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1

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

2

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

3

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

4

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

5

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

6

Annual Energy Outlook 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

7

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

8

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

9

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.

10

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

11

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

12

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

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

EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes EXHIBIT A: CRADA, WFO, PUA and NPUA Comparison Table, with suggested changes Suggested changes to the...

13

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.

14

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

E-Print Network [OSTI]

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

Perez, Richard R.

15

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

Science Journals Connector (OSTI)

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

2014-01-01T23:59:59.000Z

16

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

17

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

18

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

19

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

20

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

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

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

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


21

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

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

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

22

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

23

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

Science Journals Connector (OSTI)

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

Ralph Anker

2000-01-01T23:59:59.000Z

24

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

Science Journals Connector (OSTI)

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

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

2013-01-01T23:59:59.000Z

25

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

26

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

Science Journals Connector (OSTI)

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

Eric Gilleland

2013-01-01T23:59:59.000Z

27

Comparison of longterm forecasting of JuneAugust rainfall over changjianghuaihe valley  

Science Journals Connector (OSTI)

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

Jin Long; Luo Ying; Lin Zhenshan

1997-01-01T23:59:59.000Z

28

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

29

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

E-Print Network [OSTI]

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

30

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

E-Print Network [OSTI]

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

Joshi, Krunal Jaykant

2012-10-19T23:59:59.000Z

31

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

E-Print Network [OSTI]

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

Steinwart, Ingo

32

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

33

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

34

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

35

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

36

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

37

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

E-Print Network [OSTI]

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

Xue, Ming

38

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

December 22, 2000 (Next Release: December, 2001) Related Links Annual Energy Outlook 2001 Assumptions to the AEO2001 NEMS Conference Contacts Forecast Homepage EIA Homepage AEO Supplement Reference Case Forecast (1999-2020) (HTML) Table 1. Energy Consumption by Source and Sector (New England) Table 2. Energy Consumption by Source and Sector (Middle Atlantic) Table 3. Energy Consumption by Source and Sector (East North Central) Table 4. Energy Consumption by Source and Sector (West North Central) Table 5. Energy Consumption by Source and Sector (South Atlantic) Table 6. Energy Consumption by Source and Sector (East South Central) Table 7. Energy Consumption by Source and Sector (West South Central) Table 8. Energy Consumption by Source and Sector (Mountain)

39

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

40

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...

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


41

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.

42

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

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

43

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.

44

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

45

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

SciTech Connect (OSTI)

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

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

1989-12-01T23:59:59.000Z

46

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

E-Print Network [OSTI]

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

Droegemeier, Kelvin K.

47

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

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

48

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

Science Journals Connector (OSTI)

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

Giacomo Sbrana; Andrea Silvestrini

2013-01-01T23:59:59.000Z

49

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

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

50

Comparison of BALON2 with cladding ballooning strain tables in NUREG-0630. [PWR; BWR  

SciTech Connect (OSTI)

For this comparison study, the two computer models used for calculating fuel rod cladding failure and the resulting permanent strains were compared against experiment data. The two models considered were the mechanistic BALON2 model and the empirical model described in the NUREG-0630 report. The purpose for making this comparison was simply to gain insight into the relative strengths and weaknesses of each model. The experiment data sample consisted of data from both single and bundle tests conducted sometimes in in-pile facilities, but mostly in out-of-pile facilities. Comparisons between models indicated that the empirical NUREG-0630 model more accurately calculated the local cladding temperature and pressure conditions at rupture, but the mechanistic BALON2 model more accurately calculated the resulting cladding permanent strain at the rupture location.

Resch, S.C.; Laats, E.T.

1982-01-01T23:59:59.000Z

51

Table Search (or Ranking Tables)  

E-Print Network [OSTI]

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

Halevy, Alon

52

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

53

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

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

54

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

55

Energy Information Administration (EIA) - Supplement Tables - Supplemental  

Gasoline and Diesel Fuel Update (EIA)

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

56

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

SciTech Connect (OSTI)

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

Rogers, J.; Porter, K.

2011-03-01T23:59:59.000Z

57

Supplement Tables - Contact  

Gasoline and Diesel Fuel Update (EIA)

Supplement Tables to the AEO99 Supplement Tables to the AEO99 bullet1.gif (843 bytes) Annual Energy Outlook 1999 bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage furtherinfo.gif (5474 bytes) The Annual Energy Outlook 1999 (AEO99) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@eia.doe.gov, 202/586-2222). General questions may be addressed to Arthur T. Andersen (aanderse@eia.doe.gov, 202/586-1441), Director of the International, Economic, and Greenhouse Gas Division; Susan H. Holte (sholte@eia.doe.gov, 202/586-4838), Director of the Demand and Integration Division; James M. Kendell (jkendell@eia.doe.gov, 202/586-9646), Director of the Oil and Gas Division; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Director of the Coal and Electric Power Division; or Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Modeling Analyst. Detailed questions about the forecasts and related model components may be addressed to the following analysts:

58

Supplemental Tables to the Annual Energy Outlook 2003  

Gasoline and Diesel Fuel Update (EIA)

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

59

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

E-Print Network [OSTI]

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

Vrugt, Jasper A.

60

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

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


61

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...

62

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

63

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

64

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

65

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

66

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

67

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...

68

Conversion Tables  

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

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

69

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

70

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

71

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

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

72

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

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

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

73

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

74

Table of Contents Resilient Sustainable Communities  

E-Print Network [OSTI]

..................................... 5 Onondaga County: Sustainable Development Plan....................... 9 Comparison of the Hazard Mitigation Plan and Onondaga County Sustainable Development Plan DraftTable of Contents Resilient Sustainable Communities: Integrating Hazard Mitigation & Sustainability

75

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...

76

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

77

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

78

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.

79

1992 CBECS Detailed Tables  

Gasoline and Diesel Fuel Update (EIA)

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

80

Table 25  

Gasoline and Diesel Fuel Update (EIA)

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

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


81

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,

82

chapter 5. Detailed Tables  

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

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

83

Notices TABLE  

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

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

84

Table 4  

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

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

85

EIA - Annual Energy Outlook (AEO) 2013 Data Tables  

Gasoline and Diesel Fuel Update (EIA)

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

86

Comparison of 3D, Assist-as-Needed Robotic Arm/Hand Movement Training Provided with Pneu-WREX to Conventional Table Top Therapy Following Chronic Stroke  

E-Print Network [OSTI]

0b013e31826bce79. Comparison of 3D, Assist-as-Needed Roboticuser can move her hand in 3D space, and use the grip sensorrequire three dimensional (3D) movement against gravity and

Reinkensmeyer, David J.; Wolbrecht, Eric T.; Chan, Vicky; Chou, Cathy; Cramer, Steven C.; Bobrow, James E.

2012-01-01T23:59:59.000Z

87

1995 Detailed Tables  

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

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

88

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

89

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

90

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

91

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...

92

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.

93

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

94

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...

95

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

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

96

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

97

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

98

EIA - Annual Energy Outlook (AEO) 2012 Data Tables  

Gasoline and Diesel Fuel Update (EIA)

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

99

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

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

100

1999 CBECS Detailed Tables  

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

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

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


101

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

102

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

103

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":""}]}

104

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

105

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

106

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

107

FY 2005 Statistical Table  

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

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

108

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.

109

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

110

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

111

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...

112

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

113

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"

114

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

115

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

116

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

117

FY 2005 Laboratory Table  

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

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

118

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

119

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.

120

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

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


121

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.

122

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

123

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

124

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.

125

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

126

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

127

Louisiana Block Grant Tables | Department of Energy  

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

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

128

Mississippi Block Grant Tables | Department of Energy  

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

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

129

2003 CBECS RSE Tables  

Gasoline and Diesel Fuel Update (EIA)

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

130

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

131

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

132

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

133

CBECS Buildings Characteristics --Revised Tables  

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

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

134

Supplement Tables - Contacts  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook 2000 (AEO2000) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@ eia.doe.gov, 202/586-2222), Director, Office of Integrated Analysis and Forecasting; Susan H. Holte (sholte@eia.doe.gov, 202/586-4838), Director, Demand and Integration Division; James M. Kendell (jkendell@eia.doe.gov, 202/586-9646), Director, Oil and Gas Division; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Director, Coal and Electric Power Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Modeling Analyst: Annual Energy Outlook 2000 (AEO2000) was prepared by the Energy Information Administration (EIA), Office of Integrated Analysis and Forecasting, under the direction of Mary J. Hutzler (mhutzler@ eia.doe.gov, 202/586-2222), Director, Office of Integrated Analysis and Forecasting; Susan H. Holte (sholte@eia.doe.gov, 202/586-4838), Director, Demand and Integration Division; James M. Kendell (jkendell@eia.doe.gov, 202/586-9646), Director, Oil and Gas Division; Scott Sitzer (ssitzer@eia.doe.gov, 202/586-2308), Director, Coal and Electric Power Division; and Andy S. Kydes (akydes@eia.doe.gov, 202/586-2222), Senior Modeling Analyst: For ordering information and questions on other energy statistics available from EIA, please contact EIA’s National Energy Information Center. Addresses, telephone numbers, and hours are as follows:

135

ARM - Instrument Location Table  

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

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

136

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

137

FY 2009 State Table  

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

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

138

FY 2005 State Table  

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

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

139

FY 2010 State Table  

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

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

140

FY 2006 State Table  

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

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

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


141

FY 2010 Laboratory Table  

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

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

142

Table of Contents  

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

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

143

FY 2008 State Table  

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

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

144

EIA - Annual Energy Outlook 2009 - Comparison with Other Projections  

Gasoline and Diesel Fuel Update (EIA)

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

145

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

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

146

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

147

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

148

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

149

Supplement Tables - Contacts  

Gasoline and Diesel Fuel Update (EIA)

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

150

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...

151

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...

152

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

153

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

Science Journals Connector (OSTI)

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

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

2013-01-01T23:59:59.000Z

154

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

155

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

156

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

157

FY 2011 State Table  

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

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

158

FY 2007 Laboratory Table  

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

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

159

FY 2011 Laboratory Table  

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

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

160

FY 2008 Laboratory Table  

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

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

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


161

FY 2006 Laboratory Table  

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

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

162

Fy 2009 Laboratory Table  

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

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

163

FY 2013 Statistical Table  

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

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

164

FY 2009 Statistical Table  

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

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

165

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

166

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

167

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

168

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

169

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

170

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

171

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

172

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

173

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...

174

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

175

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...

176

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...

177

Table of Contents Page i Table of Contents  

E-Print Network [OSTI]

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

178

Cost Recovery Charge (CRC) Calculation Tables  

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

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

179

TABLE OF CONTENTS  

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

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

180

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

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


181

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

182

FY 2006 Statistical Table  

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

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

183

FY 2013 Laboratory Table  

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

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

184

FY 2010 Statistical Table  

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

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

185

FY 2012 State Table  

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

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

186

FY 2012 Statistical Table  

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

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

187

FY 2007 Statistical Table  

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

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

188

FY 2012 Laboratory Table  

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

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

189

FY 2008 Statistical Table  

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

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

190

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

191

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

192

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

193

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

194

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

195

Table of Contents  

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

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

196

CBECS Buildings Characteristics --Revised Tables  

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

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

197

2003 CBECS Detailed Tables: Summary  

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

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

198

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

199

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

200

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

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


201

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

202

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

203

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

204

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

205

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

206

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

207

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

208

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

209

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.

210

Supplement Tables - Supplemental Data  

Gasoline and Diesel Fuel Update (EIA)

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

211

EIA - Supplement Tables - Contact  

Gasoline and Diesel Fuel Update (EIA)

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

212

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

213

ORIGINAL PAPER Comparison of point forecast accuracy of model averaging  

E-Print Network [OSTI]

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

Vrugt, Jasper A.

214

Table of Contents  

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

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

215

Help:Tables | Open Energy Information  

Open Energy Info (EERE)

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

216

CBECS Buildings Characteristics --Revised Tables  

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

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

217

CBECS Buildings Characteristics --Revised Tables  

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

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

218

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

219

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

220

CARINA Data Table  

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

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

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


221

Appendix B: Summary Tables  

Gasoline and Diesel Fuel Update (EIA)

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

222

CBECS 1992 - Consumption & Expenditures, Detailed Tables  

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

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

223

Microsoft Word - table_87  

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

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

224

TABLE OF CONTENTS  

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

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

225

Reviews, Tables, and Plots  

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

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

226

Table G3  

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

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

227

TABLE OF CONTENTS  

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

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

228

TABLE OF CONTENTS  

National Nuclear Security Administration (NNSA)

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

229

Table of Contents  

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

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

230

TABLE OF CONTENTS  

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

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

231

2003 CBECS Detailed Tables: Summary  

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

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

232

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

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

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

233

FY 2014 Budget Request Summary Table | Department of Energy  

Office of Environmental Management (EM)

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

234

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

235

ARM - Instrument - s-table  

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

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

236

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

E-Print Network [OSTI]

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

Murthi, Aditya

2011-08-08T23:59:59.000Z

237

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:

238

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

239

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.

240

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

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


241

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

242

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

243

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

244

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

245

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

246

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...

247

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

248

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

249

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

250

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

251

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...

252

table14.xls  

Gasoline and Diesel Fuel Update (EIA)

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

253

Code Tables | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

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

254

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

255

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

256

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

257

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

258

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

259

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

260

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

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


261

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.

262

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

263

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

264

MECS Fuel Oil Tables  

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

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

265

TABLE OF CONTENTS  

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

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

266

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

267

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

268

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

269

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

270

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.

271

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

272

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

273

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

274

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

275

EIA - Annual Energy Outlook 2009 - chapter Tables  

Gasoline and Diesel Fuel Update (EIA)

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

276

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

277

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

278

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

279

MECS 1991 Publications and Tables  

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

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

280

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

E-Print Network [OSTI]

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

Oak Ridge National Laboratory

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


281

HEURISTIC APPROACH FOR OPTIMAL PARAMETER ESTIMATION OF ELECTRIC LOAD FORECAST MODEL  

Science Journals Connector (OSTI)

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

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

2009-01-01T23:59:59.000Z

282

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.

283

EIA - Appendix A - Reference Case Projection Tables  

Gasoline and Diesel Fuel Update (EIA)

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

284

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

285

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

286

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

287

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

288

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

289

EIA - Supplement Tables to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

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

290

EIA - Supplement Tables to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

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

291

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

292

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

293

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

294

Nature Bulletin Table of Contents  

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

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

295

COST AND QUALITY TABLES 95  

Gasoline and Diesel Fuel Update (EIA)

5 Tables 5 Tables July 1996 Energy Information Administration Office of Coal, Nuclear, Electric and Alternate Fuels U.S. Department of Energy Washington DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the Department of Energy. The information contained herein should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. Contacts The annual publication Cost and Quality of Fuels for Electric Utility Plants (C&Q) will no longer be pub- lished by the EIA. The tables presented in this docu- ment are intended to replace that annual publication. Questions regarding the availability of these data should be directed to: Coal and Electric Data and Renewables Division

296

MTS Table Top Load frame  

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

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

297

CBECS 1992 - Building Characteristics, Detailed Tables  

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

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

298

Energy Information Administration (EIA) - Supplement Tables  

Gasoline and Diesel Fuel Update (EIA)

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

299

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.

300

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 "forecast comparisons table" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

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

302

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

Science Journals Connector (OSTI)

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

Wang Yan; Li Jingwen

2008-01-01T23:59:59.000Z

303

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...

304

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

305

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

306

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...

307

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...

308

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

309

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

Gasoline and Diesel Fuel Update (EIA)

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

310

FRAUD POLICY Table of Contents  

E-Print Network [OSTI]

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

Shihadeh, Alan

311

CHP NOTEBOOK Table of Contents  

E-Print Network [OSTI]

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

Braun, Paul

312

Microsoft Word - table_04.doc  

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

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

313

PARENT HANDBOOK TABLE OF CONTENTS  

E-Print Network [OSTI]

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

Adali, Tulay

314

Automatic Construction of Diagnostic Tables  

Science Journals Connector (OSTI)

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

W. R. Willcox; S. P. Lapage

1972-08-01T23:59:59.000Z

315

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

316

An optimal filtering algorithm for table constraints  

Science Journals Connector (OSTI)

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

Jean-Baptiste Mairy; Pascal Van Hentenryck; Yves Deville

2012-10-01T23:59:59.000Z

317

Table Name query? | OpenEI Community  

Open Energy Info (EERE)

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

318

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

319

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

320

Buildings and Energy in the 80's -- Detailed Tables  

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

Detailed Tables Detailed Tables Total Residential and Commercial Primary Consumption by Type of Building Sources: Energy Information Administration, Office of Energy Markets and End Use, EIA-457 of the 1980 Residential Energy Consumption Survey and Form EIA-871 of the 1989 Commercial Buildings Energy Consumption Survey. This report introduces several innovations in energy data reporting that complement the previously published triennial reports of the Residential Energy Consumption Survey (RECS) and the Commercial Buildings Energy Consumption Survey (CBECS). (1) Both residential and commercial sector buildings data are presented together in the report. Common units of analysis, the residential or commercial building and floorspace, are used to facilitate comparison.17 (2) Unlike the triennial RECS and CBECS that

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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

Chemistry Department Assessment Table of Contents  

E-Print Network [OSTI]

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

Bogaerts, Steven

322

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

323

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

324

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

325

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

326

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

327

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

328

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.

329

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

330

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

331

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

332

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

333

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

334

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...

335

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

336

Microsoft Word - table_11.doc  

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

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

337

Microsoft Word - table_08.doc  

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

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

338

Action Codes Table | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

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

339

Description of Energy Intensity Tables (12)  

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

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

340

Sandia National Labs: PCNSC: IBA Table  

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

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

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


341

Energy Information Administration (EIA) - Supplement Tables - Supplemental  

Gasoline and Diesel Fuel Update (EIA)

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

342

TABLE OF CONTENTS 2014 ORNL NEUTRON SCIENCES STRATEGIC PLAN  

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

TABLE OF CONTENTS 2014 ORNL NEUTRON SCIENCES STRATEGIC PLAN Executive Summary Director's Message Introduction Neutron Scattering User Facilities Comparison with Leading International Neutron Facilities Strategic Planning and Research Community Involvement New Opportunities Science Priorities Introduction Quantum Materials Materials Synthesis and Performance Soft Molecular Matter Biosciences New and Upgraded Capabilities Enabling Technologies Sources Executing the Plan Strategic Timeline Appendices and Acronyms 3 6 17 55 43 51 59 9 3 EXECUTIVE SUMMARY 2014 ORNL NEUTRON SCIENCES STRATEGIC PLAN EXECUTIVE SUMMARY AND DIRECTOR'S MESSAGE * Optimizing existing instrumentation with targeted de-

343

Annual Energy Outlook 2007 - Low Price Case Tables  

Gasoline and Diesel Fuel Update (EIA)

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

344

Annual Energy Outlook 2007 - Low Economic Growth Case Tables  

Gasoline and Diesel Fuel Update (EIA)

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

345

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

346

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)

347

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

348

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

349

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.

350

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

351

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

352

Table  

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

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

353

Table  

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

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

354

Table  

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

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

355

Table  

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

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

356

Table  

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

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

357

Table  

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

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

358

Table  

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

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

359

Table  

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

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

360

Table  

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

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

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361

Table  

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

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

362

Table  

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

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

363

Table  

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

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

364

Table  

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

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

365

Table  

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

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

366

Table  

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

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

367

Table  

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

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

368

Table  

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

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

369

Table  

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

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

370

Table  

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

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

371

Table  

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

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

372

Table  

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

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

373

Table  

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

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

374

Table  

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

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

375

Table  

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

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

376

Table  

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

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

377

Table  

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

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

378

Table  

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

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

379

Table  

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

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

380

Table  

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

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

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381

Table  

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

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

382

Table  

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

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

383

Table  

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

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

384

Table  

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

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

385

Table  

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

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

386

Table  

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

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

387

Table  

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

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

388

Table  

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

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

389

Table  

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

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

390

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

391

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

392

Variable White Dwarf Data Tables  

SciTech Connect (OSTI)

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

Bradley, P. A.

1997-12-31T23:59:59.000Z

393

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

394

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

395

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

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

396

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

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

397

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

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

398

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

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

399

Table-top job analysis  

SciTech Connect (OSTI)

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

Not Available

1994-12-01T23:59:59.000Z

400

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

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


401

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

402

EIA-Annual Energy Outlook 2010 - Low Economic Growth Tables  

Gasoline and Diesel Fuel Update (EIA)

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

403

EIA-Annual Energy Outlook 2010 - High Economic Growth Tables  

Gasoline and Diesel Fuel Update (EIA)

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

404

Environmental Regulatory Update Table, October 1991  

SciTech Connect (OSTI)

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

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

1991-11-01T23:59:59.000Z

405

Environmental Regulatory Update Table, August 1991  

SciTech Connect (OSTI)

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

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

1991-09-01T23:59:59.000Z

406

Environmental Regulatory Update Table, September 1991  

SciTech Connect (OSTI)

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

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

1991-10-01T23:59:59.000Z

407

Environmental Regulatory Update Table, November 1991  

SciTech Connect (OSTI)

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

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

1991-12-01T23:59:59.000Z

408

Environmental regulatory update table, July 1991  

SciTech Connect (OSTI)

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

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

1991-08-01T23:59:59.000Z

409

Environmental Regulatory Update Table, November 1990  

SciTech Connect (OSTI)

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

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

1990-12-01T23:59:59.000Z

410

Microsoft Word - table_09.doc  

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

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

411

All Price Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

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

412

Microsoft Word - table_13.doc  

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

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

413

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

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

414

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

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

415

Microsoft Word - table_01.doc  

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

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

416

Microsoft Word - table_02.doc  

Gasoline and Diesel Fuel Update (EIA)

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

417

TableHC2.12.xls  

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

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

418

TableHC10.13.xls  

Gasoline and Diesel Fuel Update (EIA)

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

419

TABLE54.CHP:Corel VENTURA  

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

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

420

TABLE19.CHP:Corel VENTURA  

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

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

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


421

TABLE15.CHP:Corel VENTURA  

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

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

422

TABLE53.CHP:Corel VENTURA  

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

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

423

TABLE11.CHP:Corel VENTURA  

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

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

424

2011 Annual Report Table of Contents  

E-Print Network [OSTI]

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

425

Summary Statistics Table 1. Crude Oil Prices  

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

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

426

GIS DEVELOPMENT GUIDE Table of Contents  

E-Print Network [OSTI]

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

Ghelli, Giorgio

427

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

428

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,

429

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

430

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

431

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

432

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

433

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

434

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

435

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...

436

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

437

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

438

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

439

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

440

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

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


441

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

442

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

443

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

444

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

445

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

446

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

447

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

448

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

449

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

450

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

451

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

452

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

453

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

454

Annual Energy Outlook 2009 - High Price Case Tables  

Gasoline and Diesel Fuel Update (EIA)

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

455

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

456

Exhibit C Table of Contents  

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

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

457

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

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

458

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

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

459

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

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

460

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

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

Note: This page contains sample records for the topic "forecast comparisons table" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
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461

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

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

462

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

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

463

EM International Program Action Table  

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

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

464

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

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

465

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

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

466

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

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

467

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

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

468

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

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

469

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

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

470

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

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

471

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

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

472

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

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

473

Microsoft Word - table_21.doc  

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

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

474

Microsoft Word - table_05.doc  

Gasoline and Diesel Fuel Update (EIA)

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

475

Microsoft Word - table_09.doc  

Gasoline and Diesel Fuel Update (EIA)

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

476

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

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

477

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

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

478

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

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

479

Microsoft Word - table_21.doc  

Gasoline and Diesel Fuel Update (EIA)

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

480

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

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

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481

Microsoft Word - table_10.doc  

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

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

482

Microsoft Word - table_08.doc  

Gasoline and Diesel Fuel Update (EIA)

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

483

All Consumption Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

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

484

Microsoft Word - table_06.doc  

Gasoline and Diesel Fuel Update (EIA)

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

485

Microsoft Word - table_07.doc  

Gasoline and Diesel Fuel Update (EIA)

Table 7. Natural Gas Processed, Liquids Extracted, and Estimated Extraction Loss by State, 2010 Alabama .................................. 242,444 13,065 19,059 47,741 Alaska...................................... 2,731,803 17,798 20,835 79,355 Arkansas.................................. 9,599 160 213 692 California ................................. 204,327 10,400 13,244 42,509 Colorado .................................. 1,434,003 57,924 82,637 209,191 Kansas..................................... 341,778 18,424 26,251 70,425 Kentucky.................................. 66,579 3,317 4,576 13,311 Louisiana ................................. 2,207,760 71,231 102,448 262,178 Michigan .................................. 23,449 2,207 2,943 8,272 Mississippi ...............................

486

Microsoft Word - table_07.doc  

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

7 7 Table 7. Supplemental gas supplies by state, 2012 (million cubic feet) Colorado 0 99 0 4,313 4,412 Georgia 0 0 660 0 660 Hawaii 2,491 20 0 0 2,510 Illinois 0 1 0 0 1 Indiana 0 1 0 0 1 Kentucky 0 1 0 0 1 Louisiana 0 0 553 0 553 Maryland 0 116 0 0 116 Minnesota 0 9 0 0 9 Missouri * 0 0 0 * Nebraska 0 4 0 0 4 New Jersey 0 0 0 139 139 North Dakota 52,541 0 0 0 52,541 Ohio 0 6 360 0 366 Pennsylvania 0 2 0 0 2 Vermont 0 3 0 0 3 Virginia 0 48 0 0 48 Total 55,032 309 1,573 4,452 61,366 State Synthetic Natural Gas Propane-Air Biomass Gas Other Total * Volume is less than 500,000 cubic feet.

487

All Price Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

4) 4) June 2007 State Energy Price and Expenditure Estimates 1970 Through 2004 2004 Price and Expenditure Summary Tables Table S1a. Energy Price Estimates by Source, 2004 (Nominal Dollars per Million Btu) State Primary Energy Electric Power Sector d,e Retail Electricity Total Energy d,f Coal Natural Gas Petroleum Nuclear Fuel Biomass c Total d,e,f Distillate Fuel Jet Fuel LPG a Motor Gasoline Residual Fuel Other b Total Alabama 1.57 7.72 11.91 8.82 15.78 13.68 4.78 8.25 12.28 0.43 1.81 5.32 1.68 18.01 11.29 Alaska 1.91 3.59 12.43 9.61 19.64 15.55 3.63 12.09 11.05 - 6.68 9.07 3.18 32.29 11.09 Arizona 1.31 6.84 13.59 9.53 18.40 15.33 5.29 7.23 13.92 0.45 5.90 6.68 2.18 21.83 15.24 Arkansas 1.25 8.09 12.01 8.30 14.80 13.97 4.67 11.02 12.77 0.49 1.79 6.59 1.43 16.76 11.89 California 1.82 7.63 13.58

488

All Price Tables.vp  

Gasoline and Diesel Fuel Update (EIA)

7) 7) August 2009 State Energy Price and Expenditure Estimates 1970 Through 2007 2007 Price and Expenditure Summary Tables Table S1a. Energy Price Estimates by Source, 2007 (Nominal Dollars per Million Btu) State Primary Energy Electric Power Sector e,f Retail Electricity Total Energy e,g Coal Natural Gas a Petroleum Nuclear Fuel Biomass Total e,f,g Distillate Fuel Oil Jet Fuel LPG b Motor Gasoline Residual Fuel Oil Other c Total Wood and Waste d Alabama 2.17 9.06 19.43 16.20 21.84 21.26 8.46 14.19 19.62 0.42 2.71 7.47 2.29 22.46 16.01 Alaska 2.34 5.76 19.43 16.35 28.63 22.14 11.51 23.69 17.97 - 10.51 14.88 4.94 38.96 17.87 Arizona 1.61 8.44 19.84 16.24 27.16 21.95 10.04 11.27 20.50 0.57 10.86 9.61 2.78 25.02 20.72 Arkansas 1.65 9.33 19.63 15.73 21.10 21.54 8.65 18.76 20.42 0.57 2.66 9.45 1.98 20.57

489

TABLE OF CONTENTS SECTION A: PREINTERVIEW OBSERVATION  

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

TABLE OF CONTENTS TABLE OF CONTENTS SECTION A: PREINTERVIEW OBSERVATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 SECTION B: HOUSING TYPE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 SECTION C: HOME HEATING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 SECTION D: AIR CONDITIONING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 SECTION E: WATER HEATING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 SECTION F: LIGHTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 SECTION G: APPLIANCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Cooking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Refrigerators and Freezers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

490

Supplemental Tables to the Annual Energy Outlook  

Reports and Publications (EIA)

The Annual Energy Outlook (AEO) Supplemental tables were generated for the reference case of the AEO using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets. Most of the tables were not published in the AEO, but contain regional and other more detailed projections underlying the AEO projections.

2014-01-01T23:59:59.000Z

491

Tables in Context: Integrating Horizontal Displays with  

E-Print Network [OSTI]

design challenges for tabletop interfaces: integrating access to public and private information, managing a cooperative gesture to organize digital documents on an interactive table. Our tabletop interface designTables in Context: Integrating Horizontal Displays with Ubicomp Environments Abstract Our work

Klemmer, Scott

492

Tafel Musik: Formatting algorithm of tables  

Science Journals Connector (OSTI)

This paper provides a description on the formatting algorithm of tables that the authors have developed. This algorithm is an important component of the so called TafeMusik (Tafel Musik) environment. TafeMusikprovides the user with an environment to ... Keywords: First-fit algorithm, Linear programming, Optimization, Tables, Tabular formatting, Tabular layout

K. -H. Shin; K. Kobayashi; A. Suzuki

1997-07-01T23:59:59.000Z

493

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

SciTech Connect (OSTI)

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

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

1997-01-07T23:59:59.000Z

494

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

SciTech Connect (OSTI)

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

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

1993-05-01T23:59:59.000Z

495

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

E-Print Network [OSTI]

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

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

2008-09-17T23:59:59.000Z

496

Energy Information Administration (EIA) - Supplement Tables  

Gasoline and Diesel Fuel Update (EIA)

7 7 Regional Energy Consumption and Prices by Sector Energy Consumption by Sector Table 1. New England Consumption & Prices by Sector & Census Division. Need help, contact the National Energy Information Center at 202-586-8800. Table 2. Middle Atlantic Consumption & Prices by Sector & Census Division. Need help, contact the National Energy Information Center at 202-586-8800. Table 3. East North Central Consumption & Prices by Sector & Census Division. Need help, contact the National Energy Information Center at 202-586-8800. Table 4. West North Central Consumption & Prices by Sector & Census Division. Need help, contact the National Energy Information Center at 202-586-8800. Table 5. South Atlantic Consumption & Prices by Sector & Census Division. Need help, contact the National Energy Information Center at 202-586-8800.

497

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

498

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

Science Journals Connector (OSTI)

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

Nigel Meade

2010-01-01T23:59:59.000Z

499

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.

500

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

E-Print Network [OSTI]

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

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

2013-01-01T23:59:59.000Z