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1

Annual Energy Outlook Forecast Evaluation-Table 1  

Annual Energy Outlook 2012 (EIA)

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

2

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

3

ELECTRICITY DEMAND FORECAST COMPARISON REPORT  

E-Print Network (OSTI)

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

4

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

5

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

6

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

7

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

8

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

9

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

10

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

11

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.

12

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

13

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

U.S. Energy Information Administration (EIA)

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

14

Wind Power Forecasting Error Distributions: An International Comparison; Preprint  

DOE Green Energy (OSTI)

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

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

2012-09-01T23:59:59.000Z

15

Industrial production index forecast: Methods comparison  

Science Conference Proceedings (OSTI)

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

M. Filomena Teodoro

2012-01-01T23:59:59.000Z

16

On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables  

Science Conference Proceedings (OSTI)

The so-called True Skill Statistic (TSS) and the Heidke Skill Score (S), as used in the context of the contingency, table approach to forecast verification, are compared. It is shown that the TSS approaches the Probability of Detection (POD) ...

Charles A. Doswell III; Robert Davies-Jones; David L. Keller

1990-12-01T23:59:59.000Z

17

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

18

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

E-Print Network (OSTI)

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

Bolinger, Mark

2008-01-01T23:59:59.000Z

19

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

20

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

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

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

DOE Green Energy (OSTI)

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

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

2012-07-01T23:59:59.000Z

22

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

23

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

24

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

25

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

26

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

27

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

28

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

29

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

E-Print Network (OSTI)

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

Bolinger, Mark

2008-01-01T23:59:59.000Z

30

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

31

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

32

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

E-Print Network (OSTI)

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

Steinwart, Ingo

33

Property Comparison Tables: Hardness and Tensile Properties  

Science Conference Proceedings (OSTI)

Table 6   Tensile modulus of elasticity in tension at room temperature...11.7 1.7 5.9 0.86 Spruce (sitka) 11.0 1.6 ? ? Poplar (yellow) 11.0 1.6 ? ? Carbon, petroleum coke base 11.0 1.6 15.8 2.3 Indium 10.8 1.57 ? ? Basswood 10.3 1.5 ? ? Elm (rock) 10.3 1.5 ? ? Polysulfone, 30??40% glass reinforced 10.3 1.5 7.6 1.1 Cypress (Southern bald) 9.6 1.4 ? ? Nylons; 30% glass...

34

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

DOE Green Energy (OSTI)

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

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

2013-10-01T23:59:59.000Z

35

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

SciTech Connect

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

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

2013-12-18T23:59:59.000Z

36

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

E-Print Network (OSTI)

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

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

2000-04-01T23:59:59.000Z

37

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

38

C:\\WEBSHARE\\WWWROOT\\forecastactuals\\tables2_18.wpd  

Annual Energy Outlook 2012 (EIA)

Tables 2 through 18 Table 2. Total Energy Consumption, Actual vs. Forecasts Table 3. Total Petroleum Consumption, Actual vs. Forecasts Table 4. Total Natural Gas Consumption,...

39

Comparison of Energy Information Administration and Bonneville Power Administration load forecasts  

SciTech Connect

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

Reed, H.J.

1978-06-01T23:59:59.000Z

40

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

Science Conference Proceedings (OSTI)

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

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

2013-07-25T23:59:59.000Z

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

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

E-Print Network (OSTI)

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

Droegemeier, Kelvin K.

42

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

E-Print Network (OSTI)

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

Mass, Clifford F.

43

A.: Modeling and forecasting electricity loads: A comparison  

E-Print Network (OSTI)

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

Rafa? Weron

2004-01-01T23:59:59.000Z

44

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.

45

STAFF FORECAST OF 2007 PEAK STAFFREPORT  

E-Print Network (OSTI)

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

46

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

47

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

E-Print Network (OSTI)

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

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

2005-01-01T23:59:59.000Z

48

Table  

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

Table 295: Muons in water as calc from steam to check code ZA gcm 3 I eV a k m s x 0 x 1 C 0 0.55509 1.000 71.6 0.44251 3.0000 0.2000 2.0000 3.5017 0.00 T p...

49

EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes  

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

EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes On cost recovery basis, the CRADA, WFO, PUA and NPUA agreements can be distinguished as follows: Participant covers any DOE Laboratory costs? NO = NPUA 1 , no cost recovery YES = Participant uses DOE Contractor personnel (other than incidental use)? NO = PUA 2 , full cost recovery YES = Participant "hires" DOE Contractor personnel (as opposed to joint research collaboration)? NO = CRADA 3 , full or partial cost recovery YES = WFO 4 , full cost recovery We suggest the following changes to the CRADA, WFO, PUA and NPUA agreements below. 5 1. Title.......................................................................................................................................................................................................2

50

EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes  

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

EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes On cost recovery basis, the CRADA, WFO, PUA and NPUA agreements can be distinguished as follows: Participant covers any DOE Laboratory costs? NO = NPUA 1 , no cost recovery YES = Participant uses DOE Contractor personnel (other than incidental use)? NO = PUA 2 , full cost recovery YES = Participant "hires" DOE Contractor personnel (as opposed to joint research collaboration)? NO = CRADA 3 , full or partial cost recovery YES = WFO 4 , full cost recovery We suggest the following changes to the CRADA, WFO, PUA and NPUA agreements below. 5 1. Title.......................................................................................................................................................................................................2

51

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network (OSTI)

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

52

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect

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

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

53

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

SciTech Connect

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

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

54

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

DOE Green Energy (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

55

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

DOE Green Energy (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

56

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

57

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

E-Print Network (OSTI)

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

Bolinger, Mark

2008-01-01T23:59:59.000Z

58

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

59

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

60

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

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

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

62

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

Science Conference Proceedings (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-12-06T23:59:59.000Z

63

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

Science Conference Proceedings (OSTI)

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

James A. Renwick; Craig S. Thompson

2001-09-01T23:59:59.000Z

64

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

Science Conference Proceedings (OSTI)

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

Xuguang Wang; Craig H. Bishop

2003-05-01T23:59:59.000Z

65

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

66

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

Science Conference Proceedings (OSTI)

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

2013-01-01T23:59:59.000Z

67

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

Science Conference Proceedings (OSTI)

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

Harold E. Brooks; Charles A. Doswell III

1996-09-01T23:59:59.000Z

68

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

69

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

E-Print Network (OSTI)

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

Bolinger, Mark

2008-01-01T23:59:59.000Z

70

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

71

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

72

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

73

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

E-Print Network (OSTI)

History of Natural Gas Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .pdf/table13.pdf> History of Natural Gas Regulation The

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

2005-01-01T23:59:59.000Z

74

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

E-Print Network (OSTI)

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

75

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

76

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

E-Print Network (OSTI)

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

Bolinger, Mark

2009-01-01T23:59:59.000Z

77

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

SciTech Connect

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

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

2010-03-15T23:59:59.000Z

78

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

Science Conference Proceedings (OSTI)

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

Steven J. Lambert

1989-05-01T23:59:59.000Z

79

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

SciTech Connect

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

Bolinger, Mark; Wiser, Ryan

2009-01-28T23:59:59.000Z

80

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

Science Conference Proceedings (OSTI)

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

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

2008-01-07T23:59:59.000Z

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

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

Science Conference Proceedings (OSTI)

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

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

2013-10-01T23:59:59.000Z

82

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

Science Conference Proceedings (OSTI)

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

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

83

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

Science Conference Proceedings (OSTI)

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

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

2005-02-09T23:59:59.000Z

84

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

Science Conference Proceedings (OSTI)

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

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

2007-10-01T23:59:59.000Z

85

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

Science Conference Proceedings (OSTI)

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

Carolyn A. Reynolds

1999-11-01T23:59:59.000Z

86

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

Science Conference Proceedings (OSTI)

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

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

2000-01-01T23:59:59.000Z

87

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

Science Conference Proceedings (OSTI)

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

Juan Ruiz; Celeste Saulo; Eugenia Kalnay

2009-02-01T23:59:59.000Z

88

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

Science Conference Proceedings (OSTI)

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

Allan H. Murphy; Qian Ye

1990-09-01T23:59:59.000Z

89

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

Science Conference Proceedings (OSTI)

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

Eric Gilleland

2013-01-01T23:59:59.000Z

90

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

Science Conference Proceedings (OSTI)

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

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

2000-06-01T23:59:59.000Z

91

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

E-Print Network (OSTI)

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

Joshi, Krunal Jaykant

2012-08-01T23:59:59.000Z

92

Consensus Coal Production Forecast for  

E-Print Network (OSTI)

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

Mohaghegh, Shahab

93

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

E-Print Network (OSTI)

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

Mr. Rajesh Deshmukh; Dr. Amita Mahor

2011-01-01T23:59:59.000Z

94

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

SciTech Connect

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

Bolinger, Mark A.; Wiser, Ryan H.

2010-01-04T23:59:59.000Z

95

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

96

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

E-Print Network (OSTI)

1 1.1 History of Natural Gaspdf/table13.pdf> History of Natural Gas Regulation TheUnderstanding the history of the natural gas market helps to

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

2005-01-01T23:59:59.000Z

97

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

Science Conference Proceedings (OSTI)

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

Chi-Chen Wang

2011-08-01T23:59:59.000Z

98

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)

99

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

Science Conference Proceedings (OSTI)

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

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

2012-02-01T23:59:59.000Z

100

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

E-Print Network (OSTI)

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

Bolinger, Mark

2008-01-01T23:59:59.000Z

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

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-01-01T23:59:59.000Z

102

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

E-Print Network (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-01-01T23:59:59.000Z

103

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

104

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.

105

Forecast Correlation Coefficient Matrix of Stock Returns in Portfolio Analysis  

E-Print Network (OSTI)

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

Zhao, Feng

2013-01-01T23:59:59.000Z

106

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

Science Conference Proceedings (OSTI)

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

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

2009-06-01T23:59:59.000Z

107

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

Science Conference Proceedings (OSTI)

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

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

2000-06-01T23:59:59.000Z

108

Forecast Technical Document Forecast Types  

E-Print Network (OSTI)

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

109

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

Science Conference Proceedings (OSTI)

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

J. D. Price; M. R. Bush

2004-12-01T23:59:59.000Z

110

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

111

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.

112

RACORO Forecasting  

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

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

113

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

Science Conference Proceedings (OSTI)

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

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

1989-12-01T23:59:59.000Z

114

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.

115

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

Science Conference Proceedings (OSTI)

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

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

2009-08-01T23:59:59.000Z

116

R/ECON October 1999 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

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

117

R/ECON July 1999 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

R/ECON July 1999 Forecast RUTGERS ECONOMIC ADVISORY SERVICE FORECAST OF JULY 1999 NEW JERSEY forecast for New Jersey is for a continuing but slowing expansion. (See Table 1.) In 1998, employment rose increased by 0.7% in 1998. It will slow a bit over the forecast period as foreign immigration declines. #12

118

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.

119

Forecasting Forecast Skill  

Science Conference Proceedings (OSTI)

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

Eugenia Kalnay; Amnon Dalcher

1987-02-01T23:59:59.000Z

120

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

Science Conference Proceedings (OSTI)

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

William A. Gallus Jr.; James F. Bresch

2006-09-01T23:59:59.000Z

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

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

Science Conference Proceedings (OSTI)

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

Chih-Chiang Wei

2012-04-01T23:59:59.000Z

122

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

Science Conference Proceedings (OSTI)

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

N. Brunet; R. Verret; N. Yacowar

1988-12-01T23:59:59.000Z

123

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

Science Conference Proceedings (OSTI)

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

John S. Jensenius Jr.

1990-03-01T23:59:59.000Z

124

Forecast Combinations  

E-Print Network (OSTI)

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

Allan Timmermann; Jel Codes C

2006-01-01T23:59:59.000Z

125

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

Gasoline and Diesel Fuel Update (EIA)

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

126

Forecasting overview  

E-Print Network (OSTI)

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

Rob J Hyndman

2009-01-01T23:59:59.000Z

127

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

Science Conference Proceedings (OSTI)

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

Daniel Guedalia; Thierry Bergot

1994-06-01T23:59:59.000Z

128

Wavelets and Field Forecast Verification  

Science Conference Proceedings (OSTI)

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

William M. Briggs; Richard A. Levine

1997-06-01T23:59:59.000Z

129

Draft Forecast of Electricity Demand for the 5th  

E-Print Network (OSTI)

products has been below the medium-low. Future natural gas prices are expected to be higher in this power's draft natural gas price forecasts. The medium natural gas price forecast for this plan in 2015 is about Council Document 2001-23, sited above. #12;DRAFT DRAFT DRAFT 11 Table 1 Natural Gas Price Forecasts

130

Downscaling Extended Weather Forecasts for Hydrologic Prediction  

SciTech Connect

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

Leung, Lai-Yung R.; Qian, Yun

2005-03-01T23:59:59.000Z

131

Relationship between Precipitation Forecast Errors and Skill Scores of Dichotomous Forecasts  

Science Conference Proceedings (OSTI)

In this paper, the sensitivities of the equitable threat score (ETS) and the true skill score (TSS), obtained with a 2 2 contingency table, to continuous precipitation forecast errors are investigated. Two idealized error models are adopted to ...

Nazario Tartaglione

2010-02-01T23:59:59.000Z

132

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.

133

Short-term streamflow forecasting: ARIMA vs neural networks  

Science Conference Proceedings (OSTI)

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

Juan Frausto-Solis; Esmeralda Pita; Javier Lagunas

2008-03-01T23:59:59.000Z

134

Table of Exhibits..................................................................................................... iii  

E-Print Network (OSTI)

Table of Contents..................................................................................................... ii

Pjm Interconnection

2007-01-01T23:59:59.000Z

135

Energy conservation and official UK energy forecasts  

SciTech Connect

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

Pearce, D.

1980-09-01T23:59:59.000Z

136

sf01 - Summer Fuel Table.xlsx  

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

by simulation of the Short-Term Integrated Forecasting System. a Spot Price of West Texas Intermediate (WTI) crude oil Year-over-year Change (percent) Table SF01. U.S. Motor...

137

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

Science Conference Proceedings (OSTI)

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

Christophe Accadia; Stefano Zecchetto; Alfredo Lavagnini; Antonio Speranza

2007-05-01T23:59:59.000Z

138

Improving Probabilistic Ensemble Forecasts of Convection through the Application of QPFPOP Relationships  

Science Conference Proceedings (OSTI)

Four new approaches of postprocessing quantitative precipitation forecasts (QPFs) from model ensemble output were used to generate probability of precipitation (POP) tables in order to develop a forecasting method that could outperform a ...

Christopher J. Schaffer; William A. Gallus Jr.; Moti Segal

2011-06-01T23:59:59.000Z

139

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.

140

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

SciTech Connect

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

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

1983-07-01T23:59:59.000Z

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

Solar forecasting review  

E-Print Network (OSTI)

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

Inman, Richard Headen

2012-01-01T23:59:59.000Z

142

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

Science Conference Proceedings (OSTI)

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

Martin Ehrendorfer; Allan H. Murphy

1988-09-01T23:59:59.000Z

143

NFI Forecasts Methodology NFI Forecasts Methodology  

E-Print Network (OSTI)

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

144

Forecast Technical Document Restocking in the Forecast  

E-Print Network (OSTI)

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

145

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS FORECAST IMPROVEMENTS  

E-Print Network (OSTI)

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

Greenslade, Diana

146

Another Approach to Forecasting Forecast Skill  

Science Conference Proceedings (OSTI)

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

W. Y. Chen

1989-02-01T23:59:59.000Z

147

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:

148

TABLE OF CONTENTS  

E-Print Network (OSTI)

Table of Contents......i List of Tables.....ii

Ingleside Tx; Base Realignment

2010-01-01T23:59:59.000Z

149

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

E-Print Network (OSTI)

of AEO 2010 Natural Gas Price Forecast to NYMEX Futures Prices Date: January 4, 2010 1. Introduction, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better

150

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

151

The Forecast Gap: Linking Forwards and Forecasts  

Science Conference Proceedings (OSTI)

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

2008-12-15T23:59:59.000Z

152

Airborne Volcanic Ash Forecast Area Reliability  

Science Conference Proceedings (OSTI)

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

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

2007-10-01T23:59:59.000Z

153

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

Science Conference Proceedings (OSTI)

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

Mark Buehner; Ahmed Mahidjiba

2010-10-01T23:59:59.000Z

154

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)

155

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

DOE Green Energy (OSTI)

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

156

Forecasting in Meteorology  

Science Conference Proceedings (OSTI)

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

C. S. Ramage

1993-10-01T23:59:59.000Z

157

Emergency Operations Table of Contents  

E-Print Network (OSTI)

Table of Contents..................................................................................................... ii

unknown authors

2012-01-01T23:59:59.000Z

158

Global Carbon Biomass Tables  

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

Table 1c. Mixed Forest Classes Table 1d. NaturalBurnt Forest Mosaic Classes Table 1e. CropForest Mosaic Classes Table 1f. Shrub Cover Classes Table 1g. Grassland Classes Table...

159

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

E-Print Network (OSTI)

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

160

Mid-range energy-forecasting system: structure, forecasts, and critique  

SciTech Connect

The Mid-Range Energy Forecasting System (MEFS) is a large-scale, interdisciplinary model of the US energy system maintained by the US Department of Energy. MEFS provides long-run regional forecasts of delivered prices for electricity, coal, gasoline, residual, distillate, and natural gas. A number of sets of MEFS forecasts are usually issued, each set corresponding to a different scenario. Because it forecasts prices and since these forecasts are regularly disseminated, MEFS is of considerable practical interest. A critical guide of the model's output for potential users is provided in this paper. The model's logic is described, the latest forecasts from MEFS are presented, and the reasonableness of both the forecasts and the methodology are critically evaluated. The manner in which MEFS interfaces with the Oil Market Simulation Model, which forecasts crude oil price, is also discussed. The evaluation concludes that while there are serious problems with MEFS, selective use can prove very helpful. 17 references, 1 figure, 2 tables.

DeSouza, G.

1980-01-01T23:59:59.000Z

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

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

Science Conference Proceedings (OSTI)

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

George Diak; Stacey Heikkinen; John Rates

1986-01-01T23:59:59.000Z

162

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

Science Conference Proceedings (OSTI)

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

Peitao Peng; Anthony G. Barnston; Arun Kumar

2013-04-01T23:59:59.000Z

163

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

Science Conference Proceedings (OSTI)

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

Roberto Buizza

2008-09-01T23:59:59.000Z

164

Table Search (or Ranking Tables)  

E-Print Network (OSTI)

Table Search (or Ranking Tables) Alon Halevy Google DBRank @ ICDE March 1, 2010 #12;Structured Data organizations Requires infrastructure, concerns about losing control Hard to find structured data via search Search #1 store locations used cars radio stations patents recipes · Deep = not accessible through

Halevy, Alon

165

Forecasts, Meteorology Services, Environmental Sciences Department  

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

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

166

Table 4  

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

125 69 112 131 137 158 7.36 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

167

Table 4  

Gasoline and Diesel Fuel Update (EIA)

378 913 993 1,130 1,316 1,625 8.24 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

168

this table  

U.S. Energy Information Administration (EIA)

AC Argentina AR Aruba AA Bahamas, The BF Barbados BB Belize BH Bolivia BL ... Table 1.2 World Petroleum Consumption, 1980-2006 (Thousand Barrels per Day) Page 1980.00 ...

169

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

Science Conference Proceedings (OSTI)

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

Thomas M. Hamill

2003-10-01T23:59:59.000Z

170

Verifying Forecasts Spatially  

Science Conference Proceedings (OSTI)

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

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

2010-10-01T23:59:59.000Z

171

Forecasting of Supercooled Clouds  

Science Conference Proceedings (OSTI)

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

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

1995-07-01T23:59:59.000Z

172

Time Series and Forecasting  

Science Conference Proceedings (OSTI)

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

173

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

174

Colorado uranium production forecast for 1981 to 1990. [Monograph  

SciTech Connect

A decline in demand for yellowcake, a single-use commodity of which Colorado is the fourth largest producer, is influenced by several interrelated factors. The revised forecasts for 1990 assume that electric-power capacity will be lower than previous forecasts and that domestic production will supply 80% of the yellowcake. Production will be lower until inventory depletion allows a balanced market. Production rates will begin increasing after 1987. An appendix summarizes the factors influencing production rates. 10 references, 3 tables.

Morse, J.G.

1980-01-01T23:59:59.000Z

175

Forecast Technical Document Volume Increment  

E-Print Network (OSTI)

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

176

A General Analytic Method for Assessing Sensitivity to Bias of Performance Measures for Dichotomous Forecasts  

Science Conference Proceedings (OSTI)

Performance measures computed from the 2 2 contingency table of outcomes for dichotomous forecasts are sensitive to bias. The method presented here evaluates how the probability of detection (POD) must change as bias changes so that a ...

Keith F. Brill

2009-02-01T23:59:59.000Z

177

The Strategy of Professional Forecasting  

E-Print Network (OSTI)

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

Marco Ottaviani; Peter Norman Srensen

2003-01-01T23:59:59.000Z

178

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

179

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

180

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

Note: This page contains sample records for the topic "forecast comparisons 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.


181

Business forecasting methods  

E-Print Network (OSTI)

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

Rob J Hyndman

2009-01-01T23:59:59.000Z

182

ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL  

E-Print Network (OSTI)

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

Hamill, Tom

183

Bayesian Correlation Score: A Utilitarian Measure of Forecast Skill  

Science Conference Proceedings (OSTI)

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

Roman Krzysztofowicz

1992-01-01T23:59:59.000Z

184

Evaluation of Prototypical Climate Forecasts: The Sufficiency Relation  

Science Conference Proceedings (OSTI)

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

Martin Ehrendorfer; Allan H. Murphy

1992-08-01T23:59:59.000Z

185

ORNL integrated forecasting system  

SciTech Connect

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

Rizy, C.G.

1983-01-01T23:59:59.000Z

186

Solar forecasting review  

E-Print Network (OSTI)

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

Inman, Richard Headen

2012-01-01T23:59:59.000Z

187

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

188

forecast | OpenEI  

Open Energy Info (EERE)

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

189

Seasonal tropical cyclone forecasts  

E-Print Network (OSTI)

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

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

2007-01-01T23:59:59.000Z

190

Probabilistic Forecasts from the National Digital Forecast Database  

Science Conference Proceedings (OSTI)

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

Roman Krzysztofowicz; W. Britt Evans

2008-04-01T23:59:59.000Z

191

Real-Time Forecasting of Snowfall Using a Neural Network  

Science Conference Proceedings (OSTI)

A set of 53 snowfall reports was collected in real time from the 2004/05 and 2005/06 cold seasons (NovemberMarch). Three snowfall-amount forecast methods were tested: neural network, surface-temperature-based 676-USDT table, and climatological ...

Paul J. Roebber; Melissa R. Butt; Sarah J. Reinke; Thomas J. Grafenauer

2007-06-01T23:59:59.000Z

192

Certification and Training Requirements Table of Contents  

E-Print Network (OSTI)

Table of Exhibits..................................................................................................... iii

unknown authors

2008-01-01T23:59:59.000Z

193

Global and Local Skill Forecasts  

Science Conference Proceedings (OSTI)

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

P. L. Houtekamer

1993-06-01T23:59:59.000Z

194

Distortion Representation of Forecast Errors  

Science Conference Proceedings (OSTI)

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

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

1995-09-01T23:59:59.000Z

195

Composite forecasting in commodity systems  

E-Print Network (OSTI)

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

Johnson, Stanley R; Rausser, Gordon C.

1980-01-01T23:59:59.000Z

196

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network (OSTI)

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

197

CONSULTANT REPORT DEMAND FORECAST EXPERT  

E-Print Network (OSTI)

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

198

Does the term structure forecast  

E-Print Network (OSTI)

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

Berardi, Andrea; Torous, Walter

2002-01-01T23:59:59.000Z

199

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:

200

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 comparisons 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

Coefficients for Debiasing Forecasts  

Science Conference Proceedings (OSTI)

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

Thomas R. Stewart; Patricia Reagan-Cirincione

1991-08-01T23:59:59.000Z

202

Evaluating Point Forecasts  

E-Print Network (OSTI)

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

Gneiting, Tilmann

2009-01-01T23:59:59.000Z

203

Forecasters Objectives and Strategies ?  

E-Print Network (OSTI)

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

Ivn Marinovic; Marco Ottaviani; Peter Norman Srensen

2011-01-01T23:59:59.000Z

204

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.

205

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

206

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

207

A New Verification Score for Public Forecasts  

Science Conference Proceedings (OSTI)

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

Dean P. Gulezian

1981-02-01T23:59:59.000Z

208

NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS  

E-Print Network (OSTI)

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

Mohaghegh, Shahab

209

APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY  

E-Print Network (OSTI)

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

Katz, Richard

210

Why are survey forecasts superior to model forecasts?  

E-Print Network (OSTI)

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

Michael P. Clements; Michael P. Clements

2010-01-01T23:59:59.000Z

211

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

in spreadsheet format. A total of one hundred and seventeen tables is presented. The data for tables 10 and 20 match those published in AEO2004 Appendix tables A2 and A3,...

212

Supplement Tables - Supplemental Data  

Annual Energy Outlook 2012 (EIA)

are in spreadsheet format. A total of one hundred and nine tables is presented. The data for tables 10 and 20 match those published in AEO2003 Appendix tables A2 and A3,...

213

Meson Summary Table See  

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

Meson Summary Table See also the table of suggested qq quark-model assignments in the Quark Model section. * Indicates particles that appear in the preceding Meson Summary Table....

214

Supplement Tables - Supplemental Data  

Annual Energy Outlook 2012 (EIA)

Vehicle Fuel Economy Table 57. New Light-Duty Vehicle Prices Table 58. New Light-Duty Vehicle Range Table 59. Electric Power Projections for EMM Region 01- East Central Area...

215

LOAD FORECASTING Eugene A. Feinberg  

E-Print Network (OSTI)

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

Feinberg, Eugene A.

216

Demand Forecast INTRODUCTION AND SUMMARY  

E-Print Network (OSTI)

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

217

EIA - Annual Energy Outlook (AEO) 2013 Data Tables  

Gasoline and Diesel Fuel Update (EIA)

2013 (See release cycle changes) | correction | full 2013 (See release cycle changes) | correction | full report Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Market Trends Issues in Focus Legislation & Regulations Comparison Appendices View All Filter By Source Oil Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Other Topics Emissions Prices Macroeconomic International Data TablesAll Tables Reference case summary & detailed tables... + EXPAND ALL Summary Case Tables additional formats Table 1. Total Energy Supply, Disposition, and Price Summary XLS

218

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

219

All Consumption Tables  

U.S. Energy Information Administration (EIA)

2010 Consumption Summary Tables. Table C1. Energy Consumption Overview: Estimates by Energy Source and End-Use Sector, 2010 (Trillion Btu) ... Ranked by State, 2010

220

Factors Driving Prices & Forecast  

Gasoline and Diesel Fuel Update (EIA)

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

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

Modeling and Forecasting Aurora  

Science Conference Proceedings (OSTI)

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

Dirk Lummerzheim

2007-01-01T23:59:59.000Z

222

Valuing Climate Forecast Information  

Science Conference Proceedings (OSTI)

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

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

1987-09-01T23:59:59.000Z

223

Just enough tabling  

Science Conference Proceedings (OSTI)

We introduce just enough tabling (JET), a mechanism to suspend and resume the tabled execution of logic programs at an arbitrary point. In particular, JET allows pruning of tabled logic programs to be performed without resorting to any recomputation. ... Keywords: logic programming, pruning, suspension/resumption in the WAM, tabling

Konstantinos Sagonas; Peter J. Stuckey

2004-08-01T23:59:59.000Z

224

Forecasting in the Presence of Level Shifts  

E-Print Network (OSTI)

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

Smith, Aaron

2004-01-01T23:59:59.000Z

225

Multivariate Forecast Evaluation And Rationality Testing  

E-Print Network (OSTI)

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

Komunjer, Ivana; OWYANG, MICHAEL

2007-01-01T23:59:59.000Z

226

FROM ANALYSTS ' EARNINGS FORECASTS  

E-Print Network (OSTI)

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

Theodore Sougiannis; Takashi Yaekura

2000-01-01T23:59:59.000Z

227

Forecasting of mine price for central Appalachian steam coal  

SciTech Connect

In reaction to Virginia's declining share of the steam coal market and the subsequent depression in southwest Virginia's economy, an optimization model of the central Appalachian steam coal market was developed. The input to the cost vector was the delivered cost of coal, which is comprised of the mine price (FOB) and transportation cost. One objective of the study was to develop a purchasing model that could be used to minimize the cost of coal procurement over a multi-period time span. The initial case study used a six-month period (7/86-12/86); this requires short-term, forecasts of the mine price of coal. Mine-cost equations and regression models were found to be inadequate for forecasting the mine price of coal. Instead forecasts were generated using modified time series models. This paper describes the application of classical time-series modeling to forecasting the mine price of coal in central Appalachia; in particular, the special modification to the classical methodology needed to generate short-term forecasts and their confidence limits and the need to take into account market-specific considerations such as the split between long-term contracts and the spot market. Special consideration is given to forecasting the spot market. 7 references, 4 figures, 3 tables.

Smith, M.L.

1988-01-01T23:59:59.000Z

228

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

229

Future world oil prices: modeling methodologies and summary of recent forecasts  

SciTech Connect

This paper has three main objectives. First, the various methodologies that have been developed to explain historical oil price changes and forecast future price trends are reviewed and summarized. Second, the paper summarizes recent world oil price forecasts, and, then possible, discusses the methodologies used in formulating those forecasts. Third, utilizing conclusions from the reviews of the modeling methodologies and the recent price forecasts, in combination with an assessment of recent and projected oil market trends, oil price projections are given for the time period 1987 to 2022. The paper argues that modeling methodologies have undergone significant evolution during the past decade as modelers increasingly recognize the complex and constantly changing structure of the world oil market. Unfortunately, at this point in time a consensus about the appropriate methodology to use in formulating oil price forecasts is yet to be reached. There is, however, a general movement toward the opinion that both economic and political factors should be considered when making price projections. Likewise, there is no consensus about future oil price trends. Forecasts differ widely. However, in general, forecasts have been adjusted downwardly in recent years. Further, an overall assessment of the forecasts and recent oil market trends suggests that oil prices will remain constant in real terms for the remainder of the 1980s. Real oil prices are expected to increase by between 2 and 3% during the 1990s and beyond. Forecasters are quick to point out, however, that all forecasts are subject to significant uncertainty. 69 references, 3 figures, 10 tables.

Curlee, T.R.

1985-04-01T23:59:59.000Z

230

Forecasting world oil prices: the evolution of modeling methodologies and summary of recent projections  

SciTech Connect

This paper has three main objectives: (1) to review and summarize the varios methodologies that have been developed to explain historical oil price changes and forecast future price trends, (2) to summarize recent world oil price forecasts, and, when possible, discuss the methodologies used in formulating those forecasts, and (3) utilizing conclusions from the reviews of the modeling methodologies and the recent price forecasts, in combination with an assessment of recent and projected oil market trends, to give oil price projections for the time period 1987 to 2022. The paper argues that modeling methodologies have undergone significant evolution during the past decade as modelers increasingly recognize the complex and constantly changing structure of the world oil market. Unfortunately, a consensus about the appropriate methodology to use in formulating oil price forecasts is yet to be reached. There is, however, a general movement toward the opinion that both economic and political factors should be considered when making price projections. Likewise, there is no consensus about future oil price trends. Forecasts differ widely. However, in general, forecasts have been adjusted downwardly in recent years. Further, an overall assessment of the forecasts and recent oil market trends suggests that oil prices will remain constant in real terms for the remainder of the 1980s. Real oil prices are expected to increase by between 2 and 3% during the 1990s and beyond. Forecasters are quick to point out, however, that all forecasts are subject to significant uncertainty. 68 references, 1 figure, 6 tables.

Curlee, T.R.

1985-01-01T23:59:59.000Z

231

Cost Development Guidelines Table of Contents  

E-Print Network (OSTI)

Table of Contents..................................................................................................... ii Table of Exhibits...................................................................................................... v Approval.................................................................................................................. vi

unknown authors

2011-01-01T23:59:59.000Z

232

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)

233

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

234

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

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

235

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

236

table of contents  

Science Conference Proceedings (OSTI)

The Use of a Mineralogical Data Base for Production Forecasting and Trouble- shooting in Copper Leach Operations [pp. 393-408] W. Baum. A Framework for...

237

Table of Contents  

Science Conference Proceedings (OSTI)

... Weather maps and forecasts are made available by Thinking ... nwhois and other people finding tools.(3). ... is also tkWWW, an authoring tool based on ...

238

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network (OSTI)

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

239

Forecast Technical Document Technical Glossary  

E-Print Network (OSTI)

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

240

Forecast Technical Document Tree Species  

E-Print Network (OSTI)

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

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

3, 21452173, 2006 Probabilistic forecast  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

242

4, 189212, 2007 Forecast and  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

243

FINANCIAL FORECASTING USING GENETIC ALGORITHMS  

E-Print Network (OSTI)

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

Boetticher, Gary D.

244

EIA - Annual Energy Outlook (AEO) 2012 Data Tables  

Gasoline and Diesel Fuel Update (EIA)

2 2 Release Date: June 25, 2012 | Next Early Release Date: December 5, 2012 | Report Number: DOE/EIA-0383(2012) Overview Data Reference Case Side Cases Interactive Table Viewer Topics Source Oil/Liquids Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Energy Demand Other Emissions Prices Macroeconomic International Efficiency Publication Chapter Executive Summary Market Trends Issues in Focus Legislation & Regulations Comparison Appendices View All Filter By Source Oil Natural Gas Coal Electricity Renewable/Alternative Nuclear Sector Residential Commercial Industrial Transportation Other Topics Emissions Prices Macroeconomic International Data TablesAll Tables Reference case summary & detailed tables... + EXPAND ALL Summary Case Tables Additional Formats

245

Table of Contents PJM Manual [18]: PJM Capacity Market  

E-Print Network (OSTI)

Table of Contents Table of Contents..................................................................................................... ii

unknown authors

2008-01-01T23:59:59.000Z

246

Forecast of auroral activity  

Science Conference Proceedings (OSTI)

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

A. T. Y. Lui

2004-01-01T23:59:59.000Z

247

CBECS Buildings Characteristics --Revised Tables  

Gasoline and Diesel Fuel Update (EIA)

Table 37. Refrigeration Equipment, Number of Buildings and Floorspace, 1995 Table 38. Water-Heating Equipment, Number of Buildings and Floorspace, 1995 Table 39. Lighting...

248

CBECS Buildings Characteristics --Revised Tables  

Gasoline and Diesel Fuel Update (EIA)

Table 25. Cooling Energy Sources, Number of Buildings and Floorspace, 1995 Table 26. Water-Heating Energy Sources, Number of Buildings, 1995 Table 27. Water-Heating Energy...

249

Table of Contents  

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

COMMUNICATIONS REQUIREMENTS OF SMART GRID TECHNOLOGIES October 5, 2010 i Table of Contents I. Introduction and Executive Summary......

250

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.........................................................................................................

251

MSSM Forecast for the LHC  

E-Print Network (OSTI)

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

Maria Eugenia Cabrera; Alberto Casas; Roberto Ruiz de Austri

2009-11-24T23:59:59.000Z

252

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,

253

Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

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

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

2005-01-01T23:59:59.000Z

254

Information and Inference in Econometrics: Estimation, Testing and Forecasting  

E-Print Network (OSTI)

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

Tu, Yundong

2012-01-01T23:59:59.000Z

255

Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

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

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

2011-01-01T23:59:59.000Z

256

Management Earnings Forecasts and Value of Analyst Forecast Revisions  

E-Print Network (OSTI)

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

Yongtae Kim; Minsup Song

2013-01-01T23:59:59.000Z

257

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

Science Conference Proceedings (OSTI)

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

Agostino Manzato

2008-06-01T23:59:59.000Z

258

Chapter 11 Forecasting breaks and forecasting during breaks  

E-Print Network (OSTI)

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

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

2011-01-01T23:59:59.000Z

259

United States energy supply and demand forecasts 1979-1995  

SciTech Connect

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

Walton, H.L.

1979-01-01T23:59:59.000Z

260

Sensitivity of Tropical Storm Forecast to Radiative Destabilization  

Science Conference Proceedings (OSTI)

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

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

1991-09-01T23:59:59.000Z

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

Forecasting Uncertain Hotel Room Demand  

E-Print Network (OSTI)

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

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

2001-01-01T23:59:59.000Z

262

Forecast Technical Document Growing Stock Volume  

E-Print Network (OSTI)

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

263

Aviation forecasting and systems analyses  

SciTech Connect

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

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

1980-01-01T23:59:59.000Z

264

Studies of inflation and forecasting.  

E-Print Network (OSTI)

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

Bermingham, Colin

2011-01-01T23:59:59.000Z

265

UWIG Forecasting Workshop -- Albany (Presentation)  

SciTech Connect

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

Lew, D.

2011-04-01T23:59:59.000Z

266

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.

267

Table of Contents  

Science Conference Proceedings (OSTI)

Table of Contents. A, B. 1, USGCB Settings. 2, This spreadsheet captures the USGCB defined configuration settings. 3, Tab Name, Tab Description. ...

2013-11-19T23:59:59.000Z

268

FY 2007 State Table  

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

Department of Energy FY 2007 Congressional Budget Request February 2006 Office of Chief Financial Officer state tables preliminary Department of Energy FY 2007 Congressional Budget...

269

Tables - Refinery Capacity Report  

U.S. Energy Information Administration (EIA)

Tables: 1: Number and Capacity of Operable Petroleum Refineries by PAD District and State as of January 1, 2009: PDF: 2: Production Capacity of Operable ...

270

Volume 15, number 5 June/July 2010 markets products analysis research forecasts  

E-Print Network (OSTI)

fundamentals, lumber and panel demand is going to grow too slowly to support today's production base, keeping/Veneer, Particle- board/MDF, USa, Canada Table 1 WOOD MARKETS' PRicE & HOuSing FOREcASTS: 2010­2011 Product

271

A. G. A. six-month gas demand forecast July-December, 1984  

Science Conference Proceedings (OSTI)

Estimates of the total gas demand for 1984 (including pipeline fuel) range from 18,226 to 19,557 trillion (TBtu). The second half of the year shows a slower recovery rate as economic recovery moderates. The forecast show both actual and projected demand by month, and compares it with 1983 demand and by market sector. 6 tables.

Not Available

1984-01-01T23:59:59.000Z

272

Subhourly wind forecasting techniques for wind turbine operations  

DOE Green Energy (OSTI)

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

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

1984-08-01T23:59:59.000Z

273

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

E-Print Network (OSTI)

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

James Mitchell; Kenneth F. Wallis

2008-01-01T23:59:59.000Z

274

On the Prediction of Forecast Skill  

Science Conference Proceedings (OSTI)

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

T. N. Palmer; S. Tibaldi

1988-12-01T23:59:59.000Z

275

Equitable Skill Scores for Categorical Forecasts  

Science Conference Proceedings (OSTI)

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

Lev S. Gandin; Allan H. Murphy

1992-02-01T23:59:59.000Z

276

Whither the Weather Analysis and Forecasting Process?  

Science Conference Proceedings (OSTI)

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

Lance F. Bosart

2003-06-01T23:59:59.000Z

277

Lagged Ensembles, Forecast Configuration, and Seasonal Predictions  

Science Conference Proceedings (OSTI)

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

Mingyue Chen; Wanqiu Wang; Arun Kumar

278

Improving Forecast Communication: Linguistic and Cultural Considerations  

Science Conference Proceedings (OSTI)

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

Karen Pennesi

2007-07-01T23:59:59.000Z

279

Lagged Ensembles, Forecast Configuration, and Seasonal Predictions  

Science Conference Proceedings (OSTI)

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

Mingyue Chen; Wanqiu Wang; Arun Kumar

2013-10-01T23:59:59.000Z

280

Evaluation of errors in national energy forecasts.  

E-Print Network (OSTI)

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

Sakva, Denys

2005-01-01T23:59:59.000Z

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

What Is the True Value of Forecasts?  

Science Conference Proceedings (OSTI)

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

Antony Millner

2009-10-01T23:59:59.000Z

282

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Conference Proceedings (OSTI)

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

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

283

Forecasting Electric Vehicle Costs with Experience Curves  

E-Print Network (OSTI)

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

Lipman, Timonthy E.; Sperling, Daniel

2001-01-01T23:59:59.000Z

284

Probabilistic Verification of Monthly Temperature Forecasts  

Science Conference Proceedings (OSTI)

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

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

2008-12-01T23:59:59.000Z

285

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

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

286

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

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

287

A Forecast for the California Labor Market  

E-Print Network (OSTI)

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

Mitchell, Daniel J. B.

2001-01-01T23:59:59.000Z

288

Operational Forecaster Uncertainty Needs and Future Roles  

Science Conference Proceedings (OSTI)

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

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

2008-12-01T23:59:59.000Z

289

Calibration of Probabilistic Forecasts of Binary Events  

Science Conference Proceedings (OSTI)

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

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

2009-03-01T23:59:59.000Z

290

CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

291

Forecasting women's apparel sales using mathematical  

E-Print Network (OSTI)

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

Raheja, Amar

292

Calibration of Probabilistic Quantitative Precipitation Forecasts  

Science Conference Proceedings (OSTI)

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

Roman Krzysztofowicz; Ashley A. Sigrest

1999-06-01T23:59:59.000Z

293

Evaluating Probabilistic Forecasts Using Information Theory  

Science Conference Proceedings (OSTI)

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

Mark S. Roulston; Leonard A. Smith

2002-06-01T23:59:59.000Z

294

Virtual Floe Ice Drift Forecast Model Intercomparison  

Science Conference Proceedings (OSTI)

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

Robert W. Grumbine

1998-09-01T23:59:59.000Z

295

Ensemble Cloud Model Applications to Forecasting Thunderstorms  

Science Conference Proceedings (OSTI)

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

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

2002-04-01T23:59:59.000Z

296

The evolution of consensus in macroeconomic forecasting  

E-Print Network (OSTI)

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

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

2004-01-01T23:59:59.000Z

297

Background pollution forecast over bulgaria  

Science Conference Proceedings (OSTI)

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

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

2009-06-01T23:59:59.000Z

298

Frequency Dependence in Forecast Skill  

Science Conference Proceedings (OSTI)

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

H. M. van Den Dool; Suranjana Saha

1990-01-01T23:59:59.000Z

299

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

300

Improving Forecasting: A plea for historical retrospectives  

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

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

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

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

302

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

303

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)

304

DND: a model for forecasting electrical energy usage by water-resource subregion  

SciTech Connect

A forecast methodology was derived from principles of econometrics using exogenous variables, i.e., cost of electricity, consumer income, and price elasticity as indicators of growth for each consuming sector: residential, commercial, and industrial. The model was calibrated using forecast data submitted to the Department of Energy (DOE) by the nine Regional Electric Reliability Councils. Estimates on electrical energy usage by specific water-resource subregion were obtained by normalizing forecasted total electrical energy usage by state into per capita usage. The usage factor and data on forecasted population were applied for each water resource subregion. The results derived using the model are self-consistent and in good agreement with DOE Energy Information Administration projections. The differences that exist are largely the result of assumptions regarding specific aggregations and assignment of regional-system reliability and load factors. 8 references, 2 figures, 13 tables.

Sonnichsen, J.C. Jr.

1980-02-01T23:59:59.000Z

305

Model documentation: electricity market module. [15 year forecasts  

SciTech Connect

This report documents the electricity market model. This model is a component of the Intermediate Future Forecasting System (IFFS), the energy market model used to provide projections of energy markets up to 15 years into the future. The electricity market model was developed by the Supply Analysis and Integration Branch as part of building the larger system. This report is written for an audience consisting of mathematical economists, statisticians, operations research analysts, and utility planners. This report contains an overview and a mathematical specification of the electricity market module. It includes a description of the model logic and the individual subroutines in the computer code. A companion document Intermediate Future Forecasting System: Executive Summary (DOE/EIA-430) provides an overview of the components in IFFS and their linkages. 22 figures, 2 tables.

Sanders, R.C.; Murphy, F.H.

1984-12-01T23:59:59.000Z

306

EIA - AEO2010 - Comparison With Other Projections  

Gasoline and Diesel Fuel Update (EIA)

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

307

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

308

1997 Consumption and Expenditures Tables  

U.S. Energy Information Administration (EIA)

5HVLGHQWLDO (QHUJ\\ &RQVXPSWLRQ 6XUYH\\V 1997 Consumption and Expenditures Tables Appliances Consumption Tables (17 pages, 60 kb) Contents Pages CE5-1c.

309

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

310

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

E-Print Network (OSTI)

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

311

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Nuclear Generating Capacity Data Tables (2002-2025) Formats Projections of Nuclear Generating Capacity Data Tables (1 to 4 complete) Excel PDF Table Title Table F1 World Nuclear...

312

Quantitative Precipitation Forecast Techniques for Use in Hydrologic Forecasting  

Science Conference Proceedings (OSTI)

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

Konstantine P. Georgakakos; Michael D. Hudlow

1984-11-01T23:59:59.000Z

313

Load Forecast For use in Resource Adequacy  

E-Print Network (OSTI)

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

314

Forecast Technical Document Felling and Removals  

E-Print Network (OSTI)

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

315

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network (OSTI)

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

316

Combining forecast weights: Why and how?  

Science Conference Proceedings (OSTI)

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

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

2012-01-01T23:59:59.000Z

317

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

318

Using reforecasts for probabilistic forecast calibration  

E-Print Network (OSTI)

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

Hamill, Tom

319

Assessing Forecast Accuracy Measures Department of Economics  

E-Print Network (OSTI)

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

320

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

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

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

322

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

323

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

324

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

325

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

326

Table - Energy Information Administration  

U.S. Energy Information Administration (EIA)

September 2013 U.S. Energy Information 9/27/2013 9:52:45 AM Administration | Natural Gas Monthly 9 Created on: Table 4. U.S. natural gas imports ...

327

table E1  

U.S. Energy Information Administration (EIA)

AC Argentina AR Aruba AA Bahamas, The BF Barbados BB Belize BH Bolivia BL ... Table E.1 World Primary Energy Consumption (Btu), 1980-2006 (Quadrillion (10 15 ) Btu) Page

328

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

E-Print Network (OSTI)

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

329

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

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

330

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:

331

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":""}]}

332

Value of Wind Power Forecasting  

DOE Green Energy (OSTI)

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

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

2011-04-01T23:59:59.000Z

333

Fuzzy forecasting with DNA computing  

Science Conference Proceedings (OSTI)

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

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

2006-06-01T23:59:59.000Z

334

Sampling Errors in Seasonal Forecasting  

Science Conference Proceedings (OSTI)

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

Stephen Cusack; Alberto Arribas

2009-03-01T23:59:59.000Z

335

Scoring Rules for Forecast Verification  

Science Conference Proceedings (OSTI)

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

Riccardo Benedetti

2010-01-01T23:59:59.000Z

336

Richardson's Barotropic Forecast: A Reappraisal  

Science Conference Proceedings (OSTI)

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

Peter Lynch

1992-01-01T23:59:59.000Z

337

Consensus forecast of U. S. energy supply and demand to the year 2000  

DOE Green Energy (OSTI)

Methods used in forecasting energy supply and demand are described, and recent forecasts are reviewed briefly. Forecasts to the year 2000 are displayed in tables and graphs and are used to prepare consensus forecasts for each form of fuel and energy supply. Fuel demand and energy use by consuming sector are tabulated for 1972 and 1975 for the various fuel forms. The distribution of energy consumption by use sector, as projected for the years 1985 and 2000 in the ERDA-48 planning report (Scenario V), is normalized to match the consensus energy supply forecasts. The results are tabulated listing future demand for each fuel and energy form by each major energy-use category. Recent estimates of U.S. energy resources are also reviewed briefly and are presented in tables for each fuel and energy form. The outlook for fossil fuel resources to the year 2040, as developed by the Institute for Energy Analysis at the Oak Ridge Associated Universities, is also presented.

Lane, J.A.

1976-02-01T23:59:59.000Z

338

2001 Housing Characteristics Detailed Tables  

U.S. Energy Information Administration (EIA)

2001 Residential Energy Consumption Survey-Housing Characteristics, 2001 Detailed Tables, Energy Information Administration

339

Light truck forecasts  

SciTech Connect

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

Liepins, G.E.

1979-09-01T23:59:59.000Z

340

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

Science Conference Proceedings (OSTI)

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

Marion P. Mittermaier

2008-10-01T23:59:59.000Z

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

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

Science Conference Proceedings (OSTI)

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

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

2013-02-01T23:59:59.000Z

342

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

343

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

Science Conference Proceedings (OSTI)

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

Paul J. Roebber; Lance F. Bosart

1996-12-01T23:59:59.000Z

344

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

Science Conference Proceedings (OSTI)

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

Barbara G. Brown; Allan H. Murphy

1987-09-01T23:59:59.000Z

345

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

Science Conference Proceedings (OSTI)

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

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

2009-04-01T23:59:59.000Z

346

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

Science Conference Proceedings (OSTI)

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

Hae-Kyung Lee Drbohlav; V. Krishnamurthy

2010-09-01T23:59:59.000Z

347

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

Science Conference Proceedings (OSTI)

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

Allan H. Murphy

1993-06-01T23:59:59.000Z

348

Consensus forecast of U. S. electricity supply and demand to the year 2000  

SciTech Connect

Recent forecasts of total electricity generating capacity and energy demand as well as for electricity produced from nuclear energy and hydroelectric power are presented in tables and graphs to the year 2000. A forecast of the distribution of type of fuel and energy source that will supply the future electricity demand is presented. Use of electricity by each major consuming sector is presented for 1975. Projected demands for electricity in the years 1985 and 2000, as allocated to consuming sectors, are derived and presented.

Lane, J.A.

1976-02-01T23:59:59.000Z

349

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

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

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

350

Forecasting new product penetration with flexible substitution patterns  

E-Print Network (OSTI)

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

Brownstone, David; Train, Kenneth

1999-01-01T23:59:59.000Z

351

Overestimation Reduction in Forecasting Telecommuting as a TDM Policy  

E-Print Network (OSTI)

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

Tal, Gil

2008-01-01T23:59:59.000Z

352

Forecasting US CO2 Emissions Using State-Level Data  

E-Print Network (OSTI)

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

Steinhauser, Ralf; Auffhammer, Maximilian

2005-01-01T23:59:59.000Z

353

NoVaS Transformations: Flexible Inference for Volatility Forecasting  

E-Print Network (OSTI)

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

Politis, Dimitris N; Thomakos, Dimitrios D

2008-01-01T23:59:59.000Z

354

Forecasting new product penetration with flexible substitution patterns  

E-Print Network (OSTI)

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

Brownstone, David; Train, Kenneth

1999-01-01T23:59:59.000Z

355

Earthquake Forecasting in Diverse Tectonic Zones of the Globe  

E-Print Network (OSTI)

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

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

2010-01-01T23:59:59.000Z

356

Ensemble-based methods for forecasting census in hospital units  

E-Print Network (OSTI)

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

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

2013-01-01T23:59:59.000Z

357

Forecasting Danerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures  

E-Print Network (OSTI)

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

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

2005-01-01T23:59:59.000Z

358

Developing a Practical Forecasting Screener for Domestic Violence Incidents  

E-Print Network (OSTI)

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

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

2011-01-01T23:59:59.000Z

359

Forecasting with Dynamic Microsimulation: Design, Implementation, and Demonstration  

E-Print Network (OSTI)

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

Ravulaparthy, Srinath; Goulias, Konstadinos G.

2011-01-01T23:59:59.000Z

360

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:

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

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

362

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

363

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

364

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

365

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

366

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

367

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 -- --

368

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..........................................

369

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"

370

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

371

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

372

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

373

A Review of Numerical Forecast Guidance for Hurricane Hugo  

Science Conference Proceedings (OSTI)

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

John H. Ward

1990-09-01T23:59:59.000Z

374

Short-Range Ensemble Forecasts of Precipitation Type  

Science Conference Proceedings (OSTI)

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

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

2005-08-01T23:59:59.000Z

375

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Conference Proceedings (OSTI)

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

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

376

Using Customers' Reported Forecasts to Predict Future Sales  

E-Print Network (OSTI)

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

Murphy, Robert F.

377

The Automated Tropical Cyclone Forecasting System (ATCF)  

Science Conference Proceedings (OSTI)

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

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

1990-12-01T23:59:59.000Z

378

Evaluation of LFM-2 Quantitative Precipitation Forecasts  

Science Conference Proceedings (OSTI)

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

Lance F. Bosart

1980-08-01T23:59:59.000Z

379

Forecaster Workstation Design: Concepts and Issues  

Science Conference Proceedings (OSTI)

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

Charles A. Doswell III

1992-06-01T23:59:59.000Z

380

Performance of Recent Multimodel ENSO Forecasts  

Science Conference Proceedings (OSTI)

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

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

2012-03-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast comparisons 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.


381

Local Forecast Communication In The Altiplano  

Science Conference Proceedings (OSTI)

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

Jere L. Gilles; Corinne Valdivia

2009-01-01T23:59:59.000Z

382

Bayesian Model Verification of NWP Ensemble Forecasts  

Science Conference Proceedings (OSTI)

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

Andreas Rpnack; Andreas Hense; Christoph Gebhardt; Detlev Majewski

2013-01-01T23:59:59.000Z

383

Economic and Statistical Measures of Forecast Accuracy  

E-Print Network (OSTI)

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

Granger, Clive W J; Pesaran, M Hashem

2004-06-16T23:59:59.000Z

384

2013 Midyear Economic Forecast Sponsorship Opportunity  

E-Print Network (OSTI)

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

de Lijser, Peter

385

Forecasting consumer products using prediction markets  

E-Print Network (OSTI)

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

Trepte, Kai

2009-01-01T23:59:59.000Z

386

Probabilistic Visibility Forecasting Using Bayesian Model Averaging  

Science Conference Proceedings (OSTI)

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

Richard M. Chmielecki; Adrian E. Raftery

2011-05-01T23:59:59.000Z

387

Intercomparison of Spatial Forecast Verification Methods  

Science Conference Proceedings (OSTI)

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

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

2009-10-01T23:59:59.000Z

388

Forecasting with Reference to a Specific Climatology  

Science Conference Proceedings (OSTI)

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

Emily Wallace; Alberto Arribas

2012-11-01T23:59:59.000Z

389

Probabilistic Quantitative Precipitation Forecasts for River Basins  

Science Conference Proceedings (OSTI)

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

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

1993-12-01T23:59:59.000Z

390

A General Framework for Forecast Verification  

Science Conference Proceedings (OSTI)

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

Allan H. Murphy; Robert L. Winkler

1987-07-01T23:59:59.000Z

391

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Conference Proceedings (OSTI)

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

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

2003-02-01T23:59:59.000Z

392

Value from Ambiguity in Ensemble Forecasts  

Science Conference Proceedings (OSTI)

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

Mark S. Allen; F. Anthony Eckel

2012-02-01T23:59:59.000Z

393

Management of supply chain: an alternative modelling technique for forecasting  

E-Print Network (OSTI)

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

Datta, Shoumen

2007-05-23T23:59:59.000Z

394

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

395

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

396

EJ and EK Pay Table  

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

The EJ and EK pay table excludes locality pay. Refer to the General Schedule Complete Set of Locality Pay Tables to determine the locality pay for your applicable geographic area.

397

February 2013 Table of Contents  

Science Conference Proceedings (OSTI)

Inform February 2013 table of contents. February 2013 Table of Contents inform Magazine algae algal AOCS biomass business chemistry cottonseed date detergents fats filing first history inform inform Magazine international inventor law magazine me

398

Visualization of truth tables - CECM  

E-Print Network (OSTI)

Nov 19, 1997 ... Visualization of truth tables. The Figures are computer-generated tables that show the value 1 as being represented by a black pixel and 0 by a...

399

January 2013 Table of Contents  

Science Conference Proceedings (OSTI)

inform January 2013 table of contents. January 2013 Table of Contents inform Magazine algae algal AOCS biomass business chemistry cottonseed date detergents fats filing first history inform inform Magazine international inventor law magazine membe

400

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

E-Print Network (OSTI)

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

Whitaker, Jeffrey S.

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

Forecasting for energy and chemical decision analysis  

SciTech Connect

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

Cazalet, E.G.

1984-08-01T23:59:59.000Z

402

A Rank Approach to Equity Forecast Construction  

E-Print Network (OSTI)

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

Satchell, Stephen E; Wright, Stephen M

2006-03-14T23:59:59.000Z

403

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

Science Conference Proceedings (OSTI)

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

Theodore W. Funk

1991-12-01T23:59:59.000Z

404

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

Science Conference Proceedings (OSTI)

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

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

2009-06-01T23:59:59.000Z

405

May 2011 Table of Contents  

Science Conference Proceedings (OSTI)

May 2011 Table of Contents Inform Magazine Inform Archives News 266 Insect oils: nutritional and industrial applications Many

406

October 2010 Table of Contents  

Science Conference Proceedings (OSTI)

October 2010 Table of Contents Inform Magazine Inform Archives News 598 Universal detectors for determination of lipids in biodiesel producti

407

Load Forecasting for Modern Distribution Systems  

Science Conference Proceedings (OSTI)

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

2013-03-08T23:59:59.000Z

408

Modeling and Forecasting Electric Daily Peak Loads  

E-Print Network (OSTI)

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

Abdel-Aal, Radwan E.

409

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network (OSTI)

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

410

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network (OSTI)

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

411

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

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

412

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network (OSTI)

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

413

Load forecast and treatment of conservation  

E-Print Network (OSTI)

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

414

FINAL STAFF FORECAST OF 2008 PEAK DEMAND  

E-Print Network (OSTI)

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

415

STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES  

E-Print Network (OSTI)

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

416

Blue Chip Consensus US GDP Forecast  

E-Print Network (OSTI)

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

James F. Wilson

2007-01-01T23:59:59.000Z

417

5, 183218, 2008 A rainfall forecast  

E-Print Network (OSTI)

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

Paris-Sud XI, Université de

418

System Demonstration Multilingual Weather Forecast Generation System  

E-Print Network (OSTI)

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

419

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

E-Print Network (OSTI)

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

Hamill, Tom

420

2003 CBECS Detailed Tables: Summary  

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

the Tables | Estimation of Energy End-Use Consumption | CBECS Glossary | FAQs | Other Years: 1999 1995 1992 Complete Set of All Tables (Tables A1-A8, B1-B46, C1-C38, C1A-C38A,...

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

Price forecasting for notebook computers  

E-Print Network (OSTI)

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

Rutherford, Derek Paul

1997-01-01T23:59:59.000Z

422

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.

423

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%

424

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

425

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 -- -- --

426

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

427

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

428

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.......................

429

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

430

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......................

431

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

Science Conference Proceedings (OSTI)

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

2001-09-28T23:59:59.000Z

432

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

433

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

DOE Green Energy (OSTI)

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

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

2013-01-01T23:59:59.000Z

434

Residential Energy Consumption Survey Data Tables  

U.S. Energy Information Administration (EIA)

Below are historical data tables from the Residential Energy Consumption Survey (RECS). These tables cover the total number of households ...

435

EIA - Annual Energy Outlook 2009 - Comparison with Other Projections  

Gasoline and Diesel Fuel Update (EIA)

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

436

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

437

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.

438

Essays on macroeconomics and forecasting  

E-Print Network (OSTI)

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

Liu, Dandan

2005-08-01T23:59:59.000Z

439

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

Science Conference Proceedings (OSTI)

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

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

1981-01-01T23:59:59.000Z

440

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:

Note: This page contains sample records for the topic "forecast comparisons 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.


441

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.

442

Critical Operating Constraint Forecasting (COCF)  

Science Conference Proceedings (OSTI)

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

2006-06-30T23:59:59.000Z

443

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

444

Engineering Tables: Polymeric Materials  

Science Conference Proceedings (OSTI)

Table 6   Chemical resistance ratings for selected plastics and metals...B A A C C C ? B C A A A Miscellaneous Detergents Laundry and dishwashing detergents, soaps A ? A ? B ? ? A A A ? B A ? A A B Inorganic salts Zinc chloride, cupric sulfate B B B ? A ? ? A ? A ? ? A A B B B Oxidizing agents, strong 30% hydrogen peroxide, bromine (wet) C C C ? C ? B B ? C ? ? A ? C C C...

445

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

Science Conference Proceedings (OSTI)

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

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

1998-12-01T23:59:59.000Z

446

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

E-Print Network (OSTI)

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

Evans, MDD; Lyons, Richard K.

2005-01-01T23:59:59.000Z

447

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

E-Print Network (OSTI)

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

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

2010-01-01T23:59:59.000Z

448

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

E-Print Network (OSTI)

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

Letschert, Virginie

2010-01-01T23:59:59.000Z

449

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

450

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

451

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

452

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

453

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

454

March 2012 Table of Contents  

Science Conference Proceedings (OSTI)

March 2012 Table of Contents Inform Magazine Inform Archives News March 2012 World supplies of rapeseed and canola likely to remain tight in the 201

455

1993 Housing Characteristics -Detailed Tables  

U.S. Energy Information Administration (EIA)

Within each section, except for Air-conditioning and Light Usage, ... the Light Usage section includes a table that describes indoor light usage by ...

456

Characteristics of truth tables - CECM  

E-Print Network (OSTI)

Nov 19, 1997... fairly straightforward because each row represents an assignment of truth values ... A truth table is a standard binary ordering of 2-partitions.

457

EIA Table E.1C  

U.S. Energy Information Administration (EIA)

AC Argentina AR Aruba AA Bahamas, The BF Barbados BB Belize BH Bolivia BL Brazil BR Cayman Islands CJ ... World Total ww NA - - Table Posted: December 19, 2008

458

Microsoft Word - table_24.doc  

Annual Energy Outlook 2012 (EIA)

0 Table 24. Percent Distribution of Natural Gas Supply and Disposition by State, 2006 Alabama ... 1.44 1.81...

459

Microsoft Word - table_25.doc  

Gasoline and Diesel Fuel Update (EIA)

4 Table 25. Percent Distribution of Natural Gas Supply and Disposition by State, 2008 Alabama ... 1.19 1.74...

460

Microsoft Word - table_25.doc  

Annual Energy Outlook 2012 (EIA)

4 Table 25. Percent Distribution of Natural Gas Supply and Disposition by State, 2007 Alabama ... 1.31 1.83...

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

Microsoft Word - table_24.doc  

Annual Energy Outlook 2012 (EIA)

0 Table 24. Percent Distribution of Natural Gas Supply and Disposition by State, 2005 Alabama ... 1.56 1.59...

462

Microsoft Word - table_25.doc  

Annual Energy Outlook 2012 (EIA)

4 Table 25. Percent Distribution of Natural Gas Supply and Disposition by State, 2009 Alabama ... 1.1 2.0...

463

2011 22 Table of for  

U.S. Energy Information Administration (EIA)

2011 60 U.S. Energy Information Administration | Natural Gas Annual Table 22. Number of natural gas industrial consumers by type of ...

464

Microsoft Word - table_23.doc  

Gasoline and Diesel Fuel Update (EIA)

4 Table 23. Average Price of Natural Gas Delivered to Consumers by State and Sector, 2006 (Dollars per Thousand Cubic Feet) Alabama ... 18.80 100.00...

465

Faculty Search Table of Contents  

E-Print Network (OSTI)

October 28 2009 Faculty Search Committee Procedures Handbook #12;#12;#12;Table of Contents........................................................................................................................7 Charge to Search Committee................................................................................................................................8 Role of the Search Committee Chair

New Mexico, University of

466

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

E-Print Network (OSTI)

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

Wisconsin at Madison, University of

467

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

Science Conference Proceedings (OSTI)

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

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

1989-09-01T23:59:59.000Z

468

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

Science Conference Proceedings (OSTI)

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

Jianguo Liu; Zhenghui Xie

469

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

Science Conference Proceedings (OSTI)

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

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

2010-03-01T23:59:59.000Z

470

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

Science Conference Proceedings (OSTI)

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

A. Sankarasubramanian; Upmanu Lall; Susan Espinueva

2008-04-01T23:59:59.000Z

471

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

Science Conference Proceedings (OSTI)

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

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

2009-05-01T23:59:59.000Z

472

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

Science Conference Proceedings (OSTI)

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

Thomas M. Hamill; Daniel S. Wilks

1995-09-01T23:59:59.000Z

473

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

Science Conference Proceedings (OSTI)

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

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

2010-01-01T23:59:59.000Z

474

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting  

Science Conference Proceedings (OSTI)

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

Giuseppe Mascaro; Enrique R. Vivoni; Roberto Deidda

2010-02-01T23:59:59.000Z

475

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

Science Conference Proceedings (OSTI)

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

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

2007-02-01T23:59:59.000Z

476

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

Science Conference Proceedings (OSTI)

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

David J. Stensrud; Nusrat Yussouf

2007-02-01T23:59:59.000Z

477

The Impact of Writing Area Forecast Discussions on Student Forecaster Performance  

Science Conference Proceedings (OSTI)

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

Patrick S. Market

2006-02-01T23:59:59.000Z

478

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

Science Conference Proceedings (OSTI)

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

Ashok Kumar; Parvinder Maini; S. V. Singh

1999-02-01T23:59:59.000Z

479

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

Science Conference Proceedings (OSTI)

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

Doug McCollor; Roland Stull

2009-02-01T23:59:59.000Z

480

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

Science Conference Proceedings (OSTI)

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

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

1991-12-01T23:59:59.000Z

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481

R/ECON July 2000 Forecast RUTGERS ECONOMIC ADVISORY SERVICE  

E-Print Network (OSTI)

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

482

Increasing NOAA's computational capacity to improve global forecast modeling  

E-Print Network (OSTI)

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

Hamill, Tom

483

Forecasting Random Walks Under Drift Instability  

E-Print Network (OSTI)

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

Pesaran, M Hashem; Pick, Andreas

484

Solar future: 1978. [Market forecast to 1992  

SciTech Connect

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

Butt, S.H.

1978-03-01T23:59:59.000Z

485

Geothermal wells: a forecast of drilling activity  

DOE Green Energy (OSTI)

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

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

1981-07-01T23:59:59.000Z

486

Time Series Prediction Forecasting the Future and ...  

Science Conference Proceedings (OSTI)

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

2012-10-01T23:59:59.000Z

487

Promotional forecasting in the grocery retail business  

E-Print Network (OSTI)

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

Koottatep, Pakawkul

2006-01-01T23:59:59.000Z

488

Rolling 12 Month Forecast November-2008.xls  

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

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

489

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

490

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%

491

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)

492

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

493

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

494

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

495

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

496

Engineering Tables: Reinforcement Materials  

Science Conference Proceedings (OSTI)

Table 1   Properties of key reinforcement materials...3 GPa 10 6 psi GPa 10 6 psi GPa 10 6 psi Carbon fiber (pitch) E = 55 ? 10 6 psi 2.0 0.072 380 55 ? ? 190 28 E = 75 ? 10 6 psi 2.0 0.072 520 75 ? ? 260 38 E = 100 ? 10 6 psi 2.2 0.078 690 100 5 0.7 314 46 E = 120 ? 10 6 psi 2.2 0.078 830 120 5 0.7 377 55 E = 130 ? 10 6 psi 2.2 0.078 895 130 5 0.7 407...

497

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.

498

Forecasting Prices andForecasting Prices and Congestion forCongestion for  

E-Print Network (OSTI)

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

Tesfatsion, Leigh

499

Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations  

E-Print Network (OSTI)

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

Kemner, Ken

500

A Statistical Solar Flare Forecast Method  

E-Print Network (OSTI)

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

M. S. Wheatland

2005-05-14T23:59:59.000Z