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1

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

2

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,

3

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

4

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

5

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.

6

Annual Energy Outlook Forecast Evaluation 2004  

Gasoline and Diesel Fuel Update (EIA)

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

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  

Gasoline and Diesel Fuel Update (EIA)

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

9

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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.

10

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

11

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

12

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2006-12-06T23:59:59.000Z

13

Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2005-12-19T23:59:59.000Z

14

Annual Energy Outlook Forecast Evaluation 2005  

Gasoline and Diesel Fuel Update (EIA)

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

15

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

16

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

SciTech Connect (OSTI)

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

Bolinger, Mark; Wiser, Ryan

2004-12-13T23:59:59.000Z

17

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

18

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

19

AEO2010 Early Release Overview  

Gasoline and Diesel Fuel Update (EIA)

10 10 Early Release Overview December 2009 Energy Trends to 2035 In preparing the Annual Energy Outlook 2010 (AEO- 2010), the Energy Information Administration (EIA) evaluated a wide range of trends and issues that could have major implications for U.S. energy markets. This overview focuses primarily on one case, the AEO2010 reference case, which is presented and com- pared with the updated Annual Energy Outlook 2009 (updated AEO2009) reference case released in April 2009 1 (see Table 1). Because of the uncertainties in- herent in any energy market projection, particularly in periods of high price volatility, rapid market trans- formation, or active changes in legislation, the refer- ence case results should not be viewed in isolation. Readers are encouraged to review the alternative cases when the complete AEO2010 publication is re- leased in order to gain perspective on how variations

20

AEO2008 Overview - Early Release  

Gasoline and Diesel Fuel Update (EIA)

08 08 Overview Energy Trends to 2030 In preparing projections for the Annual Energy Out- look 2008 (AEO2008), the Energy Information Ad- ministration (EIA) evaluated a wide range of trends and issues that could have major implications for U.S. energy markets between today and 2030. 1 This over- view focuses on one case, the reference case, which is presented and compared with the Annual Energy Outlook 2007 (AEO2007) reference case (see Table 1). Readers are encouraged to review the full range of alternative cases included in other sections of AEO2008. As in previous editions of the Annual Energy Outlook (AEO), the reference case assumes that current poli- cies affecting the energy sector remain unchanged throughout the projection period. Some possible pol- icy changes-notably, the adoption of policies to limit or reduce greenhouse gas emissions-could change the reference case projections

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

Energy Technologies on the Horizon (released in AEO2006)  

Reports and Publications (EIA)

A key issue in mid-term forecasting is the representation of changing and developing technologies. How existing technologies will evolve, and what new technologies might emerge, cannot be known with certainty. The issue is of particular importance in Annual Energy Outlook 2006 (AEO), the first AEO with projections out to 2030.

2006-01-01T23:59:59.000Z

22

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 34  

Gasoline and Diesel Fuel Update (EIA)

4 4 Table 21. Total energy related carbon dioxide emissions, projected vs. actual Projected (million metric tons) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 5060 5130 5185 5240 5287 5335 5379 5438 5482 5529 5599 5658 5694 5738 5797 5874 5925 5984 AEO 1995 5137 5174 5188 5262 5309 5361 5394 5441 5489 5551 5621 5680 5727 5775 5841 5889 5944 AEO 1996 5182 5224 5295 5355 5417 5464 5525 5589 5660 5735 5812 5879 5925 5981 6030 6087 AEO 1997 5295 5381 5491 5586 5658 5715 5781 5863 5934 6009 6106 6184 6236 6268 6316

23

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 28  

Gasoline and Diesel Fuel Update (EIA)

8 8 Table 15. Total electricity sales, projected vs. actual Projected (billion kilowatt-hours) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 2843 2891 2928 2962 3004 3039 3071 3112 3148 3185 3228 3263 3298 3332 3371 3406 3433 3469 AEO 1995 2951 2967 2983 3026 3058 3085 3108 3134 3166 3204 3248 3285 3321 3357 3396 3433 3475 AEO 1996 2973 2998 3039 3074 3106 3137 3173 3215 3262 3317 3363 3409 3454 3505 3553 3604 AEO 1997 3075 3115 3168 3229 3290 3328 3379 3437 3497 3545 3596 3649 3697 3736 3784

24

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 14  

Gasoline and Diesel Fuel Update (EIA)

4 4 Table 4. Total petroleum consumption, projected vs. actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 6450 6566 6643 6723 6811 6880 6957 7059 7125 7205 7296 7377 7446 7523 7596 7665 7712 7775 AEO 1995 6398 6544 6555 6676 6745 6822 6888 6964 7048 7147 7245 7337 7406 7472 7537 7581 7621 AEO 1996 6490 6526 6607 6709 6782 6855 6942 7008 7085 7176 7260 7329 7384 7450 7501 7545 AEO 1997 6636 6694 6826 6953 7074 7183 7267 7369 7461 7548 7643 7731 7793 7833 7884

25

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 15  

Gasoline and Diesel Fuel Update (EIA)

5 5 Table 5. Domestic crude oil production, projected vs. actual Projected (million barrels) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 2508 2373 2256 2161 2088 2022 1953 1891 1851 1825 1799 1781 1767 1759 1778 1789 1807 1862 AEO 1995 2402 2307 2205 2095 2037 1967 1953 1924 1916 1905 1894 1883 1887 1887 1920 1945 1967 AEO 1996 2387 2310 2248 2172 2113 2062 2011 1978 1953 1938 1916 1920 1927 1949 1971 1986 AEO 1997 2362 2307 2245 2197 2143 2091 2055 2033 2015 2004 1997 1989 1982 1975 1967

26

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 24  

Gasoline and Diesel Fuel Update (EIA)

4 4 Table 12. Total coal consumption, projected vs. actual Projected (million short tons) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 920 928 933 938 943 948 953 958 962 967 978 990 987 992 1006 1035 1061 1079 AEO 1995 935 940 941 947 948 951 954 958 963 971 984 992 996 1002 1013 1025 1039 AEO 1996 937 942 954 962 983 990 1004 1017 1027 1033 1046 1067 1070 1071 1074 1082 AEO 1997 948 970 987 1003 1017 1020 1025 1034 1041 1054 1075 1086 1092 1092 1099

27

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 25  

Gasoline and Diesel Fuel Update (EIA)

5 5 Table 13. Coal production, projected vs. actual Projected (million short tons) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 999 1021 1041 1051 1056 1066 1073 1081 1087 1098 1107 1122 1121 1128 1143 1173 1201 1223 AEO 1995 1006 1010 1011 1016 1017 1021 1027 1033 1040 1051 1066 1076 1083 1090 1108 1122 1137 AEO 1996 1037 1044 1041 1045 1061 1070 1086 1100 1112 1121 1135 1156 1161 1167 1173 1184 AEO 1997 1028 1052 1072 1088 1105 1110 1115 1123 1133 1146 1171 1182 1190 1193 1201

28

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

SciTech Connect (OSTI)

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

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

2013-10-01T23:59:59.000Z

29

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

30

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

31

AEO Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for the for the Annual Energy Outlook 1997 December 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Energy Information Administration/Assumptions for the Annual Energy Outlook 1997 Contents Page Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Commercial Demand Module . . . . . . . . . . . . . . . . . .

32

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 26  

Gasoline and Diesel Fuel Update (EIA)

6 6 Table 14a. Average electricity prices, projected vs. actual Projected price in constant dollars (constant dollars, cents per kilowatt-hour in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 1992 6.80 6.80 6.90 6.90 6.90 6.90 7.00 7.00 7.10 7.10 7.20 7.20 7.20 7.30 7.30 7.40 7.50 7.60

33

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 22  

Gasoline and Diesel Fuel Update (EIA)

2 2 Table 11a. Coal prices to electric generating plants, projected vs. actual Projected price in constant dollars (constant dollars per million Btu in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 1992 1.47 1.48 1.53 1.57 1.58 1.57 1.61 1.63 1.68 1.69 1.70 1.72 1.70 1.76 1.79 1.81 1.88 1.92

34

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 17  

Gasoline and Diesel Fuel Update (EIA)

7 7 Table 7a. Natural gas wellhead prices, projected vs. actual Projected price in constant dollars (constant dollars per thousand cubic feet in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 1992 1.94 2.03 2.11 2.19 2.29 2.35 2.39 2.42 2.47 2.55 2.65 2.75 2.89 3.01 3.17 3.30 3.35 3.47

35

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 12  

Gasoline and Diesel Fuel Update (EIA)

12 12 Table 3a. Imported refiner acquisition cost of crude oil, projected vs. actual Projected price in constant dollars (constant dollars per barrel in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 1992 16.69 16.43 16.99 17.66 18.28 19.06 19.89 20.72 21.65 22.61 23.51 24.29 24.90 25.60 26.30 27.00 27.64 28.16 AEO 1995 1993 14.90 16.41 16.90 17.45 18.00 18.53 19.13 19.65 20.16 20.63 21.08 21.50 21.98 22.44 22.94 23.50 24.12

36

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

37

AEO2015 BWG  

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

purposes only - do not cite or circulate Tech. update example: commercial rooftop heat pumps AEO2015 Builldings Working Group Washington, D.C., August 7, 2014 5 Discussion...

38

EIA-Annual Energy Outlook Retrospective Review: Evaluation of Projections  

Gasoline and Diesel Fuel Update (EIA)

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

39

AEO | OpenEI  

Open Energy Info (EERE)

AEO AEO Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 95, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections United States Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - United States- Reference Case (xls, 260.9 KiB) Quality Metrics

40

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 21  

Gasoline and Diesel Fuel Update (EIA)

1 1 Table 10. Natural gas net imports, projected vs. actual Projected (trillion cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 2.02 2.40 2.66 2.74 2.81 2.85 2.89 2.93 2.95 2.97 3.00 3.16 3.31 3.50 3.57 3.63 3.74 3.85 AEO 1995 2.46 2.54 2.80 2.87 2.87 2.89 2.90 2.90 2.92 2.95 2.97 3.00 3.03 3.19 3.35 3.51 3.60 AEO 1996 2.56 2.75 2.85 2.88 2.93 2.98 3.02 3.06 3.07 3.09 3.12 3.17 3.23 3.29 3.37 3.46 AEO 1997 2.82 2.96 3.16 3.43 3.46 3.50 3.53 3.58 3.64 3.69 3.74 3.78 3.83 3.87 3.92

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

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 10  

Gasoline and Diesel Fuel Update (EIA)

10 10 Table 2. Real gross domestic product, projected vs. actual Projected Real GDP growth trend (cumulative average percent growth in projected real GDP from first year shown for each AEO) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 3.1% 3.2% 2.9% 2.8% 2.7% 2.7% 2.6% 2.6% 2.6% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.4% 2.3% 2.3% AEO 1995 3.7% 2.8% 2.5% 2.7% 2.7% 2.6% 2.6% 2.5% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.3% 2.3% 2.2% AEO 1996 2.6% 2.2% 2.5% 2.5% 2.5% 2.5% 2.4% 2.4% 2.4% 2.4% 2.4% 2.3% 2.3% 2.2% 2.2% 2.2%

42

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 32  

Gasoline and Diesel Fuel Update (EIA)

2 2 Table 19. Total delivered industrial energy consumption, projected vs. actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 25.4 25.9 26.3 26.7 27.0 27.1 26.8 26.6 26.9 27.2 27.7 28.1 28.3 28.7 29.1 29.4 29.7 30.0 AEO 1995 26.2 26.3 26.5 27.0 27.3 26.9 26.6 26.8 27.1 27.5 27.9 28.2 28.4 28.7 29.0 29.3 29.6 AEO 1996 26.5 26.6 27.3 27.5 26.9 26.5 26.7 26.9 27.2 27.6 27.9 28.2 28.3 28.5 28.7 28.9 AEO 1997 26.2 26.5 26.9 26.7 26.6 26.8 27.1 27.4 27.8 28.0 28.4 28.7 28.9 29.0 29.2

43

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 20  

Gasoline and Diesel Fuel Update (EIA)

20 20 Table 9. Natural gas production, projected vs. actual Projected (trillion cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 17.71 17.68 17.84 18.12 18.25 18.43 18.58 18.93 19.28 19.51 19.80 19.92 20.13 20.18 20.38 20.35 20.16 20.19 AEO 1995 18.28 17.98 17.92 18.21 18.63 18.92 19.08 19.20 19.36 19.52 19.75 19.94 20.17 20.28 20.60 20.59 20.88 AEO 1996 18.90 19.15 19.52 19.59 19.59 19.65 19.73 19.97 20.36 20.82 21.25 21.37 21.68 22.11 22.47 22.83 AEO 1997 19.10 19.70 20.17 20.32 20.54 20.77 21.26 21.90 22.31 22.66 22.93 23.38 23.68 23.99 24.25

44

EIA-Annual Energy Outlook Retrospective Review: Evaluation of Projections  

Gasoline and Diesel Fuel Update (EIA)

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

45

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.

46

AEO2014 results and status updates for the AEO2015  

Gasoline and Diesel Fuel Update (EIA)

modeling updates for AEO2015 * Modeling changes planned for AEO 2015 - Cross State Air Pollution Rule (Transport RuleCSAPR) - U.S. Supreme Court overturned D.C. Circuit...

47

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 13  

Gasoline and Diesel Fuel Update (EIA)

3 3 Table 3b. Imported refiner acquisition cost of crude oil, projected vs. actual Projected price in nominal dollars (nominal dollars per barrel) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 17.06 17.21 18.24 19.43 20.64 22.12 23.76 25.52 27.51 29.67 31.86 34.00 36.05 38.36 40.78 43.29 45.88 48.37 AEO 1995 15.24 17.27 18.23 19.26 20.39 21.59 22.97 24.33 25.79 27.27 28.82 30.38 32.14 33.89 35.85 37.97 40.28

48

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 27  

Gasoline and Diesel Fuel Update (EIA)

7 7 Table 14b. Average electricity prices, projected vs. actual Projected price in nominal dollars (nominal dollars, cents per kilowatt-hour) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 6.95 7.12 7.41 7.59 7.79 8.01 8.36 8.62 9.02 9.32 9.76 10.08 10.42 10.94 11.32 11.87 12.45 13.05 AEO 1995 6.95 7.16 7.23 7.40 7.59 7.81 8.04 8.42 8.70 9.12 9.43 9.75 10.24 10.57 11.10 11.47 12.02

49

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 19  

Gasoline and Diesel Fuel Update (EIA)

19 19 Table 8. Total natural gas consumption, projected vs. actual Projected (trillion cubic feet) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 19.87 20.21 20.64 20.99 21.20 21.42 21.60 21.99 22.37 22.63 22.95 23.22 23.58 23.82 24.09 24.13 24.02 24.14 AEO 1995 20.82 20.66 20.85 21.21 21.65 21.95 22.12 22.25 22.43 22.62 22.87 23.08 23.36 23.61 24.08 24.23 24.59 AEO 1996 21.32 21.64 22.11 22.21 22.26 22.34 22.46 22.74 23.14 23.63 24.08 24.25 24.63 25.11 25.56 26.00

50

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 30  

Gasoline and Diesel Fuel Update (EIA)

0 0 Table 17. Total delivered residential energy consumption, projected vs. actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 10.3 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.4 10.5 10.5 10.5 10.5 10.5 10.6 10.6 AEO 1995 10.96 10.8 10.8 10.8 10.8 10.8 10.8 10.7 10.7 10.7 10.7 10.7 10.7 10.7 10.8 10.8 10.9 AEO 1996 10.4 10.7 10.7 10.7 10.8 10.8 10.9 10.9 11.0 11.2 11.2 11.3 11.4 11.5 11.6 11.7

51

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 33  

Gasoline and Diesel Fuel Update (EIA)

3 3 Table 20. Total delivered transportation energy consumption, projected vs. actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 23.6 24.1 24.5 24.7 25.1 25.4 25.7 26.2 26.5 26.9 27.2 27.6 27.9 28.3 28.6 28.9 29.2 29.5 AEO 1995 23.3 24.0 24.2 24.7 25.1 25.5 25.9 26.2 26.5 26.9 27.3 27.7 28.0 28.3 28.5 28.7 28.9 AEO 1996 23.9 24.1 24.5 24.8 25.3 25.7 26.0 26.4 26.7 27.1 27.5 27.8 28.1 28.4 28.6 28.9

52

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 31  

Gasoline and Diesel Fuel Update (EIA)

1 1 Table 18. Total delivered commercial energy consumption, projected vs. actual Projected (quadrillion Btu) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 AEO 1994 6.8 6.9 6.9 7.0 7.1 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.4 7.5 7.5 7.5 7.5 7.6 AEO 1995 6.94 6.9 7.0 7.0 7.0 7.1 7.1 7.1 7.1 7.1 7.2 7.2 7.2 7.2 7.3 7.3 7.3 AEO 1996 7.1 7.2 7.2 7.3 7.3 7.4 7.4 7.5 7.6 7.6 7.7 7.7 7.8 7.9 8.0 8.0

53

EIA-Annual Energy Outlook Retrospective Review: Evaluation of Projections  

Gasoline and Diesel Fuel Update (EIA)

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

54

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

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

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

2013-10-01T23:59:59.000Z

55

Evaluation of hierarchical forecasting for substitutable products  

Science Journals Connector (OSTI)

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

S. Viswanathan; Handik Widiarta; R. Piplani

2008-01-01T23:59:59.000Z

56

AEO2014: Preliminary Industrial Output  

Gasoline and Diesel Fuel Update (EIA)

Elizabeth Sendich, Analyst, and Kay Smith, Team Leader Macroeconomic Analysis Team September 26, 2013 Preliminary AEO2014 Macroeconomic Industrial Results DO NOT CITE OR...

57

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

Gasoline and Diesel Fuel Update (EIA)

3 3 Table 18. Total Carbon Dioxide Emissions, Actual vs. Forecasts (million metric tons) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 AEO 1983 AEO 1984 AEO 1985 AEO 1986 AEO 1987 AEO 1989* AEO 1990 AEO 1991 AEO 1992 AEO 1993 5009 5053 5130 5207 5269 5335 5401 5449 5504 5562 5621 5672 5724 5771 AEO 1994 5060 5130 5185 5240 5287 5335 5379 5438 5482 5529 5599 5658 5694 AEO 1995 5137 5174 5188 5262 5309 5361 5394 5441 5489 5551 5621 5680 AEO 1996 5182 5224 5295 5355 5417 5464 5525 5589 5660 5735 5812 AEO 1997 5295 5381 5491 5586 5658 5715 5781 5863 5934 6009 AEO 1998 5474 5621 5711 5784 5893 5957 6026 6098 6192 AEO 1999 5522 5689 5810 5913 5976 6036 6084 6152 AEO 2000 5573 5692 5777 5865 5971 6078 6172 AEO 2001 5630 5781 5909 6023 6114 6197 AEO 2002 5723 5808 5981 6095 6210 AEO 2003 5633 5749 5856 5954 AEO 2004 5743 5826

58

AEO2013 Early Release Overview  

Gasoline and Diesel Fuel Update (EIA)

3 Early Release Overview 3 Early Release Overview AEO2013 Early Release Overview Executive summary Projections in the Annual Energy Outlook 2013 (AEO2013) Reference case focus on the factors that shape U.S. energy markets through 2040, under the assumption that current laws and regulations remain generally unchanged throughout the projection period. This early release focuses on the AEO2013 Reference case, which provides the basis for examination and discussion of energy market trends and serves as a starting point for analysis of potential changes in U.S. energy policies, rules, or regulations or potential technology breakthroughs. Readers are encouraged to review the full range of cases that will be presented when the complete AEO2013 is released in early 2013, exploring key uncertainties in the Reference case. Major highlights in the AEO2013 Reference case include:

59

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

60

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

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

AEO2014 Early Release Overview  

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

Administration | Annual Energy Outlook 2014 Early Release Overview Administration | Annual Energy Outlook 2014 Early Release Overview AEO2014 Early Release Overview Executive summary Projections in the Annual Energy Outlook 2014 (AEO2014) Reference case focus on the factors that shape U.S. energy markets through 2040, under the assumption that current laws and regulations remain generally unchanged throughout the projection period. The early release provides a basis for the examination and discussion of energy market trends and serves as a starting point for analysis of potential changes in U.S. energy policies, rules, or regulations or possible technology breakthroughs. Readers are encouraged to review the full range of cases that will be presented when the complete AEO2014 is released in 2014, exploring key

62

EIA - AEO2010 - Energy intensity trends in AEO2010  

Gasoline and Diesel Fuel Update (EIA)

intensity trends in AEO2010 intensity trends in AEO2010 Annual Energy Outlook 2010 with Projections to 2035 Figure 17. Trends in U.S. oil prices, energy consumption, and economic output, 1950-2035 Click to enlarge » Figure source and data excel logo Energy intensity trends in AEO2010 Energy intensity—energy consumption per dollar of real GDP—indicates how much energy a country uses to produce its goods and services. From the early 1950s to the early 1970s, U.S. total primary energy consumption and real GDP increased at nearly the same annual rate (Figure 17). During that period, real oil prices remained virtually flat. In contrast, from the mid-1970s to 2008, the relationship between energy consumption and real GDP growth changed, with primary energy consumption growing at less than one-third the previous average rate and real GDP growth continuing to grow at its historical rate. The decoupling of real GDP growth from energy consumption growth led to a decline in energy intensity that averaged 2.8 percent per year from 1973 to 2008. In the AEO2010 Reference case, energy intensity continues to decline, at an average annual rate of 1.9 percent from 2008 to 2035.

63

D:\0myfiles\AEO2007\Final for PDF\AEO2007\AEO2007.vp  

Gasoline and Diesel Fuel Update (EIA)

7 7 (AEO2007), pre- pared by the Energy Information Administration (EIA), presents long-term projections of energy sup- ply, demand, and prices through 2030. The projec- tions are based on results from EIA's National Energy Modeling System (NEMS). The report begins with an "Overview" summarizing the AEO2007 reference case. The next section, "Leg- islation and Regulations," discusses evolving legisla- tion and regulatory issues, including recently enacted legislation and regulation, such as the new Corporate Average Fuel Economy (CAFE) standards for light- duty trucks finalized by the National Highway Traffic Safety Administration (NHTSA) in March 2006. It also provides an update on the handling of key provi- sions in the Energy Policy Act of 2005 (EPACT2005) that could not be incorporated in the Annual Energy Outlook 2006 (AEO2006) because of the absence

64

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

E-Print Network [OSTI]

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

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

65

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

E-Print Network [OSTI]

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

Dacre, Helen

66

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

E-Print Network [OSTI]

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

Howat, Ian M.

67

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

E-Print Network [OSTI]

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

Jacobson, Mark

68

EIA - AEO2010 - Clean Air Interstate Rule: Changes and modeling in AEO2010  

Gasoline and Diesel Fuel Update (EIA)

Clean Air Interstate Rule: Changes and modeling in AEO2010 Clean Air Interstate Rule: Changes and modeling in AEO2010 Annual Energy Outlook 2010 with Projections to 2035 Clean Air Interstate Rule: Changes and modeling in AEO2010 On December 23, 2008, the D.C. Circuit Court remanded but did not vacate CAIR [17], overriding its previous decision on February 8, 2008, to remand and vacate CAIR. The December decision, which is reflected in AEO2010, allows CAIR to remain in effect, providing time for the EPA to modify the rule in order to address objections raised by the Court in its earlier decision. A similar rule, referred to as the CAMR, which was to set up a cap-and-trade system for reducing mercury emissions by approximately 70 percent, is not represented in the AEO2010 projections, because it was vacated by the D.C. Circuit Court in February 2008.

69

Workshop on Biofuels Projections in AEO Presenters Biographies  

Gasoline and Diesel Fuel Update (EIA)

Presenters' Biographies 1 Presenters' Biographies 1 March 2013 Workshop on Biofuels Projections in AEO Presenters' Biographies (by presentation order) John Conti John J. Conti is the Assistant Administrator for Energy Analysis at EIA. Mr. Conti analyzes energy supply, demand, and prices including the impact of financial markets on energy markets; prepares reports on current and future energy use; analyzes the impact of energy policies; and develops advanced techniques for conducting energy information analyses. He also oversees the planning and execution of EIA's analysis and forecasting programs to ensure that EIA models, analyses, and projections meet the highest standards of relevance, reliability, and timeliness. Mr. Conti spent nearly 30 years working for the U.S. Department of Energy. Mr. Conti

70

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.

71

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

Gasoline and Diesel Fuel Update (EIA)

8 8 Table 13. Total Coal Consumption, Actual vs. Forecasts (million short tons) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 805 825 843 868 896 936 AEO 1983 807 831 848 870 899 928 1061 AEO 1984 843 848 866 889 919 958 1110 AEO 1985 818 833 842 853 867 891 918 943 970 989 1008 AEO 1986 813 831 860 870 888 919 945 972 995 1021 1038 1051 1069 1083 1105 AEO 1987 837 837 854 879 896 912 932 954 975 1059 AEO 1989* 872 882 894 903 927 947 965 987 990 1006 1026 1045 1062 AEO 1990 884 893 984 1049 1207 AEO 1991 893 902 918 932 943 948 962 973 984 991 1003 1003 1011 1030 1056 1078 AEO 1992 905 934 919 925 934 944 953 961 971 993 1003 1011 1020 1037 1055 AEO 1993 929 931 940 947 958 965 970 972 987 997 1004 1010 1018 1032 AEO 1994 920 928 933 938 943 948 953 958 962 967 978 990 987 AEO 1995 935 940 941 947 948 951 954 958 963 971 984 992 AEO 1996 937 942 954 962 983

72

EIA - AEO2010 - Coal projections  

Gasoline and Diesel Fuel Update (EIA)

Coal Projections Coal Projections Annual Energy Outlook 2010 with Projections to 2035 Coal Projections Figure 88. Coal production by region, 1970-2035 Click to enlarge » Figure source and data excel logo Figure 89. U.S. coal production in six cases, 2008, 2020, and 2035 Click to enlarge » Figure source and data excel logo Figure 90. Average annual minemouth coal prices by region, 1990-2035 Click to enlarge » Figure source and data excel logo Figure 91. Average annual delivered coal prices in four cases, 1990-2035 Click to enlarge » Figure source and data excel logo Figure 92. Change in U.S. coal consumption by end use in two cases, 2008-2035 Click to enlarge » Figure source and data excel logo Coal production increases at a slower rate than in the past In the AEO2010 Reference case, increasing coal use for electricity generation, along with the startup of several CTL plants, leads to growth in coal production averaging 0.2 percent per year from 2008 to 2035. This is significantly less than the 0.9-percent average growth rate for U.S. coal production from 1980 to 2008.

73

AEO2011: Electricity Trade | OpenEI  

Open Energy Info (EERE)

Trade Trade Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 10, and contains only the reference case. The dataset uses billion kilowatthours. The data is broken down into Interregional Electricity trade, gross domestic sales, international electricity trade, imports and exports to Canada and Mexico. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA electricity trade Data application/vnd.ms-excel icon AEO2011: Electricity Trade- Reference Case (xls, 34.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035

74

2017 Levelized Costs AEO 2012 Early Release  

Gasoline and Diesel Fuel Update (EIA)

2018 Levelized Costs AEO 2013 1 2018 Levelized Costs AEO 2013 1 January 2013 Levelized Cost of New Generation Resources in the Annual Energy Outlook 2013 This paper presents average levelized costs for generating technologies that are brought on line in 2018 1 as represented in the National Energy Modeling System (NEMS) for the Annual Energy Outlook 2013 (AEO2013) Early Release Reference case. 2 Both national values and the minimum and maximum values across the 22 U.S. regions of the NEMS electricity market module are presented. Levelized cost is often cited as a convenient summary measure of the overall competiveness of different generating technologies. It represents the per-kilowatthour cost (in real dollars) of building and operating a generating plant over an assumed financial life and duty cycle. Key

75

Efficiency and Intensity in the AEO 2010  

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

Session 9 Session 9 Energy Efficiency: Measuring Gains and Quantifying Opportunities April 7, 2010 2010 Energy Conference Washington, DC Steve Wade, Economist Efficiency and Intensity in the AEO 2010 Steve Wade, 2010 Energy Conference, April 7, 2010 2 * What are the sources of efficiency in the AEO 2010? * What is the contribution of energy efficiency to projected U.S. energy intensity? * How do AEO scenarios relate to technical potential? Overview Steve Wade, 2010 Energy Conference, April 7, 2010 3 * Technology - Stock turnover - Progress and learning * Mandates - CAFÉ, efficiency standards (NAECA, EPACT), building codes... - Renewable fuel standards * Incentives - Tax credits, loan guarantees, grants, ...  Energy efficiency and renewables - ACESA, ARRA (stimulus bill) ...  Investment tax credits

76

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

77

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

E-Print Network [OSTI]

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

Allan, Richard P.

78

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

79

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

E-Print Network [OSTI]

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

Perez, Richard R.

80

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

Gasoline and Diesel Fuel Update (EIA)

9 9 Table 14. Coal Production, Actual vs. Forecasts (million short tons) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 914 939 963 995 1031 1080 AEO 1983 900 926 947 974 1010 1045 1191 AEO 1984 899 921 948 974 1010 1057 1221 AEO 1985 886 909 930 940 958 985 1015 1041 1072 1094 1116 AEO 1986 890 920 954 962 983 1017 1044 1073 1097 1126 1142 1156 1176 1191 1217 AEO 1987 917 914 932 962 978 996 1020 1043 1068 1149 AEO 1989* 941 946 977 990 1018 1039 1058 1082 1084 1107 1130 1152 1171 AEO 1990 973 987 1085 1178 1379 AEO 1991 1035 1002 1016 1031 1043 1054 1065 1079 1096 1111 1133 1142 1160 1193 1234 1272 AEO 1992 1004 1040 1019 1034 1052 1064 1074 1087 1102 1133 1144 1156 1173 1201 1229 AEO 1993 1039 1043 1054 1065 1076 1086 1094 1102 1125 1136 1148 1161 1178 1204 AEO 1994 999 1021 1041 1051 1056 1066 1073 1081 1087 1098 1107 1122 1121 AEO 1995

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

Renewable Electricity in the Annual Energy Outlook (AEO)  

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

For Renewable Electricity Working Group July 24, 2014 Christopher Namovicz and Gwen Bredehoeft Renewable Electricity Analysis Team AEO2014 results and status updates for the...

82

Second AEO2014 Oil and Gas Working Group Meeting Summary  

Gasoline and Diesel Fuel Update (EIA)

TEAM EXPLORATION AND PRODUCTION and NATURAL GAS MARKETS TEAMS SUBJECT: Second AEO2014 Oil and Gas Working Group Meeting Summary (presented September 26, 2013) Attendees: Robert...

83

2017 Levelized Costs AEO 2012 Early Release  

Gasoline and Diesel Fuel Update (EIA)

Host and Presentor Contact Information 1 Host and Presentor Contact Information 1 March 2013 Workshop on Biofuels Projections in AEO Host and Presentor Contact Information Hosts: Mindi Farber-DeAnda Team Lead, Energy Information Administration, Biofuels and Emerging Technologies Mindi.Farber-DeAnda@eia.gov 202-586-6419 Vishakh Mantri, Ph.D, P.E. Chemical Engineer, Energy Information Administration, Biofuels and Emerging Technologies Team Vishakh.Mantri@eia.gov 202-586-4815 Presenters: Biofuels in the United States: Context and Outlook Howard Gruenspecht Deputy Administrator, Energy Information Administration Howard.gruenspecht@eia.gov 202-586-6351 Modeling of Biofuels in the AEO, Michael Cole Operations Research Analyst, Energy Information Administration, Liquid Fuels Market Team

84

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

Science Journals Connector (OSTI)

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

M.R. Nava; Tugrul U. Daim

2007-01-01T23:59:59.000Z

85

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

Gasoline and Diesel Fuel Update (EIA)

1 1 Table 16. Total Electricity Sales, Actual vs. Forecasts (billion kilowatt-hours) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 2364 2454 2534 2626 2708 2811 AEO 1983 2318 2395 2476 2565 2650 2739 3153 AEO 1984 2321 2376 2461 2551 2637 2738 3182 AEO 1985 2317 2360 2427 2491 2570 2651 2730 2808 2879 2949 3026 AEO 1986 2363 2416 2479 2533 2608 2706 2798 2883 2966 3048 3116 3185 3255 3324 3397 AEO 1987 2460 2494 2555 2622 2683 2748 2823 2902 2977 3363 AEO 1989* 2556 2619 2689 2760 2835 2917 2994 3072 3156 3236 3313 3394 3473 AEO 1990 2612 2689 3083 3488 3870 AEO 1991 2700 2762 2806 2855 2904 2959 3022 3088 3151 3214 3282 3355 3427 3496 3563 3632 AEO 1992 2746 2845 2858 2913 2975 3030 3087 3146 3209 3276 3345 3415 3483 3552 3625 AEO 1993 2803 2840 2893 2946 2998 3052 3104 3157 3214 3271 3327 3383 3438 3491 AEO 1994 2843 2891 2928

86

EIA - AEO2010 - World oil prices and production trends in AEO2010  

Gasoline and Diesel Fuel Update (EIA)

World oil prices and production trends in AEO2010 World oil prices and production trends in AEO2010 Annual Energy Outlook 2010 with Projections to 2035 World oil prices and production trends in AEO2010 In AEO2010, the price of light, low-sulfur (or “sweet”) crude oil delivered at Cushing, Oklahoma, is tracked to represent movements in world oil prices. EIA makes projections of future supply and demand for “total liquids,” which includes conventional petroleum liquids—such as conventional crude oil, natural gas plant liquids, and refinery gain—in addition to unconventional liquids, which include biofuels, bitumen, coal-to-liquids (CTL), gas-to-liquids (GTL), extra-heavy oils, and shale oil. World oil prices can be influenced by a multitude of factors. Some tend to be short term, such as movements in exchange rates, financial markets, and weather, and some are longer term, such as expectations concerning future demand and production decisions by the Organization of the Petroleum Exporting Countries (OPEC). In 2009, the interaction of market factors led prompt month contracts (contracts for the nearest traded month) for crude oil to rise relatively steadily from a January average of $41.68 per barrel to a December average of $74.47 per barrel [38].

87

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

E-Print Network [OSTI]

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

Marquez, Ricardo

2012-01-01T23:59:59.000Z

88

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

Science Journals Connector (OSTI)

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

Haralambos Feidas; Themistoklis Kontos; Nikolaos Soulakellis; Konstantinos Lagouvardos

2007-01-01T23:59:59.000Z

89

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

90

EIA - AEO2010 - Issues in Focus  

Gasoline and Diesel Fuel Update (EIA)

Issues in Focus Issues in Focus Annual Energy Outlook 2010 with Projections to 2035 Issues in Focus Introduction Each year, the Issues in Focus section of the AEO provides an in-depth discussion on topics of special interest, including significant changes in assumptions and recent developments in technologies for energy production, supply, and consumption. The first section compares the results of two cases that adopt different assumptions about the future course of existing energy policies. One case assumes the elimination of sunset provisions in existing energy policies. The other case assumes the extension of a selected group of existing policies—CAFE standards, appliance standards, and PTCs—in addition to the elimination of sunset provisions. Other sections include a discussion of end-use energy efficiency trends in AEO2010; an analysis of the impact of incentives on the use of natural gas in heavy freight trucks; factors affecting the relationship between crude oil and natural gas prices; the sensitivity of the projection results to variations in assumptions about the availability of U.S. shale gas resources; the implications of retiring nuclear plants after 60 years of operation; and issues related to accounting for CO2 emissions from biomass energy combustion.

91

EIA - AEO2010 - Legislation and Regulations  

Gasoline and Diesel Fuel Update (EIA)

Legislation and Regulations Legislation and Regulations Annual Energy Outlook 2010 with Projections to 2035 Legislation and Regulations Introduction The Reference case projections in AEO2010 generally assume that current laws and regulations affecting the energy sector remain unchanged throughout the projection period (including the implication that laws which include sunset dates do, in fact, become ineffective at the time of those sunset dates). The potential impacts of pending or proposed legislation, regulations, and standards—or of sections of legislation that have been enacted but that require regulations for which the implementing agency will exercise major discretion, or require appropriation of funds that are not provided or specified in the legislation itself—are not reflected in the Reference case projections. However, sensitivity cases that incorporate alternative assumptions about the future of existing policies subject to periodic updates also are included. The Federal and State laws and regulations included in AEO2010 are based on those in effect as of the end of October 2009. In addition, at the request of the Administration and Congress, EIA has regularly examined the potential implications of proposed legislation in Service Reports (see EIA Service Reports released since January 2009).

92

Clean Air Interstate Rule: Changes and Modeling in AEO2010 (released in AEO2010)  

Reports and Publications (EIA)

On December 23, 2008, the D.C. Circuit Court remanded but did not vacate the Clean Air Interstate Rule (CAIR), overriding its previous decision on February 8, 2008, to remand and vacate CAIR. The December decision, which is reflected in Annual Energy Outlook 2010 (AEO) , allows CAIR to remain in effect, providing time for the Environmental Protection Agency to modify the rule in order to address objections raised by the Court in its earlier decision. A similar rule, referred to as the Clean Air Mercury Rule (CAMR), which was to set up a cap-and-trade system for reducing mercury emissions by approximately 70%, is not represented in the AEO2010 projections, because it was vacated by the D.C. Circuit Court in February 2008.

2010-01-01T23:59:59.000Z

93

AEO2011: Petroleum Product Prices | OpenEI  

Open Energy Info (EERE)

4932 4932 Varnish cache server AEO2011: Petroleum Product Prices Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 12, and contains only the reference case. The dataset uses 2009 dollars per gallon. The data is broken down into crude oil prices, residential, commercial, industrial, transportation, electric power and refined petroleum product prices. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Petroleum prices Data application/vnd.ms-excel icon AEO2011: Petroleum Product Prices- Reference Case (xls, 129.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

94

First AEO2015 Liquid Fuels Markets Working Group Meeting  

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

with Brazil for ethanol. - AEO2015 does not include the Pathways2 cellulosic to biogas provision that recently was approved for cellulosic RIN pathways (but is not in effect...

95

AEO 2015 Electricity, Coal, Nuclear and Renewables Preliminary...  

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

* Clean Air Interstate Rule is still assumed (as in AEO2014) - Not modeled: Cross State Air Pollution Rule (CSAPR) - D.C. Circuit Court still has not ruled on EPA's motion to...

96

EPACT2005: Status of Provisions (Update) (released in AEO2007)  

Reports and Publications (EIA)

The Energy Policy Act 2005 (EPACT) was signed into law by President Bush on August 8, 2005, and became Public Law 109-058. A number of provisions from EPACT2005 were included in the Annual Energy Outlook 2006 (AEO) projections. Many others were not considered in AEO2006particularly, those that require funding appropriations or further specification by federal agencies or Congress before implementation.

2007-01-01T23:59:59.000Z

97

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

E-Print Network [OSTI]

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

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

98

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

SciTech Connect (OSTI)

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

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

1985-05-01T23:59:59.000Z

99

EIA - AEO2010 - Trends in Economic Activity  

Gasoline and Diesel Fuel Update (EIA)

Trends in Economic Activity Trends in Economic Activity Annual Energy Outlook 2010 with Projections to 2035 Trends in Economic Activity Real gross domestic product returns to its pre-recession level by 2011 AEO2010 presents three views of economic growth (Figure 31). The rate of growth in real GDP depends on assumptions about labor force growth and productivity. In the Reference case, growth in real GDP averages 2.4 percent per year. Figure 31. Average annual growth rates of real GDP, labot force, and productivity in three cases, 2008-2035 Click to enlarge » Figure source and data excel logo Figure 32. Average annual inflation, interest, and unemployment rates in three cases, 2008-2035 Click to enlarge » Figure source and data excel logo Figure 33. Sectoral composition of industrial output growth rates in three cases, 2008-2035

100

Annual Energy Outlook 1998 Forecasts - Preface  

Gasoline and Diesel Fuel Update (EIA)

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

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

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

102

AEO2011: Renewable Energy Generation by Fuel - Midwest Reliability Council  

Open Energy Info (EERE)

West West Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 101, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Generation Fuel midwest Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Midwest Reliability Council / West- Reference Case (xls, 119 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

103

AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating  

Open Energy Info (EERE)

Upstate New York Upstate New York Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 105, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Renewable Energy Generation Upstate New York Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating Council / Upstate New York- Reference Case (xls, 119 KiB) Quality Metrics Level of Review Peer Reviewed

104

AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation  

Open Energy Info (EERE)

Delta Delta Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 109, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Delta EIA Renewable Energy Generation SERC Reliability Corporation Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation / Delta- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment

105

AEO2011: Renewable Energy Generation by Fuel - Texas Regional Entity |  

Open Energy Info (EERE)

Texas Regional Entity Texas Regional Entity Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 98, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Generation Fuel Texas Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Texas Regional Entity- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

106

AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating  

Open Energy Info (EERE)

Northeast Northeast Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 102, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Generation Northeast Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating Council / Northeast- Reference Case (xls, 119 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

107

AEO2011: Liquid Fuels Supply and Disposition | OpenEI  

Open Energy Info (EERE)

Liquid Fuels Supply and Disposition Liquid Fuels Supply and Disposition Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 11, and contains only the reference case. The dataset uses million barrels per day. The data is broken down into crude oil, other petroleum supply, other non petroleum supply and liquid fuel consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO disposition EIA liquid fuels Supply Data application/vnd.ms-excel icon AEO2011: Liquid Fuels Supply and Disposition- Reference Case (xls, 117 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

108

AEO2011: Electric Power Projections for EMM Region - Midwest Reliability  

Open Energy Info (EERE)

West West Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 76, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power midwest projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Midwest Reliability Council / West- Reference Case (xls, 259.1 KiB)

109

AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation  

Open Energy Info (EERE)

Virginia-Carolina Virginia-Carolina Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 113, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Carolina EIA Renewable Energy Generation SERC Reliability Corporation Virginia Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation / Virginia-Carolina- Reference Case (xls, 118.9 KiB) Quality Metrics

110

AEO2011: Electric Power Projections for EMM Region - Western Electricity  

Open Energy Info (EERE)

Northwest Power Pool Area Northwest Power Pool Area Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 93, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power Northwest Power Pool Area projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Western Electricity Coordinating Council / Northwest Power Pool Area (xls, 259.1 KiB)

111

AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation  

Open Energy Info (EERE)

Southeastern Southeastern Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 111, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Renewable Energy Generation SERC Reliability Corporation Southeastern Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation / Southeastern- Reference Case (xls, 119 KiB) Quality Metrics Level of Review Peer Reviewed

112

AEO2011: Renewable Energy Generation by Fuel - Reliability First  

Open Energy Info (EERE)

East East Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 106, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released July 25th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO East EIA Renewable Energy Generation Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Reliability First Corporation / East- Reference Case (xls, 119 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

113

AEO2011: Electric Power Projections for EMM Region - Reliability First  

Open Energy Info (EERE)

9643 9643 Varnish cache server AEO2011: Electric Power Projections for EMM Region - Reliability First Corporation / Michigan Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 82, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power Michigan projections Data Quality Metrics Level of Review Peer Reviewed

114

AEO2011: Electric Power Projections for EMM Region - SERC Reliability  

Open Energy Info (EERE)

Southeastern Southeastern Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 86, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - SERC Reliability Corporation / Southeastern- Reference Case (xls, 259.3 KiB)

115

AEO2011: Natural Gas Imports and Exports | OpenEI  

Open Energy Info (EERE)

Imports and Exports Imports and Exports Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 135, and contains only the reference case. The data is broken down into Crude oil, dry natural gas. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA exports imports Natural Gas Data application/vnd.ms-excel icon AEO2011: Natural Gas Imports and Exports- Reference Case (xls, 48.8 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata

116

AEO2011: Renewable Energy Generation by Fuel - Western Electricity  

Open Energy Info (EERE)

Southwest Southwest Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 116, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Renewable Energy Generation Southwest Western Electricity Coordinating Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Western Electricity Coordinating Council / Southwest (xls, 119.1 KiB) Quality Metrics Level of Review Peer Reviewed

117

AEO2011: Electric Power Projections for EMM Region - SERC Reliability  

Open Energy Info (EERE)

Central Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 87, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO central EIA Electric power projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - SERC Reliability Corporation / Central- Reference Case (xls, 259.1 KiB)

118

AEO2011: Renewable Energy Generation by Fuel - Reliability First  

Open Energy Info (EERE)

Michigan Michigan Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 107, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Michigan Reliability First Corporation Renewable Energy Generation Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Reliability First Corporation / Michigan- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed

119

AEO2011: Electricity Generation by Electricity Market Module Region and  

Open Energy Info (EERE)

Generation by Electricity Market Module Region and Generation by Electricity Market Module Region and Source Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 96, and contains only the reference case. The dataset uses billion kilowatthours. The data is broken down into texas regional entity, Florida reliability coordinating council, midwest reliability council and northeast power coordination council. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electricity generation Data application/vnd.ms-excel icon AEO2011: Electricity Generation by Electricity Market Module Region and Source- Reference Case (xls, 400.2 KiB) Quality Metrics

120

AEO2011: Imported Liquids by Source | OpenEI  

Open Energy Info (EERE)

Imported Liquids by Source Imported Liquids by Source Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 146, and contains only the reference case. The dataset uses million barrels per day. The data is broken down into crude oil, light refined products and heavy refined products. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA imports liquids Data application/vnd.ms-excel icon AEO2011: Imported Liquids by Source- Reference Case (xls, 85.2 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL)

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

AEO2011: Electricity Generating Capacity | OpenEI  

Open Energy Info (EERE)

Generating Capacity Generating Capacity Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 9, and contains only the reference case. The dataset uses gigawatts. The data is broken down into power only, combined heat and power, cumulative planned additions, cumulative unplanned conditions, and cumulative retirements and total electric power sector capacity . Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO capacity consumption EIA Electricity generating Data application/vnd.ms-excel icon AEO2011: Electricity Generating Capacity- Reference Case (xls, 130.1 KiB) Quality Metrics Level of Review Peer Reviewed

122

AEO2011: Renewable Energy Generation by Fuel - Midwest Reliability Council  

Open Energy Info (EERE)

East East Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 100, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Generation Fuel midwest Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Midwest Reliability Council / East- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

123

AEO2011: Electric Power Projections for EMM Region - Northeast Power  

Open Energy Info (EERE)

NYC-Westchester NYC-Westchester Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 78, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power Northeast projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Northeast Power Coordinating Council / NYC-Westchester - Reference Case (xls, 259.2 KiB)

124

AEO2011: Electric Power Projections for EMM Region - Florida Reliability  

Open Energy Info (EERE)

Florida Reliability Florida Reliability Coordinating Council Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 74, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power Florida projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Florida Reliability Coordinating Council- Reference Case (xls, 259.3 KiB)

125

AEO2011: Electric Power Projections for EMM Region - Reliability First  

Open Energy Info (EERE)

Reliability First Reliability First Corporation / West Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 83, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Reliability First Corporation / West- Reference Case (xls, 259.5 KiB)

126

AEO2011: Renewable Energy Generation by Fuel - Western Electricity  

Open Energy Info (EERE)

Northwest Power Pool Area Northwest Power Pool Area Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 118, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. This dataset contains data for the northwest power pool area of the U.S. Western Electricity Coordinating Council (WECC). Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Northwest Power Pool Area Renewable Energy Generation WECC Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Western Electricity Coordinating Council / Northwest Power Pool Area - Reference (xls, 119.3 KiB)

127

AEO2011: Electric Power Projections for EMM Region - Western Electricity  

Open Energy Info (EERE)

California California Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 92, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released August 10th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO California EIA Electric Power projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Western Electricity Coordinating Council / California- Reference Case (xls, 259.5 KiB)

128

AEO2011: Electric Power Projections for EMM Region - SERC Reliability  

Open Energy Info (EERE)

Gateway Gateway Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 85, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projection Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - SERC Reliability Corporation / Gateway - Reference Case (xls, 259 KiB)

129

AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating  

Open Energy Info (EERE)

Long Island Long Island Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 104, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Long Island Renewable Energy Generation Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating Council / Long Island- Reference Case (xls, 118.8 KiB) Quality Metrics Level of Review Peer Reviewed Comment

130

AEO2011: Coal Supply, Disposition, and Prices | OpenEI  

Open Energy Info (EERE)

Supply, Disposition, and Prices Supply, Disposition, and Prices Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 15, and contains only the reference case. The dataset uses gigawatts. The data is broken down into production, net imports, consumption by sector and price. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO coal coal supply disposition. prices EIA Data application/vnd.ms-excel icon AEO2011: Coal Supply, Disposition, and Prices- Reference Case (xls, 91.7 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL)

131

AEO2011: Electric Power Projections for EMM Region - Western Electricity  

Open Energy Info (EERE)

Southwest Southwest Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 91, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections Southwest WECC Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Western Electricity Coordinating Council / Southwest- Reference Case (xls, 259.1 KiB)

132

AEO2011: Electric Power Projections for EMM Region - Northeast Power  

Open Energy Info (EERE)

Northeast Northeast Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 77, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power Northeast projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Northeast Power Coordinating Council / Northeast- Reference Case (xls, 259.2 KiB)

133

AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation  

Open Energy Info (EERE)

Gateway Gateway Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 110, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Gateway Reliability First Corporation SERC Reliability Corporation Data application/vnd.ms-excel icon AEO2011:Renewable Energy Generation by Fuel - SERC Reliability Corporation / Gateway- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed

134

AEO2011: Renewable Energy Generation by Fuel - Reliability First  

Open Energy Info (EERE)

West West Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 108, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Reliability First Corporation Renewable Energy Generation West Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Reliability First Corporation / West- Reference Case (xls, 119 KiB) Quality Metrics Level of Review Peer Reviewed Comment

135

AEO2011: Electric Power Projections for EMM Region - Western Electricity  

Open Energy Info (EERE)

Rockies Rockies Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 94, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections Rockies Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Western Electricity Coordinating Council / Rockies- Reference Case (xls, 258.8 KiB)

136

AEO2011: Renewable Energy Generation by Fuel - Florida Reliability  

Open Energy Info (EERE)

Florida Reliability Florida Reliability Coordinating Council Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 99, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released July 20th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Generation Florida Fuel Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Florida Reliability Coordinating Council- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed

137

AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating  

Open Energy Info (EERE)

NYC-Westchester NYC-Westchester Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 103, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Generation Fuel Westchester Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Northeast Power Coordinating Council / NYC-Westchester- Reference Case (xls, 118.8 KiB) Quality Metrics Level of Review Peer Reviewed Comment

138

AEO2011: Renewable Energy Generation by Fuel - Western Electricity  

Open Energy Info (EERE)

Rockies Rockies Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 119, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. The dataset contains data for the Rockies region of WECC. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Renewable Energy Generation Rockies WECC Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Western Electricity Coordinating Council / Rockies- Reference Case (xls, 119 KiB)

139

AEO2011: Renewable Energy Generation by Fuel - Western Electricity  

Open Energy Info (EERE)

California California Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 117, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO California EIA Renewable Energy Generation Western Electricity Coordinating Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Western Electricity Coordinating Council / California (xls, 119.2 KiB) Quality Metrics Level of Review Peer Reviewed

140

2017 Levelized Costs AEO 2012 Early Release  

Gasoline and Diesel Fuel Update (EIA)

1 1 July 2012 Short-Term Energy Outlook Highlights * EIA projects the West Texas Intermediate (WTI) crude oil spot price to average about $88 per barrel over the second half of 2012 and the U.S. refiner acquisition cost (RAC) of crude oil to average $93 per barrel, both about $7 per barrel lower than last month's Outlook. EIA expects WTI and RAC crude oil prices to remain roughly at these second half levels in 2013. Beginning in this month's Outlook, EIA is also providing a forecast of Brent crude oil spot prices (see Brent Crude Oil Spot Price Added to Forecast), which are expected to average $106 per barrel for 2012 and $98 per barrel in 2013. These price forecasts assume that world oil-consumption-weighted real gross domestic product

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

California's Move Toward E10 (released in AEO2009)  

Reports and Publications (EIA)

In Annual Energy Outlook 2009, (AEO) E10a gasoline blend containing 10% ethanolis assumed to be the maximum ethanol blend allowed in California erformulated gasoline (RFG), as opposed to the 5.7% blend assumed in earlier AEOs. The 5.7% blend had reflected decisions made when California decided to phase out use of the additive methyl tertiary butyl ether in its RFG program in 2003, opting instead to use ethanol in the minimum amount that would meet the requirement for 2.0% oxygen content under the Clean Air Act provisions in effect at that time.

2009-01-01T23:59:59.000Z

142

Annual Energy Outlook Retrospective Review: Evaluation of 2013...  

Gasoline and Diesel Fuel Update (EIA)

resulted in energy intensity being overestimated for nearly all years. April 2014 U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2013 and Prior...

143

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

Science Journals Connector (OSTI)

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

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

2014-01-01T23:59:59.000Z

144

An Evaluation of Tropical Cyclone Genesis Forecasts from Global Numerical Models  

Science Journals Connector (OSTI)

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

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

2013-12-01T23:59:59.000Z

145

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

Science Journals Connector (OSTI)

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

Emma B. Suckling; Leonard A. Smith

2013-12-01T23:59:59.000Z

146

RACORO Forecasting  

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

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

147

State Renewable Energy Requirements and Goals: Update Through 2007 (Update) (released in AEO2008)  

Reports and Publications (EIA)

In recent years, the Annual Energy Outlook (AEO) has tracked the growing number of states that have adopted requirements or goals for renewable energy. While there is no federal renewable generation mandate, the states have been adopting such standards for some time. AEO2005 provided a summary of all existing programs in effect at that time, and subsequent AEOs have examined new policies or changes to existing ones. Since the publication of AEO2007, four states have enacted new renewable portfolio standards (RPS) legislation, and five others have strengthened their existing RPS programs. In total, 25 states and the District of Columbia.

2008-01-01T23:59:59.000Z

148

AEO2011: Oil and Gas Supply | OpenEI  

Open Energy Info (EERE)

Supply Supply Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 14, and contains only the reference case. The dataset uses gigawatts. The data is broken down into production, net imports, consumption by sector and price. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA gas oil Data Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset Average vote Your vote

149

EIA - AEO2010 - State renewable energy requirements and goals: Update  

Gasoline and Diesel Fuel Update (EIA)

State renewable energy requirements and goals: Update through 2009 State renewable energy requirements and goals: Update through 2009 Annual Energy Outlook 2010 with Projections to 2035 State renewable energy requirements and goals: Update through 2009 To the extent possible, AEO2010 incorporates the impacts of State laws requiring the addition of renewable generation or capacity by utilities doing business in the States. Currently, 30 States and the District of Columbia have enforceable RPS or similar laws (Table 2). Under such standards, each State determines its own levels of generation, eligible technologies, and noncompliance penalties. AEO2010 includes the impacts of all laws in effect as of September 2009 (with the exception of Hawaii, because NEMS provides electricity market projections for the continental United States only).

150

AEO2011: Electric Power Projections for EMM Region - Midwest Reliability  

Open Energy Info (EERE)

East East Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 75, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEIO EIA Electric Power projections Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Midwest Reliability Council / East - Reference Case (xls, 258.6 KiB) Quality Metrics

151

AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation  

Open Energy Info (EERE)

Central Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 112, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords undefined Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - SERC Reliability Corporation / Central- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035

152

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

Science Journals Connector (OSTI)

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

Giorgos Tigas; Panagiotis Lefakis; Konstantinos Ioannou; Athanasios Hasekioglou

2013-01-01T23:59:59.000Z

153

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

SciTech Connect (OSTI)

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

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

2008-01-24T23:59:59.000Z

154

Valuing Climate Forecast Information  

Science Journals Connector (OSTI)

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

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

1987-09-01T23:59:59.000Z

155

Federal Fuels Taxes and Tax Credits (Update) (released in AEO2008)  

Reports and Publications (EIA)

The Annual Energy Outlook 2008 (AEO) reference case incorporates current regulations that pertain to the energy industry. This section describes the handling of federal taxes and tax credits in AEO2008, focusing primarily on areas where regulations have changed or the handling of taxes or tax credits has been updated.

2008-01-01T23:59:59.000Z

156

EIA - Annual Energy Outlook 2008-Impacts of Updating the AEO2008 Reference  

Gasoline and Diesel Fuel Update (EIA)

Impacts of Updating the AEO2008 Reference Case Impacts of Updating the AEO2008 Reference Case Annual Energy Outlook 2008 with Projections to 2030 Impacts of Updating the AEO2008 Reference Case EIA's decision to update the AEO2008 early-release reference case was motivated by the enactment in December 2007 of EISA2007, which contains many provisions that will significantly influence future energy trends. The specific EISA2007 provisions modeled in AEO2008 include updates to the renewable fuel standard (RFS) and the corporate average fuel economy (CAFE) standard for new light-duty vehicles (LDVs); updated and new appliance energy efficiency standards for boilers, dehumidifiers, dish-washers, clothes washers, and commercial walk-in refrigerators and freezers; lighting energy efficiency standards; provisions to reduce energy consumption in Federal buildings; and efficiency standards for in-dustrial electric motors.

157

AEO2013 Early Release Base Overnight Project Technological Total Overnight  

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

AEO2013 Early Release AEO2013 Early Release Base Overnight Project Technological Total Overnight Variable Fixed Heatrate 6 nth-of-a- kind Online Size Lead time Cost in 2012 Contingency Optimism Cost in 2012 4 O&M 5 O&M in 2012 Heatrate Technology Year 1 (MW) (years) (2011 $/kW) Factor 2 Factor 3 (2011 $/kW) (2011 $/MWh) (2011$/kW) (Btu/kWh) (Btu/kWh) Scrubbed Coal New 7 2016 1300 4 2,694 1.07 1.00 2,883 4.39 30.64 8,800 8,740 Integrated Coal-Gasification Comb Cycle (IGCC) 7 2016 1200 4 3,475 1.07 1.00 3,718 7.09 50.49 8,700 7,450 Pulverized Coal with carbon sequestration 2017 650 4 4,662 1.07 1.03 5,138 4.37 65.31 12,000 9,316

158

EIA - AEO2010 - Liquid fuels taxes and tax credits  

Gasoline and Diesel Fuel Update (EIA)

Liquid fuels taxes and tax credits Liquid fuels taxes and tax credits Annual Energy Outlook 2010 with Projections to 2035 Liquid fuels taxes and tax credits This section provides a review of the treatment of Federal fuels taxes and tax credits in AEO2010. Excise taxes on highway fuel The treatment of Federal highway fuel taxes remains unchanged from the previous year’s AEO. Gasoline is taxed at 18.4 cents per gallon, diesel fuel at 24.4 cents per gallon, and jet fuel at 4.4 cents per gallon, consistent with current laws and regulations. Consistent with Federal budgeting procedures, which dictate that excise taxes dedicated to a trust fund, if expiring, are assumed to be extended at current rates, these taxes are maintained at their present levels, without adjustment for inflation, throughout the projection [9]. State fuel taxes are calculated on the basis of a volume-weighted average for diesel, gasoline, and jet fuels. The State fuel taxes were updated as of July 2009 [10] and are held constant in real terms over the projection period, consistent with historical experience.

159

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

Science Journals Connector (OSTI)

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

Donald R. Davis; Soronadi Nnaji

1982-04-01T23:59:59.000Z

160

Transportation Sector Energy Use by Fuel Type Within a Mode from EIA AEO  

Open Energy Info (EERE)

Sector Energy Use by Fuel Type Within a Mode from EIA AEO Sector Energy Use by Fuel Type Within a Mode from EIA AEO 2011 Early Release Dataset Summary Description Supplemental Table 46 of EIA AEO 2011 Early Release Source EIA Date Released December 08th, 2010 (3 years ago) Date Updated Unknown Keywords AEO Annual Energy Outlook EIA Energy Information Administration Fuel mode TEF transportation Transportation Energy Futures Data text/csv icon Transportation_Sector_Energy_Use_by_Fuel_Type_Within_a_Mode.csv (csv, 144.3 KiB) Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset Average vote Your vote

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

Energy Independence and Security Act of 2007: Summary of Provisions (released in AEO2008)  

Reports and Publications (EIA)

The Energy Independence and Security Act of 2007 was signed into law on December 19, 2007, and became Public Law 110-140. Provisions in EISA2007 that require funding appropriations to be implemented, whose impact is highly uncertain, or that require further specification by federal agencies or Congress are not included in Annual Energy Outlook 2008 (AEO). For example, the Energy Information Administration (EIA) does not try to anticipate policy responses to the many studies required by EISA2007, nor to predict the impact of research and development (R&D) funding authorizations included in the bill. Moreover, AEO2008 does not include any provision that addresses a level of detail beyond that modeled in the National Energy Modeling System (NEMS), which was used to develop the AEO2008 projections. AEO2008 addresses only those provisions in EISA2007 that establish specific tax credits, incentives, or standards.

2008-01-01T23:59:59.000Z

162

Workshop on Biofuels Projections in AEO Attendance List  

Gasoline and Diesel Fuel Update (EIA)

Attendance List 1 Attendance List 1 March 2013 Workshop on Biofuels Projections in AEO Attendee list In person attendees Mia Adelberg Abengoa Bioenergy Michael Bredehoeft EIA Tom Capehart USDA Terry Carter Biofuels Center of North Carolina Adam Christensen Johns Hopkins University Michael Cole EIA John Conti EIA Lauren Cooper Center for Climate and Energy Solutions Mindi Farber-DeAnda EIA Denise Gerber Fiberight Steve Gerber Fiberight Ryan Graf Policy Navigation Group David L. Greene Oak Ridge National Laboratory Peter Gross EIA Howard Gruenspecht EIA Marilyn Herman Herman & Associates Robert Hershey Robert L. Hershey, P.E. Sean Hill EIA David Hitchcock Virent, Inc. Rob Johansson USDA Sandra Jones CITGO Petroleum Corp Kendell W. Keith TRC Consulting, Ltd.

163

EIA - AEO2010 - No Sunset and Extended Policies cases  

Gasoline and Diesel Fuel Update (EIA)

No Sunset and Extended Policies cases No Sunset and Extended Policies cases Annual Energy Outlook 2010 with Projections to 2035 No Sunset and Extended Policies cases Background The AEO2010 Reference case is best described as a “current laws and regulations” case, because it generally assumes that existing laws and fully promulgated regulations will remain unchanged throughout the projection period, unless the legislation establishing them specifically calls for them to end or change. The Reference case often serves as a starting point for the analysis of proposed legislative or regulatory changes, a task that would be difficult if the Reference case included “projected” legislative or regulatory changes. As might be expected, it is sometimes difficult to draw a line between what should be included or excluded from the Reference case. Areas of particular uncertainty include:

164

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

E-Print Network [OSTI]

need to consider coal and other fuel prices. This work wascoal-fired generation, for example), for several reasons: (1) price

Bolinger, Mark

2008-01-01T23:59:59.000Z

165

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

166

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

167

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

168

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

E-Print Network [OSTI]

of better understanding fuel price risk and the role thatonly to natural gas fuel price risk (i.e. , the risk thatsuch attributes, including fuel price risk. Furthermore, our

Bolinger, Mark

2009-01-01T23:59:59.000Z

169

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

E-Print Network [OSTI]

of better understanding fuel price risk and the role thatonly to natural gas fuel price risk (i.e. , the risk thatsuch attributes, including fuel price risk. Furthermore, our

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

170

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

E-Print Network [OSTI]

of better understanding fuel price risk and the role thatonly to natural gas fuel price risk (i.e. , the risk thatsuch attributes, including fuel price risk. Furthermore, our

Bolinger, Mark

2008-01-01T23:59:59.000Z

171

Annual Energy Outlook Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Annual Energy Outlook Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections March 2012 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or

172

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

Gasoline and Diesel Fuel Update (EIA)

8 8 Table 3. Gross Domestic Product, Actual vs. Forecasts (Nominal $Billions) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO82 3,939 4,306 4,733 5,201 5,712 6,288 AEO83 3,919 4,264 4,650 5,086 5,549 6,053 9,362 AEO84 3,910 4,191 4,589 5,031 5,490 5,979 9,098 AEO85 3,882 4,103 4,436 4,793 5,207 5,658 6,158 6,702 7,252 7,836 8,486 AEO86 4,203 4,434 4,741 5,015 5,371 5,795 6,244 6,726 7,270 7,875 8,524 9,226 9,973 10,764 11,600 AEO87 4,483 4,701 5,035 5,389 5,773 6,190 6,666 7,175 7,716 11,223 AEO89* 4,857 5,182 5,575 6,013 6,483 6,987 7,525 8,106 8,756 9,400 10,103 10,863 11,632 AEO90 5,236 5,550 7,882 11,329 16,111 AEO91 5,457 5,695 6,078 6,399 6,738 7,145 7,607 8,099

173

File:AEO2012earlyrelease.pdf | Open Energy Information  

Open Energy Info (EERE)

2earlyrelease.pdf 2earlyrelease.pdf Jump to: navigation, search File File history File usage File:AEO2012earlyrelease.pdf Size of this preview: 463 × 599 pixels. Other resolution: 464 × 600 pixels. Go to page 1 2 3 4 5 6 7 8 9 10 11 12 13 Go! next page → next page → Full resolution ‎(1,275 × 1,650 pixels, file size: 1.31 MB, MIME type: application/pdf, 13 pages) File history Click on a date/time to view the file as it appeared at that time. Date/Time Thumbnail Dimensions User Comment current 11:09, 28 March 2012 Thumbnail for version as of 11:09, 28 March 2012 1,275 × 1,650, 13 pages (1.31 MB) Graham7781 (Talk | contribs) You cannot overwrite this file. Edit this file using an external application (See the setup instructions for more information) File usage The following page links to this file:

174

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

E-Print Network [OSTI]

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

Hering, Amanda S.

2010-10-12T23:59:59.000Z

175

AEO2011: Carbon Dioxide Emissions by Sector and Source - South Atlantic |  

Open Energy Info (EERE)

South Atlantic South Atlantic Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 25, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO carbon dioxide emissions EIA South Atlantic Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - South Atlantic- Reference Case (xls, 74.5 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

176

AEO2011: Carbon Dioxide Emissions by Sector and Source - East North Central  

Open Energy Info (EERE)

North Central North Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 23, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO carbon dioxide emissions East North Central Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - East North Central- Reference Case (xls, 74.5 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

177

AEO2011: Lower 48 Natural Gas Production and Wellhead Prices by Supply  

Open Energy Info (EERE)

Natural Gas Production and Wellhead Prices by Supply Natural Gas Production and Wellhead Prices by Supply Region Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 133, and contains only the reference case. The data is broken down into Production, lower 48 onshore and lower 48 offshore. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Natural Gas Wellhead prices Data application/vnd.ms-excel icon AEO2011: Lower 48 Natural Gas Production and Wellhead Prices by Supply Region- Reference Case (xls, 59.1 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License

178

AEO2011: Energy Consumption by Sector and Source - Mountain | OpenEI  

Open Energy Info (EERE)

Mountain Mountain Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 8, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption mountain region Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - Mountain- Reference Case (xls, 297.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035

179

AEO2011: World Metallurgical Coal Flows By Importing Regions and Exporting  

Open Energy Info (EERE)

Metallurgical Coal Flows By Importing Regions and Exporting Metallurgical Coal Flows By Importing Regions and Exporting Countries Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 143, and contains only the reference case. The dataset uses million short tons. The data is broken down into Metallurgical coal exports to Europe, Asia and America. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO coal EIA Data application/vnd.ms-excel icon AEO2011: World Metallurgical Coal Flows By Importing Regions and Exporting Countries- Reference Case (xls, 103.8 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

180

AEO2011: Total Energy Supply, Disposition, and Price Summary | OpenEI  

Open Energy Info (EERE)

Total Energy Supply, Disposition, and Price Summary Total Energy Supply, Disposition, and Price Summary Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 1, and contains only the reference case. The dataset uses quadrillion BTUs, and quantifies the energy prices using U.S. dollars. The data is broken down into total production, imports, exports, consumption, and prices for energy types. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO consumption EIA export import production reference case total energy Data application/vnd.ms-excel icon AEO2011: Total Energy Supply, Disposition, and Price Summary - Reference Case (xls, 112.8 KiB) Quality Metrics

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

AEO2011: Coal Minemouth Prices by Region and Type | OpenEI  

Open Energy Info (EERE)

Minemouth Prices by Region and Type Minemouth Prices by Region and Type Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 141, and contains only the reference case. The dataset uses million short tons and the US Dollar. The data is broken down into northern Appalachia, central Appalachia, southern Appalachia, eastern interior, western interior, Gulf, Dakota medium, western Montana, Wyoming, Rocky Mountain, Arizona/New Mexico and Washington/Alaska. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Coal Minemouth Prices EIA Data application/vnd.ms-excel icon AEO2011: Coal Minemouth Prices by Region and Type- Reference Case (xls, 121.6 KiB)

182

EIA - AEO2010 - American Recovery and Reinvestment Act of 2009: Summary of  

Gasoline and Diesel Fuel Update (EIA)

American Recovery and Reinvestment Act of 2009: Summary of provisions American Recovery and Reinvestment Act of 2009: Summary of provisions Annual Energy Outlook 2010 with Projections to 2035 American Recovery and Reinvestment Act of 2009: Summary of provisions ARRA, signed into law in mid-February 2009, provides significant new Federal funding, loan guarantees, and tax credits to stimulate investments in energy efficiency and renewable energy. The provisions of ARRA were incorporated initially as part of a revision to the AEO2009 Reference case that was released in April 2009 [5], and they also are included in AEO2010. However, provisions that require funding appropriations to be implemented, whose impact is highly uncertain, or that require further specification by Federal agencies or Congress, are not included. Moreover, AEO2010 does not include any provision that addresses a level of detail beyond that modeled in NEMS.

183

AEO2011: Coal Production and Minemouth Prices by Region | OpenEI  

Open Energy Info (EERE)

and Minemouth Prices by Region and Minemouth Prices by Region Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 139, and contains only the reference case. The dataset uses million short tons and the US Dollar. The data is broken down into production and minemouth prices. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Coal Production EIA Minemouth Prices Data application/vnd.ms-excel icon AEO2011: Coal Production and Minemouth Prices by Region- Reference Case (xls, 41.5 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL)

184

AEO2011: Carbon Dioxide Emissions by Sector and Source, New England |  

Open Energy Info (EERE)

Source, New England Source, New England Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 21, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO carbon dioxide emissions New England Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source, New England- Reference Case (xls, 73.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

185

AEO2011: Natural Gas Delivered Prices by End-Use Sector and Census Division  

Open Energy Info (EERE)

Delivered Prices by End-Use Sector and Census Division Delivered Prices by End-Use Sector and Census Division Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 137, and contains only the reference case. This dataset is in trillion cubic feet. The data is broken down into residential, commercial, industrial, electric power and transportation. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Natural Gas Data application/vnd.ms-excel icon AEO2011: Natural Gas Delivered Prices by End-Use Sector and Census Division- Reference Case (xls, 140.7 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

186

AEO2011: Energy Consumption by Sector and Source - Pacific | OpenEI  

Open Energy Info (EERE)

Pacific Pacific Dataset Summary Description This dataset comes from the Electric Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This data reflects Table 9, and contains only the reference case. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption Pacific Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - Pacific- Reference Case (xls, 297.5 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset

187

AEO2011: Lower 48 Crude Oil Production and Wellhead Prices by Supply Region  

Open Energy Info (EERE)

Crude Oil Production and Wellhead Prices by Supply Region Crude Oil Production and Wellhead Prices by Supply Region Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 132, and contains only the reference case. The data is broken down into Production, lower 48 onshore and lower 48 offshore. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO crude oil EIA prices Data application/vnd.ms-excel icon AEO2011: Lower 48 Crude Oil Production and Wellhead Prices by Supply Region- Reference Case (xls, 54.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL)

188

AEO2011: Oil and Gas End-of-Year Reserves and Annual Reserve Additions |  

Open Energy Info (EERE)

End-of-Year Reserves and Annual Reserve Additions End-of-Year Reserves and Annual Reserve Additions Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 134, and contains only the reference case. The data is broken down into Crude oil, dry natural gas. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA end-of-year reserves gas oil Data application/vnd.ms-excel icon AEO2011: Oil and Gas End-of-Year Reserves and Annual Reserve Additions- Reference Case (xls, 58.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL)

189

AEO2011: Energy Consumption by Sector and Source - New England | OpenEI  

Open Energy Info (EERE)

New England New England Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 1, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption New England Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - New England- Reference Case (xls, 297.3 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035

190

AEO2011: Energy Consumption by Sector and Source - West South Central |  

Open Energy Info (EERE)

South Central South Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 7, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption West South Central Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - West South Central- Reference Case (xls, 297.7 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

191

AEO2011:Total Energy Supply, Disposition, and Price Summary | OpenEI  

Open Energy Info (EERE)

Total Energy Supply, Disposition, and Price Summary Total Energy Supply, Disposition, and Price Summary Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 1, and contains only the reference case. The dataset uses quadrillion Btu and the U.S. Dollar. The data is broken down into production, imports, exports, consumption and price. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO consumption disposition energy exports imports Supply Data application/vnd.ms-excel icon AEO2011:Total Energy Supply, Disposition, and Price Summary- Reference Case (xls, 112.8 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

192

AEO2011: Electric Power Projections for EMM Region - Southwest Power Pool /  

Open Energy Info (EERE)

South South Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 90, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections South Southwest Power Pool Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Southwest Power Pool / South- Reference Case (xls, 259 KiB)

193

AEO2011: Energy Consumption by Sector and Source - Middle Atlantic | OpenEI  

Open Energy Info (EERE)

Middle Atlantic Middle Atlantic Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 2, and contains only the reference case. The dataset uses quadrillion btu. The energy consumption data is broken down by sector (residential, commercial, industrial, transportation, electric power) as well as source, and also provides total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA middle atlantic Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - Middle Atlantic- Reference Case (xls, 297.6 KiB) Quality Metrics Level of Review Peer Reviewed Comment

194

AEO2011: Electric Power Projections for EMM Region - Texas Regional Entity  

Open Energy Info (EERE)

Texas Regional Entity Texas Regional Entity Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 73, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections Texas Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Texas Regional Entity - Reference Case (xls, 259.4 KiB)

195

AEO2011: Carbon Dioxide Emissions by Sector and Source - West North Central  

Open Energy Info (EERE)

North Central North Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 24, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO carbon dioxide emissions EIA west north central Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - West North Central- Reference Case (xls, 74.3 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

196

AEO2011: Carbon Dioxide Emissions by Sector and Source - West South Central  

Open Energy Info (EERE)

South Central South Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 27, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO carbon dioxide emissions EIA West South Central Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - West South Central- Reference Case (xls, 74.6 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

197

AEO2011: Renewable Energy Generation by Fuel - United States | OpenEI  

Open Energy Info (EERE)

United States United States Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 120, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Renewable Energy Generation United States Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - United States- Reference Case (xls, 119.5 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

198

AEO2011: Carbon Dioxide Emissions by Sector and Source - Mountain | OpenEI  

Open Energy Info (EERE)

Mountain Mountain Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 28, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO carbon dioxide emissions EIA Mountain Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - Mountain- Reference Case (xls, 74.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

199

AEO2011: Energy Consumption by Sector and Source - East South Central |  

Open Energy Info (EERE)

South Central South Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 6, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Commercial East South Central EIA Electric Power Energy Consumption Industrial Residential transportation Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - East South Central- Reference Case (xls, 297.5 KiB) Quality Metrics Level of Review Peer Reviewed

200

AEO2011: World Total Coal Flows By Importing Regions and Exporting  

Open Energy Info (EERE)

Total Coal Flows By Importing Regions and Exporting Total Coal Flows By Importing Regions and Exporting Countries Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 144, and contains only the reference case. The dataset uses million short tons. The data is broken down into total coal exports to Europe, Asia and America. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO coal EIA Data application/vnd.ms-excel icon AEO2011: World Total Coal Flows By Importing Regions and Exporting Countries - Reference Case (xls, 104 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035

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

AEO2011: Electric Power Projections for EMM Region - Southwest Power Pool /  

Open Energy Info (EERE)

North North Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 89, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power Southwest Power Pool Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - Southwest Power Pool / North- Reference Case (xls, 258.6 KiB)

202

EIA - AEO2013 Early Release Energy-Related Carbon Dioxide Emissions  

Gasoline and Diesel Fuel Update (EIA)

Energy-Related CO2 Emissions Energy-Related CO2 Emissions Total U.S. energy-related CO2 emissions do not return to their 2005 level (5,997 million metric tons) by the end of the AEO2013 projection period.6 Growth in demand for transportation fuels is moderated by rising fuel prices and new, stricter federal CAFE standards for model years 2017 to 2025, which reduce transportation emissions from 2018 until they begin to rise near the end of the projection period. Transportation emissions in 2040 are 26 million metric tons below the 2011 level. Largely as a result of the inclusion of the new CAFE standards in AEO2013, transportation-related CO2 emissions in 2035 are 94 million metric tons below their level in the AEO2012 Reference case. State RPS requirements and abundant low-cost natural gas help shift the

203

AEO2011: Carbon Dioxide Emissions by Sector and Source - East South Central  

Open Energy Info (EERE)

South Central South Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 26, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO carbon dioxide emissions East South Central EIA Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - East South Central- Reference Case (xls, 74.3 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

204

AEO2011: Carbon Dioxide Emissions by Sector and Source - United States |  

Open Energy Info (EERE)

United States United States Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 30, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO carbon dioxide emissions EIA United States Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - United States- Reference Case (xls, 75.1 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

205

AEO2011: Carbon Dioxide Emissions by Sector and Source- Middle Atlantic |  

Open Energy Info (EERE)

Source- Middle Atlantic Source- Middle Atlantic Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 22, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO carbon dioxide emissions middle atlantic Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source- Middle Atlantic- Reference Case (xls, 74.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

206

AEO2011: Energy Consumption by Sector and Source - South Atlantic | OpenEI  

Open Energy Info (EERE)

South Atlantic South Atlantic Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 5, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption sector South Atlantic Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - South Atlantic- Reference Case (xls, 297.6 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

207

AEO2011: Natural Gas Consumption by End-Use Sector and Census Division |  

Open Energy Info (EERE)

Consumption by End-Use Sector and Census Division Consumption by End-Use Sector and Census Division Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 136, and contains only the reference case. This dataset is in trillion cubic feet. The data is broken down into residential, commercial, industrial, electric power and transportation. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Natural gas consumption Data application/vnd.ms-excel icon AEO2011: Natural Gas Consumption by End-Use Sector and Census Division- Reference Case (xls, 138.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

208

AEO2011: Renewable Energy Generation by Fuel - Southwest Power Pool / South  

Open Energy Info (EERE)

South South Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 115, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO EIA Renewable Energy Generation South Southwest Power Pool Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Southwest Power Pool / South- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

209

AEO2011: Renewable Energy Generation by Fuel - Southwest Power Pool / North  

Open Energy Info (EERE)

North North Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 114, and contains only the reference case. The dataset uses gigawatts, billion kilowatthours and quadrillion Btu. The data is broken down into generating capacity, electricity generation and energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA North Renewable Energy Generation Southwest Power Pool Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generation by Fuel - Southwest Power Pool / North- Reference Case (xls, 118.8 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

210

Changing Trends in the Bulk Chemicals and Pulp and Paper Industries (released in AEO2005)  

Reports and Publications (EIA)

Compared with the experience of the 1990s, rising energy prices in recent years have led to questions about expectations of growth in industrial output, particularly in energy-intensive industries. Given the higher price trends, a review of expected growth trends in selected industries was undertaken as part of the production of Annual Energy Outlook 2005 (AEO). In addition, projections for the industrial value of shipments, which were based on the Standard Industrial Classification (SIC) system in AEO2004, are based on the North American Industry Classification System (NAICS) in AEO2005. The change in industrial classification leads to lower historical growth rates for many industrial sectors. The impacts of these two changes are highlighted in this section for two of the largest energy-consuming industries in the U.S. industrial sector-bulk chemicals and pulp and paper.

2005-01-01T23:59:59.000Z

211

AEO2011: Energy Consumption by Sector and Source - West North Central |  

Open Energy Info (EERE)

North Central North Central Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 4, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - West North Central- Reference Case (xls, 297.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035

212

AEO2011: Natural Gas Supply, Disposition, and Prices | OpenEI  

Open Energy Info (EERE)

Supply, Disposition, and Prices Supply, Disposition, and Prices Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 13, and contains only the reference case. The dataset uses gigawatts. The data is broken down into production, net imports, consumption by sector and price. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO disposition EIA natural gas supply prices Data application/vnd.ms-excel icon AEO2011: Natural Gas Supply, Disposition, and Prices - Reference Case (xls, 91.8 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL)

213

AEO2011: World Steam Coal Flows By Importing Regions and Exporting  

Open Energy Info (EERE)

Steam Coal Flows By Importing Regions and Exporting Steam Coal Flows By Importing Regions and Exporting Countries Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 142, and contains only the reference case. The dataset uses million short tons. The data is broken down into steam coal exports to Europe, Asia and America. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Coal flows countries EIA exporting importing Data application/vnd.ms-excel icon AEO2011: World Steam Coal Flows By Importing Regions and Exporting Countries- Reference Case (xls, 103.7 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage

214

AEO2011: Carbon Dioxide Emissions by Sector and Source - Pacific | OpenEI  

Open Energy Info (EERE)

Pacific Pacific Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 29, and contains only the reference case. The dataset uses million metric tons carbon dioxide equivalent. The data is broken down into residential, commercial, industrial, transportation, electric power, and total by fuel. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO carbon dioxide emissions EIA Pacific Data application/vnd.ms-excel icon AEO2011: Carbon Dioxide Emissions by Sector and Source - Pacific- Reference Case (xls, 74.2 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

215

AEO2011: Primary Natural Gas Flows Entering NGTDM Region from Neighboring  

Open Energy Info (EERE)

Primary Natural Gas Flows Entering NGTDM Region from Neighboring Primary Natural Gas Flows Entering NGTDM Region from Neighboring Regions Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 138, and contains only the reference case. This dataset is in billion cubic feet per year. The data is broken down into New England, Middle Atlantic, East North Central, West Central, South Atlantic, East South Central, West South Central, Mountain, Pacific, Florida, Arizona/New Mexico, California. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIS Natural Gas Data application/vnd.ms-excel icon AEO2011: Primary Natural Gas Flows Entering NGTDM Region from Neighboring Regions- Reference Case (xls, 60 KiB)

216

AEO2011: Energy Consumption by Sector and Source - United States | OpenEI  

Open Energy Info (EERE)

United States United States Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 10, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Energy Consumption United States Data application/vnd.ms-excel icon AEO2011: Energy Consumption by Sector and Source - United States- Reference Case (xls, 298.4 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually

217

AEO2011: Electric Power Projections for EMM Region - United States | OpenEI  

Open Energy Info (EERE)

United States United States Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 95, and contains only the reference case. The data is broken down into electric power sector, cumulative planned additions,cumulative unplanned additions,cumulative retirements, end-use sector, electricity sales, net energy for load, generation by fuel type and price by service category. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Electric Power projections United States Data application/vnd.ms-excel icon AEO2011: Electric Power Projections for EMM Region - United States- Reference Case (xls, 260.9 KiB) Quality Metrics

218

State Renewable Energy Requirements and Goals: Update through 2009 (Update) (released in AEO2010)  

Reports and Publications (EIA)

To the extent possible,Annual Energy Outlook 2010 (AEO) incorporates the impacts of state laws requiring the addition of renewable generation or capacity by utilities doing business in the states. Currently, 30 states and the District of Columbia have enforceable renewable portfolio standards (RPS) or similar laws). Under such standards, each state determines its own levels of generation, eligible technologies, and noncompliance penalties. AEO2010 includes the impacts of all laws in effect as of September 2009 (with the exception of Hawaii, because the National Energy Modeling System provides electricity market projections for the continental United States only).

2010-01-01T23:59:59.000Z

219

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

Gasoline and Diesel Fuel Update (EIA)

6 6 Table 11. Natural Gas Net Imports, Actual vs. Forecasts (trillion cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 1.19 1.19 1.19 1.19 1.19 1.19 AEO 1983 1.08 1.16 1.23 1.23 1.23 1.23 1.23 AEO 1984 0.99 1.05 1.16 1.27 1.43 1.57 2.11 AEO 1985 0.94 1.00 1.19 1.45 1.58 1.86 1.94 2.06 2.17 2.32 2.44 AEO 1986 0.74 0.88 0.62 1.03 1.05 1.27 1.39 1.47 1.66 1.79 1.96 2.17 2.38 2.42 2.43 AEO 1987 0.84 0.89 1.07 1.16 1.26 1.36 1.46 1.65 1.75 2.50 AEO 1989* 1.15 1.32 1.44 1.52 1.61 1.70 1.79 1.87 1.98 2.06 2.15 2.23 2.31 AEO 1990 1.26 1.43 2.07 2.68 2.95 AEO 1991 1.36 1.53 1.70 1.82 2.11 2.30 2.33 2.36 2.42 2.49 2.56 2.70 2.75 2.83 2.90 2.95 AEO 1992 1.48 1.62 1.88 2.08 2.25 2.41 2.56 2.68 2.70 2.72 2.76 2.84 2.92 3.05 3.10 AEO 1993 1.79 2.08 2.35 2.49 2.61 2.74 2.89 2.95 3.00 3.05 3.10 3.17 3.29 3.42 AEO 1994 2.02 2.40 2.66 2.74

220

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

Gasoline and Diesel Fuel Update (EIA)

4 4 Table 19. Energy Intensity, Actual vs. Forecasts (quadrillion Btu / $Billion Nominal GDP) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 20.1 18.5 16.9 15.5 14.4 13.2 AEO 1983 19.9 18.6 17.4 16.2 15.1 14.0 9.6 AEO 1984 20.1 18.9 17.7 16.5 15.5 14.5 10.3 AEO 1985 20.0 19.1 18.0 16.9 15.9 14.7 13.7 12.7 11.8 11.1 10.3 AEO 1986 18.3 17.8 16.8 16.1 15.2 14.3 13.5 12.6 11.8 11.0 10.3 9.6 8.8 8.2 7.7 AEO 1987 17.6 17.0 16.3 15.4 14.5 13.8 13.0 12.2 11.5 8.2 AEO 1989* 16.9 16.2 15.2 14.2 13.3 12.5 11.7 11.0 10.4 9.7 9.0 8.4 8.0 AEO 1990 16.1 15.4 11.7 8.6 6.4 AEO 1991 15.5 14.9 14.2 13.6 13.0 12.5 11.9 11.3 10.8 10.3 9.7 9.2 8.7 8.3 7.9 7.4 AEO 1992 15.0 14.5 13.9 13.3 12.7 12.1 11.6 11.0 10.5 10.0 9.5 9.0 8.6 8.1 7.7 AEO 1993 14.7 13.9 13.4 12.8 12.3 11.8 11.2 10.7 10.2 9.6 9.2 8.7 8.3 7.8 AEO 1994 14.1 13.5 13.1 12.6 12.1 11.6 11.1 10.6

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

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

222

Probabilistic Verification of Global and Mesoscale Ensemble Forecasts of Tropical Cyclogenesis  

Science Journals Connector (OSTI)

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

Sharanya J. Majumdar; Ryan D. Torn

2014-10-01T23:59:59.000Z

223

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

Gasoline and Diesel Fuel Update (EIA)

0 0 Table 5. Total Petroleum Consumption, Actual vs. Forecasts (million barrels per day) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 18.00 17.89 17.55 17.24 16.98 16.99 AEO 1983 15.82 16.13 16.37 16.50 16.56 16.63 17.37 AEO 1984 15.77 15.76 16.01 16.27 16.48 16.74 18.00 AEO 1985 15.72 15.74 15.97 16.01 16.06 16.08 16.18 16.23 16.32 16.36 16.53 AEO 1986 16.07 16.29 16.05 16.07 16.15 16.31 16.37 16.42 16.44 16.46 16.50 16.64 16.80 17.21 17.38 AEO 1987 16.52 16.66 16.96 17.06 17.29 17.56 17.73 17.76 17.72 18.30 AEO 1989* 17.01 17.20 17.44 17.57 17.72 17.76 17.78 17.82 18.05 18.12 18.19 18.40 18.61 AEO 1990

224

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

Gasoline and Diesel Fuel Update (EIA)

3 3 Table 8. Natural Gas Wellhead Prices, Actual vs. Forecasts (current dollars per thousand cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 4.32 5.47 6.67 7.51 8.04 8.57 AEO 1983 2.93 3.11 3.46 3.93 4.56 5.26 12.74 AEO 1984 2.77 2.90 3.21 3.63 4.13 4.79 9.33 AEO 1985 2.60 2.61 2.66 2.71 2.94 3.35 3.85 4.46 5.10 5.83 6.67 AEO 1986 1.73 1.96 2.29 2.54 2.81 3.15 3.73 4.34 5.06 5.90 6.79 7.70 8.62 9.68 10.80 AEO 1987 1.83 1.95 2.11 2.28 2.49 2.72 3.08 3.51 4.07 7.54 AEO 1989* 1.62 1.70 1.91 2.13 2.58 3.04 3.48 3.93 4.76 5.23 5.80 6.43 6.98 AEO 1990 1.78 1.88 2.93 5.36 9.15 AEO 1991 1.77 1.90 2.11 2.30 2.42 2.51 2.60 2.74 2.91 3.29 3.75 4.31 5.07 5.77 6.45 7.29 AEO 1992 1.69 1.85 2.03 2.15 2.35 2.51 2.74 3.01 3.40 3.81 4.24 4.74 5.25 5.78 6.37 AEO 1993 1.85 1.94 2.09 2.30 2.44 2.60 2.85 3.12 3.47 3.84 4.31 4.81 5.28 5.68

225

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

Gasoline and Diesel Fuel Update (EIA)

0 0 Table 15. Average Electricity Prices, Actual vs. Forecasts (nominal cents per kilowatt-hour) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 6.38 6.96 7.63 8.23 8.83 9.49 AEO 1983 6.85 7.28 7.74 8.22 8.68 9.18 13.12 AEO 1984 6.67 7.05 7.48 7.89 8.25 8.65 11.53 AEO 1985 6.62 6.94 7.32 7.63 7.89 8.15 8.46 8.85 9.20 9.61 10.04 AEO 1986 6.67 6.88 7.05 7.18 7.35 7.52 7.65 7.87 8.31 8.83 9.41 10.01 10.61 11.33 12.02 AEO 1987 6.63 6.65 6.92 7.12 7.38 7.62 7.94 8.36 8.86 11.99 AEO 1989* 6.50 6.75 7.14 7.48 7.82 8.11 8.50 8.91 9.39 9.91 10.49 11.05 11.61 AEO 1990 6.49 6.72 8.40 10.99 14.47 AEO 1991 6.94 7.31 7.59 7.82 8.18 8.38 8.54 8.73 8.99 9.38 9.83 10.29 10.83 11.36 11.94 12.58 AEO 1992 6.97 7.16 7.32 7.56 7.78 8.04 8.29 8.57 8.93 9.38 9.82 10.26 10.73 11.25 11.83 AEO 1993 7.18 7.36 7.54 7.77 7.99 8.27 8.54 8.89 9.33 9.72 10.14

226

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

Gasoline and Diesel Fuel Update (EIA)

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

227

AEO2011: Renewable Energy Generating Capacity and Generation | OpenEI  

Open Energy Info (EERE)

electric power capacity and generation. electric power capacity and generation. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO EIA Renewable energy capacity and generation Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generating Capacity and Generation- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset Average vote Your vote Ease of access Average vote Your vote Overall rating Average vote Your vote Comments Login or register to post comments If you rate this dataset, your published comment will include your rating.

228

AEO2011: Renewable Energy Consumption by Sector and Source | OpenEI  

Open Energy Info (EERE)

Consumption by Sector and Source Consumption by Sector and Source Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 17, and contains only the reference case. The dataset uses quadrillion Btu. The data is broken down into marketed renewable energy, residential, commercial, industrial, transportation and electric power. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords Commercial Electric Power Industrial Renewable Energy Consumption Residential sector source transportation Data application/vnd.ms-excel icon AEO2011: Renewable Energy Consumption by Sector and Source- Reference Case (xls, 105 KiB) Quality Metrics Level of Review Peer Reviewed

229

Annual Energy Outlook 2000 - Table G1 - Summary of the AEO2000 Cases  

Gasoline and Diesel Fuel Update (EIA)

AEO2000 Cases AEO2000 Cases Case name Description Integration mode Reference in text Reference in Appendix G Reference Baseline economic growth, world oil price, and technology assumptions Fully integrated — — Low Economic Growth Gross domestic product grows at an average annual rate of 1.7 percent, compared to the reference case growth of 2.2 percent. Fully integrated p. 49 — High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.2 percent. Fully integrated p. 49 — Low World Oil Price World oil prices are $14.90 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully integrated p. 50 — High World Oil Price World oil prices are $28.04 per barrel in 2020, compared to $22.04 per barrel in the reference case.

230

AEO2011: Renewable Energy Generating Capacity and Generation | OpenEI  

Open Energy Info (EERE)

generation of each renewable energy source. generation of each renewable energy source. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords AEO generation renewable energy renewable energy generating capacity Data application/vnd.ms-excel icon AEO2011: Renewable Energy Generating Capacity and Generation- Reference Case (xls, 118.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 2008-2035 License License Open Data Commons Public Domain Dedication and Licence (PDDL) Comment Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset Average vote Your vote Ease of access Average vote Your vote Overall rating Average vote Your vote Comments Login or register to post comments

231

AEO2011: Coal Production by Region and Type | OpenEI  

Open Energy Info (EERE)

by Region and Type by Region and Type Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is Table 140, and contains only the reference case. The unit of measurement in this dataset is million short tons. The data is broken down into northern Appalachia, central Appalachia, southern Appalachia, eastern interior, western interior, gulf, Dakota medium, western montana, Wyoming, Rocky Mountain, Arizona/New Mexico and Washington/Alaska. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO Coal Production EIA Data application/vnd.ms-excel icon AE2011: Coal Production by Region and Type- Reference Case (xls, 122.3 KiB)

232

D:\AEO98\OV98\front.wpd  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Distribution Category UC-950 The National Energy Modeling System: An Overview 1998 February 1998 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the U.S. Department of Energy. The information contained herein should be attributed to the Energy Information Administration and should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. ii Energy Information Administration/ The National Energy Modeling System: An Overview 1998 PREFACE The National Energy Modeling System: An Overview provides a summary description of the National Energy Modeling

233

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

Gasoline and Diesel Fuel Update (EIA)

4 4 Table 9. Total Natural Gas Consumption, Actual vs. Forecasts (trillion cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 15.93 15.72 15.72 16.08 16.59 17.08 AEO 1983 17.75 17.63 17.57 17.75 17.76 17.77 16.95 AEO 1984 18.22 18.07 18.33 18.61 18.73 18.76 18.75 AEO 1985 17.79 17.80 17.89 18.30 18.58 18.71 18.79 18.88 18.82 18.82 18.81 AEO 1986 16.52 16.83 17.35 17.27 17.50 17.77 17.77 17.90 18.01 18.04 18.03 18.26 18.34 18.04 17.92 AEO 1987 16.85 16.93 17.24 17.27 17.34 17.43 17.66 18.02 18.31 19.65 AEO 1989* 17.75 17.95 17.94 18.08 18.10 18.34 18.68 18.94 19.17 19.55 19.86 19.98 20.29 AEO 1990 18.34 18.66 20.69 22.88 22.72 AEO 1991 18.53 19.21 19.34 19.56 19.76 20.01 20.21 20.66 20.93 21.23 21.64 22.03 22.33 22.35 22.32 22.27 AEO 1992 18.79 19.36 19.84 20.08 20.53 20.68 21.12 21.42 21.66 21.89 22.23

234

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

Gasoline and Diesel Fuel Update (EIA)

5 5 Table 10. Natural Gas Production, Actual vs. Forecasts (trillion cubic feet) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AEO 1982 14.74 14.26 14.33 14.89 15.39 15.88 AEO 1983 16.48 16.27 16.20 16.31 16.27 16.29 14.89 AEO 1984 17.48 17.10 17.44 17.58 17.52 17.32 16.39 AEO 1985 16.95 17.08 17.11 17.29 17.40 17.33 17.32 17.27 17.05 16.80 16.50 AEO 1986 16.30 16.27 17.15 16.68 16.90 16.97 16.87 16.93 16.86 16.62 16.40 16.33 16.57 16.23 16.12 AEO 1987 16.21 16.09 16.38 16.32 16.30 16.30 16.44 16.62 16.81 17.39 AEO 1989* 16.71 16.71 16.94 17.01 16.83 17.09 17.35 17.54 17.67 17.98 18.20 18.25 18.49 AEO 1990 16.91 17.25 18.84 20.58 20.24 AEO 1991 17.40 17.48 18.11 18.22 18.15 18.22 18.39 18.82 19.03 19.28 19.62 19.89 20.13 20.07 19.95 19.82 AEO 1992 17.43 17.69 17.95 18.00 18.29 18.27 18.51 18.75 18.97 19.19 19.48 19.78

235

EIA - AEO2010 - U.S. nuclear power plants: Continued life or replacement  

Gasoline and Diesel Fuel Update (EIA)

U.S. nuclear power plants: continued life or replacement after 60? U.S. nuclear power plants: continued life or replacement after 60? Annual Energy Outlook 2010 with Projections to 2035 U.S. nuclear power plants: Continued life or replacement after 60? Background Nuclear power plants generate approximately 20 percent of U.S. electricity, and the plants in operation today are often seen as attractive assets in the current environment of uncertainty about future fossil fuel prices, high construction costs for new power plants (particularly nuclear plants), and the potential enactment of GHG regulations. Existing nuclear power plants have low fuel costs and relatively high power output. However, there is uncertainty about how long they will be allowed to continue operating. The nuclear industry has expressed strong interest in continuing the operation of existing nuclear facilities, and no particular technical issues have been identified that would impede their continued operation. Recent AEOs had assumed that existing nuclear units would be retired after 60 years of operation (the initial 40-year license plus one 20-year license renewal). Maintaining the same assumption in AEO2010, with the projection horizon extended to 2035, would result in the retirement of more than one-third of existing U.S. nuclear capacity between 2029 and 2035. Given the uncertainty about when existing nuclear capacity actually will be retired, EIA revisited the assumption for the development of AEO2010 and modified it to allow the continued operation of all existing U.S. nuclear power plants through 2035 in the Reference case.

236

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

E-Print Network [OSTI]

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

Niyogi, Dev

237

Forecasting wireless communication technologies  

Science Journals Connector (OSTI)

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

Sabrina Patino; Jisun Kim; Tugrul U. Daim

2010-01-01T23:59:59.000Z

238

Wind Power Forecasting  

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

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

239

Wind Power Forecasting  

Science Journals Connector (OSTI)

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

Sue Ellen Haupt; William P. Mahoney; Keith Parks

2014-01-01T23:59:59.000Z

240

Energy Demand Forecasting  

Science Journals Connector (OSTI)

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

S. C. Bhattacharyya

2011-01-01T23:59:59.000Z

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

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

SciTech Connect (OSTI)

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

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

2011-08-26T23:59:59.000Z

242

Improving Inventory Control Using Forecasting  

E-Print Network [OSTI]

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

Balandran, Juan

2005-12-16T23:59:59.000Z

243

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

SciTech Connect (OSTI)

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

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

2014-05-01T23:59:59.000Z

244

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Preface Preface This report presents international energy projections through 2025, prepared by the Energy Information Administration, including outlooks for major energy fuels and associated carbon dioxide emissions. The International Energy Outlook 2005 (IEO2005) presents an assessment by the Energy Information Administration (EIA) of the outlook for international energy markets through 2025. U.S. projections appearing in IEO2005 are consistent with those published in EIA’s Annual Energy Outlook 2005 (AEO2005), which was prepared using the National Energy Modeling System (NEMS). Although the IEO typically uses the same reference case as the AEO, IEO2005 has adopted the October futures case from AEO2005 as its reference case for the United States. The October futures case, which has an assumption of higher world oil prices than the AEO2005 reference case, now appears to be a more likely projection. The reference case prices will be reconsidered for the next AEO. Based on information available as of July 2005, the AEO2006 reference case will likely reflect world oil prices higher than those in the IEO2005 reference case.

245

Technology Forecasting Scenario Development  

E-Print Network [OSTI]

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

246

CAPP 2010 Forecast.indd  

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

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

247

EIA - AEO2010 - Accounting for carbon dioxide emissions from biomass energy  

Gasoline and Diesel Fuel Update (EIA)

Accounting for carbon diioxide emissions from biomass energy combustion Accounting for carbon diioxide emissions from biomass energy combustion Annual Energy Outlook 2010 with Projections to 2035 Accounting for carbon dioxide emissions from biomass energy combustion CO2 emissions from the combustion of biomass [75] to produce energy are excluded from the energy-related CO2 emissions reported in AEO2010. According to current international convention [76], carbon released through biomass combustion is excluded from reported energy-related emissions. The release of carbon from biomass combustion is assumed to be balanced by the uptake of carbon when the feedstock is grown, resulting in zero net emissions over some period of time [77]. However, analysts have debated whether increased use of biomass energy may result in a decline in terrestrial carbon stocks, leading to a net positive release of carbon rather than the zero net release assumed by its exclusion from reported energy-related emissions.

248

AEO2011: Energy Consumption by Sector and Source - East North Central |  

Open Energy Info (EERE)

North Central North Central Dataset Summary Description http://en.openei.org/w/skins/openei/images/ui-bg_gloss_wave-medium_40_d6...); background-attachment: scroll; background-origin: initial; background-clip: initial; background-color: rgb(214, 235, 225); line-height: 17px; width: 650px; background-position: 50% 0%; background-repeat: repeat no-repeat; ">This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 3, and contains only the reference case. The dataset uses quadrillion btu. The data is broken down into residential, commercial, industrial, transportation, electric power and total energy consumption. Source EIA Date Released April 26th, 2011 (3 years ago)

249

Comparing Forecast Skill  

Science Journals Connector (OSTI)

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

Timothy DelSole; Michael K. Tippett

2014-12-01T23:59:59.000Z

250

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect (OSTI)

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

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

2010-11-02T23:59:59.000Z

251

Annual Energy Outlook Evaluation, 2005  

Gasoline and Diesel Fuel Update (EIA)

Outlook Evaluation, 2005 1 Outlook Evaluation, 2005 1 Annual Energy Outlook Evaluation, 2005 * Then Energy Information Administration (EIA) produces projections of energy supply and demand each year in the Annual Energy Outlook (AEO). The projections in the AEO are not statements of what will happen but of what might happen, given the assumptions and methodologies used. The projections are business-as-usual trend projections, given known technology, technological and demographic trends, and current laws and regulations. Thus, they provide a policy-neutral reference case that can be used to analyze policy initiatives. EIA does not propose or advocate future legislative and regulatory changes. All laws are assumed to remain as currently enacted; however, the impacts of emerging regulatory changes,

252

EIA-Annual Energy Outlook Retrospective Review-Evaluation of Projections in  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Retrospective Review: Evaluation of Projections in Past Editions (1982-2006)* Annual Energy Outlook Retrospective Review: Evaluation of Projections in Past Editions (1982-2006)* The Energy Information Administration (EIA) produces projections of energy supply and demand each year in the Annual Energy Outlook (AEO). The projections in the AEO are not statements of what will happen but of what might happen, given the assumptions and methodologies used. The projections are business-as-usual trend projections, given known technology, technological and demographic trends, and current laws and regulations. The potential impacts of pending or proposed legislation, regulations, and standards-or of sections of legislation that have been enacted but that require implementing regulations or appropriation of funds that are not provided or specified in the legislation itself-are not reflected in the projections, although there are a few exceptions. It is assumed that current laws and regulations that have sunset dates, but which are regularly renewed, are extended for modeling purposes. Thus, the AEO generally provides a policy-neutral reference case that can be used to analyze policy initiatives. While the analyses in the AEO focus primarily on a reference case, lower and higher economic growth cases, and lower and higher energy price cases; more than 30 alternative cases are generally included in the AEO. Readers are encouraged to review the full range of cases, which address many of the uncertainties inherent in long-term projections.

253

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

Science Journals Connector (OSTI)

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

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

2015-01-01T23:59:59.000Z

254

Information-Based Skill Scores for Probabilistic Forecasts  

Science Journals Connector (OSTI)

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

Bodo Ahrens; Andr Walser

2008-01-01T23:59:59.000Z

255

A methodology for forecasting carbon dioxide flooding performance  

E-Print Network [OSTI]

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

Marroquin Cabrera, Juan Carlos

2012-06-07T23:59:59.000Z

256

Sandia National Laboratories: solar forecasting  

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

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

257

Consensus Coal Production Forecast for  

E-Print Network [OSTI]

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

Mohaghegh, Shahab

258

On Sequential Probability Forecasting  

E-Print Network [OSTI]

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

McCarl, Bruce A.

259

EIA - AEO2010 - Naturall gas as a fuel for heavy trucks: Issues and  

Gasoline and Diesel Fuel Update (EIA)

gas as a fuel for heavy trucks: Issues and incentives gas as a fuel for heavy trucks: Issues and incentives Annual Energy Outlook 2010 with Projections to 2035 Natural gas as a fuel for heavy trucks: Issues and incentives Environmental and energy security concerns related to petroleum use for transportation fuels, together with recent growth in U.S. proved reserves and technically recoverable natural gas resources, including shale gas, have sparked interest in policy proposals aimed at stimulating increased use of natural gas as a vehicle fuel, particularly for heavy trucks. In 2008, U.S. freight trucks used more than 2 million barrels of petroleum-based diesel fuel per day. In the AEO2010 Reference case, they are projected to use 2.7 million barrels per day in 2035. Petroleum-based diesel use by freight trucks in 2008 accounted for 15 percent of total petroleum consumption (excluding biofuels and other non-petroleum-based products) in the transportation sector (13.2 million barrels per day) and 12 percent of the U.S. total for all sectors (18.7 million barrels per day). In the Reference case, oil use by freight trucks grows to 20 percent of total transportation use (13.7 million barrels per day) and 14 percent of the U.S. total (19.0 million barrels per day) by 2035. The following analysis examines the potential impacts of policies aimed at increasing sales of heavy-duty natural gas vehicles (HDNGVs) and the use of natural gas fuels, and key factors that lead to uncertainty in these estimates.

260

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

SciTech Connect (OSTI)

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

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

2007-06-01T23:59:59.000Z

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

Online short-term solar power forecasting  

SciTech Connect (OSTI)

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

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

2009-10-15T23:59:59.000Z

262

Price forecasting for notebook computers.  

E-Print Network [OSTI]

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

Rutherford, Derek Paul

2012-01-01T23:59:59.000Z

263

Ensemble Forecasts and their Verification  

E-Print Network [OSTI]

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

Maryland at College Park, University of

264

Energy Information Administration (EIA) - Annual Energy Outlook with  

Gasoline and Diesel Fuel Update (EIA)

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

265

Probabilistic manpower forecasting  

E-Print Network [OSTI]

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

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

266

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Journals Connector (OSTI)

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

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

267

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

E-Print Network [OSTI]

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

Jacobsen, Michael

268

Project Profile: Forecasting and Influencing Technological Progress...  

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

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

269

Forecasting with adaptive extended exponential smoothing  

Science Journals Connector (OSTI)

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

John T. Mentzer Ph.D.

270

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

271

Energy Department Forecasts Geothermal Achievements in 2015 ...  

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

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

272

Correcting and combining time series forecasters  

Science Journals Connector (OSTI)

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

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

2014-02-01T23:59:59.000Z

273

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

E-Print Network [OSTI]

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

274

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

SciTech Connect (OSTI)

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

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

2011-08-15T23:59:59.000Z

275

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":""}]}

276

Price forecasting for notebook computers  

E-Print Network [OSTI]

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

Rutherford, Derek Paul

2012-06-07T23:59:59.000Z

277

Forecasting phenology under global warming  

Science Journals Connector (OSTI)

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

2010-01-01T23:59:59.000Z

278

Demand Forecasting of New Products  

E-Print Network [OSTI]

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

Sun, Yu

279

Evaluation Framework and Tools for Distributed Energy Resources  

SciTech Connect (OSTI)

The Energy Information Administration's (EIA) 2002 Annual Energy Outlook (AEO) forecast anticipates the need for 375 MW of new generating capacity (or about one new power plant) per week for the next 20 years, most of which is forecast to be fueled by natural gas. The Distributed Energy and Electric Reliability Program (DEER) of the Department of Energy (DOE), has set a national goal for DER to capture 20 percent of new electric generation capacity additions by 2020 (Office of Energy Efficiency and Renewable Energy 2000). Cumulatively, this amounts to about 40 GW of DER capacity additions from 2000-2020. Figure ES-1 below compares the EIA forecast and DEER's assumed goal for new DER by 2020 while applying the same definition of DER to both. This figure illustrates that the EIA forecast is consistent with the overall DEER DER goal. For the purposes of this study, Berkeley Lab needed a target level of small-scale DER penetration upon which to hinge consideration of benefits and costs. Because the AEO2002 forecasted only 3.1 GW of cumulative additions from small-scale DER in the residential and commercial sectors, another approach was needed to estimate the small-scale DER target. The focus here is on small-scale DER technologies under 500 kW. The technology size limit is somewhat arbitrary, but the key results of interest are marginal additional costs and benefits around an assumed level of penetration that existing programs might achieve. Berkeley Lab assumes that small-scale DER has the same growth potential as large scale DER in AEO2002, about 38 GW. This assumption makes the small-scale goal equivalent to 380,000 DER units of average size 100 kW. This report lays out a framework whereby the consequences of meeting this goal might be estimated and tallied up. The framework is built around a list of major benefits and a set of tools that might be applied to estimate them. This study lists some of the major effects of an emerging paradigm shift away from central station power and towards a more dispersed and heterogeneous power system. Seventeen societal effects of small-scale DER are briefly summarized. Each effect is rated as high, medium or low, on three different scales that will help determine the optimal social investment. The three scales are: the magnitude of the economic benefit; the likelihood that the benefit can be monetized in efficient markets, i.e. internalized; and how tractable it might be to quantify each benefit analytically. Some of the modeling tools that may be used to estimate these effects are described in the Appendix.

Gumerman, Etan Z.; Bharvirkar, Ranjit R.; LaCommare, Kristina Hamachi; Marnay , Chris

2003-02-01T23:59:59.000Z

280

AEO2011_Newell_SENR_Testimony_2 03_11final revised  

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

NATURAL RESOURCES NATURAL RESOURCES UNITED STATES SENATE February 3, 2011 2 Mr. Chairman and Members of the Committee: I appreciate the opportunity to appear before you today to discuss the energy and oil market outlook. The U.S. Energy Information Administration (EIA) is the statistical and analytical agency within the U.S. Department of Energy. EIA collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding regarding energy and its interaction with the economy and the environment. EIA is the Nation's premier source of energy information and, by law, its data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government.

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

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

282

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

Office of Environmental Management (EM)

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

283

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

E-Print Network [OSTI]

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

Sakauchi, Tsuginosuke

2011-01-01T23:59:59.000Z

284

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"

285

The University of Reading Helen Dacre Evaluating pollution transport in  

E-Print Network [OSTI]

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

Dacre, Helen

286

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Journals Connector (OSTI)

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

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

287

Aggregate vehicle travel forecasting model  

SciTech Connect (OSTI)

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

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

1995-05-01T23:59:59.000Z

288

Communication of uncertainty in temperature forecasts  

Science Journals Connector (OSTI)

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

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

289

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

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

Sathaye, Jayant

2013-01-01T23:59:59.000Z

290

Massachusetts state airport system plan forecasts.  

E-Print Network [OSTI]

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

Mathaisel, Dennis F. X.

291

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Journals Connector (OSTI)

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

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

2003-02-01T23:59:59.000Z

292

Forecasting Water Use in Texas Cities  

E-Print Network [OSTI]

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

Shaw, Douglas T.; Maidment, David R.

293

Energy demand forecasting: industry practices and challenges  

Science Journals Connector (OSTI)

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

Mathieu Sinn

2014-06-01T23:59:59.000Z

294

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

295

Load Forecasting of Supermarket Refrigeration  

E-Print Network [OSTI]

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

296

Essays on macroeconomics and forecasting  

E-Print Network [OSTI]

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

Liu, Dandan

2006-10-30T23:59:59.000Z

297

"Table 1. Aeo Reference Case Projection Results" "Variable","Average Absolute Percent Differences","Percent of Projections Over- Estimated"  

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

Aeo Reference Case Projection Results" Aeo Reference Case Projection Results" "Variable","Average Absolute Percent Differences","Percent of Projections Over- Estimated" "Gross Domestic Product" "Real Gross Domestic Product (Average Cumulative Growth)* (Table 2)",0.9772689079,42.55319149 "Petroleum" "Imported Refiner Acquisition Cost of Crude Oil (Constant $) (Table 3a)",35.19047501,18.61702128 "Imported Refiner Acquisition Cost of Crude Oil (Nominal $) (Table 3b)",34.68652106,19.68085106 "Total Petroleum Consumption (Table 4)",6.150682783,66.4893617 "Crude Oil Production (Table 5)",5.99969572,59.57446809 "Petroleum Net Imports (Table 6)",13.27260615,67.0212766 "Natural Gas"

298

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

Science Journals Connector (OSTI)

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

Peng Kou; Deliang Liang; Lin Gao; Jianyong Lou

2015-01-01T23:59:59.000Z

299

Forecasting-based SKU classification  

Science Journals Connector (OSTI)

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

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

2013-01-01T23:59:59.000Z

300

U.S. Energy Information Administration | AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections 3  

Gasoline and Diesel Fuel Update (EIA)

3 3 Variable Average Absolute Percent Differences Percent of Projections Over- Estimated Gross Domestic Product Real Gross Domestic Product (Average Cumulative Growth)* (Table 2) 0.9 40.4 Petroleum Imported Refiner Acquisition Cost of Crude Oil (Constant $) (Table 3a) 33.3 20.5 Imported Refiner Acquisition Cost of Crude Oil (Nominal $) (Table 3b) 32.8 21.6 Total Petroleum Consumption (Table 4) 5.2 63.7 Crude Oil Production (Table 5) 6.2 62.0 Petroleum Net Imports (Table 6) 9.9 63.7

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

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

302

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

Science Journals Connector (OSTI)

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

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

2008-01-01T23:59:59.000Z

303

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

Science Journals Connector (OSTI)

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

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

2013-01-01T23:59:59.000Z

304

Forecasting wind speed financial return  

E-Print Network [OSTI]

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

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

305

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect (OSTI)

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

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

2011-03-28T23:59:59.000Z

306

Power load forecasting using data mining and knowledge discovery technology  

Science Journals Connector (OSTI)

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

Yongli Wang; Dongxiao Niu; Ling Ji

2011-01-01T23:59:59.000Z

307

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

SciTech Connect (OSTI)

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

Finley, Cathy [WindLogics

2014-04-30T23:59:59.000Z

308

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

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

Poulin, L.

2013-01-01T23:59:59.000Z

309

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

Science Journals Connector (OSTI)

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

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

310

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

Science Journals Connector (OSTI)

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

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

2010-01-01T23:59:59.000Z

311

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

Science Journals Connector (OSTI)

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

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

2015-01-01T23:59:59.000Z

312

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.

313

Funding Opportunity Announcement for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

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

314

Upcoming Funding Opportunity for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

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

315

Huge market forecast for linear LDPE  

Science Journals Connector (OSTI)

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

1980-08-25T23:59:59.000Z

316

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

E-Print Network [OSTI]

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

317

Optimal combined wind power forecasts using exogeneous variables  

E-Print Network [OSTI]

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

318

Ensemble typhoon quantitative precipitation forecasts model in Taiwan  

Science Journals Connector (OSTI)

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

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

319

Forecast of geothermal drilling activity  

SciTech Connect (OSTI)

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

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

1981-10-01T23:59:59.000Z

320

New Concepts in Wind Power Forecasting Models  

E-Print Network [OSTI]

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

Kemner, Ken

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

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

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

Malmberg, Anders

322

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

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

Malmberg, Anders

323

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

324

UHERO FORECAST PROJECT DECEMBER 5, 2014  

E-Print Network [OSTI]

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

325

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

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

Stoffelen, Ad

326

A Forecasting Support System Based on Exponential Smoothing  

Science Journals Connector (OSTI)

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

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

2010-01-01T23:59:59.000Z

327

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

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

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

328

Improved Prediction of Runway Usage for Noise Forecast :.  

E-Print Network [OSTI]

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

Dhanasekaran, D.

2014-01-01T23:59:59.000Z

329

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

Energy Savers [EERE]

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

330

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

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

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

331

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

SciTech Connect (OSTI)

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

United States. Bonneville Power Administration.

1994-02-01T23:59:59.000Z

332

AEO2014 Preliminary Results  

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

Displays Monitors (i.e. desktop PC monitors) Video Boards Audio Equipment Security Systems Portable Electric Spas PoolsPool Pumps Security Systems, Home For discussion...

333

1993 Solid Waste Reference Forecast Summary  

SciTech Connect (OSTI)

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

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

1993-08-01T23:59:59.000Z

334

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

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

335

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

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

Boyer, Edmond

336

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

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

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

337

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

SciTech Connect (OSTI)

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

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

2011-10-01T23:59:59.000Z

338

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

E-Print Network [OSTI]

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

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

339

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

E-Print Network [OSTI]

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

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

340

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

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

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

Wind Speed Forecasting for Power System Operation  

E-Print Network [OSTI]

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

Zhu, Xinxin

2013-07-22T23:59:59.000Z

342

Testing Competing High-Resolution Precipitation Forecasts  

E-Print Network [OSTI]

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

Gilleland, Eric

343

Forecasting Capital Expenditure with Plan Data  

Science Journals Connector (OSTI)

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

W. Gerstenberger

1977-01-01T23:59:59.000Z

344

Forecasting Agriculturally Driven Global Environmental Change  

Science Journals Connector (OSTI)

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

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

2001-04-13T23:59:59.000Z

345

Medium- and Long-Range Forecasting  

Science Journals Connector (OSTI)

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

A. James Wagner

1989-09-01T23:59:59.000Z

346

Updated Satellite Technique to Forecast Heavy Snow  

Science Journals Connector (OSTI)

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

Edward C. Johnston

1995-06-01T23:59:59.000Z

347

Forecasting energy markets using support vector machines  

Science Journals Connector (OSTI)

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

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

348

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

Science Journals Connector (OSTI)

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

S. Viswanathan; Handik Widiarta; Rajesh Piplani

2008-07-01T23:59:59.000Z

349

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

E-Print Network [OSTI]

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

Hammerton, James

350

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

E-Print Network [OSTI]

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

Dorta, Miguel

2012-07-16T23:59:59.000Z

351

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

Science Journals Connector (OSTI)

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

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

2014-01-01T23:59:59.000Z

352

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

E-Print Network [OSTI]

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

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

353

12-32021E2_Forecast  

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

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

354

Building Energy Software Tools Directory: Degree Day Forecasts  

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

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

355

Building Energy Software Tools Directory: Energy Usage Forecasts  

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

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

356

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

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

McBurney, Peter

357

River Forecast Application for Water Management: Oil and Water?  

Science Journals Connector (OSTI)

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

Kevin Werner; Kristen Averyt; Gigi Owen

2013-07-01T23:59:59.000Z

358

Data Mining in Load Forecasting of Power System  

Science Journals Connector (OSTI)

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

Guang Yu Zhao; Yan Yan; Chun Zhou Zhao

2013-01-01T23:59:59.000Z

359

Operational Rainfall and Flow Forecasting for the Panama Canal Watershed  

Science Journals Connector (OSTI)

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

Konstantine P. Georgakakos; Jason A. Sperfslage

2005-01-01T23:59:59.000Z

360

Power System Load Forecasting Based on EEMD and ANN  

Science Journals Connector (OSTI)

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

Wanlu Sun; Zhigang Liu; Wenfan Li

2011-01-01T23:59:59.000Z

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

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network [OSTI]

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

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

2005-01-01T23:59:59.000Z

362

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

Energy Savers [EERE]

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

363

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

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

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

1980-01-01T23:59:59.000Z

364

Forecasting Energy Demand Using Fuzzy Seasonal Time Series  

Science Journals Connector (OSTI)

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

?Irem Ual Sar?; Basar ztaysi

2012-01-01T23:59:59.000Z

365

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

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

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

2010-04-01T23:59:59.000Z

366

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

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

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

2010-04-15T23:59:59.000Z

367

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

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

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

368

Application of a Combination Forecasting Model in Logistics Parks' Demand  

Science Journals Connector (OSTI)

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

Chen Qin; Qi Ming

2010-05-01T23:59:59.000Z

369

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

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

Boyer, Edmond

370

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

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

371

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

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

Heinemann, Detlev

372

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

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

de Lijser, Peter

373

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

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

Stevenson, Paul

374

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

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

375

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

E-Print Network [OSTI]

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

Povinelli, Richard J.

376

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

E-Print Network [OSTI]

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

Lang, K.

1982-01-01T23:59:59.000Z

377

Forecasting the Locational Dynamics of Transnational Terrorism  

E-Print Network [OSTI]

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

Massachusetts at Amherst, University of

378

Do quantitative decadal forecasts from GCMs provide  

E-Print Network [OSTI]

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

Stevenson, Paul

379

Sunny outlook for space weather forecasters  

Science Journals Connector (OSTI)

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

Eric Hand

2012-04-27T23:59:59.000Z

380

Modeling of Uncertainty in Wind Energy Forecast  

E-Print Network [OSTI]

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

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

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

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

Howat, Ian M.

382

Customized forecasting tool improves reserves estimation  

SciTech Connect (OSTI)

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

Mian, M.A.

1986-04-01T23:59:59.000Z

383

Storm-in-a-Box Forecasting  

Science Journals Connector (OSTI)

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

Richard A. Kerr

2004-05-14T23:59:59.000Z

384

FORECAST OF VACANCIES Until end of 2016  

E-Print Network [OSTI]

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

385

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect (OSTI)

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

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

386

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect (OSTI)

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

Werth, D.; Garrett, A.

2009-04-15T23:59:59.000Z

387

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect (OSTI)

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

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

388

Steam System Forecasting and Management  

E-Print Network [OSTI]

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

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

1982-01-01T23:59:59.000Z

389

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

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

390

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

Science Journals Connector (OSTI)

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

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

2013-08-01T23:59:59.000Z

391

Short-Term World Oil Price Forecast  

Gasoline and Diesel Fuel Update (EIA)

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

392

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

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

393

Voluntary Green Power Market Forecast through 2015  

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

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

394

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

395

FORSITE: a geothermal site development forecasting system  

SciTech Connect (OSTI)

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

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

1981-10-01T23:59:59.000Z

396

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

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

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

2014-12-30T23:59:59.000Z

397

Exponential smoothing model selection for forecasting  

Science Journals Connector (OSTI)

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

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

2006-01-01T23:59:59.000Z

398

Solar Wind Forecasting with Coronal Holes  

E-Print Network [OSTI]

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

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

2007-01-09T23:59:59.000Z

399

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

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

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

400

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

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

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

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

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

Science Journals Connector (OSTI)

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

Weigang Zhao; Jianzhou Wang; Haiyan Lu

2014-01-01T23:59:59.000Z

402

Electric Grid - Forecasting system licensed | ornl.gov  

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

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

403

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network [OSTI]

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

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

404

Forecasting supply/demand and price of ethylene feedstocks  

SciTech Connect (OSTI)

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

Struth, B.W.

1984-08-01T23:59:59.000Z

405

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

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

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

406

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

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

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

407

Integrating agricultural pest biocontrol into forecasts of energy biomass production  

E-Print Network [OSTI]

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

Gratton, Claudio

408

Power System Modeling of 20percent Wind-Generated Electricity by 2030  

E-Print Network [OSTI]

fuel price forecast Coal prices follow AEO 2007 referencecoal- and natural gas-based electricity generation analyzed here include decreased natural gas prices,

Hand, Maureen

2008-01-01T23:59:59.000Z

409

Advanced Coal Wind Hybrid: Economic Analysis  

E-Print Network [OSTI]

Coal Prices..AEO 2007 forecast for coal prices for PRB coal. Transmissionregimes. Sensitivity to Coal Prices Figure 9 is similar to

Phadke, Amol

2008-01-01T23:59:59.000Z

410

Forecasting for inventory control with exponential smoothing  

Science Journals Connector (OSTI)

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

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

2002-01-01T23:59:59.000Z

411

Random switching exponential smoothing and inventory forecasting  

Science Journals Connector (OSTI)

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

Giacomo Sbrana; Andrea Silvestrini

2014-01-01T23:59:59.000Z

412

Voluntary Green Power Market Forecast through 2015  

SciTech Connect (OSTI)

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

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

2010-05-01T23:59:59.000Z

413

Expert Panel: Forecast Future Demand for Medical Isotopes  

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

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

414

A robust automatic phase-adjustment method for financial forecasting  

Science Journals Connector (OSTI)

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

Ricardo de A. Arajo

2012-03-01T23:59:59.000Z

415

Short term forecasting of solar radiation based on satellite data  

E-Print Network [OSTI]

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

Heinemann, Detlev

416

Developing electricity forecast web tool for Kosovo market  

Science Journals Connector (OSTI)

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

Blerim Rexha; Arben Ahmeti; Lule Ahmedi; Vjollca Komoni

2011-02-01T23:59:59.000Z

417

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

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

Keller, Arturo A.

418

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

419

Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa  

E-Print Network [OSTI]

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

420

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network [OSTI]

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

Washington at Seattle, University of

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

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network [OSTI]

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

Hansens, Jim

422

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

E-Print Network [OSTI]

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

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

423

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

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

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

424

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect (OSTI)

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

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

2014-10-27T23:59:59.000Z

425

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

426

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

427

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

428

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

429

Volatility forecasting with smooth transition exponential smoothing  

Science Journals Connector (OSTI)

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

James W. Taylor

2004-01-01T23:59:59.000Z

430

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect (OSTI)

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

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

2014-07-09T23:59:59.000Z

431

AEO Early Release 2013 - oil  

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

Growing U.S. oil output and rising vehicle fuel economy to cut Growing U.S. oil output and rising vehicle fuel economy to cut U.S. reliance on foreign oil The United States is expected to continue cutting its dependence on petroleum and liquid fuels imports over the rest of this decade because of growing domestic crude oil production and more fuel-efficient vehicles on America's highways. The new long-term outlook from the U.S. Energy Information Administration shows America's dependence on imported petroleum and liquid fuels will decline from 45 percent of domestic demand last year to 34 percent by 2019. U.S. dependence on imported oil had reached 60 percent as recently as 2005. EIA Administrator Adam Sieminski explains: "The United States will be able to meet more of its own energy needs because of two key

432

EIA - AEO2010 - Executive Summary  

Gasoline and Diesel Fuel Update (EIA)

Executive Summary Executive Summary Annual Energy Outlook 2010 with Projections to 2035 Executive Summary In 2009, U.S. energy markets continued to show the impacts of the economic downturn that began in late 2007. After falling by 1 percent in 2008, total electricity generation dropped by another 3 percent in 2009. Although other factors, including weather, contributed to the decrease, it was the first time in the 60-year data series maintained by the EIA that electricity use fell in two consecutive years. Over the next few years, the key factors influencing U.S. energy markets will be the pace of the economic recovery, any lasting impacts on capital-intensive energy projects from the turmoil in financial markets, and the potential enactment of legislation related to energy and the environment.

433

Supplement to AEO99 - Errata  

Gasoline and Diesel Fuel Update (EIA)

1999 1999 as of 9/13/99 1. Tables 36-43 which contain supplementary data for the industrial sector have been revised. These revisions were made to better reflect energy consumption that had been incorrectly allocated to the individual industrial sectors. The revisions do not affect the total industrial consumption reported in the Annual Energy Outlook 1999. (change made on 2/9/99) 2. Tables 59-71 which contain regional electric generator data have been revised. These revisions were made to cogeneration and net energy for load values. (change made on 3/19/99) 3. The historical Lower 48 average and regional crude oil wellhead prices for 1997 were incorrectly reported in Table 79 of the Supplemental Tables to the Annual Energy Outlook 1999. The correct prices are as follows:

434

EIA - AEO2010 - Emissions projections  

Gasoline and Diesel Fuel Update (EIA)

Emissions Projections Emissions Projections Annual Energy Outlook 2010 with Projections to 2035 Emissions Projections Figure 93. Carbon dioxide emissions by sector and fuel, 2008 and 2035 Click to enlarge » Figure source and data excel logo Figure 94. Sulfur dioxide emissions from electricity generation, 2000-2035 Click to enlarge » Figure source and data excel logo Figure 95. Nitrogen oxide emissions from electricity generation, 2000-2035 Click to enlarge » Figure source and data excel logo Growth of carbon dioxide emissions slows in the projections Federal and State energy policies recently enacted will stimulate increased use of renewable technologies and efficiency improvements in the future, slowing the growth of energy-related CO2 emissions through 2035. In the Reference case, emissions do not exceed pre-recession 2007 levels until 2025. In 2035, energy-related CO2 emissions total 6,320 million metric tons, about 6 percent higher than in 2007 and 9 percent higher than in 2008 (Figure 93). On average, emissions in the Reference case grow by 0.3 percent per year from 2008 to 2035, compared with 0.7 percent per year from 1980 to 2008.

435

EIA - AEO2010 - Electricity Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Demand Electricity Demand Annual Energy Outlook 2010 with Projections to 2035 Electricity Demand Figure 69. U.S. electricity demand growth 1950-2035 Click to enlarge » Figure source and data excel logo Figure 60. Average annual U.S. retail electricity prices in three cases, 1970-2035 Click to enlarge » Figure source and data excel logo Figure 61. Electricity generation by fuel in three cases, 2008 and 2035 Click to enlarge » Figure source and data excel logo Figure 62. Electricity generation capacity additions by fuel type, 2008-2035 Click to enlarge » Figure source and data excel logo Figure 63. Levelized electricity costs for new power plants, 2020 and 2035 Click to enlarge » Figure source and data excel logo Figure 64. Electricity generating capacity at U.S. nuclear power plants in three cases, 2008, 2020, and 2035

436

AEO2012 Early Release Overview  

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

BTL), gas-to- liquids (GTL), coal-to-liquids (CTL), kerogen (i.e., oil shale), and refinery gain. After the oil crises of the 1970s and 1980s, much of the debate...

437

Industrial Plans for AEO2014  

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

you for your attention 10 Industrial Team Washington DC, July 30, 2013 Macro Team: Kay Smith (202) 586-1132 | kay.smith@eia.gov Vipin Arora (202) 586-1048 | vipin.arora@eia.gov...

438

AEO2012 Early Release Overview  

Gasoline and Diesel Fuel Update (EIA)

beginning in 2016. * The electricity module was updated to incorporate the Cross-State Air Pollution Rule (CSAPR) 2 as finalized by the EPA in July 2011. CSAPR requires...

439

Coal production forecast and low carbon policies in China  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

440

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

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

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

2005-07-01T23:59:59.000Z

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

Measuring the forecasting accuracy of models: evidence from industrialised countries  

Science Journals Connector (OSTI)

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

Athanasios Koulakiotis; Apostolos Dasilas

2009-01-01T23:59:59.000Z

442

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

E-Print Network [OSTI]

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

Greenslade, Diana

443

Wind power forecast error smoothing within a wind farm  

Science Journals Connector (OSTI)

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

Nadja Saleck; Lueder von Bremen

2007-01-01T23:59:59.000Z

444

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

Open Energy Info (EERE)

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

445

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory  

Gasoline and Diesel Fuel Update (EIA)

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

446

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network [OSTI]

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

Choi, Han-Lim

2009-01-01T23:59:59.000Z

447

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network [OSTI]

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

Lim, Eun-pa

448

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network [OSTI]

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

Yang, Xiaorong

2008-01-01T23:59:59.000Z

449

Exponential smoothing with covariates applied to electricity demand forecast  

Science Journals Connector (OSTI)

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

José D. Bermúdez

2013-01-01T23:59:59.000Z

450

Initial conditions estimation for improving forecast accuracy in exponential smoothing  

Science Journals Connector (OSTI)

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

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

2012-07-01T23:59:59.000Z

451

A Bayesian approach to forecast intermittent demand for seasonal products  

Science Journals Connector (OSTI)

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

Mohammad Anwar Rahman; Bhaba R. Sarker

2012-01-01T23:59:59.000Z

452

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

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

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

453

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones  

Science Journals Connector (OSTI)

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

Matthew J. Onderlinde; Henry E. Fuelberg

2014-10-01T23:59:59.000Z

454

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

E-Print Network [OSTI]

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

Kemner, Ken

455

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network [OSTI]

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

456

Recently released EIA report presents international forecasting data  

SciTech Connect (OSTI)

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

NONE

1995-05-01T23:59:59.000Z

457

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network [OSTI]

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

458

Evolutionary Optimization of an Ice Accretion Forecasting System  

Science Journals Connector (OSTI)

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

Pawel Pytlak; Petr Musilek; Edward Lozowski; Dan Arnold

2010-07-01T23:59:59.000Z

459

Diagnosing the Origin of Extended-Range Forecast Errors  

Science Journals Connector (OSTI)

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

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

2010-06-01T23:59:59.000Z

460

Application of an Improved SVM Algorithm for Wind Speed Forecasting  

Science Journals Connector (OSTI)

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

Huaqiang Zhang; Xinsheng Wang; Yinxiao Wu

2011-01-01T23:59:59.000Z

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

Research on Development Trends of Power Load Forecasting Methods  

Science Journals Connector (OSTI)

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

Litong Dong; Jun Xu; Haibo Liu; Ying Guo

2014-01-01T23:59:59.000Z

462

Representing Forecast Error in a Convection-Permitting Ensemble System  

Science Journals Connector (OSTI)

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

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

2014-12-01T23:59:59.000Z

463

Weather Research and Forecasting Model 2.2 Documentation  

E-Print Network [OSTI]

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

Sadjadi, S. Masoud

464

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect (OSTI)

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

Yoo, Wucherl; Sim, Alex

2014-07-07T23:59:59.000Z

465

Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach  

Science Journals Connector (OSTI)

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

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

2009-11-01T23:59:59.000Z

466

A model for short term electric load forecasting  

E-Print Network [OSTI]

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

Tigue, John Robert

1975-01-01T23:59:59.000Z

467

Radiation fog forecasting using a 1-dimensional model  

E-Print Network [OSTI]

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

Peyraud, Lionel

2012-06-07T23:59:59.000Z

468

Weather-based forecasts of California crop yields  

SciTech Connect (OSTI)

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

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

2005-09-26T23:59:59.000Z

469

Wave height forecasting in Dayyer, the Persian Gulf  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

470

Errata  

Gasoline and Diesel Fuel Update (EIA)

for the Annual Energy Outlook Forecast Evaluation for the Annual Energy Outlook Forecast Evaluation as of 7/14/04 The average absolute forecast errors for Gross Domestic Product (GDP) and energy intensity have been recalculated. In the case of GDP, the data for the actual historical values for 1991 through 2002 were inadvertently given in real dollars, when they should have been in nominal dollars. In the case of intensity, it was determined that the forecast error should be based on intensity defined as the ratio between two output variables already available in the forecast evaluation: total energy consumption measured in Btu and GDP in nominal dollars. Because the GDP deflator to convert nominal dollars to real dollars varies with each AEO, the nominal approach simplifies the calculations for this

471

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.

472

Telecommunications Networks Planning and Evaluation with Techno-Economic Criteria  

E-Print Network [OSTI]

Telecommunications Networks Planning and Evaluation with Techno-Economic Criteria Dimitris Katsianis Department of Informatics and Telecommunications, University of Athens Panepistimiopolis Ilissia: Techno-economic Analysis, Telecommunications, Demand Forecast, Real Options, Game Theory, Investments

Kouroupetroglou, Georgios

473

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

474

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

SciTech Connect (OSTI)

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

Porter, K.; Rogers, J.

2012-04-01T23:59:59.000Z

475

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

476

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

477

An assessment of electrical load forecasting using artificial neural network  

Science Journals Connector (OSTI)

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

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

2012-01-01T23:59:59.000Z

478

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

Science Journals Connector (OSTI)

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

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

2014-02-01T23:59:59.000Z

479

Annual Energy Outlook Retrospective Review - Energy Information  

Gasoline and Diesel Fuel Update (EIA)

AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case AEO Retrospective Review: Evaluation of 2011 and Prior Reference Case Projections Release Date: March 16, 2012 | Next Release Date: March 2013 | Report Number: DOE/EIA-0640(2011) The U.S. Energy Information Administration (EIA) produces projections of energy production, consumption and prices each year in the Annual Energy Outlook (AEO). Each year, EIA also produces this AEO Retrospective Review document, which presents a comparison between realized energy outcomes and the Reference case projections included in previous editions of the AEO. The purpose of the AEO Retrospective is to demonstrate how the AEO projections have tracked actual energy indicators, enable trend analysis, and inform discussions of potential improvements to the AEO. The projections in the AEO are not statements of what will happen but of

480

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

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

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

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


481

Forecasting correlated time series with exponential smoothing models  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

482

Application of GIS on forecasting water disaster in coal mines  

SciTech Connect (OSTI)

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

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

1996-08-01T23:59:59.000Z

483

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

Science Journals Connector (OSTI)

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

Jong Hwan Suh

2014-01-01T23:59:59.000Z

484

Electricity price forecasting: A review of the state-of-the-art with a look into the future  

Science Journals Connector (OSTI)

Abstract A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one models outperformance of another.

Rafa? Weron

2014-01-01T23:59:59.000Z

485

NREL: Energy Analysis - Energy Forecasting and Modeling Staff  

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

Energy Forecasting and Modeling Energy Forecasting and Modeling The following includes summary bios of staff expertise and interests in analysis relating to energy economics, energy system planning, risk and uncertainty modeling, and energy infrastructure planning. Team Lead: Nate Blair Administrative Support: Geraly Amador Clayton Barrows Greg Brinkman Brian W Bush Stuart Cohen Carolyn Davidson Paul Denholm Victor Diakov Aron Dobos Easan Drury Kelly Eurek Janine Freeman Marissa Hummon Jennie Jorganson Jordan Macknick Trieu Mai David Mulcahy David Palchak Ben Sigrin Daniel Steinberg Patrick Sullivan Aaron Townsend Laura Vimmerstedt Andrew Weekley Owen Zinaman Photo of Clayton Barrows. Clayton Barrows Postdoctoral Researcher Areas of expertise Power system modeling Primary research interests Power and energy systems

486

Conceptual design of a geothermal site development forecasting system  

SciTech Connect (OSTI)

A site development forecasting system has been designed in response to the need to monitor and forecast the development of specific geothermal resource sites for electrical power generation and direct heat applications. The system is comprised of customized software, a site development status data base, and a set of complex geothermal project development schedules. The system would use site-specific development status information obtained from the Geothermal Progress Monitor and other data derived from economic and market penetration studies to produce reports on the rates of geothermal energy development, federal agency manpower requirements to ensure these developments, and capital expenditures and technical/laborer manpower required to achieve these developments.

Neham, E.A.; Entingh, D.J.

1980-03-01T23:59:59.000Z

487

CCPP-ARM Parameterization Testbed Model Forecast Data  

DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

Klein, Stephen

488

Forecast of contracting and subcontracting opportunities. Fiscal year 1996  

SciTech Connect (OSTI)

This forecast of prime and subcontracting opportunities with the U.S. Department of Energy and its MAO contractors and environmental restoration and waste management contractors, is the Department`s best estimate of small, small disadvantaged and women-owned small business procurement opportunities for fiscal year 1996. The information contained in the forecast is published in accordance with Public Law 100-656. It is not an invitation for bids, a request for proposals, or a commitment by DOE to purchase products or services. Each procurement opportunity is based on the best information available at the time of publication and may be revised or cancelled.

NONE

1996-02-01T23:59:59.000Z

489

Sales forecasting strategies for small businesses: an empirical investigation of statistical and judgemental methods  

Science Journals Connector (OSTI)

This study evolved from the mixed results shown in the reviewed forecasting literature and from the lack of sufficient forecasting research dealing with micro data. The main purpose of this study is to investigate and compare the accuracy of different quantitative and qualitative forecasting techniques, and to recommend a forecasting strategy for small businesses. Emphasis is placed on the testing of combining as a tool to improve forecasting accuracy. Of particular interest is whether combining time series and judgemental forecasts provides more accurate results than individual methods. A case study of a small business was used for this purpose to assess the accuracy and applicability of combining forecasts. The evidence indicates that combining qualitative and quantitative methods results in better and improved forecasts.

Imad J. Zbib

2006-01-01T23:59:59.000Z

490

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

E-Print Network [OSTI]

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

Bush, Sarah, 1973-

2003-01-01T23:59:59.000Z

491

Distributed quantitative precipitation forecasts combining information from radar and numerical weather prediction model outputs  

E-Print Network [OSTI]

Applications of distributed Quantitative Precipitation Forecasts (QPF) range from flood forecasting to transportation. Obtaining QPF is acknowledged to be one of the most challenging areas in hydrology and meteorology. ...

Ganguly, Auroop Ratan

2002-01-01T23:59:59.000Z

492

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

Science Journals Connector (OSTI)

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

Harold E. Brooks; Charles A. Doswell III

1996-09-01T23:59:59.000Z

493

Correspondence among the Correlation, RMSE, and Heidke Forecast Verification Measures; Refinement of the Heidke Score  

Science Journals Connector (OSTI)

The correspondence among the following three forecast verification scores, based on forecasts and their associated observations, is described: 1) the correlation score, 2) the root-mean-square error (RMSE) score, and 3) the Heidke score (based on ...

Anthony G. Barnston

1992-12-01T23:59:59.000Z

494

Improving Seasonal Forecast Skill of North American Surface Air Temperature in Fall Using a Postprocessing Method  

Science Journals Connector (OSTI)

A statistical postprocessing approach is applied to seasonal forecasts of surface air temperatures (SAT) over North America in fall, when the original uncalibrated predictions have little skill. The data used are ensemble-mean seasonal forecasts ...

XiaoJing Jia; Hai Lin; Jacques Derome

2010-05-01T23:59:59.000Z

495

Computing electricity spot price prediction intervals using quantile regression and forecast averaging  

Science Journals Connector (OSTI)

We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity

Jakub Nowotarski; Rafa? Weron

2014-08-01T23:59:59.000Z

496

Medium-term forecasting of demand prices on example of electricity prices for industry  

Science Journals Connector (OSTI)

In the paper, a method of forecasting demand prices for electric energy for the industry has been suggested. An algorithm of the forecast for 20062010 based on the data for 19972005 has been presented.

V. V. Kossov

2014-09-01T23:59:59.000Z

497

Price Forecasting and Optimal Operation of Wholesale Customers in a Competitive Electricity Market.  

E-Print Network [OSTI]

??This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning (more)

Zareipour, Hamidreza

2006-01-01T23:59:59.000Z

498

Impacts of Improved Day-Ahead Wind Forecasts on Power Grid Operations: September 2011  

SciTech Connect (OSTI)

This study analyzed the potential benefits of improving the accuracy (reducing the error) of day-ahead wind forecasts on power system operations, assuming that wind forecasts were used for day ahead security constrained unit commitment.

Piwko, R.; Jordan, G.

2011-11-01T23:59:59.000Z

499

Combining Multi Wavelet and Multi NN for Power Systems Load Forecasting  

Science Journals Connector (OSTI)

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series ... introduced. In addition, multi neural network for load forecast including...

Zhigang Liu; Qi Wang; Yajun Zhang

2008-01-01T23:59:59.000Z

500

Application of the Stretched Exponential Production Decline Model to Forecast Production in Shale Gas Reservoirs  

E-Print Network [OSTI]

Production forecasting in shale (ultra-low permeability) gas reservoirs is of great interest due to the advent of multi-stage fracturing and horizontal drilling. The well renowned production forecasting model, Arps? Hyperbolic Decline Model...

Statton, James Cody

2012-07-16T23:59:59.000Z