National Library of Energy BETA

Sample records for module forecasts consumption

  1. Module 6 - Metrics, Performance Measurements and Forecasting | Department

    Energy Savers [EERE]

    of Energy 6 - Metrics, Performance Measurements and Forecasting Module 6 - Metrics, Performance Measurements and Forecasting This module focuses on the metrics and performance measurement tools used in Earned Value. 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 estimate to complete (ETC) and estimate at completion (EAC)

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

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

    This module focuses on the metrics and performance measurement tools used in Earned Value. This module reviews metrics such as cost and schedule variance along with cost and ...

  3. 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.

  4. 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.

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. 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...

  6. 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

  7. A microbial functional group-based module for simulating methane production and consumption: Application to an incubated permafrost soil

    SciTech Connect (OSTI)

    Xu, Xiaofeng; Elias, Dwayne A.; Graham, David E.; Phelps, Tommy J.; Carroll, Sue L.; Wullschleger, Stan D.; Thornton, Peter E.

    2015-07-23

    In this study, accurately estimating methane (CH4) flux is critically important for investigating and predicting the biogeochemistry-climate feedback. Better simulating CH4 flux requires explicit representations of microbial processes on CH4 dynamics because all processes for CH4 production and consumption are actually carried out by microbes. A microbial functional group based module was developed and tested against an incubation experiment. The module considers four key mechanisms for CH4 production and consumption: methanogenesis from acetate or single-carbon compounds and CH4 oxidation using molecular oxygen or other inorganic electron acceptors. These four processes were carried out by four microbial functional groups: acetoclastic methanogens, hydrogenotrophic methanogens, aerobic methanotrophs, and anaerobic methanotrophs. This module was then linked with the decomposition subroutine of the Community Land Model, and was further used to simulate dynamics of carbon dioxide (CO2) and CH4 concentrations from an incubation experiment with permafrost soils. The results show that the model could capture the dynamics of CO2 and CH4 concentrations in microcosms with top soils, mineral layer soils and permafrost soils under natural and saturated moisture conditions and a temperature gradient of -2°C, 3°C, and 5°C. Sensitivity analysis confirmed the importance of acetic acid's direct contribution as substrate and indirect effects through pH feedback on CO2 and CH4 production and consumption. This study suggests that representing the microbial mechanisms is critical for modeling CH4 production and consumption; it is urgent to incorporate microbial mechanisms into Earth system models for better predicting the behavior of the climate system.

  8. A microbial functional group-based module for simulating methane production and consumption: Application to an incubated permafrost soil

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

    Xu, Xiaofeng; Elias, Dwayne A.; Graham, David E.; Phelps, Tommy J.; Carroll, Sue L.; Wullschleger, Stan D.; Thornton, Peter E.

    2015-07-23

    In this study, accurately estimating methane (CH4) flux is critically important for investigating and predicting the biogeochemistry-climate feedback. Better simulating CH4 flux requires explicit representations of microbial processes on CH4 dynamics because all processes for CH4 production and consumption are actually carried out by microbes. A microbial functional group based module was developed and tested against an incubation experiment. The module considers four key mechanisms for CH4 production and consumption: methanogenesis from acetate or single-carbon compounds and CH4 oxidation using molecular oxygen or other inorganic electron acceptors. These four processes were carried out by four microbial functional groups: acetoclastic methanogens,more » hydrogenotrophic methanogens, aerobic methanotrophs, and anaerobic methanotrophs. This module was then linked with the decomposition subroutine of the Community Land Model, and was further used to simulate dynamics of carbon dioxide (CO2) and CH4 concentrations from an incubation experiment with permafrost soils. The results show that the model could capture the dynamics of CO2 and CH4 concentrations in microcosms with top soils, mineral layer soils and permafrost soils under natural and saturated moisture conditions and a temperature gradient of -2°C, 3°C, and 5°C. Sensitivity analysis confirmed the importance of acetic acid's direct contribution as substrate and indirect effects through pH feedback on CO2 and CH4 production and consumption. This study suggests that representing the microbial mechanisms is critical for modeling CH4 production and consumption; it is urgent to incorporate microbial mechanisms into Earth system models for better predicting the behavior of the climate system.« less

  9. Forecasting Flu

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

    Forecasting Flu March 6, 2016 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 Valle and her team from Los Alamos National Laboratory have developed a global disease-forecasting system that will improve the way we respond to epidemics. Using this model, individuals and public health officials can monitor

  10. RACORO Forecasting

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

    Hartsock CIMMS, University of Oklahoma  ARM AAF Wiki page  Weather Briefings  Observed Weather  Cloud forecasting models  BUFKIT forecast soundings + guidance from Norman NWS enhanced pages and discussions NAM-WRF updated twice/day (12Z and 00Z) Forecast out to 84-hours RUC (updated every 3 hours) Operational RUC forecast only goes out 12 hours (developmental out 24 hours)

  11. Model documentation coal market module of the National Energy Modeling System

    SciTech Connect (OSTI)

    1997-02-01

    This report documents the objectives and the conceptual and methodological approach used in the development of the Coal Production Submodule (CPS). It provides a description of the CPS for model analysts and the public. The Coal Market Module provides annual forecasts of prices, production, and consumption of coal.

  12. Modules

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

    it's recommended to use the full version number, i.e. instead of doing "module load python" do "module load python2.7.6". This will preserve a record of the software version...

  13. Acquisition Forecast

    Broader source: Energy.gov [DOE]

    It is the policy of the Department of Energy (DOE) and the National Nuclear Security Administration (NNSA) to provide timely information to the public regarding DOE/NNSA’s forecast of future prime contracting opportunities and subcontracting opportunities which are available via the Department’s major site and facilities management contractors.

  14. Microsoft PowerPoint - FinalModule6.ppt

    Office of Environmental Management (EM)

    6: Metrics, Performance Measurements and Forecasting Prepared by: Module 6 - Metrics, Performance Measures and Forecasting 2 Prepared by: Booz Allen Hamilton Module 6: Metrics, Performance Measurements and Forecasting Welcome to Module 6. The objective of this module is to introduce you to the Metrics and Performance Measurement tools used, along with Forecasting, in Earned Value Management. The Topics that will be addressed in this Module include: * Define Cost and Schedule Variances * Define

  15. 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...

  16. 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.

  17. 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...

  18. 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...

  19. NREL: Transmission Grid Integration - Forecasting

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

    generation output by using forecasts that incorporate meteorological data to predict production. Such systems typically provide forecasts at a number of timescales, ranging from...

  20. Renewable Fuels Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the Annual Energy Outlook forecasts.

  1. 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 ...

  2. The forecast calls for flu

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

    Laboratory found a way to forecast the flu season and even next week's sickness trends. ... Laboratory found a way to forecast the flu season and even next week's sickness trends. ...

  3. Solar Forecasting | Department of Energy

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

    Systems Integration » Solar Forecasting Solar Forecasting 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. solar energy plants. Part of the SunShot Systems Integration efforts, the Solar Forecasting projects will allow power system operators to integrate more solar energy into the electricity grid, and ensure the economic and reliable delivery of

  4. 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.

  5. probabilistic energy production forecasts

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

    probabilistic energy production forecasts - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Fuel Cycle Defense Waste Management

  6. 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...

  7. 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)

  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. 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.

  10. Using Wikipedia to forecast diseases

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

    based on today's forecast." Del Valle and her team were able to successfully monitor influenza in the United States, Poland, Japan and Thailand, dengue fever in Brazil and...

  11. Detailed Course Module Description

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

    Course Module Description Module/Learning Objectives Level of Detail in Module by Audience Consumers Gen Ed/ Community College Trades 1. Energy Issues and Building Solutions High High High Learning Objectives: * Define terms of building science, ecological systems, economics of consumption * Relate building science perspective, ecology, social science * Explain historical energy and environmental issues related to buildings * Compare Site and source energy * Examine the health, safety and

  12. 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.

  13. 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:...

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

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

    Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex...

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

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

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

  16. Natural Gas Consumption and Prices Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    Natural Gas Consumption and Prices Short-Term Energy Outlook June 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Natural Gas Consumption and Prices - Short-Term Energy Outlook Model 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

  17. 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

  18. CSV File Documentation: Consumption

    Gasoline and Diesel Fuel Update (EIA)

    Consumption Estimates The State Energy Data System (SEDS) comma-separated value (CSV) files contain consumption estimates shown in the tables located on the SEDS website. There are four files that contain estimates for all states and years. Consumption in Physical Units contains the consumption estimates in physical units for all states; Consumption in Btu contains the consumption estimates in billion British thermal units (Btu) for all states. There are two data files for thermal conversion

  19. ,"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...

  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. 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.

  2. 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...

  3. Solar Forecast Improvement Project | Department of Energy

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

    Solar Forecast Improvement Project Solar Forecast Improvement Project NOAA.png 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 accurate methods for solar forecasts using their state-of-the-art weather models. APPROACH NOAA solar.png SFIP has three main goals: 1) to develop solar forecasting metrics tailored to the utility sector; 2) to improve solar

  4. Commercial Demand Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

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

    Gasoline and Diesel Fuel Update (EIA)

    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

  6. GREET Pretreatment Module

    SciTech Connect (OSTI)

    Adom, Felix K.; Dunn, Jennifer B.; Han, Jeongwoo

    2014-09-01

    A wide range of biofuels and biochemicals can be produced from biomass via different pretreatment technologies that yield sugars. This report documents the material and energy flows that occur when fermentable sugars from four lignocellulosic feedstocks (corn stover, miscanthus, switchgrass, and poplar) are produced via dilute acid pretreatment and ammonia fiber expansion. These flows are documented for inclusion in the pretreatment module of the Greenhouses Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. Process simulations of each pretreatment technology were developed in Aspen Plus. Material and energy consumption data from Aspen Plus were then compiled in the GREET pretreatment module. The module estimates the cradle-to-gate fossil energy consumption (FEC) and greenhouse gas (GHG) emissions associated with producing fermentable sugars. This report documents the data and methodology used to develop this module and the cradle-to-gate FEC and GHG emissions that result from producing fermentable sugars.

  7. Model documentation, Coal Market Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    1998-01-01

    This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The international area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.

  8. 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

  9. Introduction to the Buildings Sector Module of SEDS

    SciTech Connect (OSTI)

    DeForest, Nicholas; Bonnet, Florence; Stadler, Michael; Marnay, Chris

    2010-12-31

    SEDS is a stochastic engineering-economics model that forecasts economy-wide energy consumption in the U.S. to 2050. It is the product of multi-laboratory collaboration among the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), and Lumina Decision Systems. Among national energy models, SEDS is unique, as it is the only model written to explicitly incorporate uncertainty in its inputs and outputs. The primary purpose of SEDS is to estimate the impact of various US Department of Energy (DOE)R&D and policy programs on the performance and subsequent adoption rates of technologies relating to every energy consuming sector of the economy (shown below). It has previously been used to assist DOE in complying with the Government Performance and Results Act of 1993 (GPRA). The focus of LBNL research has been exclusively on develop the buildings model (SBEAM), which is capable of running as a stand-alone forecasting model, or as a part of SEDS as a whole. The full version of SEDS, containing all sectors and interaction is also called the 'integrated' version and is managed by NREL. Forecasts from SEDS are often compared to those coming from National Energy Modeling System (NEMS). The intention of this document is to present new users and developers with a general description of the purpose, functionality and structure of the buildings module within the Stochastic Energy Deployment System (SEDS). The Buildings module, which is capable of running as a standalone model, is also called the Stochastic Buildings Energy and Adoption Model (SBEAM). This document will focus exclusively on SBEAM and its interaction with other major sector modules present within SEDS. The methodologies and major assumptions employed in SBEAM will also be discussed. The organization of this report will parallel the organization of the model itself, being divided into major submodules. As the description progresses, the nature of modules will change from broad, easily understood concepts to lower-level data manipulation. Because SBEAM contains dozens of submodules and hundreds of variables, it would not be relevant or useful to describe each and every one. Rather, the investigation will focus more generally on the operations performed throughout the model. This manual is by no means a complete description of SBEAM; however it should provide the foundation for an introductory understanding of the model. The manual assumes a basic level of understating of Analytica{reg_sign}, the platform on which SEDS and SBEAM have been developed.

  10. 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.

  11. 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...

  12. 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...

  13. 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

  14. Forecasting Water Quality & Biodiversity

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

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability Platform Review Principle Investigator: Dr. Henriette I. Jager Organization: Oak Ridge National Laboratory This presentation does not contain any proprietary, confidential, or otherwise restricted information 2015 DOE Bioenergy Technologies Office (BETO) Project Peer Review Goal Statement Addresses the following MYPP BETO goals:  Advance scientific methods and models for measuring and understanding

  15. 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

  16. 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...

  17. 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.

  18. 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,

  19. ,"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...

  20. 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,...

  1. VERDE Analytic Modules

    Energy Science and Technology Software Center (OSTI)

    2008-01-15

    The Verde Analytic Modules permit the user to ingest openly available data feeds about phenomenology (storm tracks, wind, precipitation, earthquake, wildfires, and similar natural and manmade power grid disruptions and forecast power outages, restoration times, customers outaged, and key facilities that will lose power. Damage areas are predicted using historic damage criteria of the affected area. The modules use a cellular automata approach to estimating the distribution circuits assigned to geo-located substations. Population estimates servedmore » within the service areas are located within 1 km grid cells and converted to customer counts by conversion through demographic estimation of households and commercial firms within the population cells. Restoration times are estimated by agent-based simulation of restoration crews working according to utility published prioritization calibrated by historic performance.« less

  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. UPF Forecast | Y-12 National Security Complex

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

    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 Equipment Rental FOC 238910 TBD 3Q FY15/ 3Q

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

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

    Influencing Technological Progress in Solar Energy Project Profile: Forecasting and ... energy technologies based on estimates of future rates of progress and adoption. ...

  5. 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:...

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

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

  8. 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...

  9. 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.

  10. AVLIS: a technical and economic forecast

    SciTech Connect (OSTI)

    Davis, J.I.; Spaeth, M.L.

    1986-01-01

    The AVLIS process has intrinsically large isotopic selectivity and hence high separative capacity per module. The critical components essential to achieving the high production rates represent a small fraction (approx.10%) of the total capital cost of a production facility, and the reference production designs are based on frequent replacement of these components. The specifications for replacement frequencies in a plant are conservative with respect to our expectations; it is reasonable to expect that, as the plant is operated, the specifications will be exceeded and production costs will continue to fall. Major improvements in separator production rates and laser system efficiencies (approx.power) are expected to occur as a natural evolution in component improvements. With respect to the reference design, such improvements have only marginal economic value, but given the exigencies of moving from engineering demonstration to production operations, we continue to pursue these improvements in order to offset any unforeseen cost increases. Thus, our technical and economic forecasts for the AVLIS process remain very positive. The near-term challenge is to obtain stable funding and a commitment to bring the process to full production conditions within the next five years. If the funding and commitment are not maintained, the team will disperse and the know-how will be lost before it can be translated into production operations. The motivation to preserve the option for low-cost AVLIS SWU production is integrally tied to the motivation to maintain a competitive nuclear option. The US industry can certainly survive without AVLIS, but our tradition as technology leader in the industry will certainly be lost.

  11. EIA lowers forecast for summer gasoline prices

    Gasoline and Diesel Fuel Update (EIA)

    EIA lowers forecast for summer gasoline prices U.S. gasoline prices are expected to be lower this summer than previously thought. The price for regular gasoline this summer is now expected to average $3.53 a gallon, according to the new monthly forecast from the U.S. Energy Information Administration. That's down 10 cents from last month's forecast and 16 cents cheaper than last summer. After reaching a weekly peak of $3.78 a gallon in late February, pump prices fell nine weeks in a row to $3.52

  12. Short-Term Energy Outlook Model Documentation: Petroleum Product Prices Module

    Reports and Publications (EIA)

    2015-01-01

    The petroleum products price module of the Short-Term Energy Outlook (STEO) model is designed to provide U.S. average wholesale and retail price forecasts for motor gasoline, diesel fuel, heating oil, and jet fuel.

  13. Text-Alternative Version LED Lighting Forecast

    Broader source: Energy.gov [DOE]

    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....

  14. 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...

  15. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  16. 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...

  17. 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.

  18. 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.

  19. Building Energy Consumption Analysis

    Energy Science and Technology Software Center (OSTI)

    2005-03-02

    DOE2.1E-121SUNOS is a set of modules for energy analysis in buildings. Modules are included to calculate the heating and cooling loads for each space in a building for each hour of a year (LOADS), to simulate the operation and response of the equipment and systems that control temperature and humidity and distribute heating, cooling and ventilation to the building (SYSTEMS), to model energy conversion equipment that uses fuel or electricity to provide the required heating,more » cooling and electricity (PLANT), and to compute the cost of energy and building operation based on utility rate schedule and economic parameters (ECONOMICS).« less

  20. Module Configuration

    DOE Patents [OSTI]

    Oweis, Salah (Ellicott City, MD); D'Ussel, Louis (Bordeaux, FR); Chagnon, Guy (Cockeysville, MD); Zuhowski, Michael (Annapolis, MD); Sack, Tim (Cockeysville, MD); Laucournet, Gaullume (Paris, FR); Jackson, Edward J. (Taneytown, MD)

    2002-06-04

    A stand alone battery module including: (a) a mechanical configuration; (b) a thermal management configuration; (c) an electrical connection configuration; and (d) an electronics configuration. Such a module is fully interchangeable in a battery pack assembly, mechanically, from the thermal management point of view, and electrically. With the same hardware, the module can accommodate different cell sizes and, therefore, can easily have different capacities. The module structure is designed to accommodate the electronics monitoring, protection, and printed wiring assembly boards (PWAs), as well as to allow airflow through the module. A plurality of modules may easily be connected together to form a battery pack. The parts of the module are designed to facilitate their manufacture and assembly.

  1. 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.

  2. 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.

  3. 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...

  4. 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.

  5. ZBB EnerStore(tm): Deep Discharge Zinc-Bromine Battery Module...

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

    Long-Lasting Electrical Energy Storage Module Allows Off-Peak Power Generation Electricity consumption during ... use off-grid or during grid outages, by integrating renewable ...

  6. Building Energy Consumption Analysis

    Energy Science and Technology Software Center (OSTI)

    2005-01-24

    DOE2.1E-121 is a set of modules for energy analysis in buildings. Modules are included to calculate the heating and cooling loads for each space in a building for each hour of a year (LOADS), to simulate the operation and response of the equipment and systems that control temperature and humidity and distribute heating, cooling and ventilation to the building (SYSTEMS), to model energy conversion equipment that uses fuel or electricity to provide the required heating,more » cooling and electricity (PLANT), and to compute the cost of energy and building operation based on utility rate schedule and economic parameters (ECONOMICS). DOE2.1E-121 contains modifications to DOE2.1E which allows 1000 zones to be modeled.« less

  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. 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 ...

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

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

    rely on an array of VG forecasts suited to different purposes. Some of the most common types of VG forecasts are defined below: 2 This report is available at no cost from the...

  11. 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...

  12. 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.

  13. NREL: Resource Assessment and Forecasting - Capabilities

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

    Capabilities Best Practices Handbook Helps Industry Collect and Interpret Solar Resource Data Read about this new comprehensive resource for the solar industry. NREL's resource assessment and forecasting research staff provides expertise in renewable energy measurement and instrumentation. Major capabilities include solar resource measurement, instrument calibration, instrument characterization, solar monitoring training, and standards development and information dissemination. Solar Resource

  14. 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.

  15. 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...

  16. 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.

  17. 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.

  18. 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)

  19. 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...

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

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

    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

  1. Uncertainty Reduction in Power Generation Forecast Using Coupled

    Office of Scientific and Technical Information (OSTI)

    Wavelet-ARIMA (Conference) | SciTech Connect Conference: Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA Citation Details In-Document Search Title: Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction

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

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

    ANL Software Improves Wind Power Forecasting ANL Software Improves Wind Power Forecasting May 1, 2012 - 3:19pm Addthis This is an excerpt from the Second Quarter 2012 edition of the Wind Program R&D Newsletter. Since 2008, Argonne National Laboratory and INESC TEC (formerly INESC Porto) have conducted a research project to improve wind power forecasting and better use of forecasting in electricity markets. One of the main results from the project is ARGUS PRIMA (PRediction Intelligent

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

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

    Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical

  4. Model documentation: Electricity Market Module, Electricity Fuel Dispatch Submodule

    SciTech Connect (OSTI)

    Not Available

    1994-04-08

    This report documents the objectives, analytical approach and development of the National Energy Modeling System Electricity Fuel Dispatch Submodule (EFD), a submodule of the Electricity Market Module (EMM). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

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

    SciTech Connect (OSTI)

    Wilczak, J. 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.

    2015-10-11

    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.

  6. 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...

  7. 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.

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. Full Consumption Report.indd

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

    214(2013) July 2015 State Energy Consumption Estimates 1960 Through 2013 2013 Consumption Summary Tables S U M M A R I E S U.S. Energy Information Administration | State Energy Data 2013: Consumption 3 Table C1. Energy Consumption Overview: Estimates by Energy Source and End-Use Sector, 2013 (Trillion Btu) State Total Energy b Sources End-Use Sectors a Fossil Fuels Nuclear Electric Power Renewable Energy e Net Interstate Flow of Electricity f Net Electricity Imports g Residential Commercial

  14. 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.

  15. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [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.

  16. 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.

  17. 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

  18. 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.

  19. Thermionic modules

    DOE Patents [OSTI]

    King, Donald B. (Albuquerque, NM); Sadwick, Laurence P. (Salt Lake City, UT); Wernsman, Bernard R. (Clairton, PA)

    2002-06-18

    Modules of assembled microminiature thermionic converters (MTCs) having high energy-conversion efficiencies and variable operating temperatures manufactured using MEMS manufacturing techniques including chemical vapor deposition. The MTCs incorporate cathode to anode spacing of about 1 micron or less and use cathode and anode materials having work functions ranging from about 1 eV to about 3 eV. The MTCs also exhibit maximum efficiencies of just under 30%, and thousands of the devices and modules can be fabricated at modest costs.

  20. US NE MA Site Consumption

    Gasoline and Diesel Fuel Update (EIA)

    NE MA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 US NE MA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US NE MA Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US NE MA Expenditures dollars ELECTRICITY ONLY average per household * Massachusetts households use 109 million Btu of energy per home, 22% more than the U.S. average. * The higher than average site consumption

  1. US ENC IL Site Consumption

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

    IL Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US ENC IL Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US ENC IL Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US ENC IL Expenditures dollars ELECTRICITY ONLY average per household * Illinois households use 129 million Btu of energy per home, 44% more than the U.S. average. * High consumption, combined with low costs for heating fuels

  2. US ENC MI Site Consumption

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

    MI Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US ENC MI Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US ENC MI Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US ENC MI Expenditures dollars ELECTRICITY ONLY average per household * Michigan households use 123 million Btu of energy per home, 38% more than the U.S. average. * High consumption, combined with low costs for heating fuels

  3. 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%

  4. US NE MA Site Consumption

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

    NE MA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 US NE MA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US NE MA Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US NE MA Expenditures dollars ELECTRICITY ONLY average per household * Massachusetts households use 109 million Btu of energy per home, 22% more than the U.S. average. * The higher than average site consumption

  5. US WSC TX Site Consumption

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

    WSC TX Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US WSC TX Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 4,000 8,000 12,000 16,000 US WSC TX Site Consumption kilowatthours $0 $500 $1,000 $1,500 $2,000 US WSC TX Expenditures dollars ELECTRICITY ONLY average per household * Texas households consume an average of 77 million Btu per year, about 14% less than the U.S. average. * Average electricity consumption per Texas home is 26% higher than

  6. 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.

  7. 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...

  8. 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...

  9. 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) . . . . . . . . ....

  10. US ESC TN Site Consumption

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

    an average of 79 million Btu per year, about 12% less than the U.S. average. * Average electricity consumption for Tennessee households is 33% higher than the national average...

  11. 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.

  12. 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.

  13. 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

  14. Funding Opportunity Announcement for Wind Forecasting Improvement Project

    Office of Environmental Management (EM)

    in Complex Terrain | Department of Energy Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain April 4, 2014 - 9:47am Addthis On April 4, 2014 the U.S. Department of Energy announced a $2.5 million funding opportunity entitled "Wind Forecasting Improvement Project in Complex Terrain." By researching the physical processes that take place in complex

  15. DOE Benefits Forecasts: Report of the External Peer Review Panel |

    Office of Environmental Management (EM)

    Department of Energy Benefits Forecasts: Report of the External Peer Review Panel DOE Benefits Forecasts: Report of the External Peer Review Panel A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts. PDF icon Report of the External Peer Review Panel More Documents & Publications Industrial Technologies Funding Profile by Subprogram Survey of Emissions Models for Distributed Combined Heat and Power

  16. NREL: Resource Assessment and Forecasting - Webmaster

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

    Webmaster Use this form to send us your comments and questions, report problems with the site, or ask for help finding information on the site. Please enter your name and email address in the boxes provided, then type your message below. When you are finished, click "Send Message." NOTE: If you enter your e-mail address incorrectly, we will be unable to reply. Your name: Your email address: Your message: Send Message Printable Version Resource Assessment & Forecasting Home

  17. 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.

  18. Wind Power Forecasting Error Distributions: An International Comparison; Preprint

    SciTech Connect (OSTI)

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

    2012-09-01

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

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

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

    research project whose overarching goals are to improve the accuracy of short-term wind energy forecasts, and to demonstrate the economic value of these improvements. WFIP Round...

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

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

    Opportunity, DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and other stakeholders better forecast when, where, and how much...

  1. 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...

  2. 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...

  3. 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 " " % % &...

  4. ,"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...

  5. ,"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...

  6. 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)...

  7. 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...

  8. 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...

  9. 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...

  10. 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...

  11. 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...

  12. 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...

  13. 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...

  14. 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)...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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)...

  3. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. Thermoelectric module

    DOE Patents [OSTI]

    Kortier, William E. (Columbus, OH); Mueller, John J. (Columbus, OH); Eggers, Philip E. (Columbus, OH)

    1980-07-08

    A thermoelectric module containing lead telluride as the thermoelectric mrial is encapsulated as tightly as possible in a stainless steel canister to provide minimum void volume in the canister. The lead telluride thermoelectric elements are pressure-contacted to a tungsten hot strap and metallurgically bonded at the cold junction to iron shoes with a barrier layer of tin telluride between the iron shoe and the p-type lead telluride element.

  9. 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 uncertainty in wind power and to more efficiently operate power systems with large wind power penetrations. Moreover, in a market environment, the wind power contribution to the generation portofolio becomes important in determining the daily and hourly prices, as variations in the estimated wind power will influence the clearing prices for both energy and operating reserves. With the increasing penetration of wind power, WPF is quickly becoming an important topic for the electric power industry. System operators (SOs), generating companies (GENCOs), and regulators all support efforts to develop better, more reliable and accurate forecasting models. Wind farm owners and operators also benefit from better wind power prediction to support competitive participation in electricity markets against more stable and dispatchable energy sources. In general, WPF can be used for a number of purposes, such as: generation and transmission maintenance planning, determination of operating reserve requirements, unit commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and energy trading. The objective of this report is to review and analyze state-of-the-art WPF models and their application to power systems operations. We first give a detailed description of the methodologies underlying state-of-the-art WPF models. We then look at how WPF can be integrated into power system operations, with specific focus on the unit commitment problem.

  10. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01

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

  11. Photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan; Graves, Simon; Lenox, Carl J. S.; Culligan, Matthew; Danning, Matt

    2013-08-27

    A photovoltaic (PV) module including a PV device and a frame, The PV device has a PV laminate defining a perimeter and a major plane. The frame is assembled to and encases the laminate perimeter, and includes leading, trailing, and side frame members, and an arm that forms a support face opposite the laminate. The support face is adapted for placement against a horizontal installation surface, to support and orient the laminate in a non-parallel or tilted arrangement. Upon final assembly, the laminate and the frame combine to define a unitary structure. The frame can orient the laminate at an angle in the range of 3.degree.-7.degree. from horizontal, and can be entirely formed of a polymeric material. Optionally, the arm incorporates integral feature(s) that facilitate interconnection with corresponding features of a second, identically formed PV module.

  12. Photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan (El Cerrito, CA); Graves, Simon (Berkeley, CA); Lenox, Carl J. S. (Oakland, CA); Culligan, Matthew (Berkeley, CA); Danning, Matt (Oakland, CA)

    2012-07-17

    A photovoltaic (PV) module including a PV device and a frame. The PV device has a PV laminate defining a perimeter and a major plane. The frame is assembled to and encases the laminate perimeter, and includes leading, trailing, and side frame members, and an arm that forms a support face opposite the laminate. The support face is adapted for placement against a horizontal installation surface, to support and orient the laminate in a non-parallel or tilted arrangement. Upon final assembly, the laminate and the frame combine to define a unitary structure. The frame can orient the laminate at an angle in the range of 3.degree.-7.degree. from horizontal, and can be entirely formed of a polymeric material. Optionally, the arm incorporates integral feature(s) that facilitate interconnection with corresponding features of a second, identically formed PV module.

  13. The Impact of Using Derived Fuel Consumption Maps to Predict...

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

    The Impact of Using Derived Fuel Consumption Maps to Predict Fuel Consumption The Impact of Using Derived Fuel Consumption Maps to Predict Fuel Consumption Poster presented at the...

  14. US ENC WI Site Consumption

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

    120 US ENC WI Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US ENC WI Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US ENC WI Site Consumption kilowatthours $0 $300 $600 $900 $1,200 $1,500 US ENC WI Expenditures dollars ELECTRICITY ONLY average per household * Wisconsin households use 103 million Btu of energy per home, 15% more than the U.S. average. * Lower electricity and natural gas rates compared to

  15. US WNC MO Site Consumption

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

    WNC MO Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US WNC MO Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 3,000 6,000 9,000 12,000 15,000 US WNC MO Site Consumption kilowatthours $0 $300 $600 $900 $1,200 $1,500 US WNC MO Expenditures dollars ELECTRICITY ONLY average per household * Missouri households consume an average of 100 million Btu per year, 12% more than the U.S. average. * Average household energy costs in Missouri are slightly less

  16. Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1

    SciTech Connect (OSTI)

    Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

    1994-05-01

    This report details the data, assumptions and methodology for end-use forecasting of appliance energy use in the US residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which was developed by the Electric Power Research Institute. In this modeling framework, appliances include essentially all residential end-uses other than space conditioning end-uses. We have defined a distinct appliance model for each end-use based on a common modeling framework provided in the REEPS software. This report details our development of the following appliance models: refrigerator, freezer, dryer, water heater, clothes washer, dishwasher, lighting, cooking and miscellaneous. Taken together, appliances account for approximately 70% of electricity consumption and 30% of natural gas consumption in the US residential sector. Appliances are thus important to those residential sector policies or programs aimed at improving the efficiency of electricity and natural gas consumption. This report is primarily methodological in nature, taking the reader through the entire process of developing the baseline for residential appliance end-uses. Analysis steps documented in this report include: gathering technology and market data for each appliance end-use and specific technologies within those end-uses, developing cost data for the various technologies, and specifying decision models to forecast future purchase decisions by households. Our implementation of the REEPS 2.1 modeling framework draws on the extensive technology, cost and market data assembled by LBL for the purpose of analyzing federal energy conservation standards. The resulting residential appliance forecasting model offers a flexible and accurate tool for analyzing the effect of policies at the national level.

  17. Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

    SciTech Connect (OSTI)

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

    2012-07-01

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

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

    Energy Savers [EERE]

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

  19. Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

    Reports and Publications (EIA)

    2010-01-01

    The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

  20. 3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information

    Open Energy Info (EERE)

    TIER Environmental Forecast Group Inc 3TIER Jump to: navigation, search Name: 3TIER Environmental Forecast Group Inc (3TIER) Place: Seattle, Washington Zip: 98121 Sector: Renewable...

  1. Energy Savings Forecast of Solid-State Lighting in General Illuminatio...

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

    Forecast of Solid-State Lighting in General Illumination Applications Energy Savings Forecast of Solid-State Lighting in General Illumination Applications PDF icon...

  2. Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010

    SciTech Connect (OSTI)

    Jacobs, David E. . E-mail: dejacobs@starpower.net; Nevin, Rick

    2006-11-15

    We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels {>=}10 {mu}g/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

  3. Optimal design of reverse osmosis module networks

    SciTech Connect (OSTI)

    Maskan, F.; Wiley, D.E.; Johnston, L.P.M.; Clements, D.J.

    2000-05-01

    The structure of individual reverse osmosis modules, the configuration of the module network, and the operating conditions were optimized for seawater and brackish water desalination. The system model included simple mathematical equations to predict the performance of the reverse osmosis modules. The optimization problem was formulated as a constrained multivariable nonlinear optimization. The objective function was the annual profit for the system, consisting of the profit obtained from the permeate, capital cost for the process units, and operating costs associated with energy consumption and maintenance. Optimization of several dual-stage reverse osmosis systems were investigated and compared. It was found that optimal network designs are the ones that produce the most permeate. It may be possible to achieve economic improvements by refining current membrane module designs and their operating pressures.

  4. 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-...

  5. 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-...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

  10. 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-...

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  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 1999","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 South Central","Moun- tain","Pacific","West South Central","Moun-...

  20. 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...

  1. 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-...

  2. 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,...

  3. 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...

  4. Development and testing of improved statistical wind power forecasting methods.

    SciTech Connect (OSTI)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J.

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

  5. U.S. Regional Demand Forecasts Using NEMS and GIS

    SciTech Connect (OSTI)

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

    2005-07-01

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

  6. Supported PV module assembly

    DOE Patents [OSTI]

    Mascolo, Gianluigi; Taggart, David F.; Botkin, Jonathan D.; Edgett, Christopher S.

    2013-10-15

    A supported PV assembly may include a PV module comprising a PV panel and PV module supports including module supports having a support surface supporting the module, a module registration member engaging the PV module to properly position the PV module on the module support, and a mounting element. In some embodiments the PV module registration members engage only the external surfaces of the PV modules at the corners. In some embodiments the assembly includes a wind deflector with ballast secured to a least one of the PV module supports and the wind deflector. An array of the assemblies can be secured to one another at their corners to prevent horizontal separation of the adjacent corners while permitting the PV modules to flex relative to one another so to permit the array of PV modules to follow a contour of the support surface.

  7. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema (OSTI)

    Gonzalez, Frank

    2010-01-08

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

  8. 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.

  9. Short-Term Energy Outlook Model Documentation: Petroleum Products Supply Module

    Reports and Publications (EIA)

    2013-01-01

    The Petroleum Products Supply Module of the Short-Term Energy Outlook (STEO) model provides forecasts of petroleum refinery inputs (crude oil, unfinished oils, pentanes plus, liquefied petroleum gas, motor gasoline blending components, and aviation gasoline blending components) and refinery outputs (motor gasoline, jet fuel, distillate fuel, residual fuel, liquefied petroleum gas, and other petroleum products).

  10. Energy Forecasting Framework and Emissions Consensus Tool (EFFECT...

    Open Energy Info (EERE)

    Tool (EFFECT) EFFECT is an open, Excel-based modeling tool used to forecast greenhouse gas emissions from a range of development scenarios at the regional and national levels....

  11. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

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

  12. Solar Trackers Market Forecast | OpenEI Community

    Open Energy Info (EERE)

    Solar Trackers Market Forecast Home John55364's picture Submitted by John55364(100) Contributor 12 May, 2015 - 03:54 Solar Trackers Market - Global Industry Analysis, Size, Share,...

  13. Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels

    Reports and Publications (EIA)

    2003-01-01

    This paper presents a short-term monthly forecasting model of West Texas Intermediate crude oil spot price using Organization for Economic Cooperation and Development (OECD) petroleum inventory levels.

  14. Value of Improved Short-Term Wind Power Forecasting

    SciTech Connect (OSTI)

    Hodge, B. M.; Florita, A.; Sharp, J.; Margulis, M.; Mcreavy, D.

    2015-02-01

    This report summarizes an assessment of improved short-term wind power forecasting in the California Independent System Operator (CAISO) market and provides a quantification of its potential value.

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

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

    The Energy Department will present a live webinar titled "Solar Forecasting Metrics" on Thursday, February 13, from 3:00 p.m. to 5:00 p.m. Eastern Standard Time. During this ...

  16. Improving the Accuracy of Solar Forecasting Funding Opportunity

    Broader source: Energy.gov [DOE]

    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 other...

  17. PBL FY 2002 Second Quarter Review Forecast of Generation Accumulated...

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

    Slice true-ups, and actual expense levels. Any variation of these can change the net revenue situation. FY 2002 Forecasted Second Quarter Results 170 (418) FY 2002 Unaudited...

  18. World oil inventories forecast to grow significantly in 2016...

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

    World oil inventories forecast to grow significantly in 2016 and 2017 Global oil inventories are expected to continue strong growth over the next two years which should keep oil ...

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

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

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

  20. Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

    Broader source: Energy.gov [DOE]

    Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

  1. NERSC Python Modules

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

    NERSC Python Modules NERSC Python Modules Python Interpreter Modules To use the NERSC-built installation, type: module load python This loads the default version of the following useful core modules: python_base: Python 2.7.x module. numpy: Defines the numerical array and matrix type and basic operations on them. scipy: Uses numpy to do advanced math, signal processing, optimization, statistics and much more. matplotlib: Plotting/visualization library. ipython: Interactive python shell offering

  2. 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 the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. December 21, 2015 Los Alamos scientist Nate McDowell discusses how climate change is killing trees with PBS NewsHour reporter Miles O'Brien. Los Alamos scientist Nate McDowell discusses how climate change is

  3. Forecasting neutrino masses from combining KATRIN and the CMB observations:

    Office of Scientific and Technical Information (OSTI)

    Frequentist and Bayesian analyses (Journal Article) | SciTech Connect SciTech Connect Search Results Journal Article: Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses Citation Details In-Document Search Title: Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses We present a showcase for deriving bounds on the neutrino masses from laboratory experiments and cosmological

  4. Energy Department Forecasts Geothermal Achievements in 2015 | Department of

    Office of Environmental Management (EM)

    Energy Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector, including Jay Nathwani, Acting Director of the Energy Department's Geothermal Technologies Office. Nathwani shared achievements and challenges in the program's technical portfolio. The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector,

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

    Office of Environmental Management (EM)

    Energy Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes The Expert Panel has concluded that the Department of Energy and National Institutes of Health must develop the capability to produce a diverse supply of radioisotopes for medical use in quantities sufficient to support research and clinical activities. Such a capability would prevent shortages of isotopes, reduce American dependence on foreign radionuclide sources and

  6. State energy data report 1992: Consumption estimates

    SciTech Connect (OSTI)

    Not Available

    1994-05-01

    This is a report of energy consumption by state for the years 1960 to 1992. The report contains summaries of energy consumption for the US and by state, consumption by source, comparisons to other energy use reports, consumption by energy use sector, and describes the estimation methodologies used in the preparation of the report. Some years are not listed specifically although they are included in the summary of data.

  7. User-needs study for the 1993 residential energy consumption survey

    SciTech Connect (OSTI)

    Not Available

    1993-09-24

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

  8. National Oceanic and Atmospheric Administration Provides Forecasting Support for CLASIC and CHAPS 2007

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

    NOAA Provides Forecasting Support for CLASIC and CHAPS 2007 Forecasting Challenge While weather experiments in the heart of Tornado Alley typically focus on severe weather, the CLASIC and CHAPS programs will have different emphases. Forecasters from the National Oceanic and Atmospheric Administration in Norman, Okla. will provide weather forecasting support to these two Department of Energy experiments based in the state. Forecasting support for meteorological research field programs usually

  9. Ballasted photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan (El Cerrito, CA); Graves, Simon (Berkeley, CA); Danning, Matt (Oakland, CA)

    2011-11-29

    A photovoltaic (PV) module assembly including a PV module and a ballast tray. The PV module includes a PV device and a frame. A PV laminate is assembled to the frame, and the frame includes an arm. The ballast tray is adapted for containing ballast and is removably associated with the PV module in a ballasting state where the tray is vertically under the PV laminate and vertically over the arm to impede overt displacement of the PV module. The PV module assembly can be installed to a flat commercial rooftop, with the PV module and the ballast tray both resting upon the rooftop. In some embodiments, the ballasting state includes corresponding surfaces of the arm and the tray being spaced from one another under normal (low or no wind) conditions, such that the frame is not continuously subjected to a weight of the tray.

  10. 2014 Manufacturing Energy Consumption Survey

    Gasoline and Diesel Fuel Update (EIA)

    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 the Bureau of the Census U.S. Department of Commerce Form EIA-846 (mm-dd-yy) OMB Approval No. xxxx-xxxx Expires: mm/dd/yyyy Report Electronically: www.census.gov/ econhelp/mecs Username: Password: Reporting electronically allows you to save your work as you go through the form and could save you time If you need

  11. Electrical appliance energy consumption control methods and electrical energy consumption systems

    DOE Patents [OSTI]

    Donnelly, Matthew K.; Chassin, David P.; Dagle, Jeffery E.; Kintner-Meyer, Michael; Winiarski, David W.; Pratt, Robert G.; Boberly-Bartis, Anne Marie

    2006-03-07

    Electrical appliance energy consumption control methods and electrical energy consumption systems are described. In one aspect, an electrical appliance energy consumption control method includes providing an electrical appliance coupled with a power distribution system, receiving electrical energy within the appliance from the power distribution system, consuming the received electrical energy using a plurality of loads of the appliance, monitoring electrical energy of the power distribution system, and adjusting an amount of consumption of the received electrical energy via one of the loads of the appliance from an initial level of consumption to an other level of consumption different than the initial level of consumption responsive to the monitoring.

  12. Electrical appliance energy consumption control methods and electrical energy consumption systems

    DOE Patents [OSTI]

    Donnelly, Matthew K.; Chassin, David P.; Dagle, Jeffery E.; Kintner-Meyer, Michael; Winiarski, David W.; Pratt, Robert G.; Boberly-Bartis, Anne Marie

    2008-09-02

    Electrical appliance energy consumption control methods and electrical energy consumption systems are described. In one aspect, an electrical appliance energy consumption control method includes providing an electrical appliance coupled with a power distribution system, receiving electrical energy within the appliance from the power distribution system, consuming the received electrical energy using a plurality of loads of the appliance, monitoring electrical energy of the power distribution system, and adjusting an amount of consumption of the received electrical energy via one of the loads of the appliance from an initial level of consumption to an other level of consumption different than the initial level of consumption responsive to the monitoring.

  13. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect (OSTI)

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

  14. Forecasting the 2013–2014 influenza season using Wikipedia

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

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

  15. Thermoelectrics Partnership: Automotive Thermoelectric Modules...

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

    Documents & Publications Novel Nanostructured Interface Solution for Automotive Thermoelectric Modules Application Thermoelectrics Partnership: Automotive Thermoelectric Modules...

  16. Modules Software Environment

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

    Environment » Modules Environment Modules Software Environment NERSC uses the module utility to manage nearly all software. There are two huge advantages of the module approach: NERSC can provide many different versions and/or installations of a single software package on a given machine, including a default version as well as several older and newer versions; and Users can easily switch to different versions or installations without having to explicitly specify different paths. With modules,

  17. Energy consumption in thermomechanical pulping

    SciTech Connect (OSTI)

    Marton, R.; Tsujimoto, N.; Eskelinen, E.

    1981-08-01

    Various components of refining energy were determined experimentally and compared with those calculated on the basis of the dimensions of morphological elements of wood. The experimentally determined fiberization energy of spruce was 6 to 60 times larger than the calculated value and that of birch 3 to 15 times larger. The energy consumed in reducing the Canadian standard freeness of isolated fibers from 500 to 150 ml was found to be approximately 1/3 of the total fiber development energy for both spruce and birch TMP. Chip size affected the refining energy consumption; the total energy dropped by approximately 30% when chip size was reduced from 16 mm to 3 mm in the case of spruce and approximately 40% for birch. 6 refs.

  18. 2009 Energy Consumption Per Person | Department of Energy

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

    2009 Energy Consumption Per Person 2009 Energy Consumption Per Person 2009 Energy Consumption Per Person Per capita energy consumption across all sectors of the economy. Click on a state for more information.

  19. Household energy consumption and expenditures 1993

    SciTech Connect (OSTI)

    1995-10-05

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

  20. Table 6a. Total Electricity Consumption per Effective Occupied...

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

    a. Total Electricity Consumption per Effective Occupied Square Foot, 1992 Building Characteristics All Buildings Using Electricity (thousand) Total Electricity Consumption...

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2012-04-01

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

  2. Trends in Renewable Energy Consumption and Electricity

    Reports and Publications (EIA)

    2012-01-01

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

  3. Energy Intensity Indicators: Commercial Source Energy Consumption |

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

    Department of Energy Commercial Source Energy Consumption Energy Intensity Indicators: Commercial Source Energy Consumption Figure C1 below reports as index numbers over the period 1970 through 2011: 1) commercial building floor space, 2) energy use based on source energy consumption, 3) energy intensity, and 4) the year-to-year influence of weather. Activity: Since 1970, the quantity of commercial floor space has nearly doubled, with about half of that increase occurring after 1985. There

  4. Energy Intensity Indicators: Residential Source Energy Consumption |

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

    Department of Energy Residential Source Energy Consumption Energy Intensity Indicators: Residential Source Energy Consumption Figure R1 below reports as index numbers over the period 1970 through 2011: 1) the number of U.S. households, 2) the average size of those housing units, 3) residential source energy consumption, 4) energy intensity, and 5) an overall structural component that represents "other explanatory factors." Activity: Since 1970, the number of household (occupied

  5. ,"Natural Gas Consumption",,,"Natural Gas Expenditures"

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

    Census Division, 1999" ,"Natural Gas Consumption",,,"Natural Gas Expenditures" ,"per Building (thousand cubic feet)","per Square Foot (cubic feet)","per Worker (thousand cubic...

  6. Commercial Buildings Energy Consumption and Expenditures 1995...

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

    fuel oil, and district heat consumption and expenditures for commercial buildings by building characteristics. Previous Page Arrow Separater Bar File Last Modified: January 29,...

  7. Commercial Buildings Energy Consumption and Expenditures 1992

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

    Appendix I Related EIA Publications on Energy Consumption For information about how to obtain these publi- cations, see the inside cover of this report. Please note that the...

  8. Commercial Buildings Energy Consumption and Expenditures 1992

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

    Appendix A How the Survey Was Conducted Introduction The Commercial Buildings Energy Consumption Survey (CBECS) is conducted by the Energy Information Administration (EIA) on a...

  9. Commercial Buildings Energy Consumption and Expenditures 1992

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

    Distribution Category UC-950 Commercial Buildings Energy Consumption and Expenditures 1992 April 1995 Energy Information Adminstration Office of Energy Markets and End Use U.S....

  10. Commercial Buildings Energy Consumption and Expenditures 1992

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

    in this report were based on monthly billing records submitted by the buildings' energy suppliers. The section, "Annual Consumption and Expenditures" provide a detailed...

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

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

    Gas Consumption Natural Gas Expenditures per Building (thousand cubic feet) per Square Foot (cubic feet) Distribution of Building-Level Intensities (cubic feetsquare foot) 25th...

  12. CBECS 1992 - Consumption & Expenditures, Detailed Tables

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

    consumption by major fuel, 1992 Divider Line To View andor Print Reports (requires Adobe Acrobat Reader) - Download Adobe Acrobat Reader If you experience any difficulties,...

  13. ,"Maine Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Maine Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  14. ,"Hawaii Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Hawaii Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  15. ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; GREENHOUSES...

    Office of Scientific and Technical Information (OSTI)

    fuel-fired peak heating for geothermal greenhouses Rafferty, K. 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; GREENHOUSES; AUXILIARY HEATING; CAPITALIZED COST; OPERATING...

  16. Energy Preview: Residential Transportation Energy Consumption...

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

    t 7 Energy Preview: Residential Transportation Energy Consumption Survey, Preliminary Estimates, 1991 (See Page 1) This publication and other Energy Information Administration...

  17. Derived Annual Estimates of Manufacturing Energy Consumption...

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

    > Derived Annual Estimates - Executive Summary Derived Annual Estimates of Manufacturing Energy Consumption, 1974-1988 Figure showing Derived Estimates Executive Summary This...

  18. Household Vehicles Energy Consumption 1994 - Appendix C

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

    discusses several issues relating to the quality of the Residential Transportation Energy Consumption Survey (RTECS) data and to the interpretation of conclusions based on...

  19. Chapter 4. Fuel Economy, Consumption and Expenditures

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

    national concerns about dependence on foreign oil and the deleterious effect on the environment of fossil fuel combustion, residential vehicle fleet fuel consumption was...

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

    Gasoline and Diesel Fuel Update (EIA)

    homes plus increased use of electronics, improvements in efficiency for space heating, air conditioning, and major appliances have all led to decreased consumption per household. ...

  1. ,"Washington Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Washington Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

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

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  3. ,"Texas Natural Gas Lease Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Lease Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  4. ,"Texas Natural Gas Plant Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Plant Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

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

    Gasoline and Diesel Fuel Update (EIA)

    that extend from the foundation to the roof. Data collection for the 2012 Commercial Buildings Energy Consumption Survey (CBECS) took place between April and November 2013,...

  6. Issues in International Energy Consumption Analysis: Canadian...

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

    Canadian Energy Demand June 2015 Independent Statistics & ... DC 20585 U.S. Energy Information Administration | Issues ... change in household electricity consumption between 1990 ...

  7. Manufacturing Energy Consumption Survey (MECS) - Analysis & Projection...

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

    Manufacturing Activity between 2002 and 2010 Released: March 19, 2013 Total energy consumption in the manufacturing sector decreased by 17% from 2002 to 2010, according to data...

  8. ,"North Carolina Natural Gas Industrial Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","North Carolina Natural Gas Industrial Consumption (MMcf)",1,"Monthly","102015" ,"Release...

  9. ,"New York Natural Gas Residential Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New York Natural Gas Residential Consumption (MMcf)",1,"Monthly","92015" ,"Release...

  10. CCPP-ARM Parameterization Testbed Model Forecast Data

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

    Klein, Stephen

    2008-01-15

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

  11. CCPP-ARM Parameterization Testbed Model Forecast Data

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

    Klein, Stephen

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

  12. Coal Market Module

    Gasoline and Diesel Fuel Update (EIA)

    power generation, industrial steam generation, coal-to-liquids production, coal coke manufacturing, residentialcommercial consumption, and coal exports) within the CMM. By...

  13. Commercial Sector Demand Module

    Gasoline and Diesel Fuel Update (EIA)

    the State Energy Data System (SEDS) historical commercial sector consumption, applying an additive correction term to ensure that simulated model results correspond to published...

  14. Minimize oil field power consumption

    SciTech Connect (OSTI)

    Harris, B.; Ennis, P.

    1999-08-01

    Though electric power is a major operating cost of oil production, few producers have systematically evaluated their power consumption for ways to be more efficient. There is significant money to be saved by doing so, and now is a good time to make an evaluation because new power options are at hand. They range from small turbo generators that can run on casing head gas and power one or two lift pumps, to rebuilt major turbines and ram-jet powered generators that can be set in a multi-well field and deliver power at bargain prices. Power industry deregulation is also underway. Opportunities for more advantageous power contracts from competitive sources are not far off. This two-part series covers power efficiency and power options. This article reviews steps you can take to evaluate the efficiency of your power use and go about improving it. Part 2 will discuss opportunities for use of distributed power and changes you can expect from decentralized power.

  15. Detailed Course Module Description

    Broader source: Energy.gov [DOE]

    This document lists the course modules for building science courses offered at Cornell's Collaborator Sustainable Buildingi Practice course.

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

    SciTech Connect (OSTI)

    Piwko, R.; Jordan, G.

    2011-11-01

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

  17. DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting

    Broader source: Energy.gov [DOE]

    DOE has published a new report forecasting the energy savings of LED white-light sources compared with conventional white-light sources. The sixth iteration of the Energy Savings Forecast of Solid...

  18. Status of Centralized Wind Power Forecasting in North America: May 2009-May 2010

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2010-04-01

    Report surveys grid wind power forecasts for all wind generators, which are administered by utilities or regional transmission organizations (RTOs), typically with the assistance of one or more wind power forecasting companies.

  19. EERE Success Story-Solar Forecasting Gets a Boost from Watson...

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

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

  20. Modulating lignin in plants

    DOE Patents [OSTI]

    Apuya, Nestor; Bobzin, Steven Craig; Okamuro, Jack; Zhang, Ke

    2013-01-29

    Materials and methods for modulating (e.g., increasing or decreasing) lignin content in plants are disclosed. For example, nucleic acids encoding lignin-modulating polypeptides are disclosed as well as methods for using such nucleic acids to generate transgenic plants having a modulated lignin content.

  1. Integrating Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

  2. Energy Intensity Indicators: Industrial Source Energy Consumption |

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

    Department of Energy Industrial Source Energy Consumption Energy Intensity Indicators: Industrial Source Energy Consumption The industrial sector comprises manufacturing and other nonmanufacturing industries not included in transportation or services. Manufacturing includes 18 industry sectors, generally defined at the three-digit level of the North American Industrial Classification System (NAICS). The nonmanufacturing sectors are agriculture, forestry and fisheries, mining, and

  3. Weather forecast-based optimization of integrated energy systems.

    SciTech Connect (OSTI)

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

    2009-03-01

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

  4. Weather Research and Forecasting Model with the Immersed Boundary Method

    Energy Science and Technology Software Center (OSTI)

    2012-05-01

    The Weather Research and Forecasting (WRF) Model with the immersed boundary method is an extension of the open-source WRF Model available for wwww.wrf-model.org. The new code modifies the gridding procedure and boundary conditions in the WRF model to improve WRF's ability to simutate the atmosphere in environments with steep terrain and additionally at high-resolutions.

  5. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect (OSTI)

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

    2011-03-28

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

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

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

    Department of Energy PDF icon Wind Forecast Improvement Project Southern Study Area Final Report.pdf 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

  7. Power consumption monitoring using additional monitoring device

    SciTech Connect (OSTI)

    Tru?c?, M. R. C. Albert, ?. Tudoran, C. Soran, M. L. F?rca?, F.; Abrudean, M.

    2013-11-13

    Today, emphasis is placed on reducing power consumption. Computers are large consumers; therefore it is important to know the total consumption of computing systems. Since their optimal functioning requires quite strict environmental conditions, without much variation in temperature and humidity, reducing energy consumption cannot be made without monitoring environmental parameters. Thus, the present work uses a multifunctional electric meter UPT 210 for power consumption monitoring. Two applications were developed: software which carries meter readings provided by electronic and programming facilitates remote device and a device for temperature monitoring and control. Following temperature variations that occur both in the cooling system, as well as the ambient, can reduce energy consumption. For this purpose, some air conditioning units or some computers are stopped in different time slots. These intervals were set so that the economy is high, but the work's Datacenter is not disturbed.

  8. Central Wind Power Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2009-12-01

    The report addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America.

  9. A Public-Private-Academic Partnership to Advance Solar Power Forecasting |

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

    Department of Energy A Public-Private-Academic Partnership to Advance Solar Power Forecasting A Public-Private-Academic Partnership to Advance Solar Power Forecasting UCAR logo2.jpg The University Corporation for Atmospheric Research (UCAR) will develop a solar power forecasting system that advances the state of the science through cutting-edge research. APPROACH UCAR value chain.png The team will develop a solar power forecasting system that advances the state of the science through

  10. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

    Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V.

    2011-11-29

    The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  12. Developing a tool to estimate water withdrawal and consumption in electricity generation in the United States.

    SciTech Connect (OSTI)

    Wu, M.; Peng, J.

    2011-02-24

    Freshwater consumption for electricity generation is projected to increase dramatically in the next couple of decades in the United States. The increased demand is likely to further strain freshwater resources in regions where water has already become scarce. Meanwhile, the automotive industry has stepped up its research, development, and deployment efforts on electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Large-scale, escalated production of EVs and PHEVs nationwide would require increased electricity production, and so meeting the water demand becomes an even greater challenge. The goal of this study is to provide a baseline assessment of freshwater use in electricity generation in the United States and at the state level. Freshwater withdrawal and consumption requirements for power generated from fossil, nonfossil, and renewable sources via various technologies and by use of different cooling systems are examined. A data inventory has been developed that compiles data from government statistics, reports, and literature issued by major research institutes. A spreadsheet-based model has been developed to conduct the estimates by means of a transparent and interactive process. The model further allows us to project future water withdrawal and consumption in electricity production under the forecasted increases in demand. This tool is intended to provide decision makers with the means to make a quick comparison among various fuel, technology, and cooling system options. The model output can be used to address water resource sustainability when considering new projects or expansion of existing plants.

  13. State Energy Data Report, 1991: Consumption estimates

    SciTech Connect (OSTI)

    Not Available

    1993-05-01

    The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to the Government, policy makers, and the public; and (2) to provide the historical series necessary for EIA`s energy models.

  14. US MidAtl NJ Site Consumption

    Gasoline and Diesel Fuel Update (EIA)

    MidAtl NJ Site Consumption million Btu $0 $700 $1,400 $2,100 $2,800 $3,500 US MidAtl NJ Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US MidAtl NJ Site Consumption kilowatthours $0 $400 $800 $1,200 $1,600 US MidAtl NJ Expenditures dollars ELECTRICITY ONLY average per household * Average energy consumption (127 million Btu per year) in New Jersey homes and average household energy expenditures ($3,065 per year) are among the

  15. US Mnt(S) AZ Site Consumption

    Gasoline and Diesel Fuel Update (EIA)

    Mnt(S) AZ Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US Mnt(S) AZ Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 3,000 6,000 9,000 12,000 15,000 US Mnt(S) AZ Site Consumption kilowatthours $0 $500 $1,000 $1,500 $2,000 US Mnt(S) AZ Expenditures dollars ELECTRICITY ONLY average per household * Arizona households use 66 million Btu of energy per home, 26% less than the U.S. average. * The combination of lower than average site consumption of all

  16. US SoAtl VA Site Consumption

    Gasoline and Diesel Fuel Update (EIA)

    SoAtl VA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US SoAtl VA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 4,000 8,000 12,000 16,000 US SoAtl VA Site Consumption kilowatthours $0 $300 $600 $900 $1,200 $1,500 $1,800 US SoAtl VA Expenditures dollars ELECTRICITY ONLY average per household * Virginia households consume an average of 86 million Btu per year, about 4% less than the U.S. average. * Average electricity consumption and costs are

  17. US MidAtl NJ Site Consumption

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

    MidAtl NJ Site Consumption million Btu $0 $700 $1,400 $2,100 $2,800 $3,500 US MidAtl NJ Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US MidAtl NJ Site Consumption kilowatthours $0 $400 $800 $1,200 $1,600 US MidAtl NJ Expenditures dollars ELECTRICITY ONLY average per household * Average energy consumption (127 million Btu per year) in New Jersey homes and average household energy expenditures ($3,065 per year) are among the

  18. US Mnt(S) AZ Site Consumption

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

    Mnt(S) AZ Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US Mnt(S) AZ Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 3,000 6,000 9,000 12,000 15,000 US Mnt(S) AZ Site Consumption kilowatthours $0 $500 $1,000 $1,500 $2,000 US Mnt(S) AZ Expenditures dollars ELECTRICITY ONLY average per household * Arizona households use 66 million Btu of energy per home, 26% less than the U.S. average. * The combination of lower than average site consumption of all

  19. US SoAtl GA Site Consumption

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

    GA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US SoAtl GA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 4,000 8,000 12,000 16,000 US SoAtl GA Site Consumption kilowatthours $0 $300 $600 $900 $1,200 $1,500 $1,800 US SoAtl GA Expenditures dollars ELECTRICITY ONLY average per household * Site energy consumption (89.5 million Btu) and energy expenditures per household ($2,067) in Georgia are similar to the U.S. household averages. * Per

  20. US SoAtl VA Site Consumption

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

    SoAtl VA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US SoAtl VA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 4,000 8,000 12,000 16,000 US SoAtl VA Site Consumption kilowatthours $0 $300 $600 $900 $1,200 $1,500 $1,800 US SoAtl VA Expenditures dollars ELECTRICITY ONLY average per household * Virginia households consume an average of 86 million Btu per year, about 4% less than the U.S. average. * Average electricity consumption and costs are

  1. Residential Energy Consumption Survey: Quality Profile

    SciTech Connect (OSTI)

    1996-03-01

    The Residential Energy Consumption Survey (RECS) is a periodic national survey that provides timely information about energy consumption and expenditures of U.S. households and about energy-related characteristics of housing units. The survey was first conducted in 1978 as the National Interim Energy Consumption Survey (NIECS), and the 1979 survey was called the Household Screener Survey. From 1980 through 1982 RECS was conducted annually. The next RECS was fielded in 1984, and since then, the survey has been undertaken at 3-year intervals. The most recent RECS was conducted in 1993.

  2. State energy data report 1993: Consumption estimates

    SciTech Connect (OSTI)

    1995-07-01

    The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public; and (2) to provide the historical series necessary for EIA`s energy models.

  3. EIA model documentation: Electricity market module - electricity fuel dispatch

    SciTech Connect (OSTI)

    1997-01-01

    This report documents the National Energy Modeling System Electricity Fuel Dispatch Submodule (EFD), a submodule of the Electricity Market Module (EMM) as it was used for EIA`s Annual Energy Outlook 1997. It replaces previous documentation dated March 1994 and subsequent yearly update revisions. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This document serves four purposes. First, it is a reference document providing a detailed description of the model for reviewers and potential users of the EFD including energy experts at the Energy Information Administration (EIA), other Federal agencies, state energy agencies, private firms such as utilities and consulting firms, and non-profit groups such as consumer and environmental groups. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports. Third, it facilitates continuity in model development by providing documentation which details model enhancements that were undertaken for AE097 and since the previous documentation. Last, because the major use of the EFD is to develop forecasts, this documentation explains the calculations, major inputs and assumptions which were used to generate the AE097.

  4. State energy data report 1996: Consumption estimates

    SciTech Connect (OSTI)

    1999-02-01

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

  5. US MidAtl NY Site Consumption

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

    consumption in New York homes is much lower than the U.S. average, because many households use other fuels for major energy end uses like space heating, water heating, and cooking. ...

  6. US SoAtl GA Site Consumption

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

    household (2,067) in Georgia are similar to the U.S. household averages. * Per household electricity consumption in Georgia is among the highest in the country, but similar to...

  7. Cavity enhanced terahertz modulation

    SciTech Connect (OSTI)

    Born, N.; Scheller, M.; Moloney, J. V.; Koch, M.

    2014-03-10

    We present a versatile concept for all optical terahertz (THz) amplitude modulators based on a Fabry-Pérot semiconductor cavity design. Employing the high reflectivity of two parallel meta-surfaces allows for trapping selected THz photons within the cavity and thus only a weak optical modulation of the semiconductor absorbance is required to significantly damp the field within the cavity. The optical switching yields to modulation depths of more than 90% with insertion efficiencies of 80%.

  8. State energy data report 1994: Consumption estimates

    SciTech Connect (OSTI)

    1996-10-01

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

  9. Energy Intensity Indicators: Transportation Energy Consumption | Department

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

    of Energy Transportation Energy Consumption Energy Intensity Indicators: Transportation Energy Consumption This section contains an overview of the aggregate transportation sector, combining both passenger and freight segments of this sector. The specific energy intensity indicators for passenger and freight can be obtained from the links, passenger transportation, or freight transportation. For further detail within the transportation sector, download the appropriate Trend Data worksheet

  10. Estimates of US biomass energy consumption 1992

    SciTech Connect (OSTI)

    Not Available

    1994-05-06

    This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass-derived primary energy used by the US economy. It presents estimates of 1991 and 1992 consumption. The objective of this report is to provide updated estimates of biomass energy consumption for use by Congress, Federal and State agencies, biomass producers and end-use sectors, and the public at large.

  11. Residential Sector Demand Module

    Gasoline and Diesel Fuel Update (EIA)

    Stoves Geothermal Heat Pump Natural Gas Heat Pump Variables: HSYSSHR 2002-5,eg,b,r Benchmarking Data from Short-Term Energy Outlook Definition: Household energy consumption by...

  12. Bracket for photovoltaic modules

    DOE Patents [OSTI]

    Ciasulli, John; Jones, Jason

    2014-06-24

    Brackets for photovoltaic ("PV") modules are described. In one embodiment, a saddle bracket has a mounting surface to support one or more PV modules over a tube, a gusset coupled to the mounting surface, and a mounting feature coupled to the gusset to couple to the tube. The gusset can have a first leg and a second leg extending at an angle relative to the mounting surface. Saddle brackets can be coupled to a torque tube at predetermined locations. PV modules can be coupled to the saddle brackets. The mounting feature can be coupled to the first gusset and configured to stand the one or more PV modules off the tube.

  13. NREL: Energy Analysis - Energy Forecasting and Modeling Staff

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

    Energy Forecasting and Modeling The following includes summary bios of staff expertise and interests in analysis relating to energy economics, energy system planning, risk and uncertainty modeling, and energy infrastructure planning. Team Lead: Nate Blair Administrative Support: Elizabeth Torres Clayton Barrows Dave Bielen Aaron Bloom Greg Brinkman Brian W Bush Stuart Cohen Wesley Cole Paul Denholm Victor Diakov Nicholas DiOrio Aron Dobos Kelly Eurek Janine Freeman Bethany Frew Pieter Gagnon

  14. 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 Center (RReDC) Provides information about biomass, geothermal, solar, and wind energy resources. Measurement and Instrumentation Data Center Provides irradiance and meteorological data from stations throughout the United States. Baseline Measurement System (BMS) Provides live solar radiation data from approximately 70

  15. Towards a Science of Tumor Forecast for Clinical Oncology

    SciTech Connect (OSTI)

    Yankeelov, Tom; Quaranta, Vito; Evans, Katherine J; Rericha, Erin

    2015-01-01

    We propose that the quantitative cancer biology community make a concerted effort to apply the methods of weather forecasting to develop an analogous theory for predicting tumor growth and treatment response. Currently, the time course of response is not predicted, but rather assessed post hoc by physical exam or imaging methods. This fundamental limitation of clinical oncology makes it extraordinarily difficult to select an optimal treatment regimen for a particular tumor of an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful theory of tumor forecasting, it should be possible to integrate large tumor specific datasets of varied types, and effectively defeat cancer one patient at a time.

  16. Assessment of the possibility of forecasting future natural gas curtailments

    SciTech Connect (OSTI)

    Lemont, S.

    1980-01-01

    This study provides a preliminary assessment of the potential for determining probabilities of future natural-gas-supply interruptions by combining long-range weather forecasts and natural-gas supply/demand projections. An illustrative example which measures the probability of occurrence of heating-season natural-gas curtailments for industrial users in the southeastern US is analyzed. Based on the information on existing long-range weather forecasting techniques and natural gas supply/demand projections enumerated above, especially the high uncertainties involved in weather forecasting and the unavailability of adequate, reliable natural-gas projections that take account of seasonal weather variations and uncertainties in the nation's energy-economic system, it must be concluded that there is little possibility, at the present time, of combining the two to yield useful, believable probabilities of heating-season gas curtailments in a form useful for corporate and government decision makers and planners. Possible remedial actions are suggested that might render such data more useful for the desired purpose in the future. The task may simply require the adequate incorporation of uncertainty and seasonal weather trends into modeling systems and the courage to report projected data, so that realistic natural gas supply/demand scenarios and the probabilities of their occurrence will be available to decision makers during a time when such information is greatly needed.

  17. Silicon photonic heater-modulator

    DOE Patents [OSTI]

    Zortman, William A.; Trotter, Douglas Chandler; Watts, Michael R.

    2015-07-14

    Photonic modulators, methods of forming photonic modulators and methods of modulating an input optical signal are provided. A photonic modulator includes a disk resonator having a central axis extending along a thickness direction of the disk resonator. The disk resonator includes a modulator portion and a heater portion. The modulator portion extends in an arc around the central axis. A PN junction of the modulator portion is substantially normal to the central axis.

  18. Trends in Commercial Buildings--Trends in Energy Consumption...

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

    2 Part 1. Energy Consumption Data Tables Total Energy Intensity Intensity by Energy Source Background: Site and Primary Energy Trends in Energy Consumption and Energy Sources Part...

  19. Table 3a. Total Natural Gas Consumption per Effective Occupied...

    Gasoline and Diesel Fuel Update (EIA)

    3a. Natural Gas Consumption per Sq Ft Table 3a. Total Natural Gas Consumption per Effective Occupied Square Foot, 1992 Building Characteristics All Buildings Using Natural Gas...

  20. Fact #706: December 19, 2011 Vocational Vehicle Fuel Consumption...

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

    6: December 19, 2011 Vocational Vehicle Fuel Consumption Standards Fact 706: December 19, 2011 Vocational Vehicle Fuel Consumption Standards The National Highway Traffic Safety...

  1. Appliance Standby Power and Energy Consumption in South African...

    Open Energy Info (EERE)

    Standby Power and Energy Consumption in South African Households Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Appliance Standby Power and Energy Consumption in South...

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

    Energy Savers [EERE]

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

  3. Fuel Oil",,,"Fuel Oil Consumption",,"Fuel Oil Expenditures"

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

    1. Total Fuel Oil Consumption and Expenditures, 1999" ,"All Buildings Using Fuel Oil",,,"Fuel Oil Consumption",,"Fuel Oil Expenditures" ,"Number of Buildings (thousand)","Floorspac...

  4. Table C10. Electricity Consumption and Expenditure Intensities...

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

    Electricity Consumption and Expenditure Intensities, 1999" ,"Electricity Consumption",,,,,,"Electricity Expenditures" ,"per Building (thousand kWh)","per Square Foot (kWh)","per...

  5. Fact #749: October 15, 2012 Petroleum and Natural Gas Consumption...

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

    9: October 15, 2012 Petroleum and Natural Gas Consumption for Transportation by State, 2010 Fact 749: October 15, 2012 Petroleum and Natural Gas Consumption for Transportation by...

  6. Complex System Method to Assess Commercial Vehicle Fuel Consumption...

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

    Complex System Method to Assess Commercial Vehicle Fuel Consumption Complex System Method to Assess Commercial Vehicle Fuel Consumption Two case studies for commercial vehicle ...

  7. Impact of Driving Behavior on PHEV Fuel Consumption for Different...

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

    Driving Behavior on PHEV Fuel Consumption for Different Powertrain, Component Sizes and Control Impact of Driving Behavior on PHEV Fuel Consumption for Different Powertrain, ...

  8. 2002 Manufacturing Energy Consumption Survey - User Needs Survey

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

    2002 Manufacturing Energy Consumption Survey: User-Needs Survey View current results. We need your help in designing the next Energy Consumption Survey (MECS) As our valued...

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

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

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

  10. 1991 Manufacturing Consumption of Energy 1991 Executive Summary

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

    Summary The Manufacturing Consumption of Energy 1991 report presents statistics about the energy consumption of the manufacturing sector, based on the 1991 Manufacturing Energy...

  11. The Impact of Oil Consumption Mechanisms on Diesel Exhaust Particle...

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

    Oil Consumption Mechanisms on Diesel Exhaust Particle Size Distributions and Detailed Exhaust Chemical Composition The Impact of Oil Consumption Mechanisms on Diesel Exhaust...

  12. Residential Lighting End-Use Consumption | Department of Energy

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

    Information Resources Publications Market Studies Residential Lighting End-Use Consumption Residential Lighting End-Use Consumption The U.S. DOE Residential Lighting ...

  13. Table 5a. Total District Heat Consumption per Effective Occupied...

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

    a. Total District Heat Consumption per Effective Occupied Square Foot, 1992 Building Characteristics All Buildings Using District Heat (thousand) Total District Heat Consumption...

  14. Federal Offshore -- Gulf of Mexico Natural Gas Total Consumption...

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

    -- Gulf of Mexico Natural Gas Total Consumption (Million Cubic Feet) Federal Offshore -- Gulf of Mexico Natural Gas Total Consumption (Million Cubic Feet) Decade Year-0 Year-1...

  15. Visualization of United States Energy Consumption | Open Energy...

    Open Energy Info (EERE)

    Energy Consumption Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Visualization of United States Energy Consumption AgencyCompany Organization: Energy Information...

  16. Table 2a. Electricity Consumption and Electricity Intensities...

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

    Administration Home Page Home > Commercial Buildings Home > Sq Ft Tables > Table 2a. Electricity Consumption per Sq Ft Table 2a. Electricity Consumption and Electricity...

  17. ,"Total Fuel Oil Consumption (trillion Btu)",,,,,"Fuel Oil Energy...

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

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

  18. Membrane module assembly

    DOE Patents [OSTI]

    Kaschemekat, J.

    1994-03-15

    A membrane module assembly is described which is adapted to provide a flow path for the incoming feed stream that forces it into prolonged heat-exchanging contact with a heating or cooling mechanism. Membrane separation processes employing the module assembly are also disclosed. The assembly is particularly useful for gas separation or pervaporation. 2 figures.

  19. Module Safety Issues (Presentation)

    SciTech Connect (OSTI)

    Wohlgemuth, J.

    2012-02-01

    Description of how to make PV modules so that they are less likely to turn into safety hazards. Making modules inherently safer with minimum additional cost is the preferred approach for PV. Safety starts with module design to ensure redundancy within the electrical circuitry to minimize open circuits and proper mounting instructions to prevent installation related ground faults. Module manufacturers must control the raw materials and processes to ensure that that every module is built like those qualified through the safety tests. This is the reason behind the QA task force effort to develop a 'Guideline for PV Module Manufacturing QA'. Periodic accelerated stress testing of production products is critical to validate the safety of the product. Combining safer PV modules with better systems designs is the ultimate goal. This should be especially true for PV arrays on buildings. Use of lower voltage dc circuits - AC modules, DC-DC converters. Use of arc detectors and interrupters to detect arcs and open the circuits to extinguish the arcs.

  20. Membrane module assembly

    DOE Patents [OSTI]

    Kaschemekat, Jurgen (Palo Alto, CA)

    1994-01-01

    A membrane module assembly adapted to provide a flow path for the incoming feed stream that forces it into prolonged heat-exchanging contact with a heating or cooling mechanism. Membrane separation processes employing the module assembly are also disclosed. The assembly is particularly useful for gas separation or pervaporation.

  1. Logs Perl Module

    Energy Science and Technology Software Center (OSTI)

    2007-04-04

    A perl module designed to read and parse the voluminous set of event or accounting log files produced by a Portable Batch System (PBS) server. This module can filter on date-time and/or record type. The data can be returned in a variety of formats.

  2. Short and Long-Term Perspectives: The Impact on Low-Income Consumers of Forecasted Energy Price Increases in 2008 and A Cap & Trade Carbon Policy in 2030

    SciTech Connect (OSTI)

    Eisenberg, Joel Fred

    2008-01-01

    The Department of Energy's Energy Information Administration (EIA) recently released its short-term forecast for residential energy prices for the winter of 2007-2008. The forecast indicates increases in costs for low-income consumers in the year ahead, particularly for those using fuel oil to heat their homes. 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 report provides an update of bill estimates provided in a previous study, "The Impact Of Forecasted Energy Price Increases On Low-Income Consumers" (Eisenberg, 2005). The statistics are intended for use by 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 2008 fiscal year. In addition to providing expenditure forecasts for the year immediately ahead, this analysis uses a similar methodology to give policy makers some insight into one of the major policy debates that will impact low-income energy expenditures well into the middle decades of this century and beyond. There is now considerable discussion of employing a cap-and-trade mechanism to first limit and then reduce U.S. emissions of carbon into the atmosphere in order to combat the long-range threat of human-induced climate change. The Energy Information Administration has provided an analysis of projected energy prices in the years 2020 and 2030 for one such cap-and-trade carbon reduction proposal that, when integrated with the RECS 2001 database, provides estimates of how low-income households will be impacted over the long term by such a carbon reduction policy.

  3. Transportation Sector Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model.

  4. Energy Consumption of Die Casting Operations

    SciTech Connect (OSTI)

    Jerald Brevick; clark Mount-Campbell; Carroll Mobley

    2004-03-15

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

  5. Photovoltaic module and interlocked stack of photovoltaic modules

    DOE Patents [OSTI]

    Wares, Brian S.

    2014-09-02

    One embodiment relates to an arrangement of photovoltaic modules configured for transportation. The arrangement includes a plurality of photovoltaic modules, each photovoltaic module including a frame. A plurality of individual male alignment features and a plurality of individual female alignment features are included on each frame. Adjacent photovoltaic modules are interlocked by multiple individual male alignment features on a first module of the adjacent photovoltaic modules fitting into and being surrounded by corresponding individual female alignment features on a second module of the adjacent photovoltaic modules. Other embodiments, features and aspects are also disclosed.

  6. US MidAtl NY Site Consumption

    Gasoline and Diesel Fuel Update (EIA)

    MidAtl NY Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 US MidAtl NY Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US MidAtl NY Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US MidAtl NY Expenditures dollars ELECTRICITY ONLY average per household * New York households consume an average of 103 million Btu per year, 15% more than the U.S. average. * Electricity consumption in

  7. Economic Evaluation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results; Preprint

    SciTech Connect (OSTI)

    Orwig, K.; Hodge, B. M.; Brinkman, G.; Ela, E.; Milligan, M.; Banunarayanan, V.; Nasir, S.; Freedman, J.

    2012-09-01

    Historically, a number of wind energy integration studies have investigated the value of using day-ahead wind power forecasts for grid operational decisions. These studies have shown that there could be large cost savings gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter-term (0 to 6-hour-ahead) wind power forecasts. In 2010, the Department of Energy and National Oceanic and Atmospheric Administration partnered to fund improvements in short-term wind forecasts and to determine the economic value of these improvements to grid operators, hereafter referred to as the Wind Forecasting Improvement Project (WFIP). In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined, then the economic results of a production cost model simulation are analyzed.

  8. US MidAtl PA Site Consumption

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

    MidAtl PA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 US MidAtl PA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US MidAtl PA Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US MidAtl PA Expenditures dollars ELECTRICITY ONLY average per household * Pennsylvania households consume an average of 96 million Btu per year, 8% more than the U.S. average. Pennsylvania residents also

  9. Commercial Buildings Energy Consumption Survey - Office Buildings

    Reports and Publications (EIA)

    2010-01-01

    Provides an in-depth look at this building type as reported in the 2003 Commercial Buildings Energy Consumption Survey. Office buildings are the most common type of commercial building and they consumed more than 17% of all energy in the commercial buildings sector in 2003. This special report provides characteristics and energy consumption data by type of office building (e.g. administrative office, government office, medical office) and information on some of the types of equipment found in office buildings: heating and cooling equipment, computers, servers, printers, and photocopiers.

  10. State energy data report 1995 - consumption estimates

    SciTech Connect (OSTI)

    1997-12-01

    The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public, and (2) to provide the historical series necessary for EIA`s energy models.

  11. Sonication standard laboratory module

    DOE Patents [OSTI]

    Beugelsdijk, Tony (Los Alamos, NM); Hollen, Robert M. (Los Alamos, NM); Erkkila, Tracy H. (Los Alamos, NM); Bronisz, Lawrence E. (Los Alamos, NM); Roybal, Jeffrey E. (Santa Fe, NM); Clark, Michael Leon (Menan, ID)

    1999-01-01

    A standard laboratory module for automatically producing a solution of cominants from a soil sample. A sonication tip agitates a solution containing the soil sample in a beaker while a stepper motor rotates the sample. An aspirator tube, connected to a vacuum, draws the upper layer of solution from the beaker through a filter and into another beaker. This beaker can thereafter be removed for analysis of the solution. The standard laboratory module encloses an embedded controller providing process control, status feedback information and maintenance procedures for the equipment and operations within the standard laboratory module.

  12. Water heater control module

    DOE Patents [OSTI]

    Hammerstrom, Donald J

    2013-11-26

    An advanced electric water heater control system that interfaces with a high temperature cut-off thermostat and an upper regulating thermostat. The system includes a control module that is electrically connected to the high-temperature cut-off thermostat and the upper regulating thermostat. The control module includes a switch to open or close the high-temperature cut-off thermostat and the upper regulating thermostat. The control module further includes circuitry configured to control said switch in response to a signal selected from the group of an autonomous signal, a communicated signal, and combinations thereof.

  13. 1999 Commercial Buildings Energy Consumption Survey Detailed Tables

    Gasoline and Diesel Fuel Update (EIA)

    Consumption and Expenditures Tables Table C1. Total Energy Consumption by Major Fuel ............................................... 124 Table C2. Total Energy Expenditures by Major Fuel................................................ 130 Table C3. Consumption for Sum of Major Fuels ...................................................... 135 Table C4. Expenditures for Sum of Major Fuels....................................................... 140 Table C5. Consumption and Gross Energy Intensity by

  14. Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting

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

    Technology | Department of Energy Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting Technology Watt-Sun: A Multi-Scale, Multi-Model, Machine-Learning Solar Forecasting Technology IBM logo.png As part of this project, new solar forecasting technology will be developed that leverages big data processing, deep machine learning, and cloud modeling integrated in a universal platform with an open architecture. Similar to the Watson computer system, this proposed technology

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

    SciTech Connect (OSTI)

    Rogers, J.; Porter, K.

    2011-03-01

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

  16. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System

    Office of Environmental Management (EM)

    Operations | Department of Energy Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Clean Power Research logo.jpg This project will address the need for a more accurate approach to forecasting net utility load by taking into consideration the contribution of customer-sited PV energy generation. Tasks within the project are designed to integrate novel PV power

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

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

    The Value of Improved Short- Term Wind Power Forecasting B.-M. Hodge and A. Florita National Renewable Energy Laboratory J. Sharp Sharply Focused, LLC M. Margulis and D. Mcreavy Lockheed Martin Technical Report NREL/TP-5D00-63175 February 2015 NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL)

  18. Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)

    SciTech Connect (OSTI)

    Hodge, B.

    2013-12-01

    Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

  19. ARM - PI Product - CCPP-ARM Parameterization Testbed Model Forecast Data

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

    ProductsCCPP-ARM Parameterization Testbed Model Forecast Data ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send PI Product : CCPP-ARM Parameterization Testbed Model Forecast Data Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are

  20. Report of the external expert peer review panel: DOE benefits forecasts

    SciTech Connect (OSTI)

    None, None

    2006-12-20

    A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts.

  1. NEMS International Energy Module

    Gasoline and Diesel Fuel Update (EIA)

    EIA NEMS International Energy Module Model Documentation Report vii Mr. G. Daniel Butler U.S. Department of Energy EI-812 1000 Independence Ave., SW Washington, DC 20585 Tel:...

  2. International Energy Module

    Gasoline and Diesel Fuel Update (EIA)

    EIA NEMS International Energy Module Model Documentation Report vii Mr. G. Daniel Butler U.S. Department of Energy EI-812 1000 Independence Ave., SW Washington, DC 20585 Tel:...

  3. Digital optical conversion module

    DOE Patents [OSTI]

    Kotter, D.K.; Rankin, R.A.

    1988-07-19

    A digital optical conversion module used to convert an analog signal to a computer compatible digital signal including a voltage-to-frequency converter, frequency offset response circuitry, and an electrical-to-optical converter. Also used in conjunction with the digital optical conversion module is an optical link and an interface at the computer for converting the optical signal back to an electrical signal. Suitable for use in hostile environments having high levels of electromagnetic interference, the conversion module retains high resolution of the analog signal while eliminating the potential for errors due to noise and interference. The module can be used to link analog output scientific equipment such as an electrometer used with a mass spectrometer to a computer. 2 figs.

  4. International Energy Module

    Gasoline and Diesel Fuel Update (EIA)

    self-contained units which are linked by an integrating mechanism. The NEMS International Energy Module (IEM) computes world oil prices, provides a set of crude oil and refined...

  5. Digital optical conversion module

    DOE Patents [OSTI]

    Kotter, Dale K. (North Shelley, ID); Rankin, Richard A. (Ammon, ID)

    1991-02-26

    A digital optical conversion module used to convert an analog signal to a computer compatible digital signal including a voltage-to-frequency converter, frequency offset response circuitry, and an electrical-to-optical converter. Also used in conjunction with the digital optical conversion module is an optical link and an interface at the computer for converting the optical signal back to an electrical signal. Suitable for use in hostile environments having high levels of electromagnetic interference, the conversion module retains high resolution of the analog signal while eliminating the potential for errors due to noise and interference. The module can be used to link analog output scientific equipment such as an electrometer used with a mass spectrometer to a computer.

  6. Table 10.1 Nonswitchable Minimum and Maximum Consumption, 2002

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

    Nonswitchable Minimum and Maximum Consumption, 2002; " " Level: National and Regional Data;" " Row: Energy Sources;" " Column: Consumption Potential;" " Unit: Physical Units." ,,,,"RSE" ,"Actual","Minimum","Maximum","Row" "Energy Sources","Consumption","Consumption(a)","Consumption(b)","Factors" ,"Total United States" "RSE Column

  7. ,"North Carolina Natural Gas Residential Consumption (MMcf)"

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

    Gas Residential Consumption (MMcf)" 32523,6946 32554,5655 32582,6202 32613,3723 32643,1899 32674,1020 32704,744 32735,695 32766,745 32796,1338 32827,2823 32857,6866 32888,8535...

  8. Photovoltaic module reliability workshop

    SciTech Connect (OSTI)

    Mrig, L.

    1990-01-01

    The paper and presentations compiled in this volume form the Proceedings of the fourth in a series of Workshops sponsored by Solar Energy Research Institute (SERI/DOE) under the general theme of photovoltaic module reliability during the period 1986--1990. The reliability Photo Voltaic (PV) modules/systems is exceedingly important along with the initial cost and efficiency of modules if the PV technology has to make a major impact in the power generation market, and for it to compete with the conventional electricity producing technologies. The reliability of photovoltaic modules has progressed significantly in the last few years as evidenced by warranties available on commercial modules of as long as 12 years. However, there is still need for substantial research and testing required to improve module field reliability to levels of 30 years or more. Several small groups of researchers are involved in this research, development, and monitoring activity around the world. In the US, PV manufacturers, DOE laboratories, electric utilities and others are engaged in the photovoltaic reliability research and testing. This group of researchers and others interested in this field were brought together under SERI/DOE sponsorship to exchange the technical knowledge and field experience as related to current information in this important field. The papers presented here reflect this effort.

  9. Organotin intake through fish consumption in Finland

    SciTech Connect (OSTI)

    Airaksinen, Riikka; Rantakokko, Panu; Turunen, Anu W.; Vartiainen, Terttu; Vuorinen, Pekka J.; Lappalainen, Antti; Vihervuori, Aune; Mannio, Jaakko; Hallikainen, Anja

    2010-08-15

    Background: Organotin compounds (OTCs) are a large class of synthetic chemicals with widely varying properties. Due to their potential adverse health effects, their use has been restricted in many countries. Humans are exposed to OTCs mostly through fish consumption. Objectives: The aim of this study was to describe OTC exposure through fish consumption and to assess the associated potential health risks in a Finnish population. Methods: An extensive sampling of Finnish domestic fish was carried out in the Baltic Sea and freshwater areas in 2005-2007. In addition, samples of imported seafood were collected in 2008. The chemical analysis was performed in an accredited testing laboratory during 2005-2008. Average daily intake of the sum of dibutyltin (DBT), tributyltin (TBT), triphenyltin (TPhT) and dioctyltin (DOT) ({Sigma}OTCs) for the Finnish population was calculated on the basis of the measured concentrations and fish consumption rates. Results: The average daily intake of {Sigma}OTCs through fish consumption was 3.2 ng/kg bw day{sup -1}, which is 1.3% from the Tolerable Daily Intake (TDI) of 250 ng/kg bw day{sup -1} set by the European Food Safety Authority. In total, domestic wild fish accounted for 61% of the {Sigma}OTC intake, while the intake through domestic farmed fish was 4.0% and the intake through imported fish was 35%. The most important species were domestic perch and imported salmon and rainbow trout. Conclusions: The Finnish consumers are not likely to exceed the threshold level for adverse health effects due to OTC intake through fish consumption.

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

    SciTech Connect (OSTI)

    Finley, Cathy

    2014-04-30

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

  11. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

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

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  13. Energy Savings Forecast of Solid-State Lighting in General Illumination

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

    Applications | Department of Energy Forecast of Solid-State Lighting in General Illumination Applications Energy Savings Forecast of Solid-State Lighting in General Illumination Applications PDF icon energysavingsforecast14.pdf More Documents & Publications Energy Savings Potential of Solid-State Lighting in General Illumination Applications - Report LED ADOPTION REPORT Solid-State Lighting R&D

  14. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect (OSTI)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  15. Energy consumption series: Lighting in commercial buildings

    SciTech Connect (OSTI)

    Not Available

    1992-03-11

    Lighting represents a substantial fraction of commercial electricity consumption. A wide range of initiatives in the Department of Energy`s (DOE) National Energy Strategy have focused on commercial lighting as a potential source of energy conservation. This report provides a statistical profile of commercial lighting, to examine the potential for lighting energy conservation in commercial buildings. The principal conclusion from this analysis is that energy use for lighting could be reduced by as much as a factor of four using currently available technology. The analysis is based primarily on the Energy Information Administration`s (EIA) 1986 Commercial Buildings Energy Consumption Survey (CBECS). The more recent 1989 survey had less detail on lighting, for budget reasons. While changes have occurred in the commercial building stock since 1986, the relationships identified by this analysis are expected to remain generally valid. In addition, the analytic approach developed here can be applied to the data that will be collected in the 1992 CBECS.

  16. Power module assembly

    DOE Patents [OSTI]

    Campbell, Jeremy B. (Torrance, CA); Newson, Steve (Redondo Beach, CA)

    2011-11-15

    A power module assembly of the type suitable for deployment in a vehicular power inverter, wherein the power inverter has a grounded chassis, is provided. The power module assembly comprises a conductive base layer electrically coupled to the chassis, an insulating layer disposed on the conductive base layer, a first conductive node disposed on the insulating layer, a second conductive node disposed on the insulating layer, wherein the first and second conductive nodes are electrically isolated from each other. The power module assembly also comprises a first capacitor having a first electrode electrically connected to the conductive base layer, and a second electrode electrically connected to the first conductive node, and further comprises a second capacitor having a first electrode electrically connected to the conductive base layer, and a second electrode electrically connected to the second conductive node.

  17. Household Energy Consumption Segmentation Using Hourly Data

    SciTech Connect (OSTI)

    Kwac, J; Flora, J; Rajagopal, R

    2014-01-01

    The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.

  18. Energy Information Administration (EIA)- Manufacturing Energy Consumption

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

    Survey (MECS) Steel Analysis Brief Chemical Industry Analysis Brief Change Topic: Steel | Chemical JUMP TO: Introduction | Energy Consumption | Energy Expenditures | Producer Prices and Production | Energy Intensity | Energy Management Activities | Fuel Switching Capacity Introduction The chemical industries are a cornerstone of the U.S. economy, converting raw materials such as oil, natural gas, air, water, metals, and minerals into thousands of various products. Chemicals are key materials

  19. Energy Information Administration (EIA)- Manufacturing Energy Consumption

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

    Survey (MECS) Steel Analysis Brief Steel Industry Analysis Brief Change Topic: Steel | Chemical JUMP TO: Introduction | Energy Consumption | Energy Expenditures | Producer Prices and Production | Energy Intensity | Energy Management Activities Introduction The steel industry is critical to the U.S. economy. Steel is the material of choice for many elements of construction, transportation, manufacturing, and a variety of consumer products. It is the backbone of bridges, skyscrapers,

  20. Displacing Natural Gas Consumption and Lowering Emissions

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

    Combustion System for Refinery and Chemical Plant Process Heaters ADVANCED MANUFACTURING OFFICE Displacing Natural Gas Consumption and Lowering Emissions By enabling process heaters to utilize opportunity gaseous fuels with a fuel-flexible combustion system, this technol- ogy lowers carbon and nitrogen oxide (NO x ) emissions and reduces energy costs for industry. Introduction The refning and chemical sectors account for more than 40% of total industrial natural gas use. Prior to the completion

  1. Flexible programmable logic module

    DOE Patents [OSTI]

    Robertson, Perry J.; Hutchinson, Robert L.; Pierson, Lyndon G.

    2001-01-01

    The circuit module of this invention is a VME board containing a plurality of programmable logic devices (PLDs), a controlled impedance clock tree, and interconnecting buses. The PLDs are arranged to permit systolic processing of a problem by offering wide data buses and a plurality of processing nodes. The board contains a clock reference and clock distribution tree that can drive each of the PLDs with two critically timed clock references. External clock references can be used to drive additional circuit modules all operating from the same synchronous clock reference.

  2. U.S. Energy Information Administration | State Energy Data 2014: Consumption

    Gasoline and Diesel Fuel Update (EIA)

    25 Appendix A. Mnemonic Series Names (MSN) A P P E N D I X A This appendix contains an alphabetical listing of the variable used in the consumption module of the State Energy Data System (SEDS). Provided for each variable are: a brief description; unit of measure; and the formulas used to create the variable. If a variable is not one calculated in SEDS but is entered into the system, it is described as an independent variable. Formulas for the state calculations have "ZZ" following the

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

    SciTech Connect (OSTI)

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

    2005-02-09

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

  4. Thirty-Year Solid Waste Generation Maximum and Minimum Forecast for SRS

    SciTech Connect (OSTI)

    Thomas, L.C.

    1994-10-01

    This report is the third phase (Phase III) of the Thirty-Year Solid Waste Generation Forecast for Facilities at the Savannah River Site (SRS). Phase I of the forecast, Thirty-Year Solid Waste Generation Forecast for Facilities at SRS, forecasts the yearly quantities of low-level waste (LLW), hazardous waste, mixed waste, and transuranic (TRU) wastes generated over the next 30 years by operations, decontamination and decommissioning and environmental restoration (ER) activities at the Savannah River Site. The Phase II report, Thirty-Year Solid Waste Generation Forecast by Treatability Group (U), provides a 30-year forecast by waste treatability group for operations, decontamination and decommissioning, and ER activities. In addition, a 30-year forecast by waste stream has been provided for operations in Appendix A of the Phase II report. The solid wastes stored or generated at SRS must be treated and disposed of in accordance with federal, state, and local laws and regulations. To evaluate, select, and justify the use of promising treatment technologies and to evaluate the potential impact to the environment, the generic waste categories described in the Phase I report were divided into smaller classifications with similar physical, chemical, and radiological characteristics. These smaller classifications, defined within the Phase II report as treatability groups, can then be used in the Waste Management Environmental Impact Statement process to evaluate treatment options. The waste generation forecasts in the Phase II report includes existing waste inventories. Existing waste inventories, which include waste streams from continuing operations and stored wastes from discontinued operations, were not included in the Phase I report. Maximum and minimum forecasts serve as upper and lower boundaries for waste generation. This report provides the maximum and minimum forecast by waste treatability group for operation, decontamination and decommissioning, and ER activities.

  5. Photovoltaic module and interlocked stack of photovoltaic modules

    DOE Patents [OSTI]

    Wares, Brian S.

    2012-09-04

    One embodiment relates to an arrangement of photovoltaic modules configured for transportation. The arrangement includes a plurality of photovoltaic modules, each photovoltaic module including a frame having at least a top member and a bottom member. A plurality of alignment features are included on the top member of each frame, and a plurality of alignment features are included on the bottom member of each frame. Adjacent photovoltaic modules are interlocked by the alignment features on the top member of a lower module fitting together with the alignment features on the bottom member of an upper module. Other embodiments, features and aspects are also disclosed.

  6. Weather Research and Forecasting Model with Vertical Nesting Capability

    Energy Science and Technology Software Center (OSTI)

    2014-08-01

    The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improvesmore » WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundary conditions to be provided through the nesting procedure.« less

  7. Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

    Broader source: Energy.gov [DOE]

    Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

  8. Trends in U.S. Residential Natural Gas Consumption

    Reports and Publications (EIA)

    2010-01-01

    This report presents an analysis of residential natural gas consumption trends in the United States through 2009 and analyzes consumption trends for the United States as a whole (1990 through 2009) and for each Census division (1998 through 2009).

  9. HEAVY-DUTY TRUCK EMISSIONS AND FUEL CONSUMPTION SIMULATING REAL...

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

    HEAVY-DUTY TRUCK EMISSIONS AND FUEL CONSUMPTION SIMULATING REAL-WORLD DRIVING IN LABORATORY CONDITIONS HEAVY-DUTY TRUCK EMISSIONS AND FUEL CONSUMPTION SIMULATING REAL-WORLD DRIVING ...

  10. Fact #704: December 5, 2011 Fuel Consumption Standards for New...

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

    4: December 5, 2011 Fuel Consumption Standards for New Heavy Pickups and Vans Fact 704: December 5, 2011 Fuel Consumption Standards for New Heavy Pickups and Vans In September...

  11. Texas Natural Gas Total Consumption (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    Total Consumption (Million Cubic Feet) Texas Natural Gas Total Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's...

  12. Texas Natural Gas Lease Fuel Consumption (Million Cubic Feet...

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

    Fuel Consumption (Million Cubic Feet) Texas Natural Gas Lease Fuel Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  13. Texas Natural Gas Lease and Plant Fuel Consumption (Million Cubic...

    Gasoline and Diesel Fuel Update (EIA)

    and Plant Fuel Consumption (Million Cubic Feet) Texas Natural Gas Lease and Plant Fuel Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

  14. Texas Natural Gas Plant Fuel Consumption (Million Cubic Feet...

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

    Fuel Consumption (Million Cubic Feet) Texas Natural Gas Plant Fuel Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  15. Connecticut Natural Gas Total Consumption (Million Cubic Feet...

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

    Total Consumption (Million Cubic Feet) Connecticut Natural Gas Total Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  16. 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 (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

  17. 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 Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  18. New York Natural Gas Lease and Plant Fuel Consumption (Million...

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

    and Plant Fuel Consumption (Million Cubic Feet) New York Natural Gas Lease and Plant Fuel Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

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

    Gasoline and Diesel Fuel Update (EIA)

    Total Consumption (Million Cubic Feet) New York Natural Gas Total Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  20. Formed photovoltaic module busbars

    DOE Patents [OSTI]

    Rose, Douglas; Daroczi, Shan; Phu, Thomas

    2015-11-10

    A cell connection piece for a photovoltaic module is disclosed herein. The cell connection piece includes an interconnect bus, a plurality of bus tabs unitarily formed with the interconnect bus, and a terminal bus coupled with the interconnect bus. The plurality of bus tabs extend from the interconnect bus. The terminal bus includes a non-linear portion.

  1. State Energy Data System 2013 Consumption Technical Notes

    Gasoline and Diesel Fuel Update (EIA)

    Consumption Technical Notes U.S. Energy Information Administration | State Energy Data 2013: Consumption 3 Purpose All of the estimates contained in the state energy consumption data tables are developed using the State Energy Data System (SEDS), which is main- tained and operated by the U.S. Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy production, consumption, prices, and expenditures by state that are defined as

  2. Appliance Energy Consumption in Australia | Open Energy Information

    Open Energy Info (EERE)

    ?viewPublicatio Equivalent URI: cleanenergysolutions.orgcontentappliance-energy-consumption-australi DeploymentPrograms: Industry Codes & Standards Regulations:...

  3. Federal Government's Energy Consumption Lowest in Almost 40 Years |

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

    Department of Energy Government's Energy Consumption Lowest in Almost 40 Years Federal Government's Energy Consumption Lowest in Almost 40 Years February 11, 2015 - 3:49am Addthis Energy consumption by the federal government has been steadily declining for nearly four decades. Much of the decline in recent years can be attributed to a decrease in the use of jet fuel at agencies like the Air Force. | Air Force photo Energy consumption by the federal government has been steadily declining for

  4. 2001 Residential Energy Consumption Survey Answers to Frequently Asked Questions

    Gasoline and Diesel Fuel Update (EIA)

    D (2001) -- Household Bottled Gas (LPG or Propane) Usage Form OMB No. 1905-0092, Expiring February 29, 2004 2001 Residential Energy Consumption Survey Answers to Frequently Asked Questions About the Household Bottled Gas (LPG or Propane) Usage Form What is the purpose of the Residential Energy Consumption Survey? The Residential Energy Consumption Survey (RECS) collects data on energy consumption and expenditures in U.S. housing units. Over 5,000 statistically selected households across the U.S.

  5. Visualization of United States Renewable Consumption | Open Energy...

    Open Energy Info (EERE)

    Visualization of United States Renewable Consumption AgencyCompany Organization: Energy Information Administration Sector: Energy Resource Type: Softwaremodeling tools User...

  6. Canada's Fuel Consumption Guide Website | Open Energy Information

    Open Energy Info (EERE)

    URI: cleanenergysolutions.orgcontentcanadas-fuel-consumption-guide-websit Language: English Policies: Regulations Regulations: Fuel Efficiency Standards This website...

  7. Method of monolithic module assembly

    DOE Patents [OSTI]

    Gee, James M. (Albuquerque, NM); Garrett, Stephen E. (Albuquerque, NM); Morgan, William P. (Albuquerque, NM); Worobey, Walter (Albuquerque, NM)

    1999-01-01

    Methods for "monolithic module assembly" which translate many of the advantages of monolithic module construction of thin-film PV modules to wafered c-Si PV modules. Methods employ using back-contact solar cells positioned atop electrically conductive circuit elements affixed to a planar support so that a circuit capable of generating electric power is created. The modules are encapsulated using encapsulant materials such as EVA which are commonly used in photovoltaic module manufacture. The methods of the invention allow multiple cells to be electrically connected in a single encapsulation step rather than by sequential soldering which characterizes the currently used commercial practices.

  8. An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study

    SciTech Connect (OSTI)

    De Mello, Phillip; Lu, Ning; Makarov, Yuri V.

    2011-01-17

    This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.

  9. User-needs study for the 1992 Commercial Buildings Energy Consumption Survey. [Energy Consumption Series

    SciTech Connect (OSTI)

    Not Available

    1992-09-01

    The Commercial Buildings Energy Consumption Survey (CBECS) that is conducted by the Energy Information Administration (EIA) is the primary source of energy data for commercial buildings in the United States. The survey began in 1979 and has subsequently been conducted in 1983, 1986, and 1989. The next survey will cover energy consumption during the year 1992. The building characteristic data will be collected between August 1992 and early December 1992. Requests for energy consumption data are mailed to the energy suppliers in January 1993, with data due by March 1993. Before each survey is sent into the field, the data users' needs are thoroughly assessed. The purpose of this report is to document the findings of that user-needs assessment for the 1992 survey.

  10. Electrical energy consumption control apparatuses and electrical energy consumption control methods

    DOE Patents [OSTI]

    Hammerstrom, Donald J.

    2012-09-04

    Electrical energy consumption control apparatuses and electrical energy consumption control methods are described. According to one aspect, an electrical energy consumption control apparatus includes processing circuitry configured to receive a signal which is indicative of current of electrical energy which is consumed by a plurality of loads at a site, to compare the signal which is indicative of current of electrical energy which is consumed by the plurality of loads at the site with a desired substantially sinusoidal waveform of current of electrical energy which is received at the site from an electrical power system, and to use the comparison to control an amount of the electrical energy which is consumed by at least one of the loads of the site.

  11. Table 3.1 Fuel Consumption, 2010;

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

    1 Fuel Consumption, 2010; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Physical Units or Btu. Coke Net Residual Distillate Natural Gas(d) LPG and Coal and Breeze NAICS Total Electricity(b) Fuel Oil Fuel Oil(c) (billion NGL(e) (million (million Other(f) Code(a) Subsector and Industry (trillion Btu) (million kWh) (million bbl) (million bbl) cu ft) (million bbl) short tons) short tons) (trillion Btu) Total United States 311 Food 1,158 75,407 2 4 563 1 8 * 99

  12. Table 3.2 Fuel Consumption, 2010;

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

    2 Fuel Consumption, 2010; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Trillion Btu. NAICS Net Residual Distillate LPG and Coke Code(a) Subsector and Industry Total Electricity(b) Fuel Oil Fuel Oil(c) Natural Gas(d) NGL(e) Coal and Breeze Other(f) Total United States 311 Food 1,158 257 12 22 579 6 182 2 99 3112 Grain and Oilseed Milling 350 56 * 1 121 * 126 0 45 311221 Wet Corn Milling 214 25 * * 53 * 110 0 25 31131 Sugar Manufacturing 107 4 1 1 15 * 49 2 36

  13. Table 3.3 Fuel Consumption, 2002

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

    3 Fuel Consumption, 2002;" " Level: National and Regional Data; " " Row: Values of Shipments and Employment Sizes;" " Column: Energy Sources;" " Unit: Trillion Btu." " "," "," "," "," "," "," "," "," "," "," " " "," ",," "," ",," "," ",," ","RSE"

  14. Table 3.3 Fuel Consumption, 2010;

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

    3 Fuel Consumption, 2010; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: Trillion Btu. Economic Net Residual Distillate LPG and Coke and Characteristic(a) Total Electricity(b) Fuel Oil Fuel Oil(c) Natural Gas(d) NGL(e) Coal Breeze Other(f) Total United States Value of Shipments and Receipts (million dollars) Under 20 1,148 314 6 53 446 14 25 Q 291 20-49 1,018 297 13 22 381 18 97 5 185 50-99 1,095 305 7 13 440 6 130 9 186 100-249

  15. Photovoltaic Module Reliability Workshop 2013

    Broader source: Energy.gov [DOE]

    The Photovoltaic (PV) Module Reliability Workshop was held in Golden, Colorado, on Feb. 26–27, 2013. The objective was to share information to improve PV module reliability because such...

  16. " Column: Energy-Consumption Ratios;"

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

    3 Consumption Ratios of Fuel, 2002;" " Level: National Data; " " Row: Values of Shipments within NAICS Codes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." " "," ",,,"Consumption"," " " "," ",,"Consumption","per Dollar" " "," ","Consumption","per Dollar","of Value","RSE" "NAICS",,"per

  17. " Column: Energy-Consumption Ratios;"

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

    3 Consumption Ratios of Fuel, 2006;" " Level: National Data; " " Row: Values of Shipments within NAICS Codes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." ,,,,"Consumption" ,,,"Consumption","per Dollar" ,,"Consumption","per Dollar","of Value" "NAICS",,"per Employee","of Value Added","of Shipments" "Code(a)","Economic

  18. " Column: Energy-Consumption Ratios;"

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

    3 Consumption Ratios of Fuel, 2010;" " Level: National Data; " " Row: Values of Shipments within NAICS Codes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." ,,,,"Consumption" ,,,"Consumption","per Dollar" ,,"Consumption","per Dollar","of Value" "NAICS",,"per Employee","of Value Added","of Shipments" "Code(a)","Economic

  19. Table 6.2 Consumption Ratios of Fuel, 2002

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

    2 Consumption Ratios of Fuel, 2002;" " Level: National and Regional Data; " " Row: Values of Shipments and Employment Sizes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." ,,,"Consumption" " ",,"Consumption","per Dollar"," " " ","Consumption","per Dollar","of Value","RSE" "Economic","per Employee","of Value

  20. Table E7.1. Consumption Ratios of Fuel, 1998

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

    1. Consumption Ratios of Fuel, 1998;" " Level: National and Regional Data; " " Row: Values of Shipments and Employment Sizes;" " Column: Energy-Consumption Ratios;" " Unit: Varies." ,,,"Consumption" " ",,"Consumption","per Dollar"," " " ","Consumption","per Dollar","of Value","RSE" "Economic","per Employee","of Value

  1. Thermoelectrics Partnership: Automotive Thermoelectric Modules...

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

    More Documents & Publications Thermoelectrics Partnership: Automotive Thermoelectric Modules with Scalable Thermo- and Electro-Mechanical Interfaces Novel...

  2. Detailed Course Module Description | Department of Energy

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

    Detailed Course Module Description Detailed Course Module Description This document lists the course modules for building science courses offered at Cornell's Collaborator...

  3. SHARP Physics Modules Updated | Department of Energy

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

    Physics Modules Updated SHARP Physics Modules Updated January 29, 2013 - 12:37pm Addthis PROTEUS Development The SHARP neutronics module, PROTEUS, includes neutron and gamma ...

  4. Module Solar AG | Open Energy Information

    Open Energy Info (EERE)

    Module Solar AG Jump to: navigation, search Name: Module Solar AG Place: Baar, Switzerland Product: A Swiss PV module and materials supplier and developer Coordinates: 40.808083,...

  5. Review of Variable Generation Forecasting in the West: July 2013 - March 2014

    SciTech Connect (OSTI)

    Widiss, R.; Porter, K.

    2014-03-01

    This report interviews 13 operating entities (OEs) in the Western Interconnection about their implementation of wind and solar forecasting. The report updates and expands upon one issued by NREL in 2012. As in the 2012 report, the OEs interviewed vary in size and character; the group includes independent system operators, balancing authorities, utilities, and other entities. Respondents' advice for other utilities includes starting sooner rather than later as it can take time to plan, prepare, and train a forecast; setting realistic expectations; using multiple forecasts; and incorporating several performance metrics.

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

    Office of Environmental Management (EM)

    Department of Energy .5 Million to Improve Wind Forecasting Energy Department Announces $2.5 Million to Improve Wind Forecasting January 8, 2015 - 12:00pm Addthis The Energy Department today announced $2.5 million for a new project to research the atmospheric processes that generate wind in mountain-valley regions. This in-depth research, conducted by Vaisala of Louisville, Colorado, will be used to improve the wind industry's weather models for short-term wind forecasts, especially for

  7. Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% |

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

    Department of Energy 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 Youtube Video | Courtesy of IBM Remember when IBM's super computer Watson defeated Jeopardy! champions Ken Jennings and Brad Rutter? With funding from the U.S. Department of Energy SunShot Initiative, IBM researchers are using Watson-like technology to improve solar forecasting accuracy by as much

  8. Module isolation devices

    DOE Patents [OSTI]

    Carolan, Michael Francis; Cooke, John Albert; Buzinski, Michael David

    2010-04-27

    A gas flow isolation device includes a gas flow isolation valve movable from an opened condition to a closed condition. The module isolation valve in one embodiment includes a rupture disk in flow communication with a flow of gas when the module isolation valve is in an opened condition. The rupture disk ruptures when a predetermined pressure differential occurs across it causing the isolation valve to close. In one embodiment the valve is mechanically linked to the rupture disk to maintain the valve in an opened condition when the rupture disk is intact, and which permits the valve to move into a closed condition when the rupture disk ruptures. In another embodiment a crushable member maintains the valve in an open condition, and the flow of gas passed the valve upon rupturing of the rupture disk compresses the crushable member to close the isolation valve.

  9. Tandem resonator reflectance modulator

    DOE Patents [OSTI]

    Fritz, I.J.; Wendt, J.R.

    1994-09-06

    A wide band optical modulator is grown on a substrate as tandem Fabry-Perot resonators including three mirrors spaced by two cavities. The absorption of one cavity is changed relative to the absorption of the other cavity by an applied electric field, to cause a change in total reflected light, as light reflecting from the outer mirrors is in phase and light reflecting from the inner mirror is out of phase with light from the outer mirrors. 8 figs.

  10. PV modules modelling

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

    of the Environmental Sciences / Group of Energy / PVsyst Modeling Systems Losses in PVsyst André Mermoud Institute of the Environmental Sciences Group of energy - PVsyst andre.mermoud@unige.ch Institute of the Environmental Sciences / Group of Energy / PVsyst Summary Losses in a PV system simulation may be: - Determined by specific models (shadings) - Interpretations of models (PV module behaviour) - User's parameter specifications (soiling, wiring, etc). PVsyst provides a detailed analysis of

  11. Tandem resonator reflectance modulator

    DOE Patents [OSTI]

    Fritz, Ian J. (Albuquerque, NM); Wendt, Joel R. (Albuquerque, NM)

    1994-01-01

    A wide band optical modulator is grown on a substrate as tandem Fabry-Perot resonators including three mirrors spaced by two cavities. The absorption of one cavity is changed relative to the absorption of the other cavity by an applied electric field, to cause a change in total reflected light, as light reflecting from the outer mirrors is in phase and light reflecting from the inner mirror is out of phase with light from the outer mirrors.

  12. Resource Information and Forecasting Group; Electricity, Resources, & Building Systems Integration (ERBSI) (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2009-11-01

    Researchers in the Resource Information and Forecasting group at NREL provide scientific, engineering, and analytical expertise to help characterize renewable energy resources and facilitate the integration of these clean energy sources into the electricity grid.

  13. Integration of Behind-the-Meter PV Fleet Forecasts into Utility...

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

    Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Clean Power Research logo.jpg This project will address the need for a more accurate approach ...

  14. Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Orwig, K.; Milligan, M.

    2012-06-01

    In this paper, we examine the parameters associated with the calculation of the Renyi entropy in order to further the understanding of its application to assessing wind power forecasting errors.

  15. Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

    SciTech Connect (OSTI)

    Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.

    2013-11-01

    As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

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

    SciTech Connect (OSTI)

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

    2013-01-01

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

  17. Ramping Effect on Forecast Use: Integrated Ramping as a Mitigation Strategy; NREL (National Renewable Energy Laboratory)

    SciTech Connect (OSTI)

    Diakov, Victor; Barrows, Clayton; Brinkman, Gregory; Bloom, Aaron; Denholm, Paul

    2015-06-23

    Power generation ramping between forecasted (net) load set-points shift the generation (MWh) from its scheduled values. The Integrated Ramping is described as a method that mitigates this problem.

  18. U.S. Crude Oil Production Forecast-Analysis of Crude Types

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

    of Energy Washington, DC 20585 U.S. Energy Information Administration | U.S. Crude Oil Production Forecast-Analysis of Crude Types i This report was prepared by the U.S....

  19. U.S. diesel fuel price forecast to be 1 penny lower this summer...

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

    That's down 12 percent from last summer's record exports. Biodiesel production, which averaged 68,000 barrels a day last summer, is forecast to jump to 82,000 barrels a day this ...

  20. NOAA Teams Up with Department of Energy & Industry to Improve Wind Forecasts

    Broader source: Energy.gov [DOE]

    The growth of wind-generated power in the United States  is creating greater demand for improved wind forecasts. To address this need, the Department of Energy is working with NOAA and industry on...

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

    SciTech Connect (OSTI)

    Zhou, Nan; McNeil, Michael A.; Levine, Mark

    2009-06-01

    China's rapid economic expansion has propelled it to the rank of the largest energy consuming nation in the world, with energy demand growth continuing at a pace commensurate with its economic growth. The urban population is expected to grow by 20 million every year, accompanied by construction of 2 billion square meters of buildings every year through 2020. Thus residential energy use is very likely to continue its very rapid growth. Understanding the underlying drivers of this growth helps to identify the key areas to analyze energy efficiency potential, appropriate policies to reduce energy use, as well as to understand future energy in the building sector. This paper provides a detailed, bottom-up analysis of residential building energy consumption in China using data from a wide variety of sources and a modelling effort that relies on a very detailed characterization of China's energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast the future outlook for the critical period extending to 2020, based on assumptions of likely patterns of economic activity, availability of energy services, technology improvement and energy intensities. From this analysis, we can conclude that Chinese residential energy consumption will more than double by 2020, from 6.6 EJ in 2000 to 15.9 EJ in 2020. This increase will be driven primarily by urbanization, in combination with increases in living standards. In the urban and higher income Chinese households of the future, most major appliances will be common, and heated and cooled areas will grow on average. These shifts will offset the relatively modest efficiency gains expected according to current government plans and policies already in place. Therefore, levelling and reduction of growth in residential energy demand in China will require a new set of more aggressive efficiency policies.

  2. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  3. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    SciTech Connect (OSTI)

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  4. Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

    SciTech Connect (OSTI)

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

    2013-05-01

    Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

  5. Nuclear Theory Helps Forecast Neutron Star Temperatures | U.S. DOE Office

    Office of Science (SC) Website

    of Science (SC) Nuclear Theory Helps Forecast Neutron Star Temperatures Nuclear Physics (NP) NP Home About Research Facilities Science Highlights Benefits of NP Funding Opportunities Nuclear Science Advisory Committee (NSAC) Community Resources Contact Information Nuclear Physics U.S. Department of Energy SC-26/Germantown Building 1000 Independence Ave., SW Washington, DC 20585 P: (301) 903-3613 F: (301) 903-3833 E: Email Us More Information » 05.01.14 Nuclear Theory Helps Forecast Neutron

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

    SciTech Connect (OSTI)

    Stoffel, T.

    2012-06-01

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

  7. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  8. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect (OSTI)

    1995-01-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  9. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    SciTech Connect (OSTI)

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  10. DOE Announces Webinars on Solar Forecasting Metrics, the DOE Wind Vision,

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

    and More | Department of Energy Solar Forecasting Metrics, the DOE Wind Vision, and More DOE Announces Webinars on Solar Forecasting Metrics, the DOE Wind Vision, and More February 12, 2014 - 7:38pm Addthis EERE offers webinars to the public on a range of subjects, from adopting the latest energy efficiency and renewable energy technologies to training for the clean energy workforce. Webinars are free; however, advanced registration is typically required. You can also watch archived webinars

  11. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    SciTech Connect (OSTI)

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  12. TASMANIAN Sparse Grids Module

    Energy Science and Technology Software Center (OSTI)

    2013-09-20

    Sparse Grids are the family of methods of choice for multidimensional integration and interpolation in low to moderate number of dimensions. The method is to select extend a one dimensional set of abscissas, weights and basis functions by taking a subset of all possible tensor products. The module provides the ability to create global and local approximations based on polynomials and wavelets. The software has three components, a library, a wrapper for the library thatmore » provides a command line interface via text files ad a MATLAB interface via the command line tool.« less

  13. Processing module operating methods, processing modules, and communications systems

    DOE Patents [OSTI]

    McCown, Steven Harvey; Derr, Kurt W.; Moore, Troy

    2014-09-09

    A processing module operating method includes using a processing module physically connected to a wireless communications device, requesting that the wireless communications device retrieve encrypted code from a web site and receiving the encrypted code from the wireless communications device. The wireless communications device is unable to decrypt the encrypted code. The method further includes using the processing module, decrypting the encrypted code, executing the decrypted code, and preventing the wireless communications device from accessing the decrypted code. Another processing module operating method includes using a processing module physically connected to a host device, executing an application within the processing module, allowing the application to exchange user interaction data communicated using a user interface of the host device with the host device, and allowing the application to use the host device as a communications device for exchanging information with a remote device distinct from the host device.

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

    SciTech Connect (OSTI)

    Zhou, Nan; Nishida, Masaru; Gao, Weijun

    2008-12-01

    China's rapid economic expansion has propelled it into the ranks of the largest energy consuming nation in the world, with energy demand growth continuing at a pace commensurate with its economic growth. Even though the rapid growth is largely attributable to heavy industry, this in turn is driven by rapid urbanization process, by construction materials and equipment produced for use in buildings. Residential energy is mostly used in urban areas, where rising incomes have allowed acquisition of home appliances, as well as increased use of heating in southern China. The urban population is expected to grow by 20 million every year, accompanied by construction of 2 billion square meters of buildings every year through 2020. Thus residential energy use is very likely to continue its very rapid growth. Understanding the underlying drivers of this growth helps to identify the key areas to analyze energy efficiency potential, appropriate policies to reduce energy use, as well as to understand future energy in the building sector. This paper provides a detailed, bottom-up analysis of residential building energy consumption in China using data from a wide variety of sources and a modeling effort that relies on a very detailed characterization of China's energy demand. It assesses the current energy situation with consideration of end use, intensity, and efficiency etc, and forecast the future outlook for the critical period extending to 2020, based on assumptions of likely patterns of economic activity, availability of energy services, technology improvement and energy intensities.

  15. Phase modulation in RF tag

    DOE Patents [OSTI]

    Carrender, Curtis Lee; Gilbert, Ronald W.

    2007-02-20

    A radio frequency (RF) communication system employs phase-modulated backscatter signals for RF communication from an RF tag to an interrogator. The interrogator transmits a continuous wave interrogation signal to the RF tag, which based on an information code stored in a memory, phase-modulates the interrogation signal to produce a backscatter response signal that is transmitted back to the interrogator. A phase modulator structure in the RF tag may include a switch coupled between an antenna and a quarter-wavelength stub; and a driver coupled between the memory and a control terminal of the switch. The driver is structured to produce a modulating signal corresponding to the information code, the modulating signal alternately opening and closing the switch to respectively decrease and increase the transmission path taken by the interrogation signal and thereby modulate the phase of the response signal. Alternatively, the phase modulator may include a diode coupled between the antenna and driver. The modulating signal from the driver modulates the capacitance of the diode, which modulates the phase of the response signal reflected by the diode and antenna.

  16. SEP Request for Approval Form 7 - Other Situations for Consumption

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

    Adjustment | Department of Energy 7 - Other Situations for Consumption Adjustment SEP Request for Approval Form 7 - Other Situations for Consumption Adjustment File SEP-Request-for-Approval-Form-7_Other-Situations-for-Consumption-Adjustment.docx More Documents & Publications SEP Request for Approval Form 6 - Non-Routine Adjustments SEP Request for Approval Form 5 - Model Does Not Satisfy 3.4.1-3.4.10 Requirements SEP Request for Approval Form 4 - Alternative Adjustment Model Application

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

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

    Administration (EIA) ‹ Consumption & Efficiency Manufacturing Energy Consumption Survey (MECS) Glossary › FAQS › Overview Data 2010 2006 2002 1998 1994 1991 Archive Analysis & Projections Cost of Natural Gas Used in Manufacturing Sector Has Fallen Graph showing Cost of Natural Gas Used in Manufacturing Sector Has Fallen Source: U.S. Energy Information Administration, Manufacturing Energy Consumption Survey (MECS) 1998-2010, September 6, 2013. New 2010 Manufacturing Energy

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

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

    Buildings and Major Renovations of Federal Buildings | Department of Energy Buildings Fossil Fuel-Generated Energy Consumption Reduction for New Federal Buildings and Major Renovations of Federal Buildings Document details Fossil Fuel-Generated Energy Consumption Reduction for New Federal Buildings and Major Renovations of Federal Buildings in a Supplemental Notice of Proposed Rulemaking. File fossilfuel.docx More Documents & Publications Fossil Fuel-Generated Energy Consumption

  19. Fact #706: December 19, 2011 Vocational Vehicle Fuel Consumption Standards

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

    | Department of Energy 6: December 19, 2011 Vocational Vehicle Fuel Consumption Standards Fact #706: December 19, 2011 Vocational Vehicle Fuel Consumption Standards The National Highway Traffic Safety Administration recently published final fuel consumption standards for heavy vehicles called "vocational" vehicles. A vocational vehicle is generally a single-unit work vehicle over 8,500 lbs gross vehicle weight rating (GVWR) or a passenger vehicle over 10,000 lbs GVWR that is not a

  20. Fact #840: September 29, 2014 World Renewable Electricity Consumption is

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

    Growing | Department of Energy 40: September 29, 2014 World Renewable Electricity Consumption is Growing Fact #840: September 29, 2014 World Renewable Electricity Consumption is Growing Electricity generated from sources that are renewable - hydroelectric power, bio-fuels, geothermal, solar, wind, wood, waste - have grown 150% from 1980 to 2011 (latest year available). Of the selected countries/regions shown, Europe has consistently had the highest consumption of renewable electricity.

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

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

    U.S. Energy Information Administration (EIA) Manufacturing Energy Consumption Data Show Large Reductions in Both Manufacturing Energy Use and the Energy Intensity of Manufacturing Activity between 2002 and 2010 MECS 2010 - Release date: March 19, 2013 Total energy consumption in the manufacturing sector decreased by 17 percent from 2002 to 2010 (Figure 1), according to data from the U.S. Energy Information Administration's (EIA) Manufacturing Energy Consumption Survey (MECS). line chart:air

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

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

    Information Administration (EIA) 1 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive Data Methodology & Forms + EXPAND ALL Consumption of Energy for All Purposes (First Use) Total Primary Consumption of Energy for All Purposes by Census Region, Industry Group, and Selected Industries, 1991: Part 1 (Estimates in Btu or Physical Units) XLS Total Primary Consumption of Energy for All Purposes by Census Region, Industry Group, and Selected Industries, 1991: Part 2 (Estimates

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

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

    Information Administration (EIA) 4 MECS Survey Data 2010 | 2006 | 2002 | 1998 | 1994 | 1991 | Archive Data Methodology & Forms + EXPAND ALL Consumption of Energy for All Purposes (First Use) Total First Use (formerly Primary Consumption) of Energy for All Purposes by Census Region, Census Division, Industry Group, and Selected Industries, 1994: Part 1 (Estimates in Btu or Physical Units) XLS Total First Use (formerly Primary Consumption) of Energy for All Purposes by Census Region,

  4. Impact of Extended Daylight Saving Time on National Energy Consumption,

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

    Report to Congress | Department of Energy Report to Congress Impact of Extended Daylight Saving Time on National Energy Consumption, Report to Congress This report presents the detailed results, data, and analytical methods used in the DOE Report to Congress on the impacts of Extended Daylight Saving Time on the U.S. national energy consumption. PDF icon Report to Congress More Documents & Publications Impact of Extended Daylight Saving Time on National Energy Consumption, Technical

  5. Trends in Commercial Buildings--Energy Sources Consumption Tables

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

    ** estimates adjusted to match the 1995 CBECS definition of target population Energy Information Administration Commercial Buildings Energy Consumption Survey Table 2....

  6. Waste-to-Energy Biomass Digester with Decreased Water Consumption...

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

    Biomass and Biofuels Biomass and Biofuels Find More Like This Return to Search Waste-to-Energy Biomass Digester with Decreased Water Consumption Colorado State University Contact...

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

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

    New Water Booster Pump System Reduces Energy Consumption by 80 Percent and Increases Reliability This case study outlines how General Motors (GM) developed a highly efficient ...

  8. Fuel Consumption and Cost Benefits of DOE Vehicle Technologies...

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

    Cost Benefits of DOE Vehicle Technologies Program Fuel Consumption and Cost Benefits of DOE Vehicle Technologies Program 2012 DOE Hydrogen and Fuel Cells Program and Vehicle...

  9. ,"Total District Heat Consumption (trillion Btu)",,,,,"District...

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

    Heat Consumption (trillion Btu)",,,,,"District Heat Energy Intensity (thousand Btusquare foot)" ,"Total ","Space Heating","Water Heating","Cook- ing","Other","Total ","Space...

  10. ,"Total Natural Gas Consumption (trillion Btu)",,,,,"Natural...

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

    Gas Consumption (trillion Btu)",,,,,"Natural Gas Energy Intensity (thousand Btusquare foot)" ,"Total ","Space Heating","Water Heating","Cook- ing","Other","Total ","Space...

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

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

    Grades: All Topics: Biomass, Wind Energy, Hydropower, Solar, Geothermal Owner: The NEED Project Power to the Plug: An Introduction to Energy, Electricity, Consumption, and...

  12. Manufacturing-Industrial Energy Consumption Survey(MECS) Historical...

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

    reports, data tables and questionnaires Released: May 2008 The Manufacturing Energy Consumption Survey (MECS) is a periodic national sample survey devoted to measuring...

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

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

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

  14. Impact of Extended Daylight Saving Time on National Energy Consumption...

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

    Technical Documentation Impact of Extended Daylight Saving Time on National Energy Consumption, Technical Documentation This report presents the detailed results, data, and...

  15. Impact of Extended Daylight Saving Time on National Energy Consumption...

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

    Report to Congress Impact of Extended Daylight Saving Time on National Energy Consumption, Report to Congress This report presents the detailed results, data, and analytical...

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

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

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

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

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

    Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New Mexico Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  18. ,"New Mexico Natural Gas Plant Fuel Consumption (MMcf)"

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

    Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New Mexico Natural Gas Plant Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  19. ,"New Mexico Natural Gas Lease and Plant Fuel Consumption (MMcf...

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

    Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New Mexico Natural Gas Lease and Plant Fuel Consumption (MMcf)",1,"Annual",1998 ,"Release...

  20. ,"New Mexico Natural Gas Lease Fuel Consumption (MMcf)"

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

    Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New Mexico Natural Gas Lease Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...