National Library of Energy BETA

Sample records for forecasts quadrillion btu

  1. Expanded standards and codes case limits combined buildings delivered energy to 21 quadrillion Btu by 2035

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

    Erin Boedecker, Session Moderator April 27, 2011 | Washington, DC Energy Demand. Efficiency, and Consumer Behavior 16 17 18 19 20 21 22 23 24 25 2005 2010 2015 2020 2025 2030 2035 2010 Technology Reference Expanded Standards Expanded Standards + Codes -7.6% ≈ 0 Expanded standards and codes case limits combined buildings delivered energy to 21 quadrillion Btu by 2035 2 Erin Boedecker, EIA Energy Conference, April 27, 2011 delivered energy quadrillion Btu Source: EIA, Annual Energy Outlook 2011

  2. Table 2.4 Household Energy Consumption by Census Region, Selected Years, 1978-2009 (Quadrillion Btu, Except as Noted)

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

    Household 1 Energy Consumption by Census Region, Selected Years, 1978-2009 (Quadrillion Btu, Except as Noted) Census Region 2 1978 1979 1980 1981 1982 1984 1987 1990 1993 1997 2001 2005 2009 United States Total (does not include wood) 10.56 9.74 9.32 9.29 8.58 9.04 9.13 9.22 10.01 10.25 9.86 10.55 10.18 Natural Gas 5.58 5.31 4.97 5.27 4.74 4.98 4.83 4.86 5.27 5.28 4.84 4.79 4.69 Electricity 3 2.47 2.42 2.48 2.42 2.35 2.48 2.76 3.03 3.28 3.54 3.89 4.35 4.39 Distillate Fuel Oil and Kerosene 2.19

  3. Btu)","per Building

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

    ,"Number of Buildings (thousand)","Floorspace (million square feet)","Floorspace per Building (thousand square feet)","Total (trillion Btu)","per Building (million Btu)","per...

  4. First BTU | Open Energy Information

    Open Energy Info (EERE)

    that is consumed by the United States.3 References First BTU First BTU Green Energy About First BTU Retrieved from "http:en.openei.orgwindex.php?titleFirstBT...

  5. ,"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...

  6. BTU International Inc | Open Energy Information

    Open Energy Info (EERE)

    1862 Product: US-based manufacturer of thermal processing equipment, semiconductor packaging, and surface mount assembly. References: BTU International Inc1 This article is a...

  7. Microfabricated BTU monitoring device for system-wide natural...

    Office of Scientific and Technical Information (OSTI)

    Microfabricated BTU monitoring device for system-wide natural gas monitoring. Citation Details In-Document Search Title: Microfabricated BTU monitoring device for system-wide...

  8. 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.

  9. Method for selective detection of explosives in mass spectrometer or ion mobility spectrometer at parts-per-quadrillion level

    DOE Patents [OSTI]

    Ewing, Robert G.; Atkinson, David A.; Clowers, Brian H.

    2015-09-01

    A method for selective detection of volatile and non-volatile explosives in a mass spectrometer or ion mobility spectrometer at a parts-per-quadrillion level without preconcentration is disclosed. The method comprises the steps of ionizing a carrier gas with an ionization source to form reactant ions or reactant adduct ions comprising nitrate ions (NO.sub.3.sup.-); selectively reacting the reactant ions or reactant adduct ions with at least one volatile or non-volatile explosive analyte at a carrier gas pressure of at least about 100 Ton in a reaction region disposed between the ionization source and an ion detector, the reaction region having a length which provides a residence time (tr) for reactant ions therein of at least about 0.10 seconds, wherein the selective reaction yields product ions comprising reactant ions or reactant adduct ions that are selectively bound to the at least one explosive analyte when present therein; and detecting product ions with the ion detector to determine presence or absence of the at least one explosive analyte.

  10. ,"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...

  11. ,"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...

  12. Property:Geothermal/CapacityBtuHr | Open Energy Information

    Open Energy Info (EERE)

    to: navigation, search This is a property of type Number. Pages using the property "GeothermalCapacityBtuHr" Showing 25 pages using this property. (previous 25) (next 25) 4 4 UR...

  13. Property:Geothermal/AnnualGenBtuYr | Open Energy Information

    Open Energy Info (EERE)

    to: navigation, search This is a property of type Number. Pages using the property "GeothermalAnnualGenBtuYr" Showing 25 pages using this property. (previous 25) (next 25) 4 4 UR...

  14. ,"Henry Hub Natural Gas Spot Price (Dollars per Million Btu)...

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

    12:00:20 PM" "Back to Contents","Data 1: Henry Hub Natural Gas Spot Price (Dollars per Million Btu)" "Sourcekey","RNGWHHD" "Date","Henry Hub Natural Gas Spot Price (Dollars per ...

  15. Microfabricated BTU monitoring device for system-wide natural gas

    Office of Scientific and Technical Information (OSTI)

    monitoring. (Technical Report) | SciTech Connect Technical Report: Microfabricated BTU monitoring device for system-wide natural gas monitoring. Citation Details In-Document Search Title: Microfabricated BTU monitoring device for system-wide natural gas monitoring. The natural gas industry seeks inexpensive sensors and instrumentation to rapidly measure gas heating value in widely distributed locations. For gas pipelines, this will improve gas quality during transfer and blending, and will

  16. DYNAMIC MANUFACTURING ENERGY SANKEY TOOL (2010, UNITS: TRILLION BTU) |

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

    Department of Energy Information Resources » Energy Analysis » DYNAMIC MANUFACTURING ENERGY SANKEY TOOL (2010, UNITS: TRILLION BTU) DYNAMIC MANUFACTURING ENERGY SANKEY TOOL (2010, UNITS: TRILLION BTU) About the Energy Data Use this diagram to explore (zoom, pan, select) and compare energy flows across U.S. manufacturing and key subsectors. Line widths indicate the volume of energy flow in trillions of British thermal units (TBtu). The 15 manufacturing subsectors together consume 95% of all

  17. 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

  18. 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)

  19. EIS-0007: Low Btu Coal Gasification Facility and Industrial Park

    Broader source: Energy.gov [DOE]

    The U.S. Department of Energy (DOE) prepared this draft environmental impact statement that evaluates the potential environmental impacts that may be associated with the construction and operation of a low-Btu coal gasification facility and the attendant industrial park in Georgetown, Scott County, Kentucky. DOE cancelled this project after publication of the draft.

  20. "Economic","per Employee","of Value Added","of Shipments" "Characteristic(a)","(million Btu)","(thousand Btu)","(thousand Btu)"

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

    2 Relative Standard Errors for Table 6.2;" " Unit: Percents." ,,,"Consumption" " ",,"Consumption","per Dollar" " ","Consumption","per Dollar","of Value" "Economic","per Employee","of Value Added","of Shipments" "Characteristic(a)","(million Btu)","(thousand Btu)","(thousand Btu)" ,"Total United States" "Value

  1. "Economic","per Employee","of Value Added","of Shipments" "Characteristic(a)","(million Btu)","(thousand Btu)","(thousand Btu)"

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

    2 Relative Standard Errors for Table 6.2;" " Unit: Percents." ,,,"Consumption" ,,"Consumption","per Dollar" ,"Consumption","per Dollar","of Value" "Economic","per Employee","of Value Added","of Shipments" "Characteristic(a)","(million Btu)","(thousand Btu)","(thousand Btu)" ,"Total United States" "Value of Shipments and

  2. A Requirement for Significant Reduction in the Maximum BTU Input Rate of

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

    Decorative Vented Gas Fireplaces Would Impose Substantial Burdens on Manufacturers | Department of Energy A Requirement for Significant Reduction in the Maximum BTU Input Rate of Decorative Vented Gas Fireplaces Would Impose Substantial Burdens on Manufacturers A Requirement for Significant Reduction in the Maximum BTU Input Rate of Decorative Vented Gas Fireplaces Would Impose Substantial Burdens on Manufacturers Comment that a requirement to reduce the BTU input rate of existing decorative

  3. 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.

  4. Sectoral combustor for burning low-BTU fuel gas

    DOE Patents [OSTI]

    Vogt, Robert L. (Schenectady, NY)

    1980-01-01

    A high-temperature combustor for burning low-BTU coal gas in a gas turbine is disclosed. The combustor includes several separately removable combustion chambers each having an annular sectoral cross section and a double-walled construction permitting separation of stresses due to pressure forces and stresses due to thermal effects. Arrangements are described for air-cooling each combustion chamber using countercurrent convective cooling flow between an outer shell wall and an inner liner wall and using film cooling flow through liner panel grooves and along the inner liner wall surface, and for admitting all coolant flow to the gas path within the inner liner wall. Also described are systems for supplying coal gas, combustion air, and dilution air to the combustion zone, and a liquid fuel nozzle for use during low-load operation. The disclosed combustor is fully air-cooled, requires no transition section to interface with a turbine nozzle, and is operable at firing temperatures of up to 3000.degree. F. or within approximately 300.degree. F. of the adiabatic stoichiometric limit of the coal gas used as fuel.

  5. Annual Energy Review 2000

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

    Includes 0.07 quadrillion Btu coal coke net imports and 0.10 electricity net imports from fossil fuels. Includes, in quadrillion Btu, 0.10 electricity net imports from fossil...

  6. Energy Information Administration/Annual Energy Review

    Gasoline and Diesel Fuel Update (EIA)

    in quadrillion Btu, 0.04 coal coke net imports and 0.05 electricity net imports from fossil fuels. Includes, in quadrillion Btu, -0.09 hydroelectric pumped storage and -0.15...

  7. Recent regulatory experience of low-Btu coal gasification. Volume III. Supporting case studies

    SciTech Connect (OSTI)

    Ackerman, E.; Hart, D.; Lethi, M.; Park, W.; Rifkin, S.

    1980-02-01

    The MITRE Corporation conducted a five-month study for the Office of Resource Applications in the Department of Energy on the regulatory requirements of low-Btu coal gasification. During this study, MITRE interviewed representatives of five current low-Btu coal gasification projects and regulatory agencies in five states. From these interviews, MITRE has sought the experience of current low-Btu coal gasification users in order to recommend actions to improve the regulatory process. This report is the third of three volumes. It contains the results of interviews conducted for each of the case studies. Volume 1 of the report contains the analysis of the case studies and recommendations to potential industrial users of low-Btu coal gasification. Volume 2 contains recommendations to regulatory agencies.

  8. Commercial low-Btu coal-gasification plant. Feasibility study: General Refractories Company, Florence, Kentucky. Volume I. Project summary. [Wellman-Galusha

    SciTech Connect (OSTI)

    1981-11-01

    In response to a 1980 Department of Energy solicitation, the General Refractories Company submitted a Proposal for a feasibility study of a low Btu gasification facility for its Florence, KY plant. The proposed facility would substitute low Btu gas from a fixed bed gasifier for natural gas now used in the manufacture of insulation board. The Proposal from General Refractories was prompted by a concern over the rising costs of natural gas, and the anticipation of a severe increase in fuel costs resulting from deregulation. The proposed feasibility study is defined. The intent is to provide General Refractories with the basis upon which to determine the feasibility of incorporating such a facility in Florence. To perform the work, a Grant for which was awarded by the DOE, General Refractories selected Dravo Engineers and Contractors based upon their qualifications in the field of coal conversion, and the fact that Dravo has acquired the rights to the Wellman-Galusha technology. The LBG prices for the five-gasifier case are encouraging. Given the various natural gas forecasts available, there seems to be a reasonable possibility that the five-gasifier LBG prices will break even with natural gas prices somewhere between 1984 and 1989. General Refractories recognizes that there are many uncertainties in developing these natural gas forecasts, and if the present natural gas decontrol plan is not fully implemented some financial risks occur in undertaking the proposed gasification facility. Because of this, General Refractories has decided to wait for more substantiating evidence that natural gas prices will rise as is now being predicted.

  9. Low-Btu coal gasification in the United States: company topical. [Brick producers

    SciTech Connect (OSTI)

    Boesch, L.P.; Hylton, B.G.; Bhatt, C.S.

    1983-07-01

    Hazelton and other brick producers have proved the reliability of the commercial size Wellman-Galusha gasifier. For this energy intensive business, gas cost is the major portion of the product cost. Costs required Webster/Hazelton to go back to the old, reliable alternative energy of low Btu gasification when the natural gas supply started to be curtailed and prices escalated. Although anthracite coal prices have skyrocketed from $34/ton (1979) to over $71.50/ton (1981) because of high demand (local as well as export) and rising labor costs, the delivered natural gas cost, which reached $3.90 to 4.20/million Btu in the Hazelton area during 1981, has allowed the producer gas from the gasifier at Webster Brick to remain competitive. The low Btu gas cost (at the escalated coal price) is estimated to be $4/million Btu. In addition to producing gas that is cost competitive with natural gas at the Webster Brick Hazelton plant, Webster has the security of knowing that its gas supply will be constant. Improvements in brick business and projected deregulation of the natural gas price may yield additional, attractive cost benefits to Webster Brick through the use of low Btu gas from these gasifiers. Also, use of hot raw gas (that requires no tar or sulfur removal) keeps the overall process efficiency high. 25 references, 47 figures, 14 tables.

  10. Hawaii Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Hawaii Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,056 1,055 1,057 1,043 983 983 983 983 983 983 983 983 2014 947 946 947 947 947 947 951 978 990 968 974 962 2015 968 954 947 959 990 1,005 1,011 965 989 996 996 997 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual

  11. Wind Power Forecasting

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

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

  12. 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...

  13. Georgia Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Georgia Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,014 1,015 1,016 1,015 1,014 1,015 1,016 1,019 1,017 1,016 1,017 1,017 2014 1,018 1,018 1,018 1,018 1,021 1,022 1,023 1,023 1,027 1,026 1,026 1,025 2015 1,025 1,026 1,025 1,026 1,028 1,031 1,030 1,028 1,029 1,028 1,026 1,027 - = No Data Reported; -- = Not Applicable; NA = Not

  14. Delaware Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Delaware Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,050 1,049 1,046 1,048 1,041 1,049 1,058 1,054 1,065 1,064 1,067 1,057 2014 1,052 1,048 1,048 1,051 1,045 1,049 1,063 1,065 1,062 1,063 1,063 1,064 2015 1,061 1,061 1,062 1,051 1,055 1,055 1,044 1,044 1,043 1,051 1,051 1,049 - = No Data Reported; -- = Not Applicable; NA = Not

  15. Colorado Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Colorado Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,023 1,032 1,030 1,033 1,040 1,051 1,056 1,057 1,058 1,037 1,032 1,033 2014 1,030 1,036 1,038 1,041 1,051 1,050 1,048 1,048 1,050 1,055 1,042 1,051 2015 1,046 1,044 1,051 1,059 1,059 1,070 1,073 1,069 1,076 1,069 1,060 1,051 - = No Data Reported; -- = Not Applicable; NA = Not

  16. Florida Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Florida Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,016 1,015 1,016 1,015 1,016 1,015 1,016 1,016 1,017 1,017 1,018 1,018 2014 1,018 1,018 1,018 1,019 1,019 1,019 1,022 1,023 1,024 1,023 1,024 1,025 2015 1,024 1,025 1,024 1,024 1,026 1,026 1,026 1,024 1,024 1,023 1,023 1,023 - = No Data Reported; -- = Not Applicable; NA = Not

  17. Connecticut Heat Content of Natural Gas Deliveries to Consumers (BTU per

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

    Cubic Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Connecticut Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,018 1,025 1,011 1,022 1,028 1,024 1,032 1,028 1,030 1,030 1,026 1,024 2014 1,015 1,015 1,016 1,019 1,020 1,022 1,022 1,023 1,021 1,020 1,018 1,017 2015 1,017 1,026 1,029 1,026 1,049 1,027 1,027 1,026 1,026 1,028 1,027 1,026 - = No Data Reported; -- = Not Applicable;

  18. Iowa Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Iowa Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,025 1,029 1,029 1,030 1,031 1,030 1,030 1,027 1,028 1,032 1,033 1,032 2014 1,034 1,033 1,034 1,036 1,040 1,039 1,043 1,047 1,044 1,046 1,044 1,045 2015 1,045 1,047 1,047 1,051 1,054 1,060 1,059 1,059 1,058 1,058 1,057 1,056 - = No Data Reported; -- = Not Applicable; NA = Not

  19. U.S. Heat Content of Natural Gas Deliveries to Other Sectors Consumers (BTU

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

    per Cubic Foot) Other Sectors Consumers (BTU per Cubic Foot) U.S. Heat Content of Natural Gas Deliveries to Other Sectors Consumers (BTU per Cubic Foot) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 1,029 1,026 1,028 1,028 1,027 1,027 1,025 2010's 1,023 1,022 1,025 1,028 1,032 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 02/29/2016 Next Release Date: 03/31/2016

  20. U.S. Total Consumption of Heat Content of Natural Gas (BTU per Cubic Foot)

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

    Consumption of Heat Content of Natural Gas (BTU per Cubic Foot) U.S. Total Consumption of Heat Content of Natural Gas (BTU per Cubic Foot) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 1,028 1,026 1,028 1,028 1,027 1,027 1,025 2010's 1,023 1,022 1,024 1,027 1,032 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 02/29/2016 Next Release Date: 03/31/2016 Referring Pages:

  1. Louisiana Heat Content of Natural Gas Deliveries to Consumers (BTU per

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

    Cubic Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Louisiana Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,015 1,013 1,015 1,015 1,015 1,016 1,016 1,017 1,017 1,016 1,018 1,019 2014 1,017 1,016 1,018 1,021 1,028 1,025 1,029 1,029 1,031 1,034 1,037 1,038 2015 1,030 1,031 1,029 1,029 1,028 1,027 1,028 1,024 1,023 1,023 1,022 1,023 - = No Data Reported; -- = Not Applicable;

  2. Kansas Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Kansas Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,017 1,017 1,019 1,018 1,018 1,020 1,020 1,020 1,018 1,017 1,016 1,017 2014 1,017 1,017 1,019 1,023 1,022 1,023 1,025 1,025 1,027 1,025 1,028 1,025 2015 1,033 1,034 1,035 1,036 1,044 1,039 1,040 1,042 1,039 1,037 1,035 1,031 - = No Data Reported; -- = Not Applicable; NA = Not

  3. Kentucky Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Kentucky Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,023 1,022 1,023 1,025 1,026 1,027 1,028 1,030 1,031 1,028 1,028 1,033 2014 1,029 1,024 1,026 1,028 1,031 1,037 1,034 1,036 1,038 1,022 1,017 1,019 2015 1,023 1,018 1,015 1,016 1,023 1,021 1,024 1,015 1,020 1,024 1,021 1,024 - = No Data Reported; -- = Not Applicable; NA = Not

  4. Idaho Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Idaho Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,015 1,015 1,031 1,021 1,010 997 988 994 1,001 1,026 1,034 1,054 2014 1,048 1,036 1,030 1,022 1,006 993 984 996 1,005 1,019 1,046 1,039 2015 1,047 1,037 1,030 1,023 1,000 1,010 1,034 1,028 1,024 1,033 1,035 1,041 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  5. Illinois Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Illinois Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,013 1,013 1,014 1,015 1,015 1,014 1,015 1,015 1,016 1,017 1,019 1,018 2014 1,020 1,020 1,020 1,020 1,020 1,020 1,022 1,020 1,021 1,021 1,023 1,024 2015 1,027 1,030 1,029 1,028 1,029 1,027 1,027 1,027 1,028 1,028 1,030 1,030 - = No Data Reported; -- = Not Applicable; NA = Not

  6. Indiana Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Indiana Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,011 1,012 1,013 1,015 1,019 1,020 1,019 1,021 1,020 1,018 1,015 1,014 2014 1,016 1,017 1,019 1,019 1,023 1,023 1,025 1,030 1,028 1,027 1,025 1,029 2015 1,028 1,029 1,031 1,039 1,037 1,043 1,043 1,044 1,041 1,039 1,034 1,033 - = No Data Reported; -- = Not Applicable; NA = Not

  7. Minnesota Heat Content of Natural Gas Deliveries to Consumers (BTU per

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

    Cubic Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Minnesota Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,020 1,021 1,020 1,021 1,026 1,030 1,028 1,029 1,028 1,029 1,029 1,027 2014 1,031 1,027 1,033 1,034 1,038 1,042 1,042 1,051 1,046 1,040 1,038 1,040 2015 1,041 1,034 1,033 1,037 1,044 1,047 1,043 1,041 1,039 1,041 1,045 1,041 - = No Data Reported; -- = Not Applicable;

  8. Mississippi Heat Content of Natural Gas Deliveries to Consumers (BTU per

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

    Cubic Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Mississippi Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,013 1,013 1,014 1,014 1,015 1,018 1,018 1,021 1,022 1,025 1,020 1,020 2014 1,019 1,014 1,019 1,026 1,030 1,034 1,035 1,036 1,035 1,033 1,035 1,034 2015 1,036 1,033 1,031 1,037 1,032 1,030 1,030 1,029 1,031 1,028 1,029 1,030 - = No Data Reported; -- = Not Applicable;

  9. Missouri Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Missouri Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,015 1,014 1,014 1,013 1,014 1,013 1,017 1,015 1,016 1,019 1,013 1,014 2014 1,013 1,013 1,014 1,014 1,011 1,016 1,016 1,018 1,017 1,018 1,017 1,017 2015 1,017 1,020 1,025 1,026 1,024 1,026 1,026 1,026 1,026 1,025 1,024 1,023 - = No Data Reported; -- = Not Applicable; NA = Not

  10. Montana Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Montana Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,044 1,040 1,032 1,034 1,034 1,044 1,048 1,043 1,047 1,041 1,032 1,031 2014 1,034 1,030 1,030 1,027 1,032 1,030 1,038 1,036 1,040 1,031 1,026 1,030 2015 1,028 1,029 1,028 1,021 1,019 1,030 1,031 1,033 1,032 1,032 1,034 1,034 - = No Data Reported; -- = Not Applicable; NA = Not

  11. Maine Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Maine Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,037 1,032 1,027 1,032 1,028 1,031 1,033 1,030 1,031 1,037 1,032 1,029 2014 1,029 1,030 1,030 1,030 1,033 1,030 1,031 1,039 1,023 1,016 1,025 1,027 2015 1,033 1,035 1,030 1,025 1,022 1,020 1,020 1,018 1,019 1,026 1,025 1,027 - = No Data Reported; -- = Not Applicable; NA = Not

  12. Maryland Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Maryland Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,041 1,037 1,032 1,027 1,037 1,042 1,060 1,056 1,062 1,059 1,061 1,059 2014 1,053 1,048 1,045 1,049 1,047 1,052 1,051 1,051 1,049 1,052 1,057 1,057 2015 1,059 1,061 1,058 1,051 1,058 1,057 1,055 1,049 1,050 1,053 1,049 1,050 - = No Data Reported; -- = Not Applicable; NA = Not

  13. Massachusetts Heat Content of Natural Gas Deliveries to Consumers (BTU per

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

    Cubic Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Massachusetts Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,037 1,033 1,032 1,033 1,035 1,032 1,033 1,034 1,036 1,038 1,033 1,030 2014 1,035 1,032 1,031 1,030 1,030 1,031 1,030 1,029 1,029 1,028 1,029 1,028 2015 1,035 1,035 1,030 1,029 1,027 1,027 1,029 1,028 1,027 1,028 1,029 1,030 - = No Data Reported; -- = Not

  14. Michigan Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic

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

    Foot) Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Michigan Heat Content of Natural Gas Deliveries to Consumers (BTU per Cubic Foot) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 1,021 1,021 1,022 1,026 1,020 1,022 1,024 1,021 1,019 1,019 1,017 1,019 2014 1,019 1,021 1,021 1,017 1,020 1,019 1,015 1,028 1,022 1,023 1,026 1,029 2015 1,027 1,026 1,030 1,035 1,028 1,033 1,034 1,035 1,036 1,034 1,041 1,040 - = No Data Reported; -- = Not Applicable; NA = Not

  15. Enabling Clean Consumption of Low Btu and Reactive Fuels in Gas Turbines

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

    Fuel-Flexible, Low-Emissions Catalytic Combustor for Opportunity Fuels ADVANCED MANUFACTURING OFFICE Enabling Clean Combustion of Low-Btu and Reactive Fuels in Gas Turbines By enabling ultralow-emission, lean premixed combustion of a wide range of gaseous opportunity fuels, this unique, fuel- fexible catalytic combustor for gas turbines can reduce natural gas consumption in industry. Introduction Gas turbines are commonly used in industry for onsite power and heating needs because of their high

  16. "NAICS",,"per Employee","of Value Added","of Shipments" "Code(a)","Economic Characteristic(b)","(million Btu)","(thousand Btu)","(thousand Btu)"

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

    3 Relative Standard Errors for Table 6.3;" " Unit: Percents." ,,,,"Consumption" ,,,"Consumption","per Dollar" ,,"Consumption","per Dollar","of Value" "NAICS",,"per Employee","of Value Added","of Shipments" "Code(a)","Economic Characteristic(b)","(million Btu)","(thousand Btu)","(thousand Btu)" ,,"Total United States" "

  17. 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...

  18. Table 2.2 Manufacturing Energy Consumption for All Purposes, 2006 (Trillion Btu )

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

    Manufacturing Energy Consumption for All Purposes, 2006 (Trillion Btu ) NAICS 1 Code Manufacturing Group Coal Coal Coke and Breeze 2 Natural Gas Distillate Fuel Oil LPG 3 and NGL 4 Residual Fuel Oil Net Electricity 5 Other 6 Shipments of Energy Sources 7 Total 8 311 Food 147 1 638 16 3 26 251 105 (s) 1,186 312 Beverage and Tobacco Products 20 0 41 1 1 3 30 11 -0 107 313 Textile Mills 32 0 65 (s) (s) 2 66 12 -0 178 314 Textile Product Mills 3 0 46 (s) 1 Q 20 (s) -0 72 315 Apparel 0 0 7 (s) (s)

  19. 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 ...

  20. Fuel injection staged sectoral combustor for burning low-BTU fuel gas

    DOE Patents [OSTI]

    Vogt, Robert L. (Schenectady, NY)

    1985-02-12

    A high-temperature combustor for burning low-BTU coal gas in a gas turbine is described. The combustor comprises a plurality of individual combustor chambers. Each combustor chamber has a main burning zone and a pilot burning zone. A pipe for the low-BTU coal gas is connected to the upstream end of the pilot burning zone: this pipe surrounds a liquid fuel source and is in turn surrounded by an air supply pipe: swirling means are provided between the liquid fuel source and the coal gas pipe and between the gas pipe and the air pipe. Additional preheated air is provided by counter-current coolant air in passages formed by a double wall arrangement of the walls of the main burning zone communicating with passages of a double wall arrangement of the pilot burning zone: this preheated air is turned at the upstream end of the pilot burning zone through swirlers to mix with the original gas and air input (and the liquid fuel input when used) to provide more efficient combustion. One or more fuel injection stages (second stages) are provided for direct input of coal gas into the main burning zone. The countercurrent air coolant passages are connected to swirlers surrounding the input from each second stage to provide additional oxidant.

  1. Fuel injection staged sectoral combustor for burning low-BTU fuel gas

    DOE Patents [OSTI]

    Vogt, Robert L. (Schenectady, NY)

    1981-01-01

    A high-temperature combustor for burning low-BTU coal gas in a gas turbine is described. The combustor comprises a plurality of individual combustor chambers. Each combustor chamber has a main burning zone and a pilot burning zone. A pipe for the low-BTU coal gas is connected to the upstream end of the pilot burning zone; this pipe surrounds a liquid fuel source and is in turn surrounded by an air supply pipe; swirling means are provided between the liquid fuel source and the coal gas pipe and between the gas pipe and the air pipe. Additional preheated air is provided by counter-current coolant air in passages formed by a double wall arrangement of the walls of the main burning zone communicating with passages of a double wall arrangement of the pilot burning zone; this preheated air is turned at the upstream end of the pilot burning zone through swirlers to mix with the original gas and air input (and the liquid fuel input when used) to provide more efficient combustion. One or more fuel injection stages (second stages) are provided for direct input of coal gas into the main burning zone. The countercurrent air coolant passages are connected to swirlers surrounding the input from each second stage to provide additional oxidant.

  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. Combined compressed air storage-low BTU coal gasification power plant

    DOE Patents [OSTI]

    Kartsounes, George T.; Sather, Norman F.

    1979-01-01

    An electrical generating power plant includes a Compressed Air Energy Storage System (CAES) fueled with low BTU coal gas generated in a continuously operating high pressure coal gasifier system. This system is used in coordination with a continuously operating main power generating plant to store excess power generated during off-peak hours from the power generating plant, and to return the stored energy as peak power to the power generating plant when needed. The excess coal gas which is produced by the coal gasifier during off-peak hours is stored in a coal gas reservoir. During peak hours the stored coal gas is combined with the output of the coal gasifier to fuel the gas turbines and ultimately supply electrical power to the base power plant.

  5. Table 3.1 Fossil Fuel Production Prices, 1949-2011 (Dollars per Million Btu)

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

    Fossil Fuel Production Prices, 1949-2011 (Dollars per Million Btu) Year Coal 1 Natural Gas 2 Crude Oil 3 Fossil Fuel Composite 4 Nominal 5 Real 6 Nominal 5 Real 6 Nominal 5 Real 6 Nominal 5 Real 6 Percent Change 7 1949 0.21 1.45 0.05 0.37 0.44 3.02 0.26 1.81 – – 1950 .21 1.41 .06 .43 .43 2.95 [R] .26 1.74 -3.6 1951 .21 1.35 .06 .40 .44 2.78 .26 1.65 -5.4 1952 .21 1.31 [R] .07 .45 .44 2.73 .26 1.63 -1.0 1953 .21 1.29 .08 .50 .46 2.86 .27 1.69 3.3 1954 .19 1.18 .09 .55 .48 2.94 .28 1.70 .7 1955

  6. Annual Energy Review, 1996

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

    condensate. b Natural gas plant liquids. c Biofuels, conventional hydroelectric power, geothermal energy, solar energy, and wind energy. d Includes -0.03 quadrillion Btu for...

  7. Annual Energy Review 1997

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

    condensate. b Natural gas plant liquids. c Biofuels, conventional hydroelectric power, geothermal energy, solar energy, and wind energy. d Includes -0.04 quadrillion Btu...

  8. New Title

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

    ... concerns regarding electricity generation, and doing so at comparative costs. * The Vision 21 fuel cells ... 30 1970 1975 1980 1985 1990 1995 2000 Year quadrillion BTU ...

  9. Word Pro - Untitled1

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

    in the United States, Selected Years, 1635-1945 (Quadrillion Btu) Year Fossil Fuels Renewable Energy Electricity Net Imports Total Coal Natural Gas Petroleum Total...

  10. Slovenia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Slovenia Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code SI 3-letter ISO code SVN Numeric ISO code...

  11. Peru: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Peru Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code PE 3-letter ISO code PER Numeric ISO code...

  12. Guadeloupe: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Guadeloupe Population Unavailable GDP Unavailable Energy Consumption 0.03 Quadrillion Btu 2-letter ISO code GP 3-letter ISO code GLP Numeric ISO...

  13. Marshall Islands: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Marshall Islands Population 56,429 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code MH 3-letter ISO code MHL Numeric ISO code...

  14. Australia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Australia Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code AU 3-letter ISO code AUS Numeric ISO code...

  15. San Marino: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name San Marino Population 32,576 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code SM 3-letter ISO code SMR Numeric ISO code...

  16. Anguilla: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Anguilla Population 13,452 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code AI 3-letter ISO code AIA Numeric ISO code...

  17. Gambia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Gambia Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code GM 3-letter ISO code GMB Numeric ISO code...

  18. Antigua and Barbuda: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Antigua and Barbuda Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code AG 3-letter ISO code ATG Numeric ISO code...

  19. Thailand: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Thailand Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code TH 3-letter ISO code THA Numeric ISO code...

  20. Sierra Leone: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Sierra Leone Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code SL 3-letter ISO code SLE Numeric ISO code...

  1. Djibouti: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Djibouti Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code DJ 3-letter ISO code DJI Numeric ISO code...

  2. Saint Barthlemy: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Saint Barthlemy Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code BL 3-letter ISO code BLM Numeric ISO code...

  3. Taiwan: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Taiwan Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code TW 3-letter ISO code TWN Numeric ISO code...

  4. Georgia (country): Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Georgia Population Unavailable GDP Unavailable Energy Consumption 0.17 Quadrillion Btu 2-letter ISO code GE 3-letter ISO code GEO Numeric ISO...

  5. France: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name France Population Unavailable GDP Unavailable Energy Consumption 11.29 Quadrillion Btu 2-letter ISO code FR 3-letter ISO code FRA Numeric ISO...

  6. Croatia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Croatia Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code HR 3-letter ISO code HRV Numeric ISO code...

  7. Palau: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Palau Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code PW 3-letter ISO code PLW Numeric ISO code...

  8. Uganda: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Uganda Population Unavailable GDP Unavailable Energy Consumption 0.04 Quadrillion Btu 2-letter ISO code UG 3-letter ISO code UGA Numeric ISO...

  9. Tuvalu: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Tuvalu Population 10,837 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code TV 3-letter ISO code TUV Numeric ISO code...

  10. Ireland: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Ireland Population Unavailable GDP Unavailable Energy Consumption 0.69 Quadrillion Btu 2-letter ISO code IE 3-letter ISO code IRL Numeric ISO...

  11. Cayman Islands: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Cayman Islands Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code KY 3-letter ISO code CYM Numeric ISO code...

  12. Myanmar: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Myanmar Population Unavailable GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code MM 3-letter ISO code MMR Numeric ISO code...

  13. Armenia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    ,"inlineLabel":"","visitedicon":"" Country Profile Name Armenia Population Unavailable GDP Unavailable Energy Consumption 0.22 Quadrillion Btu 2-letter ISO code AM 3-letter ISO...

  14. Annual Energy Review 2009 - Released August 2010

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

    less than 0.1 quadrillion Btu of coal coke net exports. 4 Conventional hydroelectric power, geothermal, solarPV, wind, and biomass. 5 Includes industrial...

  15. Department of Energy Announces Winners of 2015 Federal Energy...

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

    at less than 1 quadrillion British thermal units (Btu) annually, the lowest since 1975 when data collection began. The energy savings being recognized by these awards alone...

  16. Appendix A: Reference case

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

    Reference case Energy Information Administration Annual Energy Outlook 2014 Table A17. Renewable energy consumption by sector and source (quadrillion Btu) Sector and source...

  17. Appendix A: Reference case

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

    4 Reference case Table A2. Energy consumption by sector and source (quadrillion Btu per year, unless otherwise noted) Energy Information Administration Annual Energy Outlook 2014...

  18. 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

  19. 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)

  20. 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:...

  1. 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.

  2. 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...

  3. Commercial demonstration of atmospheric medium BTU fuel gas production from biomass without oxygen the Burlington, Vermont Project

    SciTech Connect (OSTI)

    Rohrer, J.W.

    1995-12-31

    The first U.S. demonstration of a gas turbine operating on fuel gas produced by the thermal gasification of biomass occurred at Battelle Columbus Labs (BCL) during 1994 using their high throughput indirect medium Btu gasification Process Research Unit (PRU). Zurn/NEPCO was retained to build a commercial scale gas plant utilizing this technology. This plant will have a throughput rating of 8 to 12 dry tons per hour. During a subsequent phase of the Burlington project, this fuel gas will be utilized in a commercial scale gas turbine. It is felt that this process holds unique promise for economically converting a wide variety of biomass feedstocks efficiently into both a medium Btu (500 Btu/scf) gas turbine and IC engine quality fuel gas that can be burned in engines without modification, derating or efficiency loss. Others are currently demonstrating sub-commercial scale thermal biomass gasification processes for turbine gas, utilizing both atmospheric and pressurized air and oxygen-blown fluid bed processes. While some of these approaches hold merit for coal, there is significant question as to whether they will prove economically viable in biomass facilities which are typically scale limited by fuel availability and transportation logistics below 60 MW. Atmospheric air-blown technologies suffer from large sensible heat loss, high gas volume and cleaning cost, huge gas compressor power consumption and engine deratings. Pressurized units and/or oxygen-blown gas plants are extremely expensive for plant scales below 250 MW. The FERCO/BCL process shows great promise for overcoming the above limitations by utilizing an extremely high throughout circulation fluid bed (CFB) gasifier, in which biomass is fully devolitalized with hot sand from a CFB char combustor. The fuel gas can be cooled and cleaned by a conventional scrubbing system. Fuel gas compressor power consumption is reduced 3 to 4 fold verses low Btu biomass gas.

  4. 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.

  5. 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:...

  6. 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...

  7. 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...

  8. 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

  9. 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.

  10. 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

  11. Table 2.9 Commercial Buildings Consumption by Energy Source, Selected Years, 1979-2003 (Trillion Btu)

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

    9 Commercial Buildings Consumption by Energy Source, Selected Years, 1979-2003 (Trillion Btu) Energy Source and Year Square Footage Category Principal Building Activity Census Region 1 All Buildings 1,001 to 10,000 10,001 to 100,000 Over 100,000 Education Food Sales Food Service Health Care Lodging Mercantile and Service Office All Other Northeast Midwest South West Major Sources 2 1979 1,255 2,202 1,508 511 [3] 336 469 278 894 861 1,616 1,217 1,826 1,395 526 4,965 1983 1,242 1,935 1,646 480 [3]

  12. Low-Btu coal-gasification-process design report for Combustion Engineering/Gulf States Utilities coal-gasification demonstration plant. [Natural gas or No. 2 fuel oil to natural gas or No. 2 fuel oil or low Btu gas

    SciTech Connect (OSTI)

    Andrus, H E; Rebula, E; Thibeault, P R; Koucky, R W

    1982-06-01

    This report describes a coal gasification demonstration plant that was designed to retrofit an existing steam boiler. The design uses Combustion Engineering's air blown, atmospheric pressure, entrained flow coal gasification process to produce low-Btu gas and steam for Gulf States Utilities Nelson No. 3 boiler which is rated at a nominal 150 MW of electrical power. Following the retrofit, the boiler, originally designed to fire natural gas or No. 2 oil, will be able to achieve full load power output on natural gas, No. 2 oil, or low-Btu gas. The gasifier and the boiler are integrated, in that the steam generated in the gasifier is combined with steam from the boiler to produce full load. The original contract called for a complete process and mechanical design of the gasification plant. However, the contract was curtailed after the process design was completed, but before the mechanical design was started. Based on the well defined process, but limited mechanical design, a preliminary cost estimate for the installation was completed.

  13. "NAICS",,"per Employee","of Value Added","of Shipments" "Code(a)","Economic Characteristic(b)","(million Btu)","(thousand Btu)","(thousand Btu)"

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

    4 Relative Standard Errors for Table 6.4;" " Unit: Percents." " "," ",,,"Consumption" " "," ",,"Consumption","per Dollar" " "," ","Consumption","per Dollar","of Value" "NAICS",,"per Employee","of Value Added","of Shipments" "Code(a)","Economic Characteristic(b)","(million Btu)","(thousand

  14. 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

  15. Annual Energy Review 1999

    SciTech Connect (OSTI)

    Seiferlein, Katherine E.

    2000-07-01

    A generation ago the Ford Foundation convened a group of experts to explore and assess the Nations energy future, and published their conclusions in A Time To Choose: Americas Energy Future (Cambridge, MA: Ballinger, 1974). The Energy Policy Project developed scenarios of U.S. potential energy use in 1985 and 2000. Now, with 1985 well behind us and 2000 nearly on the record books, it may be of interest to take a look back to see what actually happened and consider what it means for our future. The study group sketched three primary scenarios with differing assumptions about the growth of energy use. The Historical Growth scenario assumed that U.S. energy consumption would continue to expand by 3.4 percent per year, the average rate from 1950 to 1970. This scenario assumed no intentional efforts to change the pattern of consumption, only efforts to encourage development of our energy supply. The Technical Fix scenario anticipated a conscious national effort to use energy more efficiently through engineering know-how." The Zero Energy Growth scenario, while not clamping down on the economy or calling for austerity, incorporated the Technical Fix efficiencies plus additional efficiencies. This third path anticipated that economic growth would depend less on energy-intensive industries and more on those that require less energy, i.e., the service sector. In 2000, total energy consumption was projected to be 187 quadrillion British thermal units (Btu) in the Historical Growth case, 124 quadrillion Btu in the Technical Fix case, and 100 quadrillion Btu in the Zero Energy Growth case. The Annual Energy Review 1999 reports a preliminary total consumption for 1999 of 97 quadrillion Btu (see Table 1.1), and the Energy Information Administrations Short-Term Energy Outlook (April 2000) forecasts total energy consumption of 98 quadrillion Btu in 2000. What energy consumption path did the United States actually travel to get from 1974, when the scenarios were drawn, to the end of the century? What happened to the relationship between growth and energy consumption? How did the fuel mix change over this period? What are the effects of energy usage on our environment? What level of consumption will the United Statesand the worldrecord in the Annual Energy Review 2025? We present this edition of the Annual Energy Review to help investigate these important questions and to stimulate and inform our thinking about what the future holds.

  16. 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

  17. 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

  18. Table 3.3 Consumer Price Estimates for Energy by Source, 1970-2010 (Dollars per Million Btu)

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

    Consumer Price Estimates for Energy by Source, 1970-2010 (Dollars 1 per Million Btu) Year Primary Energy 2 Electric Power Sector 11,12 Retail Electricity 13 Total Energy 9,10,14 Coal Natural Gas 3 Petroleum Nuclear Fuel Biomass 8 Total 9,10 Distillate Fuel Oil Jet Fuel 4 LPG 5 Motor Gasoline 6 Residual Fuel Oil Other 7 Total 1970 0.38 0.59 1.16 0.73 1.43 2.85 0.42 1.38 1.71 0.18 1.29 1.08 0.32 4.98 1.65 1971 .42 .63 1.22 .77 1.46 2.90 .58 1.45 1.78 .18 1.31 1.15 .38 5.30 1.76 1972 .45 .68 1.22

  19. Industrial co-generation through use of a medium BTU gas from biomass produced in a high throughput reactor

    SciTech Connect (OSTI)

    Feldmann, H.F.; Ball, D.A.; Paisley, M.A.

    1983-01-01

    A high-throughput gasification system has been developed for the steam gasification of woody biomass to produce a fuel gas with a heating value of 475 to 500 Btu/SCF without using oxygen. Recent developments have focused on the use of bark and sawdust as feedstocks in addition to wood chips and the testing of a new reactor concept, the so-called controlled turbulent zone (CTZ) reactor to increase gas production per unit of wood fed. Operating data from the original gasification system and the CTZ system are used to examine the preliminary economics of biomass gasification/gas turbine cogeneration systems. In addition, a ''generic'' pressurized oxygen-blown gasification system is evaluated. The economics of these gasification systems are compared with a conventional wood boiler/steam turbine cogeneration system.

  20. COMPCOAL{trademark}: A profitable process for production of a stable high-Btu fuel from Powder River Basin coal

    SciTech Connect (OSTI)

    Smith, V.E.; Merriam, N.W.

    1994-10-01

    Western Research Institute (WRI) is developing a process to produce a stable, clean-burning, premium fuel from Powder River Basin (PRB) coal and other low-rank coals. This process is designed to overcome the problems of spontaneous combustion, dust formation, and readsorption of moisture that are experienced with PRB coal and with processed PRB coal. This process, called COMPCOAL{trademark}, results in high-Btu product that is intended for burning in boilers designed for midwestern coals or for blending with other coals. In the COMPCOAL process, sized coal is dried to zero moisture content and additional oxygen is removed from the coal by partial decarboxylation as the coal is contacted by a stream of hot fluidizing gas in the dryer. The hot, dried coal particles flow into the pyrolyzer where they are contacted by a very small flow of air. The oxygen in the air reacts with active sites on the surface of the coal particles causing the temperature of the coal to be raised to about 700{degrees}F (371{degrees}C) and oxidizing the most reactive sites on the particles. This ``instant aging`` contributes to the stability of the product while only reducing the heating value of the product by about 50 Btu/lb. Less than 1 scf of air per pound of dried coal is used to avoid removing any of the condensible liquid or vapors from the coal particles. The pyrolyzed coal particles are mixed with fines from the dryer cyclone and dust filter and the resulting mixture at about 600{degrees}F (316{degrees}C) is fed into a briquettor. Briquettes are cooled to about 250{degrees}F (121{degrees}C) by contact with a mist of water in a gas-tight mixing conveyor. The cooled briquettes are transferred to a storage bin where they are accumulated for shipment.

  1. 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,

  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. Low NO{sub x} turbine power generation utilizing low Btu GOB gas. Final report, June--August 1995

    SciTech Connect (OSTI)

    Ortiz, I.; Anthony, R.V.; Gabrielson, J.; Glickert, R.

    1995-08-01

    Methane, a potent greenhouse gas, is second only to carbon dioxide as a contributor to potential global warming. Methane liberated by coal mines represents one of the most promising under exploited areas for profitably reducing these methane emissions. Furthermore, there is a need for apparatus and processes that reduce the nitrogen oxide (NO{sub x}) emissions from gas turbines in power generation. Consequently, this project aims to demonstrate a technology which utilizes low grade fuel (CMM) in a combustion air stream to reduce NO{sub x} emissions in the operation of a gas turbine. This technology is superior to other existing technologies because it can directly use the varying methane content gases from various streams of the mining operation. The simplicity of the process makes it useful for both new gas turbines and retrofitting existing gas turbines. This report evaluates the feasibility of using gob gas from the 11,000 acre abandoned Gateway Mine near Waynesburg, Pennsylvania as a fuel source for power generation applying low NO{sub x} gas turbine technology at a site which is currently capable of producing low grade GOB gas ({approx_equal} 600 BTU) from abandoned GOB areas.

  8. Philadelphia gas works medium-Btu coal gasification project: capital and operating cost estimate, financial/legal analysis, project implementation

    SciTech Connect (OSTI)

    Not Available

    1981-12-01

    This volume of the final report is a compilation of the estimated capital and operating costs for the project. Using the definitive design as a basis, capital and operating costs were developed by obtaining quotations for equipment delivered to the site. Tables 1.1 and 1.2 provide a summary of the capital and operating costs estimated for the PGW Coal Gasification Project. In the course of its Phase I Feasibility Study of a medium-Btu coal-gas facility, Philadelphia Gas Works (PGW) identified the financing mechanism as having great impact on gas cost. Consequently, PGW formed a Financial/Legal Task Force composed of legal, financial, and project analysis specialists to study various ownership/management options. In seeking an acceptable ownership, management, and financing arrangement, certain ownership forms were initially identified and classified. Several public ownership, private ownership, and third party ownership options for the coal-gas plant are presented. The ownership and financing forms classified as base alternatives involved tax-exempt and taxable financing arrangements and are discussed in Section 3. Project implementation would be initiated by effectively planning the methodology by which commercial operation will be realized. Areas covered in this report are sale of gas to customers, arrangements for feedstock supply and by-product disposal, a schedule of major events leading to commercialization, and a plan for managing the implementation.

  9. Low/medium Btu coal gasification assessment of central plant for the city of Philadelphia, Pennsylvania. Final report

    SciTech Connect (OSTI)

    Not Available

    1981-02-01

    The objective of this study is to assess the technical and economic feasibility of producing, distributing, selling, and using fuel gas for industrial applications in Philadelphia. The primary driving force for the assessment is the fact that oil users are encountering rapidly escalating fuel costs, and are uncertain about the future availability of low sulfur fuel oil. The situation is also complicated by legislation aimed at reducing oil consumption and by difficulties in assuring a long term supply of natural gas. Early in the gasifier selection study it was decided that the level of risk associated with the gasification process sould be minimal. It was therefore determined that the process should be selected from those commercially proven. The following processes were considered: Lurgi, KT, Winkler, and Wellman-Galusha. From past experience and a knowledge of the characteristics of each gasifier, a list of advantages and disadvantages of each process was formulated. It was concluded that a medium Btu KT gas can be manufactured and distributed at a lower average price than the conservatively projected average price of No. 6 oil, provided that the plant is operated as a base load producer of gas. The methodology used is described, assumptions are detailed and recommendations are made. (LTN)

  10. Monthly energy review: September 1996

    SciTech Connect (OSTI)

    1996-09-01

    Energy production during June 1996 totaled 5.6 quadrillion Btu, a 0.5% decrease from the level of production during June 1995. Energy consumption during June 1996 totaled 7.1 quadrillion Btu, 2.7% above the level of consumption during June 1995. Net imports of energy during June 1996 totaled 1.6 quadrillion Btu, 4.5% above the level of net imports 1 year earlier. Statistics are presented on the following topics: energy consumption, petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, and international energy. 37 figs., 59 tabs.

  11. System and process for the abatement of casting pollution, reclaiming resin bonded sand, and/or recovering a low BTU fuel from castings

    DOE Patents [OSTI]

    Scheffer, Karl D. (121 Governor Dr., Scotia, NY 12302)

    1984-07-03

    Air is caused to flow through the resin bonded mold to aid combustion of the resin binder to form a low BTU gas fuel. Casting heat is recovered for use in a waste heat boiler or other heat abstraction equipment. Foundry air pollution is reduced, the burned portion of the molding sand is recovered for immediate reuse and savings in fuel and other energy is achieved.

  12. System and process for the abatement of casting pollution, reclaiming resin bonded sand, and/or recovering a low Btu fuel from castings

    DOE Patents [OSTI]

    Scheffer, K.D.

    1984-07-03

    Air is caused to flow through the resin bonded mold to aid combustion of the resin binder to form a low Btu gas fuel. Casting heat is recovered for use in a waste heat boiler or other heat abstraction equipment. Foundry air pollutis reduced, the burned portion of the molding sand is recovered for immediate reuse and savings in fuel and other energy is achieved. 5 figs.

  13. 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

  14. 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....

  15. 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...

  16. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  17. 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...

  18. 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.

  19. 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.

  20. Annual Energy Review 2008 - Released June 2009

    Gasoline and Diesel Fuel Update (EIA)

    0.1 quadrillion Btu of coal coke net imports. 4 Conventional hydroelectric power, geothermal, solarPV, wind, and biomass. 5 Includes industrial combined-heat-and-power (CHP)...

  1. Tips: Heating and Cooling | Department of Energy

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

    us use natural gas. | Source: Buildings Energy Data Book 2011, 2.1.1 Residential Primary Energy Consumption, by Year and Fuel Type (Quadrillion Btu and Percent of Total)....

  2. MU Eneg

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

    is included, but an estimated 3.0 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  3. Ordering Information

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

    is included, but an estimated 3.4 quadrillion Btu of renewable Note 8; and Table A8. Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  4. AA

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

    is included, but an estimated 3.0 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  5. DOE/EI-003595/10

    Gasoline and Diesel Fuel Update (EIA)

    is included, but an estimated 3.0 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  6. Ordering Information

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

    is included, but an estimated 3.4 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  7. 1) E/ L I

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

    is included, but an estimated 3.0 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  8. DOE/EIA-0035(94/01) Ener Revie

    Gasoline and Diesel Fuel Update (EIA)

    is included, but an estimated 3.4 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  9. DOE/ELIA-0035(95/105), Monthly

    Gasoline and Diesel Fuel Update (EIA)

    is included, but an estimated 3.0 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  10. II IIE

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

    is included, but an estimated 3.0 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  11. II Now Available State Energy Data Report 1992

    Gasoline and Diesel Fuel Update (EIA)

    is included, but an estimated 3.4 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  12. I.

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

    is included, but an estimated 3.0 quadrillion Btu of renewable Note 8; and Table A8. * Geothermal Energy and Other: Section 2, energy used by other sectors is not included....

  13. AEO2011:Total Energy Supply, Disposition, and Price Summary ...

    Open Energy Info (EERE)

    case. The dataset uses quadrillion Btu and the U.S. Dollar. The data is broken down into production, imports, exports, consumption and price. Data and Resources AEO2011:Total...

  14. Innovative Process and Materials Technologies

    Broader source: Energy.gov [DOE]

    U. S. industry consumes approximately 30 quadrillion Btu (quads) of energy per year, which is almost one third of all energy used in the United States. Solutions that increase energy productivity ...

  15. Solomon Islands: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Solomon Islands Population 523,000 GDP 840,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code SB 3-letter ISO code SLB Numeric ISO...

  16. Kenya: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Kenya Population 38,610,097 GDP Unavailable Energy Consumption 0.21 Quadrillion Btu 2-letter ISO code KE 3-letter ISO code KEN Numeric ISO...

  17. Madagascar: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Madagascar Population 12,238,914 GDP 10,025,000,000 Energy Consumption 0.05 Quadrillion Btu 2-letter ISO code MG 3-letter ISO code MDG Numeric ISO...

  18. Mauritius: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    "","visitedicon":"" Country Profile Name Mauritius Population 1,236,817 GDP 14 Energy Consumption 0.06 Quadrillion Btu 2-letter ISO code MU 3-letter ISO code MUS Numeric ISO...

  19. Senegal: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Senegal Population 13,508,715 GDP 13,864,000,000 Energy Consumption 0.09 Quadrillion Btu 2-letter ISO code SN 3-letter ISO code SEN Numeric ISO...

  20. Greenland: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Greenland Population 56,968 GDP Unavailable Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code GL 3-letter ISO code GRL Numeric ISO...

  1. Maldives: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Maldives Population 393,500 GDP 1,944,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code MV 3-letter ISO code MDV Numeric ISO...

  2. United States: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    page. Country Profile Name United States Population 320,206,000 GDP Unavailable Energy Consumption 99.53 Quadrillion Btu 2-letter ISO code US 3-letter ISO code USA Numeric ISO...

  3. Tanzania: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    "","visitedicon":"" Country Profile Name Tanzania Population 44,928,923 GDP 37 Energy Consumption 0.12 Quadrillion Btu 2-letter ISO code TZ 3-letter ISO code TZA Numeric ISO...

  4. Syria: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Syria Population 17,951,639 GDP Unavailable Energy Consumption 0.84 Quadrillion Btu 2-letter ISO code SY 3-letter ISO code SYR Numeric ISO...

  5. Saint Lucia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Saint Lucia Population 173,765 GDP 1,239,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code LC 3-letter ISO code LCA Numeric ISO...

  6. Yemen: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Yemen Population 19,685,000 GDP 36,700,000,000 Energy Consumption 0.31 Quadrillion Btu 2-letter ISO code YE 3-letter ISO code YEM Numeric ISO...

  7. Seychelles: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Seychelles Population 84,000 GDP 2,760,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code SC 3-letter ISO code SYC Numeric ISO...

  8. South Korea: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name South Korea Population 51,302,044 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code KR 3-letter ISO code KOR Numeric ISO code...

  9. Guyana: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Guyana Population 747,884 GDP 2,788,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code GY 3-letter ISO code GUY Numeric ISO...

  10. Albania: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Albania Population 2,821,977 GDP 14,000,000,000 Energy Consumption 0.11 Quadrillion Btu 2-letter ISO code AL 3-letter ISO code ALB Numeric ISO...

  11. Romania: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Romania Population 20,121,641 GDP 191,581,000,000 Energy Consumption 1.68 Quadrillion Btu 2-letter ISO code RO 3-letter ISO code ROU Numeric ISO...

  12. Morocco: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Morocco Population 33,250,000 GDP 114,700,000,000 Energy Consumption 0.56 Quadrillion Btu 2-letter ISO code MA 3-letter ISO code MAR Numeric ISO...

  13. Dominica: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Dominica Population 72,301 GDP 497,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code DM 3-letter ISO code DMA Numeric ISO...

  14. Tonga: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Tonga Population 103,036 GDP 439,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code TO 3-letter ISO code TON Numeric ISO...

  15. Antigua and Barbuda: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Antigua and Barbuda Population 81,799 GDP 1,176,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code AG 3-letter ISO code ATG Numeric ISO...

  16. Cape Verde: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Cape Verde Population 512,096 GDP 2,071,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code CV 3-letter ISO code CPV Numeric ISO...

  17. Burundi: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Burundi Population 8,053,574 GDP 3,037,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code BI 3-letter ISO code BDI Numeric ISO...

  18. Somalia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Somalia Population 10,428,043 GDP Unavailable Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code SO 3-letter ISO code SOM Numeric ISO...

  19. Ethiopia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Ethiopia Population 73,750,932 GDP 51,000,000,000 Energy Consumption 0.12 Quadrillion Btu 2-letter ISO code ET 3-letter ISO code ETH Numeric ISO...

  20. Montserrat: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Montserrat Population 4,900 GDP Unavailable Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code MS 3-letter ISO code MSR Numeric ISO...

  1. Faroe Islands: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Faroe Islands Population 48,351 GDP 2,450,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code FO 3-letter ISO code FRO Numeric ISO...

  2. I.N

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

    heating or water heating. Fuel 011, LPG, and Kerosene. Expenditures of 11 Fuel Oil. Consumption of 1.0 quadrillion Btu of fuel billion for fuel oil, LPG, and kerosene...

  3. Nepal: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Nepal Population 26,494,504 GDP Unavailable Energy Consumption 0.08 Quadrillion Btu 2-letter ISO code NP 3-letter ISO code NPL Numeric ISO...

  4. Panama: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Panama Population 3,608,431 GDP 49,142,000,000 Energy Consumption 0.24 Quadrillion Btu 2-letter ISO code PA 3-letter ISO code PAN Numeric ISO...

  5. Iran: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Iran Population 77,176,930 GDP 402,700,000,000 Energy Consumption 8.12 Quadrillion Btu 2-letter ISO code IR 3-letter ISO code IRN Numeric ISO...

  6. Nauru: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    "","visitedicon":"" Country Profile Name Nauru Population 9,275 GDP Unavailable Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code NR 3-letter ISO code NRU Numeric ISO...

  7. Guinea: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Guinea Population 10,628,972 GDP 5,212,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code GN 3-letter ISO code GIN Numeric ISO...

  8. Tunisia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Tunisia Population 10,982,754 GDP 45,611,000,000 Energy Consumption 0.35 Quadrillion Btu 2-letter ISO code TN 3-letter ISO code TUN Numeric ISO...

  9. Lithuania: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Lithuania Population 3,043,429 GDP 51,002,000,000 Energy Consumption 0.39 Quadrillion Btu 2-letter ISO code LT 3-letter ISO code LTU Numeric ISO...

  10. Northern Mariana Islands: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Northern Mariana Islands Population 53,833 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code MP 3-letter ISO code MNP Numeric ISO code...

  11. Cambodia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Cambodia Population 13,388,910 GDP 17,250,000,000 Energy Consumption 0.07 Quadrillion Btu 2-letter ISO code KH 3-letter ISO code KHM Numeric ISO...

  12. Kosovo: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Kosovo Population 1,733,842 GDP 7,813,000,000 Energy Consumption Quadrillion Btu 2-letter ISO code XK 3-letter ISO code XKX Numeric ISO code N...

  13. Togo: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Togo Population 5,337,000 GDP 3,685,000,000 Energy Consumption 0.04 Quadrillion Btu 2-letter ISO code TG 3-letter ISO code TGO Numeric ISO...

  14. Guinea-Bissau: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Guinea-Bissau Population 1,345,479 GDP 870,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code GW 3-letter ISO code GNB Numeric ISO...

  15. Uruguay: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Uruguay Population 3,286,314 GDP 58,283,000,000 Energy Consumption 0.17 Quadrillion Btu 2-letter ISO code UY 3-letter ISO code URY Numeric ISO...

  16. Turks and Caicos Islands: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Turks and Caicos Islands Population 31,458 GDP Unavailable Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code TC 3-letter ISO code TCA Numeric ISO...

  17. Rwanda: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Rwanda Population 10,515,973 GDP 7,431,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code RW 3-letter ISO code RWA Numeric ISO...

  18. Grenada: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Grenada Population 109,590 GDP 790,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code GD 3-letter ISO code GRD Numeric ISO...

  19. Burkina Faso: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Burkina Faso Population 14,017,262 GDP 13,000,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code BF 3-letter ISO code BFA Numeric ISO...

  20. Iraq: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Iraq Population 36,004,552 GDP 164,600,000,000 Energy Consumption 1.36 Quadrillion Btu 2-letter ISO code IQ 3-letter ISO code IRQ Numeric ISO...

  1. Benin: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Benin Population 9,983,884 GDP 7,429,000,000 Energy Consumption 0.05 Quadrillion Btu 2-letter ISO code BJ 3-letter ISO code BEN Numeric ISO...

  2. Portugal: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Portugal Population 10,562,178 GDP Unavailable Energy Consumption 1.06 Quadrillion Btu 2-letter ISO code PT 3-letter ISO code PRT Numeric ISO...

  3. Oman: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Oman Population 2,773,479 GDP 78,788,000,000 Energy Consumption 0.71 Quadrillion Btu 2-letter ISO code OM 3-letter ISO code OMN Numeric ISO...

  4. Angola: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Angola Population 18,498,000 GDP 129,785,000,000 Energy Consumption 0.20 Quadrillion Btu 2-letter ISO code AO 3-letter ISO code AGO Numeric ISO...

  5. Lebanon: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Lebanon Population 4,965,914 GDP 44,967,000,000 Energy Consumption 0.20 Quadrillion Btu 2-letter ISO code LB 3-letter ISO code LBN Numeric ISO...

  6. Belize: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Belize Population 324,528 GDP 1,554,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code BZ 3-letter ISO code BLZ Numeric ISO...

  7. Republic of Macedonia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Republic of Macedonia Population 2,022,547 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code MK 3-letter ISO code MKD Numeric ISO code...

  8. Slovakia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Slovakia Population 5,397,036 GDP Unavailable Energy Consumption 0.80 Quadrillion Btu 2-letter ISO code SK 3-letter ISO code SVK Numeric ISO...

  9. Bhutan: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Bhutan Population Unavailable GDP 1,488,000,000 Energy Consumption 0.05 Quadrillion Btu 2-letter ISO code BT 3-letter ISO code BTN Numeric ISO...

  10. Comoros: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Comoros Population 798,000 GDP 655,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code KM 3-letter ISO code COM Numeric ISO...

  11. Finland: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Finland Population 5,180,000 GDP 276,275,000,000 Energy Consumption 1.29 Quadrillion Btu 2-letter ISO code FI 3-letter ISO code FIN Numeric ISO...

  12. Latvia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Latvia Population 2,070,371 GDP 34,118,000,000 Energy Consumption 0.16 Quadrillion Btu 2-letter ISO code LV 3-letter ISO code LVA Numeric ISO...

  13. Cuba: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Cuba Population 11,210,064 GDP 78,694,000,000 Energy Consumption 0.42 Quadrillion Btu 2-letter ISO code CU 3-letter ISO code CUB Numeric ISO...

  14. Barbados: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Barbados Population 277,821 GDP 4,490,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code BB 3-letter ISO code BRB Numeric ISO...

  15. Cyprus: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Cyprus Population 838,897 GDP 23,006,000,000 Energy Consumption 0.13 Quadrillion Btu 2-letter ISO code CY 3-letter ISO code CYP Numeric ISO...

  16. Kiribati: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Kiribati Population 103,500 GDP 167,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code KI 3-letter ISO code KIR Numeric ISO...

  17. Saint Helena: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Saint Helena Population 4,255 GDP Unavailable Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code SH 3-letter ISO code SHN Numeric ISO...

  18. Brunei: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Brunei Population 415,717 GDP 17,092,000,000 Energy Consumption 0.19 Quadrillion Btu 2-letter ISO code BN 3-letter ISO code BRN Numeric ISO...

  19. Kuwait: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Kuwait Population 2,213,403 GDP 173,438,000,000 Energy Consumption 1.19 Quadrillion Btu 2-letter ISO code KW 3-letter ISO code KWT Numeric ISO...

  20. Malaysia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Malaysia Population 28,334,135 GDP Unavailable Energy Consumption 2.45 Quadrillion Btu 2-letter ISO code MY 3-letter ISO code MYS Numeric ISO...

  1. New Zealand: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name New Zealand Population 4,242,048 GDP Unavailable Energy Consumption Quadrillion Btu 2-letter ISO code NZ 3-letter ISO code NZL Numeric ISO code...

  2. Zimbabwe: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    "","visitedicon":"" Country Profile Name Zimbabwe Population 13,061,239 GDP 11 Energy Consumption 0.16 Quadrillion Btu 2-letter ISO code ZW 3-letter ISO code ZWE Numeric ISO...

  3. Togo: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Togo Population 7,154,237 GDP 3,685,000,000 Energy Consumption 0.04 Quadrillion Btu 2-letter ISO code TG 3-letter ISO code TGO Numeric ISO...

  4. Estonia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Estonia Population 1,294,486 GDP 27,410,000,000 Energy Consumption 0.24 Quadrillion Btu 2-letter ISO code EE 3-letter ISO code EST Numeric ISO...

  5. Suriname: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Suriname Population 492,829 GDP 5,273,000,000 Energy Consumption 0.04 Quadrillion Btu 2-letter ISO code SR 3-letter ISO code SUR Numeric ISO...

  6. Bulgaria: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Bulgaria Population 7,364,570 GDP 57,596,000,000 Energy Consumption 0.83 Quadrillion Btu 2-letter ISO code BG 3-letter ISO code BGR Numeric ISO...

  7. Switzerland: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Switzerland Population 7,954,700 GDP 679,028,000,000 Energy Consumption 1.32 Quadrillion Btu 2-letter ISO code CH 3-letter ISO code CHE Numeric ISO...

  8. Jordan: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Jordan Population 5,611,202 GDP 33,516,000,000 Energy Consumption 0.31 Quadrillion Btu 2-letter ISO code JO 3-letter ISO code JOR Numeric ISO...

  9. Costa Rica: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Costa Rica Population 4,586,353 GDP 52,968,000,000 Energy Consumption 0.20 Quadrillion Btu 2-letter ISO code CR 3-letter ISO code CRI Numeric ISO...

  10. Guatemala: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Guatemala Population 15,806,675 GDP 49,880,000,000 Energy Consumption 0.21 Quadrillion Btu 2-letter ISO code GT 3-letter ISO code GTM Numeric ISO...

  11. Liechtenstein: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Liechtenstein Population 37,132 GDP 5,155,000,000 Energy Consumption Quadrillion Btu 2-letter ISO code LI 3-letter ISO code LIE Numeric ISO code...

  12. Gabon: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Gabon Population 1,475,000 GDP 20,664,000,000 Energy Consumption 0.05 Quadrillion Btu 2-letter ISO code GA 3-letter ISO code GAB Numeric ISO...

  13. Niger: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Niger Population 17,138,707 GDP 6,022,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code NE 3-letter ISO code NER Numeric ISO...

  14. Singapore: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    ","visitedicon":"" Country Profile Name Singapore Population 5,469,700 GDP 298 Energy Consumption 2.38 Quadrillion Btu 2-letter ISO code SG 3-letter ISO code SGP Numeric ISO...

  15. Cameroon: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Cameroon Population 17,463,836 GDP 30,000,000,000 Energy Consumption 0.10 Quadrillion Btu 2-letter ISO code CM 3-letter ISO code CMR Numeric ISO...

  16. Honduras: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Honduras Population 7,529,403 GDP 19,567,000,000 Energy Consumption 0.13 Quadrillion Btu 2-letter ISO code HN 3-letter ISO code HND Numeric ISO...

  17. Federated States of Micronesia: Energy Resources | Open Energy...

    Open Energy Info (EERE)

    Profile Name Federated States of Micronesia Population 106,104 GDP 277,000,000 Energy Consumption Quadrillion Btu 2-letter ISO code FM 3-letter ISO code FSM Numeric ISO code...

  18. Pakistan: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Pakistan Population 196,174,380 GDP Unavailable Energy Consumption 2.48 Quadrillion Btu 2-letter ISO code PK 3-letter ISO code PAK Numeric ISO...

  19. Moldova: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Moldova Population Unavailable GDP 8,738,000,000 Energy Consumption 0.14 Quadrillion Btu 2-letter ISO code MD 3-letter ISO code MDA Numeric ISO...

  20. Jamaica: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Jamaica Population 2,889,187 GDP 15,569,000,000 Energy Consumption 0.17 Quadrillion Btu 2-letter ISO code JM 3-letter ISO code JAM Numeric ISO...

  1. Hungary: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Hungary Population 9,937,628 GDP 145,153,000,000 Energy Consumption 1.11 Quadrillion Btu 2-letter ISO code HU 3-letter ISO code HUN Numeric ISO...

  2. Paraguay: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Paraguay Population 6,800,284 GDP 30,558,000,000 Energy Consumption 0.44 Quadrillion Btu 2-letter ISO code PY 3-letter ISO code PRY Numeric ISO...

  3. Algeria: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Algeria Population 37,900,000 GDP 227,802,000,000 Energy Consumption 1.71 Quadrillion Btu 2-letter ISO code DZ 3-letter ISO code DZA Numeric ISO...

  4. Bangladesh: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Bangladesh Population 156,594,962 GDP Unavailable Energy Consumption 0.87 Quadrillion Btu 2-letter ISO code BD 3-letter ISO code BGD Numeric ISO...

  5. Nigeria: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Nigeria Population 140,431,790 GDP 594,257,000,000 Energy Consumption 1.09 Quadrillion Btu 2-letter ISO code NG 3-letter ISO code NGA Numeric ISO...

  6. Chad: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Chad Population 6,279,921 GDP 15,986,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code TD 3-letter ISO code TCD Numeric ISO...

  7. Eritrea: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Eritrea Population 6,380,803 GDP 3,881,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code ER 3-letter ISO code ERI Numeric ISO...

  8. Bolivia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Bolivia Population 10,556,102 GDP 29,802 Energy Consumption 0.25 Quadrillion Btu 2-letter ISO code BO 3-letter ISO code BOL Numeric ISO...

  9. Andorra: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Andorra Population 85,458 GDP 4,510,000,000 Energy Consumption Quadrillion Btu 2-letter ISO code AD 3-letter ISO code AND Numeric ISO code...

  10. Liberia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Liberia Population 3,476,608 GDP 1,735,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code LR 3-letter ISO code LBR Numeric ISO...

  11. Bahamas: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name The Bahamas Population 254,685 GDP 8,043,000,000 Energy Consumption Quadrillion Btu 2-letter ISO code BS 3-letter ISO code BHS Numeric ISO code...

  12. Ivory Coast: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Ivory Coast Population 15,366,672 GDP 32,000,000,000 Energy Consumption Quadrillion Btu 2-letter ISO code CI 3-letter ISO code CIV Numeric ISO code...

  13. Mauritania: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Mauritania Population 3,537,368 GDP 4,547,000,000 Energy Consumption 0.04 Quadrillion Btu 2-letter ISO code MR 3-letter ISO code MRT Numeric ISO...

  14. Dominican Republic: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Dominican Republic Population 9,378,818 GDP 62,484,000,000 Energy Consumption 0.30 Quadrillion Btu 2-letter ISO code DO 3-letter ISO code DOM Numeric ISO...

  15. Bahrain: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Bahrain Population 1,234,571 GDP Unavailable Energy Consumption 0.55 Quadrillion Btu 2-letter ISO code BH 3-letter ISO code BHR Numeric ISO...

  16. Laos: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    bel":"","visitedicon":"" Country Profile Name Laos Population 4,574,848 GDP 11 Energy Consumption 0.04 Quadrillion Btu 2-letter ISO code LA 3-letter ISO code LAO Numeric ISO...

  17. Qatar: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Qatar Population 1,699,435 GDP 213,784,000,000 Energy Consumption 1.00 Quadrillion Btu 2-letter ISO code QA 3-letter ISO code QAT Numeric ISO...

  18. Lesotho: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Lesotho Population 2,031,348 GDP 2,616,000,000 Energy Consumption 0.01 Quadrillion Btu 2-letter ISO code LS 3-letter ISO code LSO Numeric ISO...

  19. Sweden: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Sweden Population 9,658,301 GDP Unavailable Energy Consumption 2.22 Quadrillion Btu 2-letter ISO code SE 3-letter ISO code SWE Numeric ISO...

  20. Vanuatu: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Vanuatu Population 243,304 GDP 743,000,000 Energy Consumption 0.00 Quadrillion Btu 2-letter ISO code VU 3-letter ISO code VUT Numeric ISO...

  1. Israel: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Energy Consumption 0.86 Quadrillion Btu 2-letter ISO code IL 3-letter ISO code ISR Numeric ISO code 376 UN Region1 Western Asia OpenEI Resources Energy Maps 0 Tools 2...

  2. Afghanistan: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    nlineLabel":"","visitedicon":"" Country Profile Name Afghanistan Population 15,500,000 GDP 21,747,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code AF 3-letter...

  3. Cape Verde: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    0.00 Quadrillion Btu 2-letter ISO code CV 3-letter ISO code CPV Numeric ISO code 132 UN Region1 Western Africa OpenEI Resources Energy Maps 0 Tools 0 Programs 4 view...

  4. U.S. Energy Information Administration (EIA) - Pub

    Gasoline and Diesel Fuel Update (EIA)

    ... not include EPA's proposed Clean Power Plan, which if it is ... period, as steel production becomes more energy efficient. ... consumed as liquids), nuclear (9.8 quadrillion Btu in ...

  5. Azerbaijan: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Azerbaijan Population 9,494,600 GDP 73,537,000,000 Energy Consumption 0.68 Quadrillion Btu 2-letter ISO code AZ 3-letter ISO code AZE Numeric ISO...

  6. Mongolia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Mongolia Population 3,000,000 GDP 11,516,000,000 Energy Consumption 0.09 Quadrillion Btu 2-letter ISO code MN 3-letter ISO code MNG Numeric ISO...

  7. Sierra Leone: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Country Profile Name Sierra Leone Population 6,190,280 GDP 3,777,000,000 Energy Consumption 0.02 Quadrillion Btu 2-letter ISO code SL 3-letter ISO code SLE Numeric ISO...

  8. Residential | Open Energy Information

    Open Energy Info (EERE)

    used 19.6 quadrillion Btu of delivered energy, or 21 percent of total U.S. energy consumption. The residential sector accounted for 57 percent of that energy use and the...

  9. Commercial | Open Energy Information

    Open Energy Info (EERE)

    used 19.6 quadrillion Btu of delivered energy, or 21 percent of total U.S. energy consumption. The residential sector accounted for 57 percent of that energy use and the...

  10. Transportation | Open Energy Information

    Open Energy Info (EERE)

    Data From AEO2011 report . Market Trends From 2009 to 2035, transportation sector energy consumption grows at an average annual rate of 0.6 percent (from 27.2 quadrillion Btu...

  11. Buildings Energy Data Book [EERE]

    1 FY 2007 Federal Primary Energy Consumption (Quadrillion Btu) Buildings and Facilities 0.88 Vehicles/Equipment 0.69 (mostly jet fuel and diesel) Total Federal Government Consumption 1.57

  12. 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.

  13. Annual Energy Outlook 2015 - Appendix A

    Gasoline and Diesel Fuel Update (EIA)

    A-3 U.S. Energy Information Administration | Annual Energy Outlook 2015 Reference case Table A2. Energy consumption by sector and source (quadrillion Btu per year, unless otherwise noted) Energy Information Administration / Annual Energy Outlook 2015 Table A2. Energy consumption by sector and source (quadrillion Btu per year, unless otherwise noted) Sector and source Reference case Annual growth 2013-2040 (percent) 2012 2013 2020 2025 2030 2035 2040 Energy consumption Residential Propane

  14. Word Pro - S2.lwp

    Gasoline and Diesel Fuel Update (EIA)

    Primary Energy Consumption by Source and Sector, 2012 (Quadrillion Btu) 1 Does not include biofuels that have been blended with petroleum-biofuels are included in "Renewable Energy." 2 Excludes supplemental gaseous fuels. 3 Includes less than 0.1 quadrillion Btu of coal coke net imports. 4 Conventional hydroelectric power, geothermal, solar/photovoltaic, wind, and biomass. 5 Includes industrial combined-heat-and-power (CHP) and industrial electricity-only plants. 6 Includes commercial

  15. Word Pro - Untitled1

    Gasoline and Diesel Fuel Update (EIA)

    F1. Primary Energy Consumption and Delivered Total Energy, 2010 (Quadrillion Btu) U.S. Energy Information Administration / Annual Energy Review 2011 347 Primary Energy Consumption by Source 1 Delivered Total Energy by Sector 8 1 Includes electricity net imports, not shown separately. 2 Does not include biofuels that have been blended with petroleum-biofuels are included in "Renewable Energy." 3 Excludes supplemental gaseous fuels. 4 Includes less than 0.1 quadrillion Btu of coal coke

  16. Word Pro - Untitled1

    Gasoline and Diesel Fuel Update (EIA)

    . Energy Consumption by Sector THIS PAGE INTENTIONALLY LEFT BLANK Figure 2.0 Primary Energy Consumption by Source and Sector, 2011 (Quadrillion Btu) U.S. Energy Information Administration / Annual Energy Review 2011 37 1 Does not include biofuels that have been blended with petroleum-biofuels are included in "Renewable Energy." 2 Excludes supplemental gaseous fuels. 3 Includes less than 0.1 quadrillion Btu of coal coke net imports. 4 Conventional hydroelectric power, geothermal,

  17. 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.

  18. 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.

  19. 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 ...

  20. 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...

  1. 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...

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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)

  8. 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...

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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.

  14. Table 1.13 U.S. Government Energy Consumption by Agency and Source, Fiscal Years 2003, 2010, and 2011 (Trillion Btu)

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

    3 U.S. Government Energy Consumption by Agency and Source, Fiscal Years 2003, 2010, and 2011 (Trillion Btu) Resource and Fiscal Years Agriculture Defense Energy GSA 1 HHS 2 Interior Justice NASA 3 Postal Service Trans- portation Veterans Affairs Other 4 Total Coal 2003 (s) 15.4 2.0 0.0 (s) (s) 0.0 0.0 0.0 0.0 0.2 0.0 17.7 2010 (s) 15.5 4.5 .0 0.0 0.0 .0 .0 (s) .0 .1 .0 20.1 2011 P 0.0 14.3 4.2 .0 .0 .0 .0 .0 (s) .0 .1 .0 18.6 Natural Gas 5 2003 1.4 76.6 7.0 7.6 3.7 1.3 8.6 2.9 10.4 .7 15.6 4.2

  15. Table 3.4 Consumer Price Estimates for Energy by End-Use Sector, 1970-2010 (Dollars per Million Btu)

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

    Consumer Price Estimates for Energy by End-Use Sector, 1970-2010 (Dollars 1 per Million Btu) Year Residential Commercial Industrial Transportation Natural Gas 2 Petroleum Retail Electricity 3 Total 4 Natural Gas 2 Petroleum 5 Retail Electricity 3 Total 6,7 Coal Natural Gas 2 Petroleum 5 Biomass 8 Retail Electricity 3 Total 7,9 Petroleum 5 Total 7,10 1970 1.06 1.54 6.51 2.10 0.75 0.90 [R] 6.09 1.97 0.45 0.38 0.98 1.59 2.99 0.84 2.31 2.31 1971 1.12 1.59 6.80 2.24 .80 1.02 6.44 2.15 .50 .41 1.05

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. Trends in energy use in commercial buildings -- Sixteen years of EIA's commercial buildings energy consumption survey

    SciTech Connect (OSTI)

    Davis, J.; Swenson, A.

    1998-07-01

    The Commercial Buildings Energy Consumption Survey (CBECS) collects basic statistical information on energy consumption and energy-related characteristics of commercial buildings in the US. The first CBECS was conducted in 1979 and the most recent was completed in 1995. Over that period, the number of commercial bindings and total amount of floorspace increased, total consumption remained flat, and total energy intensity declined. By 1995, there were 4.6 million commercial buildings and 58.8 billion square feet of floorspace. The buildings consumed a total of 5.3 quadrillion Btu (site energy), with a total intensity of 90.5 thousand Btu per square foot per year. Electricity consumption exceeded natural gas consumption (2.6 quadrillion and 1.9 quadrillion Btu, respectively). In 1995, the two major users of energy were space heating (1.7 quadrillion Btu) and lighting (1.2 quadrillion Btu). Over the period 1979 to 1995, natural gas intensity declined from 71.4 thousand to 51.0 thousand Btu per square foot per year. Electricity intensity did not show a similar decline (44.2 thousand Btu per square foot in 1979 and 45.7 thousand Btu per square foot in 1995). Two types of commercial buildings, office buildings and mercantile and service buildings, were the largest consumers of energy in 1995 (2.0 quadrillion Btu, 38% of total consumption). Three building types, health care, food service, and food sales, had significantly higher energy intensities. Buildings constructed since 1970 accounted for half of total consumption and a majority (59%) of total electricity consumption.

  4. 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.

  5. 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

  6. 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)

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. 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 " " % % &...

  17. 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.

  18. 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.

  19. 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

  20. Monthly energy review, July 1990

    SciTech Connect (OSTI)

    Not Available

    1990-10-29

    US total energy consumption in July 1990 was 6.7 quadrillion Btu Petroleum products accounted for 42 percent of the energy consumed in July 1990, while coal accounted for 26 percent and natural gas accounted for 19 percent. Residential and commercial sector consumption was 2.3 quadrillion Btu in July 1990, up 2 percent from the July 1989 level. The sector accounted for 35 percent of July 1990 total consumption, about the same share as in July 1989. Industrial sector consumption was 2.4 quadrillion Btu in July 1990, up 2 percent from the July 1989 level. The industrial sector accounted for 36 percent of July 1990 total consumption, about the same share as in July 1989. Transportation sector consumption of energy was 1.9 quadrillion Btu in July 1990, up 1 percent from the July 1989 level. The sector consumed 29 percent of July 1990 total consumption, about the same share as in July 1989. Electric utility consumption of energy totaled 2.8 quadrillion Btu in July 1990, up 2 percent from the July 1989 level. Coal contributed 53 percent of the energy consumed by electric utilities in July 1990, while nuclear electric power contributed 21 percent; natural gas, 12 percent; hydroelectric power, 9 percent; petroleum, 5 percent; and wood, waste, geothermal, wind, photovoltaic, and solar thermal energy, about 1 percent.

  1. 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.

  2. 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...

  3. 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...

  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. BTU LLC | Open Energy Information

    Open Energy Info (EERE)

    Small start-up with breakthrough technology seeking funding to prove commercial feasibility Coordinates: 45.425788, -122.765754 Show Map Loading map......

  6. 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.

  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. 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....

  9. 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.

  10. 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,...

  11. 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.

  12. 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.

  13. 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 ...

  14. Improving the Accuracy of Solar Forecasting Funding Opportunity

    Broader source: Energy.gov [DOE]

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

  15. 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...

  16. 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 ...

  17. 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

  18. 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

  19. 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

  20. 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,

  1. 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

  2. Monthly energy review, May 1994

    SciTech Connect (OSTI)

    Not Available

    1994-05-25

    Energy production during February 1994 totaled 5.3 quadrillion Btu, a 2.2% increase over February 1993. Coal production increased 9%, natural gas rose 2.5%, and petroleum decreased 3.6%; all other forms of energy production combined were down 3%. Energy consumption during the same period totaled 7.5 quadrillion Btu, 4.1% above February 1993. Natural gas consumption increased 5.8%, petroleum 5.2%, and coal 2.3%; consumption of all other energy forms combined decreased 0.7%. Net imports of energy totaled 1.4 quadrillion Btu, 16.9% above February 1993; petroleum net imports increased 10.1%, natural gas net imports were down 4.9%, and coal net exports fell 43.7%. This document is divided into: energy overview, energy consumption, petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, international energy, appendices (conversion factors, etc.), and glossary.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. U.S. Energy Information Administration (EIA) - Pub

    Gasoline and Diesel Fuel Update (EIA)

    Delivered energy consumption by sector Transportation Energy consumption in the transportation sector declines in the AEO2015 Reference case from 27.0 quadrillion Btu (13.8 million bbl/d) in 2013 to 26.4 quadrillion Btu (13.5 million bbl/d) in 2040. Energy consumption falls most rapidly through 2030, primarily as a result of improvement in light-duty vehicle (LDV) fuel economy with the implementation of corporate average fuel economy (CAFE) standards and greenhouse gas emissions (GHG) standards

  8. 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.

  9. 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).

  10. 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).

  11. 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.

  12. 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...

  13. 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.

  14. 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 ...

  15. Table 8.3a Useful Thermal Output at Combined-Heat-and-Power Plants: Total (All Sectors), 1989-2011 (Sum of Tables 8.3b and 8.3c; Billion Btu)

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

    a Useful Thermal Output at Combined-Heat-and-Power Plants: Total (All Sectors), 1989-2011 (Sum of Tables 8.3b and 8.3c; Billion Btu) Year Fossil Fuels Renewable Energy Other 7 Total Coal 1 Petroleum 2 Natural Gas 3 Other Gases 4 Total Biomass Total Wood 5 Waste 6 1989 323,191 95,675 461,905 92,556 973,327 546,354 30,217 576,571 39,041 1,588,939 1990 362,524 127,183 538,063 140,695 1,168,465 650,572 36,433 687,005 40,149 1,895,619 1991 351,834 112,144 546,755 148,216 1,158,949 623,442 36,649

  16. Table 8.3c Useful Thermal Output at Combined-Heat-and-Power Plants: Commercial and Industrial Sectors, 1989-2011 (Subset of Table 8.3a; Billion Btu)

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

    c Useful Thermal Output at Combined-Heat-and-Power Plants: Commercial and Industrial Sectors, 1989-2011 (Subset of Table 8.3a; Billion Btu) Year Fossil Fuels Renewable Energy Other 7 Total Coal 1 Petroleum 2 Natural Gas 3 Other Gases 4 Total Biomass Total Wood 5 Waste 6 Commercial Sector 8<//td> 1989 13,517 3,896 9,920 102 27,435 145 10,305 10,450 – 37,885 1990 14,670 5,406 15,515 118 35,709 387 10,193 10,580 – 46,289 1991 15,967 3,684 20,809 118 40,578 169 8,980 9,149 1 49,728 1992

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. Word Pro - Untitled1

    Gasoline and Diesel Fuel Update (EIA)

    Table 1.5 Energy Consumption, Expenditures, and Emissions Indicators Estimates, Selected Years, 1949-2011 Year Energy Consumption Energy Consumption per Capita Energy Expenditures 1 Energy Expenditures 1 per Capita Gross Output 3 Energy Expenditures 1 as Share of Gross Output 3 Gross Domestic Product (GDP) Energy Expenditures 1 as Share of GDP Gross Domestic Product (GDP) Energy Consumption per Real Dollar of GDP Carbon Dioxide Emissions 2 per Real Dollar of GDP Quadrillion Btu Million Btu

  6. Buildings Energy Data Book: 1.5 Generic Fuel Quad and Comparison

    Buildings Energy Data Book [EERE]

    1 Key Definitions Quad: Quadrillion Btu (10^15 or 1,000,000,000,000,000 Btu) Generic Quad for the Buildings Sector: One quad of primary energy consumed in the buildings sector (includes the residential and commercial sectors), apportioned between the various primary fuels used in the sector according to their relative consumption in a given year. To obtain this value, electricity is converted into its primary energy forms according to relative fuel contributions (or shares) used to produce

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 2013 Renewable Energy Data Book (Book), NREL (National Renewable...

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

    figures in later sections. 10.1% Nuclear 11.2% Renewables 24.7% Coal 34.6% Natural Gas 19.3% Crude Oil U.S. Energy Production (2013): 81.8 Quadrillion Btu U.S. Renewable...

  13. 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.

  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. 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.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. Catalytic reactor for low-Btu fuels

    DOE Patents [OSTI]

    Smith, Lance (North Haven, CT); Etemad, Shahrokh (Trumbull, CT); Karim, Hasan (Simpsonville, SC); Pfefferle, William C. (Madison, CT)

    2009-04-21

    An improved catalytic reactor includes a housing having a plate positioned therein defining a first zone and a second zone, and a plurality of conduits fabricated from a heat conducting material and adapted for conducting a fluid therethrough. The conduits are positioned within the housing such that the conduit exterior surfaces and the housing interior surface within the second zone define a first flow path while the conduit interior surfaces define a second flow path through the second zone and not in fluid communication with the first flow path. The conduit exits define a second flow path exit, the conduit exits and the first flow path exit being proximately located and interspersed. The conduits define at least one expanded section that contacts adjacent conduits thereby spacing the conduits within the second zone and forming first flow path exit flow orifices having an aggregate exit area greater than a defined percent of the housing exit plane area. Lastly, at least a portion of the first flow path defines a catalytically active surface.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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 ...

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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....

  2. 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 ...

  3. 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...

  4. 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%.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.)

  10. 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.)

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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 Southern Study Area, Final Report

    SciTech Connect (OSTI)

    Freedman, Jeffrey M.; Manobianco, John; Schroeder, John; Ancell, Brian; Brewster, Keith; Basu, Sukanta; Banunarayanan, Venkat; Hodge, Bri-Mathias; Flores, Isabel

    2014-04-30

    This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10 - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.

  16. 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.

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

    SciTech Connect (OSTI)

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04

    On December 14, 2009, the reference-case projections from Annual Energy Outlook 2010 were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in itigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings.

  18. Gasoline price forecast to stay below 3 dollar a gallon in 2015

    Gasoline and Diesel Fuel Update (EIA)

    Gasoline price forecast to stay below $3 a gallon in 2015 The national average pump price of gasoline is expected to stay below $3 per gallon during 2015. In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular gasoline should average $2.33 per gallon this year. The price of gasoline increased in early February after falling for 17 weeks in a row. But gasoline prices will continue to remain low in 2015 when compared with pump prices in recent

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

    Office of Environmental Management (EM)

    Improved by 30% | Department of Energy 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 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

  20. Annual Energy Review, 1995

    SciTech Connect (OSTI)

    1996-07-01

    This document presents statistics on energy useage for 1995. A reviving domestic economy, generally low energy prices, a heat wave in July and August, and unusually cold weather in November and December all contributed to the fourth consecutive year of growth in U.S. total energy consumption, which rose to an all-time high of almost 91 quadrillion Btu in 1995 (1.3). The increase came as a result of increases in the consumption of natural gas, coal, nuclear electric power, and renewable energy. Petroleum was the primary exception, and its use declined by only 0.3 percent. (Integrating the amount of renewable energy consumed outside the electric utility sector into U.S. total energy consumption boosted the total by about 3.4 quadrillion Btu, but even without that integration, U.S. total energy consumption would have reached a record level in 1995.)

  1. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2003 THRU FY2046 VERSION 2003.1 VOLUME 2 [SEC 1 & 2

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2003-12-01

    This report includes data requested on September 10, 2002 and includes radioactive solid waste forecasting updates through December 31, 2002. The FY2003.0 request is the primary forecast for fiscal year FY 2003.

  2. Word Pro - A

    Gasoline and Diesel Fuel Update (EIA)

    7 Appendix D Table D1. Estimated Primary Energy Consumption in the United States, Selected Years, 1635-1945 (Quadrillion Btu) Fossil Fuels Renewable Energy Electricity Net Imports b Total Coal Natural Gas Petroleum Total Conventional Hydroelectric Power Biomass Total Wood a 1635 .............. NA - - - - NA - - (s) (s) - - (s) 1645 .............. NA - - - - NA - - 0.001 0.001 - - 0.001 1655 .............. NA - - - - NA - - .002 .002 - - .002 1665 .............. NA - - - - NA - - .005 .005 - -

  3. Hydrogen & Fuel Cells - Program Overview

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

    Program Manager 2012 Annual Merit Review and Peer Evaluation Meeting May 14, 2012 Petroleum 37% Natural Gas 25% Coal 21% Nuclear Energy 9% Renewable Energy 8% Transportation Residential & Commercial Industrial Electric Power 2 U.S. Energy Consumption Total U.S. Energy = 98 Quadrillion Btu/yr Source: Energy Information Administration, Annual Energy Review 2010, Table 1.3 U.S. Primary Energy Consumption by Source and Sector Residential 16% Commercial 13% Industrial 22% Transportation 20%

  4. Monthly energy review, May 1995

    SciTech Connect (OSTI)

    1995-05-24

    Energy production during Feb 95 totaled 5.4 quadrillion Btu (Q), 3.1% over Feb 94. Energy consumption totaled 7.4 Q, 0.7% below Feb 94. Net imports of energy totaled 1.3 Q, 5.6% below Feb 94. This publication is divided into energy overview, energy consumption, petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, and international energy.

  5. Monthly energy review, July 1995

    SciTech Connect (OSTI)

    1995-07-24

    Energy production during April 1995 totaled 5.5 quadrillion Btu, a 1.0-percent decrease from the level of production during April 1994. Coal production decreased 7.7 percent, natural gas increased 1.3 percent, and production of crude oil and natural gas plant liquids increased 0.3 percent. All other forms of energy production combined were up 8.6 percent from the level of production during April 1994.

  6. Slide 1

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

    Energy in the Transportation and Power Sectors April 7 th , 2009 Energy Information Administration 2009 Energy Conference: A New Climate for Energy Energy Information Administration 0 20 40 60 80 100 120 1980 1990 2000 2010 2020 2030 Nuclear Natural Gas Liquid Fuels Coal Renewables (excl liquid biofuels) Renewable energy to contribute a growing share of supply History Projections Liquid Biofuels quadrillion Btu Source: EIA Annual Energy Outlook 2009 Reference Case Renewable Energy in The

  7. Federal Comprehensive Annual Energy Performance Data | Department of Energy

    Office of Environmental Management (EM)

    Reporting & Data » Facilities » Federal Comprehensive Annual Energy Performance Data Federal Comprehensive Annual Energy Performance Data In fiscal year (FY) 2014, federal agencies reported energy use to the Federal Energy Management Program (FEMP) that totals 0.94 quadrillion British thermal units (Btu) or "quads" of delivered energy across the three energy sectors: Buildings that are subject to statutory energy-reduction requirements Buildings that are excluded from the

  8. Outdoor Lighting | Department of Energy

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

    Outdoor Lighting Outdoor Lighting Outdoor lighting consumes a significant amount of energy-about 1.3 quadrillion Btu annually-costing about $10 billion per year. In the last five years, a number of municipalities have switched to new LED technologies which can reduce energy costs by approximately 50% over conventional lighting technologies and provide additional savings of 20 to 40% with advance lighting controls. Beyond cost and energy savings, the higher efficacy of LED lights provides other

  9. Tips: Heating and Cooling | Department of Energy

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

    Heating and Cooling Tips: Heating and Cooling Household Heating Systems: Although several different types of fuels are available to heat our homes, nearly half of us use natural gas. | Source: Buildings Energy Data Book 2011, 2.1.1 Residential Primary Energy Consumption, by Year and Fuel Type (Quadrillion Btu and Percent of Total). Household Heating Systems: Although several different types of fuels are available to heat our homes, nearly half of us use natural gas. | Source: Buildings Energy

  10. Buildings Energy Data Book: 4.1 Federal Buildings Energy Consumption

    Buildings Energy Data Book [EERE]

    1 FY 2007 Federal Primary Energy Consumption (Quadrillion Btu) Buildings and Facilities 0.88 Vehicles/Equipment 0.69 (mostly jet fuel and diesel) Total Federal Government Consumption 1.57 Source(s): DOE/FEMP, Annual Report to Congress on FEMP FY 2007, Jan. 2010, Table A-1, p. 90 for total consumption and Table A-7, p. 95 for vehicle and equipment operations

  11. Word Pro - S2.lwp

    Gasoline and Diesel Fuel Update (EIA)

    Manufacturing Energy Consumption for Heat, Power, and Electricity Generation, 2006 By Selected End Use¹ By Energy Source 48 U.S. Energy Information Administration / Annual Energy Review 2011 1 Excludes inputs of unallocated energy sources (5,820 trillion Btu). 2 Heating, ventilation, and air conditioning. Excludes steam and hot water. 3 Excludes coal coke and breeze. 4 Liquefied petroleum gases. 5 Natural gas liquids. (s)=Less than 0.05 quadrillion Btu. Source: Table 2.3. 3.3 1.7 0.7 0.2 0.2

  12. Enhanced Short-Term Wind Power Forecasting and Value to Grid Operations: Preprint

    SciTech Connect (OSTI)

    Orwig, K.; Clark, C.; Cline, J.; Benjamin, S.; Wilczak, J.; Marquis, M.; Finley, C.; Stern, A.; Freedman, J.

    2012-09-01

    The current state of the art of wind power forecasting in the 0- to 6-hour time frame has levels of uncertainty that are adding increased costs and risk on the U.S. electrical grid. It is widely recognized within the electrical grid community that improvements to these forecasts could greatly reduce the costs and risks associated with integrating higher penetrations of wind energy. The U.S. Department of Energy has sponsored a research campaign in partnership with the National Oceanic and Atmospheric Administration (NOAA) and private industry to foster improvements in wind power forecasting. The research campaign involves a three-pronged approach: 1) a 1-year field measurement campaign within two regions; 2) enhancement of NOAA's experimental 3-km High-Resolution Rapid Refresh (HRRR) model by assimilating the data from the field campaign; and 3) evaluation of the economic and reliability benefits of improved forecasts to grid operators. This paper and presentation provides an overview of the regions selected, instrumentation deployed, data quality and control, assimilation of data into HRRR, and preliminary results of HRRR performance analysis.

  13. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

    SciTech Connect (OSTI)

    Brown, C. W.; Hood, Raleigh R.; Long, Wen; Jacobs, John M.; Ramers, D. L.; Wazniak, C.; Wiggert, J. D.; Wood, R.; Xu, J.

    2013-09-01

    The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanisticempirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.

  14. Monthly energy review, July 1994

    SciTech Connect (OSTI)

    Not Available

    1994-07-26

    Energy production during April 1994 totaled 5.5 quadrillion Btu, a 2.2-percent increase from the level of production during April 1993. Coal production increased 11.8 percent, petroleum production fell 4.0 percent, and natural gas production decreased 0.3 percent. All other forms of energy production combined were down 2.9 percent from the level of production during April 1993. Energy consumption during April 1994 totaled 6.7 quadrillion Btu, 1.4 percent above the level of consumption during April 1993. Petroleum consumption increased 3.9 percent, coal consumption rose 1.1 percent, and natural gas consumption decreased 1.5 percent. Consumption of all other forms of energy combined decreased 0.4 percent from the level 1 year earlier. Net imports of energy during April 1994 totaled 1.5 quadrillion Btu, 8.7 percent above the level of net imports 1 year earlier. Net imports of petroleum increased 4.5 percent, and net imports of natural gas were up 18.5 percent. Net exports of coal fell 9.2 percent from the level in April 1993.

  15. Monthly energy review, May 1997

    SciTech Connect (OSTI)

    1997-05-01

    This is an overview of the May energy statistics by the Energy Information Administration. The contents of the report include an energy overview, US energy production, trade stocks and prices for petroleum, natural gas, oil and gas resource development, coal, electricity, nuclear energy, energy prices, and international energy. Energy production during February 1997 totaled 5.4 quadrillion Btu, a 1.9% decrease from the level of production during February 1996. Coal production increased 1.2%, natural gas production decreased 2.9%, and production of crude oil and natural gas plant liquids decreased 2.1%. All other forms of energy production combined were down 6.3% from the level of production during February 1996. Energy consumption during February 1997 totaled 7.5 quadrillion Btu, 4.0% below the level of consumption during February 1996. Consumption of petroleum products decreased 4.4%, consumption of natural gas was down 3.5%, and consumption of coal fell 2.2%. Consumption of all other forms of energy combined decreased 6.7% from the level 1 year earlier. Net imports of energy during February 1997 totaled 1.5 quadrillion Btu, 14.1% above the level of net imports 1 year earlier. Net imports of petroleum increased 12.7% and net imports of natural gas were up 7.4%. Net exports of coal fell 12.1% from the level in February 1996. 37 figs., 75 tabs.

  16. Monthly energy review, August 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-29

    Energy production during May 1994 totaled 5.6 quadrillion Btu, a 2.4-percent increase from the level of production during May 1993. Coal production increased 13.3 percent, natural gas production rose 1.7 percent, and petroleum production decreased 2.5 percent. All other forms of energy production combined were down 8.3 percent from the level of production during May 1993. Energy consumption during May 1994 totaled 6.6 quadrillion Btu, 3.6 percent above the level of consumption during May 1993. Natural gas consumption increased 8.7 percent, coal consumption rose 4.6 percent, and petroleum consumption was up 3.6 percent. Consumption of all other forms of energy combined decreased 5.8 percent from the level 1 year earlier. Net imports of energy during May 1994 totaled 1.5 quadrillion Btu, 14.3 percent above the level of net imports 1 year earlier. Net imports of petroleum increased 8.4 percent, and net imports of natural gas were up 23.2 percent. Net exports of coal fell 16.8 percent from the level in May 1993.

  17. Monthly energy review, June 1994

    SciTech Connect (OSTI)

    Not Available

    1994-06-01

    Energy production during March 1994 totaled 5.9 quadrillion Btu, a 3.7-percent increase from the level of production during March 1993. Coal production increased 15.7 percent, petroleum production fell 4.1 percent, and natural gas production decreased 1.1 percent. All other forms of energy production combined were up 0.5 percent from the level of production during March 1993. Energy consumption during March 1994 totaled 7.5 quadrillion Btu, 1.3 percent below the level of consumption during March 1993. Natural gas consumption decreased 3.6 percent, petroleum consumption fell 1.6 percent, and coal consumption remained the same. Consumption of all other forms of energy combined increased 3.7 percent from the level 1 year earlier. Net imports of energy during March 1994 totaled 1.5 quadrillion Btu, 6.7 percent above the level of net imports 1 year earlier. Net imports of petroleum increased 3.2 percent, and net imports of natural gas were up 15.7 percent. Net exports of coal rose 2.1 percent from the level in March 1993.

  18. Monthly Energy Review, February 1998

    SciTech Connect (OSTI)

    1998-02-01

    This report presents an overview of recent monthly energy statistics. Energy production during November 1997 totaled 5.6 quadrillion Btu, a 0.3-percent decrease from the level of production during November 1996. Natural gas production increased 2.8 percent, production of crude oil and natural gas plant liquids decreased 1.7 percent, and coal production decreased 1.6 percent. All other forms of energy production combined were down 1.1 percent from the level of production during November 1996. Energy consumption during November 1997 totaled 7.5 quadrillion Btu, 0.1 percent above the level of consumption during November 1996. Consumption of natural gas increased 1.5 percent, consumption of coal fell 0.3 percent, while consumption of petroleum products decreased 0.2 percent. Consumption of all other forms of energy combined decreased 0.8 percent from the level 1 year earlier. Net imports of energy during November 1997 totaled 1.7 quadrillion Btu, 8.6 percent above the level of net imports 1 year earlier. Net imports of petroleum increased 6.3 percent, and net imports of natural gas were up 1.2 percent. Net exports of coal fell 17.8 percent from the level in November 1996.

  19. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect (OSTI)

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  20. Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

    SciTech Connect (OSTI)

    Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are higher. In organized electricity markets, units that are committed for reliability reasons are paid their offer price even when prevailing market prices are lower. Often, these uplift charges are allocated to market participants that caused the inefficient dispatch in the first place. Thus, wind energy facilities are burdened with their share of costs proportional to their forecast errors. For Xcel Energy, wind energy uncertainty costs manifest depending on specific market structures. In the Public Service of Colorado (PSCo), inefficient commitment and dispatch caused by wind uncertainty increases fuel costs. Wind resources participating in the Midwest Independent System Operator (MISO) footprint make substantial payments in the real-time markets to true-up their day-ahead positions and are additionally burdened with deviation charges called a Revenue Sufficiency Guarantee (RSG) to cover out of market costs associated with operations. Southwest Public Service (SPS) wind plants cause both commitment inefficiencies and are charged Southwest Power Pool (SPP) imbalance payments due to wind uncertainty and variability. Wind energy forecasting helps mitigate these costs. Wind integration studies for the PSCo and Northern States Power (NSP) operating companies have projected increasing costs as more wind is installed on the system due to forecast error. It follows that reducing forecast error would reduce these costs. This is echoed by large scale studies in neighboring regions and states that have recommended adoption of state-of-the-art wind forecasting tools in day-ahead and real-time planning and operations. Further, Xcel Energy concluded reduction of the normalized mean absolute error by one percent would have reduced costs in 2008 by over $1 million annually in PSCo alone. The value of reducing forecast error prompted Xcel Energy to make substantial investments in wind energy forecasting research and development.

  1. A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast

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

    System A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast System Morcrette, Jean-Jacques European Centre for Medium-Range Weather Forecasts Category: Radiation A new processor for evaluating the UV-B and UV-A radiation at the surface, based on modifications to the current shortwave radiation scheme of the ECMWF forecast system is described. Sensitivity studies of the UV surface irradiance and Erythemal Dose Rate to spectral resolution, representation and atmospheric

  2. Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)

    Reports and Publications (EIA)

    1998-01-01

    The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

  3. EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day

    Gasoline and Diesel Fuel Update (EIA)

    EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day The forecast for U.S. crude oil production keeps going higher. The U.S. Energy Information Administration revised upward its projection for crude oil output in 2013 by 70,000 barrels per day and for next year by 190,000 barrels per day. U.S. oil production is now on track to average 7.5 million barrels per day this year and rise to 8.4 million barrels per day in 2014, according to EIA's latest monthly forecast.

  4. Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2005-12-19

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

  5. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

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

  6. Integration of Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Chakrabarti, Bhujanga B.; Subbarao, Krishnappa; Loutan, Clyde; Guttromson, Ross T.

    2010-04-20

    In this paper, a new approach to evaluate the uncertainty ranges for the required generation performance envelope, including the balancing capacity, ramping capability and ramp duration is presented. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (CAISO) real life data have shown the effectiveness and efficiency of the proposed approach.

  7. 2007 Wholesale Power Rate Case Final Proposal : Market Price Forecast Study.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    2006-07-01

    This study presents BPA's market price forecasts for the Final Proposal, which are based on AURORA modeling. AURORA calculates the variable cost of the marginal resource in a competitively priced energy market. In competitive market pricing, the marginal cost of production is equivalent to the market-clearing price. Market-clearing prices are important factors for informing BPA's power rates. AURORA was used as the primary tool for (a) estimating the forward price for the IOU REP Settlement benefits calculation for fiscal years (FY) 2008 and 2009, (b) estimating the uncertainty surrounding DSI payments and IOU REP Settlements benefits, (c) informing the secondary revenue forecast and (d) providing a price input used for the risk analysis. For information about the calculation of the secondary revenues, uncertainty regarding the IOU REP Settlement benefits and DSI payment uncertainty, and the risk run, see Risk Analysis Study WP-07-FS-BPA-04.

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

    SciTech Connect (OSTI)

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

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

  9. Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

    SciTech Connect (OSTI)

    Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M.

    2009-10-09

    We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

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

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energys 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 nations 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 Energys 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.

  11. Are there Gains from Pooling Real-Time Oil Price Forecasts?

    Gasoline and Diesel Fuel Update (EIA)

    Are there Gains from Pooling Real- Time Oil Price Forecasts? Christiane Baumeister, Bank of Canada Lutz Kilian, University of Michigan Thomas K. Lee, U.S. Energy Information Administration February 12, 2014 Independent Statistics & Analysis www.eia.gov U.S. Energy Information Administration Washington, DC 20585 This paper is released to encourage discussion and critical comment. The analysis and conclusions expressed here are those of the authors and not necessarily those of the U.S. Energy

  12. Validation of Global Weather Forecast and Climate Models Over the North

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

    Slope of Alaska Validation of Global Weather Forecast and Climate Models Over the North Slope of Alaska Xie, Shaocheng Lawrence Livermore National Laboratory Klein, Stephen Lawrence Livermore National Laboratory Boyle, Jim Lawrence Livermore National Laboratory Fiorino, Michael DOE/Lawrence Livermore National Laboratory Hnilo, Justin DOE/Lawrence Livermore National Laboratory Phillips, Thomas PCMDI/LLNL Potter, Gerald Lawrence Livermore National Laboratory Beljaars, Anton ECMWF Category:

  13. Executive Summary: Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts

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

    5060 Sargent & Lundy LLC Consulting Group Chicago, Illinois Executive Summary: Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401-3393 NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute * Battelle * Bechtel Contract No. DE-AC36-99-GO10337 October 2003 * NREL/SR-550-35060 Executive Summary: Assessment of Parabolic Trough and Power Tower

  14. New Forecasting Tools Enhance Wind Energy Integration In Idaho and Oregon

    Office of Environmental Management (EM)

    New Forecasting Tools Enhance Wind Energy Integration in Idaho and Oregon Page 1 Under the American Recovery and Reinvestment Act of 2009, the U.S. Department of Energy and the electricity industry have jointly invested over $7.9 billion in 99 cost-shared Smart Grid Investment Grant projects to modernize the electric grid, strengthen cybersecurity, improve interoperability, and collect an unprecedented level of data on smart grid and customer operations. 1. Summary Idaho Power Company (IPC)

  15. Forecasting the Magnitude of Sustainable Biofeedstock Supplies: the Challenges and the Rewards

    SciTech Connect (OSTI)

    Graham, Robin Lambert

    2007-01-01

    Forecasting the magnitude of sustainable biofeedstock supplies is challenging because of 1) the myriad of potential feedstock types and their management 2) the need to account for the spatial variation of both the supplies and their environmental and economic consequences, and 3) the inherent challenges of optimizing across economic and environmental considerations. Over the last two decades U.S. biomass forecasts have become increasingly complex and sensitive to environmental and economic considerations. More model development and research is needed however, to capture the landscape and regional tradeoffs of differing biofeedstock supplies especially with regards water quality concerns and wildlife/biodiversity. Forecasts need to be done in the context of the direction of change and what the probable land use and attendant environmental and economic outcomes would be if biofeedstocks were not being produced. To evaluate sustainability, process-oriented models need to be coupled or used to inform sector models and more work needs to be done on developing environmental metrics that are useful for evaluating economic and environmental tradeoffs. These challenges are exciting and worthwhile as they will enable the bioenergy industry to capture environmental and social benefits of biofeedstock production and reduce risks.

  16. Why Models Don%3CU%2B2019%3Et Forecast.

    SciTech Connect (OSTI)

    McNamara, Laura A.

    2010-08-01

    The title of this paper, Why Models Don't Forecast, has a deceptively simple answer: models don't forecast because people forecast. Yet this statement has significant implications for computational social modeling and simulation in national security decision making. Specifically, it points to the need for robust approaches to the problem of how people and organizations develop, deploy, and use computational modeling and simulation technologies. In the next twenty or so pages, I argue that the challenge of evaluating computational social modeling and simulation technologies extends far beyond verification and validation, and should include the relationship between a simulation technology and the people and organizations using it. This challenge of evaluation is not just one of usability and usefulness for technologies, but extends to the assessment of how new modeling and simulation technologies shape human and organizational judgment. The robust and systematic evaluation of organizational decision making processes, and the role of computational modeling and simulation technologies therein, is a critical problem for the organizations who promote, fund, develop, and seek to use computational social science tools, methods, and techniques in high-consequence decision making.

  17. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

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

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equationsmore » at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.« less

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

    SciTech Connect (OSTI)

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

    2008-01-24

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

  19. Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

    SciTech Connect (OSTI)

    Woolsey, Lauren N.; Cranmer, Steven R.

    2014-06-01

    Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvn waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

  20. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

    SciTech Connect (OSTI)

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equations at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.

  1. National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

    SciTech Connect (OSTI)

    Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

    1982-03-31

    The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

  2. Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay

    SciTech Connect (OSTI)

    Jacobs, John M.; Rhodes, M.; Brown, C. W.; Hood, Raleigh R.; Leight, A.; Long, Wen; Wood, R.

    2014-11-01

    The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.

  3. Short-Term Energy Outlook Supplement: Uncertainties in the Short-Term Global Petroleum and Other Liquids Supply Forecast

    Gasoline and Diesel Fuel Update (EIA)

    Uncertainties in the Short-Term Global Petroleum and Other Liquids Supply Forecast February 2014 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | STEO Supplement: Uncertainties in the Global Petroleum and Other Liquids Supply Forecast 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,

  4. FY 1996 solid waste integrated life-cycle forecast characteristics summary. Volumes 1 and 2

    SciTech Connect (OSTI)

    Templeton, K.J.

    1996-05-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the physical waste forms, hazardous waste constituents, and radionuclides of the waste expected to be shipped to the CWC from 1996 through the remaining life cycle of the Hanford Site (assumed to extend to 2070). In previous years, forecast data has been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to two previous reports: the more detailed report on waste volumes, WHC-EP-0900, FY1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary and the report on expected containers, WHC-EP-0903, FY1996 Solid Waste Integrated Life-Cycle Forecast Container Summary. All three documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on two main characteristics: the physical waste forms and hazardous waste constituents of low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major generators for each waste category and waste characteristic are also discussed. The characteristics of low-level waste (LLW) are described in Appendix A. In addition, information on radionuclides present in the waste is provided in Appendix B. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste is expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters. The range is primarily due to uncertainties associated with the Tank Waste Remediation System (TWRS) program, including uncertainties regarding retrieval of long-length equipment, scheduling, and tank retrieval technologies.

  5. Use of Data Denial Experiments to Evaluate ESA Forecast Sensitivity Patterns

    SciTech Connect (OSTI)

    Zack, J; Natenberg, E J; Knowe, G V; Manobianco, J; Waight, K; Hanley, D; Kamath, C

    2011-09-13

    The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region which encompasses the Bonneville Power Administration (BPA) wind generation area shown in Figure 1 that includes Klondike, Stateline, and Hopkins Ridge wind plants. The Ensemble Sensitivity Analysis (ESA) approach uses data generated by a set (ensemble) of perturbed numerical weather prediction (NWP) simulations for a sample time period to statistically diagnose the sensitivity of a specified forecast variable (metric) for a target location to parameters at other locations and prior times referred to as the initial condition (IC) or state variables. The ESA approach was tested on the large-scale atmospheric prediction problem by Ancell and Hakim 2007 and Torn and Hakim 2008. ESA was adapted and applied at the mesoscale by Zack et al. (2010a, b, and c) to the Tehachapi Pass, CA (warm and cools seasons) and Mid-Colombia Basin (warm season only) wind generation regions. In order to apply the ESA approach at the resolution needed at the mesoscale, Zack et al. (2010a, b, and c) developed the Multiple Observation Optimization Algorithm (MOOA). MOOA uses a multivariate regression on a few select IC parameters at one location to determine the incremental improvement of measuring multiple variables (representative of the IC parameters) at various locations. MOOA also determines how much information from each IC parameter contributes to the change in the metric variable at the target location. The Zack et al. studies (2010a, b, and c), demonstrated that forecast sensitivity can be characterized by well-defined, localized patterns for a number of IC variables such as 80-m wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective.

  6. 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.

  7. Climatic Forecasting of Net Infiltration at Yucca Montain Using Analogue Meteororological Data

    SciTech Connect (OSTI)

    B. Faybishenko

    2006-09-11

    At Yucca Mountain, Nevada, future changes in climatic conditions will most likely alter net infiltration, or the drainage below the bottom of the evapotranspiration zone within the soil profile or flow across the interface between soil and the densely welded part of the Tiva Canyon Tuff. The objectives of this paper are to: (a) develop a semi-empirical model and forecast average net infiltration rates, using the limited meteorological data from analogue meteorological stations, for interglacial (present day), and future monsoon, glacial transition, and glacial climates over the Yucca Mountain region, and (b) corroborate the computed net-infiltration rates by comparing them with the empirically and numerically determined groundwater recharge and percolation rates through the unsaturated zone from published data. In this paper, the author presents an approach for calculations of net infiltration, aridity, and precipitation-effectiveness indices, using a modified Budyko's water-balance model, with reference-surface potential evapotranspiration determined from the radiation-based Penman (1948) formula. Results of calculations show that net infiltration rates are expected to generally increase from the present-day climate to monsoon climate, to glacial transition climate, and then to the glacial climate. The forecasting results indicate the overlap between the ranges of net infiltration for different climates. For example, the mean glacial net-infiltration rate corresponds to the upper-bound glacial transition net infiltration, and the lower-bound glacial net infiltration corresponds to the glacial transition mean net infiltration. Forecasting of net infiltration for different climate states is subject to numerous uncertainties-associated with selecting climate analogue sites, using relatively short analogue meteorological records, neglecting the effects of vegetation and surface runoff and runon on a local scale, as well as possible anthropogenic climate changes.

  8. FY 1996 solid waste integrated life-cycle forecast container summary volume 1 and 2

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-04-23

    For the past six years, a waste volume forecast has been collected annually from onsite and offsite generators that currently ship or are planning to ship solid waste to the Westinghouse Hanford Company`s Central Waste Complex (CWC). This document provides a description of the containers expected to be used for these waste shipments from 1996 through the remaining life cycle of the Hanford Site. In previous years, forecast data have been reported for a 30-year time period; however, the life-cycle approach was adopted this year to maintain consistency with FY 1996 Multi-Year Program Plans. This document is a companion report to the more detailed report on waste volumes: WHC-EP0900, FY 1996 Solid Waste Integrated Life-Cycle Forecast Volume Summary. Both of these documents are based on data gathered during the FY 1995 data call and verified as of January, 1996. These documents are intended to be used in conjunction with other solid waste planning documents as references for short and long-term planning of the WHC Solid Waste Disposal Division`s treatment, storage, and disposal activities over the next several decades. This document focuses on the types of containers that will be used for packaging low-level mixed waste (LLMW) and transuranic waste (both non-mixed and mixed) (TRU(M)). The major waste generators for each waste category and container type are also discussed. Containers used for low-level waste (LLW) are described in Appendix A, since LLW requires minimal treatment and storage prior to onsite disposal in the LLW burial grounds. The FY 1996 forecast data indicate that about 100,900 cubic meters of LLMW and TRU(M) waste are expected to be received at the CWC over the remaining life cycle of the site. Based on ranges provided by the waste generators, this baseline volume could fluctuate between a minimum of about 59,720 cubic meters and a maximum of about 152,170 cubic meters.

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

    SciTech Connect (OSTI)

    none,

    2014-08-29

    With declining production costs and increasing technical capabilities, LED adoption has recently gained momentum in general illumination applications. This is a positive development for our energy infrastructure, as LEDs use significantly less electricity per lumen produced than many traditional lighting technologies. The U.S. Department of Energy’s Energy Savings Forecast of Solid-State Lighting in General Illumination Applications examines the expected market penetration and resulting energy savings of light-emitting diode, or LED, lamps and luminaires from today through 2030.

  10. U.S. oil production forecast update reflects lower rig count

    Gasoline and Diesel Fuel Update (EIA)

    U.S. oil production forecast update reflects lower rig count Lower oil prices and fewer rigs drilling for crude oil are expected to slow U.S. oil production growth this year and in 2016. U.S. crude oil production is still expected to average 9.2 million barrels per day this year. That's up half a million barrels per day from last year and the highest output level in more than four decades. A substantial part of the year-over-year increase reflects rapid production growth throughout 2014.

  11. A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China

    SciTech Connect (OSTI)

    Xu, Lilai; Gao, Peiqing; Cui, Shenghui; Liu, Chun

    2013-06-15

    Highlights: ? We propose a hybrid model that combines seasonal SARIMA model and grey system theory. ? The model is robust at multiple time scales with the anticipated accuracy. ? At month-scale, the SARIMA model shows good representation for monthly MSW generation. ? At medium-term time scale, grey relational analysis could yield the MSW generation. ? At long-term time scale, GM (1, 1) provides a basic scenario of MSW generation. - Abstract: Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term.

  12. Waste generation forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1995-FY 2002, September 1994 revision

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

    A comprehensive waste-forecasting task was initiated in FY 1991 to provide a consistent, documented estimate of the volumes of waste expected to be generated as a result of U.S. Department of Energy-Oak Ridge Operations (DOE-ORO) Environmental Restoration (ER) OR-1 Project activities. Continual changes in the scope and schedules for remedial action (RA) and decontamination and decommissioning (D&D) activities have required that an integrated data base system be developed that can be easily revised to keep pace with changes and provide appropriate tabular and graphical output. The output can then be analyzed and used to drive planning assumptions for treatment, storage, and disposal (TSD) facilities. The results of this forecasting effort and a description of the data base developed to support it are provided herein. The initial waste-generation forecast results were compiled in November 1991. Since the initial forecast report, the forecast data have been revised annually. This report reflects revisions as of September 1994.

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

    SciTech Connect (OSTI)

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

    1997-01-07

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

  14. Crude oil and alternate energy production forecasts for the twenty-first century: The end of the hydrocarbon era

    SciTech Connect (OSTI)

    Edwards, J.D.

    1997-08-01

    Predictions of production rates and ultimate recovery of crude oil are needed for intelligent planning and timely action to ensure the continuous flow of energy required by the world`s increasing population and expanding economies. Crude oil will be able to supply increasing demand until peak world production is reached. The energy gap caused by declining conventional oil production must then be filled by expanding production of coal, heavy oil and oil shales, nuclear and hydroelectric power, and renewable energy sources (solar, wind, and geothermal). Declining oil production forecasts are based on current estimated ultimate recoverable conventional crude oil resources of 329 billion barrels for the United States and close to 3 trillion barrels for the world. Peak world crude oil production is forecast to occur in 2020 at 90 million barrels per day. Conventional crude oil production in the United States is forecast to terminate by about 2090, and world production will be close to exhaustion by 2100.

  15. Department of Energy award DE-SC0004164 Climate and National Security: Securing Better Forecasts

    SciTech Connect (OSTI)

    Reno Harnish

    2011-08-16

    The Climate and National Security: Securing Better Forecasts symposium was attended by senior policy makers and distinguished scientists. The juxtaposition of these communities was creative and fruitful. They acknowledged they were speaking past each other. Scientists were urged to tell policy makers about even improbable outcomes while articulating clearly the uncertainties around the outcomes. As one policy maker put it, we are accustomed to making these types of decisions. These points were captured clearly in an article that appeared on the New York Times website and can be found with other conference materials most easily on our website, www.scripps.ucsd.edu/cens/. The symposium, generously supported by the NOAA/JIMO, benefitted the public by promoting scientifically informed decision making and by the transmission of objective information regarding climate change and national security.

  16. Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

    SciTech Connect (OSTI)

    Pennock, K.

    2012-10-01

    AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

  17. A comparison of water vapor quantities from model short-range forecasts and ARM observations

    SciTech Connect (OSTI)

    Hnilo, J J

    2006-03-17

    Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the 'Merged-sounding' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.

  18. A comparison of model short-range forecasts and the ARM Microbase data

    SciTech Connect (OSTI)

    Hnilo, J J

    2006-09-22

    For the fourth quarter ARM metric we will make use of new liquid water data that has become available, and called the 'Microbase' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Tropical West Pacific (TWP) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both TWP and NSA. The Microbase data have been averaged to 35 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3hourly data for direct comparison to our model output.

  19. Solid Waste Integrated Forecast Technical (SWIFT) Report FY2001 to FY2046 Volume 1

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2000-08-31

    This report provides up-to-date life cycle information about the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons to previous forecasts and other national data sources. This report does not include: waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); waste that has been received by the WM Project to date (i.e., inventory waste); mixed low-level waste that will be processed and disposed by the River Protection Program; and liquid waste (current or future generation). Although this report currently does not include liquid wastes, they may be added as information becomes available.

  20. HONEYWELL - KANSAS CITY PLANT FISCAL YEARS 2009 THRU 2015 SMALL BUSINESS PROGRAM RESULTS & FORECAST

    National Nuclear Security Administration (NNSA)

    HONEYWELL - KANSAS CITY PLANT FISCAL YEARS 2009 THRU 2015 SMALL BUSINESS PROGRAM RESULTS & FORECAST CATEGORY Total Procurement Total SB Small Disad. Bus Woman-Owned SB Hub-Zone SB Veteran-Owned SB Service Disabled Vet. SB FY 2009 Dollars Goal (projected) $183,949,920 $82,690,000 $4,550,000 $8,829,596 $3,370,000 $5,025,000 $460,000 FY 2009 Dollars Accomplished $143,846,731 $68,174,398 $9,247,214 $11,333,905 $4,979,858 $6,713,791 $1,612,136 FY 2009 % Goal 45.0% 2.5% 4.8% 1.8% 2.7% 0.25% FY

  1. Henry Hub Natural Gas Spot Price (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1997 3.45 2.15 1.89 2.03 2.25 2.20 2.19 2.49 2.88 3.07 3.01 2.35 1998 2.09 2.23 2.24 2.43 2.14 2.17 2.17 1.85 2.02 1.91 2.12...

  2. Natural Gas Futures Contract 2 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 2.001 1.720 2.433 2.463 2.231 2.376 2000's 4.304 4.105 3.441 5.497 6.417 9.186 7.399 7.359 9.014 4.428 2010's 4.471 4.090 2.926 3.775 4.236 2.684

  3. Natural Gas Futures Contract 2 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 2.188 2.232 2.123 2.136 1.999 2.130 2.021 1.831 1.881 1.961 1.890 1.709 1995 1.457 1.448 1.595 1.718 1.770 1.685 1.525 1.630 1.805 1.870 1.936 2.200 1996 2.177 2.175 2.205 2.297 2.317 2.582 2.506 2.120 2.134 2.601 2.862 3.260 1997 2.729 2.016 1.954 2.053 2.268 2.171 2.118 2.484 2.970 3.321 3.076 2.361 1998 2.104 2.293 2.288 2.500 2.199 2.205 2.164 1.913 2.277 2.451 2.438 1.953 1999 1.851 1.788 1.829 2.184 2.293 2.373 2.335 2.836 2.836

  4. Natural Gas Futures Contract 2 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year-Month Week 1 Week 2 Week 3 Week 4 Week 5 End Date Value End Date Value End Date Value End Date Value End Date Value 1994-Jan 01/14 2.113 01/21 2.159 01/28 2.233 1994-Feb 02/04 2.303 02/11 2.230 02/18 2.223 02/25 2.197 1994-Mar 03/04 2.144 03/11 2.150 03/18 2.148 03/25 2.095 1994-Apr 04/01 2.076 04/08 2.101 04/15 2.137 04/22 2.171 04/29 2.133 1994-May 05/06 2.056 05/13 2.017 05/20 1.987 05/27 1.938 1994-Jun 06/03 2.023 06/10 2.122 06/17 2.173 06/24 2.118 1994-Jul 07/01 2.182 07/08 2.119

  5. Natural Gas Futures Contract 3 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 2.039 1.739 2.350 2.418 2.290 2.406 2000's 4.217 4.069 3.499 5.466 6.522 9.307 7.852 7.601 9.141 4.669 2010's 4.564 4.160 3.020 3.822 4.227 2.739

  6. Natural Gas Futures Contract 3 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 2.116 2.168 2.118 2.139 2.038 2.150 2.083 2.031 2.066 2.037 1.873 1.694 1995 1.490 1.492 1.639 1.745 1.801 1.719 1.605 1.745 1.883 1.889 1.858 1.995 1996 1.964 2.056 2.100 2.277 2.307 2.572 2.485 2.222 2.272 2.572 2.571 2.817 1997 2.393 1.995 1.978 2.073 2.263 2.168 2.140 2.589 3.043 3.236 2.803 2.286 1998 2.110 2.312 2.312 2.524 2.249 2.234 2.220 2.168 2.479 2.548 2.380 1.954 1999 1.860 1.820 1.857 2.201 2.315 2.393 2.378 2.948 2.977

  7. Natural Gas Futures Contract 3 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year-Month Week 1 Week 2 Week 3 Week 4 Week 5 End Date Value End Date Value End Date Value End Date Value End Date Value 1994-Jan 01/21 2.055 01/28 2.133 1994-Feb 02/04 2.189 02/11 2.159 02/18 2.174 02/25 2.163 1994-Mar 03/04 2.127 03/11 2.136 03/18 2.141 03/25 2.103 1994-Apr 04/01 2.085 04/08 2.105 04/15 2.131 04/22 2.175 04/29 2.149 1994-May 05/06 2.076 05/13 2.045 05/20 2.034 05/27 1.994 1994-Jun 06/03 2.078 06/10 2.149 06/17 2.172 06/24 2.142 1994-Jul 07/01 2.187 07/08 2.143 07/15 2.079

  8. Natural Gas Futures Contract 4 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 1.906 2.054 1.746 2.270 2.363 2.332 2.418 2000's 4.045 4.103 3.539 5.401 6.534 9.185 8.238 7.811 9.254 4.882 2010's 4.658 4.227 3.109 3.854 4.218 2.792

  9. Natural Gas Futures Contract 4 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1993 1.906 1994 2.012 2.140 2.120 2.150 2.081 2.189 2.186 2.168 2.079 1.991 1.843 1.672 1995 1.519 1.541 1.672 1.752 1.810 1.763 1.727 1.826 1.886 1.827 1.770 1.844 1996 1.877 1.985 2.040 2.245 2.275 2.561 2.503 2.293 2.296 2.436 2.317 2.419 1997 2.227 1.999 1.987 2.084 2.249 2.194 2.274 2.689 2.997 2.873 2.532 2.204 1998 2.124 2.324 2.333 2.533 2.289 2.291 2.428 2.419 2.537 2.453 2.294 1.940 1999 1.880 1.850 1.886 2.214 2.331 2.429 2.539

  10. Natural Gas Futures Contract 4 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year-Month Week 1 Week 2 Week 3 Week 4 Week 5 End Date Value End Date Value End Date Value End Date Value End Date Value 1993-Dec 12/24 1.869 12/31 1.943 1994-Jan 01/07 1.935 01/14 1.992 01/21 2.006 01/28 2.088 1994-Feb 02/04 2.133 02/11 2.135 02/18 2.148 02/25 2.149 1994-Mar 03/04 2.118 03/11 2.125 03/18 2.139 03/25 2.113 1994-Apr 04/01 2.107 04/08 2.120 04/15 2.140 04/22 2.180 04/29 2.165 1994-May 05/06 2.103 05/13 2.081 05/20 2.076 05/27 2.061 1994-Jun 06/03 2.134 06/10 2.180 06/17 2.187

  11. Microfabricated BTU monitoring device for system-wide natural...

    Office of Scientific and Technical Information (OSTI)

    Research Org: Sandia National Laboratories Sponsoring Org: USDOE Country of Publication: United States Language: English Subject: 03 NATURAL GAS; COMBUSTION; EFFICIENCY; FEEDBACK; ...

  12. Natural Gas Futures Contract 2 (Dollars per Million Btu)

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

    Sep Oct Nov Dec 1994 2.188 2.232 2.123 2.136 1.999 2.130 2.021 1.831 1.881 1.961 1.890 1.709 1995 1.457 1.448 1.595 1.718 1.770 1.685 1.525 1.630 1.805 1.870 1.936 2.200 1996 2.177...

  13. Natural Gas Futures Contract 1 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 1.934 1.692 2.502 2.475 2.156 2.319 2000's 4.311 4.053 3.366 5.493 6.178 9.014 6.976 7.114 8.899 4.159 2010's 4.382 4.026 2.827 3.731 4.262 2.627

  14. Natural Gas Futures Contract 1 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Year-Month Week 1 Week 2 Week 3 Week 4 Week 5 End Date Value End Date Value End Date Value End Date Value End Date Value 1994-Jan 01/14 2.231 01/21 2.297 01/28 2.404 1994-Feb 02/04 2.506 02/11 2.369 02/18 2.330 02/25 2.267 1994-Mar 03/04 2.178 03/11 2.146 03/18 2.108 03/25 2.058 1994-Apr 04/01 2.065 04/08 2.092 04/15 2.127 04/22 2.126 04/29 2.097 1994-May 05/06 2.025 05/13 1.959 05/20 1.933 05/27 1.855 1994-Jun 06/03 1.938 06/10 2.052 06/17 2.128 06/24 2.065 1994-Jul 07/01 2.183 07/08 2.087

  15. Natural Gas Futures Contract 2 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Week Of Mon Tue Wed Thu Fri 1994 Jan-10 to Jan-14 2.130 2.072 2.139 1994 Jan-17 to Jan-21 2.196 2.131 2.115 2.148 2.206 1994 Jan-24 to Jan-28 2.283 2.134 2.209 2.236 2.305 1994 Jan-31 to Feb- 4 2.329 2.388 2.352 2.252 2.198 1994 Feb- 7 to Feb-11 2.207 2.256 2.220 2.231 2.236 1994 Feb-14 to Feb-18 2.180 2.189 2.253 2.240 2.254 1994 Feb-21 to Feb-25 2.220 2.168 2.179 2.221 1994 Feb-28 to Mar- 4 2.165 2.146 2.139 2.126 2.144 1994 Mar- 7 to Mar-11 2.149 2.168 2.160 2.144 2.132 1994 Mar-14 to Mar-18

  16. Natural Gas Futures Contract 3 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Week Of Mon Tue Wed Thu Fri 1994 Jan-17 to Jan-21 2.019 2.043 2.103 1994 Jan-24 to Jan-28 2.162 2.071 2.119 2.128 2.185 1994 Jan-31 to Feb- 4 2.217 2.258 2.227 2.127 2.118 1994 Feb- 7 to Feb-11 2.137 2.175 2.162 2.160 2.165 1994 Feb-14 to Feb-18 2.140 2.145 2.205 2.190 2.190 1994 Feb-21 to Feb-25 2.180 2.140 2.148 2.186 1994 Feb-28 to Mar- 4 2.148 2.134 2.122 2.110 2.124 1994 Mar- 7 to Mar-11 2.129 2.148 2.143 2.135 2.125 1994 Mar-14 to Mar-18 2.111 2.137 2.177 2.152 2.130 1994 Mar-21 to Mar-25

  17. Natural Gas Futures Contract 4 (Dollars per Million Btu)

    Gasoline and Diesel Fuel Update (EIA)

    Week Of Mon Tue Wed Thu Fri 1993 Dec-20 to Dec-24 1.894 1.830 1.859 1.895 1993 Dec-27 to Dec-31 1.965 1.965 1.943 1.901 1994 Jan- 3 to Jan- 7 1.883 1.896 1.962 1.955 1.980 1994 Jan-10 to Jan-14 1.972 2.005 2.008 1.966 2.010 1994 Jan-17 to Jan-21 2.006 1.991 1.982 2.000 2.053 1994 Jan-24 to Jan-28 2.095 2.044 2.087 2.088 2.130 1994 Jan-31 to Feb- 4 2.157 2.185 2.157 2.075 2.095 1994 Feb- 7 to Feb-11 2.115 2.145 2.142 2.135 2.140 1994 Feb-14 to Feb-18 2.128 2.125 2.175 2.160 2.155 1994 Feb-21 to

  18. Kansas Heat Content of Natural Gas Deliveries to Consumers (BTU...

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

    Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's 1,018 1,034 1,019 2010's 1,019 1,020 1,022 1,020 1,021...

  19. Natural Gas Futures Contract 1 (Dollars per Million Btu)

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

    Week Of Mon Tue Wed Thu Fri 1994 Jan-10 to Jan-14 2.194 2.268 1994 Jan-17 to Jan-21 2.360 2.318 2.252 2.250 2.305 1994 Jan-24 to Jan-28 2.470 2.246 2.359 2.417 2.528 1994 Jan-31 to Feb- 4 2.554 2.639 2.585 2.383 2.369 1994 Feb- 7 to Feb-11 2.347 2.411 2.358 2.374 2.356 1994 Feb-14 to Feb-18 2.252 2.253 2.345 2.385 2.418 1994 Feb-21 to Feb-25 2.296 2.232 2.248 2.292 1994 Feb-28 to Mar- 4 2.208 2.180 2.171 2.146 2.188 1994 Mar- 7 to Mar-11 2.167 2.196 2.156 2.116 2.096 1994 Mar-14 to Mar-18 2.050

  20. ,"Henry Hub Natural Gas Spot Price (Dollars per Million Btu)...

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

    Daily","3212016" ,"Release Date:","3232016" ,"Next Release Date:","3302016" ,"Excel File Name:","rngwhhdd.xls" ,"Available from Web Page:","http:tonto.eia.govdnavnghist...