National Library of Energy BETA

Sample records for year forecast 4

  1. Forecast of contracting and subcontracting opportunities. Fiscal year 1996

    SciTech Connect (OSTI)

    1996-02-01

    This forecast of prime and subcontracting opportunities with the U.S. Department of Energy and its MAO contractors and environmental restoration and waste management contractors, is the Department`s best estimate of small, small disadvantaged and women-owned small business procurement opportunities for fiscal year 1996. The information contained in the forecast is published in accordance with Public Law 100-656. It is not an invitation for bids, a request for proposals, or a commitment by DOE to purchase products or services. Each procurement opportunity is based on the best information available at the time of publication and may be revised or cancelled.

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

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01

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

  3. 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.

  4. 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.

  5. 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

  6. Summer gasoline price forecast slightly higher, but drivers still pay less than last year

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

    Summer gasoline price forecast slightly higher, but drivers still pay less than last year Rising crude oil prices are likely to be passed on to consumers at the pump, but U.S. drivers are still expected to pay the lowest summer gasoline prices since 2004, and for all of 2016 the average household will spend $900 less on gasoline than it did two years ago." In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular grade gasoline will average

  7. Regional forecasting with global atmospheric models; Third year report

    SciTech Connect (OSTI)

    Crowley, T.J.; North, G.R.; Smith, N.R.

    1994-05-01

    This report was prepared by the Applied Research Corporation (ARC), College Station, Texas, under subcontract to Pacific Northwest Laboratory (PNL) as part of a global climate studies task. The task supports site characterization work required for the selection of a potential high-level nuclear waste repository and is part of the Performance Assessment Scientific Support (PASS) Program at PNL. The work is under the overall direction of the Office of Civilian Radioactive Waste Management (OCRWM), US Department of Energy Headquarters, Washington, DC. The scope of the report is to present the results of the third year`s work on the atmospheric modeling part of the global climate studies task. The development testing of computer models and initial results are discussed. The appendices contain several studies that provide supporting information and guidance to the modeling work and further details on computer model development. Complete documentation of the models, including user information, will be prepared under separate reports and manuals.

  8. 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.

  9. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People`s Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  10. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect (OSTI)

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People's Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  11. 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:...

  12. Simulations of Clouds and Sensitivity Study by Weather Research and Forecast Model for Atmospheric Radiation Measurement Case 4

    SciTech Connect (OSTI)

    Wu, J.; Zhang, M.

    2005-03-18

    One of the large errors in general circulation models (GCMs) cloud simulations is from the mid-latitude, synoptic-scale frontal cloud systems. Now, with the availability of the cloud observations from Atmospheric Radiation Measurement (ARM) 2000 cloud Intensive Operational Period (IOP) and other observational datasets, the community is able to document the model biases in comparison with the observations and make progress in development of better cloud schemes in models. Xie et al. (2004) documented the errors in midlatitude frontal cloud simulations for ARM Case 4 by single-column models (SCMs) and cloud resolving models (CRMs). According to them, the errors in the model simulated cloud field might be caused by following reasons: (1) lacking of sub-grid scale variability; (2) lacking of organized mesoscale cyclonic advection of hydrometeors behind a moving cyclone which may play important role to generate the clouds there. Mesoscale model, however, can be used to better under stand these controls on the subgrid variability of clouds. Few studies have focused on applying mesoscale models to the forecasting of cloud properties. Weaver et al. (2004) used a mesoscale model RAMS to study the frontal clouds for ARM Case 4 and documented the dynamical controls on the sub-GCM-grid-scale cloud variability.

  13. Forecast Change

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

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

  14. YEAR

    National Nuclear Security Administration (NNSA)

    4 YEAR 2012 Males 65 Females 29 YEAR 2012 SES 3 EJEK 5 EN 04 3 NN (Engineering) 21 NQ (ProfTechAdmin) 61 NU (TechAdmin Support) 1 YEAR 2012 American Indian Male 0 American...

  15. YEAR

    National Nuclear Security Administration (NNSA)

    4 YEAR 2011 Males 21 Females 23 YEAR 2011 SES 3 EJEK 1 EN 03 1 NN (Engineering) 3 NQ (ProfTechAdmin) 31 NU (TechAdmin Support) 5 YEAR 2011 American Indian Male 0 American...

  16. YEAR

    National Nuclear Security Administration (NNSA)

    31 YEAR 2013 Males 20 Females 11 YEAR 2013 SES 2 EN 04 4 NN (Engineering) 12 NQ (ProfTechAdmin) 12 NU (TechAdmin Support) 1 YEAR 2013 American Indian Alaska Native Male (AIAN,...

  17. YEAR

    National Nuclear Security Administration (NNSA)

    31 YEAR 2012 Males 19 Females 12 YEAR 2012 SES 2 EN 04 4 NN (Engineering) 12 NQ (ProfTechAdmin) 12 NU (TechAdmin Support) 1 YEAR 2012 American Indian Male 0 American Indian...

  18. YEAR

    National Nuclear Security Administration (NNSA)

    137 YEAR 2013 Males 90 Females 47 YEAR 2013 SES 2 SL 1 EJEK 30 EN 04 30 EN 03 2 NN (Engineering) 23 NQ (ProfTechAdmin) 45 NU (TechAdmin Support) 4 YEAR 2013 American Indian...

  19. YEAR

    National Nuclear Security Administration (NNSA)

    of Employees 14 GENDER YEAR 2012 Males 9 Females 5 YEAR 2012 SES 2 EJEK 2 NN (Engineering) 4 NQ (ProfTechAdmin) 6 YEAR 2012 American Indian Male 0 American Indian Female 0...

  20. Solar Forecasting

    Broader source: Energy.gov [DOE]

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

  1. YEAR

    National Nuclear Security Administration (NNSA)

    5 YEAR 2013 Males 58 Females 27 YEAR 2013 SES 1 EJEK 4 EN 05 3 EN 04 21 EN 03 8 NN (Engineering) 16 NQ (ProfTechAdmin) 28 NU (TechAdmin Support) 4 YEAR 2013 American Indian...

  2. YEAR

    National Nuclear Security Administration (NNSA)

    26 YEAR 2014 Males 81 Females 45 PAY PLAN YEAR 2014 SES 1 SL1 EJEK 25 EN 04 26 EN 03 2 NN (Engineering) 23 NQ (ProfTechAdmin) 44 NU (TechAdmin Support) 4 YEAR 2014 American ...

  3. YEAR

    National Nuclear Security Administration (NNSA)

    4 YEAR 2012 Males 37 Females 7 YEAR 2012 SES 1 EJEK 6 EN 05 5 EN 04 7 EN 03 1 NN (Engineering) 17 NQ (ProfTechAdmin) 6 NU (TechAdmin Support) 1 YEAR 2012 American Indian Male 2...

  4. YEAR

    National Nuclear Security Administration (NNSA)

    6 YEAR 2012 Males 64 Females 32 YEAR 2012 SES 1 EJEK 5 EN 05 3 EN 04 23 EN 03 9 NN (Engineering) 18 NQ (ProfTechAdmin) 33 NU (TechAdmin Support) 4 YEAR 2012 American Indian...

  5. YEAR

    National Nuclear Security Administration (NNSA)

    78 YEAR 2012 Males 57 Females 21 YEAR 2012 SES 2 SL 1 EJEK 12 EN 04 21 EN 03 2 NN (Engineering) 12 NQ (ProfTechAdmin) 24 NU (TechAdmin Support) 4 YEAR 2012 American Indian Male...

  6. YEAR

    National Nuclear Security Administration (NNSA)

    8 YEAR 2014 Males 18 Females 10 PAY PLAN YEAR 2014 SES 1 EN 05 1 EN 04 4 NN (Engineering) 12 NQ (Prof/Tech/Admin) 9 NU (Tech/Admin Support) 1 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 4 African American Female (AA F) 4 Asian American Pacific Islander Male (AAPI M) 1 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 0 Hispanic Female (H F) 0 White Male (W M) 13 White Female (W F) 5

  7. 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...

  8. YEAR

    National Nuclear Security Administration (NNSA)

    2012 Males 149 Females 115 YEAR 2012 SES 17 EX 1 EJEK 7 EN 05 2 EN 04 9 EN 03 2 NN (Engineering) 56 NQ (ProfTechAdmin) 165 NU (TechAdmin Support) 4 GS 13 1 YEAR 2012 American...

  9. YEAR

    National Nuclear Security Administration (NNSA)

    5 YEAR 2014 Males 92 Females 43 YEAR 2014 SES 8 EX 1 EJ/EK 4 EN 05 9 EN 04 12 EN 03 2 NN (Engineering) 57 NQ (Prof/Tech/Admin) 42 YEAR 2014 American Indian Alaska Native Male (AIAN M) 1 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 9 African American Female (AA F) 11 Asian American Pacific Islander Male (AAPI M) 4 Asian American Pacific Islander Female (AAPI F) 2 Hispanic Male (H M) 12 Hispanic Female (H F) 7 White Male (W M) 66 White Female (W F) 22 PAY PLAN

  10. YEAR

    National Nuclear Security Administration (NNSA)

    4 YEAR 2014 Males 7 Females 7 PAY PLAN YEAR 2014 SES 1 NQ (Prof/Tech/Admin) 7 GS 15 1 GS 14 2 GS 13 2 GS 10 1 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 0 African American Male (AA M) 3 African American Female (AA F) 2 Asian American Pacific Islander Male (AAPI M) 0 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 0 Hispanic Female (H F) 0 White Male (W M) 4 White Female (W F) 5 DIVERSITY TOTAL WORKFORCE GENDER

  11. YEAR

    National Nuclear Security Administration (NNSA)

    26 YEAR 2014 Males 81 Females 45 PAY PLAN YEAR 2014 SES 1 SL 1 EJ/EK 25 EN 04 26 EN 03 2 NN (Engineering) 23 NQ (Prof/Tech/Admin) 44 NU (Tech/Admin Support) 4 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 3 African American Female (AA F) 7 Asian American Pacific Islander Male (AAPI M) 4 Asian American Pacific Islander Female (AAPI F) 1 Hispanic Male (H M) 6 Hispanic Female (H F) 6 White Male (W M) 68 White

  12. YEAR

    National Nuclear Security Administration (NNSA)

    YEAR 2012 2013 SES 2 1 -50.00% EN 05 0 1 100.00% EN 04 4 4 0.00% NN (Engineering) 13 12 -7.69% NQ (ProfTechAdmin) 13 9 -30.77% NU (TechAdmin Support) 1 1...

  13. YEAR

    National Nuclear Security Administration (NNSA)

    2 YEAR 2014 Males 57 Females 25 PAY PLAN YEAR 2014 SES 3 EJ/EK 4 EN 04 2 NN (Engineering) 20 NQ (Prof/Tech/Admin) 53 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 0 African American Male (AA M) 9 African American Female (AA F) 9 Asian American Pacific Islander Male (AAPI M) 2 Asian American Pacific Islander Female (AAPI F) 1 Hispanic Male (H M) 3 Hispanic Female (H F) 5 White Male (W M) 43 White Female (W F) 10 DIVERSITY TOTAL WORKFORCE

  14. YEAR

    National Nuclear Security Administration (NNSA)

    YEAR 2014 Males 11 Females 2 PAY PLAN YEAR 2014 SES 2 EJ/EK 1 EN 04 1 NN (Engineering) 5 NQ (Prof/Tech/Admin) 4 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 0 African American Male (AA M) 0 African American Female (AA F) 0 Asian American Pacific Islander Male (AAPI M) 1 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 0 Hispanic Female (H F) 0 White Male (W M) 10 White Female (W F) 2 DIVERSITY TOTAL WORKFORCE GENDER

  15. YEAR

    National Nuclear Security Administration (NNSA)

    89 YEAR 2014 Males 98 Females 91 PAY PLAN YEAR 2014 SES 14 EX 1 EJ/EK 3 EN 05 1 EN 04 4 EN 03 1 NN (Engineering) 32 NQ (Prof/Tech/Admin) 130 NU (Tech/Admin Support) 2 GS 15 1 YEAR 2014 American Indian Alaska Native Male (AIAN M) 1 American Indian Alaskan Native Female (AIAN F) 0 African American Male (AA M) 5 African American Female (AA F) 14 Asian American Pacific Islander Male (AAPI M) 3 Asian American Pacific Islander Female (AAPI F) 7 Hispanic Male (H M) 7 Hispanic Female (H F) 10 White Male

  16. YEAR

    National Nuclear Security Administration (NNSA)

    74 YEAR 2014 Males 96 Females 78 PAY PLAN YEAR 2014 SES 8 EJ/EK 4 EN 04 11 EN 03 1 NN (Engineering) 34 NQ (Prof/Tech/Admin) 113 NU (Tech/Admin Support) 3 YEAR 2014 American Indian Alaska Native Male (AIAN M) 2 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 3 African American Female (AA F) 11 Asian American Pacific Islander Male (AAPI M) 5 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 25 Hispanic Female (H F) 25 White Male (W M) 61 White

  17. YEAR

    National Nuclear Security Administration (NNSA)

    8 YEAR 2014 Males 18 Females 20 PAY PLAN YEAR 2014 SES 3 EJ/EK 1 EN 03 1 NN (Engineering) 3 NQ (Prof/Tech/Admin) 28 NU (Tech/Admin Support) 2 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 0 African American Male (AA M) 1 African American Female (AA F) 1 Asian American Pacific Islander Male (AAPI M) 0 Asian American Pacific Islander Female (AAPI F) 1 Hispanic Male (H M) 4 Hispanic Female (H F) 7 White Male (W M) 13 White Female (W F) 11

  18. YEAR

    National Nuclear Security Administration (NNSA)

    Females 942 YEAR 2012 SES 108 EX 4 SL 1 EJEK 96 EN 05 45 EN 04 196 EN 03 20 NN (Engineering) 452 NQ (ProfTechAdmin) 1291 NU (TechAdmin Support) 106 NV (Nuc Mat Courier) 335...

  19. YEAR

    National Nuclear Security Administration (NNSA)

    9 Females 24 PAY PLAN YEAR 2014 SES 1 EJ/EK 4 EN 05 3 EN 04 22 EN 03 8 NN (Engineering) 15 NQ (Prof/Tech/Admin) 27 NU (Tech/Admin Support) 3 YEAR 2014 American Indian Alaska Native Male (AIAN M) 2 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 5 African American Female (AA F) 2 Asian American Pacific Islander Male (AAPI M) 21 Asian American Pacific Islander Female (AAPI F) 2 Hispanic Male (H M) 5 Hispanic Female (H F) 3 White Male (W M) 26 White Female (W F) 16

  20. 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.

  1. RACORO Forecasting

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

    7a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7 26.5

  2. Taking a Look at 4.57 Billion Year Old Space Objects | Department of Energy

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

    a Look at 4.57 Billion Year Old Space Objects Taking a Look at 4.57 Billion Year Old Space Objects March 23, 2011 - 3:29pm Addthis Compositional X-ray image of the rim and margin of a ~4.6 billion year old calcium aluminum refractory inclusion (CAI)
from the Allende carbonaceous chondrite. | Courtesy of Lawrence Livermore National Laboratory Compositional X-ray image of the rim and margin of a ~4.6 billion year old calcium aluminum refractory inclusion (CAI)
from the Allende carbonaceous

  3. Microsoft PowerPoint - NEAC CASL @ 3.5 Years Report v4

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

    ... Reactor System Multiphysics Integrator 4 CASL @3.5 Years Mesh Motion Quality Improvement Geometry Management Mesh Motion Quality Improvement Geometry Management Virtual ...

  4. YEAR

    National Nuclear Security Administration (NNSA)

    69 YEAR 2014 Males 34 Females 35 YEAR 2014 SES 5 EJEK 1 EN 05 8 EN 04 5 NN (Engineering) 27 NQ (ProfTechAdmin) 22 NU (TechAdmin Support) 1 YEAR 2014 American Indian Alaska...

  5. YEAR

    National Nuclear Security Administration (NNSA)

    42 YEAR 2014 Males 36 Females 6 PAY PLAN YEAR 2014 SES 2 EJEK 5 EN 05 7 EN 04 6 EN 03 1 NN (Engineering) 15 NQ (ProfTechAdmin) 6 YEAR 2014 American Indian Alaska Native Male...

  6. YEAR

    National Nuclear Security Administration (NNSA)

    92 YEAR 2012 Males 52 Females 40 YEAR 2012 SES 1 EJEK 7 EN 04 13 EN 03 1 NN (Engineering) 27 NQ (ProfTechAdmin) 38 NU (TechAdmin Support) 5 YEAR 2012 American Indian Male 0...

  7. YEAR

    National Nuclear Security Administration (NNSA)

    558 YEAR 2013 Males 512 Females 46 YEAR 2013 SES 2 EJEK 2 EN 04 1 NN (Engineering) 11 NQ (ProfTechAdmin) 220 NU (TechAdmin Support) 1 NV (Nuc Mat Courier) 321 YEAR 2013...

  8. YEAR

    National Nuclear Security Administration (NNSA)

    11 YEAR 2012 Males 78 Females 33 YEAR 2012 SES 2 EJEK 9 EN 05 1 EN 04 33 NN (Engineering) 32 NQ (ProfTechAdmin) 31 NU (TechAdmin Support) 3 YEAR 2012 American Indian Male 2...

  9. YEAR

    National Nuclear Security Administration (NNSA)

    300 YEAR 2011 Males 109 Females 191 YEAR 2011 SES 9 EJEK 1 NN (Engineering) 2 NQ (ProfTechAdmin) 203 NU (TechAdmin Support) 38 NF (Future Ldrs) 47 YEAR 2011 American Indian...

  10. YEAR

    National Nuclear Security Administration (NNSA)

    02 YEAR 2011 Males 48 Females 54 YEAR 2011 SES 5 EJEK 1 NN (Engineering) 13 NQ (ProfTechAdmin) 80 NU (TechAdmin Support) 3 YEAR 2011 American Indian Male 0 American Indian...

  11. YEAR

    National Nuclear Security Administration (NNSA)

    8 YEAR 2013 Males 27 Females 11 YEAR 2013 SES 1 EN 05 1 EN 04 11 NN (Engineering) 8 NQ (ProfTechAdmin) 15 NU (TechAdmin Support) 2 YEAR 2013 American Indian Alaska Native Male...

  12. YEAR

    National Nuclear Security Administration (NNSA)

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  13. YEAR

    National Nuclear Security Administration (NNSA)

    34 YEAR 2012 Males 66 Females 68 YEAR 2012 SES 6 NN (Engineering) 15 NQ (ProfTechAdmin) 110 NU (TechAdmin Support) 3 YEAR 2012 American Indian Male 1 American Indian Female 2...

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    National Nuclear Security Administration (NNSA)

    86 YEAR 2012 Males 103 Females 183 YEAR 2012 SES 7 EJEK 1 NN (Engineering) 1 NQ (ProfTechAdmin) 202 NU (TechAdmin Support) 30 NF (Future Ldrs) 45 YEAR 2012 American Indian Male...

  15. YEAR

    National Nuclear Security Administration (NNSA)

    80 YEAR 2012 Males 51 Females 29 YEAR 2012 SES 1 EJEK 22 EN 04 21 NN (Engineering) 14 NQ (ProfTechAdmin) 21 NU (TechAdmin Support) 1 YEAR 2012 American Indian Male 0 American...

  16. YEAR

    National Nuclear Security Administration (NNSA)

    1 YEAR 2012 Males 30 Females 11 YEAR 2012 SES 1 EN 05 1 EN 04 11 NN (Engineering) 9 NQ (ProfTechAdmin) 17 NU (TechAdmin Support) 2 YEAR 2012 American Indian Male 0 American...

  17. YEAR

    National Nuclear Security Administration (NNSA)

    96 YEAR 2013 Males 69 Females 27 YEAR 2013 SES 1 EJEK 9 EN 04 27 NN (Engineering) 26 NQ (ProfTechAdmin) 30 NU (TechAdmin Support) 3 YEAR 2013 American Indian Alaska Native Male...

  18. YEAR

    National Nuclear Security Administration (NNSA)

    0 YEAR 2013 Males 48 Females 32 YEAR 2013 SES 2 EJEK 7 EN 04 11 EN 03 1 NN (Engineering) 23 NQ (ProfTechAdmin) 33 NU (TechAdmin Support) 3 YEAR 2013 American Indian Alaska...

  19. YEAR

    National Nuclear Security Administration (NNSA)

    40 YEAR 2011 Males 68 Females 72 YEAR 2011 SES 5 EJEK 1 NN (Engineering) 16 NQ (ProfTechAdmin) 115 NU (TechAdmin Support) 3 YEAR 2011 American Indian Male 1 American Indian...

  20. YEAR

    National Nuclear Security Administration (NNSA)

    00 YEAR 2012 Males 48 Females 52 YEAR 2012 SES 5 EJEK 1 NN (Engineering) 11 NQ (ProfTechAdmin) 80 NU (TechAdmin Support) 3 YEAR 2012 American Indian Male 0 American Indian...

  1. YEAR

    National Nuclear Security Administration (NNSA)

    3 YEAR 2012 Males 21 Females 22 YEAR 2012 SES 3 EJEK 1 EN 03 1 NN (Engineering) 3 NQ (ProfTechAdmin) 30 NU (TechAdmin Support) 5 YEAR 2012 American Indian Male 0 American...

  2. YEAR

    National Nuclear Security Administration (NNSA)

    Males 139 Females 88 YEAR 2012 SES 13 EX 1 EJEK 8 EN 05 23 EN 04 20 EN 03 2 NN (Engineering) 91 NQ (ProfTechAdmin) 62 NU (TechAdmin Support) 7 YEAR 2012 American Indian...

  3. YEAR

    National Nuclear Security Administration (NNSA)

    563 YEAR 2012 Males 518 Females 45 YEAR 2012 SES 1 EJEK 2 EN 04 1 EN 03 1 NN (Engineering) 12 NQ (ProfTechAdmin) 209 NU (TechAdmin Support) 2 NV (Nuc Mat Courier) 335 YEAR 2012...

  4. YEAR

    National Nuclear Security Administration (NNSA)

    7 YEAR 2012 Males 64 Females 33 YEAR 2012 SES 2 EJEK 3 EN 05 1 EN 04 30 EN 03 1 NN (Engineering) 26 NQ (ProfTechAdmin) 32 NU (TechAdmin Support) 2 YEAR 2012 American Indian...

  5. YEAR

    National Nuclear Security Administration (NNSA)

    7 YEAR 2011 Males 38 Females 9 YEAR 2011 SES 1 EJEK 6 EN 05 5 EN 04 7 EN 03 1 NN (Engineering) 19 NQ (ProfTechAdmin) 7 NU (TechAdmin Support) 1 YEAR 2011 American Indian Male 2...

  6. YEAR

    National Nuclear Security Administration (NNSA)

    8 YEAR 2013 Males 62 Females 26 YEAR 2013 SES 1 EJEK 3 EN 05 1 EN 04 28 EN 03 1 NN (Engineering) 25 NQ (ProfTechAdmin) 27 NU (TechAdmin Support) 2 YEAR 2013 American Indian...

  7. 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...

  8. probabilistic energy production forecasts

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

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

  9. 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...

  10. Forecasting Water Quality & Biodiversity

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

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

  11. YEAR

    National Nuclear Security Administration (NNSA)

    5 YEAR 2014 Males 61 Females 24 PAY PLAN YEAR 2014 SES 1 EJ/EK 8 EN 04 22 NN (Engineering) 23 NQ (Prof/Tech/Admin) 28 NU (Tech/Admin Support) 3 YEAR 2014 American Indian Alaska Native Male (AIAN M) 2 American Indian Alaskan Native Female (AIAN F) 3 African American Male (AA M) 0 African American Female (AA F) 0 Asian American Pacific Islander Male (AAPI M) 3 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 13 Hispanic Female (H F) 10 White Male (W M) 43 White Female (W F) 11

  12. YEAR

    National Nuclear Security Administration (NNSA)

    93 YEAR 2014 Males 50 Females 43 PAY PLAN YEAR 2014 EJ/EK 3 NN (Engineering) 13 NQ (Prof/Tech/Admin) 74 NU (Tech/Admin Support) 3 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 2 African American Male (AA M) 5 African American Female (AA F) 6 Asian American Pacific Islander Male (AAPI M) 0 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 6 Hispanic Female (H F) 14 White Male (W M) 39 White Female (W F) 21 DIVERSITY

  13. YEAR

    National Nuclear Security Administration (NNSA)

    9 YEAR 2014 Males 9 Females 10 YEAR 2014 SES 7 ED 1 EJ/EK 1 EN 05 1 NQ (Prof/Tech/Admin) 8 NU (Tech/Admin Support) 1 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 1 African American Female (AA F) 5 Asian American Pacific Islander Male (AAPI M) 1 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 0 Hispanic Female (H F) 3 White Male (W M) 7 White Female (W F) 1 PAY PLAN DIVERSITY TOTAL

  14. YEAR

    National Nuclear Security Administration (NNSA)

    563 YEAR 2014 Males 517 Females 46 PAY PLAN YEAR 2014 SES 2 EJ/EK 2 EN 04 1 NN (Engineering) 11 NQ (Prof/Tech/Admin) 218 NU (Tech/Admin Support) 2 NV (Nuc Mat Courier) 327 YEAR 2014 American Indian Alaska Native Male (AIAN M) 14 American Indian Alaskan Native Female (AIAN F) 2 African American Male (AA M) 18 African American Female (AA F) 1 Asian American Pacific Islander Male (AAPI M) 8 Asian American Pacific Islander Female (AAPI F) 2 Hispanic Male (H M) 76 Hispanic Female (H F) 21 White Male

  15. YEAR

    National Nuclear Security Administration (NNSA)

    3 YEAR 2014 Males 162 Females 81 PAY PLAN YEAR 2014 SES 26 EJ/EK 3 EN 05 7 NN (Engineering) 77 NQ (Prof/Tech/Admin) 108 NU (Tech/Admin Support) 22 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 5 African American Female (AA F) 9 Asian American Pacific Islander Male (AAPI M) 1 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 2 Hispanic Female (H F) 0 White Male (W M) 154 White Female (W F)

  16. YEAR

    National Nuclear Security Administration (NNSA)

    16 YEAR 2014 Males 72 Females 144 PAY PLAN YEAR 2014 SES 8 EJ/EK 1 NQ (Prof/Tech/Admin) 198 NU (Tech/Admin Support) 9 YEAR 2014 American Indian Alaska Native Male (AIAN M) 2 American Indian Alaskan Native Female (AIAN F) 2 African American Male (AA M) 10 African American Female (AA F) 38 Asian American Pacific Islander Male (AAPI M) 1 Asian American Pacific Islander Female (AAPI F) 3 Hispanic Male (H M) 15 Hispanic Female (H F) 33 White Male (W M) 44 White Female (W F) 68 DIVERSITY TOTAL

  17. YEAR

    National Nuclear Security Administration (NNSA)

    446 YEAR 2014 Males 1626 Females 820 YEAR 2014 SES 97 EX 2 ED 1 SL 1 EJ/EK 84 EN 05 38 EN 04 162 EN 03 18 NN (Engineering) 427 NQ (Prof/Tech/Admin) 1216 NU (Tech/Admin Support) 66 NV (Nuc Mat Courier) 327 GS 15 2 GS 14 2 GS 13 2 GS 10 1 YEAR 2014 American Indian Alaska Native Male (AIAN M) 27 American Indian Alaskan Native Female (AIAN F) 24 African American Male (AA M) 90 African American Female (AA F) 141 Asian American Pacific Islander Male (AAPI M) 63 Asian American Pacific Islander Female

  18. YEAR

    National Nuclear Security Administration (NNSA)

    1 YEAR 2014 Males 48 Females 33 PAY PLAN YEAR 2014 SES 1 EJ/EK 8 EN 04 10 EN 03 1 NN (Engineering) 27 NQ (Prof/Tech/Admin) 29 NU (Tech/Admin Support) 5 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 3 African American Male (AA M) 0 African American Female (AA F) 2 Asian American Pacific Islander Male (AAPI M) 2 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 12 Hispanic Female (H F) 12 White Male (W M) 34 White Female

  19. NREL: Transmission Grid Integration - Forecasting

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

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

  20. YEAR

    National Nuclear Security Administration (NNSA)

    White Male (W M) 26 White Female (W F) 16 DIVERSITY TOTAL WORKFORCE GENDER Livermore Field ... YEARS OF FEDERAL SERVICE SUPERVISOR RATIO AGE Livermore Field Office As of March 22, 2014 ...

  1. Flood Forecasting in River System Using ANFIS

    SciTech Connect (OSTI)

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

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

  2. YEAR

    National Nuclear Security Administration (NNSA)

    25 Females 10 YEAR 2014 SES 1 EN 04 11 NN (Engineering) 8 NQ (Prof/Tech/Admin) 13 NU (Tech/Admin Support) 2 YEAR 2014 American Indian Alaska Native Male (AIAN M) 0 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 1 African American Female (AA F) 3 Asian American Pacific Islander Male (AAPI M) 0 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 0 Hispanic Female (H F) 0 White Male (W M) 24 White Female (W F) 6 TOTAL WORKFORCE GENDER Kansas City

  3. YEAR

    National Nuclear Security Administration (NNSA)

    17 Females 18 PAY PLAN YEAR 2014 SES 1 EJ/EK 3 NQ (Prof/Tech/Admin) 30 NU (Tech/Admin Support) 1 YEAR 2014 American Indian Alaska Native Male (AIAN M) 1 American Indian Alaskan Native Female (AIAN F) 2 African American Male (AA M) 3 African American Female (AA F) 7 Asian American Pacific Islander Male (AAPI M) 1 Asian American Pacific Islander Female (AAPI F) 0 Hispanic Male (H M) 2 Hispanic Female (H F) 6 White Male (W M) 10 White Female (W F) 3 DIVERSITY TOTAL WORKFORCE GENDER Associate

  4. YEAR

    National Nuclear Security Administration (NNSA)

    8 Females 25 PAY PLAN YEAR 2014 SES 1 EJ/EK 3 EN 05 1 EN 04 25 EN 03 1 NN (Engineering) 25 NQ (Prof/Tech/Admin) 25 NU (Tech/Admin Support) 2 YEAR 2014 American Indian Alaska Native Male (AIAN M) 1 American Indian Alaskan Native Female (AIAN F) 1 African American Male (AA M) 3 African American Female (AA F) 3 Asian American Pacific Islander Male (AAPI M) 2 Asian American Pacific Islander Female (AAPI F) 2 Hispanic Male (H M) 6 Hispanic Female (H F) 6 White Male (W M) 46 White Female (W F) 13

  5. YEAR

    National Nuclear Security Administration (NNSA)

    -9.09% YEAR 2012 2013 SES 1 1 0.00% EN 05 1 1 0.00% EN 04 11 11 0.00% NN (Engineering) 8 8 0.00% NQ (ProfTechAdmin) 17 14 -17.65% NU (TechAdmin Support) 2 2...

  6. YEAR

    National Nuclear Security Administration (NNSA)

    Females 863 YEAR 2013 SES 102 EX 3 SL 1 EJEK 89 EN 05 41 EN 04 170 EN 03 18 NN (Engineering) 448 NQ (ProfTechAdmin) 1249 NU (TechAdmin Support) 76 NV (Nuc Mat Courier) 321...

  7. 2016 Solar Forecasting Workshop

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  8. 300 Area D4 Project Fiscal Year 2009 Building Completion Report

    SciTech Connect (OSTI)

    B. J. Skwarek

    2010-01-27

    This report summarizes the deactivation, decontamination, decommissioning, and demolition activities of seven facilities in the 300 Area of the Hanford Site in fiscal year 2009. The D4 of these facilities included characterization; engineering; removal of hazardous and radiologically contaminated materials; equipment removal; utility disconnection; deactivation, decontamination, demolition of the structure; and stabilization or removal of slabs and foundations. This report also summarizes the nine below-grade slabs/foundations removed in FY09 of buildings demolished in previous fiscal years.

  9. Today's Forecast: Improved Wind Predictions

    Broader source: Energy.gov [DOE]

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

  10. 300 Area D4 Project Fiscal Year 2008 Building Completion Report

    SciTech Connect (OSTI)

    R. A. Westberg

    2009-01-15

    This report documents the deactivation, decontamination, decommissioning, and demolition (D4) of eighteen buildings in the 300 Area of the Hanford Site that were demolished in Fiscal Year 2008. The D4 of these facilties included characterization, engineering, removal of hazardous and radiologically contaminated materials, equipment removal, utility disconnection, deactivation, decontamination, demolition of the structure, and stabilization or removal of the remaining slab and foundation, as appropriate.

  11. YEAR

    National Nuclear Security Administration (NNSA)

    2012 2013 SES 2 1 -50.00% EJEK 10 9 -10.00% EN 04 27 24 -11.11% NN (Engineering) 28 24 -14.29% NQ (ProfTechAdmin) 31 29 -6.45% NU (TechAdmin Support) 4...

  12. YEAR

    National Nuclear Security Administration (NNSA)

    2013 SES 2 2 0.00% EJEK 7 8 14.29% EN 04 11 11 0.00% EN 03 1 1 0.00% NN (Engineering) 23 24 4.35% NQ (ProfTechAdmin) 35 32 -8.57% NU (TechAdmin Support) 3 2...

  13. Solar Forecast Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  14. 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.

  15. Acquisition Forecast | Department of Energy

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

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

  16. National radon database documentation. Volume 4. The EPA/state residential radon surveys: Year 4. Final report 1986-1992

    SciTech Connect (OSTI)

    Not Available

    1993-01-01

    The National Radon Database has been developed by the U.S. Environmental Protection Agency (EPA) to distribute information collected in two recently completed radon surveys: the EPA/State Residential Radon Surveys, Years 1 to 6; and The National Residential Radon Survey. The goals of the state radon surveys were twofold. Some measure of the distribution of radon levels among residences was desired for major geographic areas within each state and for each state as a whole. In addition, it was desired that each state survey would be able to identify areas of potentially high residential radon concentrations (hot spots) in the state, enabling the state to focus its attention on areas where indoor radon concentrations might pose a greater health threat. The document discusses year 4, 1989-90. The areas surveyed are: California; Hawaii; Idaho; Louisiana; Nebraska; Billings, MT IHS Area; Nevada; North Carolina; Oklahoma; South Carolina; and Navajo Nation.

  17. Potentially biogenic carbon preserved in a 4.1 billion-year-old zircon

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

    Bell, Elizabeth A.; Boehnke, Patrick; Harrison, T. Mark; Mao, Wendy L.

    2015-10-19

    Here, evidence of life on Earth is manifestly preserved in the rock record. However, the microfossil record only extends to ~3.5 billion years (Ga), the chemofossil record arguably to ~3.8 Ga, and the rock record to 4.0 Ga. Detrital zircons from Jack Hills, Western Australia range in age up to nearly 4.4 Ga. From a population of over 10,000 Jack Hills zircons, we identified one >3.8-Ga zircon that contains primary graphite inclusions. Here, we report carbon isotopic measurements on these inclusions in a concordant, 4.10 ± 0.01-Ga zircon. We interpret these inclusions as primary due to their enclosure in amore » crack-free host as shown by transmission X-ray microscopy and their crystal habit. Their δ13CPDB of –24 ± 5‰ is consistent with a biogenic origin and may be evidence that a terrestrial biosphere had emerged by 4.1 Ga, or ~300 My earlier than has been previously proposed.« less

  18. Potentially biogenic carbon preserved in a 4.1 billion-year-old zircon

    SciTech Connect (OSTI)

    Bell, Elizabeth A.; Boehnke, Patrick; Harrison, T. Mark; Mao, Wendy L.

    2015-10-19

    Here, evidence of life on Earth is manifestly preserved in the rock record. However, the microfossil record only extends to ~3.5 billion years (Ga), the chemofossil record arguably to ~3.8 Ga, and the rock record to 4.0 Ga. Detrital zircons from Jack Hills, Western Australia range in age up to nearly 4.4 Ga. From a population of over 10,000 Jack Hills zircons, we identified one >3.8-Ga zircon that contains primary graphite inclusions. Here, we report carbon isotopic measurements on these inclusions in a concordant, 4.10 ± 0.01-Ga zircon. We interpret these inclusions as primary due to their enclosure in a crack-free host as shown by transmission X-ray microscopy and their crystal habit. Their δ13CPDB of –24 ± 5‰ is consistent with a biogenic origin and may be evidence that a terrestrial biosphere had emerged by 4.1 Ga, or ~300 My earlier than has been previously proposed.

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

    SciTech Connect (OSTI)

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

    2015-10-30

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

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

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

    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 ...

  1. Tennessee health studies agreement. Annual report for year 4, January 1--December 31, 1995

    SciTech Connect (OSTI)

    1996-04-01

    The Tennessee Department of Health (TDH) has completed the fourth full year of the Oak Ridge Health Studies Agreement grant. This report summarizes the accomplishments and concerns of the State for the period January 1, 1995, to December 31, 1995. The focus of work during the fourth grant year was the actual work on the dose reconstruction. The final work plan for Task 5, Plan to Perform a Systematic Document Search was received in November 1994. Final work plans for Task 1, Investigation of Radioiodine from Radioactive Lanthanum Processing; Task 2, Investigation of Mercury Releases from Lithium Enrichment; Task 3, Investigation of Releases of PCBs from Oak Ridge Facilities; and Task 4, Investigation of Releases of Radionuclides from White Oak Creek to the Clinch River, were received in February 1995. Final work plans for Task 6, Investigation of the Quality of Historical Uranium Effluent Monitoring at Oak Ridge Facilities; and Task 7, Additional Screening of Materials Not Evaluated in the Dose Reconstruction Feasibility Study, were received in April 1995. ChemRisk`s 4th Quarterly Report, for October through December 1995, is included in Attachment 1. Attachment 2 contains a study which developed a quality improvement program for data imported to the Tennessee Cancer Reporting System and Birth Defects Verification Program.

  2. 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.

  3. 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.

  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. 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)

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

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

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

  7. 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.

  8. The forecast calls for flu

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

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

  9. 300 Area D4 Project Fiscal Year 2010 Building Completion Report

    SciTech Connect (OSTI)

    Skwarek, B. J.

    2011-01-27

    This report summarizes the deactiviation, decontamination, decommissioning, and demolition activities of facilities in the 300 Area of the Hanford Site in fiscal year 2010.

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

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

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

  11. 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:...

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

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

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

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

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

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

  14. 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.

  15. 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

    (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.

  16. Diagnosis of the Marine Low Cloud Simulation in the NCAR Community Earth System Model (CESM) and the NCEP Global Forecast System (GFS)-Modular Ocean Model v4 (MOM4) coupled model

    SciTech Connect (OSTI)

    Xiao, Heng; Mechoso, C. R.; Sun, Rui; Han, J.; Pan, H. L.; Park, S.; Hannay, Cecile; Bretherton, Christopher S.; Teixeira, J.

    2014-07-25

    We present a diagnostic analysis of the marine low cloud climatology simulated by two state-of-the-art coupled atmosphere-ocean models: the NCAR Community Earth System Model (CESM) and the NCEP Global Forecasting System (GFS). In both models, the shallow convection and boundary layer turbulence parameterizations have been recently updated: both models now use a mass-flux scheme for the parameterization of shallow convection, and a turbulence parameterization capable of handling Stratocumulus (Sc)-topped Planetary Boundary Layers (PBLs). For shallow convection, both models employ a convective trigger function based on the concept of convective inhibition and both include explicit convective overshooting/penetrative entrainment formulation. For Sc-topped PBL, both models treat explicitly turbulence mixing and cloud-top entrainment driven by cloud-top radiative cooling. Our focus is on the climatological transition from Sc to shallow Cumulus (Cu)-topped PBL in the subtropical eastern oceans. We show that in the CESM the coastal Sc-topped PBLs in the subtropical Eastern Pacific are well-simulated but the climatological transition from Sc to shallow Cu is too abrupt and happens too close to the coast. By contrast, in the GFS coupled simulation the coastal Sc amount and PBL depth are severely underestimated while the transition from Sc to shallow Cu is delayed and offshore Sc cover is too extensive in the subtropical Eastern Pacific. We discuss the possible connections between such differences in the simulations and differences in the parameterizations of shallow convection and boundary layer turbulence in the two models.

  17. Fact #765: February 4, 2013 EPA's Top 10 Conventionally-Fueled Vehicles for Model Year 2013

    Broader source: Energy.gov [DOE]

    For the 2013 model year, the Toyota Prius and smaller Prius c took the top spot with a combined average of 50 mpg. All vehicles making this list are hybrid vehicles, and six of the ten cars making...

  18. 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

  19. 300 Area D4 Project Fiscal Year 2007 Building Completion Report

    SciTech Connect (OSTI)

    R. A. Westberg

    2009-01-15

    This report documents the deactivation, decontamination, decommissioning, and demolition (D4) of twenty buildings in the 300 Area of the Hanford Site. The D4 of these facilties included characterization, engineering, removal of hazardous and radiologically contaminated materials, equipment removal, utility disconnection, deactivation, decontamination, demolition of the structure, and stabilization or removal of the remaining slab and foundation, as appropriate.

  20. Arrow Lakes Reservoir Fertilization Experiment; Years 4 and 5, Technical Report 2002-2003.

    SciTech Connect (OSTI)

    Schindler, E.

    2007-02-01

    This report presents the fourth and fifth year (2002 and 2003, respectively) of a five-year fertilization experiment on the Arrow Lakes Reservoir. The goal of the experiment was to increase kokanee populations impacted from hydroelectric development on the Arrow Lakes Reservoir. The impacts resulted in declining stocks of kokanee, a native land-locked sockeye salmon (Oncorhynchus nerka), a key species of the ecosystem. Arrow Lakes Reservoir, located in southeastern British Columbia, has undergone experimental fertilization since 1999. It is modeled after the successful Kootenay Lake fertilization experiment. The amount of fertilizer added in 2002 and 2003 was similar to the previous three years. Phosphorus loading from fertilizer was 52.8 metric tons and nitrogen loading from fertilizer was 268 metric tons. As in previous years, fertilizer additions occurred between the end of April and the beginning of September. Surface temperatures were generally warmer in 2003 than in 2002 in the Arrow Lakes Reservoir from May to September. Local tributary flows to Arrow Lakes Reservoir in 2002 and 2003 were generally less than average, however not as low as had occurred in 2001. Water chemistry parameters in select rivers and streams were similar to previous years results, except for dissolved inorganic nitrogen (DIN) concentrations which were significantly less in 2001, 2002 and 2003. The reduced snow pack in 2001 and 2003 would explain the lower concentrations of DIN. The natural load of DIN to the Arrow system ranged from 7200 tonnes in 1997 to 4500 tonnes in 2003; these results coincide with the decrease in DIN measurements from water samples taken in the reservoir during this period. Water chemistry parameters in the reservoir were similar to previous years of study except for a few exceptions. Seasonal averages of total phosphorus ranged from 2.11 to 7.42 {micro}g/L from 1997 through 2003 in the entire reservoir which were indicative of oligo-mesotrophic conditions

  1. Acquisition Forecast Download | Department of Energy

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

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

  2. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01

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

  3. 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.

  4. Fuel Cell Technologies Office Multi-Year Research, Development, and Demonstration Plan - Section 3.4 Fuel Cells

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

    FUEL CELLS SECTION Multi-Year Research, Development, and Demonstration Plan Page 3.4 - 1 3.4 Fuel Cells Fuel cells efficiently convert diverse fuels directly into electricity without combustion, and they are key elements of a broad portfolio for building a competitive, secure, and sustainable clean energy economy. They offer a broad range of benefits, including reduced greenhouse gas emissions; reduced oil consumption; expanded use of renewable power (through the use of hydrogen derived from

  5. Fuel Cell Technologies Office Multi-Year Research, Development, and Demonstration Plan - Section 4.0 Systems Analysis

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

    ANALYSIS SECTION Multi-Year Research, Development, and Demonstration Plan Page 4.0 - 1 4.0 Systems Analysis The Fuel Cell Technologies Office (The Office) conducts a coordinated, comprehensive effort in modeling and analysis to clarify where hydrogen and fuel cells can be most effective from an economic, environmental, and energy security standpoint, as well as to guide RD&D priorities and set program goals. These activities support the Office's decision-making process by evaluating

  6. 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).

  7. 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).

  8. 300 Area D4 Project 3rd Quarter Fiscal Year 2006 Building Completion Report

    SciTech Connect (OSTI)

    D. S. Smith

    2006-09-25

    This report documents the deactivation, decontamination, decommissioning, and demolition of five buildings in the 300 Area of the Hanford Site. The D4 of these facilities included characterization, engineering, removal of hazardous and radiologically contaminated materials, equipment removal, utility disconnection, deactivation, decontamination, demolition of the structure, and stabilization or removal of the remaining slab and foundation as appropriate.

  9. 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

    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.

  10. 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

  11. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect (OSTI)

    Not Available

    1981-05-27

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

  12. Black liquor combustion validated recovery boiler modeling: Final year report. Volume 4 (Appendix IV)

    SciTech Connect (OSTI)

    Grace, T.M.; Frederick, W.J.; Salcudean, M.; Wessel, R.A.

    1998-08-01

    This project was initiated in October 1990, with the objective of developing and validating a new computer model of a recovery boiler furnace using a computational fluid dynamics (CFD) code specifically tailored to the requirements for solving recovery boiler flows, and using improved submodels for black liquor combustion based on continued laboratory fundamental studies. The key tasks to be accomplished were as follows: (1) Complete the development of enhanced furnace models that have the capability to accurately predict carryover, emissions behavior, dust concentrations, gas temperatures, and wall heat fluxes. (2) Validate the enhanced furnace models, so that users can have confidence in the predicted results. (3) Obtain fundamental information on aerosol formation, deposition, and hardening so as to develop the knowledge base needed to relate furnace model outputs to plugging and fouling in the convective sections of the boiler. (4) Facilitate the transfer of codes, black liquid submodels, and fundamental knowledge to the US kraft pulp industry. Volume 4 contains the following appendix sections: Radiative heat transfer properties for black liquor combustion -- Facilities and techniques and Spectral absorbance and emittance data; and Radiate heat transfer determination of the optical constants of ash samples from kraft recovery boilers -- Calculation procedure; Computation program; Density determination; Particle diameter determination; Optical constant data; and Uncertainty analysis.

  13. 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.

  14. Six-year optical monitoring of the BL Lacertae object 1ES 0806+52.4

    SciTech Connect (OSTI)

    Man, Zhongyi; Zhang, Xiaoyuan; Wu, Jianghua; Zhou, Xu; Yuan, Qirong

    2014-12-01

    We present the results of the first systematic long-term multicolor optical monitoring of the BL Lacertae object 1ES 0806+52.4. The monitoring was performed in multiple passbands with a 60/90 cm Schmidt telescope from 2005 December to 2011 February. The overall brightness of this object decreased from 2005 December to 2008 December but was regained after that. A sharp outburst probably occurred around the end of our monitoring program. Overlapping the long-term trend are some short-term small-amplitude oscillations. No intranight variability was found in the object, which is in accordance with the historical observations before 2005. By investigating the color behavior, we found a strong bluer-when-brighter chromatism for the long-term variability of 1ES 0806+52.4. The total amplitudes at the c, i, and o bands are 1.18, 1.12, and 1.02 mag, respectively. The amplitudes tend to increase toward shorter wavelengths, which may be a major cause of the bluer-when-brighter chromatism. Such bluer-when-brighter chromatisms are also found in other blazars, such as S5 0716+714, OJ 287. The hard-X-ray data collected from the Swift/BAT archive was correlated with our optical data. No positive result was found, the reason for which may be that the hard-X-ray flux is a combination of the synchrotron and inverse Compton emission, but with different timescales and cadences under the leptonic synchrotron self-Compton model.

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

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

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

  16. 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

  17. The Value of Wind Power Forecasting

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

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

  18. EIA lowers forecast for summer gasoline prices

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

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

  19. UPF Forecast | Y-12 National Security Complex

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

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

  20. 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

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

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

    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 ...

  2. Environmental support FY 1995 multi-year program plan/fiscal year work plan WBS 1.5.2/7.4.11

    SciTech Connect (OSTI)

    Moore, D.A.

    1994-09-01

    The multi-Year Program Plan (MYPP) is the programmatic planning baseline document for technical, schedule, and cost data. The MYPP contains data by which all work is managed, performed and controlled. The integrated planning process, defined by RL, is redicted on establishment of detailed data in the MYPP. The MYPP includes detailed information for the data elements including Level II critical path schedules, cost estimate detail, and updated technical data to be done annually. There will be baseline execution year and out year approval with work authorization for execution. The MYPP will concentrate on definition of the scope, schedule, cost and program element level critical path schedules that show the relationship of planned activities. The Fiscal Year Work Plan (FYWP) is prepared for each program to provide the basis for authorizing fiscal year work. The MYPP/FYWP will be structured into three main areas: (1) Program Overview; (2) Program Baselines; (3) Fiscal Year Work Plan.

  3. EnPI V4.0 Algorithm

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

    ... period is summed to determine the annual cost savings. ... SEnPI (unitless) Forecasting (Model year baseline year) Calculation for the model year: ...

  4. 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,

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

    Office of Energy Efficiency and Renewable Energy (EERE)

    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...

  6. 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

  7. LED Lighting Forecast | Department of Energy

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

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

  8. 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:...

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

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

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

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

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

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

  11. 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...

  12. NREL: Resource Assessment and Forecasting - Webmaster

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

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

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

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

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

  14. 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.

  15. Forecast of contracting and subcontracting opportunities: Fiscal year 1998

    SciTech Connect (OSTI)

    1998-01-01

    This report describes procurement procedures and opportunities for small businesses with the Department of Energy (DOE). It describes both prime and subcontracting opportunities of $100,000 and above which are being set aside for 8(a) and other small business concerns. The report contains sections on: SIC codes; procurement opportunities with headquarters offices; procurement opportunities with field offices; subcontracting opportunities with major contractors; 8(a) contracts expiring in FY 1998; other opportunities to do business with DOE; management and operating contractors--expiration dates; Office of Small and Disadvantaged Business Utilization (OSDBU) staff directory; and small business survey. This document will be updated quarterly on the home page.

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

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

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

  17. 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 ...

  18. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

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

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

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

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

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

  20. 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.

  1. 4

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

    -4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4 px x -0.006 -0.004 -0.002 0 0.002 0.004 0.006 -0.02 -0.015 -0.01 -0.005 0 0.005 0.01 0.015 0.02 px x(m) 50 100 50 100 0 10000 20000 30000 50 10

  2. Geek-Up[3.4.2011]: 3,000+ MW and 2,500 Year-Old Greek Pottery

    Broader source: Energy.gov [DOE]

    Bonneville Power Administration celebrates big windy milestone and researchers SLAC National Accelerator Laboratory study the surfaces of 2,500 year old Greek pottery -- all in this week's Geek-Up.

  3. WPN 00-4- Estimated 25% State Cost Share Requirement for the Weatherization Assistance Program for Program Year 2001

    Broader source: Energy.gov [DOE]

    To provide estimated figures for the states to begin their planning for the enacted 25% cost share requirement for funding of the low-income Weatherization Assistance Program beginning with Program Year 2001.

  4. Fact #871: May 4, 2015 Most Manufacturers Have Positive CAFE Credit Balances at the End of Model Year 2013

    Office of Energy Efficiency and Renewable Energy (EERE)

    At the end of the 2013 model year (MY), Toyota, which neither bought nor sold credits between 2010 and 2013, had by far the highest balance of Corporate Average Fuel Economy (CAFE) credits at more...

  5. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01

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

  6. Y YEAR

    National Nuclear Security Administration (NNSA)

    2 40 -4.76% YEAR 2013 2014 Males 37 35 -5.41% Females 5 5 0% YEAR 2013 2014 SES 2 2 0% EJEK 5 4 -20.00% EN 05 5 7 40.00% EN 04 6 6 0% EN 03 1 1 0% NN...

  7. 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...

  8. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  9. 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.

  10. 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.

  11. Text-Alternative Version LED Lighting Forecast

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  12. 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...

  13. Oak Ridge National Laboratory [ORNL] Review, Vol. 25, Nos. 3 and 4, 1992 [The First Fifty Years

    DOE R&D Accomplishments [OSTI]

    Krause, C.(ed.)

    1992-01-01

    In observation of the 50th anniversary of Oak Ridge National Laboratory, this special double issue of the Review contains a history of the Laboratory, complete with photographs, drawings, and short accompanying articles. Table of contents include: Wartime Laboratory; High-flux Years; Accelerating Projects; Olympian Feats; Balancing Act; Responding to Social Needs; Energy Technologies; Diversity and Sharing; Global Outreach; Epilogue

  14. 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.

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

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

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

  16. 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.

  17. 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.

  18. Electric-utility DSM programs: 1990 data and forecasts to 2000

    SciTech Connect (OSTI)

    Hirst, E.

    1992-06-01

    In April 1992, the Energy Information Administration (EIA) released data on 1989 and 1990 electric-utility demand-site management (DMS) programs. These data represent a census of US utility DSM programs, with reports of utility expenditures, energy savings, and load reductions caused by these programs. In addition, EIA published utility estimates of the costs and effects of these programs from 1991 to 2000. These data provide the first comprehensive picture of what utilities are spending and accomplishing by utility, state, and region. This report presents, summarizes, and interprets the 1990 data and the utility forecasts of their DSM-program expenditures and impacts to the year 2000. Only utilities with annual sales greater than 120 GWh were required to report data on their DSM programs to EIA. Of the 1194 such utilities, 363 reported having a DSM program that year. These 363 electric utilities spent $1.2 billion on their DSM programs in 1990, up from $0.9 billion in 1989. Estimates of energy savings (17,100 GWh in 1990 and 14,800 GWh in 1989) and potential reductions in peak demand (24,400 MW in 1990 and about 19,400 MW in 1989) also showed substantial increases. Overall, utility DSM expenditures accounted for 0.7% of total US electric revenues, while the reductions in energy and demand accounted for 0.6% and 4.9% of their respective 1990 national totals. The investor-owned utilities accounted for 70 to 90% of the totals for DSM costs, energy savings, and demand reductions. The public utilities reported larger percentage reductions in peak demand and energy smaller percentage DSM expenditures. These averages hide tremendous variations across utilities. Utility forecasts of DSM expenditures and effects show substantial growth in both absolute and relative terms.

  19. Spent nuclear fuels project: FY 1995 multi-year program plan, WBS {number_sign}1.4

    SciTech Connect (OSTI)

    Denning, J.L.

    1994-09-01

    The mission of the Spent Nuclear Fuel (SNF) program is to safely, reliably, and efficiently manage, condition, transport, and store Department of Energy (DOE)-owned SNF, so that it meets acceptance criteria for disposal in a permanent repository. The Hanford Site Spent Nuclear Fuel strategic plan for accomplishing the project mission is: Establish near-term safe storage in the 105-K Basins; Complete national Environmental Policy Act (NEPA) process to obtain a decision on how and where spent nuclear fuel will be managed on the site; Define and establish alternative interim storage on site or transport off site to support implementation of the NEPA decision; and Define and establish a waste package qualified for final disposition. This report contains descriptions of the following: Work Breakdown Structure; WBS Dictionary; Responsibility Assignment Matrix; Program Logic Diagrams; Program Master Baseline Schedule; Program Performance Baseline Schedule; Milestone List; Milestone Description Sheets; Cost Baseline Summary by Year; Basis of Estimate; Waste Type Data; Planned Staffing; and Fiscal Year Work Plan.

  20. 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.

  1. 4

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

    1663 July 2016 work in parallel on other aspects of fusion power. In addition to the U.S. Department of Energy's earlier commitment to ITER, its Advanced Research Projects Agency-Energy (ARPA-E) last year announced nine research grants "to create... new, lower-cost pathways to fusion power and to enable more rapid progress in fusion research and development." The largest of these grants, awarded jointly to Los Alamos National Laboratory and HyperV Technologies Corp., comes in at about

  2. Spent nuclear fuel project multi-year work plan WBS {number_sign}1.4.1

    SciTech Connect (OSTI)

    Wells, J.L.

    1997-03-01

    The Spent Nuclear Fuel (SNF) Project Multi-Year Work Plan (MYWP) is a controlled living document that contains the current SNF Project Technical, Schedule and Cost Baselines. These baselines reflect the current Project execution strategies and are controlled via the change control process. Other changes to the MYWP document will be controlled using the document control process. These changes will be processed as they are approved to keep the MYWP a living document. The MYWP will be maintained continuously as the project baseline through the life of the project and not revised annually. The MYWP is the one document which summarizes and links these three baselines in one place. Supporting documentation for each baseline referred to herein may be impacted by changes to the MYWP, and must also be revised through change control to maintain consistency.

  3. 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.

  4. Y YEAR

    National Nuclear Security Administration (NNSA)

    79 67 -15.19% YEAR 2013 2014 Males 44 34 -22.73% Females 35 33 -5.71% YEAR 2013 2014 SES 6 4 -33.33% EJEK 1 1 0% EN 05 9 8 -11.11% EN 04 6 5 -16.67% NN...

  5. SITE-LEVEL SUMMARY (4Q) of FINAL-4TH-QUARTER-FY-2014-SCORECARD...

    Office of Environmental Management (EM)

    ... FORECAST DATE ACTUAL DATE EA DATE STATUS NARRATIVE VARIANCE NARRATIVE REGULATORY AGREEMENT NAME DESIGNATIONS GREEN SHADED DATES ARE 4TH QUARTER FY 2014 MILESTONES ARRA Project: ...

  6. Builds in U.S. natural gas storage running above five-year average

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

    Builds in U.S. natural gas storage running above five-year average The amount of natural gas put into underground storage since the beginning of the so-called "injection season" in April has been above the five-year average by a wide margin. In its new forecast, the U.S. Energy Information Administration said natural gas inventories, which are running more than 50% above year ago levels, are on track to reach almost 4 trillion cubic feet by the end of October which marks the start of

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

    SciTech Connect (OSTI)

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

    2015-11-10

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

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

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

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

    2015-11-10

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

  9. Y YEAR

    National Nuclear Security Administration (NNSA)

    4 79 -5.95% ↓ YEAR 2013 2014 Males 59 55 -6.78% ↓ Females 25 24 -4.00% ↓ YEAR 2013 2014 SES 3 3 0% / EJ/EK 4 4 0% / EN 04 2 1 -50.00% ↓ NN (Engineering) 20 20 0% / NQ (Prof/Tech/Admin) 55 51 -7.27% ↓ YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 0 0 0% / American Indian Alaskan Native Female (AIAN,F) 0 0 0% / African American Male (AA,M) 10 10 0% / African American Female (AA,F) 9 8 -11.11% ↓ Asian American Pacific Islander Male (AAPI,M) 2 2 0% / Asian American Pacific

  10. Y YEAR

    National Nuclear Security Administration (NNSA)

    8 27 -3.57% ↓ YEAR 2013 2014 Males 18 17 -5.56% ↓ Females 10 10 0% / YEAR 2013 2014 SES 1 1 0% / EN 05 1 1 0% / EN 04 4 3 -25.00% ↓ NN (Engineering) 12 12 0% / NQ (Prof/Tech/Admin) 9 9 0% / NU (Tech/Admin Support) 1 1 0% / YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 0 0 0% / American Indian Alaskan Native Female (AIAN,F) 1 1 0% / African American Male (AA,M) 4 4 0% / African American Female (AA,F) 3 4 33.33% ↑ Asian American Pacific Islander Male (AAPI,M) 1 1 0% / Asian

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

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

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

  12. 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 ...

  13. 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.

  14. 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...

  15. 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.

  16. 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)

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

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

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

  18. 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.

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

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

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

  20. 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.

  1. Y YEAR

    National Nuclear Security Administration (NNSA)

    502 2381 -4.84% ↓ YEAR 2013 2014 Males 1663 1593 -4.21% ↓ Females 839 788 -6.08% ↓ YEAR 2013 2014 SES 104 90 -13.46% ↓ EX 2 4 100% ↑ SL 1 0 -100% ↓ EJ/EK 88 73 -17.05% ↓ EN 05 40 41 2.50% ↑ EN 04 169 157 -7.10% ↓ EN 03 18 21 100% ↑ EN 00 0 6 100% ↑ NN (Engineering) 441 416 -5.67% ↓ NQ (Prof/Tech/Admin) 1239 1190 -3.95% ↓ NU (Tech/Admin Support) 66 57 -13.64% ↓ NV (Nuc Mat Courier) 328 321 -2.13% ↓ GS 15 1 2 100% ↑ GS 13 2 2 0% / GS 10 3 1 -66.67% ↓ YEAR 2013

  2. Final Report on California Regional Wind Energy Forecasting Project:Application of NARAC Wind Prediction System

    SciTech Connect (OSTI)

    Chin, H S

    2005-07-26

    Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather forecasting has been recognized as an important factor to further improve the wind energy forecast for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather forecasting capability to develop an improved regional wind energy forecasting system. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and

  3. 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.

  4. Analysis of LaNi{sub 4.25}Al{sub 0.75} (LANA.75) tritide after five years of tritium exposure

    SciTech Connect (OSTI)

    Wermer, J.R.; Holder, J.S.; Mosley, W.C.

    1993-09-01

    Tritium aging studies have shown that LaNi{sub 4.25}Al{sub 0.75} (LANA .75) tritide storage material undergoes significant degradation with tritium aging. After 5.4 years of dormant storage at full stoichiometry, which is considered a worst-case condition for this material, the performance is still acceptable for SRS tritium processing applications. The isotherms change, decreasing the desorption pressures, increasing the isotherm plateau slopes, and decreasing the total storage capacity. Eventually, the material will degrade with time to the point where it may no longer be useful for tritium processing applications. At the end of life, the tritium heel can be exchanged with protium or deuterium to produce a final material containing very little tritium.

  5. HONEYWELL - KANSAS CITY PLANT FISCAL YEARS 2009 THRU 2015 SMALL...

    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 ...

  6. Expectations models of electric utilities' forecasts: a case study of econometric estimation with influential data points

    SciTech Connect (OSTI)

    Vellutini, R. de A.S.; Mount, T.D.

    1983-01-01

    This study develops an econometric model for explaining how electric utilities revise their forecasts of future electricity demand each year. The model specification is developed from the adaptive expectations hypothesis and it relates forecasted growth rates to actual lagged growth rates of electricity demand. Unlike other studies of the expectation phenomenon, expectations of future demand levels constitute an observable variable and thus can be incorporated explicitly into the model. The data used for the analysis were derived from the published forecasts of the nine National Electric Reliability Councils in the US for the years 1974 to 1980. Three alternative statistical methods are used for estimation purposes: ordinary least-squares, robust regression and a diagnostic analysis to identify influential observations. The results obtained with the first two methods are very similar, but are both inconsistent with the underlying economic logic of the model. The estimated model obtained from the diagnostics approach after deleting two aberrant observations is consistent with economic logic, and supports the hypothesis that the low growth demand experienced immediately following the oil embargo in 1973 were disregarded by the industry for forecasting purposes. The model includes transitory effects associated with the oil embargo that gradually disappear over time, the estimated coefficients for the lagged values of actual growth approach a structure with declining positive weights. The general shape of this asymptotic structure is similar to the findings in many economic applications using distributed lag models.

  7. Forecasting hotspots using predictive visual analytics approach

    SciTech Connect (OSTI)

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

    2014-12-30

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

  8. Y YEAR

    National Nuclear Security Administration (NNSA)

    5 79 -7.06% ↓ YEAR 2013 2014 Males 59 57 -3.39% ↓ Females 26 22 -15.38% ↓ YEAR 2013 2014 SES 1 0 -100% ↓ EJ/EK 4 3 -25.00% ↓ EN 05 3 2 -33.33% ↓ EN 04 22 22 0% / EN 03 8 8 0% / NN (Engineering) 16 15 -6.25% ↓ NQ (Prof/Tech/Admin) 28 26 -7.14% ↓ NU (Tech/Admin Support) 3 3 0% / YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 2 2 0% / American Indian Alaskan Native Female (AIAN,F) 1 1 0% / African American Male (AA,M) 5 4 -20.00% ↓ African American Female (AA,F) 3 2

  9. Y YEAR

    National Nuclear Security Administration (NNSA)

    21 -4.55% ↓ YEAR 2013 2014 Males 10 8 -20.00% ↓ Females 12 13 8.33% ↑ YEAR 2013 2014 SES 10 7 -30.00% ↓ EX 0 2 100% ↑ EJ/EK 1 1 0% / EN 05 0 1 100% ↑ EN 04 0 1 100% ↑ NQ (Prof/Tech/Admin) 9 8 -11.11% ↓ NU (Tech/Admin Support) 1 1 0% / ED 00 1 0 -100% ↓ YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 0 0 0% / American Indian Alaskan Native Female (AIAN,F) 2 1 -50.00% ↓ African American Male (AA,M) 1 1 0% / African American Female (AA,F) 5 4 -20.00% ↓ Asian

  10. Y YEAR

    National Nuclear Security Administration (NNSA)

    41 155 9.93% ↑ YEAR 2013 2014 Males 92 106 15.22% ↑ Females 49 49 0% / YEAR 2013 2014 SES 8 8 0% / EX 1 1 0% / EJ/EK 4 4 0% / EN 05 11 10 -9.09% ↓ EN 04 11 14 27.27% ↑ EN 03 2 5 150% ↑ NN (Engineering) 60 63 5.00% ↑ NQ (Prof/Tech/Admin) 44 50 13.64% ↑ YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 1 1 0% / American Indian Alaskan Native Female (AIAN,F) 1 1 0% / African American Male (AA,M) 7 10 42.86% ↑ African American Female (AA,F) 13 11 -15.38% ↓ Asian American

  11. Y YEAR

    National Nuclear Security Administration (NNSA)

    79 164 -8.38% ↓ YEAR 2013 2014 Males 100 92 -8.00% ↓ Females 79 72 -8.86% ↓ YEAR 2013 2014 SES 8 8 0% / EJ/EK 4 3 -25.00% ↓ EN 04 11 11 0% / EN 03 1 1 0% / EN 00 0 2 100% ↑ NN (Engineering) 39 32 -17.95% ↓ NQ (Prof/Tech/Admin) 111 104 -6.31% ↓ NU (Tech/Admin Support) 5 3 -40.00% ↓ YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 1 2 100% ↑ American Indian Alaskan Native Female (AIAN,F) 2 1 -50.00% ↓ African American Male (AA,M) 4 3 -25.00% ↓ African American

  12. 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.

  13. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

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

    2014-11-13

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

  14. Global disease monitoring and forecasting with Wikipedia

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

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

    2014-11-13

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

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

    SciTech Connect (OSTI)

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

    2015-08-05

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

  16. Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy

    SciTech Connect (OSTI)

    Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

    2014-08-15

    Purpose: To create models that forecast longitudinal trends in changing tumor morphology and to evaluate and compare their predictive potential throughout the course of radiation therapy. Methods: Two morphology feature vectors were used to describe 35 gross tumor volumes (GTVs) throughout the course of intensity-modulated radiation therapy for oropharyngeal tumors. The feature vectors comprised the coordinates of the GTV centroids and a description of GTV shape using either interlandmark distances or a spherical harmonic decomposition of these distances. The change in the morphology feature vector observed at 33 time points throughout the course of treatment was described using static, linear, and mean models. Models were adjusted at 0, 1, 2, 3, or 5 different time points (adjustment points) to improve prediction accuracy. The potential of these models to forecast GTV morphology was evaluated using leave-one-out cross-validation, and the accuracy of the models was compared using Wilcoxon signed-rank tests. Results: Adding a single adjustment point to the static model without any adjustment points decreased the median error in forecasting the position of GTV surface landmarks by the largest amount (1.2 mm). Additional adjustment points further decreased the forecast error by about 0.4 mm each. Selection of the linear model decreased the forecast error for both the distance-based and spherical harmonic morphology descriptors (0.2 mm), while the mean model decreased the forecast error for the distance-based descriptor only (0.2 mm). The magnitude and statistical significance of these improvements decreased with each additional adjustment point, and the effect from model selection was not as large as that from adding the initial points. Conclusions: The authors present models that anticipate longitudinal changes in tumor morphology using various models and model adjustment schemes. The accuracy of these models depended on their form, and the utility of these models

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  18. 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.

  19. Upcoming Funding Opportunity for Wind Forecasting Improvement Project in

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

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

  20. Year Modules

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

    Annual photovoltaic module shipments, 2004-2014 (peak kilowatts) Year Modules 2004 143,274 2005 204,996 2006 320,208 2007 494,148 2008 920,693 2009 1,188,879 2010 2,644,498 2011 3,772,075 2012 4,655,005 2013 4,984,881 2014 6,237,524 Source: U.S. Energy Information Administration, Form EIA-63B, 'Annual Photovoltaic Cell/Module Shipments Report.' Note: Includes both U.S. Shipments and Exports.

  1. Y YEAR

    National Nuclear Security Administration (NNSA)

    91 81 -10.99% ↓ YEAR 2013 2014 Males 67 56 -16.42% ↓ Females 24 25 4.17% ↑ YEAR 2013 2014 SES 1 2 100% ↑ EJ/EK 9 8 -11.11% ↓ EN 04 25 22 -12.00% ↓ NN (Engineering) 24 20 -16.67% ↓ NQ (Prof/Tech/Admin) 29 26 -10.34% ↓ NU (Tech/Admin Support) 3 3 0% / YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 2 2 0% / American Indian Alaskan Native Female (AIAN,F) 3 3 0% / African American Male (AA,M) 0 0 0% / African American Female (AA,F) 0 0 0% / Asian American Pacific Islander

  2. Y YEAR

    National Nuclear Security Administration (NNSA)

    97 180 -8.63% ↓ YEAR 2013 2014 Males 105 89 -15.24% ↓ Females 92 91 -1.09% ↓ YEAR 2013 2014 SES 14 13 -7.14% ↓ EX 1 1 0% / EJ/EK 3 3 0% / EN 05 1 1 0% / EN 04 4 2 -50.00% ↓ EN 03 1 1 0% / EN 00 0 3 100% ↑ NN (Engineering) 35 27 -22.86% ↓ NQ (Prof/Tech/Admin) 135 126 -6.67% ↓ NU (Tech/Admin Support) 2 2 0% / GS 15 0 1 100% ↑ GS 13 1 0 -100% ↓ YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 2 1 -50.00% ↓ American Indian Alaskan Native Female (AIAN,F) 0 0 0% /

  3. Y YEAR

    National Nuclear Security Administration (NNSA)

    8 87 -1.14% ↓ YEAR 2013 2014 Males 46 46 0% / Females 42 41 -2.38% ↓ YEAR 2013 2014 SES 1 0 -100% ↓ EJ/EK 4 2 -50.00% ↓ NN (Engineering) 12 12 0% / NQ (Prof/Tech/Admin) 68 70 2.94% ↑ NU (Tech/Admin Support) 3 3 0% / YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 0 0 0% / American Indian Alaskan Native Female (AIAN,F) 2 2 0% / African American Male (AA,M) 5 5 0% / African American Female (AA,F) 5 6 20.00% ↑ Asian American Pacific Islander Male (AAPI,M) 0 0 0% / Asian

  4. Y YEAR

    National Nuclear Security Administration (NNSA)

    1 14 27.27% ↑ YEAR 2013 2014 Males 9 12 33.33% ↑ Females 2 2 0% / YEAR 2013 2014 SES 2 2 0% / EJ/EK 1 1 0% / EN 04 0 1 100% ↑ EN 00 0 1 100% ↑ NN (Engineering) 5 5 0% / NQ (Prof/Tech/Admin) 3 4 33.33% ↑ YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 0 0 0% / American Indian Alaskan Native Female (AIAN,F) 0 0 0% / African American Male (AA,M) 0 0 0% / African American Female (AA,F) 0 0 0% / Asian American Pacific Islander Male (AAPI,M) 1 1 0% / Asian American Pacific

  5. Y YEAR

    National Nuclear Security Administration (NNSA)

    4 30 -11.76% ↓ YEAR 2013 2014 Males 16 14 -12.50% ↓ Females 18 16 -11.11% ↓ YEAR 2013 2014 SES 1 1 0% / EJ/EK 3 1 -66.67% ↓ NQ (Prof/Tech/Admin) 29 27 -6.90% ↓ NU (Tech/Admin Support) 1 1 0% / YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 1 1 0% / American Indian Alaskan Native Female (AIAN,F) 2 2 0% / African American Male (AA,M) 3 3 0% / African American Female (AA,F) 7 6 -14.29% ↓ Asian American Pacific Islander Male (AAPI,M) 1 1 0% / Asian American Pacific Islander

  6. Y YEAR

    National Nuclear Security Administration (NNSA)

    80 83 3.75% ↑ YEAR 2013 2014 Males 48 50 4.17% ↑ Females 32 33 3.13% ↑ YEAR 2013 2014 SES 2 1 -50.00% ↓ EJ/EK 8 7 -12.50% ↓ EN 04 11 9 -18.18% ↓ EN 03 1 1 0% / NN (Engineering) 24 27 12.50% ↑ NQ (Prof/Tech/Admin) 32 33 3.13% ↑ NU (Tech/Admin Support) 2 5 150% ↑ YEAR 2013 2014 American Indian Alaska Native Male (AIAN,M) 0 0 0% / American Indian Alaskan Native Female (AIAN,F) 3 3 0% / African American Male (AA,M) 0 0 0% / African American Female (AA,F) 2 2 0% / Asian American

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

    SciTech Connect (OSTI)

    Eisenberg, Joel F.

    2005-10-31

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

  8. 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 " " % % &...

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

    Office of Scientific and Technical Information (OSTI)

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

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

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

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

  11. 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.

  12. 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...

  13. 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...

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

    Energy Savers [EERE]

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

  15. 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 ...

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

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

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

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

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

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

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

  19. ANL Software Improves Wind Power Forecasting | Department of...

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

    ... The licensing arrangement helps to facilitate transfer of the statistical learning algorithms developed in the project to industry use. A leading forecast provider in the United ...

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  1. Selected papers on fuel forecasting and analysis

    SciTech Connect (OSTI)

    Gordon, R.L.; Prast, W.G.

    1983-05-01

    Of the 19 presentations at this seminar, covering coal, uranium, oil, and gas issues as well as related EPRI research projects, eleven papers are published in this volume. Nine of the papers primarily address coal-market analysis, coal transportation, and uranium supply. Two additional papers provide an evaluation and perspective on the art and use of coal-supply forecasting models and on the relationship between coal and oil prices. The authors are energy analysts and EPRI research contractors from academia, the consulting profession, and the coal industry. A separate abstract was prepared for each of the 11 papers.

  2. Twenty years after '95: What climate change means for heat waves...

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

    Twenty years after '95: What climate change means for heat waves, cities and forecasting ... "In the last few years, there's been a big push to get instruments into urban areas." ...

  3. 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.

  4. 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

  5. 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

  6. Technical analysis in short-term uranium price forecasting

    SciTech Connect (OSTI)

    Schramm, D.S.

    1990-03-01

    As market participants anticipate the end of the current uranium price decline and its subsequent reversal, increased attention will be focused upon forecasting future price movements. Although uranium is economically similar to other mineral commodities, it is questionable whether methodologies used to forecast price movements of such commodities may be successfully applied to uranium.

  7. 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

  8. Fact #871: May 4, 2015 Most Manufacturers Have Positive CAFE Credit Balances at the End of Model Year 2013 – Dataset

    Broader source: Energy.gov [DOE]

    Excel file and dataset for Most Manufacturers Have Positive CAFE Credit Balances at the End of Model Year 2013

  9. 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.

  10. 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