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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


1

Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting  

Science Journals Connector (OSTI)

Abstract Worldwide implementation of demand side management (DSM) programs has had positive impacts on electrical energy consumption (EEC) and the examination of their effects on long-term forecasting is warranted. The objective of this study is to investigate the effects of historical DSM data on accuracy of EEC modeling and long-term forecasting. To achieve the objective, optimal artificial neural network (ANN) models based on improved particle swarm optimization (IPSO) and shuffled frog-leaping (SFL) algorithms are developed for EEC forecasting. For long-term EEC modeling and forecasting for the U.S. for 2010–2030, two historical data types used in conjunction with developed models include (i) EEC and (ii) socio-economic indicators, namely, gross domestic product, energy imports, energy exports, and population for 1967–2009 period. Simulation results from IPSO-ANN and SFL-ANN models show that using socio-economic indicators as input data achieves lower mean absolute percentage error (MAPE) for long-term EEC forecasting, as compared with EEC data. Based on IPSO-ANN, it is found that, for the U.S. EEC long-term forecasting, the addition of DSM data to socio-economic indicators data reduces MAPE by 36% and results in the estimated difference of 3592.8 MBOE (5849.9 TW h) in EEC for 2010–2030.

F.J. Ardakani; M.M. Ardehali

2014-01-01T23:59:59.000Z

2

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

3

TV Energy Consumption Trends and Energy-Efficiency Improvement Options  

E-Print Network [OSTI]

a forecast for total energy consumption in network standbyconsiderable impact on total energy consumption from TVs.factors affecting total energy consumption. Although further

Park, Won Young

2011-01-01T23:59:59.000Z

4

Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy  

Science Journals Connector (OSTI)

Abstract Buildings are the dominant source of energy consumption and environmental emissions in urban areas. Therefore, the ability to forecast and characterize building energy consumption is vital to implementing urban energy management and efficiency initiatives required to curb emissions. Advances in smart metering technology have enabled researchers to develop “sensor based” approaches to forecast building energy consumption that necessitate less input data than traditional methods. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and influencing variables (e.g., weather, time of day, previous consumption). While sensor-based forecasting has been studied extensively for commercial buildings, there is a paucity of research applying this data-driven approach to the multi-family residential sector. In this paper, we build a sensor-based forecasting model using Support Vector Regression (SVR), a commonly used machine learning technique, and apply it to an empirical data-set from a multi-family residential building in New York City. We expand our study to examine the impact of temporal (i.e., daily, hourly, 10 min intervals) and spatial (i.e., whole building, by floor, by unit) granularity have on the predictive power of our single-step model. Results indicate that sensor based forecasting models can be extended to multi-family residential buildings and that the optimal monitoring granularity occurs at the by floor level in hourly intervals. In addition to implications for the development of residential energy forecasting models, our results have practical significance for the deployment and installation of advanced smart metering devices. Ultimately, accurate and cost effective wide-scale energy prediction is a vital step towards next-generation energy efficiency initiatives, which will require not only consideration of the methods, but the scales for which data can be distilled into meaningful information.

Rishee K. Jain; Kevin M. Smith; Patricia J. Culligan; John E. Taylor

2014-01-01T23:59:59.000Z

5

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

SciTech Connect (OSTI)

This report presents an analysis of the relative impacts of the base-case scenario used in the Annual Energy Outlook 1997, published by the US Department of Energy, Energy Information Administration, on income quintile groups. Projected energy consumption and expenditures, and projected energy expenditures as a share of income, for the period 1993 to 2015 are reported. Projected consumption of electricity, natural gas, distillate fuel, and liquefied petroleum gas over this period is also reported for each income group. 33 figs., 11 tabs.

Poyer, D.A.; Allison, T.

1998-03-01T23:59:59.000Z

6

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

7

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

8

Energy consumption and expenditure projections by population group on the basis on the annual energy outlook 2000 forecast.  

SciTech Connect (OSTI)

The changes in the patterns of energy use and expenditures by population group are analyzed by using the 1993 and 1997 Residential Energy Consumption Surveys. Historically, these patterns have differed among non-Hispanic White households, non-Hispanic Black households, and Hispanic households. Patterns of energy use and expenditures are influenced by geographic and metropolitan location, the composition of housing stock, economic and demographic status, and the composition of energy use by end-use category. As a consequence, as energy-related factors change across groups, patterns of energy use and expenditures also change. Over time, with changes in the composition of these factors by population group and their variable influences on energy use, the impact on energy use and expenditures has varied across these population groups.

Poyer, D. A.; Decision and Information Sciences

2001-05-31T23:59:59.000Z

9

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation Analysis Papers > Annual Energy Outlook Forecast Evaluation Release Date: February 2005 Next Release Date: February 2006 Printer-friendly version Annual Energy Outlook Forecast Evaluation* Table 1.Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Printer Friendly Version Average Absolute Percent Error Variable AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 AEO82 to AEO2004 Consumption Total Energy Consumption 1.9 2.0 2.1 2.1 2.1 2.1 Total Petroleum Consumption 2.9 3.0 3.1 3.1 3.0 2.9 Total Natural Gas Consumption 7.3 7.1 7.1 6.7 6.4 6.5 Total Coal Consumption 3.1 3.3 3.5 3.6 3.7 3.8 Total Electricity Sales 1.9 2.0 2.3 2.3 2.3 2.4 Production Crude Oil Production 4.5 4.5 4.5 4.5 4.6 4.7

10

A new adaptive fuzzy inference system for electricity consumption forecasting with hike in prices  

Science Journals Connector (OSTI)

Large increase or hike in energy prices has proven to impact electricity consumption in a way which cannot be drawn ... (FIS) to estimate and forecast long-term electricity consumption when prices experience larg...

S. M. Sajadi; S. M. Asadzadeh; V. Majazi Dalfard…

2013-12-01T23:59:59.000Z

11

Energy Consumption  

Science Journals Connector (OSTI)

We investigated the relationship between electrical power consumption per capita and GDP per capita in 130 countries using the data reported by World Bank. We found that an electrical power consumption per capita...

Aki-Hiro Sato

2014-01-01T23:59:59.000Z

12

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

13

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

14

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

15

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

16

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

17

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":""}]}

18

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.

19

Transportation Energy Consumption Surveys  

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

Energy Consumption (RTECS) - U.S. Energy Information Administration (EIA) U.S. Energy Information Administration - EIA - Independent Statistics and Analysis Sources & Uses...

20

International Energy Outlook 2001 - World Energy Consumption  

Gasoline and Diesel Fuel Update (EIA)

World Energy Consumption World Energy Consumption picture of a printer Printer Friendly Version (PDF) This report presents international energy projections through 2020, prepared by the Energy Information Administration, including outlooks for major energy fuels and issues related to electricity, transportation, and the environment. The International Energy Outlook 2001 (IEO2001) presents the Energy Information Administration (EIA) outlook for world energy markets to 2020. Current trends in world energy markets are discussed in this chapter, followed by a presentation of the IEO2001 projections for energy consumption by primary energy source and for carbon emissions by fossil fuel. Uncertainty in the forecast is highlighted by an examination of alternative assumptions about economic growth and their impacts on the

Note: This page contains sample records for the topic "forecasts energy consumption" 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

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 2620–2850 million tons and in 2020 might be 3090–3490 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

22

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

23

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"

24

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

25

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

26

Modeling energy consumption of residential furnaces and boilers in U.S. homes  

E-Print Network [OSTI]

ENERGY CONSUMPTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ENERGY CONSUMPTION . . . . . . . . . . . . . . . . . . . . . . . . . .28 ENERGY CONSUMPTION

Lutz, James; Dunham-Whitehead, Camilla; Lekov, Alex; McMahon, James

2004-01-01T23:59:59.000Z

27

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,

28

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.

29

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

30

" Column: Energy-Consumption Ratios;"  

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

" Level: National Data; " " Row: Values of Shipments within NAICS Codes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." ,,,,"Consumption" ,,,"Consumption","per...

31

Energy Consumption Profile for Energy  

E-Print Network [OSTI]

317 Chapter 12 Energy Consumption Profile for Energy Harvested WSNs T. V. Prabhakar, R Venkatesha.............................................................................................318 12.2 Energy Harvesting ...................................................................................318 12.2.1 Motivations for Energy Harvesting...............................................319 12

Langendoen, Koen

32

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

33

Household Vehicles Energy Consumption 1991  

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

methodology used to estimate these statistics relied on data from the 1990 Residential Energy Consumption Survey (RECS), the 1991 Residential Transportation Energy Consumption...

34

Office Buildings - Energy Consumption  

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

Energy Consumption Energy Consumption Office buildings consumed more than 17 percent of the total energy used by the commercial buildings sector (Table 4). At least half of total energy, electricity, and natural gas consumed by office buildings was consumed by administrative or professional office buildings (Figure 2). Table 4. Energy Consumed by Office Buildings for Major Fuels, 2003 All Buildings Total Energy Consumption (trillion Btu) Number of Buildings (thousand) Total Floorspace (million sq. ft.) Sum of Major Fuels Electricity Natural Gas Fuel Oil District Heat All Buildings 4,859 71,658 6,523 3,559 2,100 228 636 All Non-Mall Buildings 4,645 64,783 5,820 3,037 1,928 222 634 All Office Buildings 824 12,208 1,134 719 269 18 128 Type of Office Building

35

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...

36

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

37

Energy Information Administration - Transportation Energy Consumption...  

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

Energy Consumption Transportation Energy Consumption Surveys energy used by vehicles EIA conducts numerous energy-related surveys and other information programs. In general, the...

38

ENERGY CONSUMPTION SURVEY  

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

5 RESIDENTIAL TRANSPORTATION 5 RESIDENTIAL TRANSPORTATION ENERGY CONSUMPTION SURVEY Prepared for: UNITED STATES DEPARTMENT OF ENERGY ENERGY INFORMATION ADMINISTRATION OFFICE OF ENERGY MARKETS AND END USE ENERGY END USE DIVISION RESIDENTIAL AND COMMERCIAL BRANCH WASHINGTON, DC 20585 Prepared by: THE ORKAND CORPORATION 8484 GEORGIA AVENUE SILVER SPRING, MD 20910 October 1986 Contract Number DE-AC01-84EI19658 TABLE OF CONTENTS FRONT MATTER Index to Program Descriptions........................................... vi List of Exhibits ....................................................... viii Acronyms and Abbreviations ............................................. ix SECTION 1: GENERAL INFORMATION ........................................ 1-1 1.1. Summary ....................................................... 1-1

39

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

40

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

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Manufacturing Consumption of Energy 1994  

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

Detailed Tables 28 Energy Information AdministrationManufacturing Consumption of Energy 1994 1. In previous MECS, the term "primary energy" was used to denote the "first use" of...

42

Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case  

Science Journals Connector (OSTI)

Abstract Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications.

Mauro Castelli; Leonardo Vanneschi; Matteo De Felice

2015-01-01T23:59:59.000Z

43

Residential Energy Consumption Survey:  

Gasoline and Diesel Fuel Update (EIA)

E/EIA-0262/2 E/EIA-0262/2 Residential Energy Consumption Survey: 1978-1980 Consumption and Expenditures Part II: Regional Data May 1981 U.S. Department of Energy Energy Information Administration Assistant Administrator for Program Development Office of the Consumption Data System Residential and Commercial Data Systems Division -T8-aa * N uojssaooy 'SOS^-m (£03) ao£ 5925 'uofSfAfQ s^onpojj aa^ndmoo - aojAaag T BU T3gN am rcoj? aig^IT^^ '(adBx Q-naugBH) TOO/T8-JQ/30Q 30^703 OQ ' d jo :moaj ajqBfT^A^ 3J^ sjaodaa aAoqe aqa jo 's-TZTOO-eoo-Tgo 'ON ^ois odo 'g^zo-via/aoQ 'TBST Sujpjjng rXaAang uojidmnsuoo XSaaug sSu-ppjprig ON ^oo^s OdO '^/ZOZO-Via/aOQ *086T aunr '6L6I ?sn§ny og aunf ' jo suja^Bd uoj^dmnsuoo :XaAjng uo^^dmnsuoQ XSaaug OS '9$ '6-ieTOO- 00-T90 OdD 'S/ZOZO-Via/aOa C

44

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)

45

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.

46

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

47

Manufacturing Consumption of Energy 1994  

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

1 Energy Information AdministrationManufacturing Consumption of Energy 1994 Introduction The market for natural gas has been changing for quite some time. As part of natural gas...

48

Household Vehicles Energy Consumption 1991  

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

vehicle aging have an additional but unknown effect on the MPG of individual vehicles. Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 27 Of the...

49

Energy Information Administration - Commercial Energy Consumption...  

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

8A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2 Total Natural Gas Consumption (billion cubic feet) Total Floorspace...

50

Energy Information Administration - Commercial Energy Consumption...  

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

2A. Natural Gas Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of...

51

Energy Information Administration - Commercial Energy Consumption...  

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

0A. Natural Gas Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of...

52

Energy Information Administration - Commercial Energy Consumption...  

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

7A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1 Total Natural Gas Consumption (billion cubic feet) Total Floorspace...

53

Energy Information Administration - Commercial Energy Consumption...  

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

Table C22. Electricity Consumption and Conditional Energy Intensity by Year Constructed for Non-Mall Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace...

54

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

5A. Electricity Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings...

55

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

5A. Natural Gas Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of...

56

Energy Information Administration - Commercial Energy Consumption...  

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

7A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1 Total Electricity Consumption (billion kWh) Total Floorspace of...

57

Energy Information Administration - Commercial Energy Consumption...  

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

2A. Electricity Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings...

58

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

A. Consumption and Gross Energy Intensity by Year Constructed for Sum of Major Fuels for All Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of...

59

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

8A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2 Total Electricity Consumption (billion kWh) Total Floorspace of...

60

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

A. Consumption and Gross Energy Intensity by Climate Zonea for All Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet)...

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

0. Consumption and Gross Energy Intensity by Climate Zonea for Non-Mall Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square...

62

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

9A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3 Total Natural Gas Consumption (billion cubic feet) Total Floorspace...

63

Energy Information Administration - Commercial Energy Consumption...  

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

9A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3 Total Electricity Consumption (billion kWh) Total Floorspace of...

64

Energy Information Administration - Commercial Energy Consumption...  

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

0A. Electricity Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings...

65

Residential Energy Consumption Survey Results: Total Energy Consumption,  

Open Energy Info (EERE)

Survey Results: Total Energy Consumption, Survey Results: Total Energy Consumption, Expenditures, and Intensities (2005) Dataset Summary Description The Residential Energy Consumption Survey (RECS) is a national survey that collects residential energy-related data. The 2005 survey collected data from 4,381 households in housing units statistically selected to represent the 111.1 million housing units in the U.S. Data were obtained from residential energy suppliers for each unit in the sample to produce the Consumption & Expenditures data. The Consumption & Expenditures and Intensities data is divided into two parts: Part 1 provides energy consumption and expenditures by census region, population density, climate zone, type of housing unit, year of construction and ownership status; Part 2 provides the same data according to household size, income category, race and age. The next update to the RECS survey (2009 data) will be available in 2011.

66

World energy consumption  

SciTech Connect (OSTI)

Historical and projected world energy consumption information is displayed. The information is presented by region and fuel type, and includes a world total. Measurements are in quadrillion Btu. Sources of the information contained in the table are: (1) history--Energy Information Administration (EIA), International Energy Annual 1992, DOE/EIA-0219(92); (2) projections--EIA, World Energy Projections System, 1994. Country amounts include an adjustment to account for electricity trade. Regions or country groups are shown as follows: (1) Organization for Economic Cooperation and Development (OECD), US (not including US territories), which are included in other (ECD), Canada, Japan, OECD Europe, United Kingdom, France, Germany, Italy, Netherlands, other Europe, and other OECD; (2) Eurasia--China, former Soviet Union, eastern Europe; (3) rest of world--Organization of Petroleum Exporting Countries (OPEC) and other countries not included in any other group. Fuel types include oil, natural gas, coal, nuclear, and other. Other includes hydroelectricity, geothermal, solar, biomass, wind, and other renewable sources.

NONE

1995-12-01T23:59:59.000Z

67

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

to totals. Source: Energy Information Administration, Office of Energy Markets and End Use, Forms EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey....

68

Manufacturing consumption of energy 1991  

SciTech Connect (OSTI)

This report provides estimates on energy consumption in the manufacturing sector of the US economy. These estimates are based on data from the 1991 Manufacturing Energy Consumption Survey (MECS). This survey--administered by the Energy End Use and Integrated Statistics Division, Office of Energy Markets and End Use, Energy Information Administration (EIA)--is the most comprehensive source of national-level data on energy-related information for the manufacturing industries.

Not Available

1994-12-01T23:59:59.000Z

69

Reduces electric energy consumption  

E-Print Network [OSTI]

consumption · Reduces nonhazardous solid waste and wastewater generation · Potential annual savings, and recycling. Alcoa provides the packaging, automotive, aerospace, and construction markets with a variety

70

Energy Consumption | OpenEI  

Open Energy Info (EERE)

Consumption Consumption Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage

71

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

This paper presents the National Energy Board’s 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

72

Energy consumption of building 39  

E-Print Network [OSTI]

The MIT community has embarked on an initiative to the reduce energy consumption and in accordance with the Kyoto Protocol. This thesis seeks to further expand our understanding of how the MIT campus consumes energy and ...

Hopeman, Lisa Maria

2007-01-01T23:59:59.000Z

73

Manufacturing Consumption of Energy 1994  

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

S Y M n i 1 y 2 i (W i ) (W i 1) , Energy Information Administration, Manufacturing Energy Consumption Survey: Methodological Report 1985. Although this report describes 44...

74

TV Energy Consumption Trends and Energy-Efficiency Improvement Options  

E-Print Network [OSTI]

and Low Power Mode Energy Consumption”, Energy Efficiency inEnergy Consumption ..26 3.1.3. 3D TV Energy Consumption and Efficiency

Park, Won Young

2011-01-01T23:59:59.000Z

75

Energy Consumption of Minimum Energy Coding in  

E-Print Network [OSTI]

Energy Consumption of Minimum Energy Coding in CDMA Wireless Sensor Networks Benigno Zurita Ares://www.ee.kth.se/control Abstract. A theoretical framework is proposed for accurate perfor- mance analysis of minimum energy coding energy consumption is analyzed for two coding schemes proposed in the literature: Minimum Energy coding

Johansson, Karl Henrik

76

Manufacturing Consumption of Energy 1994  

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

energy data used in this report do not reflect adjustments for losses in electricity generation or transmission. energy data used in this report do not reflect adjustments for losses in electricity generation or transmission. 1 The manufacturing sector is composed of establishments classified in Standard Industrial Classification 20 through 39 of the U.S. economy as defined 2 by the Office of Management and Budget. The manufacturing sector is a part of the industrial sector, which also includes mining; construction; and agriculture, forestry, and fishing. The EIA also conducts energy consumption surveys in the residential, commercial buildings, and residential transportation sectors: the Residential Energy 3 Consumption Survey (RECS); the Commercial Buildings Energy Consumption Survey (CBECS); and, until recently, the Residential Transportation Energy Consumption Survey (RTECS).

77

Energy Information Administration - Commercial Energy Consumption...  

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

A. Consumption and Gross Energy Intensity by Census Region for Sum of Major Fuels for All Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings...

78

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

C3. Consumption and Gross Energy Intensity for Sum of Major Fuels for Non-Mall Buildings, 2003 All Buildings* Sum of Major Fuel Consumption Number of Buildings (thousand)...

79

Energy Information Administration - Commercial Energy Consumption...  

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

C7A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 1 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace...

80

Energy Information Administration - Commercial Energy Consumption...  

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

. Consumption and Gross Energy Intensity by Census Region for Sum of Major Fuels for Non-Mall Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of...

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 3 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of...

82

Energy Information Administration - Commercial Energy Consumption...  

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

. Consumption and Gross Energy Intensity by Year Constructed for Sum of Major Fuels for Non-Mall Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of...

83

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

C3A. Consumption and Gross Energy Intensity for Sum of Major Fuels for All Buildings, 2003 All Buildings Sum of Major Fuel Consumption Number of Buildings (thousand) Floorspace...

84

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

Table C8A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 2 Sum of Major Fuel Consumption (trillion Btu) Total...

85

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.

86

Electricity Demand and Energy Consumption Management System  

E-Print Network [OSTI]

This project describes the electricity demand and energy consumption management system and its application to the Smelter Plant of Southern Peru. It is composted of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks, with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows management the peak demand before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules allow the proper planning because it allows knowing the behavior of the hourly demand and the consumption patterns of the plant, in...

Sarmiento, Juan Ojeda

2008-01-01T23:59:59.000Z

87

Manufacturing Consumption of Energy 1994  

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

(MECS) > MECS 1994 Combined Consumption and Fuel Switching (MECS) > MECS 1994 Combined Consumption and Fuel Switching Manufacturing Energy Consumption Survey 1994 (Combined Consumption and Fuel Switching) Manufacturing Energy Consumption Logo Full Report - (file size 5.4 MB) pages:531 Selected Sections (PDF format) Contents (file size 56 kilobytes, 10 pages). Overview (file size 597 kilobytes, 11 pages). Chapters 1-3 (file size 265 kilobytes, 9 pages). Chapter 4 (file size 1,070 kilobytes, 15 pages). Appendix A - Detailed Tables Tables A1 - A8 (file size 1,031 kilobytes, 139 pages). Tables A9 - A23 (file size 746 kilobytes, 119 pages). Tables A24 - A29 (file size 485 kilobytes, 84 pages). Tables A30 - A44 (file size 338 kilobytes, 39 pages). Appendix B (file size 194 kilobytes, 24 pages). Appendix C (file size 116 kilobytes, 16 pages).

88

Energy Consumption in Access Networks  

Science Journals Connector (OSTI)

We present a comparison of energy consumption of access networks. We consider passive optical networks, fiber to the node, point-to-point optical systems and WiMAX. Optical access...

Baliga, Jayant; Ayre, Robert; Sorin, Wayne V; Hinton, Kerry; Tucker, Rodney S

89

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.

90

New York: Weatherizing Westbeth Reduces Energy Consumption |...  

Energy Savers [EERE]

York: Weatherizing Westbeth Reduces Energy Consumption New York: Weatherizing Westbeth Reduces Energy Consumption August 21, 2013 - 12:00am Addthis The New York State Homes and...

91

Manufacturing Consumption of Energy 1994  

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

2(94) 2(94) Distribution Category UC-950 Manufacturing Consumption of Energy 1994 December 1997 Energy Information Administration Office of Energy Markets and End Use U.S. Department of Energy Washington, DC 20585 This report was prepared by the Energy Information Administration, the independent statistical and analytical agency within the U.S. Department of Energy. The information contained herein should be attributed to the Energy Information Administration and should not be construed as advocating or reflecting any policy position of the Department of Energy or any other organization. ii Energy Information Administration/Manufacturing Consumption of Energy 1994 Contacts This publication was prepared by the Energy Information Administration (EIA) under the general direction of W. Calvin

92

Energy Information Administration - Commercial Energy Consumption...  

Gasoline and Diesel Fuel Update (EIA)

or fewer than 20 buildings were sampled. NNo responding cases in sample. Notes: Statistics for the "Energy End Uses" category represent total consumption in buildings that...

93

Energy Information Administration - Commercial Energy Consumption...  

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

sum to totals. Source: Energy Information Administration, Office of Energy Markets and End Use, Forms EIA-871A, C, and E of the 2003 Commercial Buildings Energy Consumption Survey....

94

Residential Energy Consumption Survey (RECS) - Energy Information...  

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

Heating and cooling no longer majority of U.S. home energy use Pie chart of energy consumption in homes by end uses Source: U.S. Energy Information Administration, Residential...

95

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

96

Energy Information Administration - Energy Efficiency, energy consumption  

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

Efficiency Efficiency Energy Efficiency energy consumption savings households, buildings, industry & vehicles The Energy Efficiency Page reflects EIA's information on energy efficiency and related information. This site provides an in depth discussion of the concept of energy efficiency and how it is measured, measurement, summaries of formal user meetings on energy efficiency data and measurement, as well as analysis of greenhouse gas emissions as related to energy use and energy efficiency. At the site you will find links to other sources of information, and via a listserv all interested analysts can share ideas, data, and ask for assistance on methodological problems associated with energy use, energy efficiency, and greenhouse gas issues. Contact: Behjat.Hojjati@eia.doe.gov

97

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.

98

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...

99

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

100

Manufacturing Consumption of Energy 1991--Combined Consumption and Fuel  

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

< < Welcome to the U.S. Energy Information Administration's Manufacturing Web Site. If you are having trouble, call 202-586-8800 for help. Return to Energy Information Administration Home Page. Home > Energy Users > Manufacturing > Consumption and Fuel Switching Manufacturing Consumption of Energy 1991 (Combined Consumption and Fuel Switching) Overview Full Report Tables & Spreadsheets This report presents national-level estimates about energy use and consumption in the manufacturing sector as well as manufacturers' fuel-switching capability. Contact: Stephanie.battle@eia.doe.gov Stephanie Battle Director, Energy Consumption Division Phone: (202) 586-7237 Fax: (202) 586-0018 URL: http://www.eia.gov/emeu/mecs/mecs91/consumption/mecs1a.html File Last Modified: May 25, 1996

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Energy Information Administration - Transportation Energy Consumption by  

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

Energy Consumption Energy Consumption Transportation Energy Consumption Surveys energy used by vehicles EIA conducts numerous energy-related surveys and other information programs. In general, the surveys can be divided into two broad groups: supply surveys, directed to the suppliers and marketers of specific energy sources, that measure the quantities of specific fuels produced for and/or supplied to the market; and consumption surveys, which gather information on the types of energy used by consumer groups along with the consumer characteristics that are associated with energy use. In the transportation sector, EIA's core consumption survey was the Residential Transportation Energy Consumption Survey. RTECS belongs to the consumption group because it collects information directly from the consumer, the household. For roughly a decade, EIA fielded the RTECS--data were first collected in 1983. This survey, fielded for the last time in 1994, was a triennial survey of energy use and expenditures, vehicle miles-traveled (VMT), and vehicle characteristics for household vehicles. For the 1994 survey, a national sample of more than 3,000 households that own or use some 5,500 vehicles provided data.

102

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

104

Residential Energy Consumption Survey (RECS) - Energy Information  

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

Consumption Survey (RECS) - U.S. Energy Information Consumption Survey (RECS) - U.S. Energy Information Administration (EIA) U.S. Energy Information Administration - EIA - Independent Statistics and Analysis Sources & Uses Petroleum & Other Liquids Crude oil, gasoline, heating oil, diesel, propane, and other liquids including biofuels and natural gas liquids. Natural Gas Exploration and reserves, storage, imports and exports, production, prices, sales. Electricity Sales, revenue and prices, power plants, fuel use, stocks, generation, trade, demand & emissions. Consumption & Efficiency Energy use in homes, commercial buildings, manufacturing, and transportation. Coal Reserves, production, prices, employ- ment and productivity, distribution, stocks, imports and exports. Renewable & Alternative Fuels

105

Federal Energy Management Program: Data Center Energy Consumption Trends  

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

Data Center Energy Data Center Energy Consumption Trends to someone by E-mail Share Federal Energy Management Program: Data Center Energy Consumption Trends on Facebook Tweet about Federal Energy Management Program: Data Center Energy Consumption Trends on Twitter Bookmark Federal Energy Management Program: Data Center Energy Consumption Trends on Google Bookmark Federal Energy Management Program: Data Center Energy Consumption Trends on Delicious Rank Federal Energy Management Program: Data Center Energy Consumption Trends on Digg Find More places to share Federal Energy Management Program: Data Center Energy Consumption Trends on AddThis.com... Sustainable Buildings & Campuses Operations & Maintenance Greenhouse Gases Water Efficiency Data Center Energy Efficiency Energy Consumption Trends

106

Manufacturing Consumption of Energy 1994  

Gasoline and Diesel Fuel Update (EIA)

Energy Information Administration/Manufacturing Consumption of Energy 1994 Energy Information Administration/Manufacturing Consumption of Energy 1994 Introduction The market for natural gas has been changing for quite some time. As part of natural gas restructuring, gas pipelines were opened to multiple users. Manufacturers or their representatives could go directly to the wellhead to purchase their natural gas, arrange the transportation, and have the natural gas delivered either by the local distribution company or directly through a connecting pipeline. More recently, the electricity markets have been undergoing change. When Congress passed the Energy Policy Act of 1992, requirements were included not only to open access to the ownership of electricity generation, but also to open access to the transmission lines so that wholesale trade in electricity would be possible. Now several States, including California and

107

Manufacturing consumption of energy 1994  

SciTech Connect (OSTI)

This report provides estimates on energy consumption in the manufacturing sector of the U.S. economy based on data from the Manufacturing Energy Consumption Survey. The sample used in this report represented about 250,000 of the largest manufacturing establishments which account for approximately 98 percent of U.S. economic output from manufacturing, and an expected similar proportion of manufacturing energy use. The amount of energy use was collected for all operations of each establishment surveyed. Highlights of the report include profiles for the four major energy-consuming industries (petroleum refining, chemical, paper, and primary metal industries), and an analysis of the effects of changes in the natural gas and electricity markets on the manufacturing sector. Seven appendices are included to provide detailed background information. 10 figs., 51 tabs.

NONE

1997-12-01T23:59:59.000Z

108

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

Science Journals Connector (OSTI)

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

Weigang Zhao; Jianzhou Wang; Haiyan Lu

2014-01-01T23:59:59.000Z

109

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

110

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

111

Rail Transit and Energy Consumption  

Science Journals Connector (OSTI)

...Transit and Energy Consumption In a recent issue...D.C. 20418 The Diesel's Advantages It...p. 517). The diesel car, while it has...Other types of engine can be made to meet...catalysts by using leaded fuel because it is 3 to...politically unpopular. The diesel car requires no add-on...

CHARLES A. LAVE

1977-09-02T23:59:59.000Z

112

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 200 day period.

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

113

Manufacturing Consumption of Energy 1994  

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

E E U.S. Census Regions and Divisions 489 Energy Information Administration/Manufacturing Consumption of Energy 1994 Source: U.S. Department of Commerce, Bureau of the Census, Statistical Abstract of the United States,1996 (Washington, DC, October 1996), Figure 1. Appendix E U.S. Census Regions and Divisions Appendix F Descriptions of Major Industrial Groups and Selected Industries Executive Office of the President, Office of Management and Budget, Standard Industrial Classification Manual, 1987, pp. 67-263. 54 493 Energy Information Administration/Manufacturing Consumption of Energy 1994 Appendix F Descriptions of Major Industrial Groups and Selected Industries This appendix contains descriptions of industrial groups and selected industries taken from the Standard Industrial

114

DOETEIAO32l/2 Residential Energy Consumption Survey; Consumption  

Gasoline and Diesel Fuel Update (EIA)

General information about EIA data on energy consumption may be obtained from Wray Smith, Director, Office of Energy Markets and End Use (202- 252-1617); Lynda T. Carlson,...

115

Manufacturing Consumption of Energy 1994  

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

Manufacturing Manufacturing Energy Consumption Survey Forms Form EIA-846A (4-6-95) U.S. Department of Commerce Bureau of the Census Acting as Collecting and Compiling Agent For 1994 MANUFACTURING ENERGY CONSUMPTION SURVEY Public reporting burden for this collection of information is estimated to average 9 hours per response, including the time of reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to the Energy Information Administration, Office of Statistical Standards, EI-73, 1707 H-Street, NW, Washington, DC 20585; and to the Office of Information and Regulatory Affairs, Office of

116

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 Uçal Sar?; Ba¸sar Öztay¸si

2012-01-01T23:59:59.000Z

117

Household vehicles energy consumption 1994  

SciTech Connect (OSTI)

Household Vehicles Energy Consumption 1994 reports on the results of the 1994 Residential Transportation Energy Consumption Survey (RTECS). The RTECS is a national sample survey that has been conducted every 3 years since 1985. For the 1994 survey, more than 3,000 households that own or use some 6,000 vehicles provided information to describe vehicle stock, vehicle-miles traveled, energy end-use consumption, and energy expenditures for personal vehicles. The survey results represent the characteristics of the 84.9 million households that used or had access to vehicles in 1994 nationwide. (An additional 12 million households neither owned or had access to vehicles during the survey year.) To be included in then RTECS survey, vehicles must be either owned or used by household members on a regular basis for personal transportation, or owned by a company rather than a household, but kept at home, regularly available for the use of household members. Most vehicles included in the RTECS are classified as {open_quotes}light-duty vehicles{close_quotes} (weighing less than 8,500 pounds). However, the RTECS also includes a very small number of {open_quotes}other{close_quotes} vehicles, such as motor homes and larger trucks that are available for personal use.

NONE

1997-08-01T23:59:59.000Z

118

Projecting household energy consumption within a conditional demand framework  

SciTech Connect (OSTI)

Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

Teotia, A.; Poyer, D.

1991-12-31T23:59:59.000Z

119

Projecting household energy consumption within a conditional demand framework  

SciTech Connect (OSTI)

Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

Teotia, A.; Poyer, D.

1991-01-01T23:59:59.000Z

120

Monitoring Energy Consumption In Wireless Sensor Networks  

E-Print Network [OSTI]

Monitoring Energy Consumption In Wireless Sensor Networks Matthias Witt, Christoph Weyer, it may impair the ability of the sensor network to function. Therefore, minimizing energy consumption energy consumption in both standby and active modes is the basis of wireless networks. Energy preserving

Turau, Volker

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Reduced Energy Consumption for Melting in Foundries  

E-Print Network [OSTI]

Reduced Energy Consumption for Melting in Foundries Ph.D. Thesis by Søren Skov-Hansen Supervisor-melted, and hence reduce the energy consumption for melting in foundries. Traditional gating systems are known

122

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 EIAÂ’s view on energy prices, demand, and production.

123

Manufacturing Consumption of Energy 1994  

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

A24. A24. Total Inputs of Energy for Heat, Power, and Electricity Generation by Program Sponsorship, Industry Group, Selected Industries, and Type of Energy- Management Program, 1994: Part 1 (Estimates in Trillion Btu) See footnotes at end of table. Energy Information Administration/Manufacturing Consumption of Energy 1994 285 SIC Management Any Type of Sponsored Self-Sponsored Sponsored Sponsored Code Industry Group and Industry Program Sponsorship Involvement Involvement Involvement Involvement a No Energy Electric Utility Government Third Party Type of Sponsorship of Management Programs (1992 through 1994) RSE Row Factors Federal, State, or Local RSE Column Factors: 0.7 1.1 1.0 0.7 1.9 0.9 20-39 ALL INDUSTRY GROUPS Participation in One or More of the Following Types of Programs . .

124

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.

125

Energy Consumption of Personal Computing Including Portable  

E-Print Network [OSTI]

Energy Consumption of Personal Computing Including Portable Communication Devices Pavel Somavat1 consumption, questions are being asked about the energy contribution of computing equipment. Al- though studies have documented the share of energy consumption by this type of equipment over the years, research

Namboodiri, Vinod

126

Household Vehicles Energy Consumption 1991  

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

Detailed Detailed Tables The following tables present detailed characteristics of vehicles in the residential sector. Data are from the 1991 Residential Transportation Energy Consumption Survey. The "Glossary" contains the definitions of terms used in the tables. 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. Row and Column Factors These tables present estimates

127

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.

128

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

129

Household Vehicles Energy Consumption 1991  

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

1. 1. Introduction The purpose of this report is to provide information on the use of energy in residential vehicles in the 50 States and the District of Columbia. Included are data about: the number and type of vehicles in the residential sector, the characteristics of those vehicles, the total annual Vehicle Miles Traveled (VMT), the per household and per vehicle VMT, the vehicle fuel consumption and expenditures, and vehicle fuel efficiencies. The Energy Information Administration (EIA) is mandated by Congress to collect, analyze, and disseminate impartial, comprehensive data about energy--how much is produced, who uses it, and the purposes for which it is used. To comply with this mandate, EIA collects energy data from a variety of sources covering a range of topics 1 . Background The data for this report are based on the household telephone interviews from the 1991 RTECS, conducted

130

Manufacturing Consumption of Energy 1994  

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

Manufacturing Manufacturing Sector Overview 1991-1994 Energy Information Administration/Manufacturing Consumption of Energy 1994 xiii Why Do We Investigate Energy Use in the Manufacturing Sector? What Data Do EIA Use To Investigate Energy Use in the Manufacturing Sector? In 1991, output in the manufactur- ing sector fell as the country went into a recession. After 1991, however, output increased as the country slowly came out of the recession. Between 1991 and 1994, manufacturers, especially manu- facturers of durable goods such as steel and glass, experienced strong growth. The industrial production index for durable goods during the period increased by 21 percent. Real gross domestic product for durable goods increased a corre- sponding 16 percent. The growth of nondurables was not as strong-- the production index increased by only 9 percent during this time period.

131

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

132

Trends in Renewable Energy Consumption and Electricity  

Reports and Publications (EIA)

Presents a summary of the nation’s renewable energy consumption in 2010 along with detailed historical data on renewable energy consumption by energy source and end-use sector. Data presented also includes renewable energy consumption for electricity generation and for non-electric use by energy source, and net summer capacity and net generation by energy source and state. The report covers the period from 2006 through 2010.

2012-01-01T23:59:59.000Z

133

Historical Renewable Energy Consumption by Energy Use Sector and Energy  

Open Energy Info (EERE)

Historical Renewable Energy Consumption by Energy Use Sector and Energy Historical Renewable Energy Consumption by Energy Use Sector and Energy Source, 1989-2008 Dataset Summary Description Provides annual renewable energy consumption by source and end use between 1989 and 2008. This data was published and compiled by the Energy Information Administration. Source EIA Date Released August 01st, 2010 (4 years ago) Date Updated August 01st, 2010 (4 years ago) Keywords annual energy consumption consumption EIA renewable energy Data application/vnd.ms-excel icon historical_renewable_energy_consumption_by_sector_and_energy_source_1989-2008.xls (xls, 41 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 1989-2008 License License Creative Commons CCZero Comment Rate this dataset

134

Modelling the impact of user behaviour on heat energy consumption  

E-Print Network [OSTI]

strategies impact on energy consumption in residentialBEHAVIOUR ON HEAT ENERGY CONSUMPTION Nicola Combe 1 ,2 ,nearly 60% of domestic energy consumption and 27% of total

Combe, Nicola Miss; Harrison, David Professor; Way, Celia Miss

2011-01-01T23:59:59.000Z

135

Manufacturing Consumption of Energy 1994  

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

A9. A9. Total Inputs of Energy for Heat, Power, and Electricity Generation by Fuel Type, Census Region, and End Use, 1994: Part 1 (Estimates in Btu or Physical Units) See footnotes at end of table. Energy Information Administration/Manufacturing Consumption of Energy 1994 166 End-Use Categories (trillion Btu) kWh) (1000 bbl) (1000 bbl) cu ft) (1000 bbl) tons) (trillion Btu) Total (million Fuel Oil Diesel Fuel (billion LPG (1000 short Other Net Distillate Natural and Electricity Residual Fuel Oil and Gas Breeze) a b c Coal (excluding Coal Coke d RSE Row Factors Total United States RSE Column Factors: NF 0.5 1.3 1.4 0.8 1.2 1.2 NF TOTAL INPUTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16,515 778,335 70,111 26,107 5,962 25,949 54,143 5,828 2.7 Indirect Uses-Boiler Fuel . . . . . . . . . . . . . . . . . . . . . . . --

136

Manufacturing Consumption of Energy 1994  

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

3 3 Energy Information Administration/Manufacturing Consumption of Energy 1994 Glossary Anthracite: A hard, black, lustrous coal containing a high percentage of fixed carbon and a low percentage of volatile matter. Often referred to as hard coal. Barrel: A volumetric unit of measure equivalent to 42 U.S. gallons. Biomass: Organic nonfossil material of biological origin constituting a renewable energy source. Bituminous Coal: A dense, black coal, often with well-defined bands of bright and dull material, with a moisture content usually less than 20 percent. Often referred to as soft coal. It is the most common coal. Blast Furnace: A shaft furnace in which solid fuel (coke) is burned with an air blast to smelt ore in a continuous operation. Blast Furnace Gas: The waste combustible gas generated in a blast furnace when iron ore is being reduced with coke to

137

Public perceptions of energy consumption and savings  

E-Print Network [OSTI]

Public perceptions of energy consumption and savings Shahzeen Z. Attaria,1 , Michael L. De consumption and savings for a variety of household, transportation, and recycling activities. When asked, with 98% of US emissions attributed to energy consumption (2). According to Pacala and Socolow (3

Kammen, Daniel M.

138

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

139

Household energy consumption and expenditures 1993  

SciTech Connect (OSTI)

This presents information about household end-use consumption of energy and expenditures for that energy. These data were collected in the 1993 Residential Energy Consumption Survey; more than 7,000 households were surveyed for information on their housing units, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information represents all households nationwide (97 million). Key findings: National residential energy consumption was 10.0 quadrillion Btu in 1993, a 9% increase over 1990. Weather has a significant effect on energy consumption. Consumption of electricity for appliances is increasing. Houses that use electricity for space heating have lower overall energy expenditures than households that heat with other fuels. RECS collected data for the 4 most populous states: CA, FL, NY, TX.

NONE

1995-10-05T23:59:59.000Z

140

Manufacturing Consumption of Energy 1994  

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

Survey Design, Survey Design, Implementation, and Estimates 411 Energy Information Administration/Manufacturing Consumption of Energy 1994 Overview of Changes from Previous Surveys Sample Design. The MECS has increased its sample size by roughly 40 percent since the 1991 survey, increasing the designed sample size from 16,054 establishments to 22,922. This increase in size and change in sampling criteria required a departure from using the Annual Survey of Manufactures (ASM) as the MECS sampling frame. For 1994, establishments were selected directly from the 1992 Census of Manufactures (CM) mail file, updated by 1993 ASM. Sample Frame Coverage. The coverage in the 1994 MECS is 98 percent of the manufacturing population as measured in total payroll. The sampling process itself provided that level of coverage, and no special adjustments were

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Manufacturing Consumption of Energy 1994  

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

0. 0. Number of Establishments that Actually Switched Fuels from Natural Gas to Residual Fuel Oil, by Industry Group and Selected Industries, 1994 369 Energy Information Administration/Manufacturing Consumption of Energy 1994 SIC Residual Fuel Oil Total Code Industry Group and Industry (billion cu ft) Factors (counts) (counts) (percents) (counts) (percents) a Natural Gas Switchable to Establishments RSE Row Able to Switch Actually Switched RSE Column Factors: 1.3 0.1 1.4 1.7 1.6 1.8 20 Food and Kindred Products . . . . . . . . . . . . . . . . . . . . . . . . . 81 14,698 702 4.8 262 1.8 5.6 2011 Meat Packing Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 759 23 3.0 10 1.3 9.0 2033 Canned Fruits and Vegetables . . . . . . . . . . . . . . . . . . . . . 9 531 112 21.2 33 6.2 11.6 2037 Frozen Fruits and Vegetables . . . . . . . . . . . . . . . . . . . . . . 5 232 Q 5.3

142

annual energy consumption | OpenEI  

Open Energy Info (EERE)

energy consumption energy consumption Dataset Summary Description Provides annual renewable energy consumption by source and end use between 1989 and 2008. This data was published and compiled by the Energy Information Administration. Source EIA Date Released August 01st, 2010 (4 years ago) Date Updated August 01st, 2010 (4 years ago) Keywords annual energy consumption consumption EIA renewable energy Data application/vnd.ms-excel icon historical_renewable_energy_consumption_by_sector_and_energy_source_1989-2008.xls (xls, 41 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Annually Time Period 1989-2008 License License Creative Commons CCZero Comment Rate this dataset Usefulness of the metadata Average vote Your vote Usefulness of the dataset

143

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Coal Coal Although coal use is expected to be displaced by natural gas in some parts of the world, only a slight drop in its share of total energy consumption is projected by 2025. Coal continues to dominate electricity and industrial sector fuel markets in emerging Asia. Figure 50. World Coal Consumption by Region, 1970-2025 (Billion Short Tons). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 51. Coal Share of World Energy Consumption by Sector, 2002, 2015, and 2025 (Percent). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 52. World Recoverable Coal Reserves. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data In the International Energy Outlook 2005 (IEO2005) reference case, world

144

Household Vehicles Energy Consumption 1991  

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

C C Quality of the Data Appendix C Quality of the Data Introduction This appendix discusses several issues relating to the quality of the Residential Transportation Energy Consumption Survey (RTECS) data and to the interpretation of conclusions based on these data. The first section discusses under- coverage of the vehicle stock in the residential sector. The second section discusses the effects of using July 1991 as a time reference for the survey. The remainder of this appendix discusses the treatment of sampling and nonsampling errors in the RTECS, the quality of specific data items such as the Vehicle Identification Number (VIN) and fuel prices, and poststratification procedures used in the 1991 RTECS. The quality of the data collection and the processing of the data affects the accuracy of estimates based on survey data. All the statistics published in this report such as total

145

Household vehicles energy consumption 1991  

SciTech Connect (OSTI)

The purpose of this report is to provide information on the use of energy in residential vehicles in the 50 States and the District of Columbia. Included are data about: the number and type of vehicles in the residential sector, the characteristics of those vehicles, the total annual Vehicle Miles Traveled (VMT), the per household and per vehicle VMT, the vehicle fuel consumption and expenditures, and vehicle fuel efficiencies. The data for this report are based on the household telephone interviews from the 1991 RTECS, conducted during 1991 and early 1992. The 1991 RTECS represents 94.6 million households, of which 84.6 million own or have access to 151.2 million household motor vehicles in the 50 States and the District of Columbia.

Not Available

1993-12-09T23:59:59.000Z

146

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

I: System for the Analysis of Global Energy Markets (SAGE) I: System for the Analysis of Global Energy Markets (SAGE) The projections of world energy consumption appearing in this yearÂ’s International Energy Outlook (IEO) are based on the Energy Information AdministrationÂ’s (EIAÂ’s) international energy modeling tool, System for the Analysis of Global Energy markets (SAGE). SAGE is an integrated set of regional models that provide a technology-rich basis for estimating regional energy consumption. For each region, reference case estimates of 42 end-use energy service demands (e.g., car, commercial truck, and heavy truck road travel; residential lighting; steam heat requirements in the paper industry) are developed on the basis of economic and demographic projections. Projections of energy consumption to meet the energy demands are estimated on the basis of each regionÂ’s existing energy use patterns, the existing stock of energy-using equipment, and the characteristics of available new technologies, as well as new sources of primary energy supply.

147

NREL: Energy Analysis - Energy Forecasting and Modeling Staff  

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

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

148

Issues in International Energy Consumption Analysis: Electricity...  

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

Electricity Usage in India's Housing Sector SERIES: Issues in International Energy Consumption Analysis Electricity Usage in India's Housing Sector Release date: November 7, 2014...

149

Energy consumption metrics of MIT buildings  

E-Print Network [OSTI]

With world energy demand on the rise and greenhouse gas levels breaking new records each year, lowering energy consumption and improving energy efficiency has become vital. MIT, in a mission to help improve the global ...

Schmidt, Justin David

2010-01-01T23:59:59.000Z

150

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

151

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

8A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2 8A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of Buildings Using Natural Gas (million square feet) Natural Gas Energy Intensity (cubic feet/square foot) West North Central South Atlantic East South Central West North Central South Atlantic East South Central West North Central South Atlantic East South Central All Buildings ................................ 178 238 104 3,788 7,286 2,521 47.0 32.7 41.3 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 23 27 11 346 360 218 66.6 75.8 51.9 5,001 to 10,000 .............................. 14 36 Q 321 662 Q 45.1 53.8 Q 10,001 to 25,000 ............................ 31 33 Q 796 1,102 604 39.5 29.9 Q

152

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

1A. Electricity Consumption and Conditional Energy Intensity by Building Size for All Buildings, 2003 1A. Electricity Consumption and Conditional Energy Intensity by Building Size for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet All Buildings ................................ 201 412 431 13,124 31,858 25,200 15.3 12.9 17.1 Principal Building Activity Education ....................................... 9 55 45 806 5,378 3,687 11.1 10.2 12.2 Food Sales ..................................... 36 24 Q 747 467 Q 48.8 51.1 Q

153

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

. Consumption and Gross Energy Intensity by Year Constructed for Sum of Major Fuels for Non-Mall Buildings, 2003 . Consumption and Gross Energy Intensity by Year Constructed for Sum of Major Fuels for Non-Mall Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/square foot) 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 All Buildings* ............................. 1,488 2,794 1,539 17,685 29,205 17,893 84.1 95.7 86.0 Building Floorspace (Square Feet) 1,001 to 5,000 .............................. 191 290 190 2,146 2,805 1,838 89.1 103.5 103.5 5,001 to 10,000 ............................ 131 231 154 1,972 2,917 1,696 66.2 79.2 91.0 10,001 to 25,000 .......................... 235 351 191 3,213 4,976 3,346 73.1 70.5 57.0

154

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

0A. Natural Gas Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003 0A. Natural Gas Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of Buildings Using Natural Gas (million square feet) Natural Gas Energy Intensity (cubic feet/square foot) Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 All Buildings .............................. 454 715 356 378 134 8,486 14,122 8,970 11,796 5,098 53.5 50.6 39.7 32.0 26.3 Building Floorspace (Square Feet) 1,001 to 5,000 ............................. 57 84 35 58 16 666 1,015 427 832 234 84.8 83.1 81.9 69.6 66.6 5,001 to 10,000 ........................... 50 57 33 61 17 666 1,030 639 1,243 392 75.2 54.9 51.2 49.2 44.0

155

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

A. Total Energy Consumption by Major Fuel for All Buildings, 2003 A. Total Energy Consumption by Major Fuel for All Buildings, 2003 All Buildings Total Energy Consumption (trillion Btu) Number of Buildings (thousand) Floorspace (million square feet) Sum of Major Fuels Electricity Natural Gas Fuel Oil District Heat Primary Site All Buildings ................................ 4,859 71,658 6,523 10,746 3,559 2,100 228 636 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 2,586 6,922 685 1,185 392 257 34 Q 5,001 to 10,000 .............................. 948 7,033 563 883 293 224 36 Q 10,001 to 25,000 ............................ 810 12,659 899 1,464 485 353 28 Q 25,001 to 50,000 ............................ 261 9,382 742 1,199 397 278 17 Q 50,001 to 100,000 .......................... 147 10,291 913 1,579 523 277 29 Q

156

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

0A. Electricity Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003 0A. Electricity Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 All Buildings .............................. 137 254 189 261 202 11,300 18,549 12,374 17,064 10,894 12.1 13.7 15.3 15.3 18.5 Building Floorspace (Square Feet) 1,001 to 5,000 ............................. 19 27 14 32 23 1,210 1,631 923 1,811 903 15.7 16.4 15.0 17.8 25.8 5,001 to 10,000 ........................... 12 18 15 27 14 1,175 1,639 1,062 1,855 914 10.2 10.9 14.3 14.3 15.5

157

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

5A. Electricity Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 5A. Electricity Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) North- east Mid- west South West North- east Mid- west South West North- east Mid- west South West All Buildings ................................ 172 234 452 185 13,899 17,725 26,017 12,541 12.4 13.2 17.4 14.7 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 14 30 52 19 1,031 1,742 2,410 1,296 13.5 17.4 21.5 14.6 5,001 to 10,000 .............................. 11 17 37 21 1,128 1,558 2,640 1,319 9.8 10.8 14.0 15.8 10,001 to 25,000 ............................ 22 33 59 28 2,094 3,317 4,746 2,338 10.4 10.0 12.5 12.1

158

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

5A. Natural Gas Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 5A. Natural Gas Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of Buildings Using Natural Gas (million square feet) Natural Gas Energy Intensity (cubic feet/square foot) North- east Mid- west South West North- east Mid- west South West North- east Mid- west South West All Buildings ................................ 448 728 511 350 10,162 14,144 15,260 8,907 44.1 51.5 33.5 39.3 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 50 92 68 40 547 1,086 912 629 90.6 84.6 74.5 63.7 5,001 to 10,000 .............................. 39 63 69 46 661 1,064 1,439 806 59.2 59.4 48.1 57.4 10,001 to 25,000 ............................ 58 133 81 70 1,293 2,656 2,332 1,542 45.2 50.1 34.7 45.7

159

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

1A. Natural Gas Consumption and Conditional Energy Intensity by Building Size for All Buildings, 2003 1A. Natural Gas Consumption and Conditional Energy Intensity by Building Size for All Buildings, 2003 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of Buildings Using Natural Gas (million square feet) Natural Gas Energy Intensity (cubic feet/square foot) 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet All Buildings ................................ 467 882 688 7,144 21,928 19,401 65.4 40.2 35.5 Principal Building Activity Education ....................................... Q 137 101 419 3,629 2,997 53.9 37.6 33.7 Food Sales ..................................... 16 Q Q 339 Q Q 46.6 Q Q

160

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

9A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3 9A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of Buildings Using Natural Gas (million square feet) Natural Gas Energy Intensity (cubic feet/square foot) West South Central Moun- tain Pacific West South Central Moun- tain Pacific West South Central Moun- tain Pacific All Buildings ................................ 168 185 165 5,453 3,263 5,644 30.9 56.6 29.2 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 29 18 Q 334 266 363 87.9 68.5 60.2 5,001 to 10,000 .............................. 25 Q Q 545 291 514 45.6 62.7 54.4 10,001 to 25,000 ............................ 20 45 26 626 699 844 32.1 63.9 30.6

Note: This page contains sample records for the topic "forecasts energy consumption" from the National Library of EnergyBeta (NLEBeta).
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161

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

8A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2 8A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) West North Central South Atlantic East South Central West North Central South Atlantic East South Central West North Central South Atlantic East South Central All Buildings ................................ 66 254 57 5,523 13,837 3,546 12.0 18.3 16.2 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 10 28 7 821 1,233 481 12.4 22.4 15.4 5,001 to 10,000 .............................. 7 20 5 681 1,389 386 10.8 14.4 13.3 10,001 to 25,000 ............................ 9 31 12 1,204 2,411 842 7.8 12.8 14.1

162

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

C8. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for Non-Mall Buildings, 2003: Part 2 C8. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for Non-Mall Buildings, 2003: Part 2 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) West North Central South Atlantic East South Central West North Central South Atlantic East South Central West North Central South Atlantic East South Central All Buildings* ............................... 436 1,064 309 5,485 12,258 3,393 79.5 86.8 91.1 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 60 116 36 922 1,207 538 64.9 96.5 67.8 5,001 to 10,000 .............................. 44 103 Q 722 1,387 393 60.5 74.0 Q

163

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

A. Consumption and Gross Energy Intensity by Building Size for Sum of Major Fuels for All Buildings, 2003 A. Consumption and Gross Energy Intensity by Building Size for Sum of Major Fuels for All Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet All Buildings ............................... 1,248 2,553 2,721 13,955 32,332 25,371 89.4 79.0 107.3 Principal Building Activity Education ...................................... 63 423 334 808 5,378 3,687 78.3 78.6 90.7 Food Sales ................................... 144 Q Q 765 467 Q 188.5 Q Q

164

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

0. Consumption and Gross Energy Intensity by Climate Zonea for Non-Mall Buildings, 2003 0. Consumption and Gross Energy Intensity by Climate Zonea for Non-Mall Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 All Buildings* ........................... 990 1,761 1,134 1,213 724 10,622 17,335 11,504 15,739 9,584 93.2 101.6 98.5 77.0 75.5 Building Floorspace (Square Feet) 1,001 to 5,000 ............................ 143 187 90 170 95 1,313 1,709 1,010 1,915 975 108.7 109.6 88.8 89.0 97.9 5,001 to 10,000 .......................... 110 137 91 156 69 1,248 1,725 1,077 2,024 959 88.1 79.3 84.6 77.1 71.7

165

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

. Consumption and Gross Energy Intensity by Building Size for Sum of Major Fuels for Non-Mall Buildings, 2003 . Consumption and Gross Energy Intensity by Building Size for Sum of Major Fuels for Non-Mall Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet 1,001 to 10,000 Square Feet 10,001 to 100,000 Square Feet Over 100,000 Square Feet All Buildings* ............................. 1,188 2,208 2,425 13,374 29,260 22,149 88.8 75.5 109.5 Principal Building Activity Education ...................................... 63 423 334 808 5,378 3,687 78.3 78.6 90.7

166

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for Non-Mall Buildings, 2003: Part 3 . Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for Non-Mall Buildings, 2003: Part 3 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) West South Central Moun- tain Pacific West South Central Moun- tain Pacific West South Central Moun- tain Pacific All Buildings* ............................... 575 381 530 7,837 3,675 7,635 73.4 103.8 69.4 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 87 44 64 788 464 871 110.9 94.7 73.0 5,001 to 10,000 .............................. 60 36 76 879 418 820 68.2 86.7 92.9 10,001 to 25,000 ............................ 53 76 73 1,329 831 1,256 40.2 91.7 58.4

167

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

Table C8A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 2 Table C8A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 2 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) West North Central South Atlantic East South Central West North Central South Atlantic East South Central West North Central South Atlantic East South Central All Buildings ................................ 456 1,241 340 5,680 13,999 3,719 80.2 88.7 91.4 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 60 123 37 922 1,283 547 64.9 96.2 67.6 5,001 to 10,000 .............................. 45 111 27 738 1,468 420 61.6 75.4 63.2

168

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

. Consumption and Gross Energy Intensity by Census Region for Sum of Major Fuels for Non-Mall Buildings, 2003 . Consumption and Gross Energy Intensity by Census Region for Sum of Major Fuels for Non-Mall Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) North- east Mid- west South West North- east Mid- west South West North- east Mid- west South West All Buildings* ............................. 1,271 1,690 1,948 911 12,905 17,080 23,489 11,310 98.5 98.9 82.9 80.6 Building Floorspace (Square Feet) 1,001 to 5,000 .............................. 118 206 240 108 1,025 1,895 2,533 1,336 115.1 108.5 94.9 80.6 5,001 to 10,000 ............................ 102 117 185 112 1,123 1,565 2,658 1,239 90.7 74.7 69.5 90.8

169

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 3 A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 3 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) West South Central Moun- tain Pacific West South Central Moun- tain Pacific West South Central Moun- tain Pacific All Buildings ................................ 684 446 617 9,022 4,207 8,613 75.8 106.1 71.6 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 87 44 64 788 466 871 110.9 94.8 73.0 5,001 to 10,000 .............................. 67 39 84 957 465 878 69.7 84.8 95.1 10,001 to 25,000 ............................ 77 91 89 1,555 933 1,429 49.4 97.2 62.4

170

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

C7A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 1 C7A. Consumption and Gross Energy Intensity by Census Division for Sum of Major Fuels for All Buildings, 2003: Part 1 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) New England Middle Atlantic East North Central New England Middle Atlantic East North Central New England Middle Atlantic East North Central All Buildings ................................ 345 1,052 1,343 3,452 10,543 12,424 99.8 99.7 108.1 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 37 86 147 383 676 986 95.9 127.9 148.9 5,001 to 10,000 .............................. 39 68 83 369 800 939 106.0 85.4 88.2 10,001 to 25,000 ............................ Q 121 187 674 1,448 2,113 Q 83.4 88.4

171

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

A. Consumption and Gross Energy Intensity by Year Constructed for Sum of Major Fuels for All Buildings, 2003 A. Consumption and Gross Energy Intensity by Year Constructed for Sum of Major Fuels for All Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/square foot) 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 All Buildings ............................... 1,522 3,228 1,772 18,031 33,384 20,243 84.4 96.7 87.6 Building Floorspace (Square Feet) 1,001 to 5,000 .............................. 193 300 193 2,168 2,904 1,850 89.0 103.2 104.2 5,001 to 10,000 ............................ 134 263 165 2,032 3,217 1,784 66.0 81.9 92.5 10,001 to 25,000 .......................... 241 432 226 3,273 5,679 3,707 73.6 76.1 60.9

172

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

A. Consumption and Gross Energy Intensity by Climate Zonea for All Buildings, 2003 A. Consumption and Gross Energy Intensity by Climate Zonea for All Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet) Energy Intensity for Sum of Major Fuels (thousand Btu/ square foot) Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 All Buildings ............................ 1,086 1,929 1,243 1,386 879 11,529 18,808 12,503 17,630 11,189 94.2 102.6 99.4 78.6 78.6 Building Floorspace (Square Feet) 1,001 to 5,000 ............................ 143 187 90 170 95 1,313 1,709 1,010 1,915 975 108.7 109.6 88.8 89.0 97.9 5,001 to 10,000 .......................... 110 137 91 156 69 1,248 1,725 1,077 2,024 959 88.1 79.3 84.6 77.1 71.7

173

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

9A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3 9A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) West South Central Moun- tain Pacific West South Central Moun- tain Pacific West South Central Moun- tain Pacific All Buildings ................................ 141 68 117 8,634 4,165 8,376 16.3 16.3 14.0 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 17 7 12 696 439 857 24.1 15.7 14.0 5,001 to 10,000 .............................. 12 5 15 865 451 868 13.8 12.1 17.7 10,001 to 25,000 ............................ 16 12 16 1,493 933 1,405 11.0 13.0 11.5

174

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

2A. Electricity Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003 2A. Electricity Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 All Buildings ................................ 162 538 343 17,509 32,945 19,727 9.2 16.3 17.4 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 24 54 38 2,072 2,767 1,640 11.4 19.4 23.0 5,001 to 10,000 .............................. 16 41 29 1,919 3,154 1,572 8.2 13.0 18.4 10,001 to 25,000 ............................ 28 69 45 3,201 5,610 3,683 8.7 12.3 12.2

175

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

2A. Natural Gas Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003 2A. Natural Gas Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of Buildings Using Natural Gas (million square feet) Natural Gas Energy Intensity (cubic feet/square foot) 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 All Buildings ............................... 580 986 471 12,407 22,762 13,304 46.8 43.3 35.4 Building Floorspace (Square Feet) 1,001 to 5,000 ............................... 86 103 61 1,245 1,271 659 69.0 81.0 92.1 5,001 to 10,000 ............................. 57 101 60 1,154 1,932 883 49.4 52.3 67.6 10,001 to 25,000 ........................... 105 174 65 2,452 3,390 1,982 42.6 51.2 32.7

176

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

7A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1 7A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) New England Middle Atlantic East North Central New England Middle Atlantic East North Central New England Middle Atlantic East North Central All Buildings ................................ 41 131 168 3,430 10,469 12,202 12.0 12.5 13.8 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 5 9 20 369 662 921 12.9 13.9 21.9 5,001 to 10,000 .............................. 3 8 9 360 768 877 8.4 10.4 10.8 10,001 to 25,000 ............................ Q 16 24 674 1,420 2,113 Q 11.6 11.2

177

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

5A. Fuel Oil Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 5A. Fuel Oil Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 Total Fuel Oil Consumption (million gallons) Total Floorspace of Buildings Using Fuel Oil (million square feet) Fuel Oil Energy Intensity (gallons/square foot) North- east Mid- west South West North- east Mid- west South West North- east Mid- west South West All Buildings .............................. 1,302 172 107 64 6,464 2,909 4,663 2,230 0.20 0.06 0.02 Q Building Floorspace (Square Feet) 1,001 to 10,000 ............................ 381 Q Q Q 763 Q 274 Q 0.50 Q 0.10 Q 10,001 to 100,000 ........................ 404 63 Q Q 1,806 648 985 351 0.22 0.10 Q Q Over 100,000 ............................... 517 21 45 Q 3,894 2,055 3,404 1,780 0.13 0.01 0.01 Q

178

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

7A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1 7A. Natural Gas Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1 Total Natural Gas Consumption (billion cubic feet) Total Floorspace of Buildings Using Natural Gas (million square feet) Natural Gas Energy Intensity (cubic feet/square foot) New England Middle Atlantic East North Central New England Middle Atlantic East North Central New England Middle Atlantic East North Central All Buildings ................................ 85 364 550 1,861 8,301 10,356 45.4 43.8 53.1 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ Q 42 69 Q 427 741 Q 98.4 92.9 5,001 to 10,000 .............................. Q 32 49 Q 518 743 Q 62.1 65.5 10,001 to 25,000 ............................ Q 47 102 Q 952 1,860 Q 49.7 54.6

179

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

. Total Energy Consumption by Major Fuel for Non-Mall Buildings, 2003 . Total Energy Consumption by Major Fuel for Non-Mall Buildings, 2003 All Buildings* Total Energy Consumption (trillion Btu) Number of Buildings (thousand) Floorspace (million square feet) Sum of Major Fuels Electricity Natural Gas Fuel Oil District Heat Primary Site All Buildings* ............................... 4,645 64,783 5,820 9,168 3,037 1,928 222 634 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 2,552 6,789 672 1,164 386 250 34 Q 5,001 to 10,000 .............................. 889 6,585 516 790 262 209 36 Q 10,001 to 25,000 ............................ 738 11,535 776 1,229 407 309 27 Q 25,001 to 50,000 ............................ 241 8,668 673 1,058 350 258 16 Q 50,001 to 100,000 .......................... 129 9,057 759 1,223 405 244 26 Q

180

Consumption & Efficiency - Data - U.S. Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Consumption & Efficiency Consumption & Efficiency Glossary › FAQS › Overview Data Residential Energy Consumption Survey Data Commercial Energy Consumption Survey Data Manufacturing Energy Consumption Survey Data Vehicle Energy Consumption Survey Data Energy Intensity Consumption Summaries Average cost of fossil-fuels for electricity generation All Consumption & Efficiency Data Reports Analysis & Projections All Sectors Commercial Buildings Efficiency Manufacturing Projections Residential Transportation All Reports Find statistics on energy consumption and efficiency across all fuel sources. + EXPAND ALL Residential Energy Consumption Survey Data Household characteristics Release Date: March 28, 2011 Survey data for occupied primary housing units. Residential Energy Consumption Survey (RECS)

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

C3A. Consumption and Gross Energy Intensity for Sum of Major Fuels for All Buildings, 2003 C3A. Consumption and Gross Energy Intensity for Sum of Major Fuels for All Buildings, 2003 All Buildings Sum of Major Fuel Consumption Number of Buildings (thousand) Floorspace (million square feet) Floorspace per Building (thousand square feet) Total (trillion Btu) per Building (million Btu) per Square Foot (thousand Btu) All Buildings ................................ 4,859 71,658 14.7 6,523 1,342 91.0 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 2,586 6,922 2.7 685 265 99.0 5,001 to 10,000 .............................. 948 7,033 7.4 563 594 80.0 10,001 to 25,000 ............................ 810 12,659 15.6 899 1,110 71.0 25,001 to 50,000 ............................ 261 9,382 36.0 742 2,843 79.0

182

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

C3. Consumption and Gross Energy Intensity for Sum of Major Fuels for Non-Mall Buildings, 2003 C3. Consumption and Gross Energy Intensity for Sum of Major Fuels for Non-Mall Buildings, 2003 All Buildings* Sum of Major Fuel Consumption Number of Buildings (thousand) Floorspace (million square feet) Floorspace per Building (thousand square feet) Total (trillion Btu) per Building (million Btu) per Square Foot (thousand Btu) per Worker (million Btu) All Buildings* ............................... 4,645 64,783 13.9 5,820 1,253 89.8 79.9 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 2,552 6,789 2.7 672 263 98.9 67.6 5,001 to 10,000 .............................. 889 6,585 7.4 516 580 78.3 68.7 10,001 to 25,000 ............................ 738 11,535 15.6 776 1,052 67.3 72.0 25,001 to 50,000 ............................ 241 8,668 35.9 673 2,790 77.6 75.8

183

State energy data report 1994: Consumption estimates  

SciTech Connect (OSTI)

This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA`s energy models. Division is made for each energy type and end use sector. Nuclear electric power is included.

NONE

1996-10-01T23:59:59.000Z

184

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

185

State energy data report 1996: Consumption estimates  

SciTech Connect (OSTI)

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA`s energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs.

NONE

1999-02-01T23:59:59.000Z

186

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.

187

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

188

Energy Information Administration (EIA)- Manufacturing Energy Consumption  

Gasoline and Diesel Fuel Update (EIA)

Steel Industry Analysis Brief Change Topic: Steel | Chemical Steel Industry Analysis Brief Change Topic: Steel | Chemical JUMP TO: Introduction | Energy Consumption | Energy Expenditures | Producer Prices and Production | Energy Intensity | Energy Management Activities Introduction The steel industry is critical to the U.S. economy. Steel is the material of choice for many elements of construction, transportation, manufacturing, and a variety of consumer products. It is the backbone of bridges, skyscrapers, railroads, automobiles, and appliances. Most grades of steel used today - particularly high-strength steels that are lighter and more versatile - were not available a decade ago.1 The U.S. steel industry (including iron production) relies significantly on natural gas and coal coke and breeze for fuel, and is one of the largest

189

Energy Information Administration (EIA)- Manufacturing Energy Consumption  

Gasoline and Diesel Fuel Update (EIA)

Chemical Industry Analysis Brief Change Topic: Steel | Chemical Chemical Industry Analysis Brief Change Topic: Steel | Chemical JUMP TO: Introduction | Energy Consumption | Energy Expenditures | Producer Prices and Production | Energy Intensity | Energy Management Activities | Fuel Switching Capacity Introduction The chemical industries are a cornerstone of the U.S. economy, converting raw materials such as oil, natural gas, air, water, metals, and minerals into thousands of various products. Chemicals are key materials for producing an extensive assortment of consumer goods. They are also crucial materials in creating many resources that are essential inputs to the numerous industries and sectors of the U.S. economy.1 The manufacturing sector is classified by the North American Industry Classification System (NAICS) of which the chemicals sub-sector is NAICS

190

Household Vehicles Energy Consumption 1991  

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

. . Vehicle Fuel Efficiency and Consumption Fuel consumption is estimated from RTECS data on the vehicle stock (Chapter 2) and miles traveled (Chapter 3), in combination with vehicle fuel efficiency ratings, adjusted to account for individual driving circumstances. The first two sections of this chapter present estimates of household vehicle fuel efficiency and household fuel consumption calculated from these fuel efficiency estimates. These sections also discuss variations in fuel efficiency and consumption based on differences in household and vehicle characteristics. The third section presents EIA estimates of the potential savings from replacing the oldest (and least fuel-efficient) household vehicles with new (and more fuel-efficient) vehicles. The final section of this chapter focuses on households receiving (or eligible to receive) supplemental income under

191

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

192

Renewable Energy Consumption | OpenEI  

Open Energy Info (EERE)

Consumption Consumption Dataset Summary Description Total annual renewable electricity consumption by country, 2005 to 2009 (available in Billion Kilowatt-hours or as Quadrillion Btu). Compiled by Energy Information Administration (EIA). Source EIA Date Released Unknown Date Updated Unknown Keywords EIA renewable electricity Renewable Energy Consumption world Data text/csv icon total_renewable_electricity_net_consumption_2005_2009billion_kwh.csv (csv, 8.5 KiB) text/csv icon total_renewable_electricity_net_consumption_2005_2009quadrillion_btu.csv (csv, 8.9 KiB) Quality Metrics Level of Review Peer Reviewed Comment Temporal and Spatial Coverage Frequency Time Period 2005 - 2009 License License Other or unspecified, see optional comment below Comment Rate this dataset Usefulness of the metadata

193

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

194

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

195

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

Table C22. Electricity Consumption and Conditional Energy Intensity by Year Constructed for Non-Mall Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings Using Electricity (million square feet) Electricity Energy Intensity (kWh/square foot) 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 1959 or Before 1960 to 1989 1990 to 2003 All Buildings* ............................... 155 447 288 17,163 28,766 17,378 9.0 15.5 16.6 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 23 52 37 2,049 2,668 1,628 11.3 19.6 23.0 5,001 to 10,000 .............................. 15 35 27 1,859 2,854 1,484 8.1 12.2 18.1 10,001 to 25,000 ............................ 27 55 37 3,141 4,907 3,322 8.5 11.3 11.2

196

Consumption & Efficiency - U.S. Energy Information Administration (EIA)  

Gasoline and Diesel Fuel Update (EIA)

Consumption & Efficiency Consumption & Efficiency Glossary › FAQS › Overview Data Residential Energy Consumption Survey Data Commercial Energy Consumption Survey Data Manufacturing Energy Consumption Survey Data Vehicle Energy Consumption Survey Data Energy Intensity Consumption Summaries Average cost of fossil-fuels for electricity generation All Consumption & Efficiency Data Reports Analysis & Projections All Sectors Commercial Buildings Efficiency Manufacturing Projections Residential Transportation All Reports An Assessment of EIA's Building Consumption Data Background image of CNSTAT logo The U.S. Energy Information Administration (EIA) routinely uses feedback from customers and outside experts to help improve its programs and products. As part of an assessment of its consumption

197

Energy Consumption Issues on Mobile Network Systems  

Science Journals Connector (OSTI)

This paper describes energy consumption demographic data in operating real mobile networks. We examine published data from NTT DoCoMo, which is the largest mobile telecommunication operator in Japan and operating nation-wide 3G networks, and identify ... Keywords: Moble Network, Power Consumption, Battery, CO2, Green Network

Minoru Etoh; Tomoyuki Ohya; Yuji Nakayama

2008-07-01T23:59:59.000Z

198

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

199

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.

200

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 "forecasts energy consumption" 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

Energy for 500 Million Homes: Drivers and Outlook for Residential Energy Consumption in China  

E-Print Network [OSTI]

end-use Residential primary energy consumption was 6.6 EJ inof primary energy. Primary energy consumption includes final14 Residential Primary Energy Consumption by Fuel (with

Zhou, Nan

2010-01-01T23:59:59.000Z

202

Uncertainties in Energy Consumption Introduced by Building Operations and  

E-Print Network [OSTI]

Uncertainties in Energy Consumption Introduced by Building Operations and Weather for a Medium between predicted and actual building energy consumption can be attributed to uncertainties introduced in energy consumption due to actual weather and building operational practices, using a simulation

203

Using occupancy to reduce energy consumption of buildings  

E-Print Network [OSTI]

Meter allows us to study the energy consumption patterns onThis allows us to study the energy consumption of individualgives us a good framework to study the energy consumption

Balaji, Bharathan

2011-01-01T23:59:59.000Z

204

Household Vehicles Energy Consumption 1991  

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

3. 3. Vehicle Miles Traveled This chapter presents information on household vehicle usage, as measured by the number of vehicle miles traveled (VMT). VMT is one of the two most important components used in estimating household vehicle fuel consumption. (The other, fuel efficiency, is discussed in Chapter 4). In addition, this chapter examines differences in driving behavior based on the characteristics of the household and the type of vehicle driven. Trends in household driving patterns are also examined using additional information from the Department of Transportation's Nationwide Personal Transportation Survey (NPTS). Household VMT is a measure of the demand for personal transportation. Demand for transportation may be viewed from either an economic or a social perspective. From the economic point-of-view, the use of a household vehicle represents the consumption of one

205

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

206

Energy: a historical perspective and 21st century forecast  

SciTech Connect (OSTI)

Contents are: Preface; Chapter 1: introduction, brief history, and chosen approach; Chapter 2: human population and energy consumption: the future; Chapter 4: sources of energy (including a section on coal); Chapter 5: electricity: generation and consumption; and Chapter 6: energy consumption and probable energy sources during the 21st century.

Salvador, Amos [University of Texas, Austin, TX (United States)

2005-07-01T23:59:59.000Z

207

Comparison of Real World Energy Consumption to Models and DOE...  

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

Comparison of Real World Energy Consumption to Models and DOE Test Procedures Comparison of Real World Energy Consumption to Models and DOE Test Procedures This study investigates...

208

Power to the Plug: An Introduction to Energy, Electricity, Consumption...  

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

to the Plug: An Introduction to Energy, Electricity, Consumption, and Efficiency Power to the Plug: An Introduction to Energy, Electricity, Consumption, and Efficiency Below is...

209

New Water Booster Pump System Reduces Energy Consumption by 80...  

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

Water Booster Pump System Reduces Energy Consumption by 80 Percent and Increases Reliability New Water Booster Pump System Reduces Energy Consumption by 80 Percent and Increases...

210

Manufacturing Energy Consumption Survey (MECS) - Data - U.S....  

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

| 1998 | 1994 | 1991 | Archive Data Methodology & Forms + EXPAND ALL Consumption of Energy for All Purposes (First Use) Total First Use (formerly Primary Consumption) of Energy...

211

Household Vehicles Energy Consumption 1991  

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

homes, pickup trucks, and jeeps or similar vehicles. See Vehicle. Average Household Energy Expenditures: A ratio estimate defined as the total household energy expenditures for...

212

Appliance Energy Consumption in Australia | Open Energy Information  

Open Energy Info (EERE)

Appliance Energy Consumption in Australia Appliance Energy Consumption in Australia Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Appliance Energy Consumption in Australia Focus Area: Appliances & Equipment Topics: Policy Impacts Website: www.energyrating.gov.au/resources/program-publications/?viewPublicatio Equivalent URI: cleanenergysolutions.org/content/appliance-energy-consumption-australi DeploymentPrograms: Industry Codes & Standards Regulations: Appliance & Equipment Standards and Required Labeling The document sets out the equations necessary to calculate the star rating index for appliances that carry an energy label in Australia. Equations for new air conditioner and refrigerator algorithms from April 2010 are included. Televisions, which have carried a mandatory energy label from

213

DOE/EIA-0321/HRIf Residential Energy Consumption Survey. Consumption  

Gasoline and Diesel Fuel Update (EIA)

/HRIf /HRIf Residential Energy Consumption Survey. Consumption and Expenditures, April 1981 Through March 1982 an Part I: National Data Energy Information Administration Washington, D.C. (202) 20fr02 'O'Q 'uoifkjjUSBM ujiuud juaoiujeAog 'S'n siuawnooQ jo luapuaiuuadns - 0088-292 (202) 98S02 '0'Q 8f 0-d I 6ujp|ing uoiieflSjUjiup v UOIIBUJJOJU | ABjau 3 02-13 'jaiuao UOIJBUJJOJUI XBjaug IBUO!;BN noA pasopua s; uujoi japjo uy 'MO|aq jeadde sjaqoinu auoydajaj PUB sassajppv 'OI3N 9>4i oi papajip aq pinoqs X6jaue uo suotjsenQ '(OIBN) J9»ueo aqjeiMJO^ui ASjaug (BUOIJEN s,vi3 QMi JO OdO 941 UUGJJ peuiBiqo eq ABOI suoijBonqnd (vi3) UO!JBJ;S!UILUPV UOIIBUUJO|U| XBjeug jaiflo PUB SJMJ p ssBiiojnd PUB UOIIBLUJO^JI 6uuepjQ (Od9) 90IWO Bujjuud luetuujaAOQ -g'n 'sjuaiunooa p juapuaiuuedng aqt LUOJI aiqB||BAB si uoHBOjiqnd sjt|i

214

Federal Energy Consumption and Progress Made toward Requirements  

Broader source: Energy.gov [DOE]

The Federal Energy Management Program (FEMP) tracks Federal agency energy consumption and progress toward achieving energy laws and requirements.

215

Energy Consumption of Die Casting Operations  

SciTech Connect (OSTI)

Molten metal processing is inherently energy intensive and roughly 25% of the cost of die-cast products can be traced to some form of energy consumption [1]. The obvious major energy requirements are for melting and holding molten alloy in preparation for casting. The proper selection and maintenance of melting and holding equipment are clearly important factors in minimizing energy consumption in die-casting operations [2]. In addition to energy consumption, furnace selection also influences metal loss due to oxidation, metal quality, and maintenance requirements. Other important factors influencing energy consumption in a die-casting facility include geographic location, alloy(s) cast, starting form of alloy (solid or liquid), overall process flow, casting yield, scrap rate, cycle times, number of shifts per day, days of operation per month, type and size of die-casting form of alloy (solid or liquid), overall process flow, casting yield, scrap rate, cycle times, number of shifts per day, days of operation per month, type and size of die-casting machine, related equipment (robots, trim presses), and downstream processing (machining, plating, assembly, etc.). Each of these factors also may influence the casting quality and productivity of a die-casting enterprise. In a die-casting enterprise, decisions regarding these issues are made frequently and are based on a large number of factors. Therefore, it is not surprising that energy consumption can vary significantly from one die-casting enterprise to the next, and within a single enterprise as function of time.

Jerald Brevick; clark Mount-Campbell; Carroll Mobley

2004-03-15T23:59:59.000Z

216

Energy for 500 Million Homes: Drivers and Outlook for Residential Energy Consumption in China  

E-Print Network [OSTI]

of Commercial Building Energy Consumption in China, 2008,The China Residential Energy Consumption Survey, Human andfor Residential Energy Consumption in China Nan Zhou,

Zhou, Nan

2010-01-01T23:59:59.000Z

217

Consumption & Efficiency - U.S. Energy Information Administration (EIA)  

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

Consumption & Efficiency Consumption & Efficiency Glossary › FAQS › Overview Data Residential Energy Consumption Survey Data Commercial Energy Consumption Survey Data Manufacturing Energy Consumption Survey Data Vehicle Energy Consumption Survey Data Energy Intensity Consumption Summaries Average cost of fossil-fuels for electricity generation All Consumption & Efficiency Data Reports Analysis & Projections All Sectors Commercial Buildings Efficiency Manufacturing Projections Residential Transportation All Reports Technical Workshop on Behavior Economics Presentations Technical Workshop on Behavior Economics Presentations Cost of Natural Gas Used in Manufacturing Sector Has Fallen Graph showing Cost of Natural Gas Used in Manufacturing Sector Has Fallen Source: U.S. Energy Information Administration, Manufacturing Energy

218

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

219

Energy consumption and environmental pollution: a stochastic model  

Science Journals Connector (OSTI)

......indicated that total energy consumption in sugar beet production...pollution. Although energy consumption increased sugar beet yield...and found that hybrid and electric car technologies exhibit (efficiency...ergy efficiency, affects consumption choice by Swedish households......

Charles S. Tapiero

2009-07-01T23:59:59.000Z

220

Federal Energy Management Program: Data Center Energy Consumption Trends  

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

Consumption Trends Consumption Trends Data centers can consume up to 100 times more energy than a standard office building. Often, less than 15% of original source energy is used for the information technology equipment within a data center. Figure 1 outlines typical data center energy consumption ratios. An illustration that features a graphic of a coal container representing 100 units of coal. This enters a graphic of a power plant, where those 100 units of coal are turned into 35 units of energy. The 35 units of energy are distributed by power lines, represented by a graphic of power lines, where 33 units are delivered to a pie chart representing data typical data center energy end use. The data center pie chart features 48% representing server load and computing operation consumption; 43% representing cooling equipment consumption; and 9% representing power conversion and distribution consumption.

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Manufacturing Energy Consumption Survey (MECS) - U.S. Energy Information  

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

Cost of Natural Gas Used in Manufacturing Sector Has Fallen Graph showing Cost of Natural Gas Used in Manufacturing Sector Has Fallen Source: U.S. Energy Information Administration, Manufacturing Energy Consumption Survey (MECS) 1998-2010, September 6, 2013. New 2010 Manufacturing Energy Consumption Survey (MECS) Data Released › Graph showing total U.S. manufacturing energy consumption for all purposes has declined 17 percent from 2002 to 2010. Source: U.S. Energy Information Administration, Manufacturing Energy Consumption Data Show Large Reductions in Both Manufacturing Energy Use and the Energy Intensity of Manufacturing Activity between 2002 and 2010, March 19, 2013. First Estimates from 2010 Manufacturing Energy Consumption Survey (MECS) Released ›

222

Energy Information Agency's 2003 Commercial Building Energy Consumption Survey Tables  

Broader source: Energy.gov [DOE]

Energy use intensities in commercial buildings vary widely and depend on activity and climate, as shown in this data table, which was derived from the Energy Information Agency's 2003 Commercial Building Energy Consumption Survey.

223

Monitoring and Management of Refinery Energy Consumption  

E-Print Network [OSTI]

the effects of same other nOl1"operational variables on the energy target. Figure 10 shows the results of the monitoring period in rep;Jrt form. The actual consumption for each utility is listed and converted to energy content. The base target consumption... ===============~===~.========.=.=====.=========~====================~===== ENERGY TOTAL CONTENT ENEF~GY ACTW~L CONSUMPT I ON UI\\lITS BTU/UI\\lIT MMBTU/DAY FUEL G?\\S: 441425.0 SCFH 1401.0 14842.5 FUEL OIL: O.C' BPO 6470000.0 0.0 HP STEAI1: -79344.0 tt/Hf~ 1136. C' -2163.2 MP STEAI1: 48488.0 tt/HR 952.0 1107.9 LP STEAM: BFW...

Pelham, R. O.; Moriarty, R. D.; Hudgens, P. D.

224

Household Vehicles Energy Consumption 1991  

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

Aggregate Aggregate Ratio: See Mean and Ratio Estimate. AMPD: Average miles driven per day. See Appendix B, "Estimation Methodologies." Annual Vehicle Miles Traveled: See Vehicle Miles Traveled. Automobile: Includes standard passenger car, 2-seater car and station wagons; excludes passenger vans, cargo vans, motor homes, pickup trucks, and jeeps or similar vehicles. See Vehicle. Average Household Energy Expenditures: A ratio estimate defined as the total household energy expenditures for all RTECS households divided by the total number of households. See Ratio Estimate, and Combined Household Energy Expenditures. Average Number of Vehicles per Household: The average number of vehicles used by a household for personal transportation during 1991. For this report, the average number of vehicles per household is computed as the ratio of the total number of vehicles to the

225

One of These Homes is Not Like the Other: Residential Energy Consumption Variability  

E-Print Network [OSTI]

consumption. Total energy consumption (in thousand BTUs) waselectricity and total energy consumption. Because all homesin gas, electric, and total energy consumption. Removing

Kelsven, Phillip

2013-01-01T23:59:59.000Z

226

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

227

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

228

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

7A. Total District Heat Consumption and Expenditures for All Buildings, 2003 7A. Total District Heat Consumption and Expenditures for All Buildings, 2003 All Buildings Using District Heat District Heat Consumption District Heat Expenditures Number of Buildings (thousand) Floorspace (million square feet) Floorspace per Building (thousand square feet) Total (trillion Btu) Total (million dollars) All Buildings ................................ 67 5,576 83 636 7,279 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ Q Q Q Q Q 5,001 to 10,000 .............................. Q Q Q Q Q 10,001 to 25,000 ............................ 18 289 16 Q Q 25,001 to 50,000 ............................ 10 369 35 Q Q 50,001 to 100,000 .......................... 8 574 70 Q Q 100,001 to 200,000 ........................ 9 1,399 148 165 Q

229

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

3A. Total Natural Gas Consumption and Expenditures in All Buildings, 2003 3A. Total Natural Gas Consumption and Expenditures in All Buildings, 2003 All Buildings Using Natural Gas Natural Gas Consumption Natural Gas Expenditures Number of Buildings (thousand) Floorspace (million square feet) Floorspace per Building (thousand square feet) Total (trillion Btu) Total (billion cubic feet) Total (million dollars) All Buildings ................................ 2,538 48,473 19.1 2,100 2,037 16,010 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 1,134 3,175 2.8 257 249 2,227 5,001 to 10,000 .............................. 531 3,969 7.5 224 218 1,830 10,001 to 25,000 ............................ 500 7,824 15.6 353 343 2,897 25,001 to 50,000 ............................ 185 6,604 35.8 278 270 2,054

230

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

8A. District Heat Consumption and Expenditure Intensities for All Buildings, 2003 8A. District Heat Consumption and Expenditure Intensities for All Buildings, 2003 District Heat Consumption District Heat Expenditures per Building (million Btu) per Square Foot (thousand Btu) per Building (thousand dollars) per Square Foot (dollars) per Thousand Pounds (dollars) All Buildings ................................ 9,470 113.98 108.4 1.31 11.45 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ Q Q Q Q Q 5,001 to 10,000 .............................. Q Q Q Q Q 10,001 to 25,000 ............................ Q Q Q Q Q 25,001 to 50,000 ............................ Q Q Q Q Q 50,001 to 100,000 .......................... Q Q Q Q Q 100,001 to 200,000 ........................ 17,452 118.10 Q Q Q

231

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

3A. Total Fuel Oil Consumption and Expenditures for All Buildings, 2003 3A. Total Fuel Oil Consumption and Expenditures for All Buildings, 2003 All Buildings Using Fuel Oil Fuel Oil Consumption Fuel Oil Expenditures Number of Buildings (thousand) Floorspace (million square feet) Floorspace per Building (thousand square feet) Total (trillion Btu) Total (million gallons) Total (million dollars) All Buildings ................................ 465 16,265 35 228 1,644 1,826 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 211 606 3 34 249 292 5,001 to 10,000 .............................. 102 736 7 36 262 307 10,001 to 25,000 ............................ 66 1,043 16 28 201 238 25,001 to 50,000 ............................ 24 895 38 17 124 134 50,001 to 100,000 .......................... 25 1,852 76 29 209 229

232

State Residential Energy Consumption Shares  

Gasoline and Diesel Fuel Update (EIA)

This next slide shows what fuels are used in the residential market. When a This next slide shows what fuels are used in the residential market. When a energy supply event happens, particularly severe winter weather, it is this sector that the government becomes most concerned about. As you can see, natural gas is very important to the residential sector not only in DC, MD and VA but in the United States as well. DC residents use more natural gas for home heating than do MD and VA. While residents use heating oil in all three states, this fuel plays an important role in MD and VA. Note: kerosene is included in the distillate category because it is an important fuel to rural households in MD and VA. MD and VA rely more on electricity than DC. Both MD and VA use propane as well. While there are some similarities in this chart, it is interesting to note

233

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS  

E-Print Network [OSTI]

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS Detlev Heinemann Oldenburg.girodo@uni-oldenburg.de ABSTRACT Solar energy is expected to contribute major shares of the future global energy supply. Due to its and solar energy conversion processes has to account for this behaviour in respective operating strategies

Heinemann, Detlev

234

Evaluating Texas State University Energy Consumption According to Productivity  

E-Print Network [OSTI]

The Energy Utilization Index, energy consumption per square foot of floor area, is the most commonly used index of building energy consumption. However, a building or facility exists solely to support the activities of its occupants. Floor area...

Carnes, D.; Hunn, B. D.; Jones, J. W.

1998-01-01T23:59:59.000Z

235

Manufacturing Energy Consumption Survey (MECS) - Data - U.S. Energy  

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

1 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive 1 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive Data Methodology & Forms + EXPAND ALL Consumption of Energy for All Purposes (First Use) Total Primary Consumption of Energy for All Purposes by Census Region, Industry Group, and Selected Industries, 1991: Part 1 (Estimates in Btu or Physical Units) XLS Total Primary Consumption of Energy for All Purposes by Census Region, Industry Group, and Selected Industries, 1991: Part 2 (Estimates in Trillion Btu) XLS Total Consumption of LPG, Distillate Fuel Oil, and Residual Fuel Oil for Selected Purposes by Census Region, Industry Group, and Selected Industries, 1991 (Estimates in Barrels per Day) XLS Total Primary Consumption of Energy for All Purposes by Census Region and Economic Characteristics of the Establishment, 1991 (Estimates in Btu or Physical Units) XLS

236

Energy Consumption Characteriation of Heterogeneous Servers School of Computer Science  

E-Print Network [OSTI]

Energy Consumption Characteriation of Heterogeneous Servers Xiao Zhang School of Computer Science Machine between servers to save energy. An accurate energy consumption model is the basic of energy management. Most past studies show that energy consumption has linear relation with resource utilization. We

Qin, Xiao

237

Energy Information Administration/Household Vehicles Energy Consumption 1994  

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

, , Energy Information Administration/Household Vehicles Energy Consumption 1994 ix Household Vehicles Energy Consumption 1994 presents statistics about energy-related characteristics of highway vehicles available for personal use by members of U.S. households. The data were collected in the 1994 Residential Transportation Energy Consumption Survey, the final cycle in a series of nationwide energy consumption surveys conducted during the 1980's and 1990's by the Energy Information Administrations. Engines Became More Powerful . . . Percent Distribution of Total Residential Vehicle Fleet by Number of Cylinders, 1988 and 1994 Percent Distribution of Vehicle Fleet by Engine Size, 1988 and 1994 Percent Percent 4 cyl Less than 2.50 liters 6 cyl 2.50- 4.49 liters 8 cyl 4.50 liters or greater 20 20 40 40 Vehicle

238

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

239

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

Table C13. Total Electricity Consumption and Expenditures for Non-Mall Buildings, 2003 All Buildings* Using Electricity Electricity Consumption Electricity Expenditures Number of Buildings (thousand) Floorspace (million square feet) Floorspace per Building (thousand square feet) Primary Site Total (million dollars) Total (trillion Btu) Total (trillion Btu) Total (billion kWh) All Buildings* ............................... 4,404 63,307 14.4 9,168 3,037 890 69,032 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 2,384 6,346 2.7 1,164 386 113 10,348 5,001 to 10,000 .............................. 834 6,197 7.4 790 262 77 7,296 10,001 to 25,000 ............................ 727 11,370 15.6 1,229 407 119 10,001

240

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

4A. Fuel Oil Consumption and Expenditure Intensities for All Buildings, 2003 4A. Fuel Oil Consumption and Expenditure Intensities for All Buildings, 2003 Fuel Oil Consumption Fuel Oil Expenditures per Building (gallons) per Square Foot (gallons) per Building (thousand dollars) per Square Foot (dollars) per Gallon (dollars) All Buildings ................................ 3,533 0.10 3.9 0.11 1.11 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 1,177 0.41 1.4 0.48 1.18 5,001 to 10,000 .............................. 2,573 0.36 3.0 0.42 1.17 10,001 to 25,000 ............................ 3,045 0.19 3.6 0.23 1.18 25,001 to 50,000 ............................ 5,184 0.14 5.6 0.15 1.09 50,001 to 100,000 .......................... 8,508 0.11 9.3 0.12 1.10 100,001 to 200,000 ........................ 12,639 0.09 13.1 0.09 1.03

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

4A. Electricity Consumption and Expenditure Intensities for All Buildings, 2003 4A. Electricity Consumption and Expenditure Intensities for All Buildings, 2003 Electricity Consumption Electricity Expenditures per Building (thousand kWh) per Square Foot (kWh) Distribution of Building-Level Intensities (kWh/square foot) 25th Per- centile Median 75th Per- centile per Building (thousand dollars) per Square Foot (dollars) per kWh (dollars) All Buildings ................................ 226 14.9 3.8 8.8 18.1 17.9 1.18 0.079 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 48 17.8 3.8 9.0 20.0 4.4 1.63 0.092 5,001 to 10,000 .............................. 96 12.9 4.0 8.2 15.5 9.2 1.23 0.096 10,001 to 25,000 ............................ 178 11.4 3.1 7.2 15.0 15.2 0.97 0.086

242

Analysis of water heater standby energy consumption from ELCAP homes  

Science Journals Connector (OSTI)

The Bonneville Power Administration (Bonneville) routinely prepares forecasts of future energy demands in the Pacific Northwest region of the United States. Bonneville also implements conservation programs to reduce load demands. Results from the End-Use Load and Consumer Assessment Program (ELCAP), undertaken by the Pacific Northwest Laboratory for Bonneville, indicated that single-family homes with electric space-heating equipment consume more than 4700 kWh/yr to heat water for domestic uses. This energy use amounts to about 23% of the total electricity consumed. Additionally, the peak consumption for water heating coincides with regional system peak demands. Detailed analyses of the water heating end-use data acquired for residential buildings in ELCAP reveal that the average standby load for existing homes is 1200 kWh/yr, while homes built as part of the Residential Standards Demonstration Program averaged 1100 kWh/yr. These figures are consistent with the current figure of 1300 kWh/yr that is being used in the regional energy forecast. We also determined that standby loads for some of the participants were behaviorally driven. The data indicated the occurrence of vacancy setbacks in which the participant appears to lower the thermostat to save energy while the house is vacant. Anecdotal evidence from interviews revealed that this does occur. Reasons for setting back the thermostat ranged from not thinking about using the breaker, to fear that the tank would freeze in cold weather. These types of activities also appear to create the occurrence of dueling thermostats where the upper and lower thermostats, after the vacancy period, are not returned to the same temperature. This leads to additional energy use in an attempt to maintain a uniform temperature in the tank.

R.G. Pratt; B.A. Ross; W.F. Sandusky

1993-01-01T23:59:59.000Z

243

Data Center Energy Consumption Trends | Department of Energy  

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

Program Areas » Data Center Energy Efficiency » Data Center Program Areas » Data Center Energy Efficiency » Data Center Energy Consumption Trends Data Center Energy Consumption Trends October 8, 2013 - 10:09am Addthis Data centers can consume up to 100 times more energy than a standard office building. Often, less than 15% of original source energy is used for the information technology equipment within a data center. Figure 1 outlines typical data center energy consumption ratios. An illustration that features a graphic of a coal container representing 100 units of coal. This enters a graphic of a power plant, where those 100 units of coal are turned into 35 units of energy. The 35 units of energy are distributed by power lines, represented by a graphic of power lines, where 33 units are delivered to a pie chart representing data typical data center energy end use. The data center pie chart features 48% representing server load and computing operation consumption; 43% representing cooling equipment consumption; and 9% representing power conversion and distribution consumption.

244

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks  

E-Print Network [OSTI]

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Stefan Achleitner, Tao Liu an advantage for output power prediction. Solar Energy Prediction System Our prediction model is based variability of more then 100 kW per minute. For practical usage of solar energy, predicting times of high

Cerpa, Alberto E.

245

Leveraging Weather Forecasts in Renewable Energy Navin Sharmaa,  

E-Print Network [OSTI]

Leveraging Weather Forecasts in Renewable Energy Systems Navin Sharmaa, , Jeremy Gummesonb , David, Binghamton, NY 13902 Abstract Systems that harvest environmental energy must carefully regulate their us- age to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic, since

Shenoy, Prashant

246

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems  

E-Print Network [OSTI]

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems Navin Sharma,gummeson,irwin,shenoy}@cs.umass.edu Abstract--To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands

Shenoy, Prashant

247

" Column: Energy-Consumption Ratios;"  

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

3 Consumption Ratios of Fuel, 2006;" 3 Consumption Ratios of Fuel, 2006;" " Level: National Data; " " Row: Values of Shipments within NAICS Codes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." ,,,,"Consumption" ,,,"Consumption","per Dollar" ,,"Consumption","per Dollar","of Value" "NAICS",,"per Employee","of Value Added","of Shipments" "Code(a)","Economic Characteristic(b)","(million Btu)","(thousand Btu)","(thousand Btu)" ,,"Total United States" " 311 - 339","ALL MANUFACTURING INDUSTRIES"

248

" Column: Energy-Consumption Ratios;"  

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

3 Consumption Ratios of Fuel, 2002;" 3 Consumption Ratios of Fuel, 2002;" " Level: National Data; " " Row: Values of Shipments within NAICS Codes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." " "," ",,,"Consumption"," " " "," ",,"Consumption","per Dollar" " "," ","Consumption","per Dollar","of Value","RSE" "NAICS",,"per Employee","of Value Added","of Shipments","Row" "Code(a)","Economic Characteristic(b)","(million Btu)","(thousand Btu)","(thousand Btu)","Factors"

249

On the Energy Consumption and Performance of Systems Software  

E-Print Network [OSTI]

On the Energy Consumption and Performance of Systems Software Zhichao Li, Radu Grosu, Priya Sehgal {zhicli,grosu,psehgal,sas,stoller,ezk}@cs.stonybrook.edu ABSTRACT Models of energy consumption that can balance out performance and energy use. This paper considers the energy consumption

Stoller, Scott

250

On the Interplay of Parallelization, Program Performance, and Energy Consumption  

E-Print Network [OSTI]

to either minimize the total energy consumption or minimize the energy-delay product. The impact of staticOn the Interplay of Parallelization, Program Performance, and Energy Consumption Sangyeun Cho through parallel execution of applications, suppressing the power and energy consumption remains an even

Marchal, Loris

251

Commercial Buildings Energy Consumption Survey (CBECS) - U.S. Energy  

Gasoline and Diesel Fuel Update (EIA)

Estimation of Energy End-use Consumption Estimation of Energy End-use Consumption 2003 CBECS The energy end-use consumption tables for 2003 (Detailed Tables E1-E11 and E1A-E11A) provide estimates of the amount of electricity, natural gas, fuel oil, and district heat used for ten end uses: space heating, cooling, ventilation, water heating, lighting, cooking, refrigeration, personal computers, office equipment (including servers), and other uses. Although details vary by energy source (Table 1), there are four basic steps in the end-use estimation process: Regressions of monthly consumption on degree-days to establish reference temperatures for the engineering models, Engineering modeling by end use, Cross-sectional regressions to calibrate the engineering estimates and account for additional energy uses, and

252

Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings  

E-Print Network [OSTI]

18 Figure 6 Primary Energy Consumption by End-Use in24 Figure 7 Primary Energy Consumption by Fuel in Commercialbased on total primary energy consumption (source energy),

Fridley, David G.

2008-01-01T23:59:59.000Z

253

Renewable Energy Consumption for Electricity Generation by Energy Use  

Open Energy Info (EERE)

Electricity Generation by Energy Use Electricity Generation by Energy Use Sector and Energy Source, 2004 - 2008 Dataset Summary Description Provides annual renewable energy consumption (in quadrillion btu) for electricity generation in the United States by energy use sector (commercial, industrial and electric power) and by energy source (e.g. biomass, geothermal, etc.) This data was compiled and published by the Energy Information Administration (EIA). Source EIA Date Released August 01st, 2010 (4 years ago) Date Updated Unknown Keywords biomass Commercial Electric Power Electricity Generation geothermal Industrial PV Renewable Energy Consumption solar wind Data application/vnd.ms-excel icon 2008_RE.Consumption.for_.Elec_.Gen_EIA.Aug_.2010.xls (xls, 19.5 KiB) Quality Metrics Level of Review Some Review

254

TV Energy Consumption Trends and Energy-Efficiency Improvement Options  

E-Print Network [OSTI]

global and country-specific estimates of total energyglobal and country-specific estimates of total energytotal global electricity consumption is about 5,000 TWh 68 , the energy

Park, Won Young

2011-01-01T23:59:59.000Z

255

Energy for 500 Million Homes: Drivers and Outlook for Residential Energy Consumption in China  

E-Print Network [OSTI]

accounting for 79% of non-biomass energy consumption in2000 and 2020. Biomass, the leading energy source in thehigh reliance on biomass for rural energy consumption as

Zhou, Nan

2010-01-01T23:59:59.000Z

256

On the Energy Consumption and Performance of Systems Software  

E-Print Network [OSTI]

On the Energy Consumption and Performance of Systems Software Appears in the proceedings of the 4th,grosu,psehgal,sas,stoller,ezk}@cs.stonybrook.edu ABSTRACT Models of energy consumption and performance are necessary to understand and identify system. This paper considers the energy consumption and performance of servers running a relatively simple file

Zadok, Erez

257

Energy Consumption in Coded Queues for Wireless Information Exchange  

E-Print Network [OSTI]

Energy Consumption in Coded Queues for Wireless Information Exchange Jasper Goseling, Richard J customers. We use this relation to ob- tain bounds on the energy consumption in a wireless information, for example, from the observations in [3] that using network coding can reduce the energy consumption

Boucherie, Richard J.

258

Minimizing Energy Consumption in Body Sensor Networks via Convex Optimization  

E-Print Network [OSTI]

Minimizing Energy Consumption in Body Sensor Networks via Convex Optimization Sidharth Nabar energy consumption while limiting the latency in data transfer. In this paper, we focus on polling energy consumption and latency. We show that this problem can be posed as a geometric program, which

Poovendran, Radha

259

Energino: a Hardware and Software Solution for Energy Consumption Monitoring  

E-Print Network [OSTI]

Energino: a Hardware and Software Solution for Energy Consumption Monitoring Karina Gomez, Roberto.granelli@disi.unitn.it Abstract--Accurate measurement of energy consumption of practical wireless deployments is vital in the availability of affordable and scalable energy consumption monitoring tools for the research community

Paris-Sud XI, Université de

260

GENETIC HEURISTICS FOR REDUCING MEMORY ENERGY CONSUMPTION IN EMBEDDED SYSTEMS  

E-Print Network [OSTI]

GENETIC HEURISTICS FOR REDUCING MEMORY ENERGY CONSUMPTION IN EMBEDDED SYSTEMS Maha IDRISSI AOUAD.loria.fr/zendra Keywords: Energy consumption reduction, Genetic heuristics, memory allocation management, optimizations on heuristic methods for SPMs careful management in order to reduce memory energy consumption. We propose

Schott, René - Institut de Mathématiques �lie Cartan, Université Henri Poincaré

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Reducing the Energy Consumption of Mobile Applications Behind the Scenes  

E-Print Network [OSTI]

Reducing the Energy Consumption of Mobile Applications Behind the Scenes Young-Woo Kwon and Eli, an increasing number of perfective maintenance tasks are concerned with optimizing energy consumption. However, optimizing a mobile application to reduce its energy consumption is non-trivial due to the highly volatile

Tilevich, Eli

262

Optimization of Energy and Water Consumption in Cornbased Ethanol Plants  

E-Print Network [OSTI]

1 Optimization of Energy and Water Consumption in Corn­based Ethanol Plants Elvis Ahmetovi). First, we review the major alternatives in the optimization of energy consumption and its impact for the water streams. We show that minimizing energy consumption leads to process water networks with minimum

Grossmann, Ignacio E.

263

Automated Analysis of Performance and Energy Consumption for Cloud Applications  

E-Print Network [OSTI]

Automated Analysis of Performance and Energy Consumption for Cloud Applications Feifei Chen, John providers is thus to develop resource provisioning and management solutions at minimum energy consumption system performance and energy consumption patterns in complex cloud systems is imperative to achieve

Schneider, Jean-Guy

264

Optimizing Communication Energy Consumption in Perpetual Wireless Nanosensor Networks  

E-Print Network [OSTI]

Optimizing Communication Energy Consumption in Perpetual Wireless Nanosensor Networks Shahram}@cs.odu.edu Abstract--This paper investigates the effect of various param- eters of energy consumption. Finding the optimum combination of parameters to minimize energy consumption while satisfying the Qo

Weigle, Michele

265

The Impact of Distributed Programming Abstractions on Application Energy Consumption  

E-Print Network [OSTI]

The Impact of Distributed Programming Abstractions on Application Energy Consumption Young-Woo Kwon of their energy consumption patterns. By varying the abstractions with the rest of the functionality fixed, we measure and analyze the impact of distributed programming abstractions on application energy consumption

Tilevich, Eli

266

INCREASED FOOD AND ENERGY CONSUMPTION OF LACTATING NORTHERN FUR SEALS,  

E-Print Network [OSTI]

respectively. Fish accounted for 66.4% of food biomass (69.4% of total energy consumption); squidINCREASED FOOD AND ENERGY CONSUMPTION OF LACTATING NORTHERN FUR SEALS, CALWRHINUS URSINUS MICHAEL A on ter- restrial mammals have specifically shown increased energy consumption by lactating females

267

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

268

International Energy Outlook 2000 - World Energy Consumption  

Gasoline and Diesel Fuel Update (EIA)

The IEO2000 projections indicate continued growth in world energy use, including large increases for the developing economies of Asia and South America. Energy resources are thought to be adequate to support the growth expected through 2020. The IEO2000 projections indicate continued growth in world energy use, including large increases for the developing economies of Asia and South America. Energy resources are thought to be adequate to support the growth expected through 2020. Current Trends Influencing World Energy Demand Changing world events and their effects on world energy markets shape the long-term view of trends in energy demand. Several developments in 1999—shifting short-term world oil markets, the recovery of developing Asian markets, and a faster than expected recovery in the economies of the former Soviet Union— are reflected in the projections presented in this year’s International Energy Outlook 2000 (IEO2000). In 1998, oil prices reached 20-year lows as a result of oil surpluses

269

Modeling and optimization of HVAC energy consumption  

Science Journals Connector (OSTI)

A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%.

Andrew Kusiak; Mingyang Li; Fan Tang

2010-01-01T23:59:59.000Z

270

Commercial Buildings Energy Consumption and Expenditures 1992...  

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

1992 Consumption and Expenditures 1992 Consumption & Expenditures Overview Full Report Tables National estimates of electricity, natural gas, fuel oil, and district heat...

271

International Energy Outlook 1999 - World Energy Consumption  

Gasoline and Diesel Fuel Update (EIA)

world.gif (5615 bytes) world.gif (5615 bytes) The IEO99 projections indicate substantial growth in world energy use,including substantial increases for the developing economies of Asia and South America. Resource availability is not expected to limit the growth of energy markets. In 1998, expectations for economic growth and energy market performance in many areas of the world were dashed. The Asian economic crisis proved to be deeper and more persistent than originally anticipated, and the threat and reality of spillover effects grew through the year. Oil prices crashed. RussiaÂ’s economy collapsed. Economic and social problems intensified in energy- exporting countries and in emerging economies of Asia and South America. Deepening recession in Japan made recovery more difficult in Asia

272

The Impact of Residential Density on Vehicle Usage and Energy Consumption  

E-Print Network [OSTI]

Residential Density on Vehicle Usage and Energy ConsumptionResidential Density on Vehicle Usage and Energy ConsumptionResidential Density on Vehicle Usage and Energy Consumption

Golob, Thomas F; Brownstone, David

2005-01-01T23:59:59.000Z

273

Cost and Energy Consumption Optimization of Product Manufacture in a Flexible Manufacturing System  

E-Print Network [OSTI]

Selection for Energy Consumption Reduction in Machining,Dornfeld, D. (2011): Energy Consumption Characterization and2011): Unit Process Energy Consumption Models for Material

Diaz, Nancy; Dornfeld, David

2012-01-01T23:59:59.000Z

274

The Impact of Residential Density on Vehicle Usage and Energy Consumption  

E-Print Network [OSTI]

Vehicle Usage and Energy Consumption Table 2 Housing Unitsresidential vehicular energy consumption is graphed as aon Vehicle Usage and Energy Consumption with vehicles, but

Golob, Thomas F.; Brownstone, David

2005-01-01T23:59:59.000Z

275

Energy Consumption, Efficiency, Conservation, and Greenhouse Gas Mitigation in Japan's Building Sector  

E-Print Network [OSTI]

comparison o f energy consumption i n housing (1998) (Trends i n household energy consumption (Jyukankyo Research4) Average (N=2976) Energy consumption [GJ / household-year

2006-01-01T23:59:59.000Z

276

Energy Consumption Scheduling in Smart Grid: A Non-Cooperative Game Approach  

E-Print Network [OSTI]

on Game- Theoretic Energy Consumption Scheduling for theIn this paper, energy consumption scheduling based on non-Energy Consumption Scheduling in Smart Grid: A Non-

Ma, Kai; Hu, Guoqiang; Spanos, Costas J

2014-01-01T23:59:59.000Z

277

Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings  

E-Print Network [OSTI]

Estimating Total Energy Consumption and Emissions of China’sof China’s total energy consumption mix. However, accuratelyof China’s total energy consumption, while others estimate

Fridley, David G.

2008-01-01T23:59:59.000Z

278

ResPoNSe: modeling the wide variability of residential energy consumption.  

E-Print Network [OSTI]

affect appliance energy consumption. For example, differentStates, 2005 Residential Energy Consumption Survey: HousingModeling of End-Use Energy Consumption in the Residential

Peffer, Therese; Burke, William; Auslander, David

2010-01-01T23:59:59.000Z

279

One of These Homes is Not Like the Other: Residential Energy Consumption Variability  

E-Print Network [OSTI]

the total annual energy consumption. The behavior patternsin total residential energy consumption per home, even whenthe variability in energy consumption can vary by factors of

Kelsven, Phillip

2013-01-01T23:59:59.000Z

280

Window-Related Energy Consumption in the US Residential and Commercial Building Stock  

E-Print Network [OSTI]

2001). "Residential Energy Consumption Survey." 2006, fromCommercial Building Energy Consumption Survey." from http://Scale window-related energy consumption to account for new

Apte, Joshua; Arasteh, Dariush

2008-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Current Status and Future Scenarios of Residential Building Energy Consumption in China  

E-Print Network [OSTI]

The China Residential Energy Consumption Survey, Human andof Residential Building Energy Consumption in China Nan ZhouResidential Building Energy Consumption in China Nan Zhou*,

Zhou, Nan

2010-01-01T23:59:59.000Z

282

Current Status and Future Scenarios of Residential Building Energy Consumption in China  

E-Print Network [OSTI]

accounting for 79% of non-biomass energy consumption inreliance on biomass for rural energy consumption shows thereliance on biomass for rural energy consumption shows the

Zhou, Nan

2010-01-01T23:59:59.000Z

283

Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings  

E-Print Network [OSTI]

ABORATORY Estimating Total Energy Consumption and Emissionscomponent of China’s total energy consumption mix. However,about 19% of China’s total energy consumption, while others

Fridley, David G.

2008-01-01T23:59:59.000Z

284

Total and Peak Energy Consumption Minimization of Building HVAC Systems Using Model Predictive Control  

E-Print Network [OSTI]

combination of the total energy consumption and the peakalso reduces the total energy consumption of the occupancyTotal and Peak Energy Consumption Minimization of Building

Maasoumy, Mehdi; Sangiovanni-Vincentelli, Alberto

2012-01-01T23:59:59.000Z

285

Window-Related Energy Consumption in the US Residential and Commercial Building Stock  

E-Print Network [OSTI]

the fraction of total energy consumption attributable toFraction of Total Energy Consumption Background Although thewindow fraction of total energy consumption. We believe that

Apte, Joshua; Arasteh, Dariush

2008-01-01T23:59:59.000Z

286

Video game console usage and national energy consumption: Results from a field-metering study  

E-Print Network [OSTI]

about half of the total energy consumption from Wii consolescan estimate total national energy consumption due to videoof on mode energy consumption to the total AEC. For most

Desroches, Louis-Benoit

2013-01-01T23:59:59.000Z

287

Current Status and Future Scenarios of Residential Building Energy Consumption in China  

E-Print Network [OSTI]

liters Figure 7 Primary Energy Consumption (EJ) Refrigeratorby Efficiency Class Primary Energy Consumption (EJ) Figure 8by Fuel Figure 1 Primary Energy Consumption by End-use)

Zhou, Nan

2010-01-01T23:59:59.000Z

288

Manufacturing Energy Consumption Survey (MECS) - Residential - U.S. Energy  

Gasoline and Diesel Fuel Update (EIA)

About the MECS About the MECS Survey forms Maps MECS Terminology Archives Features First 2010 Data Press Release 2010 Data Brief Other End Use Surveys Commercial Buildings - CBECS Residential - RECS Transportation DOE Uses MECS Data Manufacturing Energy and Carbon Footprints Associated Analysis Early-release estimates from the 2010 MECS show that energy consumption in the manufacturing sector decreased between 2006 and 2010 MECS 2006-2010 - Release date: March 28, 2012 Energy consumption in the U.S. manufacturing sector fell from 21,098 trillion Btu (tBtu) in 2006 to 19,062 tBtu in 2010, a decline of almost 10 percent, based on preliminary estimates released from the 2010 Manufacturing Energy Consumption Survey (MECS). This decline continues the downward trend in manufacturing energy use since the 1998 MECS report.

289

Table 24. Refining Industry Energy Consumption  

Gasoline and Diesel Fuel Update (EIA)

- Corrections to Tables 24 to 32 - Corrections to Tables 24 to 32 Table 24. Refining Industry Energy Consumption 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2002- 2025 Carbon Dioxide Emissions 4/ (million metric tons) 190.4 185.7 188.0 191.3 207.3 215.6 220.0 222.8 225.1 226.3 228.0 230.7 234.1 237.5 238.5 239.4 239.4 238.6 240.6 240.5 242.2 244.2 245.9 246.3 246.6 1.2% Table 25. Food Industry Energy Consumption 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2002- 2025 Carbon Dioxide Emissions 3/ (million metric tons) 87.8 89.4 87.5 87.8 89.2 90.2 90.9 91.4 92.2 93.5 94.5 95.7 96.7 97.7 98.6 99.6 100.8 101.9 102.9 104.1 105.4 107.0 108.7 110.3 112.1 1.0% Table 26. Paper Industry Energy Consumption 2001 2002 2003 2004 2005 2006 2007

290

Power to the Plug: An Introduction to Energy, Electricity, Consumption...  

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

Owner: The NEED Project Power to the Plug: An Introduction to Energy, Electricity, Consumption, and Efficiency ENERGY EDUCATION AND WORKFORCE DEVELOPMENT This educational...

291

Fossil Fuel-Generated Energy Consumption Reduction for New Federal...  

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

Fossil Fuel-Generated Energy Consumption Reduction for New Federal Buildings and Major Renovations of Federal Buildings OIRA Comparison Document Fossil Fuel-Generated Energy...

292

Commercial Buildings Energy Consumption Survey 2003 - Detailed Tables  

Reports and Publications (EIA)

The tables contain information about energy consumption and expenditures in U.S. commercial buildings and information about energy-related characteristics of these buildings.

2008-01-01T23:59:59.000Z

293

Today in Energy - commercial consumption & efficiency  

Reports and Publications (EIA)

Short, timely articles with graphs about recent commercial consumption and efficiency issues and trends.

2028-01-01T23:59:59.000Z

294

Continuous Improvement Energy Projects Reduce Energy Consumption  

E-Print Network [OSTI]

) located in Conroe, Texas. The facility manufactures a specialty chemical product, Soltex® Additive, which is used in drilling mud. The plant is ISO 9001 certified and is one of CPChem’s smallest production facilities, representing less than 1... evaluated, with viable ones prioritized, developed, and implemented. • The successes of the Drilling Specialties plant will be shared with other Chevron Phillips facilities within the context of the company’s Energy Best Practice Team. ESL-IE-14...

Niemeyer, E.

2014-01-01T23:59:59.000Z

295

User-needs study for the 1993 residential energy consumption survey  

SciTech Connect (OSTI)

During 1992, the Energy Information Administration (EIA) conducted a user-needs study for the 1993 Residential Energy Consumption Survey (RECS). Every 3 years, the RECS collects information on energy consumption and expenditures for various classes of households and residential buildings. The RECS is the only source of such information within EIA, and one of only a few sources of such information anywhere. EIA sent letters to more than 750 persons, received responses from 56, and held 15 meetings with users. Written responses were also solicited by notices published in the April 14, 1992 Federal Register and in several energy-related publications. To ensure that the 1993 RECS meets current information needs, EIA made a specific effort to get input from policy makers and persons needing data for forecasting efforts. These particular needs relate mainly to development of the National Energy Modeling System and new energy legislation being considered at the time of the user needs survey.

Not Available

1993-09-24T23:59:59.000Z

296

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

. Total Energy Expenditures by Major Fuel for Non-Mall Buildings, 2003 . Total Energy Expenditures by Major Fuel for Non-Mall Buildings, 2003 All Buildings* Total Energy Expenditures (million dollars) Number of Buildings (thousand) Floorspace (million square feet) Sum of Major Fuels Electricity Natural Gas Fuel Oil District Heat All Buildings* ............................... 4,645 64,783 92,577 69,032 14,525 1,776 7,245 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 2,552 6,789 12,812 10,348 2,155 292 Q 5,001 to 10,000 .............................. 889 6,585 9,398 7,296 1,689 307 Q 10,001 to 25,000 ............................ 738 11,535 13,140 10,001 2,524 232 Q 25,001 to 50,000 ............................ 241 8,668 10,392 7,871 1,865 127 Q 50,001 to 100,000 .......................... 129 9,057 11,897 8,717 1,868 203 Q

297

Energy Information Administration - Commercial Energy Consumption Survey-  

Gasoline and Diesel Fuel Update (EIA)

C2A. Total Energy Expenditures by Major Fuel for All Buildings, 2003 C2A. Total Energy Expenditures by Major Fuel for All Buildings, 2003 All Buildings Total Energy Expenditures (million dollars) Number of Buildings (thousand) Floorspace (million square feet) Sum of Major Fuels Electricity Natural Gas Fuel Oil District Heat All Buildings ................................ 4,859 71,658 107,897 82,783 16,010 1,826 7,279 Building Floorspace (Square Feet) 1,001 to 5,000 ................................ 2,586 6,922 13,083 10,547 2,227 292 Q 5,001 to 10,000 .............................. 948 7,033 10,443 8,199 1,830 307 Q 10,001 to 25,000 ............................ 810 12,659 15,689 12,172 2,897 238 Q 25,001 to 50,000 ............................ 261 9,382 11,898 9,179 2,054 134 Q 50,001 to 100,000 .......................... 147 10,291 15,171 11,694 2,140 229 Q

298

Commercial Buildings Energy Consumption and Expenditures 1992 - Executive  

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

& Expenditures > Executive Summary & Expenditures > Executive Summary 1992 Consumption & Expenditures Executive Summary Commercial Buildings Energy Consumption and Expenditures 1992 presents statistics about the amount of energy consumed in commercial buildings and the corresponding expenditures for that energy. These data are based on the 1992 Commercial Buildings Energy Consumption Survey (CBECS), a national energy survey of buildings in the commercial sector, conducted by the Energy Information Administration (EIA) of the U.S. Department of Energy. Figure ES1. Energy Consumption is Commercial Buidings by Energy Source, 1992 Energy Consumption: In 1992, the 4.8 million commercial buildings in the United States consumed 5.5 quadrillion Btu of electricity, natural gas, fuel oil, and district heat. Of those 5.5 quadrillion Btu, consumption of site electricity accounted for 2.6 quadrillion Btu, or 48.0 percent, and consumption of natural gas accounted for 2.2 quadrillion Btu, or 39.6 percent. Fuel oil consumption made up 0.3 quadrillion Btu, or 4.0 percent of the total, while consumption of district heat made up 0.4 quadrillion Btu, or 7.9 percent of energy consumption in that sector. When the energy losses that occur at the electricity generating plants are included, the overall energy consumed by commercial buildings increases to about 10.8 quadrillion Btu (Figure ES1).

299

A macroeconomic analysis of electricity consumption in Tunisia: energy policy implications  

Science Journals Connector (OSTI)

Electricity demand forecasting is becoming an essential instrument for energy management policy in a liberalised electricity market. To address the needs, an electricity consumption forecasting model based on macroeconomic factors for Tunisia during the period of 1971 to 2008 has been investigated. A cointegration and error correction model incorporated with causality analysis present an appropriate framework for studying the aggregate electricity demand. It is found that the there are long run relationships between electricity consumption, real income, population and the consumer price index. For causality results, it was found there are unidirectional relationships from electricity consumption to real income, from electricity consumption to consumer price index and from population to real income. The results from our study might be useful for the government in forming appropriate energy policies. Indeed, the policymaker would visibly pose problems for electricity security by increasing investment in the electricity supply sector in order to cope with the increasing demand and undertaking more research to sustain their social, economic and environmental needs by implementing an energy efficiency measures.

NĂ©jib ChouaĂŻbi; Tahar Abdessalem

2011-01-01T23:59:59.000Z

300

1999 Commercial Buildings Energy Consumption Survey Detailed Tables  

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

Consumption and Expenditures Tables Table C1. Total Energy Consumption by Major Fuel ............................................... 124 Table C2. Total Energy Expenditures by Major Fuel................................................ 130 Table C3. Consumption for Sum of Major Fuels ...................................................... 135 Table C4. Expenditures for Sum of Major Fuels....................................................... 140 Table C5. Consumption and Gross Energy Intensity by Census Region for Sum of Major Fuels................................................................................................... 145 Table C6. Expenditures by Census Region for Sum of Major Fuels......................... 150 Table C7. Consumption and Gross Energy Intensity by Building Size for Sum of

Note: This page contains sample records for the topic "forecasts energy consumption" 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

(1) Who owns energy consumption data  

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

Elster July 12, 2010 Reply to DOE Request for Information of May 11, 2010 Elster July 12, 2010 Reply to DOE Request for Information of May 11, 2010 regarding Data Privacy The DOE questions are restated followed by an answer. Please note that this matter is also related to the May 11, 2010 RFI on needs for utility communications. If data is provided to third parties there is a data processing and communications cost that depends on how many parties data is provided to and by how often data is communicated. These costs are minimized if an in-home display and/or smart thermostat are provided data directly from a smart meter. (1) Q. Who owns energy consumption data? A. Typically by state law the consumer owns the data. (2) Q. Who should be entitled to privacy protections relating to energy information? A. The consumer.

302

China's Top-1000 Energy-Consuming Enterprises Program: Reducing Energy Consumption of the 1000 Largest Industrial Enterprises in China  

E-Print Network [OSTI]

Monitoring of Direct Energy Consumption in Long-Term2007. “Constraining Energy Consumption of China’s LargestProgram: Reducing Energy Consumption of the 1000 Largest

Price, Lynn

2008-01-01T23:59:59.000Z

303

Visualization of United States Energy Consumption | Open Energy Information  

Open Energy Info (EERE)

Visualization of United States Energy Consumption Visualization of United States Energy Consumption Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Visualization of United States Energy Consumption Agency/Company /Organization: Energy Information Administration Sector: Energy Resource Type: Software/modeling tools User Interface: Website Website: en.openei.org/wiki/Visualization_of_United_States_Energy_Consumption Country: United States Cost: Free OpenEI Keyword(s): Community Generated UN Region: Northern America Coordinates: 37.09024°, -95.712891° 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":37.09024,"lon":-95.712891,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

304

Research on Building Energy Consumption Situation in Shanghai  

E-Print Network [OSTI]

for building energy-saving. REFERENCES [1] Weiding Long. A consider on strategy of building energy-saving in China. HV&AC, 2005, (35):1-8.(In Chinese) [2] Energy Information Administration, Commercial Buildings Energy Consumption Survey. http: //www... for building energy-saving. REFERENCES [1] Weiding Long. A consider on strategy of building energy-saving in China. HV&AC, 2005, (35):1-8.(In Chinese) [2] Energy Information Administration, Commercial Buildings Energy Consumption Survey. http: //www...

Yang, X.; Tan, H.

2006-01-01T23:59:59.000Z

305

The Reality and Future Scenarios of Commercial Building Energy Consumption in China  

E-Print Network [OSTI]

the total primary energy consumption in 2000. Furthermore,The Commercial Primary Energy Consumption by Sector GDP

Zhou, Nan

2008-01-01T23:59:59.000Z

306

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Energy-Related Carbon Dioxide Emissions Energy-Related Carbon Dioxide Emissions In the coming decades, responses to environmental issues could affect patterns of energy use around the world. Actions to limit greenhouse gas emissions could alter the level and composition of energy-related carbon dioxide emissions by energy source. Figure 67. World Carbon Dioxide Emissions by Region, 2002-2025 (Gigawatts). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Carbon dioxide is one of the most prevalent greenhouse gases in the atmosphere. Anthropogenic (human-caused) emissions of carbon dioxide result primarily from the combustion of fossil fuels for energy, and as a result world energy use has emerged at the center of the climate change debate. In the International Energy Outlook 2005 (IEO2005) reference case, world

307

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

G: Key Assumptions for the IEO2005 Kyoto Protocol Case G: Key Assumptions for the IEO2005 Kyoto Protocol Case Energy-Related Emissions of Greenhouse Gases The System for the Analysis of Global energy Markets (SAGE)—the model used by the Energy Information Administration (EIA) to prepare the International Energy Outlook 2005 (IEO2005) mid-term projections—does not include non-energy-related emissions of greenhouse gases, which are estimated at about 15 to 20 percent of total greenhouse gas emissions, based on inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC). SAGE models global energy supply and demand and, therefore, does not address agricultural and other non-energy-related emissions. EIA implicitly assumes that percentage reductions of non-energy-related emissions and their associated abatement costs will be similar to those for energy-related emissions. Non-energy-related greenhouse gas emissions are likely to grow faster than energy-related emissions; however, the marginal abatement costs for non-energy-related greenhouse gas emissions are not known and cannot be estimated reliably. In SAGE, each region’s emissions reduction goal under the Kyoto Protocol is based only on the corresponding estimate of that region’s energy-related carbon dioxide emissions, as determined by EIA data. It is assumed that the required reductions will also be proportionately less than if all gases were included.

308

Household Vehicles Energy Consumption 1994 - Appendix C  

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

Introduction This appendix discusses several issues relating to the quality of the Residential Transportation Energy Consumption Survey (RTECS) data and to the interpretation of conclusions based on these data. The first section discusses undercoverage of the vehicle stock in the residential sector. The second section discusses the effects of using July 1994 as a time reference for the survey. The remainder of this appendix discusses the treatment of sampling and nonsampling errors in the RTECS, the quality of specific data items such as the Vehicle Identification Number (VIN) and fuel prices, and poststratification procedures used in the 1994 RTECS. The quality of the data collection and the processing of the data affects the accuracy of estimates based on survey data. All the statistics

309

Canada's Fuel Consumption Guide | Open Energy Information  

Open Energy Info (EERE)

Canada's Fuel Consumption Guide Canada's Fuel Consumption Guide Jump to: navigation, search Tool Summary Name: Canada's Fuel Consumption Guide Agency/Company /Organization: Natural Resources Canada Focus Area: Fuels & Efficiency Topics: Analysis Tools Website: oee.nrcan.gc.ca/transportation/tools/fuel-consumption-guide/fuel-consu Natural Resources Canada has compiled fuel consumption ratings for passenger cars and light-duty pickup trucks, vans, and special purpose vehicles sold in Canada. The website links to the Fuel Consumption Guide and allows users to search for vehicles from current and past model years. It also provides information about vehicle maintenance and other practices to reduce fuel consumption. How to Use This Tool This tool is most helpful when using these strategies:

310

The relationship between economic growth and biomass energy consumption  

Science Journals Connector (OSTI)

This paper investigates the relationship analysis between biomass energy consumption and economic growth by using Autoregressive Distributed Lag (ARDL) bounds testing approach of cointegration and vector error-correction models. The cointegration test results show that there is cointegration between the biomasss energy consumption and the economic growth in five of the seven countries (Bolivia Brazil Chile Colombia and Guatemala) and there is no cointegration between the biomasss energy consumption and the economic growth in two of the seven countries (Argentina and Jamaica).

Melike E. Bildirici

2012-01-01T23:59:59.000Z

311

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

Science Journals Connector (OSTI)

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

Giorgos Tigas; Panagiotis Lefakis; Konstantinos Ioannou; Athanasios Hasekioglou

2013-01-01T23:59:59.000Z

312

Solar Adoption and Energy Consumption in the Residential Sector.  

E-Print Network [OSTI]

??This dissertation analyzes the energy consumption behavior of residential adopters of solar photovoltaic systems (solar-PV). Based on large data sets from the San Diego region… (more)

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

313

On Minimizing the Energy Consumption of an Electrical Vehicle  

E-Print Network [OSTI]

Apr 20, 2011 ... The problem that we focus on, is the minimization of the energy consumption of an electrical vehicle achievable on a given driving cycle.

Abdelkader Merakeb

2011-04-20T23:59:59.000Z

314

2003 Commercial Buildings Energy Consumption - What is an RSE  

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

Home > Households, Buildings & Industry > Commercial Buildings Energy Consumption Survey (CBECS) > 2003 Detailed Tables > What is an RSE? What is an RSE? The estimates in the...

315

Long-term energy consumptions of urban transportation: A prospective...  

Open Energy Info (EERE)

Bangalore can significantly curb the trajectories of energy consumption and the ensuing carbon dioxide emissions, if and only if they are implemented in the framework of...

316

Reducing Network Energy Consumption via Sleeping and Rate-Adaptation  

E-Print Network [OSTI]

Reducing Network Energy Consumption via Sleeping and Rate-Adaptation Sergiu Nedevschi Lucian Popa of two forms of power management schemes that reduce the energy consumption of networks. The first the energy consumed when actively processing packets. For real-world traffic workloads and topologies and us

California at Irvine, University of

317

Statistical Mechanics of Money, Income, Debt, and Energy Consumption  

E-Print Network [OSTI]

Statistical Mechanics of Money, Income, Debt, and Energy Consumption Physics Colloquium Presented in financial markets. Globally, data analysis of energy consumption per capita around the world shows@american.edu Similarly to the probability distribution of energy in physics, the probability distribution of money among

Hill, Wendell T.

318

Bounds on the Energy Consumption of Computational Andrew Gearhart  

E-Print Network [OSTI]

Bounds on the Energy Consumption of Computational Kernels Andrew Gearhart Electrical Engineering Fall 2014 #12;Bounds on the Energy Consumption of Computational Kernels Copyright 2014 by Andrew Scott, little consideration was given to the potential energy efficiency of algorithms them- selves. A dominant

California at Berkeley, University of

319

Tuning Fuzzy Logic Controllers for Energy Efficiency Consumption in Buildings  

E-Print Network [OSTI]

- tems 1 Introduction In EU countries, primary energy consumption in build- ings represents about 40Tuning Fuzzy Logic Controllers for Energy Efficiency Consumption in Buildings R. Alcal´a DECSAI 18071 ­ Granada, Spain e-mail: A.Gonzalez@decsai.ugr.es Abstract In EU countries, primary energy consump

Casillas Barranquero, Jorge

320

GreenSlot: Scheduling Energy Consumption in Green Datacenters  

E-Print Network [OSTI]

GreenSlot: Scheduling Energy Consumption in Green Datacenters Íñigo Goiri UPC/BSC and Rutgers Univ grid (as a backup). GreenSlot predicts the amount of solar energy that will be available in the near future, and schedules the workload to maximize the green energy consumption while meet- ing the jobs

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


321

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect (OSTI)

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

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

2011-03-28T23:59:59.000Z

322

Weather forecast-based optimization of integrated energy systems.  

SciTech Connect (OSTI)

In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

2009-03-01T23:59:59.000Z

323

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

324

Trends in Commercial Buildings--Trends in Energy Consumption and Energy  

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

Energy Consumption and Energy Sources - Part 1 Energy Consumption and Energy Sources - Part 1 Part 2. Energy Intensity Data Tables Total Energy Consumption Consumption by Energy Source Background: Site and Primary Energy Trends in Energy Consumption and Energy Sources Part 1. Energy Consumption The CBECS collects energy consumption statistics from energy suppliers for four major energy sources—electricity, natural gas, fuel oil, and district heat—and collects information from the sampled buildings on the use of the four major sources and other energy sources (e.g., district chilled water, solar, wood). Energy consumed in commercial buildings is a significant fraction of that consumed in all end-use sectors. In 2000, about 17 percent of total energy was consumed in the commercial sector. Total Energy Consumption

325

Manufacturing Energy Consumption Survey (MECS) - Data - U.S. Energy  

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

10 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive 10 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive Data Methodology & Forms + EXPAND ALL Consumption of Energy for All Purposes (First Use) Table 1.1 By Mfg. Industry & Region (physical units) XLS PDF Table 1.2 By Mfg. Industry & Region (trillion Btu) XLS PDF Table 1.3 By Value of Shipments & Employment Size Category & Region XLS PDF Table 1.5 By Further Classification of "Other" Energy Sources XLS PDF Energy Used as a Nonfuel (Feedstock) Table 2.1 By Mfg. Industry & Region (physical units) XLS PDF Table 2.2 By Mfg. Industry & Region (trillion Btu) XLS PDF Table 2.3 By Value of Shipments & Employment Size Category XLS PDF Energy Consumption as a Fuel Table 3.1 By Mfg. Industry & Region (physical units) XLS PDF

326

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

327

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Regional Definitions in the International Energy Outlook 2005 Regional Definitions in the International Energy Outlook 2005 Regular readers of the International Energy Outlook (IEO) will notice that, in this edition, the names used to describe country groupings have been changed. Although the organization of countries within the three major groupings has not changed, the nomenclature used in previous editions to describe the groups— namely, industrialized, EE/FSU, and developing— had become somewhat dated and did not accurately reflect the countries within them. Some analysts have argued that several of the countries in the “developing” group (South Korea and China, for instance) could fairly be called “industrialized” today. IEO2005 World Regions Map. Need help, contact the National Energy Information Center at 202-586-8800.

328

Three Essays on Energy Economics and Forecasting  

E-Print Network [OSTI]

This dissertation contains three independent essays relating energy economics. The first essay investigates price asymmetry of diesel in South Korea by using the error correction model. Analyzing weekly market prices in the pass-through of crude oil...

Shin, Yoon Sung

2012-02-14T23:59:59.000Z

329

Wind Forecasting Improvement Project | Department of Energy  

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

3, 2011 - 12:12pm Addthis This is an excerpt from the Third Quarter 2011 edition of the Wind Program R&D Newsletter. In July, the Department of Energy launched a 6 million...

330

Residential Energy Consumption Survey: Housing Characteristics,  

Gasoline and Diesel Fuel Update (EIA)

tni tni Residential Energy Consumption Survey: Housing Characteristics, 1981 Energy Information Administration Washington. D.C August 1983 T86T -UJ9AO9 aiji uuojj pasenojnd uaaq (OdO) i|oii)/v\ suoijdijosqns o; Ajdde jou saop aoiiou :e|ON asBa|d 'pjBo^sod at|j noA j| 3Sj| Suiije'Lu vi3 3M1 uo ;u!Buuaj o^sn o} }i ujnja> isnoi nox 'pJBOisod iuB»jodoi! UB aABL) pjnons hoA '}s\\ BujUBUJ VI3 9L|} uo ajB noA|| 'MaiAaj jsij SUJMBUJ suouBOjiqnd |BnuuBS}j BUJ -jonpuoo Sj (vi3) uoijej^siujuupv UOIJBLUJOIUI Afijau^ agj 'uoiieinBaj iuaoiujaAOQ Aq pajmbaj sv 30HON 02-13 maoj aapao ay 05. pa^oajjp aq pus siuamnooa jo 0088-353 (303) S8SOZ "D'Q 'uoiSu-pqsBtt T rao°H 50 UOT^BOLIOJUI

331

Commercial Buildings Energy Consumption Survey (CBECS) - U.S. Energy  

Gasoline and Diesel Fuel Update (EIA)

‹ Consumption & Efficiency ‹ Consumption & Efficiency Commercial Buildings Energy Consumption Survey (CBECS) Glossary › FAQS › Overview Data 2003 1999 1995 1992 Previous Analysis & Projections Maps U. S. Census Regions and Divisions U. S. Climate Zones for 2003 CBECS U. S. Climate Zones for 1979-1999 CBECS How are U.S. Climate Zones defined? U. S. Census Regions and Divisions: U.S. Census Regions and Divisions Map U. S. Climate Zones for 2003 CBECS: U.S. Census Regions and Divisions Map U. S. Climate Zones for 1979-1999 CBECS: U.S. Census Regions and Divisions Map How are U.S. Climate Zones defined? The CBECS climate zones are groups of climate divisions, as defined by the National Oceanic and Atmospheric Administration (NOAA), which are regions within a state that are as climatically homogeneous as possible. Each NOAA

332

Energy Department Announces $2.5 Million to Improve Wind Forecasting...  

Energy Savers [EERE]

better forecasts, wind energy plant operators and industry professionals can ensure wind turbines operate closer to maximum capacity, leading to lower energy costs for consumers....

333

E-Print Network 3.0 - analytical energy forecasting Sample Search...  

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

of PV energy production using... Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer... , Detlev Heinemann Energy and Semiconductor...

334

2002 Manufacturing Energy Consumption Survey - User Needs Survey  

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

2002 Manufacturing Energy Consumption Survey: User-Needs Survey 2002 Manufacturing Energy Consumption Survey: User-Needs Survey View current results. We need your help in designing the next “ Energy Consumption Survey” (MECS)! As our valued customer, you are in an important position to tell us what kinds of data are most useful in helping you understand energy consumption in the U.S. manufacturing sector. Below is a short electronic survey with just a few questions. We will stop collecting responses for user feedback on May 17, 2002. This deadline serves to meet our intended release date of April/May 2003 for fielding MECS2002. The MECS is designed to produce estimates of energy consumption and other energy-related activities in manufacturing. The survey also collects information on energy expenditures, average prices, onsite generation of

335

Fact #792: August 12, 2013 Energy Consumption by Sector and Energy...  

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

2: August 12, 2013 Energy Consumption by Sector and Energy Source, 1982 and 2012 Fact 792: August 12, 2013 Energy Consumption by Sector and Energy Source, 1982 and 2012 In the...

336

Residential Energy Consumption Survey (RECS) - U.S. Energy Information  

Gasoline and Diesel Fuel Update (EIA)

About the RECS About the RECS RECS Survey Forms RECS Maps RECS Terminology Archived Reports State fact sheets Arizona household graph See state fact sheets › graph of U.S. electricity end use, as explained in the article text U.S. electricity sales have decreased in four of the past five years December 20, 2013 Gas furnace efficiency has large implications for residential natural gas use December 5, 2013 EIA publishes state fact sheets on residential energy consumption and characteristics August 19, 2013 All 48 related articles › Other End Use Surveys Commercial Buildings - CBECS Manufacturing - MECS Transportation About the RECS EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Specially trained interviewers collect energy characteristics on the housing unit, usage

337

Manufacturing Energy Consumption Survey (MECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

Manufacturing Energy Consumption Data Show Large Reductions in Both Manufacturing Energy Consumption Data Show Large Reductions in Both Manufacturing Energy Use and the Energy Intensity of Manufacturing Activity between 2002 and 2010 MECS 2010 - Release date: March 19, 2013 Total energy consumption in the manufacturing sector decreased by 17 percent from 2002 to 2010 (Figure 1), according to data from the U.S. Energy Information Administration's (EIA) Manufacturing Energy Consumption Survey (MECS). line chart:air conditioning in U.S. Manufacturing gross output decreased by only 3 percent over the same period. Taken together, these data indicate a significant decline in the amount of energy used per unit of gross manufacturing output. The significant decline in energy intensity reflects both improvements in energy efficiency and changes in

338

UK Energy Consumption by Sector | OpenEI  

Open Energy Info (EERE)

68 68 Varnish cache server Browse Upload data GDR 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2142278068 Varnish cache server UK Energy Consumption by Sector Dataset Summary Description The energy consumption data consists of five spreadsheets: "overall data tables" plus energy consumption data for each of the following sectors: transport, domestic, industrial and service. Each of the five spreadsheets contains a page of commentary and interpretation. In addition, a user guide is available as a supplement to the full set of spreadsheets to explain the technical concepts and vocabulary found within Energy Consumption in the UK (http://www.decc.gov.uk/assets/decc/Statistics/publications/ecuk/272-ecuk-user-guide.pdf). Energy Consumption in the United Kingdom is an annual publication currently published by the UK Department of Energy and Climate Change (DECC) for varying time periods, generally 1970 to 2009 (though some time periods are shorter).

339

Energy consumption of subscriber devices in broadband network  

Science Journals Connector (OSTI)

The paper provides estimates how deployment of fast fixed broadband may affect consumption of energy by subscriber's electronic devices. New subscribers are expected to buy additional equipment: PCs, laptops, TV sets, game consoles, etc. and more intensively ... Keywords: Broadband Access Network, Energy Consumption, Home Electronics, Next-Generation Access, Power Networks

Krzysztof Borzycki

2012-10-01T23:59:59.000Z

340

Green mining: energy consumption of advertisement blocking methods  

Science Journals Connector (OSTI)

Extending battery life on mobile devices has become an important topic recently due to the increasing frequency of smartphone adoption. A primary component of smart phone energy consumption is the apps that run on these devices. Many apps have embedded ... Keywords: Advertising, Software Energy Consumption

Kent Rasmussen; Alex Wilson; Abram Hindle

2014-06-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Exposing Datapath Elements to Reduce Microprocessor Energy Consumption  

E-Print Network [OSTI]

to Reduce Microprocessor Energy Consumption by Mark Jerome Hampton Submitted to the Department of ElectricalExposing Datapath Elements to Reduce Microprocessor Energy Consumption by Mark Jerome Hampton B Submitted to the Department of Electrical Engineering and Computer Science in partial ful llment

342

Modular Exponentiation Algorithm Analysis for Energy Consumption and Performance  

E-Print Network [OSTI]

Modular Exponentiation Algorithm Analysis for Energy Consumption and Performance Lin Zhong lzhong of their complexity, parallelism and latency. Insights are found for tradeoff between energy consumption of a tree structure. For example, Figure 1.3 shows to add 5 k-bit integers together in a tree sequence. It

Zhong, Lin

343

Manufacturing Energy Consumption Survey (MECS) - Data - U.S. Energy  

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

8 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive 8 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive Data Methodology & Forms + EXPAND ALL Consumption of Energy for All Purposes (First Use) Values SIC RSE Number of Establishments by First Use of Energy for All Purposes (Fuel and Nonfuel), 1998; Level: National Data; Row: NAICS Codes; Column: Energy Sources and Shipments; Unit: Establishment Counts XLS XLS XLS First Use of Energy for All Purposes (Fuel and Nonfuel), 1998; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources and Shipments; Unit: Trillion Btu XLS XLS XLS First Use of Energy for All Purposes (Fuel and Nonfuel), 1998; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources and Shipments; Unit: Physical Units or Btu XLS XLS

344

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book [EERE]

7 7 Range 10 4 48 Clothes Dryer 359 (2) 4 49 Water Heating Water Heater-Family of 4 40 64 (3) 26 294 Water Heater-Family of 2 40 32 (3) 12 140 Note(s): Source(s): 1) $1.139/therm. 2) Cycles/year. 3) Gallons/day. A.D. Little, EIA-Technology Forecast Updates - Residential and Commercial Building Technologies - Reference Case, Sept. 2, 1998, p. 30 for range and clothes dryer; LBNL, Energy Data Sourcebook for the U.S. Residential Sector, LBNL-40297, Sept. 1997, p. 62-67 for water heating; GAMA, Consumers' Directory of Certified Efficiency Ratings for Heating and Water Heating Equipment, Apr. 2002, for water heater capacity; and American Gas Association, Gas Facts 1998, December 1999, www.aga.org for range and clothes dryer consumption. Operating Characteristics of Natural Gas Appliances in the Residential Sector

345

EIA - Annual Energy Outlook 2008 (Early Release)-Energy-Energy Consumption  

Gasoline and Diesel Fuel Update (EIA)

Consumption Consumption Annual Energy Outlook 2008 (Early Release) Energy Consumption Total primary energy consumption in the AEO2008 reference case increases at an average rate of 0.9 percent per year, from 100.0 quadrillion Btu in 2006 to 123.8 quadrillion Btu in 2030—7.4 quadrillion Btu less than in the AEO2007 reference case. In 2030, the levels of consumption projected for liquid fuels, natural gas, and coal are all lower in the AEO2008 reference case than in the AEO2007 reference case. Among the most important factors resulting in lower total energy demand in the AEO2008 reference case are lower economic growth, higher energy prices, greater use of more efficient appliances, and slower growth in energy-intensive industries. Figure 2. Delivered energy consumption by sector, 1980-2030 (quadrillion Btu). Need help, contact the National Energy Information Center at 202-586-8800.

346

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

J: Regional Definitions J: Regional Definitions Figure J1. Map of the Six Basic Country Groupings. Need help, contact the National Energy Information Center at 202-586-8800. The six basic country groupings used in this report (Figure J1) are defined as follows: Mature Market Economies (15 percent of the 2005 world population): North America—United States, Canada, and Mexico; Western Europe—Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom; Mature Market Asia—Japan, Australia, and New Zealand. Transitional Economies (6 percent of the 2005 world population): Eastern Europe (EE)—Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Hungary, Macedonia, Poland, Romania, Serbia and Montenegro,

347

Energy Consumption Patterns of the Rural Photovoltaic Market In Spain  

Science Journals Connector (OSTI)

This paper presents an analysis of the energy consumption of photovoltaic-powered rural dwellings in a representative region of Spain. We have measured the actual consumed electrical energy in several dwelling...

A. Krenzinger; M. Montero

1987-01-01T23:59:59.000Z

348

ENERGY USE AND DOMESTIC HOT WATER CONSUMPTION Final Report  

Office of Scientific and Technical Information (OSTI)

DOMESTIC HOT WATER CONSUMPTION Final Report Phase 1 Prepared for THE N E W YORK STATE ENERGY RESEARCH AND DEVELOPMENT AUTHORITY Project Manager Norine H. Karins Prepared by ENERGY...

349

2001 Residential Energy Consumption Survey Answers to Frequently Asked Questions  

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

D (2001) -- Household Bottled Gas (LPG or Propane) Usage Form D (2001) -- Household Bottled Gas (LPG or Propane) Usage Form OMB No. 1905-0092, Expiring February 29, 2004 2001 Residential Energy Consumption Survey Answers to Frequently Asked Questions About the Household Bottled Gas (LPG or Propane) Usage Form What is the purpose of the Residential Energy Consumption Survey? The Residential Energy Consumption Survey (RECS) collects data on energy consumption and expenditures in U.S. housing units. Over 5,000 statistically selected households across the U.S. have already provided information about their household, the physical characteristics of their housing unit, their energy-using equipment, and their energy suppliers. Now we are requesting the energy billing records for these households from each of their energy suppliers. After all this information has been collected, the information will be used to

350

Energy Consumption Reduction with Low Computational Needs in Multicore Systems with Energy-Performance Tradeoff  

E-Print Network [OSTI]

Energy Consumption Reduction with Low Computational Needs in Multicore Systems with Energy rules) in order to decrease the energy consumption. We proposed in a previous paper a robust control of the energy consumption. I. INTRODUCTION An energy-performance tradeoff is required in many em- bedded

Paris-Sud XI, Université de

351

Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption  

E-Print Network [OSTI]

the functioning of a multi-energy district boiler (La Rochelle, west coast of France), adding to the plant. Substituting the prediction task of an original time series of high variability by the estimation of its, outdoor temperature, thermal power consumption, hot water distribution network, district boiler. 1

Boyer, Edmond

352

Development of Energy Consumption Database Management System of Existing Large Public Buildings  

E-Print Network [OSTI]

The statistic data of energy consumption are the base of analyzing energy consumption. The scientific management method of energy consumption data and the development of database management system plays an important role in building energy...

Li, Y.; Zhang, J.; Sun, D.

2006-01-01T23:59:59.000Z

353

Global energy consumption due to friction in passenger cars  

Science Journals Connector (OSTI)

This study presents calculations on the global fuel energy consumption used to overcome friction in passenger cars in terms of friction in the engine, transmission, tires, and brakes. Friction in tribocontacts was estimated according to prevailing contact mechanisms such as elastohydrodynamic, hydrodynamic, mixed, and boundary lubrication. Coefficients of friction in the tribocontacts were estimated based on available information in the literature on the average passenger car in use today, a car with today’s advanced commercial tribological technology, a car with today’s best advanced technology based upon recent research and development, and a car with the best technology forecasted in the next 10 years. The following conclusions were reached: • In passenger cars, one-third of the fuel energy is used to overcome friction in the engine, transmission, tires, and brakes. The direct frictional losses, with braking friction excluded, are 28% of the fuel energy. In total, 21.5% of the fuel energy is used to move the car. • Worldwide, 208,000 million liters of fuel (gasoline and diesel) was used in 2009 to overcome friction in passenger cars. This equals 360 million tonne oil equivalent per year (Mtoe/a) or 7.3 million TJ/a. Reductions in frictional losses will lead to a threefold improvement in fuel economy as it will reduce both the exhaust and cooling losses also at the same ratio. • Globally, one passenger car uses on average of 340 l of fuel per year to overcome friction, which would cost 510 euros according to the average European gas price in 2011 and corresponds to an average driving distance of 13,000 km/a. • By taking advantage of new technology for friction reduction in passenger cars, friction losses could be reduced by 18% in the short term (5–10 years) and by 61% in the long term (15–25 years). This would equal worldwide economic savings of 174,000 million euros and 576,000 million euros, respectively; fuel savings of 117,000 million and 385,000 million liters, respectively; and CO2 emission reduction of 290 million and 960 million tonnes, respectively. • The friction-related energy losses in an electric car are estimated to be only about half those of an internal combustion passenger car. Potential actions to reduce friction in passenger cars include the use of advanced coatings and surface texturing technology on engine and transmission components, new low-viscosity and low-shear lubricants and additives, and tire designs that reduce rolling friction.

Kenneth Holmberg; Peter Andersson; Ali Erdemir

2012-01-01T23:59:59.000Z

354

Industrial Biomass Energy Consumption and Electricity Net Generation by  

Open Energy Info (EERE)

47 47 Varnish cache server Browse Upload data GDR 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2142281847 Varnish cache server Industrial Biomass Energy Consumption and Electricity Net Generation by Industry and Energy Source, 2008 Dataset Summary Description Biomass energy consumption and electricity net generation in the industrial sector by industry and energy source in 2008. This data is published and compiled by the U.S. Energy Information Administration (EIA). Source EIA Date Released August 01st, 2010 (4 years ago) Date Updated August 01st, 2010 (4 years ago) Keywords 2008 biomass consumption industrial sector Data application/vnd.ms-excel icon industrial_biomass_energy_consumption_and_electricity_2008.xls (xls, 27.6 KiB)

355

Consumption & Efficiency - Analysis & Projections - U.S. Energy Information  

Gasoline and Diesel Fuel Update (EIA)

Consumption & Efficiency Consumption & Efficiency Glossary › FAQS › Overview Data Residential Energy Consumption Survey Data Commercial Energy Consumption Survey Data Manufacturing Energy Consumption Survey Data Vehicle Energy Consumption Survey Data Energy Intensity Consumption Summaries Average cost of fossil-fuels for electricity generation All Consumption & Efficiency Data Reports Analysis & Projections All Sectors Commercial Buildings Efficiency Manufacturing Projections Residential Transportation All Reports All Sectors Change category... All Sectors Commercial Buildings Efficiency Manufacturing Projections Residential Transportation All Reports Filter by: All Data Analysis Projections Today in Energy - Commercial Consumption & Efficiency Short, timely articles with graphs about recent commercial consumption and

356

China's Industrial Energy Consumption Trends and Impacts of the Top-1000 Enterprises Energy-Saving Program and the Ten Key Energy-Saving Projects  

E-Print Network [OSTI]

Choices, and Energy Consumption. Praeger Publishers,The decomposition effect of energy consumption in China'sThe challenge of reducing energy consumption of the Top-1000

Ke, Jing

2014-01-01T23:59:59.000Z

357

Residential Energy Consumption Survey (RECS) - U.S. Energy Information  

Gasoline and Diesel Fuel Update (EIA)

About the RECS About the RECS RECS Survey Forms RECS Maps RECS Terminology Archived Reports State fact sheets Arizona household graph See state fact sheets › 2009 RECS Features Heating and cooling no longer majority of U.S. home energy use March 7, 2013 Newer U.S. homes are 30% larger but consume about as much energy as older homes February 12, 2013 Where does RECS square footage data come from? July 11, 2012 RECS data show decreased energy consumption per household June 6, 2012 The impact of increasing home size on energy demand April 19, 2012 Did you know that air conditioning is in nearly 100 million U.S. homes? August 19, 2011 See more > graph of U.S. electricity end use, as explained in the article text U.S. electricity sales have decreased in four of the past five years

358

Manufacturing Energy Consumption Survey (MECS) - Data - U.S. Energy  

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

2 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive 2 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive Data Methodology & Forms all tables + EXPAND ALL Consumption of Energy for All Purposes (First Use) Values RSE Table 1.1 By Mfg. Industry & Region (physical units) XLS PDF XLS Table 1.2 By Mfg. Industry & Region (trillion Btu) XLS PDF XLS Table 1.3 By Value of Shipments & Employment Size Category & Region XLS PDF Table 1.4 Number of Establishments Using Energy Consumed for All Purpose XLSPDF Table 1.5 By Further Classification of "Other" Energy Sources XLS PDF Energy Used as a Nonfuel (Feedstock) Values RSE Table 2.1 By Mfg. Industry & Region (physical units) XLS PDF XLS Table 2.2 By Mfg. Industry & Region (trillion Btu) XLS PDF XLS Table 2.3 By Value of Shipments & Employment Size Category XLS PDF

359

EnergyBox: A Trace-driven Tool for Data Transmission Energy Consumption Studies  

E-Print Network [OSTI]

EnergyBox: A Trace-driven Tool for Data Transmission Energy Consumption Studies Ekhiotz Jon Vergara-awareness and propose EnergyBox, a tool that provides accurate and repeatable en- ergy consumption studies for 3G and WiFi transmissions at the user end. We recognize that the energy consumption of data transmission is highly

360

The effects of energy policies in China on energy consumption and GDP1  

E-Print Network [OSTI]

The effects of energy policies in China on energy consumption and GDP1 Ming-Jie Lu, C.-Y. Cynthia consumption and GDP for several industries. We not only analyze the effects of multiple types of energy impact different kinds of energy consumption and the GDP of different kinds of industries using

Lin, C.-Y. Cynthia

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


361

World Energy Consumption by Fuel Type, 1970-2020  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption by Fuel Type, 1970-2020 Energy Consumption by Fuel Type, 1970-2020 Source: EIA, International Energy Outlook 2000 Previous slide Next slide Back to first slide View graphic version Notes: Natural gas is projected to be the fastest-growing component of primary world energy consumption, more than doubling between 1997 and 2020. Gas accounts for the largest increment in electricity generation (41 percent of the total increment of energy used for electricity generation). Combined-cycle gas turbine power plants offer some of the highest commercially available plant efficiencies, and natural gas is environmentally attractive because it emits less sulfur dioxide, carbon dioxide, and particulate matter than does oil or coal. In the IEO2000 projection, world natural gas consumption reaches the level of coal by

362

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching  

E-Print Network [OSTI]

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime at a wind energy site and fits a conditional predictive model for each regime. Geographically dispersed was applied to 2-hour-ahead forecasts of hourly average wind speed near the Stateline wind energy center

Genton, Marc G.

363

Energy consumption testing of innovative refrigerator-freezers  

SciTech Connect (OSTI)

The high ambient temperature of the Canadian Standards Association (CSA) and the AHAM/DOE Refrigerator-Freezer Energy Consumption Standards is intended to compensate for the lack of door openings and other heat loads. Recently published results by Meier and Jansky (1993) indicate labeled consumption overpredicting typical field consumption by 15%. In-house field studies on conventional models showed labeled consumption overpredicting by about 22%. The Refrigerator-Freezer Technology Assessment (RFTA) test was developed to more accurately predict field consumption. This test has ambient temperature and humidity, door openings, and condensation control set at levels intended to typify Canadian household conditions. It also assesses consumption at exactly defined compartment rating temperatures. Ten conventional and energy-efficient production models were laboratory tested. The RFTA results were about 30% lower than labeled. Similarly, the four innovative refrigerator-freezer models, when field tested, also had an average of 30% lower consumption than labeled. Thus, the results of the limited testing suggest that the RFTA test may be a more accurate predictor of field use. Further testing with a larger sample is recommended. Experimental results also indicated that some innovative models could save up to 50% of the energy consumption compared with similar conventional units. The technologies that contributed to this performance included dual compressors, more efficient compressors and fan motors, off-state refrigerant control valve, fuzzy logic control, and thicker insulation. The larger savings were on limited production models, for which additional production engineering is required for full marketability.

Wong, M.T.; Howell, B.T.; Jones, W.R. [Ontario Hydro Technologies, Toronto, Ontario (Canada); Long, D.L. [Statistical Solutions, Mississauga, Ontario (Canada)

1995-12-31T23:59:59.000Z

364

Empirical analysis of energy consumption behaviour An input to an effective energy plan in Nigeria  

Science Journals Connector (OSTI)

Shortages in commercial energy (gas, fuel oil, electricity and kerosene) supplies have become increasingly marked in Nigeria, and this has been graphically illustrated by incessant power failures. Primarily such shortages have been created by unprecedented increases in energy use derived from the growth in demand for consumer goods such as electrical equipment and motor vehicles, in addition to a huge expansion of the services sector and light manufacturing following the oil boom of the 1970s. The long-run implications of such demand increases were not fully appreciated or adequately forecast because of the general feeling that Nigeria had the privileged position of being a net oil exporter and producer of gas. However, the reality of energy shortages has led to the consequences of lost output and restrictions placed upon the further expansion of the service sector. With little hope of a short-run solution to the problem, a long-term view needs to begin by acquiring a comprehensive knowledge of the energy consumption behaviour of the household sector as an input into a more wide ranging energy plan for the country. This article focuses on how such knowledge can help achieve this objective.

Obas John Ebohon

1992-01-01T23:59:59.000Z

365

BURNING BURIED SUNSHINE: HUMAN CONSUMPTION OF ANCIENT SOLAR ENERGY  

E-Print Network [OSTI]

BURNING BURIED SUNSHINE: HUMAN CONSUMPTION OF ANCIENT SOLAR ENERGY JEFFREY S. DUKES Department of as a vast store of solar energy from which society meets >80% of its current energy needs. Here, using of ancient solar energy decline, humans are likely to use an increasing share of modern solar resources. I

Dukes, Jeffrey

366

Energy Information Administration - Energy Efficiency-Table 5b. Consumption  

Gasoline and Diesel Fuel Update (EIA)

b b Page Last Modified: June 2010 Table 5b. Consumption of Energy for All Purposes (First Use) per Ton of Steel, 1998, 2002, and 2006 (Million Btu per ton) MECS Survey Years Iron and Steel Mills (NAICS1 331111) 19982 20022 20062 Total 3 17 16 13 Net Electricity 4 2 2 2 Natural Gas 5 5 4 Coal 7 6 4 Notes: 1. The North American Industry Classification System (NAICS) has replaced the Standard Industrial Classification (SIC) system. NAICS 331111 includes steel works, blast furnaces (including coke ovens), and rolling mills. 2. Denominators represent the entire steel industry, not those based mainly on electric, natural gas, residual fuel oil or coal.

367

Fact #792: August 12, 2013 Energy Consumption by Sector and Energy Source, 1982 and 2012  

Broader source: Energy.gov [DOE]

In the last 30 years, overall energy consumption has grown by about 22 quadrillion Btu. The share of energy consumption by the transportation sector has seen modest growth in that time – from about...

368

Public perceptions of energy consumption and savings  

Science Journals Connector (OSTI)

...Energy, Washington, DC). Available at http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl...Efficiency and Renewable Energy. Available at: http://apps1.eere.energy.gov/consumer/your_home/appliances/index...

Shahzeen Z. Attari; Michael L. DeKay; Cliff I. Davidson; Wändi Bruine de Bruin

2010-01-01T23:59:59.000Z

369

November 2012 Key Performance Indicator (KPI): Energy Consumption  

E-Print Network [OSTI]

and district heating scheme* data. Year Energy Consumption (KWh) Percentage Change 2005/06 65,916,243 N/A 2006 buildings are connected to the Nottingham District Heating Scheme. This service meets all the heating

Evans, Paul

370

Fishery Bulletin Index Energetics 125 Energy consumption rates 332  

E-Print Network [OSTI]

655 Fishery Bulletin Index Energetics 125 Energy consumption rates 332 Volume 103(1­4), 2005 Apodichthys flavidus 476 Coral reefs 360 Food habits 445, 626 Argentina 482 Correspondence analysis 256

371

Signatures of Heating and Cooling Energy Consumption for Typical AHUs  

E-Print Network [OSTI]

An analysis is performed to investigate the signatures of different parameters on the heating and cooling energy consumption of typical air handling units (AHUs). The results are presented in graphic format. HVAC simulation engineers can use...

Wei, G.; Liu, M.; Claridge, D. E.

1998-01-01T23:59:59.000Z

372

RECS Data Show Decreased Energy Consumption per Household  

Reports and Publications (EIA)

Total United States energy consumption in homes has remained relatively stable for many years as increased energy efficiency has offset the increase in the number and average size of housing units, according to the newly released data from the Residential Energy Consumption Survey (RECS). The average household consumed 90 million British thermal units (Btu) in 2009 based on RECS. This continues the downward trend in average residential energy consumption of the last 30 years. Despite increases in the number and the average size of homes plus increased use of electronics, improvements in efficiency for space heating, air conditioning, and major appliances have all led to decreased consumption per household. Newer homes also tend to feature better insulation and other characteristics, such as double-pane windows, that improve the building envelope.

2012-01-01T23:59:59.000Z

373

Efficiency alone as a solution to increasing energy consumption  

E-Print Network [OSTI]

A statistical analysis was performed to determine the effect of efficiency on the total US energy consumption of automobiles and refrigerators. Review of literature shows that there are many different opinions regarding ...

Haidorfer, Luke

2005-01-01T23:59:59.000Z

374

The Effects of Structural Changes on Danish Energy Consumption  

Science Journals Connector (OSTI)

The aim of this paper is to present some preliminary results from a study of how changes in output-mix have influenced the energy consumption in the Danish manufacturing industries.

Ellen Plřger

1985-01-01T23:59:59.000Z

375

Distributed Energy Consumption Control via Real-Time Pricing Feedback in Smart Grid  

E-Print Network [OSTI]

on game- theoretic energy consumption scheduling for theK }). We denote the energy consumption of consumers as l kwhere l i k is the energy consumption of consumer i (i ? N )

Ma, Kai; Hu, Guoqiang; Spanos, Costas J

2014-01-01T23:59:59.000Z

376

The Reality and Future Scenarios of Commercial Building Energy Consumption in China  

E-Print Network [OSTI]

of Commercial Building Energy Consumption in China Nan Zhou,Commercial Building Energy Consumption in China* Nan Zhou, 1whether and how the energy consumption trend can be changed

Zhou, Nan

2008-01-01T23:59:59.000Z

377

The Impact of Residential Density on Vehicle Usage and Energy Consumption  

E-Print Network [OSTI]

on Vehicle Usage and Energy Consumption References Bento,Vehicle Usage and Energy Consumption UCI-ITS-WP-05-1 Thomason Vehicle Usage and Energy Consumption Thomas F. Golob

Golob, Thomas F; Brownstone, David

2005-01-01T23:59:59.000Z

378

Energy Consumption, Efficiency, Conservation, and Greenhouse Gas Mitigation in Japan's Building Sector  

E-Print Network [OSTI]

i n g s 2.1 Total Energy Consumption i n Japan's Residentialhouses. 2.1 Total Energy Consumption in Japan's Residentialorder to reduce total energy consumption. Figure 2 suggests

2006-01-01T23:59:59.000Z

379

Impacts of Electric Vehicles on Primary Energy Consumption and Petroleum Displacement  

E-Print Network [OSTI]

L.von 2. The EV primary energy consumption relative to that~ Fig. 3. The EV primary energy consumption relative to thatVehicles on Primary Energy Consumption and Petroleum

Wang, Quanlu; Delucchi, Mark A.

1991-01-01T23:59:59.000Z

380

Window-Related Energy Consumption in the US Residential and Commercial Building Stock  

E-Print Network [OSTI]

window related primary energy consumption of the US building= 1.056 EJ. “Primary” energy consumption includes a site-to-the amount of primary energy consumption required by space

Apte, Joshua; Arasteh, Dariush

2008-01-01T23:59:59.000Z

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


381

Energy Consumption, Efficiency, Conservation, and Greenhouse Gas Mitigation in Japan's Building Sector  

E-Print Network [OSTI]

e d u c i n g Primary Energy Consumption and C O 2 emissionssystem can reduce primary energy consumption by about 22system can reduce primary energy consumption by about 26

2006-01-01T23:59:59.000Z

382

Window-Related Energy Consumption in the US Residential and Commercial Building Stock  

E-Print Network [OSTI]

roughly 2.7% of total US energy consumption. The final tworoughly 1.5% of total US energy consumption. The final twoSpace Conditioning Energy Consumption in US Buildings Annual

Apte, Joshua; Arasteh, Dariush

2008-01-01T23:59:59.000Z

383

Energy Consumption ESPRIMO E7935 E80+  

E-Print Network [OSTI]

joined the "Green Grid" and "Climate Savers Computing" initiatives and publishes SPECpower benchmark (WOL enabled) 4) 96.7 kWh/year Heat dissipation, WOL enabled (MJ, 1 W = 3.6 kJ/h) 348.3 MJ/year Heat Consumption (WOL enabled) 4) 103.6 kWh/year Heat dissipation, WOL enabled (MJ, 1 W = 3.6 kJ/h) 373.0 MJ

Ott, Albrecht

384

"Table A15. Selected Energy Operating Ratios for Total Energy Consumption for"  

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

Selected Energy Operating Ratios for Total Energy Consumption for" Selected Energy Operating Ratios for Total Energy Consumption for" " Heat, Power, and Electricity Generation by Census Region and Economic" " Characteristics of the Establishment, 1991" ,,,"Consumption","Major" " "," ","Consumption","per Dollar","Byproducts(b)","Fuel Oil(c)"," " " ","Consumption","per Dollar","of Value","as a Percent","as a Percent","RSE" " ","per Employee","of Value Added","of Shipments","of Consumption","of Natural Gas","Row" "Economic Characteristics(a)","(million Btu)","(thousand Btu)","(thousand Btu)","(percent)","(percent)","Factors"

385

"Table A45. Selected Energy Operating Ratios for Total Energy Consumption"  

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

5. Selected Energy Operating Ratios for Total Energy Consumption" 5. Selected Energy Operating Ratios for Total Energy Consumption" " for Heat, Power, and Electricity Generation by Industry Group," " Selected Industries, and Value of Shipment Categories, 1994" ,,,,,"Major" ,,,"Consumption","Consumption per","Byproducts(c)","Fuel Oil(d)" ,,"Consumption","per Dollar","Dollar of Value","as a Percent","as a Percent","RSE" "SIC",,"per Employee","of Value Added","of Shipments","of Consumption","of Natural Gas","Row" "Code(a)","Economic Characteristics(b)","(million Btu)","(thousand Btu)","(thousand Btu)","(percents)","(percents)","Factors"

386

"Table A46. Selected Energy Operating Ratios for Total Energy Consumption"  

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

Selected Energy Operating Ratios for Total Energy Consumption" Selected Energy Operating Ratios for Total Energy Consumption" " for Heat, Power, and Electricity Generation by Industry Group," " Selected Industries, and Employment Size Categories, 1994" ,,,,,"Major" ,,,"Consumption","Consumption per","Byproducts(c)","Fuel Oil(d)" ,,"Consumption","per Dollar","Dollar of Value","as a Percent","as a Percent","RSE" "SIC",,"per Employee","of Value Added","of Shipments","of Consumption","of Natural Gas","Row" "Code(a)","Economic Characteristics(b)","(million Btu)","(thousand Btu)","(thousand Btu)","(percents)","(percents)","Factors"

387

"Table A48. Selected Energy Operating Ratios for Total Energy Consumption for"  

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

8. Selected Energy Operating Ratios for Total Energy Consumption for" 8. Selected Energy Operating Ratios for Total Energy Consumption for" " Heat, Power, and Electricity Generation by Census Region, Census Division, and Economic" " Characteristics of the Establishment, 1994" ,,,"Consumption","Major" " "," ","Consumption","per Dollar","Byproducts(b)","Fuel Oil(c)"," " " ","Consumption","per Dollar","of Value","as a Percent","as a Percent","RSE" " ","per Employee","of Value Added","of Shipments","of Consumption","of Natural Gas","Row"

388

"Table A51. Selected Energy Operating Ratios for Total Energy Consumption for"  

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

1. Selected Energy Operating Ratios for Total Energy Consumption for" 1. Selected Energy Operating Ratios for Total Energy Consumption for" " Heat, Power, and Electricity Generation by Census Region and Economic" " Characteristics of the Establishment, 1991 " ,,,,,"Major" ,,,"Consumption","Consumption per","Byproducts(c)","Fuel Oil(d)" ,,"Consumption","per Dollar","Dollar of Value","as a Percent","as a Percent","RSE" "SIC",,"per Employee","of Value Added","of Shipments","of Consumption","of Natural Gas","Row" "Code(a)","Economic Characteristics(b)","(million Btu)","(thousand Btu)","(thousand Btu)","(percent)","(percent)","Factors"

389

"Table A47. Selected Energy Operating Ratios for Total Energy Consumption for"  

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

7. Selected Energy Operating Ratios for Total Energy Consumption for" 7. Selected Energy Operating Ratios for Total Energy Consumption for" " Heat, Power, and Electricity Generation by Census Region, Census Division, Industry Group, and" " Selected Industries, 1994" ,,,,,"Major" ,,,,"Consumption","Byproducts(b)" ,,,"Consumption","per Dollar","as a","Fuel Oil(c) as" ,,"Consumption","per Dollar","of Value","Percent of","a Percent of","RSE" "SIC"," ","per Employee","of Value Added","of Shipments","Consumption","Natural Gas","Row" "Code(a)","Industry Group and Industry","(million Btu)","(thousand Btu)","(thousand Btu)","(percents)","(percents)","Factors"

390

"Table A50. Selected Energy Operating Ratios for Total Energy Consumption for"  

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

0. Selected Energy Operating Ratios for Total Energy Consumption for" 0. Selected Energy Operating Ratios for Total Energy Consumption for" " Heat, Power, and Electricity Generation by Industry Group," " Selected Industries, and Economic Characteristics of the" " Establishment, 1991 (Continued)" ,,,,,"Major" ,,,"Consumption","Consumption per","Byproducts(c)","Fuel Oil(d)" ,,"Consumption","per Dollar","Dollar of Value","as a Percent of","as a Percent","RSE" "SIC",,"per Employee","of Value Added","of Shipments","of Consumption","of Natural Gas","Row" "Code(a)","Economic Characteristics(b)","(million Btu)","(thousand Btu)","(thousand Btu)","(Percent)","(percent)","Factors"

391

Residential Energy Consumption Survey (RECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

How does EIA estimate energy consumption and end uses in U.S. homes? How does EIA estimate energy consumption and end uses in U.S. homes? RECS 2009 - Release date: March 28, 2011 EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and household demographics. This information is combined with data from energy suppliers to these homes to estimate energy costs and usage for heating, cooling, appliances and other end uses â€" information critical to meeting future energy demand and improving efficiency and building design. RECS uses a multi-stage area probability design to select sample methodology figure A multi-stage area probability design ensures the selection

392

OE/EIA-0272 The National Interim Energy Consumption Survey:  

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

272 272 The National Interim Energy Consumption Survey: Exploring the Variability in Energy Consumption July 1981 U.S. Department of Energy Energy Information Administration Assistant Administrator for Program Development Office of the Consumption Data System Industrial Data Systems Division This publication is available from the Superintendent of Documents, U.S. Government Printing Office, at the following address: Superintendent of Documents U.S. Government Printing Office Washington, D.C. 20402 Order Desk: (202) 783-3238 Stock Number: 061-003-00205-6 Price: $4.25 For questions on energy statistics or information on availability of other EIA publications, contact: National Energy Information Center, El-20 Forrestal Building U.S. Department of Energy Washington, D.C. 20585

393

Appliance Standby Power and Energy Consumption in South African Households  

Open Energy Info (EERE)

Appliance Standby Power and Energy Consumption in South African Households Appliance Standby Power and Energy Consumption in South African Households Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Appliance Standby Power and Energy Consumption in South African Households Focus Area: Appliances & Equipment Topics: Policy Impacts Website: active.cput.ac.za/energy/web/DUE/DOCS/422/Paper%20-%20Shuma-Iwisi%20M. Equivalent URI: cleanenergysolutions.org/content/appliance-standby-power-and-energy-co Language: English Policies: Deployment Programs DeploymentPrograms: Technical Assistance A modified engineering model is proposed to estimate standby power and energy losses in households. The modified model accounts for the randomness of standby power and energy losses due to unpredicted user appliance operational behavior.

394

Energy Data Sourcebook for the U.S. Residential Sector  

E-Print Network [OSTI]

that forecast US residential energy consumption by end-use.new unit energy consumption in the U.S. DOE appliancethe Residential Energy Consumption Survey, or RECS (US DOE

Wenzel, T.P.

2010-01-01T23:59:59.000Z

395

Effects of financial developments and income on energy consumption  

Science Journals Connector (OSTI)

Abstract Extending Sadorsky (2010), this paper focuses on nonlinear effects of financial development and income on energy consumption. Utilizing five alternative measures of financial development, it employs a panel threshold regression approach to reexamine the effect of financial development and income on energy consumption. The analysis relies on a sample of 53 countries for the period 1999–2008, showing a single-threshold effect on energy consumption when private credit, domestic credit, value of traded stocks, and stock market turnover are used as financial development indicators. It implies that the sample can be split into two regimes: high income, and non-high income. Energy consumption increases with income in emerging market and developing economies, while in advanced economies energy consumption increases with income beyond a point at which the economy achieves a threshold level of income. In addition, in the non–high income regime, energy consumption increases with financial development when both private and domestic credit are used as financial development indicators. However, when the value of traded stocks and stock market turnover are used as financial development indicators, it slightly declines with financial development in advanced economies, especially in high-income countries, but increases in the higher income countries of emerging market and developing economies.

Shu-Chen Chang

2015-01-01T23:59:59.000Z

396

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

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

Beyond "Partly Sunny": A Better Solar Forecast 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 forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. Minh Le Minh Le Program Manager, Solar Program What Do These Projects Do? The Energy Department is investing $8 million in two cutting-edge projects to increase the accuracy of solar forecasting at sub-hourly,

397

Uncertainties in Energy Consumption Introduced by Building Operations and Weather for a Medium-Size Office Building  

E-Print Network [OSTI]

Uncertainties in Energy Consumption Introduced by Buildingand actual building energy consumption can be attributed touncertainties in energy consumption due to actual weather

Wang, Liping

2014-01-01T23:59:59.000Z

398

Solar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting Questionnaire As someone who is familiar with solar energy issues, we hope that you will tak  

E-Print Network [OSTI]

is familiar with solar energy issues, we hope that you will take a few moments to answer this short survey on your needs for information on solar energy resources and forecasting. This survey is conducted with the California Solar Energy Collaborative (CSEC) and the California Solar Initiative (CSI) our objective

Islam, M. Saif

399

The Impact of Residential Density on Vehicle Usage and Energy Consumption  

E-Print Network [OSTI]

Residential Density on Vehicle Usage and Energy ConsumptionType Choice, and Fuel Usage Total annual residentialResidential Density on Vehicle Usage and Energy Consumption

Golob, Thomas F.; Brownstone, David

2005-01-01T23:59:59.000Z

400

Commercial Buildings Energy Consumption Survey (CBECS) - U.S. Energy  

Gasoline and Diesel Fuel Update (EIA)

Relationship of CBECS Coverage to EIA Supply Surveys Relationship of CBECS Coverage to EIA Supply Surveys The primary purpose of the CBECS is to collect accurate statistics of energy consumption by individual buildings. EIA also collects data on total energy supply (sales). For the information on sales totals, a different reporting system is used for each fuel and the boundaries between the different sectors (e.g., residential, commercial, industrial) are drawn differently for each fuel. Background EIA sales data on the different fuels are compiled in individual fuel reports. Annual electricity sales data are currently collected on Form EIA-861, "Annual Electric Utility Report," which is sent to all electric utilities in the United States. Supply data for natural gas are collected on Form EIA-176, "Annual Report of Natural and Supplemental Gas

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Residential Energy Consumption Survey (RECS) - U.S. Energy Information  

Gasoline and Diesel Fuel Update (EIA)

RECS Terminology RECS Terminology A B C D E F G H I J K L M N O P Q R S T U V W XYZ A Account Classification: The method in which suppliers of electricity, natural gas, or fuel oil classify and bill their customers. Commonly used account classifications are "Commercial," "Industrial," "Residential," and "Other" Suppliers' definitions of these terms vary from supplier to supplier and from the definitions used in the Residential Energy Consumption Survey (RECS). In addition, the same customer may be classified differently by each of its energy suppliers. Adequacy of Insulation: The respondent's perception of the adequacy of the housing unit's insulation. Aggregate Ratio: The ratio of two population aggregates (totals). For

402

Energy consumption analysis for CO2 separation from gas mixtures  

Science Journals Connector (OSTI)

Abstract CO2 separation is an energy intensive process, which plays an important role in both energy saving and CO2 capture and storage (CCS) implementation to deal with global warming. To quantitatively investigate the energy consumption of CO2 separation from different CO2 streams and analyze the effect of temperature, pressure and composition on energy consumption, in this work, the theoretical energy consumption of CO2 separation from flue gas, lime kiln gas, biogas and bio-syngas was calculated. The results show that the energy consumption of CO2 separation from flue gas is the highest and that from biogas is the lowest, and the concentration of CO2 is the most important factor affecting the energy consumption when the CO2 concentration is lower than 0.15 in mole fraction. Furthermore, if the CO2 captured from flue gases in CCS was replaced with that from biogases, i.e. bio-CO2, the energy saving would be equivalent to 7.31 million ton standard coal for China and 28.13 million ton standard coal globally, which corresponds to 0.30 billion US$ that can be saved for China and 1.36 billion US$ saved globally. This observation reveals the importance of trading fossil fuel-based CO2 with bio-CO2.

Yingying Zhang; Xiaoyan Ji; Xiaohua Lu

2014-01-01T23:59:59.000Z

403

Nodes Placement for reducing Energy Consumption in Multimedia Transmissions  

E-Print Network [OSTI]

quality of multimedia traffic. Index Terms--Wireless Sensor Networks, Multimedia, Energy Saving, Quality on the energy saving by extending the lifetime of the network up to more than 15% while preserving video qualityNodes Placement for reducing Energy Consumption in Multimedia Transmissions Pasquale Pace Valeria

Paris-Sud XI, Université de

404

Understanding Energy Consumption of Sensor Enabled Applications on Mobile Phones  

E-Print Network [OSTI]

is with the House n Lab at MIT, Cambridge, MA 02142 USA. in energy efficiency of portable applications. We discussUnderstanding Energy Consumption of Sensor Enabled Applications on Mobile Phones Igor Crk, Member of this project is to measure and reduce the energy demand placed on mobile phones that monitor individuals

Gniady, Chris

405

How Efficient Can We Be?: Bounds on Algorithm Energy Consumption  

E-Print Network [OSTI]

How Efficient Can We Be?: Bounds on Algorithm Energy Consumption Andrew Gearhart #12;Relation design use feedback to "cotune" compute kernel energy efficiency #12;Previous Work: Communication Lower-optimal" algorithms #12;Communication is energy inefficient! · On-chip/Off-chip gap isn't going to improve much Data

California at Irvine, University of

406

Improving Energy Use Forecast for Campus Micro-grids using Indirect Indicators Department of Computer Science  

E-Print Network [OSTI]

.32%, and a reduction in error from baseline models by up to 53%. Keywords-energy forecast models; energy informatics I that physically char- acterize a building, or are based on measured building performance data. Smart meters have analysis and machine learning methods can be used to mine sensor data and extract forecast models

Prasanna, Viktor K.

407

New York: Weatherizing Westbeth Reduces Energy Consumption  

Office of Energy Efficiency and Renewable Energy (EERE)

Project provides energy savings and the improved health and safety of the residents within the building.

408

EIA - International Energy Outlook 2007-Energy Consumption by End-Use  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption by End Use Sector Energy Consumption by End Use Sector International Energy Outlook 2007 Figure 25. OECD and Non-OECD Transportation Sector Delivered Energy Consumption, 2004-2030 Figure 25 Data. Need help, contact the National Energy Information Center at 202-586-8800. Figure 26. OECD and Non-OECD Residential Sector Delivered Energy Consumption, 2004-2030 Figure 26 Data. Need help, contact the National Energy Information Center at 202-586-8800. Figure 27. Growth in OECD and Non-OECD Residential Sector Delivered Energy Consumption by Fuel, 2004 and 2030 Figure 27 Data. Need help, contact the National Energy Information Center at 202-586-8800. Figure 28. OECD and Non-OECD Commercial Sector Delivered Energy Consumption, 2004-2030 Figure 28 Data. Need help, contact the National Energy Information Center at 202-586-8800.

409

TV Energy Consumption Trends and Energy-Efficiency Improvement Options  

E-Print Network [OSTI]

=0&o=10005442 DisplaySearch. 2009. “India TV Data. ” August.DisplaySearch. 2010a. “Brazil TV Data. ” January.Quarterly Advanced Global TV Shipment and Forecast Report,

Park, Won Young

2011-01-01T23:59:59.000Z

410

Delivered Energy Consumption Projections by Industry in the Annual Energy Outlook 2002  

Reports and Publications (EIA)

This paper presents delivered energy consumption and intensity projections for the industries included in the industrial sector of the National Energy Modeling System.

2002-01-01T23:59:59.000Z

411

Renewable Energy Consumption by Energy Use Sector and Energy Source, 2004 -  

Open Energy Info (EERE)

by Energy Use Sector and Energy Source, 2004 - by Energy Use Sector and Energy Source, 2004 - 2008 Dataset Summary Description Provides annual consumption (in quadrillion Btu) of renewable energy by energy use sector (residential, commercial, industrial, transportation and electricity) and by energy source (e.g. solar, biofuel) for 2004 through 2008. Original sources for data are cited on spreadsheet. Also available from: www.eia.gov/cneaf/solar.renewables/page/trends/table1_2.xls Source EIA Date Released August 01st, 2010 (4 years ago) Date Updated Unknown Keywords annual energy consumption biodiesel Biofuels biomass energy use by sector ethanol geothermal Hydroelectric Conventional Landfill Gas MSW Biogenic Other Biomass renewable energy Solar Thermal/PV Waste wind Wood and Derived Fuels Data application/vnd.ms-excel icon RE Consumption by Energy Use Sector, Excel file (xls, 32.8 KiB)

412

Public perceptions of energy consumption and savings  

Science Journals Connector (OSTI)

...efficiency for small appliances, conducted an energy audit of home, weatherized home, installed double-pane...decisions? _Yes _No Have you ever had an energy audit of your home? (A home energy audit is done to evaluate measures you can take to...

Shahzeen Z. Attari; Michael L. DeKay; Cliff I. Davidson; Wändi Bruine de Bruin

2010-01-01T23:59:59.000Z

413

Constraining Energy Consumption of China's Largest Industrial Enterprises Through the Top-1000 Energy-Consuming Enterprise Program  

E-Print Network [OSTI]

Industry Constraining Energy Consumption of China’s Largestone-to-one ratio of energy consumption to GDP – given China’goal of reducing energy consumption per unit of GDP by 20%

Price, Lynn; Wang, Xuejun

2007-01-01T23:59:59.000Z

414

China's Top-1000 Energy-Consuming Enterprises Program: Reducing Energy Consumption of the 1000 Largest Industrial Enterprises in China  

E-Print Network [OSTI]

recently. In 2006, total energy consumption reached 2,4577.5% per year, total energy consumption in 2010 will reachof Enterprises Total Energy Consumption Mtce pe tro iro le

Price, Lynn

2008-01-01T23:59:59.000Z

415

Constraining Energy Consumption of China's Largest Industrial Enterprises Through the Top-1000 Energy-Consuming Enterprise Program  

E-Print Network [OSTI]

recently. In 2005, total energy consumption reached 2,2257.5% per year, total energy consumption in 2010 will reachof Enterprises and Total Energy Consumption by Sector of the

Price, Lynn; Wang, Xuejun

2007-01-01T23:59:59.000Z

416

China's Top-1000 Energy-Consuming Enterprises Program: Reducing Energy Consumption of the 1000 Largest Industrial Enterprises in China  

E-Print Network [OSTI]

China’s total primary energy consumption in 2005, along withof China’s total primary energy consumption (Lin et al. ,accounted for, the primary energy consumption of the Top-

Price, Lynn

2008-01-01T23:59:59.000Z

417

Constraining Energy Consumption of China's Largest Industrial Enterprises Through the Top-1000 Energy-Consuming Enterprise Program  

E-Print Network [OSTI]

China’s total primary energy consumption in 2005, along withthe industrial sector primary energy consumption was 1,416of China’s total primary energy consumption (Lin et al. ,

Price, Lynn; Wang, Xuejun

2007-01-01T23:59:59.000Z

418

Energy Information Administration - Energy Efficiency-Table 5a. Consumption  

Gasoline and Diesel Fuel Update (EIA)

5a 5a Page Last Modified: June 2010 Table 5a. Consumption of Energy for All Purposes (First Use) per Value of Production, 1998, 2002, and 2006 (1000 Btu per constant 2000 dollar 1) MECS Survey Years Iron and Steel Mills (NAICS2 331111) 1998 3 2002 3 2006 3 Total 4 30 27 17 Net Electricity5 3 4 3 Natural Gas 9 9 6 Coal 13 10 6 Notes:1. Value of production is deflated by the chain-type price indices for iron and steel mills shipments. 2. The North American Industry Classification System (NAICS) has replaced the Standard Industrial Classification (SIC) system. NAICS 331111 includes steel works, blast furnaces (including coke ovens), and rolling mills. 3. Denominators represent the value of production for the entire iron and still mills (NAICS 331111), not those based mainly on electric, natural gas or coal.

419

Manufacturing Energy Consumption Survey (MECS) - Data - U.S. Energy  

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

6 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive 6 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive Data Methodology & Forms 2006 Data Tables Revision notice (November 2009): Tables 1.1, 1.2, 2.1, 2.2, 3.1, 3.2, 3.5, 4.1 and 4.2 have been slightly revised due to further editing. The revisions in XLS are indicated with a value of "R" in an adjacent column. In the PDF versions, the revised values are superscripted with an "R". No further revisions are anticipated for these tables. all tables + EXPAND ALL Consumption of Energy for All Purposes (First Use) Values RSE Table 1.1 By Mfg. Industry & Region (physical units) XLS PDF XLS Table 1.2 By Mfg. Industry & Region (trillion Btu) XLS PDF XLS Table 1.3 By Value of Shipments & Employment Size Category & Region XLS PDF XLS

420

Commercial Buildings Energy Consumption Survey (CBECS) - U.S. Energy  

Gasoline and Diesel Fuel Update (EIA)

Building Type Definitions Building Type Definitions In the Commercial Buildings Energy Consumption Survey (CBECS), buildings are classified according to principal activity, which is the primary business, commerce, or function carried on within each building. Buildings used for more than one of the activities described below are assigned to the activity occupying the most floorspace at the time of the interview. Thus, a building assigned to a particular principal activity category may be used for other activities in a portion of its space or at some time during the year. In the 1999 and 2003 CBECS, respondents were asked to place their building into a sub-category that was a more specific activity than has been collected in prior surveys. This was done to ensure the quality of the data; after data collection, the subcategories were combined

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Energy Consumption of Refrigerators in Ghana - Outcomes of Household  

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

Energy Consumption of Refrigerators in Ghana - Outcomes of Household Energy Consumption of Refrigerators in Ghana - Outcomes of Household Surveys Speaker(s): Essel Ben Hagan Date: July 12, 2007 - 12:00pm Location: 90-3122 Seminar Host/Point of Contact: Robert Van Buskirk Galen Barbose As part of activities to develop refrigerator efficiency standards regulations in Ghana, a national survey on the energy consumption of refrigerators and refrigerator-freezers has been conducted. The survey covered 1000 households in urban, peri-urban and rural communities in various parts of the country. The survey found that, on average, refrigerators and refrigerator-freezers in Ghana use almost three times what is allowed by minimum efficiency standards in the U.S., and a few refrigerators had energy use at levels almost ten times the U.S.

422

World Energy Consumption by Fuel Type, 1970-2020  

Gasoline and Diesel Fuel Update (EIA)

0 0 Notes: Natural gas is projected to be the fastest-growing component of primary world energy consumption, more than doubling between 1997 and 2020. Gas accounts for the largest increment in electricity generation (41 percent of the total increment of energy used for electricity generation). Combined-cycle gas turbine power plants offer some of the highest commercially available plant efficiencies, and natural gas is environmentally attractive because it emits less sulfur dioxide, carbon dioxide, and particulate matter than does oil or coal. In the IEO2000 projection, world natural gas consumption reaches the level of coal by 2005, and by 2020 gas use exceeds coal by 29 percent. Oil currently provides a larger share of world energy consumption than any other energy source and is expected to remain in that position

423

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

Broader source: Energy.gov [DOE]

The Wind Forecast Improvement Project (WFIP) is a U. S. Department of Energy (DOE) sponsored research project whose overarching goals are to improve the accuracy of short-term wind energy forecasts, and to demonstrate the economic value of these improvements.

424

Energy Consumption and Energy Density in Optical and Electronic Signal Processing  

E-Print Network [OSTI]

Energy Consumption and Energy Density in Optical and Electronic Signal Processing Volume 3, Number-0655/$26.00 ©2011 IEEE #12;Energy Consumption and Energy Density in Optical and Electronic Signal Processing Rodney optical and digital electronic signal processing circuits, including the contributions to energy

Tucker, Rod

425

"Table A8. Selected Energy Operating Ratios for Total Energy Consumption for"  

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

A8. Selected Energy Operating Ratios for Total Energy Consumption for" A8. Selected Energy Operating Ratios for Total Energy Consumption for" " Heat, Power, and Electricity Generation by Census Region, Industry Group, and" " Selected Industries, 1991" ,,,,,"Major" ,,,,"Consumption","Byproducts(b)" ,,,"Consumption","per Dollar","as a","Fuel Oil(c) as" ,,"Consumption","per Dollar","of Value","Percent of","a Percent of","RSE" "SIC"," ","per Employee","of Value Added","of Shipments","Consumsption","Natural Gas","Row" "Code(a)","Industry Groups and Industry","(million Btu)","(thousand Btu)","(thousand Btu)","(PERCENT)","(percent)","Factors"

426

Issues in International Energy Consumption Analysis: Electricity...  

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

usage in a developing country. Relationships between these factors are important for energy planning in India and around the globe. Just as electrification was a huge undertaking...

427

NCAR WRF-based data assimilation and forecasting systems for wind energy applications power  

E-Print Network [OSTI]

NCAR WRF-based data assimilation and forecasting systems for wind energy applications power Yuewei of these modeling technologies w.r.t. wind energy applications. Then I'll discuss wind farm

Kim, Guebuem

428

Energy Demand Forecasting in China Based on Dynamic RBF Neural Network  

Science Journals Connector (OSTI)

A dynamic radial basis function (RBF) network model is proposed for energy demand forecasting in this paper. Firstly, we ... detail. At last, the data of total energy demand in China are analyzed and experimental...

Dongqing Zhang; Kaiping Ma; Yuexia Zhao

2011-01-01T23:59:59.000Z

429

Vending Machine Energy Consumption and VendingMiser Evaluation  

E-Print Network [OSTI]

As an effort to decrease the amount of non-critical energy used on the Texas A&M campus, and to assist Dixie Narco in evaluating the efficiency of their vending machines, the Texas A&M Energy Systems Laboratory investigated the power consumption...

Ritter, J.; Hugghins, J.

2000-01-01T23:59:59.000Z

430

World Energy Consumption and Carbon Dioxide Emissions: 1950 2050  

E-Print Network [OSTI]

-U" relation with a within- sample peak between carbon dioxide emissions (and energy use) per capita and perWorld Energy Consumption and Carbon Dioxide Emissions: 1950 Ă? 2050 Richard Schmalensee, Thomas M. Stoker, andRuth A. Judson* Emissions of carbon dioxide from combustion of fossil fuels, which may

431

Modeling energy consumption in cellular networks L. Decreusefond  

E-Print Network [OSTI]

, the mobile industry continues to look for ways to reduce energy needs. Air conditioning is being replaced by fans or passive air flows whenever possible. Several programs are aiming to deploy solar, wind countries by 2012. Network optimization upgrades currently can reduce energy consumption by 44% and solar

Boyer, Edmond

432

Residential Energy Consumption Survey (RECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

Share of energy used by appliances and consumer electronics increases in Share of energy used by appliances and consumer electronics increases in U.S. homes RECS 2009 - Release date: March 28, 2011 Over the past three decades, the share of residential electricity used by appliances and electronics in U.S. homes has nearly doubled from 17 percent to 31 percent, growing from 1.77 quadrillion Btu (quads) to 3.25 quads. This rise has occurred while Federal energy efficiency standards were enacted on every major appliance, overall household energy consumption actually decreased from 10.58 quads to 10.55 quads, and energy use per household fell 31 percent. Federal energy efficiency standards have greatly reduced consumption for home heating Total energy use in all U.S. homes occupied as primary residences decreased slightly from 10.58 quads in 1978 to 10.55 quads in 2005 as reported by the

433

Energy Consumption Tools Pack Leandro Fontoura Cupertino, Georges DaCosta,  

E-Print Network [OSTI]

Energy Consumption Tools Pack Leandro Fontoura Cupertino, Georges DaCosta, Amal Sayah, Jean Consumption Tools Pack 1 / 23 #12;Outline 1 Introduction Motivation Our proposal 2 Energy Consumption Tools Energy Consumption Library Data Acquisition Tool Data Monitoring Tool Energy Profiler 3 Conclusions

Lefèvre, Laurent

434

A Parallel Statistical Learning Approach to the Prediction of Building Energy Consumption Based on Large Datasets  

E-Print Network [OSTI]

A Parallel Statistical Learning Approach to the Prediction of Building Energy Consumption Based consumption of buildings based on historical performances is an important approach to achieve energy consumption plays an important role in the total energy consumption of end use. Energy efficiency in building

Paris-Sud XI, Université de

435

Mixed-Criticality Multiprocessor Real-Time Systems: Energy Consumption vs Deadline Misses  

E-Print Network [OSTI]

Mixed-Criticality Multiprocessor Real-Time Systems: Energy Consumption vs Deadline Misses Vincent that using the best compromise, the energy consumption can be reduced up to 17% while the percentage the energy consumption of MC systems. The energy consumption of embedded real-time systems is indeed

Paris-Sud XI, Université de

436

uFLIP: Understanding the Energy Consumption of Flash Devices Matias Bjrling  

E-Print Network [OSTI]

uFLIP: Understanding the Energy Consumption of Flash Devices Matias Bjørling IT University Abstract Understanding the energy consumption of flash devices is important for two reasons. First, energy about the energy consumption of flash devices beyond their approximate aggregate consumption (low power

Paris-Sud XI, Université de

437

Manufacturing-Industrial Energy Consumption Survey(MECS) Historical  

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

> Historical Publications > Historical Publications Manufacturing Establishments reports, data tables and questionnaires Released: May 2008 The Manufacturing Energy Consumption Survey (MECS) is a periodic national sample survey devoted to measuring energy consumption and related issues in the manufacturing sector. The MECS collects data on energy consumption, purchases and expenditures, and related issues and behaviors. Links to previously published documents are given below. Beginning in 1998, reports were only issued electronically. Additional electronic releases are available on the MECS Homepage. The basic unit of data collection for this survey is the manufacturing establishment. Industries are selected according to definitions found in the North American Industry Classification System (NAICS), which replace the earlier Standard Industrial Classification (SIC) system.

438

Residential Energy Consumption for Water Heating (2005) | OpenEI  

Open Energy Info (EERE)

for Water Heating (2005) for Water Heating (2005) Dataset Summary Description Provides total and average annual residential energy consumption for water heating in U.S. households in 2005, measured in both physical units and Btus. The data is presented for numerous categories including: Census Region and Climate Zone; Housing Unit Characteristics (type, year of construction, size, income, race, age); and Water Heater and Water-using Appliance Characteristics (size, age, frequency of use, EnergyStar rating). Source EIA Date Released September 01st, 2008 (6 years ago) Date Updated January 01st, 2009 (5 years ago) Keywords Energy Consumption Residential Water Heating Data application/vnd.ms-excel icon 2005_Consumption.for_.Water_.Heating.Phys_.Units_EIA.Sep_.2008.xls (xls, 67.6 KiB)

439

Smart Meters Help Balance Energy Consumption at Solar Decathlon |  

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

Smart Meters Help Balance Energy Consumption at Solar Decathlon Smart Meters Help Balance Energy Consumption at Solar Decathlon Smart Meters Help Balance Energy Consumption at Solar Decathlon September 28, 2011 - 10:57am Addthis The Team Tidewater Virginia smart meter, as seen on opening day, indicates the team generated 5 kW hours of electricity in the first several hours of the competition. | Image courtesy of Lachlan Fletcher, Studio 18a The Team Tidewater Virginia smart meter, as seen on opening day, indicates the team generated 5 kW hours of electricity in the first several hours of the competition. | Image courtesy of Lachlan Fletcher, Studio 18a Liisa O'Neill Liisa O'Neill Former New Media Specialist, Office of Public Affairs Clouds, rain, thunderstorms... at Solar Decathlon Village? Oh my, you may say. But less-than-ideal weather conditions are no match for this year's

440

Smart Meters Help Balance Energy Consumption at Solar Decathlon |  

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

Smart Meters Help Balance Energy Consumption at Solar Decathlon Smart Meters Help Balance Energy Consumption at Solar Decathlon Smart Meters Help Balance Energy Consumption at Solar Decathlon September 28, 2011 - 10:57am Addthis The Team Tidewater Virginia smart meter, as seen on opening day, indicates the team generated 5 kW hours of electricity in the first several hours of the competition. | Image courtesy of Lachlan Fletcher, Studio 18a The Team Tidewater Virginia smart meter, as seen on opening day, indicates the team generated 5 kW hours of electricity in the first several hours of the competition. | Image courtesy of Lachlan Fletcher, Studio 18a Liisa O'Neill Liisa O'Neill Former New Media Specialist, Office of Public Affairs Clouds, rain, thunderstorms... at Solar Decathlon Village? Oh my, you may say. But less-than-ideal weather conditions are no match for this year's

Note: This page contains sample records for the topic "forecasts energy consumption" 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

Model for electric energy consumption in eastern Saudi Arabia  

SciTech Connect (OSTI)

Electrical energy consumption in the eastern province of Saudi Arabia is modeled as a function of weather data, global solar radiation, population, and gross domestic product per capita. Five years of data have been used to develop the energy consumption model. Variable selection in the regression model is carried out by using the general stepping-regression technique. Model adequacy is determined from a residual analysis technique. Model validation aims to determine if the model will function successfully in its intended operating field. In this regard, new energy consumption data for a sixth year are collected, and the results predicted by the regression model are compared with the new data set. Finally, the sensitivity of the model is examined. It is found that the model is strongly influenced by the ambient temperature.

Al-Garni, A.Z.; Al-Nassar, Y.N.; Zubair, S.M.; Al-Shehri, A. [King Fahd Univ. of Petroleum and Minerals, Dhahran (Saudi Arabia)

1997-05-01T23:59:59.000Z

442

Commercial Buildings Energy Consumption and Expenditures 1992 - Publication  

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

and Expenditures > Publication and Tables and Expenditures > Publication and Tables 1992 Consumption & Expenditures Publication and Tables Figure ES1. Energy Consumption in Commercial Buildings by Energy Sources, 1992 Separater Bar To View and/or Print Reports (requires Adobe Acrobat Reader) - Download Adobe Acrobat Reader . If you experience any difficulties, visit our Technical Frequently Asked Questions. You have the option of downloading the entire report or selected sections of the report. Separater Bar Full Report - Commercial Buildings Energy Consumption and Expenditures, 1992 (file size 1.07 MB) pages: 214 Selected Sections Main Text - requires Adobe Acrobat Reader (file size 193,634 bytes) pages: 28, includes the following: Contacts Contents Executive Summary Introduction Background

443

Simulation Models to Optimize the Energy Consumption of Buildings  

E-Print Network [OSTI]

Page 1 of paper submitted to ICEBO 2008 Berlin SIMULATION MODELS TO OPTIMIZE THE ENERGY CONSUMPTION OF BUILDINGS Sebastian Burhenne Fraunhofer-Institute for Solar Energy Systems Freiburg, Germany Dirk Jacob Fraunhofer...-Institute for Solar Energy Systems Freiburg, Germany ABSTRACT In practice, building operation systems are only adjusted during commissioning. This is done manually and leads to failure-free but often inefficient operation. This work deals...

Burhenne, S.; Jacob, D.

444

Energy Forecast, ForskEL (Smart Grid Project) | Open Energy Information  

Open Energy Info (EERE)

Forecast, ForskEL (Smart Grid Project) Forecast, ForskEL (Smart Grid Project) Jump to: navigation, search Project Name Energy Forecast, ForskEL Country Denmark Coordinates 56.26392°, 9.501785° 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":56.26392,"lon":9.501785,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

445

Estimating the rebound effect in US manufacturing energy consumption  

Science Journals Connector (OSTI)

The energy price shocks of the 1970s are usually assumed to have increased the search for new energy saving technologies where eventual gains in energy efficiencies will reduce the real per unit price of energy services and hence, the consumption of energy will rise and partially offset the initial reduction in the usage of energy sources. This is the ‘rebound effect’, which is estimated for the US manufacturing sector using time series data applying the dynamic OLS method (DOLS). When allowing for asymmetric price effects the rebound effect is found to be approximately 24% for the US manufacturing sector.

Jan Bentzen

2004-01-01T23:59:59.000Z

446

Manufacturing Energy Consumption Survey (MECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

All Reports & Publications All Reports & Publications Search By: Go Pick a date range: From: To: Go ManufacturingAvailable formats Cost of Natural Gas Used in Manufacturing Sector Has Fallen Released: September 6, 2013 Natural gas has been an important exception to the trend of rising prices for energy sources used by manufacturers. Production of natural gas in the United States increased rapidly beginning in 2007 as a result of resources found in shale formations. That increase in supply has in turn lowered the price of natural gas to manufacturers Manufacturing Energy Consumption Data Show Large Reductions in Both Manufacturing Energy Use and the Energy Intensity of Manufacturing Activity between 2002 and 2010 Released: March 19, 2013 Total energy consumption in the manufacturing sector decreased by 17

447

Residential Energy Consumption Survey (RECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

The impact of increasing home size on energy demand The impact of increasing home size on energy demand RECS 2009 - Release date: April 19, 2012 Homes built since 1990 are on average 27% larger than homes built in earlier decades, a significant trend because most energy end-uses are correlated with the size of the home. As square footage increases, the burden on heating and cooling equipment rises, lighting requirements increase, and the likelihood that the household uses more than one refrigerator increases. Square footage typically stays fixed over the life of a home and it is a characteristic that is expensive, even impractical to alter to reduce energy consumption. According to results from EIA's 2009 Residential Energy Consumption Survey (RECS), the stock of homes built in the 1970s and 1980s averages less than

448

Derived Annual Estimates of Manufacturing Energy Consumption, 1974-1988  

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

Manufacturing > Derived Annual Estimates - Executive Summary Manufacturing > Derived Annual Estimates - Executive Summary Derived Annual Estimates of Manufacturing Energy Consumption, 1974-1988 Figure showing Derived Estimates Executive Summary This report presents a complete series of annual estimates of purchased energy used by the manufacturing sector of the U.S. economy, for the years 1974 to 1988. These estimates interpolate over gaps in the actual data collections, by deriving estimates for the missing years 1982-84 and 1986-87. For the purposes of this report, "purchased" energy is energy brought from offsite for use at manufacturing establishments, whether the energy is purchased from an energy vendor or procured from some other source. The actual data on purchased energy comes from two sources, the U.S. Department of Commerce Bureau of the Census's Annual Survey of Manufactures (ASM) and EIA's Manufacturing Energy Consumption Survey (MECS). The ASM provides annual estimates for the years 1974 to 1981. However, in 1982 (and subsequent years) the scope of the ASM energy data was reduced to collect only electricity consumption and expenditures and total expenditures for other purchased energy. In 1985, EIA initiated the triennial MECS collecting complete energy data. The series equivalent to the ASM is referred to in the MECS as "offsite-produced fuels." The completed annual series for 1974 to 1988 developed in this report links the ASM and MECS "offsite" series, estimating for the missing years. Estimates are provided for the manufacturing sector as a whole and at the two-digit Standard Industrial Classification (SIC) level for total energy consumption and for the consumption of individual fuels. There are no direct sources of data for the missing years (1982-1984 and 1986-1987). To derive consumption estimates, a comparison was made between the ASM, MECS, and other economic series to see whether there were any good predictors for the missing data. Various estimation schemes were analyzed to fill in the gaps in data after 1981 by trying to match known data for the 1974 to 1981 period.

449

EIA - International Energy Outlook 2007 - Energy Consumption by End-Use  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption by End-Use Sector Energy Consumption by End-Use Sector International Energy Outlook 2007 Chapter 2 - Energy Consumption by End-Use Sector In the IEO2007 projections, end-use energy consumption depends on resource endowment, economic growth, and other political, social, and demographic factors.. 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 dominated by petroleum-based liquids products at present, the mix of energy use in the residential, commercial, and industrial sectors varies widely by region, depending on a combination of regional factors, such as the availability of energy resources, the level of economic development, and political, social,

450

Optimal and Autonomous Incentive-based Energy Consumption Scheduling Algorithm for Smart Grid  

E-Print Network [OSTI]

1 Optimal and Autonomous Incentive-based Energy Consumption Scheduling Algorithm for Smart Grid by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber consumption occurs in buildings. This represents 39% of the total energy consumption among all sectors

Wong, Vincent

451

Energy Consumption Analysis and Energy Conservation Evaluation of a Commercial Building in Shanghai  

E-Print Network [OSTI]

The paper presents a model of a commercial building in Shanghai with energy simulation software, and after calibration, the energy consumption of this building is calculated. On the basis of the simulation and calculation, a series of energy saving...

Chen, C.; Pan, Y.; Huang, Z.; Wu, G.

2006-01-01T23:59:59.000Z

452

The importance of population growth in future commercial energy consumption  

SciTech Connect (OSTI)

This paper estimates the contribution of population growth to commercial energy consumption, which is considered a major cause of increases in air pollution and greenhouse gases. This paper first summarizes some of the recent estimates of future energy use developed by well-known models. It then develops several alternative scenarios that use different assumptions about population growth and energy use per capita for 122 countries for the years 2020 and 2050. It calculates the relative contribution of population growth to the change in total commercial energy use and demonstrates the sensitivity of the results to different assumptions. Individual country data are separately summed to totals for more-developed countries (MDCs) and less-developed countries (LDCs). Under a business as usual scenario for both MDCs and LDCs, population growth is important, but not the most important factor, in future increases in global energy consumption. Analysis of other scenarios shows that while slower population growth always contributes to a slowing of future global energy consumption, such changes are not as effective as reductions in per capita commercial energy use. Calculations on a global basis are made in two ways: from global aggregates and by summing individual country data. Comparison of the results shows that the first method is misleading because of the heterogeneity of population growth rates and energy consumption rates of individual countries. The tentative conclusions reached in this paper are only small pieces of a much larger puzzle. More work needs to be done to better understand the dynamics of these relationships before the analysis is extended to the broader questions of population growth and environmental change.

Kolsrud, G. [Congress, Washington, DC (United States); Torrey, B.B. [Bureau of the Census, Washington, DC (United States)

1992-12-31T23:59:59.000Z

453

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network [OSTI]

for the information in this report; nor does any party represent that the uses of this information will not infringe of transportation fuel and crude oil import requirements to establish the quantitative baseline to support its fuels, integration of energy use and land use planning, and transportation fuel infrastructure

454

1991 Manufacturing Consumption of Energy 1991 Executive Summary  

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

1991 Executive Summary 1991 Executive Summary 1991 Figure showing the Largest Energy Consumers in the Manufacturing Sector Executive Summary The Manufacturing Consumption of Energy 1991 report presents statistics about the energy consumption of the manufacturing sector, based on the 1991 Manufacturing Energy Consumption Survey (MECS). The MECS is the only comprehensive source of national-level data on U.S. manufacturing energy use. The 1991 MECS is the third in an ongoing series of surveys conducted at 3-year intervals beginning in 1985. Pursuant to a provision of the Energy Policy Act of 1992, the MECS will be conducted biennially beginning in 1994. The MECS surveys a nationally representative sample of manufacturing establishments by means of mailed questionnaires. The 1991 sample represented 98 percent of the U.S. manufacturing sector universe, which consists of all manufacturing establishments in the 50 States and the District of Columbia. Compared with the 1988 MECS, the designed sample size for 1991 was increased from 12,065 manufacturing establishments to 16,054 establishments.

455

Buildings Energy Data Book: 4.4 Legislation Affecting Energy Consumption of Federal Buildings and Facilities  

Buildings Energy Data Book [EERE]

1 1 Energy Policy Act of 2005, Provisions Affecting Energy Consumption in Federal Buildings Source(s): Energy Management Requirements - Amended reduction goals set by the National Energy Conservation Policy Act, and requires increasing percentage reductions in energy consumption through FY 2015, with a final energy consumption reduction goal of 20 percent savings in FY 2015, as compared to the baseline energy consumption of Federal buildings in FY 2003. (These goals were superseded by Section 431 of the Energy Independence and Security Act of 2007.) [Section 102] Energy Use Measurement and Accountability - Requires that all Federal buildings be metered to measure electricity use by 2012. [Section 103] Procurement of Energy Efficient Products - Requires all Federal agencies to procure ENERGY STAR qualified products, for product

456

Residential Energy Consumption Survey (RECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

What's new in our home energy use? What's new in our home energy use? RECS 2009 - Release date: March 28, 2011 First results from EIA's 2009 Residential Energy Consumption Survey (RECS) The 2009 RECS collected home energy characteristics data from over 12,000 U.S. households. This report highlights findings from the survey, with details presented in the Household Energy Characteristics tables. How we use energy in our homes has changed substantially over the past three decades. Over this period U.S. homes on average have become larger, have fewer occupants, and are more energy-efficient. In 2005, energy use per household was 95 million British thermal units (Btu) of energy compared with 138 million Btu per household in 1978, a drop of 31 percent. Did You Know? Over 50 million U.S. homes have three or more televisions.

457

Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study  

Science Journals Connector (OSTI)

Due to the worldwide growth of energy consumption, analysis of energy issues and the development ... become an important issue. In this study, electricity energy consumption of Turkey is predicted by artificial b...

Feyza Gürbüz; Celal Öztürk; Panos Pardalos

2013-09-01T23:59:59.000Z

458

Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings  

E-Print Network [OSTI]

were used to calculate the energy mix in manufacturing,of China’s total energy consumption mix. However, accuratelyof China’s total energy consumption mix. However, accurately

Fridley, David G.

2008-01-01T23:59:59.000Z

459

Energy Consumption, Efficiency, Conservation, and Greenhouse Gas Mitigation in Japan's Building Sector  

E-Print Network [OSTI]

from household energy consumption i n Japan increased b y 20is that household energy consumption i n Japan has notfrom a l l households i n Japan, through 2050 (with energy-

2006-01-01T23:59:59.000Z

460

Solar Adoption and Energy Consumption in the Residential Sector  

E-Print Network [OSTI]

E. Kahn (2011). Electricity Consumption and Durable Housing:49 3.3.3. Pre-installation electricity consumption of CSIon Electricity Consumption .

McAllister, Joseph Andrew

2012-01-01T23:59:59.000Z

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


461

Commercial Buildings Energy Consumption Survey (CBECS) - Analysis &  

Gasoline and Diesel Fuel Update (EIA)

How Will Buildings Be Selected for the 2012 CBECS? How Will Buildings Be Selected for the 2012 CBECS? Background and Overview Did You Know? In the CBECS, commercial refers to any structure that is neither residential, manufacturing/ industrial, nor agricultural. Building refers to a structure that is totally enclosed by walls that extend from the foundation to the roof. Data collection for the 2012 Commercial Buildings Energy Consumption Survey (CBECS) will begin in April 2013, collecting data for reference year 2012. The goal of the CBECS is to provide basic statistical information about energy consumption and expenditures in U.S. commercial buildings and information about energy-related characteristics of these buildings. The 2003 CBECS estimated that there were 4.9 million commercial buildings in the US. Because it would be completely impractical and prohibitively

462

Residential Energy Consumption Survey (RECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

Where does RECS square footage data come from? Where does RECS square footage data come from? RECS 2009 - Release date: July 11, 2012 The size of a home is a fixed characteristic strongly associated with the amount of energy consumed within it, particularly for space heating, air conditioning, lighting, and other appliances. As a part of the Residential Energy Consumption Survey (RECS), trained interviewers measure the square footage of each housing unit. RECS square footage data allow comparison of homes with varying characteristics. In-person measurements are vital because many alternate data sources, including property tax records, real estate listings, and, respondent estimates use varying definitions and under-estimate square footage as defined for the purposes of evaluating residential energy consumption.

463

Factors affecting the energy consumption of two refrigerator-freezers  

SciTech Connect (OSTI)

Two refrigerator-freezers, one with a top-mounted freezer and one with side-by-side doors, were tested in the laboratory to determine the sensitivity of their energy consumption to various operational factors. Room temperature, room humidity, door openings, and the setting of the anti-sweat heater switch were the factors examined. The results indicated that the room temperature and door openings had a significantly greater effect on energy consumption than the other two factors. More detailed tests were then performed under different room temperature and door-opening combinations. The relationship of door openings and the equivalent test room temperature was established. Finally, the effect on energy of different temperature settings was studied. Test results are presented and discussed.

Kao, J.Y.; Kelley, G.E. [National Inst. of Standards and Technology, Gaithersburg, MD (United States). Building and Fire Research Lab.

1996-12-31T23:59:59.000Z

464

Residential Energy Consumption Survey (RECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

EIA household energy use data now includes detail on 16 States EIA household energy use data now includes detail on 16 States RECS 2009 - Release date: March 28, 2011 EIA is releasing new benchmark estimates for home energy use for the year 2009 that include detailed data for 16 States, 12 more than in past EIA residential energy surveys. EIA has conducted the Residential Energy Consumption Survey (RECS) since 1978 to provide data on home energy characteristics, end uses of energy, and expenses for the four Census Regions and nine Divisions. In 1997, EIA produced additional tabulations for the four most populous States (California, New York, Texas, and Florida). A threefold increase in the number of households included in the 2009 RECS offers more accuracy and coverage for understanding energy usage for all estimated States, Regions and Divisions.

465

" Row: NAICS Codes; Column: Energy-Consumption Ratios;"  

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

N7.1. Consumption Ratios of Fuel, 1998;" N7.1. Consumption Ratios of Fuel, 1998;" " Level: National and Regional Data; " " Row: NAICS Codes; Column: Energy-Consumption Ratios;" " Unit: Varies." " "," ",,,"Consumption"," " " "," ",,"Consumption","per Dollar"," " " "," ","Consumption","per Dollar","of Value","RSE" "NAICS"," ","per Employee","of Value Added","of Shipments","Row" "Code(a)","Subsector and Industry","(million Btu)","(thousand Btu)","(thousand Btu)","Factors"

466

" Row: NAICS Codes; Column: Energy-Consumption Ratios;"  

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

1 Consumption Ratios of Fuel, 2002;" 1 Consumption Ratios of Fuel, 2002;" " Level: National and Regional Data; " " Row: NAICS Codes; Column: Energy-Consumption Ratios;" " Unit: Varies." " "," ",,,"Consumption"," " " "," ",,"Consumption","per Dollar"," " " "," ","Consumption","per Dollar","of Value","RSE" "NAICS"," ","per Employee","of Value Added","of Shipments","Row" "Code(a)","Subsector and Industry","(million Btu)","(thousand Btu)","(thousand Btu)","Factors"

467

Analyzing the Impact of Useless Write-Backs on the Endurance and Energy Consumption of PCM  

E-Print Network [OSTI]

. With an estimated annual cost of 7.4 billion dollars for 2011, energy consumption has become a primary factorAnalyzing the Impact of Useless Write-Backs on the Endurance and Energy Consumption of PCM Main-effective and energy-efficient alternative to traditional DRAM main memory. Due to the high energy consumption

Zhang, Youtao

468

Mechanism design for aggregating energy consumption and quality of service in speed  

E-Print Network [OSTI]

in a way that minimizes energy while respecting the jobs' deadlines. The energy consumption is then chargedScale. Higher speed means that jobs finish earlier at the price of a higher energy consumption. Each job hasMechanism design for aggregating energy consumption and quality of service in speed scaling

Paris-Sud XI, Université de

469

Manufacturing Energy Consumption Survey (MECS) - Analysis & Projections -  

Gasoline and Diesel Fuel Update (EIA)

Manufacturing Energy Consumption Survey (MECS) Manufacturing Energy Consumption Survey (MECS) Glossary › FAQS › Overview Data 2010 2006 2002 1998 1994 1991 Archive Analysis & Projections MECS Industry Analysis Briefs Steel Industry Analysis The steel industry is critical to the U.S. economy. Steel is the material of choice for many elements of construction, transportation, manufacturing, and a variety of consumer products. It is the backbone of bridges, skyscrapers, railroads, automobiles, and appliances. Most grades of steel used today - particularly high-strength steels that are lighter and more versatile - were not available a decade ago. Chemical Industry Analysis The chemical industries are a cornerstone of the U.S. economy, converting raw materials such as oil, natural gas, air, water, metals, and minerals

470

3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information  

Open Energy Info (EERE)

TIER Environmental Forecast Group Inc 3TIER TIER Environmental Forecast Group Inc 3TIER Jump to: navigation, search Name 3TIER Environmental Forecast Group Inc (3TIER) Place Seattle, Washington Zip 98121 Sector Renewable Energy Product Seattle-based, renewable energy assessment and forecasting company. Coordinates 47.60356°, -122.329439° 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":47.60356,"lon":-122.329439,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

471

Analysis of energy consumption and indicators of energy use in Bangladesh  

Science Journals Connector (OSTI)

Rapid growth in the demand for commercial energy in Bangladesh poses serious development constraints in recent years. Per capita energy consumption of Bangladesh is one of the lowest in the world (252 kgoe in 200...

Joarder Mohammad Abdul Munim; Md. Mahbubul Hakim…

2010-11-01T23:59:59.000Z

472

U.S. Department of Energy Workshop Report: Solar Resources and Forecasting  

SciTech Connect (OSTI)

This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

Stoffel, T.

2012-06-01T23:59:59.000Z

473

Constraining Energy Consumption of China's Largest Industrial Enterprises Through the Top-1000 Energy-Consuming Enterprise Program  

E-Print Network [OSTI]

Daily, 2007. Energy consumption per unit GDP down 1.23%increase in energy use per unit of GDP after 2002 following2006, the energy consumption per unit of GDP declined 1.23%

Price, Lynn; Wang, Xuejun

2007-01-01T23:59:59.000Z

474

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book [EERE]

4 4 Ownership (1) Owned 54.9 104.5 40.3 78% Rented 77.4 71.7 28.4 22% Public Housing 75.7 62.7 28.7 2% Not Public Housing 77.7 73.0 28.4 19% 100% Note(s): Source(s): 1) Energy consumption per square foot was calculated using estimates of average heated floor space per household. According to the 2005 Residential Energy Consumption Survey (RECS), the average heated floor space per household in the U.S. was 1,618 square feet. Average total floor space, which includes garages, attics and unfinished basements, equaled 2,309 square feet. EIA, 2005 Residential Energy Consumption Survey, Oct. 2008 2005 Residential Delivered Energy Consumption Intensities, by Ownership of Unit Per Square Per Household Per Household Percent of Foot (thousand Btu) (million Btu) Members (million Btu) Total Consumption

475

Household energy consumption and its demand elasticity in Thailand  

Science Journals Connector (OSTI)

This study concentrates on the analysis of energy consumption, expenditure on oil and LPG use in cars and aims to examine the elasticity effect of various types of oil consumption. By using the Deaton's analysis framework, the cross-sectional data of Thai households economic survey 2009 were used. By defining energy goods in the scope of automobile fuel, the results reflect the low importance of high-quality automobile fuel on all income level households. Thai households tend to vary the quality rather than the quantity of thermal energy. All income groups have a tendency to switch to lower quality fuel. Middle and high-middle households (Q3 and Q4) are the income groups with the greatest tendency to switch to lower-quality fuel when a surge in the price of oil price occurs. The poorest households (Q1) are normally insensitive to a change of energy expenditure in terms of quality and quantity. This finding illustrates the LPG price subsidy policy favours middle and high-middle income households. The price elasticity of energy quantity demand is negative in all income levels. High to middle income families are the most sensitive to changes in the price of energy.

Montchai Pinitjitsamut

2012-01-01T23:59:59.000Z

476

MPC for Wind Power Gradients --Utilizing Forecasts, Rotor Inertia, and Central Energy Storage  

E-Print Network [OSTI]

MPC for Wind Power Gradients -- Utilizing Forecasts, Rotor Inertia, and Central Energy Storage iterations. We demonstrate our method in simulations with various wind scenarios and prices for energy. INTRODUCTION Today, wind power is the most important renewable energy source. For the years to come, many

477

European Wind Energy Conference -Brussels, Belgium, April 2008 Data mining for wind power forecasting  

E-Print Network [OSTI]

European Wind Energy Conference - Brussels, Belgium, April 2008 Data mining for wind power-term forecasting of wind energy produc- tion up to 2-3 days ahead is recognized as a major contribution the improvement of predic- tion systems performance is recognised as one of the priorities in wind energy research

Paris-Sud XI, Université de

478

DOE/EIA-0314(82) Residential Energy Consumption Survey:  

Gasoline and Diesel Fuel Update (EIA)

4(82) 4(82) Residential Energy Consumption Survey: Housing Characteri stics 1982 Published: August 1984 U-'VVv*' ^**" ^ Energy Information Administration Washington, D.C. This public ation is availa ble from the Supe rinten dent of Docu ments , U.S. Gove rnme nt Printin g Office (GPO ). Order ing inform ation and purch ase of this and other Energ y Inform ation Admi nistra tion (EIA) public ations may be obtain ed from the GPO or the ElA's Natio nal Energ y Inform ation Cente r (NEIC ). Ques tions on energ y statis tics

479

The electricity consumption impacts of commercial energy management systems  

SciTech Connect (OSTI)

An investigation of energy management systems (EMS) in large commercial and institutional buildings in North Carolina was undertaken to determine how EMS currently affect electricity consumption and what their potential is for being used to reduce on-peak electricity demand. A survey was mailed to 5000 commercial customers; the 430 responses were tabulated and analyzed; EMS vendors were interviewed, and 30 sites were investigated in detail. The detailed assessments included a site interview and reconstruction of historic billing data to evaluate EMS impact, if any. The results indicate that well-tuned EMS can result in a 10 to 40 percent reduction in billed demand, and smaller reductions in energy.

Buchanan, S.; Taylor, R.; Paulos, S.; Warren, W.; Hay, J.

1989-02-01T23:59:59.000Z

480

TAPPI survey of energy consumption: A snapshot of industry trends  

SciTech Connect (OSTI)

Energy management is one of the most important aspects of mill operation. Mills compete chiefly on the basis of price and product quality. Because pulp and paper production consumes tremendous amount of energy, the mill that can reduce the energy consumed per ton of production gains a competitive edge. The opportunities for savings range from investment in new equipment to simply increasing the efficiency of existing operations. The authors wanted to learn what mills are doing to reduce energy consumption in 1994. He also wanted to know if energy management at the mill is as important today as it was a decade ago. The results presented here are based on the 105 responses from a survey.

Burke, D.J.

1994-09-01T23:59:59.000Z

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


481

Global Inequality in Energy Consumption from 1980 to 2010  

E-Print Network [OSTI]

We study the global probability distribution of energy consumption per capita around the world using data from the U.S. Energy Information Administration (EIA) for 1980-2010. We find that the Lorenz curves have moved up during this time period, and the Gini coefficient G has decreased from 0.66 in 1980 to 0.55 in 2010, indicating a decrease in inequality. The global probability distribution of energy consumption per capita in 2010 is close to the exponential distribution with G=0.5. We attribute this result to the globalization of the world economy, which mixes the world and brings it closer to the state of maximal entropy. We argue that global energy production is a limited resource that is partitioned among the world population. The most probable partition is the one that maximizes entropy, thus resulting in the exponential distribution function. A consequence of the latter is the law of 1/3: the top 1/3 of the world population consumes 2/3 of produced energy. We also find similar results for the global pro...

Lawrence, Scott; Yakovenko, Victor M

2013-01-01T23:59:59.000Z

482

Cherish every Joule: Maximizing throughput with an eye on network-wide energy consumption  

E-Print Network [OSTI]

Cherish every Joule: Maximizing throughput with an eye on network-wide energy consumption Canming: {jcm, yshi, thou, wjlou}@vt.edu Abstract Conserving network-wide energy consumption is becoming of wireless networks, the concern of energy consumption is becoming in- creasingly important for network

Hou, Y. Thomas

483

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1 Coverage and Energy Consumption Control in  

E-Print Network [OSTI]

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1 Coverage and Energy Consumption Control in Mobile Abstract--In this paper 1 , we investigate the coverage and energy consumption control in mobile. Meanwhile, we can operate a tradeoff control between coverage performance and energy consumption

Wang, Xinbing

484

Optimal Energy Consumption Scheduling Using Mechanism Design for the Future Smart Grid  

E-Print Network [OSTI]

Optimal Energy Consumption Scheduling Using Mechanism Design for the Future Smart Grid Pedram may need to collect various information about users and their energy consumption behavior, which can this problem, different programs have been proposed to shape the energy consumption pattern of the users

Wong, Vincent

485

An off-line multiprocessor real-time scheduling algorithm to reduce static energy consumption  

E-Print Network [OSTI]

An off-line multiprocessor real-time scheduling algorithm to reduce static energy consumption, France laurent.pautet@telecom-paristech.fr Abstract--Energy consumption of highly reliable real dynamic energy consumption. This paper aims to propose a new off-line schedul- ing algorithm to put

Paris-Sud XI, Université de

486

Energy management of HEV to optimize fuel consumption and pollutant emissions  

E-Print Network [OSTI]

AVEC'12 Energy management of HEV to optimize fuel consumption and pollutant emissions Pierre Michel, several energy management strategies are proposed to optimize jointly the fuel consumption and pollutant-line strategy are given. Keywords: Hybrid Electric Vehicle (HEV), energy management, pollution, fuel consumption

Paris-Sud XI, Université de

487

Measuring the Client Performance and Energy Consumption in Mobile Cloud Gaming  

E-Print Network [OSTI]

Measuring the Client Performance and Energy Consumption in Mobile Cloud Gaming Chun-Ying Huang1, Po-constrained devices may lead to inferior performance and high energy consumption. For example, the gaming frame rate and energy consumption of mobile clients is critical to the success of the new mobile cloud gaming ecosystem

Chen, Sheng-Wei

488

An Analysis of Hard Drive Energy Consumption Anthony Hylick, Ripduman Sohan, Andrew Rice, and Brian Jones  

E-Print Network [OSTI]

An Analysis of Hard Drive Energy Consumption Anthony Hylick, Ripduman Sohan, Andrew Rice, and Brian consumed by the electronics of a drive is just as important as the mechanical energy consumption; (ii consumption was a concern pri- marily for mobile computing domains. The rising cost of energy and increased

Cambridge, University of

489

Energy-Aware Networks: Reducing Power Consumption By Switching Off Network Elements  

E-Print Network [OSTI]

Energy-Aware Networks: Reducing Power Consumption By Switching Off Network Elements Luca% of the worldwide energy consumption, and several initiatives are being punt into place to reduce the power power consumption, even without taking into account the energy necessary for equipment cooling [4

Mellia, Marco

490

INFLUENCES OF RAKE RECEIVER/TURBO DECODER PARAMETERS ON ENERGY CONSUMPTION AND QUALITY  

E-Print Network [OSTI]

INFLUENCES OF RAKE RECEIVER/TURBO DECODER PARAMETERS ON ENERGY CONSUMPTION AND QUALITY Lodewijk T are selected and their influences on the energy consumption and quality are investigated by means power hardware is needed to save energy consumption. Furthermore, an adequate quality of the wireless

Al Hanbali, Ahmad

491

UBC Social Ecological Economic Development Studies (SEEDS) Student Report Elevator Drive Systems Energy Consumption Study Report  

E-Print Network [OSTI]

Energy Consumption Study Report Benny ChunYin Chan University of British Columbia EECE 492 April 6th the current status of the subject matter of a project/report". #12;Elevator Drive Systems Energy Consumption Study Report April 2012 0 2012 Elevator Drive Systems Energy Consumption Study Report Benny CY Chan UBC

492

Mobile Location Sharing: An Energy Consumption Study Ekhiotz Jon Vergara, Mihails Prihodko, Simin Nadjm-Tehrani  

E-Print Network [OSTI]

Mobile Location Sharing: An Energy Consumption Study Ekhiotz Jon Vergara, Mihails Prihodko, Simin- packet interval) highly influences the energy consumption of the mobile device. Our work focuses other clients' location updates (similar to pull behaviour). In order to evaluate the energy consumption and the

493

THE NEXUS BETWEEN ENERGY CONSUMPTION AND ECONOMIC GROWTH IN OECD COUNTRIES: A DECOMPOSITION ANALYSIS  

E-Print Network [OSTI]

1 THE NEXUS BETWEEN ENERGY CONSUMPTION AND ECONOMIC GROWTH IN OECD COUNTRIES: A DECOMPOSITION the impacts of renewable and non-renewable energy consumption on economic activities to find out whether and both renewable and non-renewable energy consumption in the short- and long run. This finding confirms

494

IEEE INFOCOM 2001 1 Investigating the Energy Consumption of a Wireless  

E-Print Network [OSTI]

IEEE INFOCOM 2001 1 Investigating the Energy Consumption of a Wireless Network Interface in an Ad and evaluation of network protocols re­ quires knowledge of the energy consumption behavior of actual wireless interfaces. But little practical information is available about the energy consumption behavior of well

495

Accounting for the Energy Consumption of Personal Computing Including Portable Devices  

E-Print Network [OSTI]

Accounting for the Energy Consumption of Personal Computing Including Portable Devices Pavel.S.A vinod.namboodiri@wichita.edu ABSTRACT In light of the increased awareness of global energy consumption the share of energy consumption due to these equipment over the years, these have rarely characterized

Namboodiri, Vinod

496

Balancing Energy and Water Consumption in an Urban Desert Environment: A Case  

E-Print Network [OSTI]

Balancing Energy and Water Consumption in an Urban Desert Environment: A Case Study on Phoenix, AZ effect, water scarcity, and energy consumption. The transformation of native landscapes into built to cool homes. Identifying Direct and Indirect Costs of Water and Energy Consumption Study Area Although

Hall, Sharon J.

497

Energy-Aware Networks: Reducing Power Consumption By Switching Off Network Elements  

E-Print Network [OSTI]

Energy-Aware Networks: Reducing Power Consumption By Switching Off Network Elements Luca% of the worldwide energy consumption, and several initiatives are being put into place to reduce the power power consumption, even without taking into account the energy necessary for equipment cooling [4

Mellia, Marco

498

Experimental Study on the Energy Consumption in IaaS Cloud Environments  

E-Print Network [OSTI]

Experimental Study on the Energy Consumption in IaaS Cloud Environments Alexandra Carpen.morin@inria.fr Abstract--Energy consumption has always been a major concern in the design and cost of datacenters the energy consumption of a cloud system, the hardware-component level is one of the most intensively studied

Paris-Sud XI, Université de

499

Unit Testing of Energy Consumption of Software Libraries Adel Noureddine1,2  

E-Print Network [OSTI]

Unit Testing of Energy Consumption of Software Libraries Adel Noureddine1,2 , Romain Rouvoy1. In this paper, we therefore introduce JalenUnit, a software framework that infers the energy consumption model, and comparing software libraries against their energy consumption. Categories and Subject Descriptors D.2

Boyer, Edmond

500

CONSUMPTION AND CHANGES IN HOME ENERGY COSTS: HOW PREVALENT IS THE `HEAT OR EAT' DECISION?  

E-Print Network [OSTI]

CONSUMPTION AND CHANGES IN HOME ENERGY COSTS: HOW PREVALENT IS THE `HEAT OR EAT' DECISION?· Julie how household consumption responds to changes in home energy outlays over the course of the year. We specify Euler equations describing nondurable and food consumption and then rely on changes in energy

Sadoulet, Elisabeth