National Library of Energy BETA

Sample records for ieo2004 forecast consumption

  1. Energy consumption and expenditure projections by population group on the basis of the annual energy outlook 1999 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Balsley, J.H.

    2000-01-07

    This report presents an analysis of the relative impact of the base-case scenario used in Annual Energy Outlook 1999 on different population groups. Projections of energy consumption and expenditures, as well as energy expenditure as a share of income, from 1996 to 2020 are given. The projected consumption of electricty, natural gas, distillate fuel, and liquefied petroleum gas during this period is also reported for each population group. In addition, this report compares the findings of the Annual Energy Outlook 1999 report with the 1998 report. Changes in certain indicators and information affect energy use forecasts, and these effects are analyzed and discussed.

  2. Consumption

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

    A. Fuel Oil Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,"Total Floorspace of Buildings...

  3. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Climate Zonea for Non-Mall Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,,,"Total Floorspace of...

  4. Consumption

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

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

  5. Consumption

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

    A. Electricity Consumption and Conditional Energy Intensity by Building Size for All Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of...

  6. Consumption

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

    3. Electricity Consumption and Conditional Energy Intensity, 1999" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of Buildings Using Electricity (million square...

  7. Consumption

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

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

  8. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Building Size for Non-Mall Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of...

  9. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Division for Non-Mall Buildings, 2003: Part 1" ,"Total Electricity Consumption (billion kWh)",,,"Total...

  10. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Division for Non-Mall Buildings, 2003: Part 2" ,"Total Electricity Consumption (billion kWh)",,,"Total...

  11. Consumption

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

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

  12. Consumption

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

    Electricity Consumption and Conditional Energy Intensity by Census Region, 1999" ,"Total Electricity Consumption (billion kWh)",,,,"Total Floorspace of Buildings Using Electricity...

  13. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Region for Non-Mall Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,,"Total Floorspace of...

  14. Consumption

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

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

  15. Consumption

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

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

  16. Consumption

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

    4. Electricity Consumption and Conditional Energy Intensity by Year Constructed, 1999" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of Buildings Using...

  17. Consumption

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

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

  18. Consumption

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

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

  19. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Division for Non-Mall Buildings, 2003: Part 3" ,"Total Electricity Consumption (billion kWh)",,,"Total...

  20. Consumption

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

    5. Fuel Oil Consumption and Conditional Energy Intensity by Census Region for Non-Mall Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,"Total Floorspace of...

  1. Consumption

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

    3. Fuel Oil Consumption and Conditional Energy Intensity by Census Region, 1999" ,"Total Fuel Oil Consumption (million gallons)",,,,"Total Floorspace of Buildings Using Fuel Oil...

  2. Consumption

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

    . 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...

  3. LED Lighting Forecast | Department of Energy

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

    Publications » Market Studies » LED Lighting Forecast LED Lighting Forecast The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030. With declining costs and improving performance, LED products have been seeing increased adoption for general illumination applications. This is a positive development in terms of energy consumption, as LEDs use significantly

  4. Solar Forecasting

    Broader source: Energy.gov [DOE]

    On December 7, 2012, DOE announced $8 million to fund two solar projects that are helping utilities and grid operators better forecast when, where, and how much solar power will be produced at U.S....

  5. Survey Consumption

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

    purchase diaries from a subset of respondents composing a Household Transportation Panel and is reported separately. Residential Energy Consumption Survey: Consumption and...

  6. Forecast Change

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

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,151 3,302 4.8% Price (cents/kWh) 12.06 12.09 12.58 13.04 12.95 12.84 -0.9% Expenditures $415 $405 $393 $396 $408 $424 3.9% New England Usage (kWh) 2,122 2,188 2,173 1,930 1,992 2,082 4.5% Price (cents/kWh) 15.85 15.50 16.04 17.63 18.64 18.37 -1.5% Expenditures $336 $339 $348 $340 $371 $382 3.0% Mid-Atlantic Usage (kWh) 2,531 2,548 2,447 2,234 2,371 2,497 5.3% Price (cents/kWh) 16.39 15.63

  7. Short-Term Energy Outlook Model Documentation: Motor Gasoline Consumption Model

    Reports and Publications (EIA)

    2011-01-01

    The motor gasoline consumption module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of total U.S. consumption of motor gasolien based on estimates of vehicle miles traveled and average vehicle fuel economy.

  8. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01

    This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

  9. Wind Power Forecasting Data

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

    Operations Call 2012 Retrospective Reports 2012 Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email...

  10. probabilistic energy production forecasts

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

    energy production forecasts - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary ...

  11. Wind Power Forecasting

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

    data Presentations BPA Super Forecast Methodology Related Links Near Real-time Wind Animation Meteorological Data Customer Supplied Generation Imbalance Dynamic Transfer Limits...

  12. Forecasting Water Quality & Biodiversity

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

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability ... that measure feedstock production, water quality, water quantity, and biodiversity. ...

  13. NREL: Transmission Grid Integration - Forecasting

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

    Forecasting NREL researchers use solar and wind resource assessment and forecasting techniques to develop models that better characterize the potential benefits and impacts of ...

  14. 2016 Solar Forecasting Workshop

    Office of Energy Efficiency and Renewable Energy (EERE)

    On August 3, 2016, the SunShot Initiative's systems integration subprogram hosted the Solar Forecasting Workshop to convene experts in the areas of bulk power system operations, distribution system operations, weather and solar irradiance forecasting, and photovoltaic system operation and modeling. The goal was to identify the technical challenges and opportunities in solar forecasting as a capability that can significantly reduce the integration cost of high levels of solar energy into the electricity grid. This will help SunShot to assess current technology and practices in this field and identify the gaps and needs for further research.

  15. Today's Forecast: Improved Wind Predictions

    Broader source: Energy.gov [DOE]

    Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable.

  16. Solar Forecast Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE)

    For the Solar Forecast Improvement Project (SFIP), the Earth System Research Laboratory (ESRL) is partnering with the National Center for Atmospheric Research (NCAR) and IBM to develop more...

  17. Acquisition Forecast | Department of Energy

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

    Acquisition Forecast Acquisition Forecast Acquisition Forecast It is the policy of the U.S. Department of Energy (DOE) to provide timely information to the public regarding DOE's forecast of future prime contracting opportunities and subcontracting opportunities which are available via the Department's major site and facilities management contractors. This forecast has been expanded to also provide timely status information for ongoing prime contracting actions that are valued in excess of the

  18. Manufacturing Consumption of Energy 1991--Combined Consumption...

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

    call 202-586-8800 for help. Return to Energy Information Administration Home Page. Home > Energy Users > Manufacturing > Consumption and Fuel Switching Manufacturing Consumption of...

  19. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J.

    2011-02-23

    The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

  20. ,"Total Fuel Oil Consumption

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

    A. Fuel Oil Consumption (gallons) and Energy Intensities by End Use for All Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,,"Fuel Oil Energy Intensity...

  1. ,"Total Fuel Oil Consumption

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

    0. Fuel Oil Consumption (gallons) and Energy Intensities by End Use for Non-Mall Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,,"Fuel Oil Energy Intensity...

  2. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect (OSTI)

    Not Available

    1981-05-27

    This report presents an overview of forecasts of domestic energy consumption, production, and prices for the year 1990. These results are selected from more detailed projections prepared and published in Volume 3 of the Energy Information Administration 1980 Annual Report to Congress. This report focuses specifically upon the 1980's and concentrates upon similarities and differences in the domestic energy system, as forecast, compared to the national experience in the years immediately following the 1973--1974 oil embargo. Interest in the 1980's stems not only from its immediacy in time, but also from its importance as a time in which certain adjustments to higher energy prices are expected to take place. The forecasts presented do not attempt to account for all of this wide range of potentially important forces that could conceivably alter the energy situation. Instead, the projections are based on a particular set of assumptions that seems reasonable in light of what is currently known. 9 figs., 25 tabs.

  3. Manufacturing Consumption of Energy 1994

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

    (MECS) > MECS 1994 Combined Consumption and Fuel Switching Manufacturing Energy Consumption Survey 1994 (Combined Consumption and Fuel Switching) Manufacturing Energy Consumption...

  4. Using Wikipedia to forecast diseases

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

    Using Wikipedia to forecast diseases Using Wikipedia to forecast diseases Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. November 13, 2014 Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Contact Nancy Ambrosiano Communications Office (505)

  5. Baseline and Target Values for PV Forecasts: Toward Improved...

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

    Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting ... Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting Jie ...

  6. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01

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

  7. The forecast calls for flu

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

    Science on the Hill: The forecast calls for flu Using mathematics, computer programs, ... We're getting close. Using mathematics, computer programs, statistics and information ...

  8. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

    Zip: 94965 Region: Bay Area Sector: Services Product: Intelligent Monitoring and Forecasting Services Year Founded: 2010 Website: www.forecastenergy.net Coordinates:...

  9. U.S. gasoline consumption highest in 8 years

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

    U.S. gasoline consumption highest in 8 years U.S. gasoline consumption this year is expected to be at the highest level since the record fuel demand seen back in 2007 as lower gasoline prices and more people finding jobs means more sales at the gasoline pump. In its new monthly forecast, the U.S. Energy Information Administration said gasoline consumption increased by 2.7% during the first eight months of 2015 and should rise by an average of 190,000 barrels per day this year to 9.1 million

  10. Short-Term Energy Outlook Model Documentation: Other Petroleum Products Consumption Model

    Reports and Publications (EIA)

    2011-01-01

    The other petroleum product consumption module of the Short-Term Energy Outlook (STEO) model is designed to provide U.S. consumption forecasts for 6 petroleum product categories: asphalt and road oil, petrochemical feedstocks, petroleum coke, refinery still gas, unfinished oils, and other miscvellaneous products

  11. Upcoming Funding Opportunity for Wind Forecasting Improvement...

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

    Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am ...

  12. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

    Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast AgencyCompany Organization: United States Department of Energy Sector:...

  13. Project Profile: Forecasting and Influencing Technological Progress...

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

    Soft Costs Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar ...

  14. National Oceanic and Atmospheric Administration Provides Forecasting...

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

    ... will share their expertise with CLASIC and CHAPS forecasters and project leaders as they consult on the forecast that will determine the day's operations plan. -- Storm Prediction ...

  15. Intermediate future forecasting system

    SciTech Connect (OSTI)

    Gass, S.I.; Murphy, F.H.; Shaw, S.H.

    1983-12-01

    The purposes of the Symposium on the Department of Energy's Intermediate Future Forecasting System (IFFS) were: (1) to present to the energy community details of DOE's new energy market model IFFS; and (2) to have an open forum in which IFFS and its major elements could be reviewed and critiqued by external experts. DOE speakers discussed the total system, its software design, and the modeling aspects of oil and gas supply, refineries, electric utilities, coal, and the energy economy. Invited experts critiqued each of these topics and offered suggestions for modifications and improvement. This volume documents the proceedings (papers and discussion) of the Symposium. Separate abstracts have been prepared for each presentation for inclusion in the Energy Data Base.

  16. Commercial Buildings Energy Consumption and Expenditures 1992...

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

    Consumption and Expenditures Electricity Consumption Natural Gas Consumption Wood and Solar Energy Consumption Fuel Oil and District Heat Consumption Energy Consumption in...

  17. National Lighting Energy Consumption

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

    Lighting Energy National Lighting Energy Consumption Consumption 390 Billion kWh used for lighting in all 390 Billion kWh used for lighting in all commercial buildings in commercial buildings in 2001 2001 LED (<.1% ) Incandescent 40% HID 22% Fluorescent 38% Lighting Energy Consumption by Lighting Energy Consumption by Breakdown of Lighting Energy Breakdown of Lighting Energy Major Sector and Light Source Type Major Sector and Light Source Type Source: Navigant Consulting, Inc., U.S. Lighting

  18. Residential Energy Consumption Survey:

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

    ... ...*...,,.<,<,...,,.,,.,,. 97 Table 6. Residential Fuel Oil and Kerosene Consumption and Expenditures April 1979 Through March 1980 Northeast...

  19. Science on Tap - Forecasting illness

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

    Science on Tap - Forecasting illness Science on Tap - Forecasting illness WHEN: Mar 17, 2016 5:30 PM - 7:00 PM WHERE: UnQuarked Wine Room 145 Central Park Square, Los Alamos, New Mexico 87544 USA CONTACT: Linda Anderman (505) 665-9196 CATEGORY: Bradbury INTERNAL: Calendar Login Event Description Mark your calendars for this event held every third Thursday from 5:30 to 7 p.m. A short presentation is followed by a lively discussion on a different subject each month. Forecasting the flu (and other

  20. Acquisition Forecast Download | Department of Energy

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

    Acquisition Forecast Download Acquisition Forecast Download Click on the link to download a copy of the DOE HQ Acquisition Forecast. Acquisition-Forecast-2016-07-20.xlsx (72.85 KB) More Documents & Publications Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment Assessment Report: OAS-V-15-01

  1. All Consumption Tables.vp

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

    4) June 2007 State Energy Consumption Estimates 1960 Through 2004 2004 Consumption Summary Tables Table S1. Energy Consumption Estimates by Source and End-Use Sector, 2004...

  2. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01

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

  3. Office Buildings - Energy Consumption

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

    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,...

  4. ,"Total Natural Gas Consumption

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

    Gas Consumption (billion cubic feet)",,,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"Total ","Space Heating","Water Heating","Cook- ing","Other","Total ","Space...

  5. Forecast of transportation energy demand through the year 2010

    SciTech Connect (OSTI)

    Mintz, M.M.; Vyas, A.D.

    1991-04-01

    Since 1979, the Center for Transportation Research (CTR) at Argonne National Laboratory (ANL) has produced baseline projections of US transportation activity and energy demand. These projections and the methodologies used to compute them are documented in a series of reports and research papers. As the lastest in this series of projections, this report documents the assumptions, methodologies, and results of the most recent projection -- termed ANL-90N -- and compares those results with other forecasts from the current literature, as well as with the selection of earlier Argonne forecasts. This current forecast may be used as a baseline against which to analyze trends and evaluate existing and proposed energy conservation programs and as an illustration of how the Transportation Energy and Emission Modeling System (TEEMS) works. (TEEMS links disaggregate models to produce an aggregate forecast of transportation activity, energy use, and emissions). This report and the projections it contains were developed for the US Department of Energy's Office of Transportation Technologies (OTT). The projections are not completely comprehensive. Time and modeling effort have been focused on the major energy consumers -- automobiles, trucks, commercial aircraft, rail and waterborne freight carriers, and pipelines. Because buses, rail passengers services, and general aviation consume relatively little energy, they are projected in the aggregate, as other'' modes, and used primarily as scaling factors. These projections are also limited to direct energy consumption. Projections of indirect energy consumption, such as energy consumed in vehicle and equipment manufacturing, infrastructure, fuel refining, etc., were judged outside the scope of this effort. The document is organized into two complementary sections -- one discussing passenger transportation modes, and the other discussing freight transportation modes. 99 refs., 10 figs., 43 tabs.

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

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

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

  7. Picture of the Week: Forecasting Flu

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

    3 Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? March 6, 2016 flu epidemics modellled using social media Watch the video on YouTube. Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? Using real-time data from Wikipedia and social media, Sara del

  8. The Value of Wind Power Forecasting

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

    ... day-ahead wind generation forecasts yields an average of 195M savings in annual operating costs. Figure 6 shows how operating cost savings vary with improvements in forecasting. ...

  9. EIA lowers forecast for summer gasoline prices

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

    EIA lowers forecast for summer gasoline prices U.S. gasoline prices are expected to be ... according to the new monthly forecast from the U.S. Energy Information Administration. ...

  10. UPF Forecast | Y-12 National Security Complex

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

    Subcontracting / Subcontracting Forecasts / UPF Forecast UPF Forecast UPF Procurement provides the following forecast of subcontracting opportunities. Keep in mind that these requirements may be revised or cancelled, depending on program budget funding or departmental needs. If you have questions or would like to express an interest in any of the opportunities listed below, contact UPF Procurement. Descriptiona Methodb NAICS Est. Dollar Range RFP release/ Award datec Buyer/ Phone Commodities

  11. Wind Forecasting Improvement Project | Department of Energy

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

    Forecasting Improvement Project Wind Forecasting Improvement Project October 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 project with the National Oceanic and Atmospheric Administration (NOAA) and private partners to improve wind forecasting. Wind power forecasting allows system operators to anticipate the electrical output of wind plants and adjust the electrical

  12. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect (OSTI)

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

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

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

    purchase diaries from a subset of respondents composing a Household Transportation Panel and is reported separately. Residential Energy Consumption Survey: Consumption and...

  14. Short-Term Energy Outlook Model Documentation: Electricity Generation and Fuel Consumption Models

    Gasoline and Diesel Fuel Update (EIA)

    Model Documentation: Electricity Generation and Fuel Consumption Models January 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Model Documentation: Electricity Generation and Fuel Consumption Models i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts

  15. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-01-01

    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).

  16. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-12-31

    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).

  17. US WNC MO Site Consumption

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

    to historically lower residential electricity prices in the state. * Missouri ... CONSUMPTION BY END USE Consumption of energy for the four major end uses in Missouri homes is ...

  18. US ESC TN Site Consumption

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

    than the U.S. average. * Average electricity consumption for Tennessee households is 33% ... CONSUMPTION BY END USE Compared to other areas of the United States, the warmer ...

  19. Supply Forecast and Analysis (SFA)

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

    Matthew Langholtz Science Team Leader Oak Ridge National Laboratory DOE Bioenergy Technologies Office (BETO) 2015 Project Peer Review Supply Forecast and Analysis (SFA) 2 | Bioenergy Technologies Office Goal Statement * Provide timely and credible estimates of feedstock supplies and prices to support - the development of a bioeconomy; feedstock demand analysis of EISA, RFS2, and RPS mandates - the data and analysis of other projects in Analysis and Sustainability, Feedstock Supply and Logistics,

  20. Changes in Natural Gas Monthly Consumption Data Collection and the Short-Term Energy Outlook

    Reports and Publications (EIA)

    2010-01-01

    Beginning with the December 2010 issue of the Short-Term Energy Outlook (STEO), the Energy Information Administration (EIA) will present natural gas consumption forecasts for the residential and commercial sectors that are consistent with recent changes to the Form EIA-857 monthly natural gas survey.

  1. Baseline data for the residential sector and development of a residential forecasting database

    SciTech Connect (OSTI)

    Hanford, J.W.; Koomey, J.G.; Stewart, L.E.; Lecar, M.E.; Brown, R.E.; Johnson, F.X.; Hwang, R.J.; Price, L.K.

    1994-05-01

    This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. It provides a description of the methodology used to develop the database and describes the data used for heating and cooling end-uses as well as for typical household appliances. This report provides information on end-use unit energy consumption (UEC) values of appliances and equipment historical and current appliance and equipment market shares, appliance and equipment efficiency and sales trends, cost vs efficiency data for appliances and equipment, product lifetime estimates, thermal shell characteristics of buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings, baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and other economic drivers. Model inputs and outputs, as well as all other information in the database, are fully documented with the source and an explanation of how they were derived.

  2. ARM - CARES - Tracer Forecast for CARES

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

    CampaignsCarbonaceous Aerosols and Radiative Effects Study (CARES)Tracer Forecast for CARES Related Links CARES Home AAF Home ARM Data Discovery Browse Data Post-Campaign Data Sets Field Updates CARES Wiki Campaign Images Experiment Planning Proposal Abstract and Related Campaigns Science Plan Operations Plan Measurements Forecasts News News & Press Backgrounder (PDF, 1.45MB) G-1 Aircraft Fact Sheet (PDF, 1.3MB) Contacts Rahul Zaveri, Lead Scientist Tracer Forecasts for CARES This webpage

  3. NREL: Resource Assessment and Forecasting Home Page

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

    are used to plan and develop renewable energy technologies and support climate change research. Learn more about NREL's resource assessment and forecasting research:...

  4. Funding Opportunity Announcement for Wind Forecasting Improvement...

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

    There is no cost to participate and all applicants are encouraged to attend. To join the ... Related Articles Upcoming Funding Opportunity for Wind Forecasting Improvement Project in ...

  5. Module 6 - Metrics, Performance Measurements and Forecasting...

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

    This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices. In addition, this module will outline forecasting tools such as ...

  6. Forecast and Funding Arrangements - Hanford Site

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

    Annual Waste Forecast and Funding Arrangements About Us Hanford Site Solid Waste Acceptance Program What's New Acceptance Criteria Acceptance Process Becoming a new Hanford...

  7. NREL: Resource Assessment and Forecasting - Webmaster

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

    email address: Your message: Send Message Printable Version Resource Assessment & Forecasting Home Capabilities Facilities Working with Us Research Staff Data & Resources Did...

  8. Development and Demonstration of Advanced Forecasting, Power...

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

    and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices 63wateruseoptimizationprojectanlgasper.ppt (7.72 MB) More ...

  9. Navy mobility fuels forecasting system report: World petroleum trade forecasts for the year 2000

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01

    The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

  10. Health Care Buildings: Consumption Tables

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

    Consumption Tables Sum of Major Fuel Consumption by Size and Type of Health Care Building Total (trillion Btu) per Building (million Btu) per Square Foot (thousand Btu) Dollars per...

  11. US ENC WI Site Consumption

    Gasoline and Diesel Fuel Update (EIA)

    electricity consumption in the state low relative to other parts of the U.S. * Wisconsin homes are typically larger and older than homes in other states. CONSUMPTION BY END USE ...

  12. US WSC TX Site Consumption

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

    than the U.S. average. * Average electricity consumption per Texas home is 26% higher than ... CONSUMPTION BY END USE Compared to other areas of the United States, the warmer ...

  13. US ENC MI Site Consumption

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

    site electricity consumption in the state low relative to other parts of the U.S. * Michigan homes are typically older than homes in other states. CONSUMPTION BY END USE Since ...

  14. US ENC IL Site Consumption

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

    electricity consumption in the state low relative to other parts of the U.S. * Over 80% of Illinois households use natural gas as their main space heating fuel. CONSUMPTION BY END ...

  15. Sensing, Measurement, and Forecasting | Grid Modernization | NREL

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

    Sensing, Measurement, and Forecasting NREL measures weather resources and power systems, forecasts renewable resources and grid conditions, and converts measurements into operational intelligence to support a modern grid. Photo of solar resource monitoring equipment Modernizing the grid involves assessing its health in real time, predicting its behavior and potential disruptions, and quickly responding to events-which requires understanding vital parameters throughout the electric

  16. Study forecasts disappearance of conifers due to climate change

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

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in ...

  17. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

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

    Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen MJ ... Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen, ...

  18. Data Collection and Comparison with Forecasted Unit Sales of...

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

    Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types PDF icon Data Collection ...

  19. Commercial Miscellaneous Electric Loads Report: Energy Consumption...

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

    loads account for an increasingly large portion of commercial electricity consumption. ... This includes analysis of their unit energy consumption and annual electricity consumption ...

  20. Developing an industrial end-use forecast: A case study at the Los Angeles department of water and power

    SciTech Connect (OSTI)

    Mureau, T.H.; Francis, D.M.

    1995-05-01

    The Los Angeles Department of Water and Power (LADWP) uses INFORM 1.0 to forecast industrial sector energy. INFORM 1.0 provides an end-use framework that can be used to forecast electricity, natural gas or other fuels consumption. Included with INFORM 1.0 is a default date set including the input data and equations necessary to solve each model. LADWP has substituted service area specific data for the default data wherever possible. This paper briefly describes the steps LADWP follows in developing those inputs and application in INFORM 1.0.

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

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01

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

  2. DOETEIAO32l/2 Residential Energy Consumption Survey; Consumption

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

    purchase diaries from a subset of respondents comprising a Household Transportation Panel and is reported separately. * Wood used for heating. Although wood consumption data...

  3. Coal Fired Power Generation Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Coal Fired Power Generation Market Forecast Home There are currently no posts in this category. Syndicate...

  4. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Offshore Lubricants Market Forecast Home There are currently no posts in this category. Syndicate...

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

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

    SciTech Connect (OSTI)

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

    2011-10-01

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

  7. Resource demand growth and sustainability due to increased world consumption

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Balatsky, Alexander V.; Balatsky, Galina I.; Borysov, Stanislav S.

    2015-03-20

    The paper aims at continuing the discussion on sustainability and attempts to forecast the impossibility of the expanding consumption worldwide due to the planet’s limited resources. As the population of China, India and other developing countries continue to increase, they would also require more natural and financial resources to sustain their growth. We coarsely estimate the volumes of these resources (energy, food, freshwater) and the gross domestic product (GDP) that would need to be achieved to bring the population of India and China to the current levels of consumption in the United States. We also provide estimations for potentially neededmore » immediate growth of the world resource consumption to meet this equality requirement. Given the tight historical correlation between GDP and energy consumption, the needed increase of GDP per capita in the developing world to the levels of the U.S. would deplete explored fossil fuel reserves in less than two decades. These estimates predict that the world economy would need to find a development model where growth would be achieved without heavy dependence on fossil fuels.« less

  8. Resource demand growth and sustainability due to increased world consumption

    SciTech Connect (OSTI)

    Balatsky, Alexander V.; Balatsky, Galina I.; Borysov, Stanislav S.

    2015-03-20

    The paper aims at continuing the discussion on sustainability and attempts to forecast the impossibility of the expanding consumption worldwide due to the planet’s limited resources. As the population of China, India and other developing countries continue to increase, they would also require more natural and financial resources to sustain their growth. We coarsely estimate the volumes of these resources (energy, food, freshwater) and the gross domestic product (GDP) that would need to be achieved to bring the population of India and China to the current levels of consumption in the United States. We also provide estimations for potentially needed immediate growth of the world resource consumption to meet this equality requirement. Given the tight historical correlation between GDP and energy consumption, the needed increase of GDP per capita in the developing world to the levels of the U.S. would deplete explored fossil fuel reserves in less than two decades. These estimates predict that the world economy would need to find a development model where growth would be achieved without heavy dependence on fossil fuels.

  9. Flood Forecasting in River System Using ANFIS

    SciTech Connect (OSTI)

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  10. Text-Alternative Version LED Lighting Forecast

    Office of Energy Efficiency and Renewable Energy (EERE)

    The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030....

  11. energy data + forecasting | OpenEI Community

    Open Energy Info (EERE)

    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...

  12. Using Electricity",,,"Electricity Consumption",,,"Electricity...

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

    . Total Electricity Consumption and Expenditures, 2003" ,"All Buildings* Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number of Buildings...

  13. Household Vehicles Energy Consumption 1991

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

    or commercial trucks (See Table 1). Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 5 The 1991 RTECS count includes vehicles that were owned or used...

  14. Household Vehicles Energy Consumption 1991

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

    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...

  15. Manufacturing Consumption of Energy 1994

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

    Natural Gas to Residual Fuel Oil, by Industry Group and Selected Industries, 1994 369 Energy Information AdministrationManufacturing Consumption of Energy 1994 SIC Residual...

  16. Household Vehicles Energy Consumption 1991

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

    logo printer-friendly version logo for Portable Document Format file Household Vehicles Energy Consumption 1991 December 1993 Release Next Update: August 1997. Based on the 1991...

  17. 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...

  18. US ENC MI Site Consumption

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

    Illinois, Indiana, Michigan, Ohio, Wisconsin All data from EIA's 2009 Residential Energy Consumption Survey www.eia.govconsumptionresidential Space heating Water ...

  19. US ENC WI Site Consumption

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

    Illinois, Indiana, Michigan, Ohio, Wisconsin All data from EIA's 2009 Residential Energy Consumption Survey www.eia.govconsumptionresidential Space heating Water ...

  20. US ENC IL Site Consumption

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

    Illinois, Indiana, Michigan, Ohio, Wisconsin All data from EIA's 2009 Residential Energy Consumption Survey www.eia.govconsumptionresidential Space heating Water ...

  1. Science on the Hill: The forecast calls for flu

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

    The forecast calls for flu The forecast calls for flu Using mathematics, computer programs, statistics and information about how disease develops and spreads, a research team at Los Alamos National Laboratory found a way to forecast the flu season and even next week's sickness trends. January 15, 2016 Forecasting flu A team from Los Alamos has developed a method to predict flu outbreaks based in part on influenza-related searches of Wikipedia. The forecast calls for flu Beyond the familiar flu,

  2. Drivers of U.S. Household Energy Consumption, 1980-2009

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

    Drivers of U.S. Household Energy Consumption, 1980-2009 February 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Drivers of U.S. Household Energy Consumption, 1980-2009 i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any

  3. US WNC MO Site Consumption

    Gasoline and Diesel Fuel Update (EIA)

    WNC MO Site Consumption million Btu 0 500 1,000 1,500 2,000 2,500 US WNC MO ... 9,000 12,000 15,000 US WNC MO Site Consumption kilowatthours 0 300 600 900 1,200 ...

  4. US WSC TX Site Consumption

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

    WSC TX Site Consumption million Btu 0 500 1,000 1,500 2,000 2,500 US WSC TX ... 8,000 12,000 16,000 US WSC TX Site Consumption kilowatthours 0 500 1,000 1,500 ...

  5. US NE MA Site Consumption

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

    NE MA Site Consumption million Btu 0 500 1,000 1,500 2,000 2,500 3,000 US NE MA ... 8,000 10,000 12,000 US NE MA Site Consumption kilowatthours 0 250 500 750 1,000 ...

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

    SciTech Connect (OSTI)

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

    2014-05-01

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

  7. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

    Templeton, K.J.; Clary, J.L.

    1994-09-01

    This report describes a 30-year forecast of the solid waste volumes by container type. The volumes described are low-level mixed waste (LLMW) and transuranic/transuranic mixed (TRU/TRUM) waste. These volumes and their associated container types will be generated or received at the US Department of Energy Hanford Site for storage, treatment, and disposal at Westinghouse Hanford Company`s Solid Waste Operations Complex (SWOC) during a 30-year period from FY 1994 through FY 2023. The forecast data for the 30-year period indicates that approximately 307,150 m{sup 3} of LLMW and TRU/TRUM waste will be managed by the SWOC. The main container type for this waste is 55-gallon drums, which will be used to ship 36% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of 55-gallon drums is Past Practice Remediation. This waste will be generated by the Environmental Restoration Program during remediation of Hanford`s past practice sites. Although Past Practice Remediation is the primary generator of 55-gallon drums, most waste generators are planning to ship some percentage of their waste in 55-gallon drums. Long-length equipment containers (LECs) are forecasted to contain 32% of the LLMW and TRU/TRUM waste. The main waste generator forecasting the use of LECs is the Long-Length Equipment waste generator, which is responsible for retrieving contaminated long-length equipment from the tank farms. Boxes are forecasted to contain 21% of the waste. These containers are primarily forecasted for use by the Environmental Restoration Operations--D&D of Surplus Facilities waste generator. This waste generator is responsible for the solid waste generated during decontamination and decommissioning (D&D) of the facilities currently on the Surplus Facilities Program Plan. The remaining LLMW and TRU/TRUM waste volume is planned to be shipped in casks and other miscellaneous containers.

  8. The impact of forecasted energy price increases on low-income consumers

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energy’s Energy Information Administration (EIA) recently released its short term forecast for residential energy prices for the winter of 2005-2006. The forecast indicates significant increases in fuel costs, particularly for natural gas, propane, and home heating oil, for the year ahead. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nation’s low-income households by primary heating fuel type, nationally and by Census Region. The statistics are intended for the use of policymakers in the Department of Energy’s Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2006 fiscal year.

  9. US ESC TN Site Consumption

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

    ESC TN Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US ESC TN Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 4,000 8,000 12,000 16,000 US ESC TN Site Consumption kilowatthours $0 $400 $800 $1,200 $1,600 US ESC TN Expenditures dollars ELECTRICITY ONLY average per household * Tennessee households consume an average of 79 million Btu per year, about 12% less than the U.S. average. * Average electricity consumption for Tennessee households is 33%

  10. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    SciTech Connect (OSTI)

    Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.

  11. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

  12. Household Vehicles Energy Consumption 1991

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

    for 1994, will continue the 3-year cycle. The RTECS, a subsample of the Residential Energy Consumption Survey (RECS), is an integral part of a series of surveys designed by...

  13. Household Vehicles Energy Consumption 1991

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

    16.8 17.4 18.6 18.9 1.7 2.2 0.6 1.5 Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 15 Vehicle Miles Traveled per Vehicle (Thousand) . . . . . . . . ....

  14. US NE MA Site Consumption

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

    the U.S. However, spending on electricity is closer to the national average due to higher prices in New England. CONSUMPTION BY END USE Since the weather in Massachusetts and New ...

  15. 2014 Manufacturing Energy Consumption Survey

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

    U S C E N S U S B U R E A U 2014 Manufacturing Energy Consumption Survey Sponsored by the Energy Information Administration U.S. Department of Energy Administered and Compiled by ...

  16. Manufacturing Consumption of Energy 1994

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

    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...

  17. Manufacturing consumption of energy 1991

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

    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.

  18. The Value of Improved Short-Term Wind Power Forecasting

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

    ... up-ramp reserves c down cost in MWh of down-ramp reserves R down MW range for ... power forecasting and the increased gas usage that comes with less-accurate forecasting. ...

  19. PBL FY 2003 Second Quarter Review Forecast of Generation Accumulated...

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

    the rate period (i.e., FY 2002-2006), a forecast of that end-of-year Accumulated Net Revenue (ANR) will be completed. If the ANR at the end of the forecast year falls below the...

  20. Solar Forecasting Gets a Boost from Watson, Accuracy Improved...

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

    Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM ...

  1. Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01

    This presentation presents some statistical analysis of wind power forecast errors and error distributions, with examples using ERCOT data.

  2. Combined Heat And Power Installation Market Forecast | OpenEI...

    Open Energy Info (EERE)

    Combined Heat And Power Installation Market Forecast Home There are currently no posts in this category. Syndicate...

  3. Wind power forecasting in U.S. electricity markets.

    SciTech Connect (OSTI)

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

    2010-04-01

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

  4. Wind power forecasting in U.S. Electricity markets

    SciTech Connect (OSTI)

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

    2010-04-15

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

  5. DOE Taking Wind Forecasting to New Heights | Department of Energy

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

    Taking Wind Forecasting to New Heights DOE Taking Wind Forecasting to New Heights May 18, 2015 - 3:24pm Addthis A 2013 study conducted for the U.S. Department of Energy (DOE) by the National Oceanic and Atmospheric Administration (NOAA), AWS Truepower, and WindLogics in the Great Plains and Western Texas, demonstrated that wind power forecasts can be improved substantially using data collected from tall towers, remote sensors, and other devices, and incorporated into improved forecasting models

  6. ,"Oklahoma Natural Gas Consumption by End Use"

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

    Data for" ,"Data 1","Oklahoma Natural Gas Consumption by End ... 11:05:14 AM" "Back to Contents","Data 1: Oklahoma Natural Gas Consumption by End Use" ...

  7. ,"Oklahoma Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Data for" ,"Data 1","Oklahoma Natural Gas Vehicle Fuel Consumption ... 12:00:19 PM" "Back to Contents","Data 1: Oklahoma Natural Gas Vehicle Fuel Consumption ...

  8. ,"Kansas Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Data for" ,"Data 1","Kansas Natural Gas Vehicle Fuel Consumption ... 7:09:38 AM" "Back to Contents","Data 1: Kansas Natural Gas Vehicle Fuel Consumption ...

  9. ,"Nevada Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Data for" ,"Data 1","Nevada Natural Gas Vehicle Fuel Consumption ... 1:24:58 AM" "Back to Contents","Data 1: Nevada Natural Gas Vehicle Fuel Consumption ...

  10. Energy Information Administration - Commercial Energy Consumption...

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

    4A. Electricity Consumption and Expenditure Intensities for All Buildings, 2003 Electricity Consumption Electricity Expenditures per Building (thousand kWh) per Square Foot (kWh)...

  11. Energy Information Administration - Commercial Energy Consumption...

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

    3A. Total Electricity Consumption and Expenditures for All Buildings, 2003 All Buildings Using Electricity Electricity Consumption Electricity Expenditures Number of Buildings...

  12. 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...

  13. 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...

  14. Using Electricity",,,"Electricity Consumption",,,"Electricity...

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

    A. Total Electricity Consumption and Expenditures for All Buildings, 2003" ,"All Buildings Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number of...

  15. Electricity",,,"Electricity Consumption",,,"Electricity Expenditures...

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

    C9. Total Electricity Consumption and Expenditures, 1999" ,"All Buildings Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number of Buildings...

  16. Electricity",,,"Electricity Consumption",,,"Electricity Expenditures...

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

    DIV. Total Electricity Consumption and Expenditures by Census Division, 1999" ,"All Buildings Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number...

  17. US SoAtl VA Site Consumption

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

    than the U.S. average. * Average electricity consumption and costs are higher for Virginia ... CONSUMPTION BY END USE While Virginia's weather is similar to the national average, ...

  18. ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures"

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

    4. Fuel Oil Consumption and Expenditure Intensities for Non-Mall Buildings, 2003" ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures" ,"per Building (gallons)","per Square Foot...

  19. ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures"

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

    2. Fuel Oil Consumption and Expenditure Intensities, 1999" ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures" ,"per Building (gallons)","per Square Foot (gallons)","per Worker...

  20. ,"California Natural Gas Consumption by End Use"

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

    Data for" ,"Data 1","California Natural Gas Consumption by End ... AM" "Back to Contents","Data 1: California Natural Gas Consumption by End Use" ...

  1. ,"Texas Natural Gas Consumption by End Use"

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

    Data for" ,"Data 1","Texas Natural Gas Consumption by End ... 6:36:11 AM" "Back to Contents","Data 1: Texas Natural Gas Consumption by End Use" ...

  2. ,"Texas Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Data for" ,"Data 1","Texas Natural Gas Vehicle Fuel Consumption ... 7:09:53 AM" "Back to Contents","Data 1: Texas Natural Gas Vehicle Fuel Consumption ...

  3. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    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...

  4. Energy Information Administration - Commercial Energy Consumption...

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

    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)...

  5. Energy Information Administration - Commercial Energy Consumption...

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

    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...

  6. Displacing Natural Gas Consumption and Lowering Emissions

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

    ADVANCED MANUFACTURING OFFICE Displacing Natural Gas Consumption and Lowering Emissions By ... full advantage of op- portunity fuels and thereby reduce their natural gas consumption. ...

  7. Commercial Buildings Energy Consumption Survey (CBECS) - Analysis...

    Gasoline and Diesel Fuel Update (EIA)

    The Commercial Buildings Energy Consumption Survey (CBECS) project cycle spans at least ... Data collection for the 2012 Commercial Buildings Energy Consumption Survey (CBECS) took ...

  8. Vehicle Energy Consumption and Performance Analysis | Argonne...

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

    Consumption and Performance Analysis Vehicle Energy Consumption and Performance Analysis Argonne researchers have applied their expertise in modeling, simulation and control to ...

  9. ,"Virginia Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Data for" ,"Data 1","Virginia Natural Gas Vehicle Fuel Consumption ... 12:00:27 PM" "Back to Contents","Data 1: Virginia Natural Gas Vehicle Fuel Consumption ...

  10. ,"West Virginia Natural Gas Residential Consumption (MMcf)"

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

    AM" "Back to Contents","Data 1: West Virginia Natural Gas Residential Consumption (MMcf)" "Sourcekey","N3010WV2" "Date","West Virginia Natural Gas Residential Consumption ...

  11. ,"Virginia Natural Gas Consumption by End Use"

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

    Data for" ,"Data 1","Virginia Natural Gas Consumption by End ... 11:05:20 AM" "Back to Contents","Data 1: Virginia Natural Gas Consumption by End Use" ...

  12. ,"West Virginia Natural Gas Industrial Consumption (MMcf)"

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

    AM" "Back to Contents","Data 1: West Virginia Natural Gas Industrial Consumption (MMcf)" "Sourcekey","N3035WV2" "Date","West Virginia Natural Gas Industrial Consumption ...

  13. ,"West Virginia Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","West Virginia Natural Gas Total Consumption ... AM" "Back to Contents","Data 1: West Virginia Natural Gas Total Consumption (MMcf)" ...

  14. ,"New Mexico Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","New Mexico Natural Gas Total Consumption ... AM" "Back to Contents","Data 1: New Mexico Natural Gas Total Consumption (MMcf)" ...

  15. ,"North Dakota Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","North Dakota Natural Gas Total Consumption ... 9:10:34 AM" "Back to Contents","Data 1: North Dakota Natural Gas Total Consumption ...

  16. ,"North Carolina Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","North Carolina Natural Gas Total Consumption ... 9:10:33 AM" "Back to Contents","Data 1: North Carolina Natural Gas Total Consumption ...

  17. ,"Minnesota Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Data for" ,"Data 1","Minnesota Natural Gas Vehicle Fuel Consumption ... 7:09:42 AM" "Back to Contents","Data 1: Minnesota Natural Gas Vehicle Fuel Consumption ...

  18. 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...

  19. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (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...

  20. 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...

  1. Energy Information Administration - Commercial Energy Consumption...

    Annual Energy Outlook [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...

  2. Energy Information Administration - Commercial Energy Consumption...

    Annual Energy Outlook [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...

  3. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (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...

  4. 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)...

  5. Energy Information Administration - Commercial Energy Consumption...

    Annual Energy Outlook [U.S. Energy Information Administration (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...

  6. Energy Information Administration - Commercial Energy Consumption...

    Annual Energy Outlook [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...

  7. Energy Information Administration - Commercial Energy Consumption...

    Annual Energy Outlook [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...

  8. 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...

  9. Energy Information Administration - Commercial Energy Consumption...

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

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

  10. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

    Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`s National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.

  11. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

    This document consists of papers which cover topics in analysis and modeling that underlie the Annual Energy Outlook 1996. Topics include: The Potential Impact of Technological Progress on U.S. Energy Markets; The Outlook for U.S. Import Dependence; Fuel Economy, Vehicle Choice, and Changing Demographics, and Annual Energy Outlook Forecast Evaluation.

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

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

    Department of Energy Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report.pdf (15.76 MB) More Documents & Publications QER - Comment of Edison Electric Institute (EEI) 1 QER - Comment of Canadian Hydropower Association QER - Comment of Edison Electric Institute (EEI) 2

  13. Inconsistent Investment and Consumption Problems

    SciTech Connect (OSTI)

    Kronborg, Morten Tolver; Steffensen, Mogens

    2015-06-15

    In a traditional Black–Scholes market we develop a verification theorem for a general class of investment and consumption problems where the standard dynamic programming principle does not hold. The theorem is an extension of the standard Hamilton–Jacobi–Bellman equation in the form of a system of non-linear differential equations. We derive the optimal investment and consumption strategy for a mean-variance investor without pre-commitment endowed with labor income. In the case of constant risk aversion it turns out that the optimal amount of money to invest in stocks is independent of wealth. The optimal consumption strategy is given as a deterministic bang-bang strategy. In order to have a more realistic model we allow the risk aversion to be time and state dependent. Of special interest is the case were the risk aversion is inversely proportional to present wealth plus the financial value of future labor income net of consumption. Using the verification theorem we give a detailed analysis of this problem. It turns out that the optimal amount of money to invest in stocks is given by a linear function of wealth plus the financial value of future labor income net of consumption. The optimal consumption strategy is again given as a deterministic bang-bang strategy. We also calculate, for a general time and state dependent risk aversion function, the optimal investment and consumption strategy for a mean-standard deviation investor without pre-commitment. In that case, it turns out that it is optimal to take no risk at all.

  14. The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

    SciTech Connect (OSTI)

    Wilczak, James M.; Finley, Cathy; Freedman, Jeff; Cline, Joel; Bianco, L.; Olson, J.; Djalaova, I.; Sheridan, L.; Ahlstrom, M.; Manobianco, J.; Zack, J.; Carley, J.; Benjamin, S.; Coulter, R. L.; Berg, Larry K.; Mirocha, Jeff D.; Clawson, K.; Natenberg, E.; Marquis, M.

    2015-10-30

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

  15. Operational forecasting based on a modified Weather Research and Forecasting model

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

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

  16. West Virginia Natural Gas Total Consumption (Million Cubic Feet...

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

    Total Consumption (Million Cubic Feet) West Virginia Natural Gas Total Consumption ... Referring Pages: Natural Gas Consumption West Virginia Natural Gas Consumption by End Use ...

  17. Virginia Natural Gas Lease Fuel Consumption (Million Cubic Feet...

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

    Fuel Consumption (Million Cubic Feet) Virginia Natural Gas Lease Fuel Consumption (Million ... Referring Pages: Natural Gas Lease Fuel Consumption Virginia Natural Gas Consumption by ...

  18. West Virginia Natural Gas Lease Fuel Consumption (Million Cubic...

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

    Fuel Consumption (Million Cubic Feet) West Virginia Natural Gas Lease Fuel Consumption ... Referring Pages: Natural Gas Lease Fuel Consumption West Virginia Natural Gas Consumption ...

  19. Virginia Natural Gas Total Consumption (Million Cubic Feet)

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

    Total Consumption (Million Cubic Feet) Virginia Natural Gas Total Consumption (Million ... Referring Pages: Natural Gas Consumption Virginia Natural Gas Consumption by End Use ...

  20. New York Natural Gas Lease Fuel Consumption (Million Cubic Feet...

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

    Fuel Consumption (Million Cubic Feet) New York Natural Gas Lease Fuel Consumption (Million ... Referring Pages: Natural Gas Lease Fuel Consumption New York Natural Gas Consumption by ...

  1. New Mexico Natural Gas Lease Fuel Consumption (Million Cubic...

    Gasoline and Diesel Fuel Update (EIA)

    Fuel Consumption (Million Cubic Feet) New Mexico Natural Gas Lease Fuel Consumption ... Referring Pages: Natural Gas Lease Fuel Consumption New Mexico Natural Gas Consumption by ...

  2. New Jersey Natural Gas Total Consumption (Million Cubic Feet...

    Gasoline and Diesel Fuel Update (EIA)

    Total Consumption (Million Cubic Feet) New Jersey Natural Gas Total Consumption (Million ... Referring Pages: Natural Gas Consumption New Jersey Natural Gas Consumption by End Use ...

  3. New York Natural Gas Total Consumption (Million Cubic Feet)

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

    Total Consumption (Million Cubic Feet) New York Natural Gas Total Consumption (Million ... Referring Pages: Natural Gas Consumption New York Natural Gas Consumption by End Use ...

  4. New Mexico Natural Gas Total Consumption (Million Cubic Feet...

    Gasoline and Diesel Fuel Update (EIA)

    Total Consumption (Million Cubic Feet) New Mexico Natural Gas Total Consumption (Million ... Referring Pages: Natural Gas Consumption New Mexico Natural Gas Consumption by End Use ...

  5. New Mexico Natural Gas Plant Fuel Consumption (Million Cubic...

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

    Fuel Consumption (Million Cubic Feet) New Mexico Natural Gas Plant Fuel Consumption ... Referring Pages: Natural Gas Plant Fuel Consumption New Mexico Natural Gas Consumption by ...

  6. North Dakota Natural Gas Lease Fuel Consumption (Million Cubic...

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

    Fuel Consumption (Million Cubic Feet) North Dakota Natural Gas Lease Fuel Consumption ... Referring Pages: Natural Gas Lease Fuel Consumption North Dakota Natural Gas Consumption ...

  7. North Carolina Natural Gas Total Consumption (Million Cubic Feet...

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

    Total Consumption (Million Cubic Feet) North Carolina Natural Gas Total Consumption ... Referring Pages: Natural Gas Consumption North Carolina Natural Gas Consumption by End Use ...

  8. North Dakota Natural Gas Total Consumption (Million Cubic Feet...

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

    Total Consumption (Million Cubic Feet) North Dakota Natural Gas Total Consumption (Million ... Referring Pages: Natural Gas Consumption North Dakota Natural Gas Consumption by End Use ...

  9. Minnesota Natural Gas Total Consumption (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    Total Consumption (Million Cubic Feet) Minnesota Natural Gas Total Consumption (Million ... Referring Pages: Natural Gas Consumption Minnesota Natural Gas Consumption by End Use ...

  10. Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01

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

  11. Technology data characterizing water heating in commercial buildings: Application to end-use forecasting

    SciTech Connect (OSTI)

    Sezgen, O.; Koomey, J.G.

    1995-12-01

    Commercial-sector conservation analyses have traditionally focused on lighting and space conditioning because of their relatively-large shares of electricity and fuel consumption in commercial buildings. In this report we focus on water heating, which is one of the neglected end uses in the commercial sector. The share of the water-heating end use in commercial-sector electricity consumption is 3%, which corresponds to 0.3 quadrillion Btu (quads) of primary energy consumption. Water heating accounts for 15% of commercial-sector fuel use, which corresponds to 1.6 quads of primary energy consumption. Although smaller in absolute size than the savings associated with lighting and space conditioning, the potential cost-effective energy savings from water heaters are large enough in percentage terms to warrant closer attention. In addition, water heating is much more important in particular building types than in the commercial sector as a whole. Fuel consumption for water heating is highest in lodging establishments, hospitals, and restaurants (0.27, 0.22, and 0.19 quads, respectively); water heating`s share of fuel consumption for these building types is 35%, 18% and 32%, respectively. At the Lawrence Berkeley National Laboratory, we have developed and refined a base-year data set characterizing water heating technologies in commercial buildings as well as a modeling framework. We present the data and modeling framework in this report. The present commercial floorstock is characterized in terms of water heating requirements and technology saturations. Cost-efficiency data for water heating technologies are also developed. These data are intended to support models used for forecasting energy use of water heating in the commercial sector.

  12. Forecasting hotspots using predictive visual analytics approach

    SciTech Connect (OSTI)

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

    2014-12-30

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

  13. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

  14. Global disease monitoring and forecasting with Wikipedia

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: accessmore » logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.« less

  15. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-08-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  16. Consumption

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

    Using Natural Gas (million square feet)",,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"North- east","Mid- west","South","West","North- east","Mid-...

  17. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"West South Central","Moun- tain","Pacific","West South Central","Moun-...

  18. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"1959 or Before","1960 to 1989","1990 to 2003","1959 or Before","1960 to...

  19. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"West North Central","South Atlantic","East South Central","West North...

  20. Consumption

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

    Using Natural Gas (million square feet)",,,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"Zone 1","Zone 2","Zone 3","Zone 4","Zone 5","Zone 1","Zone 2","Zone 3","Zone...

  1. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"1959 or Before","1960 to 1989","1990 to 2003","1959 or Before","1960 to...

  2. Consumption

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

    (million square feet)",,,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"North- east","Mid- west","South","West","North- east","Mid-...

  3. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"New England","Middle Atlantic","East North Central","New England","Middle...

  4. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"West North Central","South Atlantic","East South Central","West North...

  5. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"1,001 to 10,000 Square Feet","10,001 to 100,000 Square Feet","Over 100,000...

  6. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"1,001 to 10,000 Square Feet","10,001 to 100,000 Square Feet","Over 100,000...

  7. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"New England","Middle Atlantic","East North Central","New England","Middle...

  8. Consumption

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

    (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...

  9. Consumption

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

    (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...

  10. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"1959 or Before","1960 to 1989","1990 to 1999","1959 or Before","1960 to...

  11. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"West South Central","Moun- tain","Pacific","West South Central","Moun-...

  12. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"1959 or Before","1960 to 1989","1990 to 1999","1959 or Before","1960 to...

  13. Consumption

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

    (million square feet)",,,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"North- east","Mid- west","South","West","North- east","Mid-...

  14. Consumption

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

    90,1024,3251,1511,"Q",106.6,97.3,100.6 "Office ...",305,325,329,175,3012,2989,3782,2425,101.2,108.8,87,72.1 "Public Assembly ...",93,103,109,64,1048,...

  15. Consumption

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

    9,60,56.7,43.1,31.4,22.1 "1990 to 1999 ...",69,87,51,93,34,1735,1988,1202,3012,1267,40,43.8,42.4,30.9,26.9 "2000 to 2003 ...",23,40,"Q",28,15,693,1086,7...

  16. Manufacturing consumption of energy 1994

    SciTech Connect (OSTI)

    1997-12-01

    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.

  17. Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

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

    2013-10-01

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

  18. Household vehicles energy consumption 1994

    SciTech Connect (OSTI)

    1997-08-01

    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.

  19. Upcoming Funding Opportunity for Wind Forecasting Improvement Project in

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

    Complex Terrain | Department of Energy Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am Addthis On February 11, 2014 the Wind Program announced a Notice of Intent to issue a funding opportunity entitled "Wind Forecasting Improvement Project in Complex Terrain." By researching the physical processes that take place in complex terrain, this funding would improve

  20. Wind power forecasting : state-of-the-art 2009.

    SciTech Connect (OSTI)

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

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and

  1. Roel Neggers European Centre for Medium-range Weather Forecasts

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

    transition from shallow to deep convection using a dual mass flux boundary layer scheme Roel Neggers European Centre for Medium-range Weather Forecasts Introduction " " % % &...

  2. Radar Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. ...

  3. DOE Announces Webinars on Solar Forecasting Metrics, the DOE...

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

    DOE Announces Webinars on Solar Forecasting Metrics, the DOE ... from adopting the latest energy efficiency and renewable ... to liquids technology, advantages of using natural gas, ...

  4. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    SciTech Connect (OSTI)

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

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

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

    Bonneville Power Administration Power Business Line Generation (PBL) Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

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

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

    2003 Bonneville Power Administration Power Business Line Generation Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

  7. Improving the Accuracy of Solar Forecasting Funding Opportunity...

    Energy Savers [EERE]

    Through the Improving the Accuracy of Solar Forecasting Funding Opportunity, DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and ...

  8. NREL: Resource Assessment and Forecasting - Data and Resources

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

    Data and Resources National Solar Radiation Database NREL resource assessment and forecasting research information is available from the following sources. Renewable Resource Data ...

  9. New Forecasting Tools Enhance Wind Energy Integration In Idaho...

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

    ... RIT forecasting is saving costs and improving operational practices for IPC and helping integrate wind power more efficiently and cost effectively. Figure 3 shows how the ...

  10. A Review of Variable Generation Forecasting in the West: July...

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

    ... Cost Assignment - Only a few respondents partly or fully recover forecasting costs from variable generators. Many simply absorb the costs, possibly viewing them as relatively ...