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  1. 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

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

    SciTech Connect (OSTI)

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

    1994-05-01

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

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

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-04-23

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

  4. 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.

  5. Residential sector end-use forecasting with EPRI-Reeps 2.1: Summary input assumptions and results

    SciTech Connect (OSTI)

    Koomey, J.G.; Brown, R.E.; Richey, R.

    1995-12-01

    This paper describes current and projected future energy use by end-use and fuel for the U.S. residential sector, and assesses which end-uses are growing most rapidly over time. The inputs to this forecast are based on a multi-year data compilation effort funded by the U.S. Department of Energy. We use the Electric Power Research Institute`s (EPRI`s) REEPS model, as reconfigured to reflect the latest end-use technology data. Residential primary energy use is expected to grow 0.3% per year between 1995 and 2010, while electricity demand is projected to grow at about 0.7% per year over this period. The number of households is expected to grow at about 0.8% per year, which implies that the overall primary energy intensity per household of the residential sector is declining, and the electricity intensity per household is remaining roughly constant over the forecast period. These relatively low growth rates are dependent on the assumed growth rate for miscellaneous electricity, which is the single largest contributor to demand growth in many recent forecasts.

  6. 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.

  7. 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

  8. 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

  9. SOLID WASTE INTEGRATED FORECAST TECHNICAL (SWIFT) REPORT FY2005 THRU FY2035 2005.0 VOLUME 2

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2005-08-17

    This report provides up-to-date life cycle information about the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: (1) an overview of Hanford-wide solid waste to be managed by the WM Project; (2) multi-level and waste class-specific estimates; (3) background information on waste sources; and (4) comparisons to previous forecasts and other national data sources. The focus of this report is low-level waste (LLW), mixed low-level waste (MLLW), and transuranic waste, both non-mixed and mixed (TRU(M)). Some details on hazardous waste are also provided, however, this information is not considered comprehensive. This report includes data requested in December, 2004 with updates through March 31,2005. The data represent a life cycle forecast covering all reported activities from FY2005 through the end of each program's life cycle and are an update of the previous FY2004.1 data version.

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

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

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

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

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

    turbines operate closer to maximum capacity, leading to lower energy costs for consumers. ... for the Weather Research and Forecasting model, a widely used weather prediction system. ...

  12. 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....

  13. U.S. diesel fuel price forecast to be 1 penny lower this summer at $3.94 a gallon

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

    diesel fuel price forecast to be 1 penny lower this summer at $3.94 a gallon The retail price of diesel fuel is expected to average $3.94 a gallon during the summer driving season that which runs from April through September. That's close to last summer's pump price of $3.95, according to the latest monthly energy outlook from the U.S. Energy Information Administration. Demand for distillate fuel, which includes diesel fuel, is expected to be up less than 1 percent from last summer. Daily

  14. Simulations of arctic mixed-phase clouds in forecasts with CAM3 and AM2 for M-PACE

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

    Xie, Shaocheng; Boyle, James; Klein, Stephen A.; Liu, Xiaohong; Ghan, Steven

    2008-02-27

    [1] Simulations of mixed-phase clouds in forecasts with the NCAR Atmosphere Model version 3 (CAM3) and the GFDL Atmospheric Model version 2 (AM2) for the Mixed-Phase Arctic Cloud Experiment (M-PACE) are performed using analysis data from numerical weather prediction centers. CAM3 significantly underestimates the observed boundary layer mixed-phase cloud fraction and cannot realistically simulate the variations of liquid water fraction with temperature and cloud height due to its oversimplified cloud microphysical scheme. In contrast, AM2 reasonably reproduces the observed boundary layer cloud fraction while its clouds contain much less cloud condensate than CAM3 and the observations. The simulation of themore » boundary layer mixed-phase clouds and their microphysical properties is considerably improved in CAM3 when a new physically based cloud microphysical scheme is used (CAM3LIU). The new scheme also leads to an improved simulation of the surface and top of the atmosphere longwave radiative fluxes. Sensitivity tests show that these results are not sensitive to the analysis data used for model initialization. Increasing model horizontal resolution helps capture the subgrid-scale features in Arctic frontal clouds but does not help improve the simulation of the single-layer boundary layer clouds. AM2 simulated cloud fraction and LWP are sensitive to the change in cloud ice number concentrations used in the Wegener-Bergeron-Findeisen process while CAM3LIU only shows moderate sensitivity in its cloud fields to this change. Furthermore, this paper shows that the Wegener-Bergeron-Findeisen process is important for these models to correctly simulate the observed features of mixed-phase clouds.« less

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

    SciTech Connect (OSTI)

    McNamara, Laura A.

    2010-08-01

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

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

    SciTech Connect (OSTI)

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

    1997-01-07

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

  17. 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.

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2000-08-31

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

  19. 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.

  20. Forecasting the market for SO sub 2 emission allowances under uncertainty

    SciTech Connect (OSTI)

    Hanson, D.; Molburg, J.; Fisher, R.; Boyd, G.; Pandola, G.; Lurie, G.; Taxon, T.

    1991-01-01

    This paper deals with the effects of uncertainty and risk aversion on market outcomes for SO{sub 2} emission allowance prices and on electric utility compliance choices. The 1990 Clean Air Act Amendments (CAAA), which are briefly reviewed here, provide for about twice as many SO{sub 2} allowances to be issued per year in Phase 1 (1995--1999) than in Phase 2. Considering the scrubber incentives in Phase 1, there is likely to be substantial emission banking for use in Phase 2. Allowance prices are expected to increase over time at a rate less than the return on alternative investments, so utilities which are risk neutral, or potential speculators in the allowance market, are not expected to bank allowances. The allowances will be banked by utilities that are risk averse. The Argonne Utility Simulation Model (ARGUS2) is being revised to incorporate the provisions of the CAAA acid rain title and to simulate SO{sub 2} allowance prices, compliance choices, capacity expansion, system dispatch, fuel use, and emissions using a unit level data base and alternative scenario assumptions. 1 fig.

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

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

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

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

    SciTech Connect (OSTI)

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

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

  4. Beamline 2.1

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

    2.1 Beamline 2.1 Print Tuesday, 20 October 2009 08:16 National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate

  5. Beamline 2.1

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

    linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (EE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field...

  6. Slide 1

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

    the 2 nd Quarter Forecast of negative 125 million. This forecast reflects: - Reduced Revenue forecast due to lower streamflows and dropping prices to date as well as expectations...

  7. Beamline 2.1

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

    2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x 2048-pixel

  8. Beamline 2.1

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

    2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x 2048-pixel

  9. Beamline 2.1

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

    Beamline 2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x

  10. Beamline 2.1

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

    2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x 2048-pixel

  11. Beamline 2.1

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

    Beamline 2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x

  12. Beamline 2.1

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

    Beamline 2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x

  13. Beamline 2.1

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

    Beamline 2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x

  14. Beamline 2.1

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

    2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x 2048-pixel

  15. Beamline 2.1

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

    2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x 2048-pixel

  16. Beamline 2.1

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

    2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x 2048-pixel

  17. Beamline 2.1

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

    2.1 Print National Center for X-Ray Tomography (NCXT) Scientific discipline: Biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 400 - 1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 2048 x 2048 pixels Resolving power (E/ĢE) 500-700 Endstations X-ray microscope (XM-2) Characteristics Full-field soft x-ray transmission microscope Spatial resolution Zone-plate dependent Detectors Back-thinned 2048- x 2048-pixel

  18. 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

  19. Slide 1

    Office of Environmental Management (EM)

    ... Project Success by Fiscal Year Baselined (Original CD-2) EM Project Success by Original ... Forecast for active CD-23 projects 0% 0 of 1 0% 0 of 1 0% 0 of 2 -- 50% 1 of 2 -- -- 100% ...

  20. Hawaii demand-side management resource assessment. Final report, Reference Volume 5: The DOETRAN user`s manual; The DOE-2/DBEDT DSM forecasting model interface

    SciTech Connect (OSTI)

    1995-04-01

    The DOETRAN model is a DSM database manager, developed to act as an intermediary between the whole building energy simulation model, DOE-2, and the DBEDT DSM Forecasting Model. DOETRAN accepts output data from DOE-2 and TRANslates that into the format required by the forecasting model. DOETRAN operates in the Windows environment and was developed using the relational database management software, Paradox 5.0 for Windows. It is not necessary to have any knowledge of Paradox to use DOETRAN. DOETRAN utilizes the powerful database manager capabilities of Paradox through a series of customized user-friendly windows displaying buttons and menus with simple and clear functions. The DOETRAN model performs three basic functions, with an optional fourth. The first function is to configure the user`s computer for DOETRAN. The second function is to import DOE-2 files with energy and loadshape data for each building type. The third main function is to then process the data into the forecasting model format. As DOETRAN processes the DOE-2 data, graphs of the total electric monthly impacts for each DSM measure appear, providing the user with a visual means of inspecting DOE-2 data, as well as following program execution. DOETRAN provides three tables for each building type for the forecasting model, one for electric measures, gas measures, and basecases. The optional fourth function provided by DOETRAN is to view graphs of total electric annual impacts by measure. This last option allows a comparative view of how one measure rates against another. A section in this manual is devoted to each of the four functions mentioned above, as well as computer requirements and exiting DOETRAN.

  1. Waste Generation Forecast for DOE-ORO`s Environmental Restoration OR-1 Project: FY 1994--FY 2001. Environmental Restoration Program, September 1993 Revision

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

    This Waste Generation Forecast for DOE-ORO`s Environmental Restoration OR-1 Project. FY 1994--FY 2001 is the third in a series of documents that report current estimates of the waste volumes expected to be generated as a result of Environmental Restoration activities at Department of Energy, Oak Ridge Operations Office (DOE-ORO), sites. Considered in the scope of this document are volumes of waste expected to be generated as a result of remedial action and decontamination and decommissioning activities taking place at these sites. Sites contributing to the total estimates make up the DOE-ORO Environmental Restoration OR-1 Project: the Oak Ridge K-25 Site, the Oak Ridge National Laboratory, the Y-12 Plant, the Paducah Gaseous Diffusion Plant, the Portsmouth Gaseous Diffusion Plant, and the off-site contaminated areas adjacent to the Oak Ridge facilities (collectively referred to as the Oak Ridge Reservation Off-Site area). Estimates are available for the entire fife of all waste generating activities. This document summarizes waste estimates forecasted for the 8-year period of FY 1994-FY 2001. Updates with varying degrees of change are expected throughout the refinement of restoration strategies currently in progress at each of the sites. Waste forecast data are relatively fluid, and this document represents remediation plans only as reported through September 1993.

  2. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect (OSTI)

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

    2015-06-23

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

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

    SciTech Connect (OSTI)

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

    2015-06-23

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

  4. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

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

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

    2015-06-23

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

  5. 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.

  6. " East North Central",1.7,1.7,1.8,1.8,1.9,2

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

    " 5 or More Persons",2.1,2.2,2.3,2,2.2,2.5 "Household Composition" " Households With Children","NA","NA",2,2,2,2.2 " Age of Oldest Child" " Under 7 Years","NA","NA",1.8,1.8,1.8,2...

  7. Slide 1

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

    ... 2,494,329 1,008,279 28.8% 08121-2801-02 GOMEX 3-D Operational Ocean Forecast System Pilot Project Portland State University 031110 030114 1,560,000 1,248,000 312,000 ...

  8. 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...

  9. 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 ...

  10. 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...

  11. 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. ...

  12. Beamline 6.1.2

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

    Beamline 6.1.2 Beamline 6.1.2 Print Tuesday, 20 October 2009 08:41 Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics,...

  13. Beamline 6.1.2

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

    6.1.2 Beamline 6.1.2 Print Tuesday, 20 October 2009 08:41 Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics,...

  14. MPX V1.2

    Energy Science and Technology Software Center (OSTI)

    002209WKSTN00 Hardware Counter Multiplexing V1.2 https://computation.llnl.gov/casc/mpx/mpx.home.html

  15. Solid waste integrated forecast technical (SWEFT) report: FY1997 to FY 2070 - Document number changed to HNF-0918 at revision 1 - 1/7/97

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-10-03

    This web site provides an up-to-date report on the radioactive solid waste expected to be managed at Hanford`s Solid Waste (SW) Program from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the SW Program; program- level and waste class-specific estimates; background information on waste sources; and Li comparisons with previous forecasts and with other national data sources. The focus of this web site is on low- level mixed waste (LLMW), and transuranic waste (both non-mixed and mixed) (TRU(M)). Some details on low-level waste and hazardous waste are also provided. Currently, this site is reporting data current as of 9/96. The data represent a life cycle forecast covering all reported activities from FY97 through the end of each program`s life cycle.

  16. 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 ...

  17. 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,

  18. 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.

  19. 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.

  20. Th1A.2.pdf

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

    Th1A.2.pdf OFC 2015 © OSA 2015 WAN Virtualization and Dynamic End-to-End Bandwidth Provisioning Using SDN Adrian Lara 1 , Byrav Ramamurthy 1 , Eric Pouyoul 2 and Inder Monga 2 1 University of Nebraska-Lincoln, Lincoln NE 68504 {alara,byrav}@cse.unl.edu 2 Energy Science Network, Lawrence Berkeley National Laboratory, Berkeley CA 94720 {lomax,imonga}@es.net Abstract: We evaluate a WAN-virtualization framework in terms of delay and scalability and demonstrate that adding a virtual layer between

  1. 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.

  2. 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...

  3. 1

    Office of Legacy Management (LM)

    ... 2014 Groundwater Monitoring Report Project Shoal Area Subsurface, CAU 447 March 2015 Doc. ... The contaminant boundary (Figure 2) is a probabilistic forecast of the maximum extent over ...

  4. TOUGH V2.1

    Energy Science and Technology Software Center (OSTI)

    2011-06-01

    TOUGH2 is a numerical simulator for nonisothermal flows of multicomponent, multiphase fluids in one-, two-, and three-dimensional porous and fracture media. The chief applications for which TOUGH2 is designed are in geothermal reservoir engineering, nuclear waste disposal, environmental assessment and remediation, geologic carbon sequestration, and unsaturated and saturated zone hydrology. The TOUGH2 V2.1 package is an upgrade of TOUGH2 V2.0 (CR-1574) and includes the following improvements and enhancements relative to TOUGH2 V2.0: - Includes allmore » known bug fixes - The module TMVOC (CR-1820) is fully integrated - The module ECO2N (CR-2202) is fully integrated - A fluid property module ECO2M has been added, that can seamlessly model CO2 storage and leakage scenarios, including transitions between super- and sub-critical fluids, and phase changes between liquid and gaseous CO2. The TOUGH2 V2.1 package also supports all legacy fluid property modules, i.e., those packaged into TOUGH2 V2.0 (CR-1574), which includes T2VOC (CR-1254).« less

  5. table1.2_02

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

    2 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources and Shipments; Unit: Trillion Btu. Shipments RSE NAICS Net Residual Distillate Natural LPG and Coke and of Energy Sources Row Code(a) Subsector and Industry Total(b) Electricity(c) Fuel Oil Fuel Oil(d) Gas(e) NGL(f) Coal Breeze Other(g) Produced Onsite(h) Factors Total United States RSE Column Factors: 0.9 1 1.2 1.8 1 1.6 0.8 0.9 1.2 0.4 311 Food 1,123 230

  6. 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

  7. 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. ...

  8. Beamline 6.1.2

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

    Beamline 6.1.2 Beamline 6.1.2 Print Tuesday, 20 October 2009 08:41 Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE)

  9. Beamline 6.1.2

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

    1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE...

  10. Beamline 6.1.2

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

    1000 x 1000 pixels, 1000 photonspixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (EE) 500-700 Endstations X-ray microscope (XM-1) Characteristics Full-field soft...

  11. Beamline 6.1.2

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

    recorded in 3 s at 517 eV with 0.2% BW Resolving power (EE) 500-700 Endstations X-ray microscope (XM-1) Characteristics Full-field soft x-ray transmission microscope Spatial...

  12. ex1ex2v2 Version 2.10

    Energy Science and Technology Software Center (OSTI)

    2007-09-12

    ex1ex2v2 is a translator program which will convert a database in exodus1 format to exodusII format. The exodus 1 format is a defined series of Fortran unformatted writes/reads; the exodusII format is defined by the ExodusII API.

  13. Beamline 6.1.2

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

    1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE) 500-700 Endstations X-ray microscope (XM-1)

  14. Beamline 6.1.2

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

    1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE) 500-700 Endstations X-ray microscope (XM-1)

  15. Beamline 6.1.2

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

    Beamline 6.1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE) 500-700 Endstations X-ray microscope (XM-1)

  16. Beamline 6.1.2

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

    Beamline 6.1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE) 500-700 Endstations X-ray microscope (XM-1)

  17. Beamline 6.1.2

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

    Beamline 6.1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE) 500-700 Endstations X-ray microscope (XM-1)

  18. Beamline 6.1.2

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

    1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE) 500-700 Endstations X-ray microscope (XM-1)

  19. Beamline 6.1.2

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

    1.2 Print Center for X-Ray Optics Soft X-Ray Microscopy Scientific disciplines: Magnetism, spin dynamics, x-ray optics, environmental science, materials science GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 500-1300 eV Monochromator Zone-plate linear Measured flux (1.9 GeV, 400 mA) Images with 1000 x 1000 pixels, 1000 photons/pixel recorded in 3 s at 517 eV with 0.2% BW Resolving power (E/ΔE) 500-700 Endstations X-ray microscope (XM-1)

  20. A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 1. Wind and Turbulence

    SciTech Connect (OSTI)

    Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.; Bieringer, Paul E.; Annunzio, Andrew; Bieberbach, George; Meech, Scott

    2015-09-25

    We found that numerical-weather-prediction models are often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense horizontally- and vertically-resolved geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City and the simulated fields important to transport and dispersion models were compared to measurements from a number of sodars, tower-based sonic anemometers, and balloon soundings located in the greater metropolitan area. Time histories of computed wind speed, wind direction, turbulent kinetic energy (e), friction velocity (u* ), and reciprocal Obukhov length (1 / L) were compared to measurements over the 1-month field campaign. Vertical profiles of wind speed, potential temperature (θ ), and e were compared during short intensive operating periods. The WRF model was typically able to replicate the measured diurnal variation of the wind fields, but with an average absolute wind direction and speed difference of 35° and 1.9 m s-1 , respectively. Then, using the Mellor-Yamada-Janjic (MYJ) surface-layer scheme, the WRF model was found to generally underpredict surface-layer TKE but overpredict u* that was observed above a suburban region of Oklahoma City. The TKE-threshold method used by the WRF model’s MYJ surface-layer scheme to compute the boundary-layer height (h) consistently overestimated h derived from a θ gradient method whether using observed or modelled θ profiles.

  1. A Case Study of the Weather Research and Forecasting Model Applied to the Joint Urban 2003 Tracer Field Experiment. Part 1. Wind and Turbulence

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

    Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.; Bieringer, Paul E.; Annunzio, Andrew; Bieberbach, George; Meech, Scott

    2015-09-25

    We found that numerical-weather-prediction models are often used to supply the mean wind and turbulence fields for atmospheric transport and dispersion plume models as they provide dense horizontally- and vertically-resolved geographic coverage in comparison to typically sparse monitoring networks. Here, the Weather Research and Forecasting (WRF) model was run over the month-long period of the Joint Urban 2003 field campaign conducted in Oklahoma City and the simulated fields important to transport and dispersion models were compared to measurements from a number of sodars, tower-based sonic anemometers, and balloon soundings located in the greater metropolitan area. Time histories of computed windmore » speed, wind direction, turbulent kinetic energy (e), friction velocity (u* ), and reciprocal Obukhov length (1 / L) were compared to measurements over the 1-month field campaign. Vertical profiles of wind speed, potential temperature (θ ), and e were compared during short intensive operating periods. The WRF model was typically able to replicate the measured diurnal variation of the wind fields, but with an average absolute wind direction and speed difference of 35° and 1.9 m s-1 , respectively. Then, using the Mellor-Yamada-Janjic (MYJ) surface-layer scheme, the WRF model was found to generally underpredict surface-layer TKE but overpredict u* that was observed above a suburban region of Oklahoma City. The TKE-threshold method used by the WRF model’s MYJ surface-layer scheme to compute the boundary-layer height (h) consistently overestimated h derived from a θ gradient method whether using observed or modelled θ profiles.« less

  2. Beamline 8.2.1

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

    1 Beamline 8.2.1 Print Tuesday, 20 October 2009 08:53 Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec

  3. Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption

    Buildings Energy Data Book [EERE]

    7 Range 10 4 48 Clothes Dryer 359 (2) 4 49 Water Heating Water Heater-Family of 4 40 64 (3) 26 294 Water Heater-Family of 2 40 32 (3) 12 140 Note(s): Source(s): 1) $1.139/therm. 2) Cycles/year. 3) Gallons/day. A.D. Little, EIA-Technology Forecast Updates - Residential and Commercial Building Technologies - Reference Case, Sept. 2, 1998, p. 30 for range and clothes dryer; LBNL, Energy Data Sourcebook for the U.S. Residential Sector, LBNL-40297, Sept. 1997, p. 62-67 for water heating; GAMA,

  4. SUPESv.4.1.2

    Energy Science and Technology Software Center (OSTI)

    2001-04-25

    SUPES is a collection of subprograms that perform frequently used non-numerical services for the engineering applications programmer. The three functional categories of SUPES are: (1) input command parsing, (2) dynamic memory management, and (3) system dependent utilities. The subprograms in categories one and two are written in standard FORTRAN-77, while the subprograms in category three are written provide a standarized FORTRAN interface to several system dependent features.

  5. Appendix I1-2 to Wind HUI Initiative 1: Field Campaign Report

    SciTech Connect (OSTI)

    John Zack; Deborah Hanley; Dora Nakafuji

    2012-07-15

    This report is an appendix to the Hawaii WindHUI efforts to dev elop and operationalize short-term wind forecasting and wind ramp event forecasting capabilities. The report summarizes the WindNET field campaign deployment experiences and challenges. As part of the WindNET project on the Big Island of Hawaii, AWS Truepower (AWST) conducted a field campaign to assess the viability of deploying a network of monitoring systems to aid in local wind energy forecasting. The data provided at these monitoring locations, which were strategically placed around the Big Island of Hawaii based upon results from the Oahu Wind Integration and Transmission Study (OWITS) observational targeting study (Figure 1), provided predictive indicators for improving wind forecasts and developing responsive strategies for managing real-time, wind-related system events. The goal of the field campaign was to make measurements from a network of remote monitoring devices to improve 1- to 3-hour look ahead forecasts for wind facilities.

  6. Beamline 3.2.1

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

    2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson

  7. Beamline 3.2.1

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

    2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson

  8. Beamline 3.2.1

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

    2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson

  9. Beamline 3.2.1

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

    2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson

  10. Beamline 3.2.1

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

    2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson

  11. Beamline 3.2.1

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

    2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson

  12. 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

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

    Reports and Publications (EIA)

    2010-01-01

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

  14. 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)

  15. 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 ...

  16. 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.

  17. 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 ...

  18. 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:...

  19. table2.1_02.xls

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

    Total United States RSE Column Factors: 1.4 0.4 1.6 1.2 1.2 1.1 0.7 1.2 ... 0 0 0 0 * 324 Petroleum and Coal Products 3,689 * * * 0 Q * 3,407 324110 Petroleum Refineries 3,307 0 0 0 0 ...

  20. EnergyPlus 1.2.2

    Energy Science and Technology Software Center (OSTI)

    2005-05-01

    EnergyPlus (E+) is a new whole-building energy analysis program that combines the best capabilities and features from BLAST and DOE-2 along with new capabilities. E+ modular implementation facilitates extending the program and adding links to other programs. The fluid loops and HVAC components support a "manager-interface" simulation protocol that allows for the independent simulation of subsystems, each possibly using a customized solution procedure. Thus, the E+ program structure allows the solution to a particular subsystemmore » to be computed without affecting the solution schemes used for the other subsystems. This fundamental requirement enables the integration of external models in the El+ building systems simulation.« less

  1. 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

  2. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural...

  3. A s o f 1 2 / 3 1 / 2 0 1 5

    Wind Powering America (EERE)

    2 0 1 5 Y e a r E n d Wi n d P o w e r C a p a c i t y ( MW)

  4. 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 ...

  5. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

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

  6. 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 ...

  7. 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 ...

  8. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max

  9. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max

  10. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max

  11. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max

  12. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max

  13. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max

  14. Beamline 8.2.1

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

    1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV (standard monochromator); 10-13 keV (multilayer) Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max

  15. Beamline 8.2.1

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

    flux (1.9 GeV, 400 mA) 3.0 x 1011 photonssec Resolving power (EE) 7,000 Divergence (max at sample) 3.0 (h) x 0.5 (v) mrad Measured spot size (FWHM) 100 m Endstations...

  16. MOSS2D V1

    Energy Science and Technology Software Center (OSTI)

    2001-01-31

    This software reduces the data from two-dimensional kSA MOS program, k-Space Associates, Ann Arbor, MI. Initial MOS data is recorded without headers in 38 columns, with one row of data per acquisition per lase beam tracked. The final MOSS 2d data file is reduced, graphed, and saved in a tab-delimited column format with headers that can be plotted in any graphing software.

  17. Science on Tap - Forecasting illness

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

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

  18. 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. ARM - VAP Product - 2rlprofasr1ferr

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

    us a note below or call us at 1-888-ARM-DATA. Send VAP Output : 2RLPROFASR1FERR 2-minute Raman Lidar: aerosol scattering ratio and backscattering coefficient profiles, from first...

  20. Acquisition Forecast Download | Department of Energy

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

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

  1. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01

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

  2. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

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

  3. USE OF AN EQUILIBRIUM MODEL TO FORECAST DISSOLUTION EFFECTIVENESS, SAFETY IMPACTS, AND DOWNSTREAM PROCESSABILITY FROM OXALIC ACID AIDED SLUDGE REMOVAL IN SAVANNAH RIVER SITE HIGH LEVEL WASTE TANKS 1-15

    SciTech Connect (OSTI)

    KETUSKY, EDWARD

    2005-10-31

    This thesis details a graduate research effort written to fulfill the Magister of Technologiae in Chemical Engineering requirements at the University of South Africa. The research evaluates the ability of equilibrium based software to forecast dissolution, evaluate safety impacts, and determine downstream processability changes associated with using oxalic acid solutions to dissolve sludge heels in Savannah River Site High Level Waste (HLW) Tanks 1-15. First, a dissolution model is constructed and validated. Coupled with a model, a material balance determines the fate of hypothetical worst-case sludge in the treatment and neutralization tanks during each chemical adjustment. Although sludge is dissolved, after neutralization more is created within HLW. An energy balance determines overpressurization and overheating to be unlikely. Corrosion induced hydrogen may overwhelm the purge ventilation. Limiting the heel volume treated/acid added and processing the solids through vitrification is preferred and should not significantly increase the number of glass canisters.

  4. Substituted 1,2-azaborine heterocycles

    DOE Patents [OSTI]

    Liu, Shih-Yuan; Lamm, Ashley

    2014-12-30

    Aromatic heterocycles incorporating boron and nitrogen atoms, in particular, 1,2-azaborine compounds having the formula ##STR00001## and their use as synthetic intermediates.

  5. 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 ...

  6. 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

  7. Microsoft Word - UPDATE 2 - Unit 1.doc

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

    2 to: A Dispersion Modeling Analysis of Downwash from Mirant's Potomac River Power Plant Modeling Unit 1 Emissions at Maximum and Minimum Loads ENSR Corporation December 20, 2005 Document Number 10350-002-410 (Update 2) December, 2005 1-1 1.0 INTRODUCTION This report describes AERMOD modeling results performed for Unit 1 at Mirant's Potomac River Generating Station. The purpose of these runs was to demonstrate that operation of Unit 1 for 24 hours a day at loads from 35 MW to 88 MW with the use

  8. Microsoft Word - CPR DID-MGMT-81466 - Final _2_

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

    contractor's Integrated Master Schedule (IMS)for the project. 1.1.7. Format 7 defines the time-phased historical & forecast cost submission 1.2. The IPMR's primary value to the ...

  9. 1-2-3 - Economic Indicators.123

    National Nuclear Security Administration (NNSA)

    Airline Passengers December 2009 3,097,382 3,234,705 3,164,313 -4.2% -2.1% Gaming Revenue December 2009 753,169,630 750,798,061 771,800,158 0.3% -2.4% Visitor Volume (all...

  10. Slide 1

    Office of Environmental Management (EM)

    Hydropower System Status Report 1LT James P. Trombly April 2016 BUILDING STRONG ® Current System Status BUILDING STRONG ® Forecasted System Status BUILDING STRONG ® Forecasted System Status

  11. Preparation of 1,1'-dinitro-3,3'-azo-1,2,4-triazole. [1,1'-dinitro-3,3'-azo-1,2,4-triazole

    DOE Patents [OSTI]

    Lee, K.Y.

    1985-03-05

    A new high density composition of matter, 1,1'-dinitro-3,3'-azo-1,2,4-triazole, has been synthesized using inexpensive, commonly available compounds. This compound has been found to be an explosive, and its use as a propellant is anticipated. 1 fig., 1 tab.

  12. Induction of cytochromes P450 1A1 and 1A2 by tanshinones in human...

    Office of Scientific and Technical Information (OSTI)

    human HepG2 hepatoma cell line Citation Details In-Document Search Title: Induction of cytochromes P450 1A1 and 1A2 by tanshinones in human HepG2 hepatoma cell line Diterpenoid ...

  13. 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. ...

  14. 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. ...

  15. 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

  16. 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

  17. ARM - VAP Product - 2rlprofdep1turn

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

    Productsrlprof2rlprofdep1turn Documentation Data Management Facility Plots (Quick Looks) Citation DOI: 10.5439/1027735 [ What is this? ] Generate Citation ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send VAP Output : 2RLPROFDEP1TURN 2-minute Raman Lidar: aerosol depolarization profiles and single layer cloud optical depths Active Dates 2004.10.01 - 2015.09.25 Originating VAP Process Raman LIDAR Vertical Profiles :

  18. Beamline 5.3.2.1

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

    5.3.2.1 Beamline 5.3.2.1 Print Thursday, 26 February 2015 12:20 Scanning Transmission X-Ray Microscopy (STXM) GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x 107 photons/s at sample Resolving power (E/ΔE) ≤ 5,000 Endstations Scanning transmission x-ray microscope (STXM) Characteristics Active servo-stabilized toroidal

  19. 3:2:1 Crack Spread

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

    To calculate the 3:2:1 crack spread for a Gulf Coast refinery that processes Louisiana Light Sweet (LLS) crude oil, add the spot price for two barrels of Gulf Coast conventional ...

  20. Stockpile Stewardship Quarterly, Volume 2, Number 1

    National Nuclear Security Administration (NNSA)

    1 * May 2012 Message from the Assistant Deputy Administrator for Stockpile Stewardship, Chris Deeney Defense Programs Stockpile Stewardship in Action Volume 2, Number 1 Inside this Issue 2 LANL and ANL Complete Groundbreaking Shock Experiments at the Advanced Photon Source 3 Characterization of Activity-Size-Distribution of Nuclear Fallout 5 Modeling Mix in High-Energy-Density Plasma 6 Quality Input for Microscopic Fission Theory 8 Fiber Reinforced Composites Under Pressure: A Case Study in

  1. Book2.xls?attach=1

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

    Units 1&2 Off, Units 3,4,5 on 12 hrs @ 100% load and 12 hrs at 35% load. (100% load 107 MW). SO2 0.22 lbMBtu on all three units. Run 0600 - 1800 at 100% load, and the rest at ...

  2. Operating Plan 573.1-2

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

    OPERATING PLAN 573.1-2 Title: U.S. POSTAL MAIL, SHIPPING AND RECEIVING SECURITY Owner: Cindy Mullens, ESS&H Division, Office of Institutional Operations Approving Official: Thomas Wilson, Jr., Director, Office of Institutional Operations {signature} /s/ Thomas Wilson, Jr. Approval Date: 9/20/12 Last Reviewed Date: 9/20/12 Cancellation: None TABLE OF CONTENTS 1. PURPOSE

  3. The NUHM2 after LHC Run 1

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

    Buchmueller, O.; Cavanaugh, R.; Citron, M.; De Roeck, A.; Dolan, M. J.; Ellis, J. R.; Flächer, H.; Heinemeyer, S.; Malik, S.; Marrouche, J.; et al

    2014-12-17

    We make a frequentist analysis of the parameter space of the NUHM2, in which the soft supersymmetry (SUSY)-breaking contributions to the masses of the two Higgs multiplets, m2Hu,d, vary independently from the universal soft SUSY-breaking contributions m20 to the masses of squarks and sleptons. Our analysis uses the MultiNest sampling algorithm with over 4 × 10⁸ points to sample the NUHM2 parameter space. It includes the ATLAS and CMS Higgs mass measurements as well as the ATLAS search for supersymmetric jets + /ET signals using the full LHC Run 1 data, the measurements of BR(Bs→μ⁺μ⁻) by LHCb and CMS togethermore » with other B-physics observables, electroweak precision observables and the XENON100 and LUX searches for spin-independent dark-matter scattering. We find that the preferred regions of the NUHM2 parameter space have negative SUSY-breaking scalar masses squared at the GUT scale for squarks and sleptons, m20 < 0, as well as m2Hu < m2Hd < 0. The tension present in the CMSSM and NUHM1 between the supersymmetric interpretation of (g – 2)μ and the absence to date of SUSY at the LHC is not significantly alleviated in the NUHM2. We find that the minimum χ2 = 32.5 with 21 degrees of freedom (dof) in the NUHM2, to be compared with χ2/dof = 35.0/23 in the CMSSM, and χ2/dof = 32.7/22 in the NUHM1. We find that the one-dimensional likelihood functions for sparticle masses and other observables are similar to those found previously in the CMSSM and NUHM1.« less

  4. RSE Table S1.1 and S1.2. Relative Standard Errors for Tables S1.1 and S1.2

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

    S1.1 and S1.2. Relative Standard Errors for Tables S1.1 and S1.2;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," "," ",," "," ",," "," ",," ","Shipments" "SIC"," ",,"Net","Residual","Distillate",,"LPG

  5. Graphs from Volume 1 Book 2

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

     We want USDOE to vitrify all Low Activity Waste (second LAW plant) -- Alternative 2B. 2  For all glass options, most of the impacts come from secondary waste. Secondary waste causes significant groundwater impacts and needs robust mitigation to get below levels of concern. Peak Groundwater Results from Various Waste Forms and Secondary Waste Glass Glass and Bulk Vit Glass and Cast Stone Glass and Steam Reforming Benchmark iodine-129 (pCi/L) 1.4 1.7 10.7 10.7 1 technetium-99 (pCi/L) 471

  6. 1 and 2-Dimensional Line Transfer Package

    Energy Science and Technology Software Center (OSTI)

    1990-07-01

    LXF1D is a one dimensional steady-state line transfer package designed to handle: overlapping and or interacting lines, planar, cylindrical, spherical (and special) geometries, doppler shifts, complete redistribution (CRD), partial redistribution (PRD). PRD requires the use of REDIST or some other package to produce emission profiles. LXF2D is a two dimensional version of LXF1D for xy and rz geometries. Both LXF1D and LXF2D are designed to be added to existing non-local thermodynamic equilibrium (NLTE) codes withmore » a minimum of effort.« less

  7. 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).

  8. 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).

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

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01

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

  10. DOE-2 supplement: Version 2.1E

    SciTech Connect (OSTI)

    Winkelmann, F.C.; Birdsall, B.E.; Buhl, W.F.; Ellington, K.L.; Erdem, A.E.; Hirsch, J.J.; Gates, S.

    1993-11-01

    This publication updates the DOE-2 Supplement form version 2.1D to version to 2.1E. It contains detailed discussions and instructions for using the features and enhancements introduced into the 2.1B, 2.1C, 2.1D, and 2.1E versions of the program. The building description section contains information on input functions in loads and systems, hourly report frequencies, saving files of hourly output for post processing, sharing hourly report data among program modules, the metric option, and input macros and general library features. The loads section contains information on sunspaces, sunspace modeling, window management and solar radiation, daylighting, trombe walls, fixed shades, fins and overhangs, shade schedules, self shades, heat distribution from lights, the Sherman-Grimsrud infiltrations method. terrain and height modification to wind speed, floor multipliers and interior wall types, improved exterior infrared radiation loss calculation, improved outside air film conductance calculation, window library, window frames, and switchable glazing. The systems section contains information on energy end use and meters, powered induction units, a packaged variable volume -- variable temperature system, a residential variable volume -- variable temperature system, air source heat pump enhancements, water loop heat pump enhancements, variable speed electric heat pump, gas heat pumps, hot water heaters, evaporative cooling, total gas solid-desiccant systems, add on desiccant cooling, water cooled condensers, evaporative precoolers outside air economizer control, optimum fan start, heat recovery from refrigerated case work, night ventilation, baseboard heating, moisture balance calculations, a residential natural ventilation algorithm, improved cooling coil model, system sizing and independent cooling and heating sizing ratios. The plant section contains information on energy meters, gas fired absorption chillers, engine driven compressor chillers, and ice energy storage.

  11. RSE Table N1.1 and N1.2. Relative Standard Errors for Tables N1.1 and N1.2

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

    1 and N1.2. Relative Standard Errors for Tables N1.1 and N1.2;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," "," ",," "," ",," "," ",," ","Shipments" "NAICS"," ",,"Net","Residual","Distillate",,"LPG

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

    SciTech Connect (OSTI)

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

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are

  13. Spin-1/2 Optical Lattice Clock

    SciTech Connect (OSTI)

    Lemke, N. D.; Ludlow, A. D.; Barber, Z. W.; Fortier, T. M.; Diddams, S. A.; Jiang, Y.; Jefferts, S. R.; Heavner, T. P.; Parker, T. E.; Oates, C. W.

    2009-08-07

    We experimentally investigate an optical clock based on {sup 171}Yb (I=1/2) atoms confined in an optical lattice. We have evaluated all known frequency shifts to the clock transition, including a density-dependent collision shift, with a fractional uncertainty of 3.4x10{sup -16}, limited principally by uncertainty in the blackbody radiation Stark shift. We measured the absolute clock transition frequency relative to the NIST-F1 Cs fountain clock and find the frequency to be 518 295 836 590 865.2(0.7) Hz.

  14. Beamline 5.3.2.1

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

    5.3.2.1 Print Scanning Transmission X-Ray Microscopy (STXM) GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x 107 photons/s at sample Resolving power (E/ΔE) ≤ 5,000 Endstations Scanning transmission x-ray microscope (STXM) Characteristics Active servo-stabilized toroidal premirror Spot size at sample (FWHM) 25-100 nm

  15. Beamline 5.3.2.1

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

    5.3.2.1 Print Scanning Transmission X-Ray Microscopy (STXM) GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x 107 photons/s at sample Resolving power (E/ΔE) ≤ 5,000 Endstations Scanning transmission x-ray microscope (STXM) Characteristics Active servo-stabilized toroidal premirror Spot size at sample (FWHM) 25-100 nm

  16. Beamline 5.3.2.1

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

    Beamline 5.3.2.1 Print Scanning Transmission X-Ray Microscopy (STXM) GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x 107 photons/s at sample Resolving power (E/ΔE) ≤ 5,000 Endstations Scanning transmission x-ray microscope (STXM) Characteristics Active servo-stabilized toroidal premirror Spot size at sample (FWHM) 25-100

  17. Beamline 5.3.2.1

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

    5.3.2.1 Print Scanning Transmission X-Ray Microscopy (STXM) GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x 107 photons/s at sample Resolving power (E/ΔE) ≤ 5,000 Endstations Scanning transmission x-ray microscope (STXM) Characteristics Active servo-stabilized toroidal premirror Spot size at sample (FWHM) 25-100 nm

  18. Beamline 5.3.2.1

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

    5.3.2.1 Print Scanning Transmission X-Ray Microscopy (STXM) GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x 107 photons/s at sample Resolving power (E/ΔE) ≤ 5,000 Endstations Scanning transmission x-ray microscope (STXM) Characteristics Active servo-stabilized toroidal premirror Spot size at sample (FWHM) 25-100 nm

  19. Beamline 5.3.2.1

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

    5.3.2.1 Print Scanning Transmission X-Ray Microscopy (STXM) GENERAL BEAMLINE INFORMATION Operational Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x 107 photons/s at sample Resolving power (E/ΔE) ≤ 5,000 Endstations Scanning transmission x-ray microscope (STXM) Characteristics Active servo-stabilized toroidal premirror Spot size at sample (FWHM) 25-100 nm

  20. RSE Table N2.1 and N2.2. Relative Standard Errors for Tables N2.1 and N2.2

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

    N2.1 and N2.2. Relative Standard Errors for Tables N2.1 and N2.2;" " Unit: Percents." " "," " "NAICS"," "," ","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Subsector and Industry","Total","Fuel Oil","Fuel Oil(b)","Natural Gas(c)","NGL(d)","Coal","and Breeze","Other(e)"

  1. RSE Table S2.1 and S2.2. Relative Standard Errors for Tables S2.1 and S2.2

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

    S2.1 and S2.2. Relative Standard Errors for Tables S2.1 and S2.2;" " Unit: Percents." " "," "," ",," "," "," "," "," "," ",," " "SIC"," "," ","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Major Group and Industry","Total","Fuel Oil","Fuel

  2. Waste Receiving and Processing Facility Module 2A: Advanced Conceptual Design Report. Volume 1

    SciTech Connect (OSTI)

    Not Available

    1994-03-01

    This ACDR was performed following completed of the Conceptual Design Report in July 1992; the work encompassed August 1992 to January 1994. Mission of the WRAP Module 2A facility is to receive, process, package, certify, and ship for permanent burial at the Hanford site disposal facilities the Category 1 and 3 contact handled low-level radioactive mixed wastes that are currently in retrievable storage at Hanford and are forecast to be generated over the next 30 years by Hanford, and waste to be shipped to Hanford from about DOE sites. This volume provides an introduction to the ACDR process and the scope of the task along with a project summary of the facility, treatment technologies, cost, and schedule. Major areas of departure from the CDR are highlighted. Descriptions of the facility layout and operations are included.

  3. 1,2,3-triazolium ionic liquids

    DOE Patents [OSTI]

    Luebke, David; Nulwala, Hunaid; Tang, Chau

    2014-12-09

    The present invention relates to compositions of matter that are ionic liquids, the compositions comprising substituted 1,2,3-triazolium cations combined with any anion. Compositions of the invention should be useful in the separation of gases and, perhaps, as catalysts for many reactions.

  4. 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.

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

    SciTech Connect (OSTI)

    Graham, Robin Lambert

    2007-01-01

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

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

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

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

    2015-02-14

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

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

    SciTech Connect (OSTI)

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

    2015-02-14

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

  8. 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

  9. Economic Benefits, Carbon Dioxide (CO2) Emissions Reduction, and Water Conservation Benefits from 1,000 Megawatts (MW) of New Wind Power in Georgia (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2008-06-01

    The U.S. Department of Energy's Wind Powering America Program is committed to educating state-level policy makers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Georgia. We forecast the cumulative economic benefits from 1000 MW of development in Georgia to be $2.1 billion, annual CO2 reductions are estimated at 3.0 million tons, and annual water savings are 1,628 million gallons.

  10. 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:...

  11. 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 ...

  12. 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 ...

  13. 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...

  14. 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...

  15. 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 ...

  16. Reinvestigating the clusters Koposov 1 and 2

    SciTech Connect (OSTI)

    Paust, Nathaniel; Wilson, Danielle; Van Belle, Gerard

    2014-07-01

    We investigate the fundamental parameters of age, distance, and mass function slope for the poorly studied clusters Koposov 1 and Koposov 2. These clusters were discovered recently and tentatively classified as globular clusters. Using the Large Monolithic Imager on Lowell Observatory's Discovery Channel Telescope, we present photometry extending to V = 25, three to four magnitudes below the main sequence turnoffs for the clusters. We find the clusters have tidal radii of 15 pc and 10.7 pc and distances of 34.9 kpc and 33.3 kpc for Koposov 1 and Koposov 2, respectively. Studying the stellar content of the clusters, we use completeness-corrected star counts to reveal extremely faint total magnitudes of 2.01 and 0.03 in V, and steep Salpeter-like present-day mass functions. Finally, we show that the spatial positions of the clusters agree well with the position of the Sagittarius stream and conclude that these two objects are open clusters removed from the Sagittarius galaxy.

  17. 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

  18. Slide 1

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

    Efficiency Opportunities and Barriers Steven Nadel, Executive Director American Council for an Energy-Efficient Economy April 2010 Share of Maryland Electricity Sales That Can Be Met by Efficiency Policies - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5 2 0 1 7 2 0 1 9 2 0 2 1 2 0 2 3 2 0 2 5 Electricity Demand (GWh) CHP Building Codes RD&D Initiative Appliance Standards State and Utility Programs 15% reduction in forecasted consumption by

  19. Economic Benefits, Carbon Dioxide (CO2) Emissions Reductions, and Water Conservation Benefits from 1,000 Megawatts (MW) of New Wind Power in Arizona (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2008-10-01

    The U.S. Department of Energy?s Wind Powering America Program is committed to educating state-level policymakers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Arizona. Although construction and operation of 1000 MW of wind power is a significant effort, six states have already reached the 1000-MW mark. We forecast the cumulative economic benefits from 1000 MW of development in Arizona to be $1.15 billion, annual CO2 reductions are estimated at 2.0 million tons, and annual water savings are 818 million gallons.

  20. 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 ...

  1. 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, ...

  2. 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 ...

  3. PSA Vol 1 Tables Revised Ver 2 Print.xls

    Gasoline and Diesel Fuel Update (EIA)

    11 0 -2 0 0 -1 -1 Honduras 0 0 -1 0 0 -3 -3 India 0 0 0 8 0 2 2 Italy 0 0 0 3 0 16 16 Japan 0 0 0 0 0 1 1 Korea, South 0 0 0 1 0 4 4 Latvia 0 0 0 0 1 0 1 Lithuania 0 0 0 0 0 19...

  4. Dynamic characterization of crystalline and glass phases of deuterated 1,1,2,2 tetrachloroethane

    SciTech Connect (OSTI)

    Pérez, Silvina C. Zuriaga, Mariano Serra, Pablo Wolfenson, Alberto; Negrier, Philippe; Tamarit, Josep Lluis

    2015-10-07

    A thorough characterization of the γ, β, and glass phases of deuterated 1,1,2,2 tetrachloroethane (C{sub 2}D{sub 2}Cl{sub 4}) via nuclear quadrupole resonance and Molecular Dynamic Simulations (MDSs) is reported. The presence of molecular reorientations was experimentally observed in the glass phase and in the β phase. In the β phase, and from MDS, these reorientations are attributed to two possible movements, i.e., a 180°  reorientation around the C{sub 2} molecular symmetry axis and a reorientation of the molecule between two non-equivalent positions. In the glass phase, the spin-lattice relaxation time T{sub 1} is of the order of 16 times lower than in the crystalline phase and varies as T{sup −1} below 100 K in good agreement with the strong quadrupolar relaxation observed in amorphous materials and in the glassy state of molecular organic systems. The activation energy of molecular reorientations in the glass phase (19 kJ/mol) is comparable to that observed in the glassy crystal of a “molecular cousin” compound, Freon 112 (C{sub 2}F{sub 2}Cl{sub 4}), for the secondary β-relaxation. Moreover, the on-site orientational motion of tetrachloroethane molecules offers a new indirect evidence of the prominent role of such orientational disorder in glassy dynamics.

  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. updated_supplemental_lists_1k-2k-3j-_1-10-2012.xlsx | Department...

    Energy Savers [EERE]

    k-2k-3j-1-10-2012.xlsx updatedsupplementallists1k-2k-3j-1-10-2012.xlsx File updatedsupplementallists1k-2k-3j-1-10-2012.xlsx More Documents & Publications...

  7. Microsoft Word - Documentation - Price Forecast Uncertainty.doc

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

    October 2009 1 October 2009 Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1 Summary It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energy- related financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the market- clearing process

  8. Preparation of 1,1'-dinitro-3,3'-azo-1,2,4-triazole

    SciTech Connect (OSTI)

    Lee, Kien-Yin

    1986-01-01

    A new high density composition of matter, 1,1'-dinitro-3,3'-azo-1,2,4-triazole, has been synthesized using inexpensive, commonly available compounds. This compound has been found to be an explosive, and its use as a propellant is anticipated.

  9. 1

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

    predicts once-mysterious chemical reactions June 28, 2016 Understanding of molecular hydrogen has implications from industry to medicine LOS ALAMOS, N.M., June 28, 2016-A team of researchers from Los Alamos National Laboratory and Curtin University in Australia developed a theoretical model to forecast the fundamental chemical reactions involving molecular hydrogen (H 2 ), which after many decades and attempts by scientists had remained largely unpredicted and unsolved. "Chemical reactions

  10. CERES progress report: Phases 1 and 2

    SciTech Connect (OSTI)

    Dannevik, W.P.; Ambrosiano, J.; Kercher, J.; Penner, J.E.; Emanuel, W.

    1994-05-27

    The CERES project represents a long-term commitment of LLNL`s Global Climate Research Division to the EPA. The goal is to build an Earth System Model (ESM) with the ability in the near future to assist EPA in carrying out its responsibilities in the environmental policy and assessment arena, with particular emphasis on the terrestrial ecosystem components of the Earth system. There are two complementary aspects of the CERES development plan. The first is to provide a computational framework and modeling infrastructure for ESM development. The goal is to create an ``open architecture`` enabling submodels from different research groups studying terrestrial ecosystems to become part of a fully-coupled model of the Earth`s climate system. The second goal is to contribute fundamentally to understanding of the terrestrial component of the Earth system by developing advanced models. During this first phase of the CERES project, these two activities have been somewhat separate; the software engineering and framework building activity having been done in parallel with terrestrial model development. These two activities are merging as the framework becomes more mature, with robust software tools, and with a growing complement of tuned and benchmarked submodels and as the ecosystem models become fully incorporated into the ESM modeling framework. Two appendices contain the following papers: (1) ``Research Recommendations to the EPA in Support of Earth System Modeling Activities,`` LLNL CERES project report; and (2) ``Progress Report on Terrestrial Model Development: Research in Support of the CERES Earth System Modeling Project,`` LLNL CERES project report.

  11. Microsoft Word - Policy Flash 2011-2 Attachment 1 | Department...

    Energy Savers [EERE]

    Microsoft Word - Policy Flash 2011-2 Attachment 1 Microsoft Word - Policy Flash 2011-2 Attachment 1 Microsoft Word - Policy Flash 2011-2 Attachment 1 More Documents & Publications...

  12. Table HC1.2.1. Living Space Characteristics by

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

    Space Characteristics by" " Total, Heated, and Cooled Floorspace, 2005" ,,,"Total Square Footage" ,"Housing Units",,"Total1",,"Heated",,"Cooled" "Living Space Characteristics","Mil...

  13. RSE Table 2.1 Relative Standard Errors for Table 2.1

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

    2.1 Relative Standard Errors for Table 2.1;" " Unit: Percents." " "," " " "," " "NAICS"," "," ","Residual","Distillate","Natural ","LPG and",,"Coke"," " "Code(a)","Subsector and Industry","Total","Fuel Oil","Fuel Oil(b)","Gas(c)","NGL(d)","Coal","and

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

    SciTech Connect (OSTI)

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

    2013-06-15

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

  15. Magnetostructural phase transformations in Tb 1-x Mn 2 (Journal...

    Office of Scientific and Technical Information (OSTI)

    phase transformations in Tb 1-x Mn 2 Citation Details In-Document Search Title: Magnetostructural phase transformations in Tb 1-x Mn 2 Magnetism and phase transformations ...

  16. Beamline 5.3.2.1

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

    Yes Source characteristics Bend magnet Energy range 600-2000eV eV Monochromator Low-dispersion, spherical-grating monochromator, two gratings Calculated flux (1.9 GeV, 500 mA) 1 x...

  17. R. Driben,1,2,*A. V. Yulin,1and ...

    Office of Scientific and Technical Information (OSTI)

    ... Lett. QE-23, 510-524 (1987). 30. B. A. Malomed, "Perturbation-induced perestroika of a ... Nonlinear dynamics of solitary waves 1 is a broad and vibrant research area. ...

  18. Construction of Building 9201-1 (Alpha 1) - part 2

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

    arriving daily. The target start up date was now November 1, 1943. General Groves, in his book, Now it can be told, said, "The work on the first racetrack was well underway before...

  19. 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...

  20. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  1. 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.

  2. 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.

  3. 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.

  4. 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....

  5. 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...

  6. 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,

  7. H2 Safety Snapshot - Vol. 2, Issue 1, Nov. 2010

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

    ... See related lessons learned at www.h2incidents.orgincident.asp?inc245. Topic suggestions? Comments? Contact us at snapshot@pnl.gov A safety knowledge tool from PNNL-SA-75299 ...

  8. Aiping Chen1, Narayan Poudyal2, ...

    Office of Scientific and Technical Information (OSTI)

    hydrogen has also been reported.2 Cu films have been synthesized by the reduction of copper oxide thin films with hydrogen plasma.3 And reduction of graphene oxide films to ...

  9. Purevdorj Munkhbaatar1, Zsolt Marton2...

    Office of Scientific and Technical Information (OSTI)

    optical birefringence was found in a single crystal of a charge-orbital ordered man- ganese oxide, Lao.5Sr1.5MnO4. The birefringence was at- tributed to the melting of the ...

  10. Dual Selectivity Expressed in [2+2+1] Dynamic Clipping of Unsymmetrical [2]Catenanes

    SciTech Connect (OSTI)

    Liu, Yi

    2010-06-11

    A {pi}-templated dynamic [2+2+1] clipping protocol is established for the synthesis of [2]catenanes from two parts dialdehyde, two parts diamine and one part tetracationic cyclophane. It is further diversified for the selective formation of an unsymmetrical [2]catenane showing great translational selectivity by employing two different dialdehydes in a one-pot reaction. The dual selectivity and the dynamic nature are verified by {sup 1}H NMR spectroscopy, X-ray single crystal structural studies and exchange experiments.

  11. Forecasting the oil-gasoline price relationship: should we care about the Rockets and the Feathers?

    Gasoline and Diesel Fuel Update (EIA)

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,153 3,253 3.2% Price (cents/kWh) 12.06 12.09 12.58 13.04 12.95 12.98 0.2% Expenditures $415 $405 $393 $396 $408 $422 3.3% New England Usage (kWh) 2,122 2,188 2,173 1,930 1,993 2,051 2.9% Price (cents/kWh) 15.85 15.50 16.04 17.63 18.64 18.36 -1.5% Expenditures $336 $339 $348 $340 $372 $377 1.3% Mid-Atlantic Usage (kWh) 2,531 2,548 2,447 2,234 2,372 2,431 2.5% Price (cents/kWh) 16.39 15.63

  12. CESP Tool 2.1: Stakeholder Matrix | Department of Energy

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

    1: Stakeholder Matrix CESP Tool 2.1: Stakeholder Matrix Tool 2.1: Stakeholder Matrix from Step 2: Identify and Engage Stakeholders of the Guide to Community Energy Strategic Planning. CESP Tool 2.1: Stakeholder Matrix (27.87 KB) More Documents & Publications Guide to Community Energy Strategic Planning: Step 2 Guide to Community Energy Strategic Planning CESP Tool 9.1: Monitoring Plan Template

  13. Crystal structures of nitrato-(2-[2-(1-pyridine-2-ylethylidene)hydrazine]-1,3-benzothiazolo) aquacopper and chloro-(2-[2-phenyl(pyridine-2-ylethylidene)hydrazine]-1,3-benzothiazolo) copper

    SciTech Connect (OSTI)

    Chumakov, Yu. M.; Paholnitcaia, A. Yu.; Petrenko, P. A.; Tsapkov, V. I.; Poirier, D.; Gulea, A. P.

    2015-01-15

    Two crystal modifications of nitrato-(2-[2-(1-pyridine-2-ylethylidene)hydrazine]-1,3-benzothiazolo) aquacopper (I and II) and two modifications of chloro-(2-[2-phenyl(pyridine-2-ylethylidene)hydrazine]-1,3-benzothiazolo) copper (III and IV) have been synthesized and studied by X-ray diffraction. In structures I and II, the copper atoms coordinate a monodeprotonated molecule of the organic ligand, nitrate ions, and a water molecule. In crystals of I, the complexes are monomeric, whereas complexes II are linked via nitrate ions to form polymeric chains. In both structures the coordination polyhedron of the copper atom can be described as a distorted tetragonal bipyramid—(4 + 1 + 1) in I and (4 + 2) in II. These coordination polyherdra have different compositions. In structures III and IV, the metal atoms coordinate a monodeprotonated (2-[2-phenyl(pyridine-2-ylethylidene)hydrazine]-1,3-benzothiazole molecule and chloride ions. In III the complex-forming ion has square-planar coordination geometry, whereas structure IV consists of centrosymmetric dimers with two bridging chlorine atoms. It was found that nitrato-(2-[2-(1-pyridine-2-ylethylidene)hydrazine]-1,3-benzothiazolo) aquacopper possesses antitumor activity.

  14. Buildings Energy Data Book: 1.2 Building Sector Expenditures

    Buildings Energy Data Book [EERE]

    2 Building Energy Prices, by Year and Fuel Type ($2010) (cents/therm) (cents/gal) ($/gal) 1980 12.42 83.51 1.53 2.24 12.70 77.01 1.43 2.05 1981 13.14 88.83 1.47 2.51 13.33 82.90 1.63 2.32 1982 13.70 100.83 1.54 2.30 13.70 93.95 1.40 2.11 1983 13.79 113.04 1.51 2.14 13.48 104.33 1.30 1.75 1984 13.24 110.16 1.46 2.10 13.20 100.01 1.37 1.68 1985 13.28 106.80 1.37 1.96 13.06 95.96 1.21 1.56 1986 13.05 99.76 1.25 1.54 12.66 86.86 0.71 1.01 1987 12.72 92.16 1.22 1.42 11.92 79.32 0.79 1.05 1988 12.36

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

    SciTech Connect (OSTI)

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

    2011-09-13

    wind speed and vertical temperature difference. Ideally, the data assimilation scheme used in the experiments would have been based upon an ensemble Kalman filter (EnKF) that was similar to the ESA method used to diagnose the Mid-Colombia Basin sensitivity patterns in the previous studies. However, the use of an EnKF system at high resolution is impractical because of the very high computational cost. Thus, it was decided to use the three-dimensional variational analysis data assimilation that is less computationally intensive and more economically practical for generating operational forecasts. There are two tasks in the current project effort designed to validate the ESA observational system deployment approach in order to move closer to the overall goal: (1) Perform an Observing System Experiment (OSE) using a data denial approach which is the focus of this task and report; and (2) Conduct a set of Observing System Simulation Experiments (OSSE) for the Mid-Colombia basin region. The results of this task are presented in a separate report. The objective of the OSE task involves validating the ESA-MOOA results from the previous sensitivity studies for the Mid-Columbia Basin by testing the impact of existing meteorological tower measurements on the 0- to 6-hour ahead 80-m wind forecasts at the target locations. The testing of the ESA-MOOA method used a combination of data assimilation techniques and data denial experiments to accomplish the task objective.

  16. 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.

  17. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01

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

  18. F2PYV1.81

    Energy Science and Technology Software Center (OSTI)

    2005-03-23

    Python is a freely distributed programming language used for rapid application development. F2PY is an open source program which reads in FORTRAN 77/90/95 source code and produces a Python wrapper module for it. Functions and variables in the original FORTRAN program can be accessed from Python using this wrapper module. This new version adds support for derived types to F2PY. Derived types are very much like structs in C. They are used to group togethermore » variables that are part of one larger whole. For example, first name, last name, and id number could be part of an employee derived type.« less

  19. Magnetic properties of Er1-xDyxAl2 (0 ≤ x ≤ 1) compounds...

    Office of Scientific and Technical Information (OSTI)

    Er1-xDyxAl2 (0 x 1) compounds in low applied fields Citation Details In-Document Search Title: Magnetic properties of Er1-xDyxAl2 (0 x 1) compounds in low applied ...

  20. DOE-2 sample run book: Version 2.1E

    SciTech Connect (OSTI)

    Winkelmann, F.C.; Birdsall, B.E.; Buhl, W.F.; Ellington, K.L.; Erdem, A.E.; Hirsch, J.J.; Gates, S.

    1993-11-01

    The DOE-2 Sample Run Book shows inputs and outputs for a variety of building and system types. The samples start with a simple structure and continue to a high-rise office building, a medical building, three small office buildings, a bar/lounge, a single-family residence, a small office building with daylighting, a single family residence with an attached sunspace, a ``parameterized`` building using input macros, and a metric input/output example. All of the samples use Chicago TRY weather. The main purpose of the Sample Run Book is instructional. It shows the relationship of LOADS-SYSTEMS-PLANT-ECONOMICS inputs, displays various input styles, and illustrates many of the basic and advanced features of the program. Many of the sample runs are preceded by a sketch of the building showing its general appearance and the zoning used in the input. In some cases we also show a 3-D rendering of the building as produced by the program DrawBDL. Descriptive material has been added as comments in the input itself. We find that a number of users have loaded these samples onto their editing systems and use them as ``templates`` for creating new inputs. Another way of using them would be to store various portions as files that can be read into the input using the {number_sign}{number_sign} include command, which is part of the Input Macro feature introduced in version DOE-2.lD. Note that the energy rate structures here are the same as in the DOE-2.lD samples, but have been rewritten using the new DOE-2.lE commands and keywords for ECONOMICS. The samples contained in this report are the same as those found on the DOE-2 release files. However, the output numbers that appear here may differ slightly from those obtained from the release files. The output on the release files can be used as a check set to compare results on your computer.

  1. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    SciTech Connect (OSTI)

    Vrugt, Jasper A; Wohling, Thomas

    2008-01-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  2. PSA Vol 1 Tables Revised Ver 2 Print.xls

    Gasoline and Diesel Fuel Update (EIA)

    Refining Districts, 2005 East Coast Appalachian No. 1 Total IN, IL, KY MN, WI, ND, SD OK, KS, MO Total Liquefied Refinery Gases 2.5 0.9 2.4 4.2 1.2 0.9 3.1 Finished Motor...

  3. 2-15-14_Event Release-1

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

    Possible Radiological Event at WIPP CARLSBAD, N.M., February 15, 2014, 2:49 PM (MST) - Operations personnel are responding to a possible radiological event at the U.S. Department of Energy's (DOE) Waste Isolation Pilot Plant (WIPP). NEW INFORMATION * At 11:30 PM Friday, a continuous air monitor detected airborne radiation in the underground * WIPP officials are on site to assess the situation and determine next steps * There were no employees working underground at the time and employees on the

  4. Coupling of TRAC-PF1/MOD2, Version 5.4.25, with NESTLE

    SciTech Connect (OSTI)

    Knepper, P.L.; Hochreiter, L.E.; Ivanov, K.N.; Feltus, M.A.

    1999-09-01

    A three-dimensional (3-D) spatial kinetics capability within a thermal-hydraulics system code provides a more correct description of the core physics during reactor transients that involve significant variations in the neutron flux distribution. Coupled codes provide the ability to forecast safety margins in a best-estimate manner. The behavior of a reactor core and the feedback to the plant dynamics can be accurately simulated. For each time step, coupled codes are capable of resolving system interaction effects on neutronics feedback and are capable of describing local neutronics effects caused by the thermal hydraulics and neutronics coupling. With the improvements in computational technology, modeling complex reactor behaviors with coupled thermal hydraulics and spatial kinetics is feasible. Previously, reactor analysis codes were limited to either a detailed thermal-hydraulics model with simplified kinetics or multidimensional neutron kinetics with a simplified thermal-hydraulics model. The authors discuss the coupling of the Transient Reactor Analysis Code (TRAC)-PF1/MOD2, Version 5.4.25, with the NESTLE code.

  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. Acquisition Guide Chapter 1.2 - Balanced Scorecard Performance...

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

    2 - Balanced Scorecard Performance Assessment Program - (March 2004) Acquisition Guide Chapter 1.2 - Balanced Scorecard Performance Assessment Program - (March 2004) This chapter ...

  7. Cancellation of DOE G 440.2B-1A

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2011-02-15

    Cancels DOE G 440.2B-1A, Implementation Guide - Performance Indicators (Metrics) for Use with Doe O 440.2B, Aviation Management and Safety.

  8. High pressure-temperature polymorphism of 1,1-diamino-2,2-dinitroethylene

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

    Bishop, M. M.; Chellappa, R. S.; Liu, Z.; Preston, D. N.; Sandstrom, M. M.; Dattelbaum, D. M.; Vohra, Y. K.; Velisavljevic, N.

    2014-05-07

    Here, 1,1-diamino-2,2-dinitroethylene (FOX-7) is a low sensitivity energetic material with performance comparable to commonly used secondary explosives such as RDX and HMX. At ambient pressure, FOX-7 exhibits complex polymorphism with at least three structurally distinct phases (α, β, and γ). In this study, we have investigated the high pressure-temperature stability of FOX-7 polymorphs using synchrotron mid-infrared (MIR) spectroscopy. At ambient pressure, our MIR spectra and corresponding differential scanning calorimetry (DSC) measurements confirmed the known α → β (~110 °C) and β → γ (~160 °C) structural phase transitions; as well as, indicated an additional transition γ → δ (~210 °C),more » with the δ phase being stable up to ~251 degree C prior to decomposition. In situ MIR spectra obtained during isobaric heating at 0.9 GPa, revealed a potential α → β transition that could occur as early as 180 degree C, while β → β+δ phase transition shifted to ~300 °C with suppression of γ phase. Decomposition was observed slightly above 325 °C at 0.9 GPa..« less

  9. Implications of SU(2)_L x U(1) Symmetry for SIM(2) Invariant...

    Office of Scientific and Technical Information (OSTI)

    Implications of SU(2)L x U(1) Symmetry for SIM(2) Invariant Neutrino Masses Citation Details In-Document Search Title: Implications of SU(2)L x U(1) Symmetry for SIM(2) Invariant ...

  10. NMAC 1.2.2 Public Regulations Commission Rules of Procedure ...

    Open Energy Info (EERE)

    NMAC 1.2.2 Public Regulations Commission Rules of Procedure Jump to: navigation, search OpenEI Reference LibraryAdd to library Legal Document- RegulationRegulation: NMAC 1.2.2...

  11. 1,2-HOIQO--A highly versatile 1,2-HOPO analog

    SciTech Connect (OSTI)

    Seitz, Michael; Pluth, Michael D.; Raymond, Kenneth N.

    2006-08-07

    A cyclic, bidentate hydroxamic acid binding unit based on an isoquinoline scaffold has been utilized for the synthesis of a hexadentate tripodal ligand based on the TREN backbone. This prototype for a new class of multidentate chelators forms mononuclear iron(III) complexes and one-dimensional coordination polymers with lanthanide(III) cations. The latter has been determined by single crystal X-ray analysis of the cerium species. The solid state structure in the monoclinic space group P2{sub 1}/c (C{sub 36}H{sub 34}CeN{sub 7}O{sub 11}, a = 12.341(2){angstrom}, b = 26.649(4){angstrom}, c = 10.621(2){angstrom}, {alpha} = {gamma} = 90{sup o}, {beta} = 96.753(3){sup o}, V = 3468.6(9) {angstrom}{sup 3}, Z = 4) exhibits a trigonal-dodecahedral environment around the cerium cation. The proof of concept for the versatility of the new scaffold has been shown by the modification of the crucial precursor 3-carboxyiso-coumarin through electrophilic aromatic substitutions to yield the corresponding chlorosulfonated and nitrated analogs.

  12. Propellant Containing 3, 6bis(1h-1,2,3,4-Tetrazol-5-Ylamino)-1,2,4,5- Tetrazine Or Salt Thereof

    DOE Patents [OSTI]

    Hiskey, Michael A.; Chavez, David E.; Naud, Darren

    2003-12-02

    The compound 3,6-bis(1H-1,2,3,4-tetrazol-5-ylamino)-1,2,4,5-tetrazine and its salts are provided together with a propellant composition including an oxidizer, a binder and 3,6-bis(1H-1,2,3,4-tetrazol-5-ylamino)-1,2,4,5-tetrazine or its salts.

  13. Microsoft Word - Table_1_2_PC_.doc

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

    Ray Data Booklet Table 1-2. Photon energies, in electron volts, of principal K-, L-, and M-shell emission lines. Element Kα α α α 1 Kα α α α 2 Kβ β β β 1 Lα α α α 1 Lα α α α 2 Lβ β β β 1 Lβ β β β 2 Lγ γ γ γ 1 Mα α α α 1 3 Li 54.3 4 Be 108.5 5 B 183.3 6 C 277 7 N 392.4 8 O 524.9 9 F 676.8 10 Ne 848.6 848.6 11 Na 1,040.98 1,040.98 1,071.1 12 Mg 1,253.60 1,253.60 1,302.2 13 Al 1,486.70 1,486.27 1,557.45 14 Si 1,739.98 1,739.38 1,835.94 15 P 2,013.7 2,012.7

  14. NMAC 17.1.2 Utility Applications | Open Energy Information

    Open Energy Info (EERE)

    1.2 Utility Applications Jump to: navigation, search OpenEI Reference LibraryAdd to library Legal Document- StatuteStatute: NMAC 17.1.2 Utility ApplicationsLegal Abstract These...

  15. Department of Energy Offers $2.1 Billion Conditional Commitment...

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

    commitment for a 2.1 billion loan guarantee to support Units 1 and 2 of the Blythe Solar Power Project, sponsored by Solar Trust of America, LLC. The concentrating solar thermal ...

  16. 1

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

    Longwave Radiation in the ECMWF Forecast System J.-J. Morcrette European Centre for Medium-Range Weather Forecasts Shinfield Park, Reading Berkshire, United Kingdom Abstract The surface downward longwave radiation (LWR) was computed by the European Centre for Medium- Range Weather Forecasts (ECMWF) forecast system used for the 40-year reanalysis. The LWR is compared with surface radiation measurements for the April to May 1999 period, available as part of the Baseline Surface Radiation Network

  17. 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.

  18. 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

  19. Cytoplasmic Domain Structures of Kir2.1 and Kir3.1 Shows Sites for

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

    Modulating Gating and Rectification Cytoplasmic Domain Structures of Kir2.1 and Kir3.1 Shows Sites for Modulating Gating and Rectification Scott Pegan1, Christine Arrabit2, Wei Zhou1, Witek Kwiatkowski1, Anthony Collins3, Paul Slesinger2 and Senyon Choe1 Structural Biology1 and Peptide Biology2 Laboratories, The Salk Institute, La Jolla, Ca 92037; Department of Pharmaceutical Sciences3, College of Pharmacy, Oregon State University, Corvallis, OR 97331 Figure 1. Kir2.1 cytoplasmic domain's

  20. Extension of DOE M 474.1-2

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2002-02-01

    This Notice extends DOE M 474.1-2, Nuclear Materials Management and Safeguards System Reporting and Data Submission, dated 2-10-98, until 2-10-03, unless sooner rescinded.

  1. Extension of DOE M 474.1-2

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2003-02-11

    This Notice extends DOE M 474.1-2, Nuclear Materials Management and Safeguards System Reporting and Data Submission, dated 2-10-98, until 2-11-04, unless sooner rescinded.

  2. Microsoft Word - AcqGuide1pt2.doc

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

    -Chapter 1.2 (January 2007) 1 Head of Contracting Acitivty (HCA) Authority, Functions, and Responsilitites Reference: FAR 1.601 Overview This chapter provides a summary of HCA ...

  3. Economic Benefits, Carbon Dioxide (CO2) Emissions Reductions, and Water Conservation Benefits from 1,000 Megawatts (MW) of New Wind Power in North Carolina (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2009-03-01

    The U.S. Department of Energy?s Wind Powering America Program is committed to educating state-level policymakers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in North Carolina. Although construction and operation of 1000 MW of wind power is a significant effort, seven states have already reached the 1000-MW mark. We forecast the cumulative economic benefits from 1000 MW of development in North Carolina to be $1.1 billion, annual CO2 reductions are estimated at 2.9 million tons, and annual water savings are 1,558 million gallons.

  4. 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. ...

  5. 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...

  6. 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 ...

  7. Implications of SU(2)_L x U(1) Symmetry for SIM(2) Invariant...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Implications of SU(2)L x U(1) Symmetry for SIM(2) Invariant Neutrino Masses Citation Details In-Document Search Title: Implications of SU(2)L x U(1) Symmetry for ...

  8. H2 Safety Snapshot - Vol. 2, Issue 1, Nov. 2010 | Department of Energy

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

    1, Nov. 2010 H2 Safety Snapshot - Vol. 2, Issue 1, Nov. 2010 This second issue outlines good practices for the safe handling of gas cylinders. h2_snapshot_v2i1.pdf (691.48 KB) More Documents & Publications Safetygram Gaseous Hydrogen H2 Safety Snapshot, Vol. 1, Issue 1, April 2009 International Hydrogen Fuel and Pressure Vessel Forum 2010 Proceedings

  9. 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.

  10. 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...