarter 2007 Atmospheric Radiation Measurement metric report. In the current report, we document the performance of these microphysical schemes in short-range weather forecasts using the Climate Chagne Prediction Program Atmospheric Radiation Measurement Parameterizaiton Testbest strategy, in which we initialize CAM3 and AM2 with realistic atmospheric states from numerical weather prediction analyses for the period when Mixed-Phase Arctic Cloud Experiment was conducted.

Xie, S; Boyle, J; Klein, S; Liu, X; Ghan, S

2007-06-01T23:59:59.000Z

105

Online short-term solar power forecasting  

SciTech Connect

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

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

2009-10-15T23:59:59.000Z

106

1995 Detailed Tables  

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

Households, Buildings & Industry > Commercial Buildings Energy Households, Buildings & Industry > Commercial Buildings Energy Consumption Survey > Detailed Tables 1995 Detailed Tables Data from the 1995 Commercial Buildings Energy Consumption Survey (CBECS) are presented in three groups of detailed tables: Buildings Characteristics Tables, number of buildings and amount of floorspace for major building characteristics. Energy Consumption and Expenditures Tables, energy consumption and expenditures for major energy sources. Energy End-Use Data, total, electricity and natural gas consumption and energy intensities for nine specific end-uses. Summary Table—All Principal Buildings Activities (HTML Format) Background information on detailed tables: Description of Detailed Tables and Categories of Data Statistical Significance of Data

107

Price forecasting for notebook computers.  

E-Print Network (OSTI)

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

Rutherford, Derek Paul

2012-01-01T23:59:59.000Z

108

Ensemble Forecasts and their Verification  

E-Print Network (OSTI)

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

Maryland at College Park, University of

109

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Journals Connector (OSTI)

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

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

110

Toward evaluating the effect of climate change on investments in the water resources sector: insights from the forecast and analysis of hydrological indicators in developing countries  

E-Print Network (OSTI)

The World Bank has recently developed a method to evaluate the effects of climate change on six hydrological indicators across 8951 basins of the world. The indicators are designed for decision-makers and stakeholders to ...

Jacobsen, Michael

111

Project Profile: Forecasting and Influencing Technological Progress...  

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

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

112

Forecasting with adaptive extended exponential smoothing  

Science Journals Connector (OSTI)

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

John T. Mentzer Ph.D.

113

Electricity price forecasting in a grid environment.  

E-Print Network (OSTI)

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

Li, Guang, 1974-

2007-01-01T23:59:59.000Z

114

Energy Department Forecasts Geothermal Achievements in 2015 ...  

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

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

115

Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California  

Science Journals Connector (OSTI)

...given in Table 1, as well as background earthquakes...in the test region as well as forecasts that excluded...about 50 km south of the MexicoUnited States border...this is the Cerra Prieto geothermal area...earthquake in northern Mexico. This earthquake occurred...

Ya-Ting Lee; Donald L. Turcotte; James R. Holliday; Michael K. Sachs; John B. Rundle; Chien-Chih Chen; Kristy F. Tiampo

2011-01-01T23:59:59.000Z

116

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF format. Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF format. MS Excel Viewer Spreadsheets are provided in excel Errata - August 25, 2004 1 to117 - Complete set of of Supplemental Tables PDF Table 1. Energy Consumption by Source and Sector (New England) XLS PDF Table 2. Energy Consumption by Source and Sector (Middle Atlantic) XLS PDF Table 3. Energy Consumption by Source and Sector (East North Central) XLS PDF Table 4. Energy Consumption by Source and Sector (West North Central) XLS PDF Table 5. Energy Consumption by Source and Sector (South Atlantic) XLS PDF Table 6. Energy Consumption by Source and Sector (East South Central) XLS PDF Table 7. Energy Consumption by Source and Sector (West South Central) XLS PDF Table 8. Energy Consumption by Source and Sector (Mountain)

117

1999 CBECS Detailed Tables  

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

Commercial Buildings Energy Consumption Survey (CBECS) > Detailed Tables Commercial Buildings Energy Consumption Survey (CBECS) > Detailed Tables 1999 CBECS Detailed Tables Building Characteristics | Consumption & Expenditures Data from the 1999 Commercial Buildings Energy Consumption Survey (CBECS) are presented in the Building Characteristics tables, which include number of buildings and total floorspace for various Building Characteristics, and Consumption and Expenditures tables, which include energy usage figures for major energy sources. A table of Relative Standard Errors (RSEs) is included as a worksheet tab in each Excel tables. Complete sets of RSE tables are also available in .pdf format. (What is an RSE?) Preliminary End-Use Consumption Estimates for 1999 | Description of 1999 Detailed Tables and Categories of Data

118

MTS Table Top Load frame  

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

MTS Table Top Load frame MTS Table Top Load frame The Non-destructive Evaluation group operates an MTS Table Top Load frame for ultimate strength and life cycle testing of various ceramic, ceramic-matrix (FGI), carbon, carbon fiber, cermet (CMC) and metal alloy engineering samples. The load frame is a servo-hydraulic type designed to function in a closed loop configuration under computer control. The system can perform non-cyclic, tension, compression and flexure testing and cyclic fatigue tests. The system is comprised of two parts: * The Load Frame and * The Control System. Load Frame The Load Frame (figure 1) is a cross-head assembly which includes a single moving grip, a stationary grip and LVDT position sensor. It can generate up to 25 kN (5.5 kip) of force in the sample under test and can

119

Correcting and combining time series forecasters  

Science Journals Connector (OSTI)

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

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

2014-02-01T23:59:59.000Z

120

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

E-Print Network (OSTI)

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

Note: This page contains sample records for the topic "forecast evaluation table" 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

Application of a medium-range global hydrologic probabilistic forecast scheme to the Ohio River Basin  

SciTech Connect

A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003-2007. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium Range Weather Forecasts (ECMWF) analysis temperatures and wind, and Tropical Rainfall Monitoring Mission Multi Satellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF ensemble prediction system (EPS) 10-day ensemble forecast. A parallel set up was used where ECMWF EPS forecasts were interpolated to the spatial scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effect of initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The flood prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. Initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.

Voisin, Nathalie; Pappenberger, Florian; Lettenmaier, D. P.; Buizza, Roberto; Schaake, John

2011-08-15T23:59:59.000Z

122

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":""}]}

123

Price forecasting for notebook computers  

E-Print Network (OSTI)

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

Rutherford, Derek Paul

2012-06-07T23:59:59.000Z

124

Forecasting phenology under global warming  

Science Journals Connector (OSTI)

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

2010-01-01T23:59:59.000Z

125

Demand Forecasting of New Products  

E-Print Network (OSTI)

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

Sun, Yu

126

FY 2005 Statistical Table  

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

Statistical Table by Appropriation Statistical Table by Appropriation (dollars in thousands - OMB Scoring) Table of Contents Summary...................................................................................................... 1 Mandatory Funding....................................................................................... 3 Energy Supply.............................................................................................. 4 Non-Defense site acceleration completion................................................... 6 Uranium enrichment D&D fund.................................................................... 6 Non-Defense environmental services.......................................................... 6 Science.........................................................................................................

127

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

128

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

Office of Environmental Management (EM)

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

129

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

E-Print Network (OSTI)

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

Sakauchi, Tsuginosuke

2011-01-01T23:59:59.000Z

130

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"

131

The University of Reading Helen Dacre Evaluating pollution transport in  

E-Print Network (OSTI)

The University of Reading Helen Dacre Evaluating pollution transport in weather prediction models Outline Air pollution forecasting Offline forecasting Online forecasting Aim Overview of ETEX 2 case Conclusions and future work #12;The University of Reading Helen Dacre Offline Air Pollution Forecasting

Dacre, Helen

132

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Journals Connector (OSTI)

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

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

133

Aggregate vehicle travel forecasting model  

SciTech Connect

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

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

1995-05-01T23:59:59.000Z

134

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.

135

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network (OSTI)

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

Sathaye, Jayant

2013-01-01T23:59:59.000Z

136

Massachusetts state airport system plan forecasts.  

E-Print Network (OSTI)

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

Mathaisel, Dennis F. X.

137

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Journals Connector (OSTI)

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

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

2003-02-01T23:59:59.000Z

138

Forecasting Water Use in Texas Cities  

E-Print Network (OSTI)

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

Shaw, Douglas T.; Maidment, David R.

139

Energy demand forecasting: industry practices and challenges  

Science Journals Connector (OSTI)

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

Mathieu Sinn

2014-06-01T23:59:59.000Z

140

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Projection Tables (1990-2025) Projection Tables (1990-2025) Formats All Reference Case Data Projection Tables (1 to 14 complete) Excel PDF Table Title Table A1 World Total Primary Energy Consumption by Region, Reference Case Excel PDF Table A2 World Total Energy Consumption by Region and Fuel, Reference Case Excel PDF Table A3 World Gross Domestic Product (GDP) by Region, Reference Case Excel PDF Table A4 World Oil Consumption by Region, Reference Case Excel PDF Table A5 World Natural Gas Consumption by Region, Reference Case Excel PDF Table A6 World Coal Consumption by Region, Reference Case Excel PDF Table A7 World Nuclear Energy Consumption by Region, Reference Case Excel PDF Table A8 World Consumption of Hydroelectricity and Other Renewable Energy by Region, Reference Case Excel PDF Table A9 World Net Electricity Consumption by Region, Reference Case

Note: This page contains sample records for the topic "forecast evaluation table" 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

Load Forecasting of Supermarket Refrigeration  

E-Print Network (OSTI)

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

142

Essays on macroeconomics and forecasting  

E-Print Network (OSTI)

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

Liu, Dandan

2006-10-30T23:59:59.000Z

143

Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning  

Science Journals Connector (OSTI)

Abstract Electricity price forecasting is essential for the market participants in their decision making. Nevertheless, the accuracy of such forecasting cannot be guaranteed due to the high variability of the price data. For this reason, in many cases, rather than merely point forecasting results, market participants are more interested in the probabilistic price forecasting results, i.e., the prediction intervals of the electricity price. Focusing on this issue, this paper proposes a new model for the probabilistic electricity price forecasting. This model is based on the active learning technique and the variational heteroscedastic Gaussian process (VHGP). It provides the heteroscedastic Gaussian prediction intervals, which effectively quantify the heteroscedastic uncertainties associated with the price data. Because the high computational effort of VHGP hinders its application to the large-scale electricity price forecasting tasks, we design an active learning algorithm to select a most informative training subset from the whole available training set. By constructing the forecasting model on this smaller subset, the computational efforts can be significantly reduced. In this way, the practical applicability of the proposed model is enhanced. The forecasting performance and the computational time of the proposed model are evaluated using the real-world electricity price data, which is obtained from the ANEM, PJM, and New England ISO.

Peng Kou; Deliang Liang; Lin Gao; Jianyong Lou

2015-01-01T23:59:59.000Z

144

Louisiana Block Grant Tables | Department of Energy  

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

Louisiana Block Grant Tables Louisiana Block Grant Tables This table details funding for state, city, and county governments in the state of Louisiana. Louisiana Block Grant Tables...

145

Mississippi Block Grant Tables | Department of Energy  

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

Mississippi Block Grant Tables Mississippi Block Grant Tables A table describing where state funding is being distributed Mississippi Block Grant Tables More Documents &...

146

2003 CBECS RSE Tables  

Gasoline and Diesel Fuel Update (EIA)

cbecs/cbecs2003/detailed_tables_2003/2003rsetables_files/plainlink.css" cbecs/cbecs2003/detailed_tables_2003/2003rsetables_files/plainlink.css" type=text/css rel=stylesheet> Home > Households, Buildings & Industry > Commercial Buildings Energy Consumption Survey (CBECS) > 2003 Detailed Tables > RSE Tables 2003 CBECS Relative Standard Error (RSE) Tables Released: Dec 2006 Next CBECS will be conducted in 2007 Standard error is a measure of the reliability or precision of the survey statistic. The value for the standard error can be used to construct confidence intervals and to perform hypothesis tests by standard statistical methods. Relative Standard Error (RSE) is defined as the standard error (square root of the variance) of a survey estimate, divided by the survey estimate and multiplied by 100. (More information on RSEs)

147

Forecasting-based SKU classification  

Science Journals Connector (OSTI)

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

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

2013-01-01T23:59:59.000Z

148

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Low Economic Growth Case Projection Tables (1990-2025) Low Economic Growth Case Projection Tables (1990-2025) Formats Low Economic Growth Case Data Projection Tables (1 to 13 complete) Excel PDF Table Title Table C1 World Total Primary Energy Consumption by Region, Low Economic Growth Case Excel PDF Table C2 World Total Energy Consumption by Region and Fuel, Low Economic Growth Case Excel PDF Table C3 World Gross Domestic Product (GDP) by Region, Low Economic Growth Case Excel PDF Table C4 World Oil Consumption by Region, Low Economic Growth Case Excel PDF Table C5 World Natural Cas Consumption by Region, Low Economic Growth Case Excel PDF Table C6 World Coal Consumption by Region, Low Economic Growth Case Excel PDF Table C7 World Nuclear Energy Consumption by Region, Low Economic Growth Case Excel PDF Table C8 World Consumption of Hydroelectricity and Other Renewable Energy by Region, Low Economic Growth Case

149

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

High Economic Growth Case Projection Tables (1990-2025) High Economic Growth Case Projection Tables (1990-2025) Formats High Economic Growth Case Data Projection Tables (1 to 13 complete) Excel PDF Table Title Table B1 World Total Primary Energy Consumption by Region, High Economic Growth Case Excel PDF Table B2 World Total Energy Consumption by Region and Fuel, High Economic Growth Case Excel PDF Table B3 World Gross Domestic Product (GDP) by Region, High Economic Growth Case Excel PDF Table B4 World Oil Consumption by Region, High Economic Growth Case Excel PDF Table B5 World Natural Cas Consumption by Region, High Economic Growth Case Excel PDF Table B6 World Coal Consumption by Region, High Economic Growth Case Excel PDF Table B7 World Nuclear Energy Consumption by Region, High Economic Growth Case Excel PDF Table B8 World Consumption of Hydroelectricity and Other Renewable Energy by Region, High Economic Growth Case

150

An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street  

Science Journals Connector (OSTI)

Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.

Zhi-Peng LI; Hong YU; Yun-Cai LIU; Fu-Qiang LIU

2008-01-01T23:59:59.000Z

151

CBECS Buildings Characteristics --Revised Tables  

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

Buildings Use Tables Buildings Use Tables (24 pages, 129 kb) CONTENTS PAGES Table 12. Employment Size Category, Number of Buildings, 1995 Table 13. Employment Size Category, Floorspace, 1995 Table 14. Weekly Operating Hours, Number of Buildings, 1995 Table 15. Weekly Operating Hours, Floorspace, 1995 Table 16. Occupancy of Nongovernment-Owned and Government-Owned Buildings, Number of Buildings, 1995 Table 17. Occupancy of Nongovernment-Owned and Government-Owned Buildings, Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of commercial buildings sponsored by the Energy Information Administration, that provides information on the use of energy in commercial buildings in the

152

Supplement Tables - Contacts  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2000 (AEO2000) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@ eia.doe.gov, 202/586-2222), Director, Office of Integrated Analysis and Forecasting; 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; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Director, Coal and Electric Power Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Modeling Analyst: Annual Energy Outlook 2000 (AEO2000) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@ eia.doe.gov, 202/586-2222), Director, Office of Integrated Analysis and Forecasting; 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; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Director, 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:

153

ARM - Instrument Location Table  

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

govInstrumentsLocation Table govInstrumentsLocation Table Instruments Location Table Contacts Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Instrument Locations Site abbreviations explained in the key. Instrument Name Abbreviation NSA SGP TWP AMF C1 C2 EF BF CF EF IF C1 C2 C3 EF IF Aerosol Chemical Speciation Monitor ACSM Atmospheric Emitted Radiance Interferometer AERI Aethalometer AETH Ameriflux Measurement Component AMC Aerosol Observing System AOS Meteorological Measurements associated with the Aerosol Observing System AOSMET Broadband Radiometer Station BRS

154

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

Science Journals Connector (OSTI)

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

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

2013-01-01T23:59:59.000Z

155

Forecasting wind speed financial return  

E-Print Network (OSTI)

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

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

156

FY 2009 State Table  

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

State Tables State Tables Preliminary February 2008 Office of Chief Financial Officer Department of Energy FY 2009 Congressional Budget Request State Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Printed with soy ink on recycled paper State Index Page Number FY 2009 Congressional Budget 1/30/2008 Department Of Energy (Dollars In Thousands) 9:01:45AM Page 1 of 2 FY 2007 Appropriation FY 2008 Appropriation FY 2009 Request State Table 1 1 $27,588

157

FY 2010 State Table  

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

State Tables State Tables Preliminary May 2009 Office of Chief Financial Officer FY 2010 Congressional Budget Request State Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Printed with soy ink on recycled paper State Index Page Number FY 2010 Congressional Budget 5/4/2009 Department Of Energy (Dollars In Thousands) 2:13:22PM Page 1 of 2 FY 2008 Appropriation FY 2009 Appropriation FY 2010 Request State Table 1 1 $46,946 $48,781 $38,844 Alabama 2 $6,569

158

FY 2010 Laboratory Table  

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

Laboratory Tables Laboratory Tables Preliminary May 2009 Office of Chief Financial Officer FY 2010 Congressional Budget Request Laboratory Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Printed with soy ink on recycled paper Laboratory / Facility Index FY 2010 Congressional Budget Page 1 of 3 (Dollars In Thousands) 2:08:56PM Department Of Energy 5/4/2009 Page Number FY 2008 Appropriation FY 2009 Appropriation FY 2010 Request Laboratory Table 1 1 $1,200

159

Table of Contents  

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

E N N E E R R A A L L Semiannual Report toCongress DOEIG-0065 April 1 - September 30, 2013 TABLE OF CONTENTS From the Desk of the Inspector General ......

160

FY 2008 State Table  

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

State Table State Table Preliminary Department of Energy FY 2008 Congressional Budget Request February 2007 Office of Chief Financial Officer State Table Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. State Index Page Number FY 2008 Congressional Budget 2/1/2007 Department Of Energy (Dollars In Thousands) 6:53:08AM Page 1 of 2 FY 2006 Appropriation FY 2007 Request FY 2008 Request State Table 1 1 $28,332 $30,341

Note: This page contains sample records for the topic "forecast evaluation table" 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

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect

Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

2011-03-28T23:59:59.000Z

162

Power load forecasting using data mining and knowledge discovery technology  

Science Journals Connector (OSTI)

Considering the importance of the peak load to the dispatching and management of the electric system, the error of peak load is proposed in this paper as criteria to evaluate the effect of the forecasting model. This paper proposes a systemic framework that attempts to use data mining and knowledge discovery (DMKD) to pretreat the data. And a new model is proposed which combines artificial neural networks with data mining and knowledge discovery for electric load forecasting. With DMKD technology, the system not only could mine the historical daily loading which had the same meteorological category as the forecasting day to compose data sequence with highly similar meteorological features, but also could eliminate the redundant influential factors. Then an artificial neural network is constructed to predict according to its characteristics. Using this new model, it could eliminate the redundant information, accelerate the training speed of neural network and improve the stability of the convergence. Compared with single BP neural network, this new method can achieve greater forecasting accuracy.

Yongli Wang; Dongxiao Niu; Ling Ji

2011-01-01T23:59:59.000Z

163

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

SciTech Connect

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times. A comprehensive analysis of wind energy forecast errors for the various model-based power forecasts was presented for a suite of wind energy ramp definitions. The results compiled over the year-long study period showed that the power forecasts based on the research models (ESRL_RAP, HRRR) more accurately predict wind energy ramp events than the current operational forecast models, both at the system aggregate level and at the local wind plant level. At the system level, the ESRL_RAP-based forecasts most accurately predict both the total number of ramp events and the occurrence of the events themselves, but the HRRR-based forecasts more accurately predict the ramp rate. At the individual site level, the HRRR-based forecasts most accurately predicted the actual ramp occurrence, the total number of ramps and the ramp rates (40-60% improvement in ramp rates over the coarser resolution forecast

Finley, Cathy [WindLogics

2014-04-30T23:59:59.000Z

164

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network (OSTI)

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

Poulin, L.

2013-01-01T23:59:59.000Z

165

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

Science Journals Connector (OSTI)

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

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

166

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

Science Journals Connector (OSTI)

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

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

2010-01-01T23:59:59.000Z

167

A hybrid short-term load forecasting with a new data preprocessing framework  

Science Journals Connector (OSTI)

Abstract This paper proposes a hybrid load forecasting framework with a new data preprocessing algorithm to enhance the accuracy of prediction. Bayesian neural network (BNN) is used to predict the load. A discrete wavelet transform (DWT) decomposes the load components into proper levels of resolution determined by an entropy-based criterion. Time series and regression analysis are used to select the best set of inputs among the input candidates. A correlation analysis together with a neural network provides an estimation of the predictions for the forecasting outputs. A standardization procedure is proposed to take into account the correlation estimations of the outputs with their associated input series. The preprocessing algorithm uses the input selection, wavelet decomposition and the proposed standardization to provide the most appropriate inputs for BNNs. Genetic Algorithm (GA) is then used to optimize the weighting coefficients of different forecast components and minimize the forecast error. The performance and accuracy of the proposed short-term load forecasting (STLF) method is evaluated using New England load data. Our results show a significant improvement in the forecast accuracy when compared to the existing state-of-the-art forecasting techniques.

M. Ghayekhloo; M.B. Menhaj; M. Ghofrani

2015-01-01T23:59:59.000Z

168

Supplement Tables - Contacts  

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:

169

Funding Opportunity Announcement for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

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

170

Upcoming Funding Opportunity for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

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

171

Huge market forecast for linear LDPE  

Science Journals Connector (OSTI)

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

1980-08-25T23:59:59.000Z

172

Advanced Vehicle Technologies Awards Table | Department of Energy  

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

Vehicle Technologies Awards Table Vehicle Technologies Awards Table Advanced Vehicle Technologies Awards Table The table contains a listing of the applicants, their locations, the amounts of the awards, and description of each project. The sub-categories of the table include: Advanced fuels and lubricants Light-weighting materials Demonstration Project for a Multi-Material Light-Weight Prototype Vehicle Advanced cells and design technology for electric drive batteries Advanced power electronics and electric motor technology Solid State Thermoelectric Energy Conversion Devices Fleet Efficiency Advanced Vehicle Testing and Evaluation Microsoft Word - VTP $175 Advanced Vehicle Tech project descriptions draft v5 8-2-11 More Documents & Publications Advanced Vehicle Technologies Awards advanced vehicle technologies awards table

173

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

E-Print Network (OSTI)

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

174

Optimal combined wind power forecasts using exogeneous variables  

E-Print Network (OSTI)

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

175

Ensemble typhoon quantitative precipitation forecasts model in Taiwan  

Science Journals Connector (OSTI)

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

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

176

FY 2011 State Table  

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

State Tables State Tables Department of Energy FY 2011 Congressional Budget Request DOE/CF-0054 March 2010 Office of Chief Financial Officer State Tables Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Department of Energy FY 2011 Congressional Budget Request DOE/CF-0054 State Index Page Number FY 2011 Congressional Budget 1/29/2010 Department Of Energy (Dollars In Thousands) 6:34:40AM Page 1 of 2 FY 2009 Appropriation

177

FY 2007 Laboratory Table  

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

Laboratory tables Laboratory tables preliminary Department of Energy FY 2007 Congressional Budget Request February 2006 Printed with soy ink on recycled paper Office of Chief Financial Officer Laboratory tables preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Laboratory / Facility Index FY 2007 Congressional Budget Page 1 of 3 (Dollars In Thousands) 12:10:40PM Department Of Energy 1/31/2006 Page Number FY 2005 Appropriation FY 2006 Appropriation FY 2007

178

FY 2011 Laboratory Table  

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

Laboratory Tables Laboratory Tables Department of Energy FY 2011 Congressional Budget Request DOE/CF-0055 March 2010 Office of Chief Financial Officer Laboratory Tables Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Department of Energy FY 2011 Congressional Budget Request DOE/CF-0055 Laboratory / Facility Index FY 2011 Congressional Budget Page 1 of 3 (Dollars In Thousands) 6:24:57AM Department Of Energy 1/29/2010 Page

179

FY 2008 Laboratory Table  

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

Laboratory Table Laboratory Table Preliminary Department of Energy FY 2008 Congressional Budget Request February 2007 Office of Chief Financial Officer Laboratory Table Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Laboratory / Facility Index FY 2008 Congressional Budget Page 1 of 3 (Dollars In Thousands) 6:51:02AM Department Of Energy 2/1/2007 Page Number FY 2006 Appropriation FY 2007 Request FY 2008 Request

180

Fy 2009 Laboratory Table  

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

Laboratory Tables Laboratory Tables Preliminary February 2008 Office of Chief Financial Officer Department of Energy FY 2009 Congressional Budget Request Laboratory Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. Printed with soy ink on recycled paper Laboratory / Facility Index FY 2009 Congressional Budget Page 1 of 3 (Dollars In Thousands) 8:59:25AM Department Of Energy 1/30/2008 Page Number FY 2007 Appropriation FY 2008 Appropriation FY 2009

Note: This page contains sample records for the topic "forecast evaluation table" from the National Library of EnergyBeta (NLEBeta).
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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

FY 2013 Statistical Table  

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

Statistical Table by Appropriation Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2011 FY 2012 FY 2013 Current Enacted Congressional Approp. Approp. * Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy........................................ 1,771,721 1,809,638 2,337,000 +527,362 +29.1% Electricity delivery and energy reliability......................................... 138,170 139,103 143,015 +3,912 +2.8% Nuclear energy................................................................................ 717,817 765,391 770,445 +5,054 +0.7% Fossil energy programs Clean coal technology.................................................................. -16,500 -- --

182

FY 2009 Statistical Table  

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

Statistical Table by Appropriation Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2007 FY 2008 FY 2009 Current Current Congressional Op. Plan Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy.......................... -- 1,722,407 1,255,393 -467,014 -27.1% Electricity delivery and energy reliability........................... -- 138,556 134,000 -4,556 -3.3% Nuclear energy................................................................. -- 961,665 853,644 -108,021 -11.2% Legacy management........................................................ -- 33,872 -- -33,872 -100.0% Energy supply and conservation Operation and maintenance..........................................

183

EERE Program Management Guide- Cover and Table of Contents  

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

Table of contents includes an introduction and eight chapters on the program's background, management, outreach planning, budget formulation, implementation, analysis and evaluation, and information/business management. Also includes appendices.

184

Forecast of geothermal drilling activity  

SciTech Connect

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

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

1981-10-01T23:59:59.000Z

185

New Concepts in Wind Power Forecasting Models  

E-Print Network (OSTI)

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

Kemner, Ken

186

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network (OSTI)

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

Malmberg, Anders

187

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network (OSTI)

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

Malmberg, Anders

188

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

189

UHERO FORECAST PROJECT DECEMBER 5, 2014  

E-Print Network (OSTI)

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

190

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network (OSTI)

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

Stoffelen, Ad

191

A Forecasting Support System Based on Exponential Smoothing  

Science Journals Connector (OSTI)

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

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

2010-01-01T23:59:59.000Z

192

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

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

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

193

Improved Prediction of Runway Usage for Noise Forecast :.  

E-Print Network (OSTI)

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

Dhanasekaran, D.

2014-01-01T23:59:59.000Z

194

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

Energy Savers (EERE)

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

195

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

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

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

196

Table of Contents Page i Table of Contents  

E-Print Network (OSTI)

Table of Contents Page i Table of Contents 4. Building HVAC Requirements ....................................................................................1 4.1.2 What's New for the 2013 Standards.............................................................................................3 4.1.4 California Appliance Standards and Equipment Certification

197

Cost Recovery Charge (CRC) Calculation Tables  

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

Cost Recovery Charge (CRC) Calculation Table Updated: October 6, 2014 FY 2016 September 2014 CRC Calculation Table (pdf) Final FY 2015 CRC Letter & Table (pdf) Note: The Cost...

198

TABLE OF CONTENTS  

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

/2011 /2011 Decades of Discovery Decades of Discovery Page 2 6/1/2011 TABLE OF CONTENTS 1 INTRODUCTION ...................................................................................................................... 6 2 BASIC ENERGY SCIENCES .................................................................................................. 7 2.1 Adenosine Triphosphate: The Energy Currency of Life .............................................. 7 2.2 Making Better Catalysts .............................................................................................. 8 2.3 Understanding Chemical Reactions............................................................................ 9 2.4 New Types of Superconductors ................................................................................ 10

199

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

SciTech Connect

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

200

Evaluation of the 2000 Predictions of the Run-Timing of Wild Migrant Yearling Chinook  

E-Print Network (OSTI)

outmigration for arrival timing, water temperature, total dissolved gas, flow, and spill at various dams. CRi as they are reported because in-season forecasts are based on whatever is available at the time. #12;A iii Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1: Flow and Spill Forecasts

Washington at Seattle, University of

Note: This page contains sample records for the topic "forecast evaluation table" 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

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

202

FY 2006 Statistical Table  

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

Statistical Table by Appropriation Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2004 FY 2005 FY 2006 Comparable Comparable Request to FY 2006 vs. FY 2005 Approp Approp Congress Discretionary Summary By Appropriation Energy And Water Development Appropriation Summary: Energy Programs Energy supply Operation and maintenance................................................. 787,941 909,903 862,499 -47,404 -5.2% Construction......................................................................... 6,956 22,416 40,175 17,759 +79.2% Total, Energy supply................................................................ 794,897 932,319 902,674 -29,645 -3.2% Non-Defense site acceleration completion............................. 167,272 157,316 172,400 15,084 +9.6%

203

FY 2013 Laboratory Table  

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

8 8 Department of Energy FY 2013 Congressional Budget Request Laboratory Tables y Preliminary February 2012 Office of Chief Financial Officer DOE/CF-0078 Department of Energy FY 2013 Congressional Budget Request Laboratory Tables P li i Preliminary h b d i d i hi d h l l f b d h i f h The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. February 2012 Office of Chief Financial Officer Printed with soy ink on recycled paper Laboratory / Facility Index FY 2013 Congressional Budget

204

FY 2010 Statistical Table  

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

Statistical Table by Appropriation Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2008 FY 2009 FY 2009 FY 2010 Current Current Current Congressional Approp. Approp. Recovery Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy....................................... 1,704,112 2,178,540 16,800,000 2,318,602 +140,062 +6.4% Electricity delivery and energy reliability........................................ 136,170 137,000 4,500,000 208,008 +71,008 +51.8% Nuclear energy.............................................................................. 960,903 792,000 -- 761,274 -30,726 -3.9% Legacy management..................................................................... 33,872 -- -- --

205

FY 2012 State Table  

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

6 6 Department of Energy FY 2012 Congressional Budget Request State Tables P li i Preliminary February 2012 Office of Chief Financial Officer DOE/CF-0066 Department of Energy FY 2012 Congressional Budget Request State Tables P li i Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. February 2012 Office of Chief Financial Officer Printed with soy ink on recycled

206

FY 2012 Statistical Table  

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

2Statistical Table by Appropriation 2Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2010 FY 2011 FY 2011 FY 2012 Current Congressional Annualized Congressional Approp. Request CR Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy....................................... 2,216,392 2,355,473 2,242,500 3,200,053 +983,661 +44.4% Electricity delivery and energy reliability........................................ 168,484 185,930 171,982 237,717 +69,233 +41.1% Nuclear energy............................................................................. 774,578 824,052 786,637 754,028 -20,550 -2.7% Fossil energy programs Fossil energy research and development................................... 659,770 586,583 672,383 452,975

207

FY 2007 Statistical Table  

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

Statistical Table by Appropriation Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2005 FY 2006 FY 2007 Current Current Congressional Approp. Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy supply and conservation Operation and maintenance............................................ 1,779,399 1,791,372 1,917,331 +125,959 +7.0% Construction................................................................... 22,416 21,255 6,030 -15,225 -71.6% Total, Energy supply and conservation.............................. 1,801,815 1,812,627 1,923,361 +110,734 +6.1% Fossil energy programs Clean coal technology..................................................... -160,000 -20,000 -- +20,000 +100.0% Fossil energy research and development.......................

208

FY 2012 Laboratory Table  

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

5 5 Department of Energy FY 2012 Congressional Budget Request Laboratory Tables y Preliminary February 2012 Office of Chief Financial Officer DOE/CF-0065 Department of Energy FY 2012 Congressional Budget Request Laboratory Tables P li i Preliminary h b d i d i hi d h l l f b d h i f h The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. February 2012 Office of Chief Financial Officer Printed with soy ink on recycled paper Laboratory / Facility Index FY 2012 Congressional Budget

209

FY 2008 Statistical Table  

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

Statistical Table by Appropriation Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2006 FY 2007 FY 2008 Current Congressional Congressional Approp. Request Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy supply and conservation Operation and maintenance........................................... 1,781,242 1,917,331 2,187,943 +270,612 +14.1% Construction.................................................................... 31,155 6,030 -- -6,030 -100.0% Total, Energy supply and conservation............................. 1,812,397 1,923,361 2,187,943 +264,582 +13.8% Fossil energy programs Clean coal technology.................................................... -20,000 -- -58,000 -58,000 N/A Fossil energy research and development......................

210

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network (OSTI)

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

211

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network (OSTI)

Forecasting Uncertainty Related to Ramps of Wind Power Production Arthur Bossavy, Robin Girard - The continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power study. Key words : wind power forecast, ramps, phase er- rors, forecasts ensemble. 1 Introduction Most

Boyer, Edmond

212

The effect of multinationality on management earnings forecasts  

E-Print Network (OSTI)

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

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

213

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

SciTech Connect

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

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

2011-10-01T23:59:59.000Z

214

Table of Contents  

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

COMMUNICATIONS REQUIREMENTS COMMUNICATIONS REQUIREMENTS OF SMART GRID TECHNOLOGIES October 5, 2010 i Table of Contents I. Introduction and Executive Summary.......................................................... 1 a. Overview of Smart Grid Benefits and Communications Needs................. 2 b. Summary of Recommendations .................................................................... 5 II. Federal Government Smart Grid Initiatives ................................................ 7 a. DOE Request for Information ....................................................................... 7 b. Other Federal Government Smart Grid Initiatives .................................... 9 III. Communications Requirements of Smart Grid Applications .................. 11 a. Advanced Metering Infrastructure ............................................................12

215

CBECS Buildings Characteristics --Revised Tables  

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

Geographic Location Tables Geographic Location Tables (24 pages, 136kb) CONTENTS PAGES Table 3. Census Region, Number of Buildings and Floorspace, 1995 Table 4. Census Region and Division, Number of Buildings, 1995 Table 5. Census Region and Division, Floorspace, 1995 Table 6. Climate Zone, Number of Buildings and Floorspace, 1995 Table 7. Metropolitan Status, Number of Buildings and Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of commercial buildings sponsored by the Energy Information Administration, that provides information on the use of energy in commercial buildings in the United States. The 1995 CBECS was the sixth survey in a series begun in 1979. The data were collected from a sample of 6,639 buildings representing 4.6 million commercial buildings

216

2003 CBECS Detailed Tables: Summary  

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

2003 Detailed Tables 2003 Detailed Tables 2003 CBECS Detailed Tables most recent available Released: September 2008 Building Characteristics | Consumption & Expenditures | End-Use Consumption In the 2003 CBECS, the survey procedures for strip shopping centers and enclosed malls ("mall buildings") were changed from those used in previous surveys, and, as a result, mall buildings are now excluded from most of the 2003 CBECS tables. Therefore, some data in the majority of the tables are not directly comparable with previous CBECS tables, all of which included mall buildings. Some numbers in the 2003 tables will be slightly lower than earlier surveys since the 2003 figures do not include mall buildings. See "Change in Data Collection Procedures for Malls" for a more detailed explanation.

217

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Projections by End-Use Sector and Region Tables (2002-2025) Projections by End-Use Sector and Region Tables (2002-2025) Formats Reference Case Projections by End-Use Sector and Region Data Tables (1 to 15 complete) Excel PDF Table Title Table D1 Delivered Energy Consumption in the United States by End-Use Sector and Fuel Excel PDF Table D2 Delivered Energy Consumption in Canada by End-Use Sector and Fuel Excel PDF Table D3 Delivered Energy Consumption in Mexico by End-Use Sector and Fuel Excel PDF Table D4 Delivered Energy Consumption in Western Europe by End-Use Sector and Fuel Excel PDF Table D5 Delivered Energy Consumption in Japan by End-Use Sector and Fuel Excel PDF Table D6 Delivered Energy Consumption in Australia/New Zealand by End-Use Sector and Fuel Excel PDF Table D7 Delivered Energy Consumption in the Former Soviet Union by End-Use Sector and Fuel

218

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

E-Print Network (OSTI)

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

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

219

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

E-Print Network (OSTI)

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

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

220

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

Note: This page contains sample records for the topic "forecast evaluation table" 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

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

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

222

Wind Speed Forecasting for Power System Operation  

E-Print Network (OSTI)

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

Zhu, Xinxin

2013-07-22T23:59:59.000Z

223

Testing Competing High-Resolution Precipitation Forecasts  

E-Print Network (OSTI)

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

Gilleland, Eric

224

Forecasting Capital Expenditure with Plan Data  

Science Journals Connector (OSTI)

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

W. Gerstenberger

1977-01-01T23:59:59.000Z

225

Forecasting Agriculturally Driven Global Environmental Change  

Science Journals Connector (OSTI)

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

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

2001-04-13T23:59:59.000Z

226

Medium- and Long-Range Forecasting  

Science Journals Connector (OSTI)

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

A. James Wagner

1989-09-01T23:59:59.000Z

227

Updated Satellite Technique to Forecast Heavy Snow  

Science Journals Connector (OSTI)

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

Edward C. Johnston

1995-06-01T23:59:59.000Z

228

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

5 5 For Further Information . . . The Annual Energy Outlook 2005 (AEO2005) was prepared by the Energy Information Administration (EIA), under the direction of John J. Conti (john.conti@eia.doe.gov, 202/586-2222), Director, Integrated Analysis and Forecasting and Acting Director, International, Economic and Greenhouse Gases Division; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (joseph.beamon@eia.doe.gov, 202-586-2025), Director, Coal and Electric Power Division; James M. Kendell (james.kendell@eia.doe.gov, 202/586-9646), Director, Oil and Gas Division; and Andy S. Kydes (andy.kydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor. For ordering information and questions on other energy statistics available from EIA, please contact EIA's National Energy Information Center. Addresses, telephone numbers, and hours are as follows:

229

EIA - Supplement Tables - Contact  

Gasoline and Diesel Fuel Update (EIA)

8 8 For Further Information . . . The Annual Energy Outlook 2008 (AEO2008) was prepared by the Energy Information Administration (EIA), under the direction of John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Integrated Analysis and Forecasting; Paul D. Holtberg (paul.holtberg@eia.doe.gov, 202/586-1284), Director, Demand and Integration Division; Joseph A. Beamon (jbeamon@eia.doe.gov, 202/586-2025), Director, Coal and Electric Power Division; A. Michael Schaal (michael.schaal@eia.doe.gov, 202/586-5590), Director, Oil and Gas Division; Glen E. Sweetnam (glen.sweetnam@eia.doe.gov, 202/586-2188), Director, International, Economic, and Greenhouse Gases Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Technical Advisor.

230

Forecasting energy markets using support vector machines  

Science Journals Connector (OSTI)

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

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

231

Table of Contents  

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

NT0005638 NT0005638 Cruise Report 1-19 July 2009 HYFLUX Sea Truth Cruise Northern Gulf of Mexico Submitted by: Texas A&M University - Corpus Christi 6300 Ocean Dr. Corpus Christi, TX 78412 Principal Authors: Ian R. MacDonald and Thomas Naehr Prepared for: United States Department of Energy National Energy Technology Laboratory October 30, 2009 Office of Fossil Energy HYFLUX Seatruth Cruise Report -1- Texas A&M University - Corpus Christi Table of Contents Summary ............................................................................................................................. 2 Participating Organizations ................................................................................................. 3 Major Equipment ................................................................................................................ 4

232

Help:Tables | Open Energy Information  

Open Energy Info (EERE)

Tables Tables Jump to: navigation, search Tables may be authored in wiki pages using either XHTML table elements directly, or using wikicode formatting to define the table. XHTML table elements and their use are well described on various web pages and will not be discussed here. The benefit of wikicode is that the table is constructed of character symbols which tend to make it easier to perceive the table structure in the article editing view compared to XHTML table elements. As a general rule, it is best to avoid using a table unless you need one. Table markup often complicates page editing. Contents 1 Wiki table markup summary 2 Basics 2.1 Table headers 2.2 Caption 3 XHTML attributes 3.1 Attributes on tables 3.2 Attributes on cells 3.3 Attributes on rows 3.4 HTML colspan and rowspan

233

CBECS Buildings Characteristics --Revised Tables  

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

Conservation Tables Conservation Tables (16 pages, 86 kb) CONTENTS PAGES Table 41. Energy Conservation Features, Number of Buildings and Floorspace, 1995 Table 42. Building Shell Conservation Features, Number of Buildings, 1995 Table 43. Building Shell Conservation Features, Floorspace, 1995 Table 44. Reduction in Equipment Use During Off Hours, Number of Buildings and Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of commercial buildings sponsored by the Energy Information Administration, that provides information on the use of energy in commercial buildings in the United States. The 1995 CBECS was the sixth survey in a series begun in 1979. The data were collected from a sample of 6,639 buildings representing 4.6 million commercial buildings

234

CBECS Buildings Characteristics --Revised Tables  

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

Structure Tables Structure Tables (16 pages, 93 kb) CONTENTS PAGES Table 8. Building Size, Number of Buildings, 1995 Table 9. Building Size, Floorspace, 1995 Table 10. Year Constructed, Number of Buildings, 1995 Table 11. Year Constructed, Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of commercial buildings sponsored by the Energy Information Administration, that provides information on the use of energy in commercial buildings in the United States. The 1995 CBECS was the sixth survey in a series begun in 1979. The data were collected from a sample of 6,639 buildings representing 4.6 million commercial buildings and 58.8 billion square feet of commercial floorspace in the U.S. The 1995 data are available for the four Census

235

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

Science Journals Connector (OSTI)

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

S. Viswanathan; Handik Widiarta; Rajesh Piplani

2008-07-01T23:59:59.000Z

236

CARINA Data Table  

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

Cruise Summary Table and Data Cruise Summary Table and Data Users are requested to report any data or metadata errors in the CARINA cruise files to CDIAC. Parameter units in all CARINA data files are in CCHDO exchange format. No Cruise Namea (Alias) Areab Number of Stations Datec Ship Chief Scientist Carbon PI Oxygen Nutrients TCO2d TALK pCO2e pHf CFC Other Measurements Data Files 1 06AQ19920929g (06ANTX_6) (See map) 2 118 9/29-11/30/1992 Polarstern V. Smetacek M. Stoll, J. Rommets, H. De Baar, D. Bakker 62 114h 53 54i U C 0 Choloroa,b Fluorescence, NH4 Data Files (Metadata) 2 06AQ19930806 (06ARKIX_4) (See map) 4 64 8/6-10/5/1993 Polarstern D.K. Fütterer L. Anderson 64 63 63j, bb 0 0 0 59he 3H, 3He, 18O, 14C, 85Kr, Bak Data Files

237

Appendix B: Summary Tables  

Gasoline and Diesel Fuel Update (EIA)

U.S. Energy Information Administration | Analysis of Impacts of a Clean Energy Standard as requested by Chairman Bingaman U.S. Energy Information Administration | Analysis of Impacts of a Clean Energy Standard as requested by Chairman Bingaman Appendix B: Summary Tables Table B1. The BCES and alternative cases compared to the Reference case, 2025 2009 2025 Ref Ref BCES All Clean Partial Credit Revised Baseline Small Utilities Credit Cap 2.1 Credit Cap 3.0 Stnds + Cds Generation (billion kilowatthours) Coal 1,772 2,049 1,431 1,305 1,387 1,180 1,767 1,714 1,571 1,358 Petroleum 41 45 43 44 44 44 45 45 45 43 Natural Gas 931 1,002 1,341 1,342 1,269 1,486 1,164 1,193 1,243 1,314 Nuclear 799 871 859 906 942 889 878 857 843 826 Conventional Hydropower 274 306 322 319 300 321 316 298 312 322 Geothermal 15 25 28 25 31 24 27 22 23 24 Municipal Waste 18 17 17 17 17 17 17 17 17 17 Wood and Other Biomass 38 162 303 289 295 301 241 266

238

Forecasting the belief of the population: Prediction Markets, Social Media & Swine Flu  

E-Print Network (OSTI)

Forecasting the belief of the population: Prediction Markets, Social Media & Swine Flu Daniel outbreak as a novel example to evaluate our models on the belief that it would turn into a pandemic. We and slow to run. Recently prediction markets have become a popular method to aggregate information

Hammerton, James

239

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

E-Print Network (OSTI)

This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied...

Dorta, Miguel

2012-07-16T23:59:59.000Z

240

CBECS 1992 - Consumption & Expenditures, Detailed Tables  

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

Detailed Tables Detailed Tables Detailed Tables Figure on Energy Consumption in Commercial Buildings by Energy Source, 1992 Divider Line The 49 tables present detailed energy consumption and expenditure data for buildings in the commercial sector. This section provides assistance in reading the tables by explaining some of the headings for the data categories. It will also explain the use of row and column factors to compute both the confidence levels of the estimates given in the tables and the statistical significance of differences between the data in two or more categories. The section concludes with a "Quick-Reference Guide" to the statistics in the different tables. Categories of Data in the Tables After Table 3.1, which is a summary table, the tables are grouped into the major fuel tables (Tables 3.2 through 3.13) and the specific fuel tables (Tables 3.14 through 3.29 for electricity, Tables 3.30 through 3.40 for natural gas, Tables 3.41 through 3.45 for fuel oil, and Tables 3.46 through 3.47 for district heat). Table 3.48 presents energy management and DSM data as reported by the building respondent. Table 3.49 presents data on participation in electric utility-sponsored DSM programs as reported by both the building respondent and the electricity supplier.

Note: This page contains sample records for the topic "forecast evaluation table" 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

Application of a statistical post-processing technique to a gridded, operational, air quality forecast  

Science Journals Connector (OSTI)

Abstract An automated air quality forecast bias correction scheme based on the short-term persistence of model bias with respect to recent observations is described. The scheme has been implemented in the operational Met Office five day regional air quality forecast for the UK. It has been evaluated against routine hourly pollution observations for a year-long hindcast. The results demonstrate the value of the scheme in improving performance. For the first day of the forecast the post-processing reduces the bias from 7.02 to 0.53?gm?3 for O3, from?4.70 to?0.63?gm?3 for NO2, from?4.00 to?0.13?gm?3 for PM2.5 and from?7.70 to?0.25?gm?3 for PM10. Other metrics also improve for all species. An analysis of the variation of forecast skill with lead-time is presented and demonstrates that the post-processing increases forecast skill out to five days ahead.

L.S. Neal; P. Agnew; S. Moseley; C. Ordez; N.H. Savage; M. Tilbee

2014-01-01T23:59:59.000Z

242

Microsoft Word - table_87  

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

5 5 Table 6. Natural gas processed, liquids extracted, and natural gas plant liquids production, by state, 2012 Alabama 87,269 5,309 7,110 Alabama Onshore Alabama 33,921 2,614 3,132 Alabama Offshore Alabama 53,348 2,695 3,978 Alaska 2,788,997 18,339 21,470 Alaska 2,788,997 18,339 21,470 Arkansas 6,872 336 424 Arkansas 6,872 336 424 California 169,203 9,923 12,755 California Onshore California 169,203 9,923 12,755 California Offshore California NA NA NA Federal Offshore California NA NA NA

243

TABLE OF CONTENTS  

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

2 2 TABLE OF CONTENTS Page A. Project Summary 1. Technical Progress 3 2. Cost Reporting 5 B. Detailed Reports 1.1 Magnets & Supports 8 1.2 Vacuum System 12 1.3 Power Supplies 14 1.4 RF System 16 1.5 Instrumentation & Controls 17 1.6 Cable Plant 18 1.7 Beam Line Front Ends 19 1.8 Facilities 19 1.9 Installation 20 2.1 Accelerator Physics 21 2 A. SPEAR 3 PROJECT SUMMARY 1. Technical Progress The progress and highlights of each major technical system are summarized below. Additional details are provided in Section B. Magnets - As of the end of this quarter (March 31, 2002), the status of magnet fabrication is as follows: Magnet Type Number Received % of Total Received Dipoles 40 100% Quadrupoles 102 100% Sextupoles 76 100%

244

Reviews, Tables, and Plots  

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

4 Review of Particle Physics 4 Review of Particle Physics Please use this CITATION: S. Eidelman et al. (Particle Data Group), Phys. Lett. B 592, 1 (2004) (bibtex) Standalone figures are now available for these reviews. Categories: * Constants, Units, Atomic and Nuclear Properties * Standard Model and Related Topics * Particle Properties * Hypothetical Particles * Astrophysics and Cosmology * Experimental Methods and Colliders * Mathematical Tools * Kinematics, Cross-Section Formulae, and Plots * Authors, Introductory Text, History plots PostScript help file PDF help file Constants, Units, Atomic and Nuclear Properties Physical constants (Rev.) PS PDF (1 page) Astrophysical constants (Rev.) PS PDF (2 pages) International System of units (SI) PS PDF (2 pages) Periodic table of the elements (Rev.) errata PS PDF (1 page)

245

Table G3  

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

1905-0194 1905-0194 Expiration Date: 07/31/2013 May 28, 2010 Voluntary Reporting of Greenhouse Gases 14 Table G3. Decision Chart for a Start Year Report for a Large Emitter Intending To Register Reductions Report Characteristics Reporting Requirements Schedule I Schedule II (For Each Subentity) Schedule III Schedule IV Sec. 1 Sec. 2 Sec. 3 Sec. 4 Sec. 1 Sec. 2 & Add. A Sec. 3 Sec. 1 Sec. 2 Sec. 1 Sec. 2 Part A Part B Part C Part D Part E Part A Part B Part C Independent Verification? All A- or B-Rated Methods? Foreign Emissions? Entity-Wide Reductions Only? Entity Statement Aggregated Emissions by Gas (Domestic and Foreign) † Emissions Inventory by Source

246

TABLE OF CONTENTS  

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

through June 2001 2 TABLE OF CONTENTS Page A. Project Summary 1. Technical Progress 3 2. Cost Reporting 4 B. Detailed Reports 1.1 Magnets & Supports 9 1.2 Vacuum System 16 1.3 Power Supplies 21 1.4 RF System 25 1.5 Instrumentation & Controls 26 1.6 Cable Plant 28 1.8 Facilities 28 2.0 Accelerator Physics 29 2.1 ES&H 31 3 A. SPEAR 3 PROJECT SUMMARY 1. Technical Progress Magnet System - The project has received three shipments of magnets from IHEP. A total of 55 dipole, quadrupole and sextupole magnets out of 218 have arrived. All main magnets will arrive by December. The additional mechanical and electrical checks of the magnets at SSRL have been successful. Only minor mechanical problems were found and corrected. The prototype

247

TABLE OF CONTENTS  

National Nuclear Security Administration (NNSA)

AC05-00OR22800 AC05-00OR22800 TABLE OF CONTENTS Contents Page # TOC - i SECTION A - SOLICITATION/OFFER AND AWARD ......................................................................... A-i SECTION B - SUPPLIES OR SERVICES AND PRICES/COSTS ........................................................ B-i B.1 SERVICES BEING ACQUIRED ....................................................................................B-2 B.2 TRANSITION COST, ESTIMATED COST, MAXIMUM AVAILABLE FEE, AND AVAILABLE FEE (Modification 295, 290, 284, 280, 270, 257, 239, 238, 219, M201, M180, M162, M153, M150, M141, M132, M103, M092, M080, M055, M051, M049, M034, M022, M003, A002) ..........................................................B-2 SECTION C - DESCRIPTION/SPECIFICATION/WORK STATEMENT DESCRIPTION OF

248

Table of Contents  

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

U U U . . S S . . D D E E P P A A R R T T M M E E N N T T O O F F E E N N E E R R G G Y Y O O F F F F I I C C E E O O F F I I N N S S P P E E C C T T O O R R G G E E N N E E R R A A L L Semiannual Report toCongress DOE/IG-0065 April 1 - September 30, 2013 TABLE OF CONTENTS From the Desk of the Inspector General ..................................................... 2 Impacts Key Accomplishments ............................................................................................... 3 Positive Outcomes ...................................................................................................... 3 Reports Investigative Outcomes .............................................................................................. 6 Audits ......................................................................................................................... 8

249

TABLE OF CONTENTS  

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

October October through December 2001 2 TABLE OF CONTENTS Page A. Project Summary 1. Technical Progress 3 2. Cost Reporting 4 B. Detailed Reports 1.1 Magnets & Supports 7 1.2 Vacuum System 9 1.3 Power Supplies 13 1.4 RF System 16 1.5 Instrumentation & Controls 17 1.6 Cable Plant 18 1.9 Installation 19 2.0 Accelerator Physics 20 3 A. SPEAR 3 PROJECT SUMMARY 1. Technical Progress In the magnet area, the production of all major components (dipoles, quadrupoles, and sextupoles) has been completed on schedule. This results from a highly successful collaboration with our colleagues at the Institute of High Energy Physics (IHEP) in Beijing. The production of corrector magnets is still in progress with completion scheduled for May 2002.

250

2003 CBECS Detailed Tables: Summary  

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

Energy Expenditures by Major Fuel c2-pdf c2.xls c2.html Table C3. Consumption and Gross Energy Intensity for Sum of Major Fuels c3.pdf c3.xls c3.html Table C4. Expenditures for...

251

2014 Headquarters Facilities Master Security Plan - Table of...  

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

Table of Contents 2014 Headquarters Facilities Master Security Plan - Table of Contents June 2014 2014 Headquarters Facilities Master Security Plan - Table of Contents The Table of...

252

FY 2014 Budget Request Summary Table | Department of Energy  

Office of Environmental Management (EM)

Summary Table FY 2014 Budget Request Summary Table Summary Table by Appropriations Summary Table by Organization More Documents & Publications FY 2014 Budget Request Statistical...

253

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

254

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

E-Print Network (OSTI)

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

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

255

ARM - Instrument - s-table  

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

govInstrumentss-table govInstrumentss-table Documentation S-TABLE : Instrument Mentor Monthly Summary (IMMS) reports S-TABLE : Data Quality Assessment (DQA) reports ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Instrument : Stabilized Platform (S-TABLE) Instrument Categories Ocean Observations For ship-based deployments, some instruments require actively stabilized platforms to compensate for the ship's motion, especially rotations around the long axis of the ship (roll), short axis (pitch), and, for some instruments, vertical axis (yaw). ARM currently employs two types of stabilized platforms: one electrically controlled for lighter instruments that includes yaw control (dubbed RPY for Roll, Pitch, Yaw) and one

256

12-32021E2_Forecast  

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

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

257

Building Energy Software Tools Directory: Degree Day Forecasts  

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

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

258

Building Energy Software Tools Directory: Energy Usage Forecasts  

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

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

259

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network (OSTI)

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

McBurney, Peter

260

River Forecast Application for Water Management: Oil and Water?  

Science Journals Connector (OSTI)

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

Kevin Werner; Kristen Averyt; Gigi Owen

2013-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast evaluation table" 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

Data Mining in Load Forecasting of Power System  

Science Journals Connector (OSTI)

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

Guang Yu Zhao; Yan Yan; Chun Zhou Zhao

2013-01-01T23:59:59.000Z

262

Operational Rainfall and Flow Forecasting for the Panama Canal Watershed  

Science Journals Connector (OSTI)

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

Konstantine P. Georgakakos; Jason A. Sperfslage

2005-01-01T23:59:59.000Z

263

Power System Load Forecasting Based on EEMD and ANN  

Science Journals Connector (OSTI)

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

Wanlu Sun; Zhigang Liu; Wenfan Li

2011-01-01T23:59:59.000Z

264

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network (OSTI)

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

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

2005-01-01T23:59:59.000Z

265

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

Energy Savers (EERE)

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

266

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

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

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

1980-01-01T23:59:59.000Z

267

Forecasting Energy Demand Using Fuzzy Seasonal Time Series  

Science Journals Connector (OSTI)

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

?Irem Ual Sar?; Basar ztaysi

2012-01-01T23:59:59.000Z

268

Wind power forecasting in U.S. electricity markets.  

SciTech Connect

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

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

2010-04-01T23:59:59.000Z

269

Wind power forecasting in U.S. Electricity markets  

SciTech Connect

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

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

2010-04-15T23:59:59.000Z

270

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

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

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

271

table14.xls  

Gasoline and Diesel Fuel Update (EIA)

Table 14. Natural Gas Wellhead Prices, Actual vs. Reference Case Projections Table 14. Natural Gas Wellhead Prices, Actual vs. Reference Case Projections (current dollars per thousand cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 AEO 1982 4.32 5.47 6.67 7.51 8.04 8.57 AEO 1983 2.93 3.11 3.46 3.93 4.56 5.26 12.74 AEO 1984 2.77 2.90 3.21 3.63 4.13 4.79 9.33 AEO 1985 2.60 2.61 2.66 2.71 2.94 3.35 3.85 4.46 5.10 5.83 6.67 AEO 1986 1.73 1.96 2.29 2.54 2.81 3.15 3.73 4.34 5.06 5.90 6.79 7.70 8.62 9.68 10.80 AEO 1987 1.83 1.95 2.11 2.28 2.49 2.72 3.08 3.51 4.07 7.54 AEO 1989* 1.62 1.70 1.91 2.13 2.58 3.04 3.48 3.93 4.76 5.23 5.80 6.43 6.98 AEO 1990 1.78 1.88 2.93 5.36 AEO 1991 1.77 1.90 2.11 2.30 2.42 2.51 2.60 2.74 2.91 3.29 3.75 4.31 5.07 5.77 6.45 AEO 1992 1.69 1.85 2.03 2.15 2.35 2.51 2.74 3.01 3.40 3.81 4.24 4.74 5.25 5.78 AEO 1993 1.85 1.94 2.09 2.30 2.44 2.60 2.85 3.12 3.47 3.84 4.31 4.81 5.28

272

Code Tables | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

System NMMSS Information, Reports & Forms Code Tables Code Tables U.S. Department of Energy U.S. Nuclear Regulatory Commission Nuclear Materials Management & Safeguards...

273

Application of a Combination Forecasting Model in Logistics Parks' Demand  

Science Journals Connector (OSTI)

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

Chen Qin; Qi Ming

2010-05-01T23:59:59.000Z

274

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network (OSTI)

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

Boyer, Edmond

275

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network (OSTI)

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

276

Accuracy of near real time updates in wind power forecasting  

E-Print Network (OSTI)

· advantage: no NWP data necessary ­ very actual shortest term forecasts possible · wind power inputAccuracy of near real time updates in wind power forecasting with regard to different weather October 2007 #12;EMS/ECAM 2007 ­ Nadja Saleck Outline · Study site · Wind power forecasting - method

Heinemann, Detlev

277

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network (OSTI)

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

de Lijser, Peter

278

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network (OSTI)

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

Stevenson, Paul

279

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network (OSTI)

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

280

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

E-Print Network (OSTI)

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

Povinelli, Richard J.

Note: This page contains sample records for the topic "forecast evaluation table" 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

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

E-Print Network (OSTI)

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

Lang, K.

1982-01-01T23:59:59.000Z

282

MECS Fuel Oil Tables  

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

: Actual, Minimum and Maximum Use Values for Fuel Oils and Natural Gas : Actual, Minimum and Maximum Use Values for Fuel Oils and Natural Gas Year Distillate Fuel Oil (TBtu) Actual Minimum Maximum Discretionary Rate 1985 185 148 1224 3.4% 1994 152 125 1020 3.1% Residual Fuel Oil (TBtu) Actual Minimum Maximum Discretionary Rate 1985 505 290 1577 16.7% 1994 441 241 1249 19.8% Natural Gas (TBtu) Actual Minimum Maximum Discretionary Rate 1985 4656 2702 5233 77.2% 1994 6141 4435 6758 73.4% Source: Energy Information Administration, Office of Energy Markets and End Use, 1985 and 1994 Manufacturing Energy Consumption Surveys. Table 2: Establishments That Actually Switched Between Natural Gas and Residual Fuel Oil Type of Switch Number of Establishments in Population Number That Use Original Fuel Percentage That Use Original Fuel Number That Can Switch to Another Fuel Percentage That Can Switch to Another Fuel Number That Actually Made a Switch Percentage That Actually Made a Switch

283

TABLE OF CONTENTS  

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

Turbines The Gas Turbine Handbook The Gas Turbine Handbook TABLE OF CONTENTS Acknowledgements Updated Author Contact Information Introduction - Rich Dennis, Turbines Technology Manager 1.1 Simple and Combined Cycles - Claire Soares 1.1-1 Introduction 1.1-2 Applications 1.1-3 Applications versatility 1.1-4 The History of the Gas Turbine 1.1-5 Gas Turbine, Major Components, Modules, and systems 1.1-6 Design development with Gas Turbines 1.1-7 Gas Turbine Performance 1.1-8 Combined Cycles 1.1-9 Notes 1.2 Integrated Coal Gasification Combined Cycle (IGCC) - Massod Ramezan and Gary Stiegel 1.2-1 Introduction 1.2-2 The Gasification Process 1.2-3 IGCC Systems 1.2-4 Gasifier Improvements 1.2-5 Gas Separation Improvements 1.2-6 Conclusions 1.2-7 Notes 1.2.1 Different Types of Gasifiers and Their Integration with Gas Turbines - Jeffrey Phillips

284

Forecasting the Locational Dynamics of Transnational Terrorism  

E-Print Network (OSTI)

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

Massachusetts at Amherst, University of

285

Do quantitative decadal forecasts from GCMs provide  

E-Print Network (OSTI)

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

Stevenson, Paul

286

Sunny outlook for space weather forecasters  

Science Journals Connector (OSTI)

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

Eric Hand

2012-04-27T23:59:59.000Z

287

Modeling of Uncertainty in Wind Energy Forecast  

E-Print Network (OSTI)

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

288

Prediction versus Projection: How weather forecasting and  

E-Print Network (OSTI)

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

Howat, Ian M.

289

Customized forecasting tool improves reserves estimation  

SciTech Connect

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

Mian, M.A.

1986-04-01T23:59:59.000Z

290

Storm-in-a-Box Forecasting  

Science Journals Connector (OSTI)

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

Richard A. Kerr

2004-05-14T23:59:59.000Z

291

FORECAST OF VACANCIES Until end of 2016  

E-Print Network (OSTI)

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

292

EIA - Annual Energy Outlook 2009 - chapter Tables  

Gasoline and Diesel Fuel Update (EIA)

Chapter Tables Chapter Tables Annual Energy Outlook 2009 with Projections to 2030 Chapter Tables Table 1. Estimated fuel economy for light-duty vehicles, based on proposed CAFE standards, 2010-2015 Table 2. State appliance efficiency standards and potential future actions Table 3. State renewable portfolio standards Table 4. Key analyses from "issues in Focus" in recent AEOs Table 5. Liquid fuels production in three cases, 2007 and 2030 Table 6. Assumptions used in comparing conventional and plug-in hybrid electric vehicles Table 7. Conventional vehicle and plug-in hybrid system component costs for mid-size vehicles at volume production Table 8. Technically recoverable resources of crude oil and natural gas in the Outer Continental Shelf, as of January 1, 2007

293

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect

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

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

294

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect

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

Werth, D.; Garrett, A.

2009-04-15T23:59:59.000Z

295

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect

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

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

296

MECS 1991 Publications and Tables  

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

Publication and Tables Publication and Tables Publication and Tables Figure showing the Largest Energy Consumers in the Manufacturing Sector You have the option of downloading the entire report or selected sections of the report. Full Report - Manufacturing Consumption of Energy 1991 (file size 17.2 MB) pages:566 Selected Sections Main Text (file size 380,153 bytes) pages: 33, includes the following: Contacts Contents Executive Summary Introduction Energy Consumption in the Manufacturing Sector: An Overview Energy Consumption in the Manufacturing Sector, 1991 Manufacturing Capability To Switch Fuels Appendices Appendix A. Detailed Tables Appendix B. Survey Design, Implementation, and Estimates (file size 141,211 bytes) pages: 22. Appendix C. Quality of the Data (file size 135,511 bytes) pages: 8.

297

TABLE OF CONTENTS ABSTRACT . . .. . . .. . . . . . . . . . . . . . . . . . . . . . v  

E-Print Network (OSTI)

............................................... 12 Water-Source Heat Pump Performance ............................ 18 Air-Source Heat Pump OF PERFORMANCE OF WATER-SOURCE HEAT PUMP .............................. ................. 23 FIGURE 2. NODAL. MONTHLY HEAT GAIN/LOSS FACTORS ........................... 5 TABLE 2. BASE TEMPERATURES

Oak Ridge National Laboratory

298

Steam System Forecasting and Management  

E-Print Network (OSTI)

by manipulation of operating schedules to avoid steam balances that result in steam venting, off gas-flaring, excessive condensing on extraction/condensing turbines, and ineffective use of extraction turbines. For example, during the fourth quarter of 1981... minimum turndown levels. Several boilers would have oeen shut down; by-product fuel gas would have been flared; and surplus low level steam would have been vented to the atmosphere. Several scenarios were studied with SFC and evaluated based...

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

1982-01-01T23:59:59.000Z

299

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

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

300

EIA - Appendix A - Reference Case Projection Tables  

Gasoline and Diesel Fuel Update (EIA)

Tables (2005-2035) Tables (2005-2035) International Energy Outlook 2010 Reference Case Projections Tables (2005-2035) Formats Data Table Titles (1 to 14 complete) Reference Case Projections Tables (1990-2030). Need help, contact the National Energy Information Center at 202-586-8800. Appendix A. Reference Case Projections Tables. Need help, contact the National Energy Information Center at 202-586-8800. Table A1 World Total Primary Energy Consumption by Region Table A1. World Total Primary Energy Consumption by Region. Need help, contact the National Energy Information Center at 202-586-8800. Table A2 World Total Energy Consumption by Region and Fuel Table A2. World Total Energy Consumption by Region and Fuel. Need help, contact the National Energy Information Center at 202-586-8800.

Note: This page contains sample records for the topic "forecast evaluation table" 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

Evaluation of WRF-Predicted Near-Hub-Height Winds and Ramp Events over a Pacific Northwest Site with Complex Terrain  

Science Journals Connector (OSTI)

One challenge with wind-power forecasts is the accurate prediction of rapid changes in wind speed (ramps). To evaluate the Weather Research and Forecasting (WRF) model's ability to predict such events, model simulations, conducted over an area of ...

Qing Yang; Larry K. Berg; Mikhail Pekour; Jerome D. Fast; Rob K. Newsom; Mark Stoelinga; Catherine Finley

2013-08-01T23:59:59.000Z

302

EA-1909: South Table Wind Farm Project, Kimball County, Nebraska  

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

DOEs Western Area Power Administration is preparing this EA to evaluate the environmental impacts of interconnecting the proposed South Table Wind Project, which would generate approximately 60 megawatts from about 40 turbines, to Westerns existing Archer-Sidney 115-kV Transmission Line in Kimball County, Nebraska.

303

Short-Term World Oil Price Forecast  

Gasoline and Diesel Fuel Update (EIA)

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

304

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

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

305

Voluntary Green Power Market Forecast through 2015  

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

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

306

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

307

FORSITE: a geothermal site development forecasting system  

SciTech Connect

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

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

1981-10-01T23:59:59.000Z

308

EA-1909: South Table Wind Project, Kimball County, NE | Department of  

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

09: South Table Wind Project, Kimball County, NE 09: South Table Wind Project, Kimball County, NE EA-1909: South Table Wind Project, Kimball County, NE Summary DOE's Western Area Power Administration is preparing this EA to evaluate the environmental impacts of interconnecting the proposed South Table Wind Project, which would generate approximately 60 megawatts from about 40 turbines, to Western's existing Archer-Sidney 115-kV Transmission Line in Kimball County, Nebraska. Public Comment Opportunities None available at this time. Documents Available for Download August 28, 2012 EA-1909: Finding of No Significant Impact South Table Wind Project, Kimball County, NE July 16, 2012 EA-1909: Final Environmental Assessment South Table Wind Project, Kimball County, NE February 29, 2012 EA-1909: Draft Environmental Assessment

309

EIA - Supplement Tables to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

10 10 Regional Energy Consumption and Prices by Sector Energy Consumption by Sector and Source Table 1. New England Excel Gif Table 2. Middle Atlantic Excel Gif Table 3. East North Central Excel Gif Table 4. West North Central Excel Gif Table 5. South Atlantic Excel Gif Table 6. East South Central Excel Gif Table 7. West South Central Excel Gif Table 8. Mountain Excel Gif Table 9. Pacific Excel Gif Table 10. Total United States Excel Gif Energy Prices by Sector and Source Table 11. New England Excel Gif Table 12. Middle Atlantic Excel Gif Table 13. East North Central Excel Gif Table 14. West North Central Excel Gif Table 15. South Atlantic Excel Gif Table 16. East South Central Excel Gif Table 17. West South Central Excel Gif Table 18. Mountain Excel Gif Table 19. Pacific

310

EIA - Supplement Tables to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

09 09 Regional Energy Consumption and Prices by Sector Energy Consumption by Sector and Source Table 1. New England Excel Gif Table 2. Middle Atlantic Excel Gif Table 3. East North Central Excel Gif Table 4. West North Central Excel Gif Table 5. South Atlantic Excel Gif Table 6. East South Central Excel Gif Table 7. West South Central Excel Gif Table 8. Mountain Excel Gif Table 9. Pacific Excel Gif Table 10. Total United States Excel Gif Energy Prices by Sector and Source Table 11. New England Excel Gif Table 12. Middle Atlantic Excel Gif Table 13. East North Central Excel Gif Table 14. West North Central Excel Gif Table 15. South Atlantic Excel Gif Table 16. East South Central Excel Gif Table 17. West South Central Excel Gif Table 18. Mountain Excel Gif Table 19. Pacific

311

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect

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

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

2014-12-30T23:59:59.000Z

312

Exponential smoothing model selection for forecasting  

Science Journals Connector (OSTI)

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

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

2006-01-01T23:59:59.000Z

313

Solar Wind Forecasting with Coronal Holes  

E-Print Network (OSTI)

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

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

2007-01-09T23:59:59.000Z

314

Nature Bulletin Table of Contents  

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

Table of Contents: Table of Contents: Here is our table of contents for the Forset Preserve District of Cook Country Nature Bulletins. To search, go to the Natuere Bulletin's Search Engine and type in your topic. You can also use your browser's "FIND" command to search the 750+ article titles here for a specific subject! Fish Smother Under Ice Coyotes in Cook County Tough Times for the Muskrats Wild Geese and Ducks Fly North Squirrels Spring Frogs Snapping Turtles A Phenomenal Spring Good People Do Not Pick Wildflowers Fire is the Enemy of Field and Forest Crows Earthworms Bees Crayfish Floods Handaxes and Knives in the Forest Preserves Ant Sanctuary Conservation Mosquitoes More About Mosquitoes Fishing in the Forest Preserve Our River Grasshoppers Chiggers Ticks Poison Ivy Fireflies

315

COST AND QUALITY TABLES 95  

Gasoline and Diesel Fuel Update (EIA)

5 Tables 5 Tables July 1996 Energy Information Administration Office of Coal, Nuclear, Electric and Alternate Fuels 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. Contacts The annual publication Cost and Quality of Fuels for Electric Utility Plants (C&Q) will no longer be pub- lished by the EIA. The tables presented in this docu- ment are intended to replace that annual publication. Questions regarding the availability of these data should be directed to: Coal and Electric Data and Renewables Division

316

CBECS 1992 - Building Characteristics, Detailed Tables  

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

Detailed Tables Detailed Tables Detailed Tables Percent of Buildings and Floorspace by Census Region, 1992 Percent of Buildings and Floorspace by Census Region, 1992 The following 70 tables present extensive cross-tabulations of commercial buildings characteristics. These data are from the Buildings Characteristics Survey portion of the 1992 CBECS. The "Quick-Reference Guide," indicates the major topics of each table. Directions for calculating an approximate relative standard error (RSE) for each estimate in the tables are presented in Figure A1, "Use of RSE Row and Column Factor." The Glossary contains the definitions of the terms used in the tables. See the preceding "At A Glance" section for highlights of the detailed tables. Table Organization

317

Energy Information Administration (EIA) - Supplement Tables  

Gasoline and Diesel Fuel Update (EIA)

6 6 1 to 116 Complete set of Supplemental Tables Complete set of Supplemental Tables. Need help, please contact the National Energy Information Center at 202-586-8800. Regional Energy Consumption and Prices by Sector Energy Consumption by Sector Table 1. New England Consumption & Prices by Sector & Census Division Tables. Need help, contact the National Energy Information Center at 202-586-8800. Table 2. Middle Atlantic Consumption & Prices by Sector & Census Division Tables. Need help, contact the National Energy Information Center at 202-586-8800. Table 3. East North Central Consumption & Prices by Sector & Census Division Tables. Need help, contact the National Energy Information Center at 202-586-8800. Table 4. West North Central

318

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

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

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

319

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

320

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

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

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

Note: This page contains sample records for the topic "forecast evaluation table" 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

Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model  

Science Journals Connector (OSTI)

Abstract Electricity consumption forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may cause wastes of scarce energy resource or electricity shortages. However, forecasting electricity consumption has proven to be a challenging task due to various unstable factors. Especially, China is undergoing a period of economic transition, which highlights this difficulty. This paper proposes a time-varying-weight combining method, i.e. High-order Markov chain based Time-varying Weighted Average (HM-TWA) method to predict the monthly electricity consumption in China. HM-TWA first calculates the in-sample time-varying combining weights by quadratic programming for the individual forecasts. Then it predicts the out-of-sample time-varying adaptive weights through extrapolating these in-sample weights using a high-order Markov chain model. Finally, the combined forecasts can be obtained. In addition, to ensure that the sample data have the same properties as the required forecasts, a reasonable multi-step-ahead forecasting scheme is designed for HM-TWA. The out-of-sample forecasting performance evaluation shows that HM-TWA outperforms the component models and traditional combining methods, and its effectiveness is further verified by comparing it with some other existing models.

Weigang Zhao; Jianzhou Wang; Haiyan Lu

2014-01-01T23:59:59.000Z

322

Analytic flux formulas and tables of shielding functions  

SciTech Connect

Hand calculations of radiation flux and dose rates are often useful in evaluating radiation shielding and in determining the scope of a problem. The flux formulas appropriate to such calculations are almost always based on the point kernel and allow for at most the consideration of laminar slab shields. These formulas often require access to tables of values of integral functions for effective use. Flux formulas and function tables appropriate to calculations involving homogeneous source regions with the shapes of lines, disks, slabs, truncated cones, cylinders, and spheres are presented. Slab shields may be included in most of these calculations, and the effect of a cylindrical shield surrounding a cylindrical source may be estimated. Detector points may be located axially, laterally, or interior to a cylindrical source. Line sources may be tilted with respect to a slab shield. All function tables are given for a wide range of arguments.

Wallace, O.J.

1981-06-01T23:59:59.000Z

323

Electric Grid - Forecasting system licensed | ornl.gov  

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

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

324

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network (OSTI)

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

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

325

Forecasting supply/demand and price of ethylene feedstocks  

SciTech Connect

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

Struth, B.W.

1984-08-01T23:59:59.000Z

326

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

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

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

327

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

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

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

328

Integrating agricultural pest biocontrol into forecasts of energy biomass production  

E-Print Network (OSTI)

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

Gratton, Claudio

329

FRAUD POLICY Table of Contents  

E-Print Network (OSTI)

FRAUD POLICY Table of Contents Section 1 - General Statement Section 2 - Management's Responsibility for Preventing Fraud Section 3 - Consequences for Fraudulent Acts Section 4 - Procedures for Reporting Fraud Section 5 - Procedures for the Investigation of Alleged Fraud Section 6 - Protection Under

Shihadeh, Alan

330

CHP NOTEBOOK Table of Contents  

E-Print Network (OSTI)

-Specific Standard Operating Procedures (SOPs) Section 8 Employee Training Section 9 Inspections and Exposure1 CHP NOTEBOOK Table of Contents Section 1 Safety Program Key Personnel Section 2 Laboratory Protective Equipment (PPE) Assessment Section 18 Hazard Assessment Information and PPE Selection Information

Braun, Paul

331

Microsoft Word - table_04.doc  

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

2 Table 4. Offshore gross withdrawals of natural gas by state and the Gulf of Mexico, 2009-2013 (million cubic feet) 2009 Total 259,848 327,105 586,953 1,878,928 606,403 2,485,331...

332

PARENT HANDBOOK TABLE OF CONTENTS  

E-Print Network (OSTI)

PARENT HANDBOOK 1 TABLE OF CONTENTS The Parent's Role 3 Academics 7 Academic Advising 7 Academic Services 26 Athletics, Physical Education and Recreation 28 Campus Resources and Student Services 30 to seeing you in person and connecting with you online! PARENT HANDBOOK THEPARENT'SROLE PARENT HANDBOOK 3

Adali, Tulay

333

Automatic Construction of Diagnostic Tables  

Science Journals Connector (OSTI)

......more usual, at least in microbiology.) Keys and diagnostic tables...Mechanization and Data Handling in Microbiology, Society for Applied Bacteriology...by A. Baillie and R. J. Gilbert, London: Academic Press...cultures, Canadian Journal of Microbiology, Vol. 14, pp. 271-279......

W. R. Willcox; S. P. Lapage

1972-08-01T23:59:59.000Z

334

An optimal filtering algorithm for table constraints  

Science Journals Connector (OSTI)

Filtering algorithms for table constraints are constraint-based, which means that the propagation queue only contains information on the constraints that must be reconsidered. This paper proposes four efficient value-based algorithms for table constraints, ...

Jean-Baptiste Mairy; Pascal Van Hentenryck; Yves Deville

2012-10-01T23:59:59.000Z

335

Table Name query? | OpenEI Community  

Open Energy Info (EERE)

Table Name query? Home > Groups > Databus Is there an API feature which returns the names of tables? Submitted by Hopcroft on 28 October, 2013 - 15:37 1 answer Points: 0 if you are...

336

Forecasting for inventory control with exponential smoothing  

Science Journals Connector (OSTI)

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

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

2002-01-01T23:59:59.000Z

337

Chemistry Department Assessment Table of Contents  

E-Print Network (OSTI)

0 Chemistry Department Assessment May, 2006 Table of Contents Page Executive Summary 1 Prelude 1 Mission Statement and Learning Goals 1 Facilities 2 Staffing 3 Students: Chemistry Majors and Student Taking Service Courses Table: 1997-2005 graduates profile Table: GRE Score for Chemistry Majors, 1993

Bogaerts, Steven

338

Random switching exponential smoothing and inventory forecasting  

Science Journals Connector (OSTI)

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

Giacomo Sbrana; Andrea Silvestrini

2014-01-01T23:59:59.000Z

339

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

340

Expert Panel: Forecast Future Demand for Medical Isotopes  

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

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

Note: This page contains sample records for the topic "forecast evaluation table" 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

A robust automatic phase-adjustment method for financial forecasting  

Science Journals Connector (OSTI)

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

Ricardo de A. Arajo

2012-03-01T23:59:59.000Z

342

Short term forecasting of solar radiation based on satellite data  

E-Print Network (OSTI)

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

Heinemann, Detlev

343

Developing electricity forecast web tool for Kosovo market  

Science Journals Connector (OSTI)

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

Blerim Rexha; Arben Ahmeti; Lule Ahmedi; Vjollca Komoni

2011-02-01T23:59:59.000Z

344

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network (OSTI)

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

Keller, Arturo A.

345

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

346

Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa  

E-Print Network (OSTI)

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

347

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network (OSTI)

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

Washington at Seattle, University of

348

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network (OSTI)

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

Hansens, Jim

349

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

E-Print Network (OSTI)

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

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

350

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

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

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

351

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect

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

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

2014-10-27T23:59:59.000Z

352

Microsoft Word - table_11.doc  

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

25 25 Table 11 Created on: 12/12/2013 2:10:53 PM Table 11. Underground natural gas storage - storage fields other than salt caverns, 2008-2013 (volumes in billion cubic feet) Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Year and Month Base Gas Working Gas Total Volume Percent Injections Withdrawals Net Withdrawals a 2008 Total b -- -- -- -- -- 2,900 2,976 76 2009 Total b -- -- -- -- -- 2,856 2,563 -293 2010 Total b -- -- -- -- -- 2,781 2,822 41 2011 January 4,166 2,131 6,298 -63 -2.9 27 780 753 February 4,166 1,597 5,763 -10 -0.6 51 586 535 March 4,165 1,426 5,591 -114 -7.4 117 288 172

353

Microsoft Word - table_08.doc  

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

1 1 Table 8 Created on: 12/12/2013 2:07:39 PM Table 8. Underground natural gas storage - all operators, 2008-2013 (million cubic feet) Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Year and Month Base Gas Working Gas Total a Volume Percent Injections Withdrawals Net Withdrawals b 2008 Total c -- -- -- -- -- 3,340 3,374 34 2009 Total c -- -- -- -- -- 3,315 2,966 -349 2010 Total c -- -- -- -- -- 3,291 3,274 -17 2011 January 4,303 2,306 6,609 2 0.1 50 849 799 February 4,302 1,722 6,024 39 2.3 82 666 584 March 4,302 1,577 5,879 -75 -4.6 168 314 146 April 4,304 1,788 6,092 -223 -11.1 312 100

354

Action Codes Table | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

Action Codes Table | National Nuclear Security Administration Action Codes Table | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Action Codes Table Home > About Us > Our Programs > Nuclear Security > Nuclear Materials Management & Safeguards System > NMMSS Information, Reports & Forms > Code Tables > Action Codes Table

355

T-583: Linux Kernel OSF Partition Table Buffer Overflow Lets Local Users  

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

583: Linux Kernel OSF Partition Table Buffer Overflow Lets Local 583: Linux Kernel OSF Partition Table Buffer Overflow Lets Local Users Obtain Information T-583: Linux Kernel OSF Partition Table Buffer Overflow Lets Local Users Obtain Information March 18, 2011 - 5:15pm Addthis PROBLEM: A vulnerability was reported in the Linux Kernel. A local user can obtain information from kernel memory. PLATFORM: Version(s): 2.4.x, 2.6.x ABSTRACT: A local user can create a storage device with specially crafted OSF partition tables. When the kernel automatically evaluates the partition tables, a buffer overflow may occur and data from kernel heap space may leak to user-space. reference LINKS: http://www.securitytracker.com/id/1025225 CVE-2011-1163 http://www.kernel.org/ IMPACT ASSESSMENT: Moderate Discussion: A local user can create a storage device with specially crafted OSF

356

Description of Energy Intensity Tables (12)  

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

3. Description of Energy Intensity Data Tables 3. Description of Energy Intensity Data Tables There are 12 data tables used as references for this report. Specifically, these tables are categorized as tables 1 and 2 present unadjusted energy-intensity ratios for Offsite-Produced Energy and Total Inputs of Energy for 1985, 1988, 1991, and 1994; along with the percentage changes between 1985 and the three subsequent years (1988, 1991, and 1994) tables 3 and 4 present 1988, 1991, and 1994 energy-intensity ratios that have been adjusted to the mix of products shipped from manufacturing establishments in 1985 tables 5 and 6 present unadjusted energy-intensity ratios for Offsite-Produced Energy and Total Inputs of Energy for 1988, 1991, and 1994; along with the percentage changes between 1988 and the two subsequent

357

Sandia National Labs: PCNSC: IBA Table  

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

Home Home About Us Departments Radiation, Nano Materials, & Interface Sciences > Radiation & Solid Interactions > Nanomaterials Sciences > Surface & Interface Sciences Semiconductor & Optical Sciences Energy Sciences Small Science Cluster Business Office News Partnering Research Ion Beam Analysis (IBA) Periodic Table (HTML) IBA Table (HTML) | IBA Table (135KB GIF) | IBA Table (1.2MB PDF) | IBA Table (33MB TIF) | Heavy Ion Backscattering Spectrometry (HIBS) | Virtual Lab Tour (6MB) The purpose of this table is to quickly give the visitor to this site information on the sensitivity, depth of analysis and depth resolution of most of the modern ion beam analysis techniques in a single easy to use format: a periodic table. Note that you can click on each panel of this

358

Energy Information Administration (EIA) - Supplement Tables - Supplemental  

Gasoline and Diesel Fuel Update (EIA)

7 7 Supplemental Tables to the Annual Energy Outlook 2007 The AEO Supplemental tables were generated for the reference case of the Annual Energy Outlook 2007 (AEO2007) using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets for 2005 to 2030. Most of the tables were not published in the AEO2007, but contain regional and other more detailed projections underlying the AEO2007 projections. The files containing these tables are in spreadsheet format. A total of one hundred and eighteen tables is presented. The data for tables 10 and 20 match those published in AEO2007 Appendix tables A2 and A3, respectively. Projections for 2006 and 2007 may differ slightly from values published in the Short Term Energy Outlook, which are the official EIA short-term projections and are based on more current information than the AEO.

359

Annual Energy Outlook 2007 - Low Price Case Tables  

Gasoline and Diesel Fuel Update (EIA)

4-2030) 4-2030) Annual Energy Outlook 2007 with Projections to 2030 MS Excel Viewer Spreadsheets are provided in Excel Low Price Case Tables (2004-2030) Table Title Formats Summary Low Price Case Tables Low Price Case Tables Table 1. Total Energy Supply and Disposition Summary Table 2. Energy Consumption by Sector and Source Table 3. Energy Prices by Sector and Source Table 4. Residential Sector Key Indicators and Consumption Table 5. Commercial Sector Indicators and Consumption Table 6. Industrial Sector Key Indicators and Consumption Table 7. Transportation Sector Key Indicators and Delivered Energy Consumption Table 8. Electricity Supply, Disposition, Prices, and Emissions Table 9. Electricity Generating Capacity Table 10. Electricity Trade Table 11. Petroleum Supply and Disposition Balance

360

Annual Energy Outlook 2007 - Low Economic Growth Case Tables  

Gasoline and Diesel Fuel Update (EIA)

Low Macroeconomic Growth Case Tables (2004-2030) Low Macroeconomic Growth Case Tables (2004-2030) Annual Energy Outlook 2007 with Projections to 2030 MS Excel Viewer Spreadsheets are provided in Excel Low Economic Growth Case Tables (2004-2030) Table Title Formats Summary Low Economic Growth Case Tables Low Economic Growth Case Tables Table 1. Total Energy Supply and Disposition Summary Table 2. Energy Consumption by Sector and Source Table 3. Energy Prices by Sector and Source Table 4. Residential Sector Key Indicators and Consumption Table 5. Commercial Sector Indicators and Consumption Table 6. Industrial Sector Key Indicators and Consumption Table 7. Transportation Sector Key Indicators and Delivered Energy Consumption Table 8. Electricity Supply, Disposition, Prices, and Emissions Table 9. Electricity Generating Capacity

Note: This page contains sample records for the topic "forecast evaluation table" 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

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

362

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

363

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

364

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

365

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

366

Volatility forecasting with smooth transition exponential smoothing  

Science Journals Connector (OSTI)

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

James W. Taylor

2004-01-01T23:59:59.000Z

367

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect

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

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

2014-07-09T23:59:59.000Z

368

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Muons Muons in B-100 Bone-equivalent plastic Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.52740 1.450 85.9 0.05268 3.7365 0.1252 3.0420 3.4528 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.435 7.435 7.443 × 10 -1 14.0 MeV 5.616 × 10 1 5.803 5.803 1.360 × 10 0 20.0 MeV 6.802 × 10 1 4.535 4.535 2.543 × 10 0 30.0 MeV 8.509 × 10 1 3.521 3.521 5.080 × 10 0 40.0 MeV 1.003 × 10 2 3.008 3.008 8.173 × 10 0 80.0 MeV 1.527 × 10 2 2.256 2.256 2.401 × 10 1 100. MeV 1.764 × 10 2 2.115 2.115 3.319 × 10 1 140. MeV 2.218 × 10 2 1.971 1.971 5.287 × 10 1 200. MeV 2.868 × 10 2 1.889 1.889 8.408 × 10 1 300. MeV 3.917 × 10 2 1.859 0.000 1.859 1.376 × 10 2 314. MeV 4.065 × 10 2 1.859 0.000 1.859 Minimum ionization 400. MeV 4.945 × 10 2 1.866 0.000 1.866 1.913 × 10 2 800. MeV 8.995 × 10 2 1.940 0.000 0.000 1.940 4.016 × 10 2 1.00 GeV 1.101 × 10 3 1.973 0.000 0.000 1.974 5.037 × 10 2 1.40

369

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Muons Muons in Sodium monoxide Na 2 O Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.48404 2.270 148.8 0.07501 3.6943 0.1652 2.9793 4.1892 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.330 6.330 8.793 × 10 -1 14.0 MeV 5.616 × 10 1 4.955 4.956 1.601 × 10 0 20.0 MeV 6.802 × 10 1 3.883 3.884 2.984 × 10 0 30.0 MeV 8.509 × 10 1 3.024 3.024 5.943 × 10 0 40.0 MeV 1.003 × 10 2 2.588 2.588 9.541 × 10 0 80.0 MeV 1.527 × 10 2 1.954 1.954 2.789 × 10 1 100. MeV 1.764 × 10 2 1.840 1.840 3.846 × 10 1 140. MeV 2.218 × 10 2 1.725 1.725 6.102 × 10 1 200. MeV 2.868 × 10 2 1.663 1.664 9.656 × 10 1 283. MeV 3.738 × 10 2 1.646 0.000 1.647 Minimum ionization 300. MeV 3.917 × 10 2 1.647 0.000 1.647 1.571 × 10 2 400. MeV 4.945 × 10 2 1.659 0.000 1.660 2.177 × 10 2 800. MeV 8.995 × 10 2 1.738 0.000 0.000 1.738 4.531 × 10 2 1.00 GeV 1.101 × 10 3 1.771 0.000 0.000 1.772 5.670 × 10 2 1.40 GeV 1.502

370

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Muons Muons in Tissue-equivalent gas (Propane based) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.55027 1.826 × 10 -3 59.5 0.09802 3.5159 1.5139 3.9916 9.3529 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 8.132 8.132 6.782 × 10 -1 14.0 MeV 5.616 × 10 1 6.337 6.337 1.241 × 10 0 20.0 MeV 6.802 × 10 1 4.943 4.944 2.326 × 10 0 30.0 MeV 8.509 × 10 1 3.831 3.831 4.656 × 10 0 40.0 MeV 1.003 × 10 2 3.269 3.269 7.500 × 10 0 80.0 MeV 1.527 × 10 2 2.450 2.450 2.209 × 10 1 100. MeV 1.764 × 10 2 2.303 2.303 3.053 × 10 1 140. MeV 2.218 × 10 2 2.158 2.158 4.855 × 10 1 200. MeV 2.868 × 10 2 2.084 2.084 7.695 × 10 1 263. MeV 3.527 × 10 2 2.068 0.000 2.069 Minimum ionization 300. MeV 3.917 × 10 2 2.071 0.000 2.072 1.252 × 10 2 400. MeV 4.945 × 10 2 2.097 0.000 2.097 1.732 × 10 2 800. MeV 8.995 × 10 2 2.232 0.000 0.000 2.232 3.580 × 10 2 1.00 GeV 1.101 × 10 3 2.289 0.000 0.000 2.290

371

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Muons Muons in Lead oxide (PbO) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.40323 9.530 766.7 0.19645 2.7299 0.0356 3.5456 6.2162 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 4.046 4.046 1.411 × 10 0 14.0 MeV 5.616 × 10 1 3.207 3.207 2.532 × 10 0 20.0 MeV 6.802 × 10 1 2.542 2.542 4.656 × 10 0 30.0 MeV 8.509 × 10 1 2.003 2.003 9.146 × 10 0 40.0 MeV 1.003 × 10 2 1.727 1.727 1.455 × 10 1 80.0 MeV 1.527 × 10 2 1.327 1.327 4.176 × 10 1 100. MeV 1.764 × 10 2 1.256 1.256 5.729 × 10 1 140. MeV 2.218 × 10 2 1.188 1.189 9.017 × 10 1 200. MeV 2.868 × 10 2 1.158 1.158 1.415 × 10 2 236. MeV 3.250 × 10 2 1.155 0.000 1.155 Minimum ionization 300. MeV 3.917 × 10 2 1.161 0.000 0.000 1.161 2.279 × 10 2 400. MeV 4.945 × 10 2 1.181 0.000 0.000 1.181 3.133 × 10 2 800. MeV 8.995 × 10 2 1.266 0.001 0.000 1.267 6.398 × 10 2 1.00 GeV 1.101 × 10 3 1.299 0.001 0.000 1.301 7.955 × 10 2 1.40

372

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Muons Muons in Liquid argon (Ar) Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 18 (Ar) 39.948 (1) 1.396 188.0 0.19559 3.0000 0.2000 3.0000 5.2146 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 5.687 5.687 9.833 × 10 -1 14.0 MeV 5.616 × 10 1 4.461 4.461 1.786 × 10 0 20.0 MeV 6.802 × 10 1 3.502 3.502 3.321 × 10 0 30.0 MeV 8.509 × 10 1 2.731 2.731 6.598 × 10 0 40.0 MeV 1.003 × 10 2 2.340 2.340 1.058 × 10 1 80.0 MeV 1.527 × 10 2 1.771 1.771 3.084 × 10 1 100. MeV 1.764 × 10 2 1.669 1.670 4.250 × 10 1 140. MeV 2.218 × 10 2 1.570 1.570 6.732 × 10 1 200. MeV 2.868 × 10 2 1.518 1.519 1.063 × 10 2 266. MeV 3.567 × 10 2 1.508 0.000 1.508 Minimum ionization 300. MeV 3.917 × 10 2 1.509 0.000 1.510 1.725 × 10 2 400. MeV 4.945 × 10 2 1.526 0.000 0.000 1.526 2.385 × 10 2 800. MeV 8.995 × 10 2 1.610 0.000 0.000 1.610 4.934 × 10 2 1.00 GeV 1.101 × 10 3 1.644 0.000 0.000 1.645 6.163

373

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Muons Muons in Freon-13 (CF 3 Cl) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.47966 0.950 126.6 0.07238 3.5551 0.3659 3.2337 4.7483 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.416 6.416 8.659 × 10 -1 14.0 MeV 5.616 × 10 1 5.019 5.019 1.578 × 10 0 20.0 MeV 6.802 × 10 1 3.930 3.930 2.945 × 10 0 30.0 MeV 8.509 × 10 1 3.057 3.057 5.870 × 10 0 40.0 MeV 1.003 × 10 2 2.615 2.615 9.430 × 10 0 80.0 MeV 1.527 × 10 2 1.971 1.971 2.760 × 10 1 100. MeV 1.764 × 10 2 1.857 1.857 3.809 × 10 1 140. MeV 2.218 × 10 2 1.745 1.745 6.041 × 10 1 200. MeV 2.868 × 10 2 1.685 1.685 9.551 × 10 1 283. MeV 3.738 × 10 2 1.668 0.000 1.668 Minimum ionization 300. MeV 3.917 × 10 2 1.668 0.000 1.668 1.553 × 10 2 400. MeV 4.945 × 10 2 1.681 0.000 1.681 2.151 × 10 2 800. MeV 8.995 × 10 2 1.762 0.000 0.000 1.763 4.473 × 10 2 1.00 GeV 1.101 × 10 3 1.796 0.000 0.000 1.797 5.596 × 10 2 1.40 GeV 1.502

374

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Muons Muons in Lutetium silicon oxide [Lu 2 SiO 5 ] Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.42793 7.400 472.0 0.20623 3.0000 0.2732 3.0000 5.4394 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 4.679 4.679 1.209 × 10 0 14.0 MeV 5.616 × 10 1 3.692 3.693 2.181 × 10 0 20.0 MeV 6.802 × 10 1 2.916 2.916 4.029 × 10 0 30.0 MeV 8.509 × 10 1 2.287 2.287 7.953 × 10 0 40.0 MeV 1.003 × 10 2 1.968 1.968 1.270 × 10 1 80.0 MeV 1.527 × 10 2 1.503 1.503 3.666 × 10 1 100. MeV 1.764 × 10 2 1.421 1.422 5.038 × 10 1 140. MeV 2.218 × 10 2 1.344 1.344 7.944 × 10 1 200. MeV 2.868 × 10 2 1.308 1.308 1.248 × 10 2 242. MeV 3.316 × 10 2 1.304 1.304 Minimum ionization 300. MeV 3.917 × 10 2 1.309 0.000 0.000 1.309 2.014 × 10 2 400. MeV 4.945 × 10 2 1.329 0.000 0.000 1.329 2.773 × 10 2 800. MeV 8.995 × 10 2 1.415 0.001 0.000 1.416 5.684 × 10 2 1.00 GeV 1.101 × 10 3 1.449 0.001 0.000 1.450 7.080

375

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Muons Muons in Boron oxide (B 2 O 3 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.49839 1.812 99.6 0.11548 3.3832 0.1843 2.7379 3.6027 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.889 6.889 8.045 × 10 -1 14.0 MeV 5.616 × 10 1 5.381 5.381 1.468 × 10 0 20.0 MeV 6.802 × 10 1 4.208 4.208 2.744 × 10 0 30.0 MeV 8.509 × 10 1 3.269 3.269 5.477 × 10 0 40.0 MeV 1.003 × 10 2 2.794 2.794 8.807 × 10 0 80.0 MeV 1.527 × 10 2 2.102 2.103 2.583 × 10 1 100. MeV 1.764 × 10 2 1.975 1.975 3.567 × 10 1 140. MeV 2.218 × 10 2 1.843 1.843 5.674 × 10 1 200. MeV 2.868 × 10 2 1.768 1.768 9.010 × 10 1 300. MeV 3.917 × 10 2 1.742 0.000 1.742 1.472 × 10 2 307. MeV 3.990 × 10 2 1.742 0.000 1.742 Minimum ionization 400. MeV 4.945 × 10 2 1.750 0.000 1.750 2.045 × 10 2 800. MeV 8.995 × 10 2 1.822 0.000 0.000 1.823 4.285 × 10 2 1.00 GeV 1.101 × 10 3 1.854 0.000 0.000 1.855 5.373 × 10 2 1.40 GeV 1.502

376

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Muons Muons in Liquid H-note density shift (H 2 ) Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 1 (H) 1.00794 (7) 7.080 × 10 -2 21.8 0.32969 3.0000 0.1641 1.9641 2.6783 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 16.508 16.508 3.316 × 10 -1 14.0 MeV 5.616 × 10 1 12.812 12.812 6.097 × 10 -1 20.0 MeV 6.802 × 10 1 9.956 9.956 1.147 × 10 0 30.0 MeV 8.509 × 10 1 7.684 7.684 2.307 × 10 0 40.0 MeV 1.003 × 10 2 6.539 6.539 3.727 × 10 0 80.0 MeV 1.527 × 10 2 4.870 4.870 1.105 × 10 1 100. MeV 1.764 × 10 2 4.550 4.550 1.531 × 10 1 140. MeV 2.218 × 10 2 4.217 4.217 2.448 × 10 1 200. MeV 2.868 × 10 2 4.018 0.000 4.018 3.912 × 10 1 300. MeV 3.917 × 10 2 3.926 0.000 3.926 6.438 × 10 1 356. MeV 4.497 × 10 2 3.919 0.000 3.919 Minimum ionization 400. MeV 4.945 × 10 2 3.922 0.000 3.922 8.988 × 10 1 800. MeV 8.995 × 10 2 4.029 0.000 4.030 1.906 × 10 2 1.00 GeV 1.101 × 10 3 4.084 0.001

377

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Muons Muons in Cortical bone (ICRP) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.52130 1.850 106.4 0.06198 3.5919 0.1161 3.0919 3.6488 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.142 7.142 7.765 × 10 -1 14.0 MeV 5.616 × 10 1 5.581 5.581 1.417 × 10 0 20.0 MeV 6.802 × 10 1 4.366 4.366 2.646 × 10 0 30.0 MeV 8.509 × 10 1 3.393 3.393 5.281 × 10 0 40.0 MeV 1.003 × 10 2 2.900 2.901 8.489 × 10 0 80.0 MeV 1.527 × 10 2 2.179 2.179 2.489 × 10 1 100. MeV 1.764 × 10 2 2.044 2.044 3.440 × 10 1 140. MeV 2.218 × 10 2 1.907 1.907 5.475 × 10 1 200. MeV 2.868 × 10 2 1.830 1.830 8.700 × 10 1 300. MeV 3.917 × 10 2 1.803 0.000 1.803 1.422 × 10 2 303. MeV 3.950 × 10 2 1.803 0.000 1.803 Minimum ionization 400. MeV 4.945 × 10 2 1.812 0.000 1.812 1.976 × 10 2 800. MeV 8.995 × 10 2 1.888 0.000 0.000 1.889 4.138 × 10 2 1.00 GeV 1.101 × 10 3 1.922 0.000 0.000 1.923 5.187 × 10 2 1.40 GeV 1.502

378

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Muons Muons in Freon-13B1 (CF 3 Br) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.45665 1.500 210.5 0.03925 3.7194 0.3522 3.7554 5.3555 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 5.678 5.678 9.844 × 10 -1 14.0 MeV 5.616 × 10 1 4.454 4.454 1.788 × 10 0 20.0 MeV 6.802 × 10 1 3.498 3.498 3.325 × 10 0 30.0 MeV 8.509 × 10 1 2.729 2.729 6.606 × 10 0 40.0 MeV 1.003 × 10 2 2.339 2.339 1.059 × 10 1 80.0 MeV 1.527 × 10 2 1.771 1.771 3.086 × 10 1 100. MeV 1.764 × 10 2 1.671 1.671 4.251 × 10 1 140. MeV 2.218 × 10 2 1.574 1.574 6.729 × 10 1 200. MeV 2.868 × 10 2 1.524 1.524 1.062 × 10 2 266. MeV 3.567 × 10 2 1.513 0.000 1.513 Minimum ionization 300. MeV 3.917 × 10 2 1.515 0.000 1.515 1.721 × 10 2 400. MeV 4.945 × 10 2 1.531 0.000 0.000 1.532 2.378 × 10 2 800. MeV 8.995 × 10 2 1.616 0.000 0.000 1.616 4.919 × 10 2 1.00 GeV 1.101 × 10 3 1.650 0.001 0.000 1.651 6.142 × 10 2 1.40 GeV

379

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Muons Muons in Sodium carbonate (Na 2 CO 3 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.49062 2.532 125.0 0.08715 3.5638 0.1287 2.8591 3.7178 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.575 6.575 8.449 × 10 -1 14.0 MeV 5.616 × 10 1 5.142 5.142 1.540 × 10 0 20.0 MeV 6.802 × 10 1 4.026 4.026 2.874 × 10 0 30.0 MeV 8.509 × 10 1 3.131 3.131 5.729 × 10 0 40.0 MeV 1.003 × 10 2 2.679 2.679 9.204 × 10 0 80.0 MeV 1.527 × 10 2 2.017 2.017 2.695 × 10 1 100. MeV 1.764 × 10 2 1.895 1.895 3.721 × 10 1 140. MeV 2.218 × 10 2 1.771 1.772 5.914 × 10 1 200. MeV 2.868 × 10 2 1.703 1.703 9.381 × 10 1 298. MeV 3.894 × 10 2 1.681 0.000 1.681 Minimum ionization 300. MeV 3.917 × 10 2 1.681 0.000 1.681 1.531 × 10 2 400. MeV 4.945 × 10 2 1.690 0.000 1.691 2.125 × 10 2 800. MeV 8.995 × 10 2 1.764 0.000 0.000 1.764 4.440 × 10 2 1.00 GeV 1.101 × 10 3 1.796 0.000 0.000 1.797 5.563 × 10 2 1.40

380

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Muons Muons in Tungsten hexafluoride (WF 6 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.42976 2.400 354.4 0.03658 3.5134 0.3020 4.2602 5.9881 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 4.928 4.928 1.143 × 10 0 14.0 MeV 5.616 × 10 1 3.880 3.880 2.067 × 10 0 20.0 MeV 6.802 × 10 1 3.057 3.057 3.828 × 10 0 30.0 MeV 8.509 × 10 1 2.393 2.393 7.574 × 10 0 40.0 MeV 1.003 × 10 2 2.056 2.056 1.211 × 10 1 80.0 MeV 1.527 × 10 2 1.565 1.565 3.509 × 10 1 100. MeV 1.764 × 10 2 1.479 1.479 4.827 × 10 1 140. MeV 2.218 × 10 2 1.396 1.396 7.623 × 10 1 200. MeV 2.868 × 10 2 1.353 1.353 1.200 × 10 2 253. MeV 3.431 × 10 2 1.346 0.000 1.346 Minimum ionization 300. MeV 3.917 × 10 2 1.349 0.000 0.000 1.349 1.942 × 10 2 400. MeV 4.945 × 10 2 1.367 0.000 0.000 1.367 2.679 × 10 2 800. MeV 8.995 × 10 2 1.451 0.001 0.000 1.452 5.516 × 10 2 1.00 GeV 1.101 × 10 3 1.485 0.001 0.000 1.486 6.877

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381

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Muons Muons in Standard rock Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.50000 2.650 136.4 0.08301 3.4120 0.0492 3.0549 3.7738 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.619 6.619 8.400 × 10 -1 14.0 MeV 5.616 × 10 1 5.180 5.180 1.530 × 10 0 20.0 MeV 6.802 × 10 1 4.057 4.057 2.854 × 10 0 30.0 MeV 8.509 × 10 1 3.157 3.157 5.687 × 10 0 40.0 MeV 1.003 × 10 2 2.701 2.702 9.133 × 10 0 80.0 MeV 1.527 × 10 2 2.028 2.029 2.675 × 10 1 100. MeV 1.764 × 10 2 1.904 1.904 3.695 × 10 1 140. MeV 2.218 × 10 2 1.779 1.779 5.878 × 10 1 200. MeV 2.868 × 10 2 1.710 1.710 9.331 × 10 1 297. MeV 3.884 × 10 2 1.688 0.000 1.688 Minimum ionization 300. MeV 3.917 × 10 2 1.688 0.000 1.688 1.523 × 10 2 400. MeV 4.945 × 10 2 1.698 0.000 1.698 2.114 × 10 2 800. MeV 8.995 × 10 2 1.774 0.000 0.000 1.775 4.418 × 10 2 1.00 GeV 1.101 × 10 3 1.808 0.000 0.000 1.808 5.534 × 10 2 1.40 GeV 1.502 × 10

382

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

Muons Muons in Ceric sulfate dosimeter solution Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.55279 1.030 76.7 0.07666 3.5607 0.2363 2.8769 3.5212 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.909 7.909 6.989 × 10 -1 14.0 MeV 5.616 × 10 1 6.170 6.170 1.278 × 10 0 20.0 MeV 6.802 × 10 1 4.819 4.819 2.391 × 10 0 30.0 MeV 8.509 × 10 1 3.739 3.739 4.779 × 10 0 40.0 MeV 1.003 × 10 2 3.193 3.193 7.693 × 10 0 80.0 MeV 1.527 × 10 2 2.398 2.398 2.261 × 10 1 100. MeV 1.764 × 10 2 2.255 2.255 3.123 × 10 1 140. MeV 2.218 × 10 2 2.102 2.102 4.968 × 10 1 200. MeV 2.868 × 10 2 2.013 2.014 7.896 × 10 1 300. MeV 3.917 × 10 2 1.980 0.000 1.980 1.292 × 10 2 317. MeV 4.096 × 10 2 1.979 0.000 1.979 Minimum ionization 400. MeV 4.945 × 10 2 1.986 0.000 1.986 1.797 × 10 2 800. MeV 8.995 × 10 2 2.062 0.000 0.000 2.062 3.774 × 10 2 1.00 GeV 1.101 × 10 3 2.096 0.000 0.000 2.097 4.735 × 10

383

Table  

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

Muons Muons in Silicon Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 14 (Si) 28.0855 (3) 2.329 173.0 0.14921 3.2546 0.2015 2.8716 4.4355 0.14 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.363 6.363 8.779 × 10 -1 14.0 MeV 5.616 × 10 1 4.987 4.987 1.595 × 10 0 20.0 MeV 6.802 × 10 1 3.912 3.912 2.969 × 10 0 30.0 MeV 8.509 × 10 1 3.047 3.047 5.905 × 10 0 40.0 MeV 1.003 × 10 2 2.608 2.608 9.476 × 10 0 80.0 MeV 1.527 × 10 2 1.965 1.965 2.770 × 10 1 100. MeV 1.764 × 10 2 1.849 1.849 3.822 × 10 1 140. MeV 2.218 × 10 2 1.737 1.737 6.064 × 10 1 200. MeV 2.868 × 10 2 1.678 1.678 9.590 × 10 1 273. MeV 3.633 × 10 2 1.664 0.000 1.664 Minimum ionization 300. MeV 3.917 × 10 2 1.665 0.000 1.666 1.559 × 10 2 400. MeV 4.945 × 10 2 1.681 0.000 1.681 2.157 × 10 2 800. MeV 8.995 × 10 2 1.767 0.000 0.000 1.768 4.475 × 10 2 1.00 GeV 1.101 × 10 3 1.803 0.000 0.000 1.804 5.595 × 10 2 1.40 GeV

384

Table  

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

Muons Muons in Polyethylene terephthalate (Mylar) (C 10 H 8 O 4 ) n Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.52037 1.400 78.7 0.12679 3.3076 0.1562 2.6507 3.3262 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.420 7.420 7.451 × 10 -1 14.0 MeV 5.616 × 10 1 5.789 5.789 1.362 × 10 0 20.0 MeV 6.802 × 10 1 4.522 4.522 2.548 × 10 0 30.0 MeV 8.509 × 10 1 3.509 3.509 5.093 × 10 0 40.0 MeV 1.003 × 10 2 2.997 2.997 8.197 × 10 0 80.0 MeV 1.527 × 10 2 2.250 2.250 2.409 × 10 1 100. MeV 1.764 × 10 2 2.108 2.108 3.329 × 10 1 140. MeV 2.218 × 10 2 1.963 1.964 5.305 × 10 1 200. MeV 2.868 × 10 2 1.880 1.880 8.440 × 10 1 300. MeV 3.917 × 10 2 1.849 0.000 1.849 1.382 × 10 2 317. MeV 4.096 × 10 2 1.848 0.000 1.849 Minimum ionization 400. MeV 4.945 × 10 2 1.855 0.000 1.855 1.922 × 10 2 800. MeV 8.995 × 10 2 1.926 0.000 0.000 1.926 4.039 × 10 2 1.00 GeV 1.101 × 10 3 1.958 0.000 0.000 1.959

385

Table  

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

Muons Muons in Dichlorodiethyl ether C 4 Cl 2 H 8 O Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.51744 1.220 103.3 0.06799 3.5250 0.1773 3.1586 4.0135 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.117 7.117 7.789 × 10 -1 14.0 MeV 5.616 × 10 1 5.561 5.561 1.421 × 10 0 20.0 MeV 6.802 × 10 1 4.349 4.349 2.655 × 10 0 30.0 MeV 8.509 × 10 1 3.380 3.380 5.300 × 10 0 40.0 MeV 1.003 × 10 2 2.889 2.889 8.521 × 10 0 80.0 MeV 1.527 × 10 2 2.174 2.174 2.499 × 10 1 100. MeV 1.764 × 10 2 2.042 2.042 3.450 × 10 1 140. MeV 2.218 × 10 2 1.907 1.907 5.486 × 10 1 200. MeV 2.868 × 10 2 1.832 1.832 8.708 × 10 1 298. MeV 3.894 × 10 2 1.807 0.000 1.807 Minimum ionization 300. MeV 3.917 × 10 2 1.807 0.000 1.807 1.422 × 10 2 400. MeV 4.945 × 10 2 1.817 0.000 1.817 1.974 × 10 2 800. MeV 8.995 × 10 2 1.895 0.000 0.000 1.896 4.129 × 10 2 1.00 GeV 1.101 × 10 3 1.930 0.000 0.000 1.931 5.174 × 10

386

Table  

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

Muons Muons in Lead Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 82 (Pb) 207.2 (1) 11.350 823.0 0.09359 3.1608 0.3776 3.8073 6.2018 0.14 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 3.823 3.823 1.524 × 10 0 14.0 MeV 5.616 × 10 1 3.054 3.054 2.705 × 10 0 20.0 MeV 6.802 × 10 1 2.436 2.436 4.927 × 10 0 30.0 MeV 8.509 × 10 1 1.928 1.928 9.600 × 10 0 40.0 MeV 1.003 × 10 2 1.666 1.666 1.521 × 10 1 80.0 MeV 1.527 × 10 2 1.283 1.283 4.338 × 10 1 100. MeV 1.764 × 10 2 1.215 1.215 5.943 × 10 1 140. MeV 2.218 × 10 2 1.151 1.152 9.339 × 10 1 200. MeV 2.868 × 10 2 1.124 1.124 1.463 × 10 2 226. MeV 3.145 × 10 2 1.122 0.000 1.123 Minimum ionization 300. MeV 3.917 × 10 2 1.130 0.000 0.000 1.131 2.352 × 10 2 400. MeV 4.945 × 10 2 1.151 0.000 0.000 1.152 3.228 × 10 2 800. MeV 8.995 × 10 2 1.237 0.001 0.000 1.238 6.572 × 10 2 1.00 GeV 1.101 × 10 3 1.270 0.001 0.000 1.272 8.165 × 10 2 1.40

387

Table  

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

Muons Muons in Sodium iodide (NaI) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.42697 3.667 452.0 0.12516 3.0398 0.1203 3.5920 6.0572 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 4.703 4.703 1.202 × 10 0 14.0 MeV 5.616 × 10 1 3.710 3.710 2.169 × 10 0 20.0 MeV 6.802 × 10 1 2.928 2.928 4.009 × 10 0 30.0 MeV 8.509 × 10 1 2.297 2.297 7.917 × 10 0 40.0 MeV 1.003 × 10 2 1.975 1.975 1.264 × 10 1 80.0 MeV 1.527 × 10 2 1.509 1.509 3.652 × 10 1 100. MeV 1.764 × 10 2 1.427 1.427 5.019 × 10 1 140. MeV 2.218 × 10 2 1.347 1.348 7.916 × 10 1 200. MeV 2.868 × 10 2 1.310 1.310 1.245 × 10 2 243. MeV 3.325 × 10 2 1.305 1.305 Minimum ionization 300. MeV 3.917 × 10 2 1.310 0.000 0.000 1.310 2.010 × 10 2 400. MeV 4.945 × 10 2 1.329 0.000 0.000 1.330 2.768 × 10 2 800. MeV 8.995 × 10 2 1.417 0.001 0.000 1.418 5.677 × 10 2 1.00 GeV 1.101 × 10 3 1.452 0.001 0.000 1.453 7.070 × 10 2 1.40 GeV

388

Table  

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

Muons Muons in Polyvinyl alcohol (C 2 H3-O-H) n Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.54480 1.300 69.7 0.11178 3.3893 0.1401 2.6315 3.1115 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.891 7.891 6.999 × 10 -1 14.0 MeV 5.616 × 10 1 6.153 6.153 1.280 × 10 0 20.0 MeV 6.802 × 10 1 4.804 4.804 2.396 × 10 0 30.0 MeV 8.509 × 10 1 3.726 3.726 4.793 × 10 0 40.0 MeV 1.003 × 10 2 3.181 3.181 7.717 × 10 0 80.0 MeV 1.527 × 10 2 2.383 2.384 2.270 × 10 1 100. MeV 1.764 × 10 2 2.231 2.232 3.140 × 10 1 140. MeV 2.218 × 10 2 2.076 2.076 5.007 × 10 1 200. MeV 2.868 × 10 2 1.986 1.986 7.974 × 10 1 300. MeV 3.917 × 10 2 1.950 0.000 1.950 1.307 × 10 2 324. MeV 4.161 × 10 2 1.949 0.000 1.949 Minimum ionization 400. MeV 4.945 × 10 2 1.955 0.000 1.955 1.820 × 10 2 800. MeV 8.995 × 10 2 2.026 0.000 0.000 2.026 3.830 × 10 2 1.00 GeV 1.101 × 10 3 2.059 0.000 0.000 2.059 4.809 × 10 2 1.40

389

Table  

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

Muons Muons in Cesium Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 55 (Cs)132.9054519 (2) 1.873 488.0 0.18233 2.8866 0.5473 3.5914 6.9135 0.14 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 4.464 4.464 1.277 × 10 0 14.0 MeV 5.616 × 10 1 3.532 3.532 2.294 × 10 0 20.0 MeV 6.802 × 10 1 2.794 2.794 4.224 × 10 0 30.0 MeV 8.509 × 10 1 2.195 2.195 8.315 × 10 0 40.0 MeV 1.003 × 10 2 1.890 1.890 1.325 × 10 1 80.0 MeV 1.527 × 10 2 1.444 1.444 3.820 × 10 1 100. MeV 1.764 × 10 2 1.366 1.366 5.248 × 10 1 140. MeV 2.218 × 10 2 1.291 1.291 8.274 × 10 1 200. MeV 2.868 × 10 2 1.257 1.257 1.300 × 10 2 236. MeV 3.250 × 10 2 1.254 1.254 Minimum ionization 300. MeV 3.917 × 10 2 1.261 0.000 0.000 1.261 2.096 × 10 2 400. MeV 4.945 × 10 2 1.284 0.000 0.000 1.285 2.882 × 10 2 800. MeV 8.995 × 10 2 1.378 0.001 0.000 1.380 5.881 × 10 2 1.00 GeV 1.101 × 10 3 1.415 0.001 0.000 1.417 7.311 × 10 2

390

Table  

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

Muons Muons in Propane (C 3 H 8 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.58962 1.868 × 10 -3 47.1 0.09916 3.5920 1.4339 3.8011 8.7939 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 8.969 8.969 6.137 × 10 -1 14.0 MeV 5.616 × 10 1 6.982 6.982 1.125 × 10 0 20.0 MeV 6.802 × 10 1 5.441 5.441 2.109 × 10 0 30.0 MeV 8.509 × 10 1 4.212 4.213 4.228 × 10 0 40.0 MeV 1.003 × 10 2 3.592 3.592 6.815 × 10 0 80.0 MeV 1.527 × 10 2 2.688 2.688 2.010 × 10 1 100. MeV 1.764 × 10 2 2.525 2.526 2.780 × 10 1 140. MeV 2.218 × 10 2 2.365 2.365 4.424 × 10 1 200. MeV 2.868 × 10 2 2.281 2.281 7.018 × 10 1 267. MeV 3.577 × 10 2 2.262 0.000 2.263 Minimum ionization 300. MeV 3.917 × 10 2 2.265 0.000 2.265 1.143 × 10 2 400. MeV 4.945 × 10 2 2.291 0.000 2.291 1.582 × 10 2 800. MeV 8.995 × 10 2 2.434 0.000 0.000 2.435 3.275 × 10 2 1.00 GeV 1.101 × 10 3 2.495 0.000 0.000 2.496 4.086 × 10 2 1.40 GeV 1.502

391

Table  

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

Muons Muons in Polystyrene ([C 6 H 5 CHCH 2 ] n ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.53768 1.060 68.7 0.16454 3.2224 0.1647 2.5031 3.2999 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.803 7.803 7.077 × 10 -1 14.0 MeV 5.616 × 10 1 6.084 6.084 1.294 × 10 0 20.0 MeV 6.802 × 10 1 4.749 4.749 2.424 × 10 0 30.0 MeV 8.509 × 10 1 3.683 3.683 4.848 × 10 0 40.0 MeV 1.003 × 10 2 3.144 3.144 7.806 × 10 0 80.0 MeV 1.527 × 10 2 2.359 2.359 2.296 × 10 1 100. MeV 1.764 × 10 2 2.210 2.211 3.174 × 10 1 140. MeV 2.218 × 10 2 2.058 2.058 5.059 × 10 1 200. MeV 2.868 × 10 2 1.970 1.971 8.049 × 10 1 300. MeV 3.917 × 10 2 1.937 0.000 1.937 1.318 × 10 2 318. MeV 4.105 × 10 2 1.936 0.000 1.936 Minimum ionization 400. MeV 4.945 × 10 2 1.942 0.000 1.943 1.834 × 10 2 800. MeV 8.995 × 10 2 2.015 0.000 0.000 2.015 3.856 × 10 2 1.00 GeV 1.101 × 10 3 2.048 0.000 0.000 2.049 4.841 × 10 2 1.40

392

Table  

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

Muons Muons in Air (dry, 1 atm) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.49919 1.205 × 10 -3 85.7 0.10914 3.3994 1.7418 4.2759 10.5961 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.039 7.039 7.862 × 10 -1 14.0 MeV 5.616 × 10 1 5.494 5.495 1.436 × 10 0 20.0 MeV 6.802 × 10 1 4.294 4.294 2.686 × 10 0 30.0 MeV 8.509 × 10 1 3.333 3.333 5.366 × 10 0 40.0 MeV 1.003 × 10 2 2.847 2.847 8.633 × 10 0 80.0 MeV 1.527 × 10 2 2.140 2.140 2.535 × 10 1 100. MeV 1.764 × 10 2 2.013 2.014 3.501 × 10 1 140. MeV 2.218 × 10 2 1.889 1.889 5.562 × 10 1 200. MeV 2.868 × 10 2 1.827 1.827 8.803 × 10 1 257. MeV 3.471 × 10 2 1.815 0.000 1.816 Minimum ionization 300. MeV 3.917 × 10 2 1.819 0.000 1.819 1.430 × 10 2 400. MeV 4.945 × 10 2 1.844 0.000 1.844 1.977 × 10 2 800. MeV 8.995 × 10 2 1.968 0.000 0.000 1.968 4.074 × 10 2 1.00 GeV 1.101 × 10 3 2.020 0.000 0.000 2.021 5.077 × 10 2 1.40 GeV 1.502

393

Table  

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

Muons Muons in Lead tungstate (PbWO 4 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.41315 8.300 600.7 0.22758 3.0000 0.4068 3.0023 5.8528 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 4.333 4.333 1.311 × 10 0 14.0 MeV 5.616 × 10 1 3.426 3.426 2.360 × 10 0 20.0 MeV 6.802 × 10 1 2.710 2.711 4.350 × 10 0 30.0 MeV 8.509 × 10 1 2.131 2.131 8.566 × 10 0 40.0 MeV 1.003 × 10 2 1.835 1.835 1.365 × 10 1 80.0 MeV 1.527 × 10 2 1.406 1.406 3.931 × 10 1 100. MeV 1.764 × 10 2 1.331 1.331 5.397 × 10 1 140. MeV 2.218 × 10 2 1.261 1.261 8.498 × 10 1 200. MeV 2.868 × 10 2 1.231 1.231 1.333 × 10 2 227. MeV 3.154 × 10 2 1.229 1.230 Minimum ionization 300. MeV 3.917 × 10 2 1.237 0.000 0.000 1.238 2.145 × 10 2 400. MeV 4.945 × 10 2 1.260 0.000 0.000 1.260 2.946 × 10 2 800. MeV 8.995 × 10 2 1.349 0.001 0.000 1.350 6.007 × 10 2 1.00 GeV 1.101 × 10 3 1.383 0.001 0.000 1.385 7.469 × 10 2 1.40

394

Table  

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

Muons Muons in Carbon (compact) Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 6 (C) [12.0107 (8)] 2.265 78.0 0.26142 2.8697 -0.0178 2.3415 2.8680 0.12 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.116 7.116 7.772 × 10 -1 14.0 MeV 5.616 × 10 1 5.549 5.549 1.420 × 10 0 20.0 MeV 6.802 × 10 1 4.331 4.331 2.658 × 10 0 30.0 MeV 8.509 × 10 1 3.355 3.355 5.318 × 10 0 40.0 MeV 1.003 × 10 2 2.861 2.861 8.567 × 10 0 80.0 MeV 1.527 × 10 2 2.126 2.127 2.531 × 10 1 100. MeV 1.764 × 10 2 1.991 1.992 3.505 × 10 1 140. MeV 2.218 × 10 2 1.854 1.854 5.597 × 10 1 200. MeV 2.868 × 10 2 1.775 1.775 8.917 × 10 1 300. MeV 3.917 × 10 2 1.745 0.000 1.745 1.462 × 10 2 317. MeV 4.096 × 10 2 1.745 0.000 1.745 Minimum ionization 400. MeV 4.945 × 10 2 1.751 0.000 1.751 2.034 × 10 2 800. MeV 8.995 × 10 2 1.819 0.000 0.000 1.820 4.275 × 10 2 1.00 GeV 1.101 × 10 3 1.850 0.000 0.000 1.851 5.365 × 10

395

Table  

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

Muons Muons in Methanol (CH 3 OH) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.56176 0.791 67.6 0.08970 3.5477 0.2529 2.7639 3.5160 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 8.169 8.169 6.759 × 10 -1 14.0 MeV 5.616 × 10 1 6.369 6.369 1.236 × 10 0 20.0 MeV 6.802 × 10 1 4.972 4.972 2.315 × 10 0 30.0 MeV 8.509 × 10 1 3.855 3.855 4.631 × 10 0 40.0 MeV 1.003 × 10 2 3.291 3.291 7.457 × 10 0 80.0 MeV 1.527 × 10 2 2.469 2.469 2.194 × 10 1 100. MeV 1.764 × 10 2 2.321 2.322 3.032 × 10 1 140. MeV 2.218 × 10 2 2.166 2.166 4.823 × 10 1 200. MeV 2.868 × 10 2 2.074 2.074 7.664 × 10 1 300. MeV 3.917 × 10 2 2.039 0.000 2.039 1.254 × 10 2 318. MeV 4.105 × 10 2 2.038 0.000 2.039 Minimum ionization 400. MeV 4.945 × 10 2 2.045 0.000 2.045 1.744 × 10 2 800. MeV 8.995 × 10 2 2.121 0.000 0.000 2.122 3.665 × 10 2 1.00 GeV 1.101 × 10 3 2.156 0.000 0.000 2.157 4.600 × 10 2 1.40 GeV 1.502 ×

396

Table  

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

Muons Muons in Carbon (amorphous) Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 6 (C) 12.0107 (8) 2.000 78.0 0.20240 3.0036 -0.0351 2.4860 2.9925 0.10 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.117 7.117 7.771 × 10 -1 14.0 MeV 5.616 × 10 1 5.550 5.551 1.420 × 10 0 20.0 MeV 6.802 × 10 1 4.332 4.332 2.658 × 10 0 30.0 MeV 8.509 × 10 1 3.357 3.357 5.317 × 10 0 40.0 MeV 1.003 × 10 2 2.862 2.862 8.564 × 10 0 80.0 MeV 1.527 × 10 2 2.129 2.129 2.529 × 10 1 100. MeV 1.764 × 10 2 1.994 1.994 3.502 × 10 1 140. MeV 2.218 × 10 2 1.857 1.857 5.591 × 10 1 200. MeV 2.868 × 10 2 1.778 1.779 8.905 × 10 1 300. MeV 3.917 × 10 2 1.749 0.000 1.749 1.459 × 10 2 313. MeV 4.055 × 10 2 1.749 0.000 1.749 Minimum ionization 400. MeV 4.945 × 10 2 1.755 0.000 1.756 2.030 × 10 2 800. MeV 8.995 × 10 2 1.824 0.000 0.000 1.825 4.266 × 10 2 1.00 GeV 1.101 × 10 3 1.855 0.000 0.000 1.856 5.353 × 10

397

Table  

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

Muons Muons in Mix D wax Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.56479 0.990 60.9 0.07490 3.6823 0.1371 2.7145 3.0780 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 8.322 8.322 6.628 × 10 -1 14.0 MeV 5.616 × 10 1 6.485 6.486 1.213 × 10 0 20.0 MeV 6.802 × 10 1 5.060 5.060 2.273 × 10 0 30.0 MeV 8.509 × 10 1 3.922 3.922 4.549 × 10 0 40.0 MeV 1.003 × 10 2 3.347 3.347 7.327 × 10 0 80.0 MeV 1.527 × 10 2 2.505 2.506 2.158 × 10 1 100. MeV 1.764 × 10 2 2.346 2.346 2.985 × 10 1 140. MeV 2.218 × 10 2 2.182 2.182 4.761 × 10 1 200. MeV 2.868 × 10 2 2.087 2.087 7.584 × 10 1 300. MeV 3.917 × 10 2 2.049 0.000 2.049 1.243 × 10 2 328. MeV 4.201 × 10 2 2.048 0.000 2.048 Minimum ionization 400. MeV 4.945 × 10 2 2.053 0.000 2.053 1.731 × 10 2 800. MeV 8.995 × 10 2 2.125 0.000 0.000 2.125 3.647 × 10 2 1.00 GeV 1.101 × 10 3 2.158 0.000 0.000 2.159 4.581 × 10 2 1.40 GeV 1.502 × 10 3 2.213

398

Table  

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

Muons Muons in Sodium nitrate NaNO 3 Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.49415 2.261 114.6 0.09391 3.5097 0.1534 2.8221 3.6502 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.702 6.702 8.281 × 10 -1 14.0 MeV 5.616 × 10 1 5.239 5.239 1.510 × 10 0 20.0 MeV 6.802 × 10 1 4.100 4.100 2.820 × 10 0 30.0 MeV 8.509 × 10 1 3.187 3.187 5.624 × 10 0 40.0 MeV 1.003 × 10 2 2.726 2.726 9.039 × 10 0 80.0 MeV 1.527 × 10 2 2.053 2.053 2.648 × 10 1 100. MeV 1.764 × 10 2 1.927 1.927 3.656 × 10 1 140. MeV 2.218 × 10 2 1.800 1.800 5.814 × 10 1 200. MeV 2.868 × 10 2 1.729 1.729 9.228 × 10 1 298. MeV 3.894 × 10 2 1.705 0.000 1.705 Minimum ionization 300. MeV 3.917 × 10 2 1.705 0.000 1.705 1.507 × 10 2 400. MeV 4.945 × 10 2 1.714 0.000 1.714 2.092 × 10 2 800. MeV 8.995 × 10 2 1.787 0.000 0.000 1.787 4.377 × 10 2 1.00 GeV 1.101 × 10 3 1.819 0.000 0.000 1.819 5.486 × 10 2 1.40 GeV 1.502

399

Table  

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

Muons Muons in Freon-12B2 (CF 2 Br 2 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.44901 1.800 284.9 0.05144 3.5565 0.3406 3.7956 5.7976 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 5.330 5.330 1.053 × 10 0 14.0 MeV 5.616 × 10 1 4.190 4.190 1.908 × 10 0 20.0 MeV 6.802 × 10 1 3.297 3.297 3.540 × 10 0 30.0 MeV 8.509 × 10 1 2.577 2.577 7.017 × 10 0 40.0 MeV 1.003 × 10 2 2.212 2.212 1.123 × 10 1 80.0 MeV 1.527 × 10 2 1.680 1.680 3.263 × 10 1 100. MeV 1.764 × 10 2 1.586 1.586 4.491 × 10 1 140. MeV 2.218 × 10 2 1.496 1.496 7.099 × 10 1 200. MeV 2.868 × 10 2 1.452 1.452 1.118 × 10 2 252. MeV 3.421 × 10 2 1.445 0.000 1.445 Minimum ionization 300. MeV 3.917 × 10 2 1.448 0.000 1.449 1.809 × 10 2 400. MeV 4.945 × 10 2 1.467 0.000 0.000 1.468 2.496 × 10 2 800. MeV 8.995 × 10 2 1.556 0.000 0.000 1.557 5.139 × 10 2 1.00 GeV 1.101 × 10 3 1.592 0.001 0.000 1.593 6.409 × 10 2 1.40 GeV

400

Table  

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

Muons Muons in Eye lens (ICRP) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.54977 1.100 73.3 0.09690 3.4550 0.2070 2.7446 3.3720 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.912 7.912 6.984 × 10 -1 14.0 MeV 5.616 × 10 1 6.171 6.171 1.277 × 10 0 20.0 MeV 6.802 × 10 1 4.819 4.819 2.390 × 10 0 30.0 MeV 8.509 × 10 1 3.738 3.738 4.779 × 10 0 40.0 MeV 1.003 × 10 2 3.192 3.192 7.693 × 10 0 80.0 MeV 1.527 × 10 2 2.396 2.396 2.262 × 10 1 100. MeV 1.764 × 10 2 2.251 2.251 3.125 × 10 1 140. MeV 2.218 × 10 2 2.095 2.096 4.976 × 10 1 200. MeV 2.868 × 10 2 2.006 2.006 7.914 × 10 1 300. MeV 3.917 × 10 2 1.971 0.000 1.971 1.296 × 10 2 318. MeV 4.105 × 10 2 1.971 0.000 1.971 Minimum ionization 400. MeV 4.945 × 10 2 1.977 0.000 1.977 1.803 × 10 2 800. MeV 8.995 × 10 2 2.051 0.000 0.000 2.051 3.790 × 10 2 1.00 GeV 1.101 × 10 3 2.085 0.000 0.000 2.085 4.756 × 10 2 1.40 GeV 1.502 × 10

Note: This page contains sample records for the topic "forecast evaluation table" 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

Table  

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

Muons Muons in Compact bone (ICRU) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.53010 1.850 91.9 0.05822 3.6419 0.0944 3.0201 3.3390 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.406 7.406 7.477 × 10 -1 14.0 MeV 5.616 × 10 1 5.783 5.783 1.365 × 10 0 20.0 MeV 6.802 × 10 1 4.521 4.521 2.552 × 10 0 30.0 MeV 8.509 × 10 1 3.511 3.511 5.097 × 10 0 40.0 MeV 1.003 × 10 2 3.000 3.000 8.199 × 10 0 80.0 MeV 1.527 × 10 2 2.247 2.247 2.408 × 10 1 100. MeV 1.764 × 10 2 2.106 2.106 3.330 × 10 1 140. MeV 2.218 × 10 2 1.962 1.962 5.307 × 10 1 200. MeV 2.868 × 10 2 1.880 1.880 8.444 × 10 1 300. MeV 3.917 × 10 2 1.849 0.000 1.850 1.382 × 10 2 314. MeV 4.065 × 10 2 1.849 0.000 1.849 Minimum ionization 400. MeV 4.945 × 10 2 1.856 0.000 1.857 1.922 × 10 2 800. MeV 8.995 × 10 2 1.930 0.000 0.000 1.930 4.036 × 10 2 1.00 GeV 1.101 × 10 3 1.963 0.000 0.000 1.964 5.063 × 10 2 1.40 GeV 1.502

402

Table  

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

Muons Muons in Polyimide film (C 22 H 10 N 2 O 5 ) n Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.51264 1.420 79.6 0.15972 3.1921 0.1509 2.5631 3.3497 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.299 7.299 7.576 × 10 -1 14.0 MeV 5.616 × 10 1 5.695 5.695 1.385 × 10 0 20.0 MeV 6.802 × 10 1 4.449 4.449 2.590 × 10 0 30.0 MeV 8.509 × 10 1 3.453 3.453 5.177 × 10 0 40.0 MeV 1.003 × 10 2 2.949 2.949 8.332 × 10 0 80.0 MeV 1.527 × 10 2 2.214 2.214 2.448 × 10 1 100. MeV 1.764 × 10 2 2.074 2.074 3.384 × 10 1 140. MeV 2.218 × 10 2 1.932 1.932 5.392 × 10 1 200. MeV 2.868 × 10 2 1.851 1.851 8.577 × 10 1 300. MeV 3.917 × 10 2 1.820 0.000 1.820 1.404 × 10 2 314. MeV 4.065 × 10 2 1.820 0.000 1.820 Minimum ionization 400. MeV 4.945 × 10 2 1.826 0.000 1.827 1.953 × 10 2 800. MeV 8.995 × 10 2 1.897 0.000 0.000 1.898 4.102 × 10 2 1.00 GeV 1.101 × 10 3 1.929 0.000 0.000 1.930 5.147 × 10 2 1.40

403

Table  

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

Muons Muons in Silicon dioxide (fused quartz) (SiO 2 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.49930 2.200 139.2 0.08408 3.5064 0.1500 3.0140 4.0560 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 6.591 6.591 8.438 × 10 -1 14.0 MeV 5.616 × 10 1 5.158 5.158 1.537 × 10 0 20.0 MeV 6.802 × 10 1 4.041 4.041 2.866 × 10 0 30.0 MeV 8.509 × 10 1 3.145 3.145 5.710 × 10 0 40.0 MeV 1.003 × 10 2 2.691 2.691 9.170 × 10 0 80.0 MeV 1.527 × 10 2 2.030 2.030 2.682 × 10 1 100. MeV 1.764 × 10 2 1.908 1.908 3.701 × 10 1 140. MeV 2.218 × 10 2 1.786 1.786 5.878 × 10 1 200. MeV 2.868 × 10 2 1.719 1.719 9.315 × 10 1 288. MeV 3.788 × 10 2 1.699 0.000 1.699 Minimum ionization 300. MeV 3.917 × 10 2 1.699 0.000 1.699 1.518 × 10 2 400. MeV 4.945 × 10 2 1.711 0.000 1.711 2.105 × 10 2 800. MeV 8.995 × 10 2 1.789 0.000 0.000 1.790 4.391 × 10 2 1.00 GeV 1.101 × 10 3 1.823 0.000 0.000 1.824 5.497

404

Table  

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

Muons Muons in Radon Z A [g/mol] ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 86 (Rn) [222.01758 (2)]9.066 × 10 -3 794.0 0.20798 2.7409 1.5368 4.9889 13.2839 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 3.782 3.782 1.535 × 10 0 14.0 MeV 5.616 × 10 1 3.018 3.018 2.730 × 10 0 20.0 MeV 6.802 × 10 1 2.405 2.405 4.980 × 10 0 30.0 MeV 8.509 × 10 1 1.902 1.902 9.715 × 10 0 40.0 MeV 1.003 × 10 2 1.644 1.644 1.540 × 10 1 80.0 MeV 1.527 × 10 2 1.267 1.267 4.394 × 10 1 100. MeV 1.764 × 10 2 1.201 1.201 6.019 × 10 1 140. MeV 2.218 × 10 2 1.140 1.140 9.452 × 10 1 200. MeV 2.868 × 10 2 1.116 1.117 1.479 × 10 2 216. MeV 3.039 × 10 2 1.116 1.116 Minimum ionization 300. MeV 3.917 × 10 2 1.127 0.000 0.000 1.128 2.372 × 10 2 400. MeV 4.945 × 10 2 1.154 0.000 0.000 1.154 3.249 × 10 2 800. MeV 8.995 × 10 2 1.258 0.001 0.000 1.260 6.559 × 10 2 1.00 GeV 1.101 × 10 3 1.300 0.001 0.000 1.302 8.119

405

Table  

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

Muons Muons in Solid carbon dioxide (dry ice; CO 2 ) Z/A ρ [g/cm 3 ] I [eV] a k = m s x 0 x 1 C δ 0 0.49989 1.563 85.0 0.43387 3.0000 0.2000 2.0000 3.4513 0.00 T p Ionization Brems Pair prod Photonucl Total CSDA range [MeV/c] [MeV cm 2 /g] [g/cm 2 ] 10.0 MeV 4.704 × 10 1 7.057 7.057 7.841 × 10 -1 14.0 MeV 5.616 × 10 1 5.508 5.508 1.432 × 10 0 20.0 MeV 6.802 × 10 1 4.304 4.304 2.679 × 10 0 30.0 MeV 8.509 × 10 1 3.341 3.341 5.353 × 10 0 40.0 MeV 1.003 × 10 2 2.854 2.854 8.612 × 10 0 80.0 MeV 1.527 × 10 2 2.145 2.145 2.529 × 10 1 100. MeV 1.764 × 10 2 2.017 2.017 3.493 × 10 1 140. MeV 2.218 × 10 2 1.886 1.886 5.554 × 10 1 200. MeV 2.868 × 10 2 1.812 1.812 8.811 × 10 1 300. MeV 3.917 × 10 2 1.787 0.000 1.787 1.438 × 10 2 303. MeV 3.950 × 10 2 1.787 0.000 1.787 Minimum ionization 400. MeV 4.945 × 10 2 1.795 0.000 1.795 1.997 × 10 2 800. MeV 8.995 × 10 2 1.866 0.000 0.000 1.866 4.182 × 10 2 1.00 GeV 1.101 × 10 3 1.896 0.000 0.000 1.897 5.245 × 10

406

Coal production forecast and low carbon policies in China  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

407

The table of isotopes-8th edition and beyond  

SciTech Connect

A new edition of the Table of Isotopes has been published this year by John Wiley and Sons, Inc. This edition is the eighth in a series started by Glenn T. Seaborg in 1940. The two-volume, 3168-page, cloth-bound edition is twice the size of the previous edition published in 1978. It contains nuclear structure and decay data, based mainly on the Evaluated Nuclear Structure Data File (ENSDF), for >3100 isotopes and isomers. Approximately 24000 references are cited, and the appendices have been updated and extended. The book is packaged with an interactive CD-ROM that contains the Table of Isotopes in Adobe Acrobat Portable Document Format for convenient viewing on personal computer (PC) and UNIX workstations. The CD-ROM version contains a chart of the nuclides graphical index and separate indices organized for radioisotope users and nuclear structure physicists. More than 100000 hypertext links are provided to move the user quickly through related information free from the limitations of page size. Complete references with keyword abstracts are provided. The CD-ROM also contains the Table of Super-deformed Nuclear Bands and Fission Isomers; Tables of Atoms, Atomic Nuclei, and Subatomic Particles by Ivan P. Selinov; the ENSDF and nuclear structure reference (NSR) databases; the ENSDF manual by Jagdish K. Tuli; and Abode Acrobat Reader software.

Firestone, R.B. [Lawrence Berkeley Laboratory, CA (United States)

1996-12-31T23:59:59.000Z

408

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

409

Variable White Dwarf Data Tables  

SciTech Connect

Below, I give a brief explanation of the information in these tables. In all cases, I list the WD {number_sign}, either from the catalog of McCook {ampersand} Sion (1987) or determined by me from the epoch 1950 coordinates. Next, I list the most commonly used name (or alias), then I list the variable star designation if it is available. If not, I list the constellation name and a V** or?? depending on what the last designated variable star for that constellation is. I present epoch 2000 coordinates for all of the stars, which I precessed from the 1950 ones in most cases. I do not include proper motion effects; this is negligible for all except the largest proper motion DAV stars, such as L 19-2, BPM 37093, B 808, and G 29-38. Even in these cases, the error is no more than 30` in declination and 2 s in right ascension. I culled effective temperatures from the latest work (listed under each table); they are now much more homogeneous than before. I pulled the magnitude estimates from the appropriate paper, and they are mean values integrated over several cycles. The amplitude given is for the height of a typical pulse in the light curve. The periods correspond the dominant ones found in the light curve. In some cases, there is a band of power in a given period range, or the light curve is very complex, and I indicate this in the table. In the references, I generally list the paper with the most comprehensive pulsation analysis for the star in question. In some cases, there is more than one good reference, and I list them as well.

Bradley, P. A.

1997-12-31T23:59:59.000Z

410

Measuring the forecasting accuracy of models: evidence from industrialised countries  

Science Journals Connector (OSTI)

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

Athanasios Koulakiotis; Apostolos Dasilas

2009-01-01T23:59:59.000Z

411

Tables and graphs of electron-interaction cross sections from 10 eV to 100 GeV derived from the LLNL Evaluated Electron Data Library (EEDL), Z = 1--100  

SciTech Connect

Energy-dependent evaluated electron interaction cross sections and related parameters are presented for elements H through Fm (Z = 1 to 100). Data are given over the energy range from 10 eV to 100 GeV. Cross sections and average energy deposits are presented in tabulated and graphic form. In addition, ionization cross sections and average energy deposits for each shell are presented in graphic form. This information is derived from the Livermore Evaluated Electron Data Library (EEDL) as of July, 1991.

Perkins, S.T.; Cullen, D.E. (Lawrence Livermore National Lab., CA (United States)); Seltzer, S.M. (National Inst. of Standards and Technology (NML), Gaithersburg, MD (United States). Center for Radiation Research)

1991-11-12T23:59:59.000Z

412

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 8. Supplemental Gas Supplies by State, 2008 (Million Cubic Feet) Colorado ......................... 0 2 0 6,256 6,258 Delaware ........................ 0 2 0 0 2 Georgia........................... 0 * 0 0 * Hawaii............................. 2,554 5 0 0 2,559 Illinois.............................. 0 15 0 0 15 Indiana............................ 0 30 0 0 30 Iowa ................................ 0 24 3 0 27 Kentucky......................... 0 15 0 0 15 Maryland ......................... 0 181 0 0 181 Massachusetts................ 0 13 0 0 13 Minnesota ....................... 0 46 0 0 46 Missouri .......................... * 6 0 0 6 Nebraska ........................ 0 28 0 0 28 New Hampshire .............. 0 44 0 0 44 New Jersey ..................... 0 0 0 489 489 New York ........................

413

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 8. Supplemental Gas Supplies by State, 2009 (Million Cubic Feet) Colorado ......................... 0 3 0 7,525 7,527 Connecticut..................... 0 * 0 0 * Delaware ........................ 0 2 0 0 2 Georgia........................... 0 0 52 * 52 Hawaii............................. 2,438 9 0 0 2,447 Illinois.............................. 0 20 0 0 20 Indiana............................ 0 * 0 0 * Iowa ................................ 0 3 0 0 3 Kentucky......................... 0 18 0 0 18 Maryland ......................... 0 170 0 0 170 Massachusetts................ 0 10 0 0 10 Minnesota ....................... 0 47 0 0 47 Missouri .......................... * 10 0 0 10 Nebraska ........................ 0 18 0 0 18 New Jersey ..................... 0 0 0 454 454 New York ........................

414

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 8. Supplemental Gas Supplies by State, 2010 (Million Cubic Feet) Colorado ......................... 0 4 0 5,144 5,148 Delaware ........................ 0 1 0 0 1 Georgia........................... 0 0 732 0 732 Hawaii............................. 2,465 6 0 0 2,472 Illinois.............................. 0 17 0 0 17 Indiana............................ 0 1 0 0 1 Iowa ................................ 0 2 0 0 2 Kentucky......................... 0 5 0 0 5 Louisiana ........................ 0 0 249 0 249 Maryland ......................... 0 115 0 0 115 Massachusetts................ 0 * 0 0 * Minnesota ....................... 0 12 0 0 12 Missouri .......................... * 18 0 0 18 Nebraska ........................ 0 12 0 0 12 New Jersey ..................... 0 0 0 457 457 New York ........................

415

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 8. Supplemental Gas Supplies by State, 2007 (Million Cubic Feet) Colorado ......................... 0 3 0 6,866 6,869 Delaware ........................ 0 5 0 0 5 Georgia........................... 0 2 0 0 2 Hawaii............................. 2,679 4 0 0 2,683 Illinois.............................. 0 11 0 0 11 Indiana............................ 0 81 0 554 635 Iowa ................................ 0 2 38 0 40 Kentucky......................... 0 124 0 0 124 Maryland ......................... 0 245 0 0 245 Massachusetts................ 0 15 0 0 15 Minnesota ....................... 0 54 0 0 54 Missouri .......................... 7 60 0 0 66 Nebraska ........................ 0 33 0 0 33 New Hampshire .............. 0 9 0 0 9 New Jersey ..................... 0 0 0 379 379 New York ........................

416

Table-top job analysis  

SciTech Connect

The purpose of this Handbook is to establish general training program guidelines for training personnel in developing training for operation, maintenance, and technical support personnel at Department of Energy (DOE) nuclear facilities. TTJA is not the only method of job analysis; however, when conducted properly TTJA can be cost effective, efficient, and self-validating, and represents an effective method of defining job requirements. The table-top job analysis is suggested in the DOE Training Accreditation Program manuals as an acceptable alternative to traditional methods of analyzing job requirements. DOE 5480-20A strongly endorses and recommends it as the preferred method for analyzing jobs for positions addressed by the Order.

Not Available

1994-12-01T23:59:59.000Z

417

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

E-Print Network (OSTI)

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

Greenslade, Diana

418

EIA-Annual Energy Outlook 2010 - Low Economic Growth Tables  

Gasoline and Diesel Fuel Update (EIA)

Economic Growth Tables (2007- 2035) Economic Growth Tables (2007- 2035) Annual Energy Outlook 2010 Main Low Economic Growth Tables (2007- 2035) Table Title Formats Summary Low Economic Growth Case Tables PDF Gif Year-by-Year Low Economic Growth Case Tables Excel Gif Table 1. Total Energy Supply, Disposition, and Price Summary Excel Gif Table 2. Energy Consumption by Sector and Source Excel Gif Table 3. Energy Prices by Sector and Source Excel Gif Table 4. Residential Sector Key Indicators and Consumption Excel Gif Table 5. Commercial Sector Indicators and Consumption Excel Gif Table 6. Industrial Sector Key Indicators and Consumption Excel Gif Table 7. Transportation Sector Key Indicators and Delivered Energy Consumption Excel Gif Table 8. Electricity Supply, Disposition, Prices, and Emissions

419

EIA-Annual Energy Outlook 2010 - High Economic Growth Tables  

Gasoline and Diesel Fuel Update (EIA)

Economic Growth Tables (2007-2035) Economic Growth Tables (2007-2035) Annual Energy Outlook 2010 Main High Economic Growth Tables (2007- 2035) Table Title Formats Summary High Economic Growth Case Tables PDF Gif Year-by-Year High Economic Growth Case Tables Excel Gif Table 1. Total Energy Supply and Disposition Summary Excel Gif Table 2. Energy Consumption by Sector and Source Excel Gif Table 3. Energy Prices by Sector and Source Excel Gif Table 4. Residential Sector Key Indicators and Consumption Excel Gif Table 5. Commercial Sector Indicators and Consumption Excel Gif Table 6. Industrial Sector Key Indicators and Consumption Excel Gif Table 7. Transportation Sector Key Indicators and Delivered Energy Consumption Excel Gif Table 8. Electricity Supply, Disposition, Prices, and Emissions Excel Gif

420

Wind power forecast error smoothing within a wind farm  

Science Journals Connector (OSTI)

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

Nadja Saleck; Lueder von Bremen

2007-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast evaluation table" 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

Environmental Regulatory Update Table, October 1991  

SciTech Connect

The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

1991-11-01T23:59:59.000Z

422

Environmental Regulatory Update Table, August 1991  

SciTech Connect

This Environmental Regulatory Update Table (August 1991) provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

Houlberg, L.M., Hawkins, G.T.; Salk, M.S.

1991-09-01T23:59:59.000Z

423

Environmental Regulatory Update Table, September 1991  

SciTech Connect

The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

1991-10-01T23:59:59.000Z

424

Environmental Regulatory Update Table, November 1991  

SciTech Connect

The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

1991-12-01T23:59:59.000Z

425

Environmental regulatory update table, July 1991  

SciTech Connect

This Environmental Regulatory Update Table (July 1991) provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

1991-08-01T23:59:59.000Z

426

Environmental Regulatory Update Table, November 1990  

SciTech Connect

The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

Hawkins, G.T.; Houlberg, L.M.; Noghrei-Nikbakht, P.A.; Salk, M.S.

1990-12-01T23:59:59.000Z

427

Microsoft Word - table_09.doc  

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

3 3 Table 9 Created on: 12/12/2013 2:08:24 PM Table 9. Underground natural gas storage - by season, 2011-2013 (volumes in billion cubic feet) Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Year, Season, and Month Base Gas Working Gas Total Volume Percent Injections Withdrawals Net Withdrawals a 2011 Refill Season April 4,304 1,788 6,092 -223 -11.1 312 100 -212 May 4,304 2,187 6,491 -233 -9.6 458 58 -399 June 4,302 2,530 6,831 -210 -7.7 421 80 -340 July 4,300 2,775 7,075 -190 -6.4 359 116 -244 August 4,300 3,019 7,319 -134 -4.2 370 126 -244 September 4,301 3,416 7,717 -92 -2.6 454 55

428

All Price Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

1) 1) June 2013 State Energy Price and Expenditure Estimates 1970 Through 2011 2011 Price and Expenditure Summary Tables Table E1. Primary Energy, Electricity, and Total Energy Price Estimates, 2011 (Dollars per Million Btu) State Primary Energy Electric Power Sector g,h Retail Electricity Total Energy g,i Coal Natural Gas a Petroleum Nuclear Fuel Biomass Total g,h,i Distillate Fuel Oil Jet Fuel b LPG c Motor Gasoline d Residual Fuel Oil Other e Total Wood and Waste f Alabama 3.09 5.66 26.37 22.77 25.54 27.12 13.18 19.42 25.90 0.61 3.01 8.75 2.56 27.08 19.85 Alaska 3.64 6.70 29.33 23.12 29.76 31.60 20.07 34.62 26.61 - 14.42 20.85 6.36 47.13 25.17 Arizona 1.99 7.07 27.73 22.84 31.95 26.97 17.00 17.23 26.71 0.75 6.31 10.79 2.16 28.46 25.23 Arkansas 1.93 6.94 26.37 22.45 26.66 27.35 17.35 33.22

429

Microsoft Word - table_13.doc  

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

U.S. Energy Information Administration | Natural Gas Monthly 31 Table 13 Created on: 12/12/2013 2:28:44 PM Table 13. Activities of underground natural gas storage operators, by state, September 2013 (volumes in million cubic feet) State Field Count Total Storage Capacity Working Gas Storage Capacity Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Base Gas Working Gas Total Volume Percent Injections Withdrawals Alabama 2 35,400 27,350 8,050 21,262 29,312 2,852 15.5 1,743 450 Alaska a 5 83,592 67,915 14,197 20,455 34,652 NA NA 1,981 30 Arkansas 2 21,853 12,178 9,648 3,372 13,020 -1,050 -23.7 204 0 California 14 599,711 374,296

430

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

4) 4) June 2007 State Energy Consumption Estimates 1960 Through 2004 2004 Consumption Summary Tables Table S1. Energy Consumption Estimates by Source and End-Use Sector, 2004 (Trillion Btu) State Total Energy b Sources End-Use Sectors a Coal Natural Gas c Petroleum Nuclear Electric Power Hydro- electric Power d Biomass e Other f Net Interstate Flow of Electricity/Losses g Residential Commercial Industrial b Transportation Alabama 2,159.7 853.9 404.0 638.5 329.9 106.5 185.0 0.1 -358.2 393.7 270.2 1,001.1 494.7 Alaska 779.1 14.1 411.8 334.8 0.0 15.0 3.3 0.1 0.0 56.4 63.4 393.4 266.0 Arizona 1,436.6 425.4 354.9 562.8 293.1 69.9 8.7 3.6 -281.7 368.5 326.0 231.2 511.0 Arkansas 1,135.9 270.2 228.9 388.3 161.1 36.5 76.0 0.6 -25.7 218.3 154.7 473.9 288.9 California 8,364.6 68.9 2,474.2 3,787.8 315.6 342.2

431

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

9) 9) June 2011 State Energy Consumption Estimates 1960 Through 2009 2009 Consumption Summary Tables Table C1. Energy Consumption Overview: Estimates by Energy Source and End-Use Sector, 2009 (Trillion Btu) State Total Energy b Sources End-Use Sectors a Fossil Fuels Nuclear Electric Power Renewable Energy e Net Interstate Flow of Electricity/ Losses f Net Electricity Imports Residential Commercial Industrial b Transportation Coal Natural Gas c Petroleum d Total Alabama 1,906.8 631.0 473.9 583.9 1,688.8 415.4 272.9 -470.3 0.0 383.2 266.0 788.5 469.2 Alaska 630.4 14.5 344.0 255.7 614.1 0.0 16.3 0.0 (s) 53.4 61.0 325.4 190.6 Arizona 1,454.3 413.3 376.7 520.8 1,310.8 320.7 103.5 -279.9 -0.8 400.8 352.1 207.8 493.6 Arkansas 1,054.8 264.1 248.1 343.1 855.3 158.7 126.5 -85.7 0.0 226.3 167.0 372.5

432

Microsoft Word - table_01.doc  

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

3 3 Table 1 Table 1. Summary of natural gas supply and disposition in the United States, 2008-2013 (billion cubic feet) Year and Month Gross Withdrawals Marketed Production NGPL Production a Dry Gas Production b Supplemental Gaseous Fuels c Net Imports Net Storage Withdrawals d Balancing Item e Consumption f 2008 Total 25,636 21,112 953 20,159 61 3,021 34 2 23,277 2009 Total 26,057 21,648 1,024 20,624 65 2,679 -355 -103 22,910 2010 Total 26,816 22,382 1,066 21,316 65 2,604 -13 115 24,087 2011 January 2,299 1,953 92 1,861 5 236 811 R -24 R 2,889 February 2,104 1,729 82 1,647 4 186 594 R 20 R 2,452 March 2,411 2,002 95 1,908 5 171 151 R -4 R 2,230 April 2,350 1,961 93 1,868 5 R 152 -216 R 17 R 1,825 May 2,411 2,031

433

Microsoft Word - table_02.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 2. Natural gas production, transmission, and consumption, by state, 2012 (million cubic feet) U.S. Energy Information Administration | Natural Gas Annual 4 Table 2 Alabama 215,710 7,110 -162,223 617,883 0 -2,478 0 666,738 Alaska 351,259 21,470 22,663 0 -9,342 0 0 343,110 Arizona 117 0 -13,236 389,036 -43,838 0 0 332,079 Arkansas 1,146,168 424 -18,281 -831,755 0 -103 0 295,811 California 246,822 12,755 104,820 2,222,355 -109,787 48,071 0 2,403,385 Colorado 1,709,376 81,943 -107,940 -1,077,968 0 2,570 4,412 443,367 Connecticut 0 0 4,191 225,228 0 260 0 229,159 Delaware 0 0 21,035 80,692 0 51 * 101,676 District of Columbia 0 0 497 28,075 0 0 0 28,572 Florida 18,681 0 15,168 1,294,620 0 0 0 1,328,469

434

TableHC2.12.xls  

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

Detached Attached 2 to 4 Units Energy Information Administration: 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing...

435

TableHC10.13.xls  

Gasoline and Diesel Fuel Update (EIA)

or More... 0.3 Q Q Q Q Lighting Usage Indicators U.S. Census Region Northeast Midwest Table HC10.13 Lighting Usage...

436

TABLE54.CHP:Corel VENTURA  

Annual Energy Outlook 2012 (EIA)

Administration (EIA) Forms EIA-812, "Monthly Product Pipeline Report," and EIA-813, Monthly Crude Oil Report." Table 54. Movements of Crude Oil and Petroleum Products by Pipeline...

437

TABLE19.CHP:Corel VENTURA  

Annual Energy Outlook 2012 (EIA)

Table 19. PAD District IV-Year-to-Date Supply, Disposition, and Ending Stocks of Crude Oil and Petroleum (Thousand Barrels) January-July 2004 Products, Crude Oil...

438

TABLE15.CHP:Corel VENTURA  

Annual Energy Outlook 2012 (EIA)

Table 15. PAD District III-Year-to-Date Supply, Disposition, and Ending Stocks of Crude Oil and Petroleum (Thousand Barrels) January-July 2004 Products, Crude Oil...

439

TABLE53.CHP:Corel VENTURA  

Annual Energy Outlook 2012 (EIA)

Table 53. Movements of Crude Oil and Petroleum Products by Pipeline, Tanker, and Barge Between July 2004 Crude Oil ... 0 383 0...

440

TABLE11.CHP:Corel VENTURA  

Annual Energy Outlook 2012 (EIA)

(Thousand Barrels) Table 11. PAD District II-Year-to-Date Supply, Disposition, and Ending Stocks of Crude Oil and Petroleum January-July 2004 Products, Crude Oil...

Note: This page contains sample records for the topic "forecast evaluation table" from the National Library of EnergyBeta (NLEBeta).
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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

2011 Annual Report Table of Contents  

E-Print Network (OSTI)

) ...................12 Smart Grid Cyber Security.....................................................13 ICT Supply ChainComputer Security Division 2011 Annual Report #12;Table of Contents Welcome ................................................................. 1 Division Organization .................................................2 The Computer Security

442

Summary Statistics Table 1. Crude Oil Prices  

Annual Energy Outlook 2012 (EIA)

Cost Report." Figure Energy Information Administration Petroleum Marketing Annual 1996 3 Table 2. U.S. Refiner Prices of Petroleum Products to End Users (Cents per Gallon...

443

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

Open Energy Info (EERE)

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

444

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory  

Gasoline and Diesel Fuel Update (EIA)

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

445

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network (OSTI)

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

Choi, Han-Lim

2009-01-01T23:59:59.000Z

446

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network (OSTI)

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

Lim, Eun-pa

447

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network (OSTI)

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

Yang, Xiaorong

2008-01-01T23:59:59.000Z

448

Exponential smoothing with covariates applied to electricity demand forecast  

Science Journals Connector (OSTI)

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

José D. Bermúdez

2013-01-01T23:59:59.000Z

449

Initial conditions estimation for improving forecast accuracy in exponential smoothing  

Science Journals Connector (OSTI)

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

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

2012-07-01T23:59:59.000Z

450

A Bayesian approach to forecast intermittent demand for seasonal products  

Science Journals Connector (OSTI)

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

Mohammad Anwar Rahman; Bhaba R. Sarker

2012-01-01T23:59:59.000Z

451

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...

452

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones  

Science Journals Connector (OSTI)

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

Matthew J. Onderlinde; Henry E. Fuelberg

2014-10-01T23:59:59.000Z

453

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

E-Print Network (OSTI)

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

Kemner, Ken

454

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network (OSTI)

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

455

Recently released EIA report presents international forecasting data  

SciTech Connect

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

NONE

1995-05-01T23:59:59.000Z

456

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network (OSTI)

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

457

Evolutionary Optimization of an Ice Accretion Forecasting System  

Science Journals Connector (OSTI)

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

Pawel Pytlak; Petr Musilek; Edward Lozowski; Dan Arnold

2010-07-01T23:59:59.000Z

458

Diagnosing the Origin of Extended-Range Forecast Errors  

Science Journals Connector (OSTI)

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

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

2010-06-01T23:59:59.000Z

459

Application of an Improved SVM Algorithm for Wind Speed Forecasting  

Science Journals Connector (OSTI)

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

Huaqiang Zhang; Xinsheng Wang; Yinxiao Wu

2011-01-01T23:59:59.000Z

460

Research on Development Trends of Power Load Forecasting Methods  

Science Journals Connector (OSTI)

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

Litong Dong; Jun Xu; Haibo Liu; Ying Guo

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast evaluation table" 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

Representing Forecast Error in a Convection-Permitting Ensemble System  

Science Journals Connector (OSTI)

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

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

2014-12-01T23:59:59.000Z

462

Weather Research and Forecasting Model 2.2 Documentation  

E-Print Network (OSTI)

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

Sadjadi, S. Masoud

463

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect

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

Yoo, Wucherl; Sim, Alex

2014-07-07T23:59:59.000Z

464

Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach  

Science Journals Connector (OSTI)

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

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

2009-11-01T23:59:59.000Z

465

Tables and graphs of photon-interaction cross sections from 0. 1 keV to 100 MeV derived from the LLL evaluated-nuclear-data library  

SciTech Connect

Energy-dependent evaluated photon interaction cross sections and related parameters are presented for elements H through Cf(Z = 1 to 98). Data are given over the energy range from 0.1 keV to 100 MeV. The related parameters include form factors and average energy deposits per collision (with and without fluorescence). Fluorescence information is given for all atomic shells that can emit a photon with a kinetic energy of 0.1 keV or more. In addition, the following macroscopic properties are given: total mean free path and energy deposit per centimeter. This information is derived from the Livermore Evaluated-Nuclear-Data Library (ENDL) as of October 1978.

Plechaty, E.F.; Cullen, D.E.; Howerton, R.J.

1981-11-11T23:59:59.000Z

466

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

467

A model for short term electric load forecasting  

E-Print Network (OSTI)

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

Tigue, John Robert

1975-01-01T23:59:59.000Z

468

Radiation fog forecasting using a 1-dimensional model  

E-Print Network (OSTI)

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

Peyraud, Lionel

2012-06-07T23:59:59.000Z

469

Annual Energy Outlook 2009 - High Price Case Tables  

Gasoline and Diesel Fuel Update (EIA)

6-2030) 6-2030) Annual Energy Outlook 2009 with Projections to 2030 XLS GIF Spreadsheets are provided in Excel High Price Case Tables (2006-2030) Table Title Formats Summary High Price Case Tables PDF GIF High Price Case Tables XLS GIF Table 1. Total Energy Supply and Disposition Summary XLS GIF Table 2. Energy Consumption by Sector and Source XLS GIF Table 3. Energy Prices by Sector and Source XLS GIF Table 4. Residential Sector Key Indicators and Consumption XLS GIF Table 5. Commercial Sector Indicators and Consumption XLS GIF Table 6. Industrial Sector Key Indicators and Consumption XLS GIF Table 7. Transportation Sector Key Indicators and Delivered Energy Consumption XLS GIF Table 8. Electricity Supply, Disposition, Prices, and Emissions XLS GIF Table 9. Electricity Generating Capacity

470

Wave height forecasting in Dayyer, the Persian Gulf  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

471

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

E-Print Network (OSTI)

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

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

472

Exhibit C Table of Contents  

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

Exhibit C Schedules and Lists Exhibit C Schedules and Lists Dated 5-20-13 Subcontract No. 241314 Page 1 of 5 EXHIBIT "C" SCHEDULES AND LISTS TABLE OF CONTENTS Form Title A Schedule of Quantities and Prices B Milestone and Payment Schedule C Lower-Tier Subcontractor and Vendor List Exhibit C Schedules and Lists Dated 5-20-13 Subcontract No. 241314 Page 2 of 5 EXHIBIT "C" FORM A SCHEDULE OF QUANTITIES AND PRICES NOTE: This Exhibit "C" Form A is part of the model subcontract for Trinity and is provided to Offerors for informational purposes only. It is not intended that this form be returned with the Offeror's proposal. 1.0 WORK TO BE PERFORMED Work shall be performed strictly in accordance with requirements of the Subcontract

473

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 7. Natural Gas Processed, Liquids Extracted, and Estimated Extraction Loss by State, 2005 Alabama .................................. 255,157 9,748 13,759 37,048 Alaska...................................... 3,089,229 23,700 27,956 105,449 Arkansas.................................. 16,756 177 231 786 California ................................. 226,230 11,101 13,748 45,926 Colorado .................................. 730,948 25,603 34,782 95,881 Florida...................................... 3,584 359 495 1,400 Illinois....................................... 280 37 46 129 Kansas..................................... 476,656 22,165 31,521 85,737 Kentucky.................................. 38,792 1,411 1,716 5,725 Louisiana ................................. 2,527,636 73,035 103,381

474

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 5. Number of Producing Gas Wells by State and the Gulf of Mexico, December 31, 2006-2010 Alabama .......................................................... 6,227 6,591 6,860 6,913 7,026 Alaska.............................................................. 231 239 261 261 269 Arizona ............................................................ 7 7 6 6 5 Arkansas.......................................................... 3,814 4,773 5,592 6,314 7,397 California ......................................................... 1,451 1,540 1,645 1,643 1,580 Colorado .......................................................... 20,568 22,949 25,716 27,021 28,813 Gulf of Mexico.................................................. 2,419 2,552 1,527 1,984 1,852 Illinois...............................................................

475

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 6. Wellhead Value and Marketed Production of Natural Gas, 2004-2008, and by State, 2008 2004 Total ............................ 15,223,749 -- 5.46 19,517,491 106,521,974 2005 Total ............................ 15,425,867 -- 7.33 18,927,095 138,750,746 2006 Total ............................ 15,981,421 -- 6.39 19,409,674 124,074,399 2007 Total ............................ R 16,335,710 -- R 6.25 R 20,196,346 R 126,164,553 2008 Total ............................ 18,424,440 -- 7.96 21,239,516 169,038,089 Alabama ............................... 246,747 2,382,188 9.65 257,884 2,489,704 Alaska................................... 337,359 2,493,128 7.39 398,442 2,944,546 Arizona ................................. 503 3,568 7.09 523 3,710 Arkansas...............................

476

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

0 0 Table 21. Number of Natural Gas Industrial Consumers by Type of Service and State, 2008-2009 Alabama ...................... 2,476 281 2,757 2,789 271 3,060 Alaska.......................... 2 4 6 2 1 3 Arizona ........................ 285 98 383 274 116 390 Arkansas...................... 648 456 1,104 582 443 1,025 California ..................... 36,124 R 3,467 R 39,591 35,126 3,762 38,888 Colorado ...................... 341 4,475 4,816 297 4,787 5,084 Connecticut.................. 2,386 810 3,196 2,228 910 3,138 Delaware ..................... 96 69 165 39 73 112 Florida.......................... 161 288 449 123 484 607 Georgia........................ 1,003 1,887 2,890 956 1,298 2,254 Hawaii.......................... 27 0 27 25 0 25 Idaho............................ 108 91 199 109 78 187 Illinois...........................

477

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

8 8 Table 21. Number of Natural Gas Industrial Consumers by Type of Service and State, 2004-2005 Alabama ...................... 2,495 R 304 R 2,799 2,487 299 2,786 Alaska.......................... 6 4 10 7 5 12 Arizona ........................ 328 86 414 319 106 425 Arkansas...................... 782 R 441 R 1,223 671 449 1,120 California ..................... 39,426 2,061 41,487 38,150 2,076 40,226 Colorado ...................... 393 3,782 4,175 364 3,954 4,318 Connecticut.................. 2,625 845 3,470 2,618 819 3,437 Delaware ..................... 134 52 186 124 55 179 Florida.......................... R 174 224 R 398 159 273 432 Georgia........................ R 993 2,168 R 3,161 854 2,599 3,453 Hawaii.......................... 29 0 29 28 0 28 Idaho............................ 117 79 196 116 79 195

478

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

0 0 Table 5. Number of Wells Producing Gas and Gas Condensate by State and the Gulf of Mexico, December 31, 2001-2005 Alabama .......................................................... 4,597 4,803 5,157 5,526 5,523 Alaska.............................................................. 170 165 195 224 227 Arizona ............................................................ 8 7 9 6 6 Arkansas.......................................................... 4,825 6,755 7,606 3,460 2,878 California ......................................................... 1,244 1,232 1,249 1,272 1,356 Colorado .......................................................... 22,117 23,554 18,774 16,718 22,691 Gulf of Mexico.................................................. 3,271 3,245 3,039 2,781 2,123 Illinois...............................................................

479

EM International Program Action Table  

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

EM INTERNATIONAL COOPERATIVE PROGRAM] October, 2012 EM INTERNATIONAL COOPERATIVE PROGRAM] October, 2012 E M I n t e r n a t i o n a l P r o g r a m s Page 1 ACTION TABLE Subject Lead Office Engaging Country Meeting Location Purpose Status Date of Event 3 rd US/German Workshop on Salt Repository Research, Design and Operations N. Buschman, EM-22 Germany Albuquerque & Carlsbad, NM Continue collaboration with Germans on salt repository research, design and operations. Draft agenda prepared. October 8-12, 2012 International Framework for Nuclear Energy Cooperation (IFNEC) Ministerial R. Elmetti, EM- 2.1 Multilateral Marrakech, Morocco To support the development of nuclear energy infrastructure globally through workforce training, information sharing, and approaches related to the safe, secure and responsible use of

480

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 7. Natural Gas Processed, Liquids Extracted, and Estimated Extraction Loss by State, 2009 Alabama .................................. 248,232 11,667 17,232 42,984 Alaska...................................... 2,830,034 19,542 22,925 86,767 Arkansas.................................. 2,352 125 168 541 California ................................. 198,213 11,042 13,722 45,669 Colorado .................................. 1,233,260 47,705 67,607 174,337 Illinois....................................... 164 24 31 84 Kansas..................................... 370,670 18,863 26,948 72,922 Kentucky.................................. 60,167 2,469 3,270 9,982 Louisiana ................................. 2,175,026 67,067 95,359 250,586 Michigan .................................. 23,819 2,409

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481

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 8. Supplemental Gas Supplies by State, 2006 (Million Cubic Feet) Colorado ...................... 0 11 0 0 6,138 6,149 Connecticut.................. 0 91 0 0 0 91 Delaware ..................... 0 * 0 0 0 * Georgia........................ 0 3 0 0 0 3 Hawaii.......................... 2,610 3 0 0 0 2,613 Illinois........................... 0 13 0 0 0 13 Indiana......................... 0 2 0 0 1,640 1,642 Iowa ............................. 0 * 0 0 46 46 Kentucky...................... 0 3 0 0 0 3 Maryland ...................... 0 41 0 0 0 41 Massachusetts............. 0 51 0 0 0 51 Minnesota .................... 0 13 0 0 0 13 Missouri ....................... 0 78 0 0 0 78 Nebraska ..................... 0 19 0 0 0 19 New Hampshire ........... 0 92 0 0 0 92 New Jersey .................. 0 0 0 0 175 175 New York .....................

482

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

20 20 Table 9. Summary of U.S. Natural Gas Imports and Exports, 2004-2008 Imports Volume (million cubic feet) Pipeline Canada a .................................................... 3,606,543 3,700,454 3,589,995 3,782,708 3,589,221 Mexico ...................................................... 0 9,320 12,749 54,062 43,314 Total Pipeline Imports............................. 3,606,543 3,709,774 3,602,744 3,836,770 3,632,535 LNG Algeria....................................................... 120,343 97,157 17,449 77,299 0 Australia.................................................... 14,990 0 0 0 0 Egypt......................................................... 0 72,540 119,528 114,580 54,839 Equatorial Guinea .....................................

483

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 7. Natural Gas Processed, Liquids Extracted, and Estimated Extraction Loss by State, 2007 Alabama .................................. 257,443 13,381 19,831 48,922 Alaska...................................... 2,965,956 22,419 26,332 99,472 Arkansas.................................. 11,532 126 162 552 California ................................. 206,239 11,388 13,521 47,045 Colorado .................................. 888,705 27,447 38,180 102,563 Florida...................................... 2,422 103 132 423 Illinois....................................... 235 38 48 131 Kansas..................................... 391,022 19,600 28,063 74,941 Kentucky.................................. 38,158 1,455 1,957 5,917 Louisiana ................................. 2,857,443 77,905 110,745

484

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

6 6 State Energy Data 2011: Consumption Table C11. Energy Consumption by Source, Ranked by State, 2011 Rank Coal Natural Gas a Petroleum b Retail Electricity Sales State Trillion Btu State Trillion Btu State Trillion Btu State Trillion Btu 1 Texas 1,695.2 Texas 3,756.9 Texas 5,934.3 Texas 1,283.1 2 Indiana 1,333.4 California 2,196.6 California 3,511.4 California 893.7 3 Ohio 1,222.6 Louisiana 1,502.9 Louisiana 1,925.7 Florida 768.0 4 Pennsylvania 1,213.0 New York 1,246.9 Florida 1,680.3 Ohio 528.0 5 Illinois 1,052.2 Florida 1,236.6 New York 1,304.0 Pennsylvania 507.6 6 Kentucky 1,010.6 Pennsylvania 998.6 Pennsylvania 1,255.6 New York 491.5

485

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 7. Natural Gas Processed, Liquids Extracted, and Estimated Extraction Loss by State, 2008 Alabama .................................. 253,028 11,753 17,222 43,191 Alaska...................................... 2,901,760 20,779 24,337 92,305 Arkansas.................................. 6,531 103 139 446 California ................................. 195,272 11,179 13,972 46,176 Colorado .................................. 1,029,641 37,804 53,590 139,332 Florida...................................... 300 16 22 65 Illinois....................................... 233 33 42 115 Kansas..................................... 397,587 19,856 28,302 76,021 Kentucky.................................. 58,899 1,783 2,401 7,233 Louisiana ................................. 2,208,920 66,369 94,785 245,631

486

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

8 8 Table 9. Summary of U.S. Natural Gas Imports and Exports, 2002-2006 Imports Volume (million cubic feet) Pipeline Canada a .................................................... 3,784,978 3,437,230 3,606,543 3,700,454 3,589,995 Mexico ...................................................... 1,755 0 0 9,320 12,749 Total Pipeline Imports............................. 3,786,733 3,437,230 3,606,543 3,709,774 3,602,744 LNG Algeria....................................................... 26,584 53,423 120,343 97,157 17,449 Australia.................................................... 0 0 14,990 0 0 Brunei ....................................................... 2,401 0 0 0 0 Egypt.........................................................

487

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

8 8 Table 9. Summary of U.S. Natural Gas Imports and Exports, 2001-2005 Imports Volume (million cubic feet) Pipeline Canada a .................................................... 3,728,537 3,784,978 3,437,230 3,606,543 3,700,454 Mexico ...................................................... 10,276 1,755 0 0 9,320 Total Pipeline Imports............................. 3,738,814 3,786,733 3,437,230 3,606,543 3,709,774 LNG Algeria....................................................... 64,945 26,584 53,423 120,343 97,157 Australia.................................................... 2,394 0 0 14,990 0 Brunei ....................................................... 0 2,401 0 0 0 Egypt.........................................................

488

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 5. Number of Wells Producing Gas and Gas Condensate by State and the Gulf of Mexico, December 31, 2002-2006 Alabama .......................................................... 4,803 5,157 5,526 5,523 6,227 Alaska.............................................................. 165 195 224 227 231 Arizona ............................................................ 7 9 6 6 7 Arkansas.......................................................... 6,755 7,606 3,460 R 3,462 3,811 California ......................................................... 1,232 1,249 1,272 1,356 1,451 Colorado .......................................................... 23,554 18,774 16,718 22,691 20,568 Gulf of Mexico.................................................. 3,245 3,039 2,781 2,123 1,946 Illinois...............................................................

489

Microsoft Word - table_21.doc  

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

9 9 Table 21. Number of natural gas commercial consumers by type of service and state, 2011-2012 R Revised data. Note: Totals may not equal sum of components due to independent rounding. Source: Energy Information Administration (EIA), Form EIA-176, "Annual Report of Natural and Supplemental Gas Supply and Disposition." Please see the cautionary note regarding the number of residential and commercial customers located on the second page of Appendix A of this report. Alabama R 67,561 135 R 67,696 67,099 135 67,234 Alaska R 12,724 303 R 13,027 13,073 61 13,134 Arizona 56,349 198 56,547 56,252 280 56,532 Arkansas 67,454 361 67,815 68,151 614 68,765

490

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 5. Number of Wells Producing by State and the Gulf of Mexico, December 31, 2003-2007 Alabama .......................................................... 5,157 5,526 5,523 6,227 6,591 Alaska.............................................................. 195 224 227 231 239 Arizona ............................................................ 9 6 6 7 7 Arkansas.......................................................... 7,606 3,460 3,462 R 3,814 4,773 California ......................................................... 1,249 1,272 1,356 1,451 1,540 Colorado .......................................................... 18,774 16,718 22,691 20,568 22,949 Gulf of Mexico.................................................. 3,039 2,781 2,123 R 2,419 2,552 Illinois...............................................................

491

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

0 0 Table 9. Summary of U.S. Natural Gas Imports and Exports, 2003-2007 Imports Volume (million cubic feet) Pipeline Canada a .................................................... 3,437,230 3,606,543 3,700,454 3,589,995 3,782,708 Mexico ...................................................... 0 0 9,320 12,749 54,062 Total Pipeline Imports............................. 3,437,230 3,606,543 3,709,774 3,602,744 3,836,770 LNG Algeria....................................................... 53,423 120,343 97,157 17,449 77,299 Australia.................................................... 0 14,990 0 0 0 Egypt......................................................... 0 0 72,540 119,528 114,580 Equatorial Guinea .....................................

492

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

0 0 Table 21. Number of Natural Gas Industrial Consumers by Type of Service and State, 2007-2008 Alabama ...................... 2,409 295 2,704 2,476 281 2,757 Alaska.......................... 7 4 11 2 4 6 Arizona ........................ 296 99 395 285 98 383 Arkansas...................... 637 418 1,055 648 456 1,104 California ..................... 35,814 3,320 39,134 36,124 3,533 39,657 Colorado ...................... 298 4,294 4,592 341 4,475 4,816 Connecticut.................. 2,472 845 3,317 2,386 810 3,196 Delaware ..................... 125 60 185 96 69 165 Florida.......................... 156 311 467 161 288 449 Georgia........................ R 1,013 1,900 R 2,913 1,003 1,887 2,890 Hawaii.......................... 27 0 27 27 0 27 Idaho............................ 109 79 188 108 91 199 Illinois...........................

493

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 7. Natural Gas Processed, Liquids Extracted, and Estimated Extraction Loss by State, 2006 Alabama .................................. 287,278 14,736 21,065 54,529 Alaska...................................... 2,665,742 20,993 24,638 93,346 Arkansas.................................. 13,702 166 212 734 California ................................. 223,580 11,267 14,056 46,641 Colorado .................................. 751,036 26,111 36,317 97,697 Florida...................................... 3,972 357 485 1,416 Illinois....................................... 242 37 47 128 Kansas..................................... 453,111 21,509 30,726 83,137 Kentucky.................................. 39,559 1,666 2,252 6,763 Louisiana ................................. 2,511,802 73,551 105,236

494

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 6. Wellhead Value and Marketed Production of Natural Gas by State, 2005-2009 2005 Total ............................ 15,425,867 -- 7.33 18,927,095 138,750,746 2006 Total ............................ 15,981,421 -- 6.39 19,409,674 124,074,399 2007 Total ............................ 16,335,710 -- 6.25 20,196,346 126,164,553 2008 Total ............................ R 18,305,411 -- R 7.97 R 21,112,053 R 168,342,230 2009 Total ............................ 18,763,726 -- 3.67 21,604,158 79,188,096 Alabama ............................... 225,666 975,789 4.32 236,029 1,020,599 Alaska................................... 397,077 1,163,555 2.93 397,077 1,163,554 Arizona ................................. 695 2,214 3.19 712 2,269 Arkansas............................... 680,613 2,332,956 3.43

495

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

9 9 Table 21. Number of natural gas commercial consumers by type of service and state, 2010-2011 R Revised data. Note: Totals may not equal sum of components due to independent rounding. Source: Energy Information Administration (EIA), Form EIA-176, "Annual Report of Natural and Supplemental Gas Supply and Disposition." Please see the cautionary note regarding the number of residential and commercial customers located on the second page of Appendix A of this report. Alabama R 68,017 146 R 68,163 67,522 135 67,657 Alaska 12,673 325 12,998 12,721 303 13,024 Arizona 56,510 166 56,676 56,349 198 56,547 Arkansas 67,676 311 67,987 67,454 361 67,815 California 399,290 40,282

496

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 6. Wellhead Value and Marketed Production of Natural Gas by State, 2006-2010 2006 Total ............................ 15,981,421 -- 6.39 19,409,674 124,074,399 2007 Total ............................ 16,335,710 -- 6.25 20,196,346 126,164,553 2008 Total ............................ 18,305,411 -- 7.97 21,112,053 168,342,230 2009 Total ............................ 18,763,726 -- 3.67 R 21,647,936 R 79,348,561 2010 Total ............................ 19,262,198 -- 4.48 22,402,141 100,272,654 Alabama ............................... 212,769 949,340 4.46 222,932 994,688 Alaska................................... 316,546 1,002,566 3.17 374,226 1,185,249 Arizona ................................. 165 676 4.11 183 753 Arkansas............................... 936,600 3,594,843 3.84

497

Microsoft Word - table_10.doc  

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

4 4 Created on: 12/12/2013 2:09:15 PM Table 10. Underground natural gas storage - salt cavern storage fields, 2008-2013 (volumes in billion cubic feet) Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Year and Month Base Gas Working Gas Total Volume Percent Injections Withdrawals Net Withdrawals a 2008 Total b -- -- -- -- -- 440 398 -42 2009 Total b -- -- -- -- -- 459 403 -56 2010 Total b -- -- -- -- -- 511 452 -58 2011 January 137 174 311 65 59.3 23 69 46 February 137 125 262 48 62.5 30 80 49 March 137 151 288 39 34.8 51 25 -25 April 140 172 312 17 11.2 42 21 -22 May 140 211 352

498

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

4 4 Table 8. Supplemental Gas Supplies by State, 2005 (Million Cubic Feet) Colorado ...................... 0 2 0 0 5,283 5,285 Connecticut.................. 0 273 0 0 0 273 Delaware ..................... 0 * 0 0 0 * Georgia........................ 0 * 0 0 0 * Hawaii.......................... 2,593 14 0 0 0 2,606 Illinois........................... 0 11 0 4 0 15 Indiana......................... 0 30 0 0 1,958 1,988 Iowa ............................. 0 2 0 30 0 31 Kentucky...................... 0 15 0 0 0 15 Maryland ...................... 0 382 0 0 0 382 Massachusetts............. 0 46 0 0 0 46 Minnesota .................... 0 154 0 0 0 154 Missouri ....................... 0 15 0 0 0 15 Nebraska ..................... 0 16 0 * 0 16 New Hampshire ........... 0 84 0 0 0 84 New Jersey .................. 0 0 0 0 435 435 New York

499

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

17 17 Table C12. Total Energy Consumption, Gross Domestic Product (GDP), Energy Consumption per Real Dollar of GDP, Ranked by State, 2011 Rank Total Energy Consumption Gross Domestic Product (GDP) Energy Consumption per Real Dollar of GDP State Trillion Btu State Billion Chained (2005) Dollars State Thousand Btu per Chained (2005) Dollar 1 Texas 12,206.6 California 1,735.4 Louisiana 19.7 2 California 7,858.4 Texas 1,149.9 Wyoming 17.5 3 Florida 4,217.1 New York 1,016.4 North Dakota 15.4 4 Louisiana 4,055.3 Florida 661.1 Alaska 14.3 5 Illinois 3,977.8 Illinois 582.1 Mississippi 13.8 6 Ohio 3,827.6 Pennsylvania 500.4 Kentucky 13.5

500

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 6. Wellhead Value and Marketed Production of Natural Gas, 2003-2007, and by State, 2007 2003 Total ............................ 14,589,545 -- 4.88 19,974,360 97,555,375 2004 Total ............................ 15,223,749 -- 5.46 19,517,491 106,521,974 2005 Total ............................ 15,425,867 -- 7.33 18,927,095 138,750,746 2006 Total ............................ R 15,981,421 -- R 6.39 R 19,409,674 R 124,074,399 2007 Total ............................ 16,031,199 -- 6.37 20,019,321 127,530,680 Alabama ............................... 259,062 1,926,374 7.44 270,407 2,010,736 Alaska................................... 368,344 2,072,647 5.63 433,485 2,439,193 Arizona ................................. 634 3,791 5.98 655 3,913 Arkansas...............................