National Library of Energy BETA

Sample records for forecast evaluation table

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  3. SECTION M EVALUATION FACTORS FOR AWARD TABLE OF CONTENTS M-1 EVALUATION OF PROPOSALS .....................................................................................2

    National Nuclear Security Administration (NNSA)

    TABLE OF CONTENTS M-1 EVALUATION OF PROPOSALS .....................................................................................2 M-2 BASIS FOR CONTRACT AWARD ...................................................................................3 M-3 TECHNICAL AND MANAGEMENT CRITERIA ..........................................................3 M-4 COST EVALUATION CRITERION .................................................................................5 Section M, Evaluation Factors Request for

  4. SECTION M EVALUATION FACTORS FOR AWARD TABLE OF CONTENTS M-1 EVALUATION OF PROPOSALS .....................................................................................2

    National Nuclear Security Administration (NNSA)

    M EVALUATION FACTORS FOR AWARD TABLE OF CONTENTS M-1 EVALUATION OF PROPOSALS .....................................................................................2 M-2 BASIS FOR CONTRACT AWARD ...................................................................................3 M-3 TECHNICAL AND MANAGEMENT CRITERIA ..........................................................3 M-4 COST CRITERION

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

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

    SciTech Connect (OSTI)

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

    2008-01-24

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

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

    SciTech Connect (OSTI)

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

    2011-09-13

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

  8. FY 2005 Laboratory Table

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

    Congressional Budget Request Laboratory Tables Preliminary Department of Energy FY 2005 Congressional Budget Request Office of Management, Budget and Evaluation/CFO February 2004 Laboratory Tables Preliminary Department of Energy Department of Energy FY 2005 Congressional Budget FY 2005 Congressional Budget Request Request Office of Management, Budget and Evaluation/CFO February 2004 Laboratory Tables Laboratory Tables Printed with soy ink on recycled paper Preliminary Preliminary The numbers

  9. FY 2005 State Table

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

    Office of Management, Budget and Evaluation/CFO February 2004 State Tables State Tables Preliminary Preliminary Department of Energy Department of Energy FY 2005 Congressional Budget FY 2005 Congressional Budget Request Request Office of Management, Budget and Evaluation/CFO February 2004 State Tables State Tables Printed with soy ink on recycled paper Preliminary Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The

  10. 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....

  11. 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 3,153 3,143 -0.3% Price (centskWh) 12.06 12.09 12.58 13.04 12.95 12.96 ...

  12. 10Li General Tables

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

    Li General Table The General Table for 10Li is subdivided into the following categories: Reviews Theoretical Ground State Properties Shell Model Cluster Model Other Models Special States Astrophysical Electromagnetic Transitions Hypernuclei Photodisintegration Light-Ion and Neutron Induced Reactions These General Tables correspond to the 2003 preliminary evaluation of ``Energy Levels of Light Nuclei, A = 10''. The prepublication version of A = 10 is available on this website in PDF format: A =

  13. 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.

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

    SciTech Connect (OSTI)

    Rogers, J.; Porter, K.

    2011-03-01

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

  15. FY 2006 Laboratory Table

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

    Laboratory Tables Preliminary Department of Energy FY 2006 Congressional Budget Request Office of Management, Budget and Evaluation/CFO February 2005 Laboratory Tables Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals,

  16. FY 2006 State Table

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

    State Tables Preliminary Department of Energy FY 2006 Congressional Budget Request Office of Management, Budget and Evaluation/CFO February 2005 State Tables Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or

  17. Table 4

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

    112 70 83 98 99 117 150 5.89 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

  18. Table 4

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

    125 43 101 95 99 130 149 8.25 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

  19. Table 4

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

    125 69 112 131 137 158 7.36 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

  20. EERE Guide for Managing General Program Evaluation Studies

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

    ... 34 Table 6. Selection of Evaluation Research Design for Impact Evaluations... 35 Table 7. Example:...

  1. Table 4

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

    10.8 0.3 0.8 1.6 2.0 2.2 4.0 11.94 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

  2. Table 4

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

    10.8 0.9 2.9 1.9 2.8 2.3 9.84 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

  3. Table 4

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

    0.6 0.8 0.6 1.4 2.3 1.9 2.5 12.69 Notes: -- To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. -- Because of rounding, data may...

  4. A = 5 General Tables

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

    5 General Tables The General Table for 5H is subdivided into the following categories: Cluster Model Hypernuclei Model Calculations Photodisintegration Pions The General Table for...

  5. Table 7

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

    1 Table 7 Created on: 4/27/2016 9:48:20 AM Table 7. Marketed production of natural gas in selected states and the Federal Gulf of Mexico, 2011-2016 (million cubic feet) Year and Month Alaska Arkansas California Colorado Kansas Louisiana Montana New Mexico North Dakota Ohio 2011 Total 356,225 1,072,212 250,177 1,637,576 309,124 3,029,206 74,624 1,237,303 97,102 78,858 2012 Total 351,259 1,146,168 246,822 1,709,376 296,299 2,955,437 66,954 1,215,773 172,242 84,482 2013 Total 338,182 1,139,654

  6. 8C General Tables

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

    C General Tables The General Table for 8C is subdivided into the following categories: Reviews Other Theoretical Work

  7. 1999 CBECS Detailed Tables

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

    Commercial Buildings Energy Consumption Survey (CBECS) > Detailed Tables 1999 CBECS Detailed Tables Building Characteristics | Consumption & Expenditures Data from the 1999...

  8. 6Be General Tables

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

    6Be General Table The General Table for 6Be is subdivided into the following categories: Cluster Model Model Calculations...

  9. A=18 Tables

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

    (1959) Adobe Reader Download Tables from (1995TI07): Introductory Table 3 in PS or PDF. Table 18.1 in PS or PDF. Table 18.2 in PS or PDF. Table 18.3 in PS or PDF. Table 18.4...

  10. A=19 Tables

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

    (1959) Adobe Reader Download Tables from (1995TI07): Introductory Table 3 in PS or PDF. Table 19.1 in PS or PDF. Table 19.2 in PS or PDF. Table 19.3 in PS or PDF. Table 19.4...

  11. A=20 Tables

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

    (1959) Adobe Reader Download Tables from (1998TI06): Introductory Table 3 in PS or PDF. Table 20.1 in PS or PDF. Table 20.2 in PS or PDF. Table 20.3 in PS or PDF. Table 20.4...

  12. 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 ...

  13. 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...

  14. Forecasting Water Quality & Biodiversity

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

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

  15. 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...

  16. Report Tables

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

    3945 8191 10161 10799 Infrastructure 1087 7608 14186 33190 Mooring/Foundation 1264 8330 37594 72750 Device Structural Components 3817 26838 118871 230967 Power Conversion Chain 588 4723 20734 39382 Subsystem Integration & Profit Margin 441 3156 13961 27035 Installation 5909 9082 21531 37860 Contingency 1705 6793 23704 45198 Total 18756 74721 260742 497181 Capex and Opex Table Rounding 1 # of digits to zero Capex 1 Unit 10 Units 50 Units 100 Units $ / kW $ / kW $ / kW $ / kW Development 10950

  17. Report Tables

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

    5205 22990 31387 30857 Infrastructure 1111 7850 16188 35610 Mooring/Foundation 835 7442 36179 70300 Device Structural Components 7682 54017 239250 464862 Power Conversion Chain 1637 9749 34184 58813 Subsystem Integration & Profit Margin 932 6377 27343 52367 Installation 5909 9082 21531 37860 Contingency 2331 11751 40606 75067 Total 25642 129258 446668 825736 Capex and Opex Table Rounding 1 # of digits to zero Capex 1 Unit 10 Units 50 Units 100 Units $ / kW $ / kW $ / kW $ / kW Development

  18. A critical evaluation of the upper ocean heat budget in the Climate Forecast System Reanalysis data for the south central equatorial Pacific

    SciTech Connect (OSTI)

    Liu H.; Lin W.; Liu, X.; Zhang, M.

    2011-08-26

    Coupled ocean-atmospheric models suffer from the common bias of a spurious rain belt south of the central equatorial Pacific throughout the year. Observational constraints on key processes responsible for this bias are scarce. The recently available reanalysis from a coupled model system for the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data is a potential benchmark for climate models in this region. Its suitability for model evaluation and validation, however, needs to be established. This paper examines the mixed layer heat budget and the ocean surface currents - key factors for the sea surface temperature control in the double Inter-Tropical Convergence Zone in the central Pacific - from 5{sup o}S to 10{sup o}S and 170{sup o}E to 150{sup o}W. Two independent approaches are used. The first approach is through comparison of CFSR data with collocated station observations from field experiments; the second is through the residual analysis of the heat budget of the mixed layer. We show that the CFSR overestimates the net surface flux in this region by 23 W m{sup -2}. The overestimated net surface flux is mainly due to an even larger overestimation of shortwave radiation by 44 W m{sup -2}, which is compensated by a surface latent heat flux overestimated by 14 W m{sup -2}. However, the quality of surface currents and the associated oceanic heat transport in CFSR are not compromised by the surface flux biases, and they agree with the best available estimates. The uncertainties of the observational data from field experiments are also briefly discussed in the present study.

  19. Using Wikipedia to forecast disease

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

    Using Wikipedia to forecast disease Using Wikipedia to forecast disease Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. December 22, 2014 Using Wikipedia to forecast disease Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. Contact Nancy Ambrosiano Communications Office (505) 667-0471 Email "A global disease-forecasting system will improve

  20. 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 ...

  1. 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.

  2. FY 2005 Statistical Table

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) Table of Contents Summary...................................................................................................... 1 Mandatory Funding....................................................................................... 3 Energy Supply.............................................................................................. 4 Non-Defense site acceleration

  3. 1995 Detailed Tables

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

    Households, Buildings & Industry > Commercial Buildings Energy Consumption Survey > Detailed Tables 1995 Detailed Tables Data from the 1995 Commercial Buildings Energy Consumption...

  4. 7He General Tables

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

    He General Table The General Table for 7He is subdivided into the following categories: Experimental Theoretical Model Calculations Hypernuclei and Mesons Pions

  5. 9He General Tables

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

    He General Table The General Table for 9He is subdivided into the following categories: Shell Model Other Model Calculations Theoretical

  6. A = 10 General Tables

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

    Table for 10He is subdivided into the following categories: Theoretical Shell Model Cluster Model Other Models Special States Electromagnetic Transitions The General Table for...

  7. 5H General Tables

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

    H General Table The General Table for 5H is subdivided into the following categories: Cluster Model Hypernuclei Model Calculations Photodisintegration Pions...

  8. 10He General Tables

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

    General Table The General Table for 10He is subdivided into the following categories: Theoretical Shell Model Cluster Model Other Models Special States Electromagnetic Transitions...

  9. 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

  10. The forecast calls for flu

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

    The forecast calls for flu Science on the Hill: 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

  11. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

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

  12. Final Report- Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Broader source: Energy.gov [DOE]

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California independent system operator’s load forecasts by integrating behind-the-meter photovoltaic forecasts.

  13. CBECS Buildings Characteristics --Revised Tables

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

    Energy Sources and End Use Tables (27 pages, 152 kb) CONTENTS PAGES Table 18. Energy Sources, Number of Buildings, 1995 Table 19. Energy Sources, Floorspace, 1995 Table 20. Energy End Uses, Number of Buildings and Floorspace, 1995 Table 21. Space-Heating Energy Sources, Number of Buildings, 1995 Table 22. Space-Heating Energy Sources, Floorspace, 1995 Table 23. Primary Space-Heating Energy Sources, Number of Buildings, 1995 Table 24. Primary Space-Heating Energy Sources, Floorspace, 1995 Table

  14. CBECS Buildings Characteristics --Revised Tables

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

    End-Use Equipment Tables (27 pages, 151 kb) CONTENTS PAGES Table 33. Heating Equipment, Number of Buildings, 1995 Table 34. Heating Equipment, Floorspace, 1995 Table 35. Cooling Equipment,Number of Buildings, 1995 Table 36. Cooling Equipment, Floorspace, 1995 Table 37. Refrigeration Equipment, Number of Buildings and Floorspace, 1995 Table 38. Water-Heating Equipment, Number of Buildings and Floorspace, 1995 Table 39. Lighting Equipment, Number of Buildings, 1995 Table 40. Lighting Equipment,

  15. Tabled Execution in Scheme

    SciTech Connect (OSTI)

    Willcock, J J; Lumsdaine, A; Quinlan, D J

    2008-08-19

    Tabled execution is a generalization of memorization developed by the logic programming community. It not only saves results from tabled predicates, but also stores the set of currently active calls to them; tabled execution can thus provide meaningful semantics for programs that seemingly contain infinite recursions with the same arguments. In logic programming, tabled execution is used for many purposes, both for improving the efficiency of programs, and making tasks simpler and more direct to express than with normal logic programs. However, tabled execution is only infrequently applied in mainstream functional languages such as Scheme. We demonstrate an elegant implementation of tabled execution in Scheme, using a mix of continuation-passing style and mutable data. We also show the use of tabled execution in Scheme for a problem in formal language and automata theory, demonstrating that tabled execution can be a valuable tool for Scheme users.

  16. 8Be General Tables

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

    Be General Tables The General Table for 8Be is subdivided into the following categories: Reviews Ground State Properties Shell Model Cluster Model Other Models Photodisintegration Fission and Fusion Astrophysical b-decay Hypernuclei

  17. 8He General Tables

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

    He General Tables The General Table for 8He is subdivided into the following categories: Reviews Ground-state Properties Shell Model Cluster Model Other Theoretical Work Elastic and Inelastic Scattering b-decay

  18. 9B General Tables

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

    B General Table The General Table for 9B is subdivided into the following categories: Shell Model Cluster Model Theoretical Other Model Calculations Complex Reactions Beta-Decay Pions Light-ion and Neutron Induced Reactions Hypernuclei

  19. 9C General Tables

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

    C General Table The General Table for 9C is subdivided into the following categories: Shell Model Cluster Model Other Models Theoretical Beta-Decay Light-ion and Neutron Induced Reactions Astrophysical

  20. A = 7 General Tables

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

    7 General Tables The General Table for 7He is subdivided into the following categories: Experimental Theoretical Model Calculations Hypernuclei and Mesons Pions The General Table for 7Li is subdivided into the following categories: Reviews Ground State Properties Shell Model Cluster Model Other Theoretical Work Model Calculations Photodisintegration Polarization Fission and Fusion Elastic and Inelastic Scattering Projectile Fragmentation and Multifragmentation Astrophysical Hyperfine Structure

  1. Hot cell examination table

    DOE Patents [OSTI]

    Gaal, Peter S.; Ebejer, Lino P.; Kareis, James H.; Schlegel, Gary L.

    1991-01-01

    A table for use in a hot cell or similar controlled environment for use in examining specimens. The table has a movable table top that can be moved relative to a table frame. A shaft is fixedly mounted to the frame for axial rotation. A shaft traveler having a plurality of tilted rollers biased against the shaft is connected to the table top such that rotation of the shaft causes the shaft traveler to roll along the shaft. An electromagnetic drive is connected to the shaft and the frame for controllably rotating the shaft.

  2. 1997 Housing Characteristics Tables Housing Unit Tables

    Gasoline and Diesel Fuel Update (EIA)

    Contact: Robert Latta, Survey Manager (rlatta@eia.doe.gov) World Wide Web: http:www.eia.doe.govemeuconsumption Table HC1-1a. Housing Unit Characteristics by Climate Zone, ...

  3. 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)

  4. Supply Forecast and Analysis (SFA)

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

    Science Team Leader Oak Ridge National Laboratory DOE Bioenergy Technologies Office (BETO) 2015 Project Peer Review Supply Forecast and Analysis (SFA) 2 | Bioenergy Technologies ...

  5. 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.

  6. 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:...

  7. CBECS Buildings Characteristics --Revised Tables

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

    Buildings Use Tables (24 pages, 129 kb) CONTENTS PAGES Table 12. Employment Size Category, Number of Buildings, 1995 Table 13. Employment Size Category, Floorspace, 1995 Table 14. Weekly Operating Hours, Number of Buildings, 1995 Table 15. Weekly Operating Hours, Floorspace, 1995 Table 16. Occupancy of Nongovernment-Owned and Government-Owned Buildings, Number of Buildings, 1995 Table 17. Occupancy of Nongovernment-Owned and Government-Owned Buildings, Floorspace, 1995 These data are from the

  8. Description of Detailed Tables

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

    for the 1999 Commercial Buildings Energy Consumption Survey (CBECS) consists of building characteristics tables B1 through B39, which contain the number of buildings and...

  9. 1997 MWD comparison tables

    SciTech Connect (OSTI)

    Wiseman, K.

    1997-05-01

    This year`s MWD Comparison Tables include a Quick Reference Guide listing MWD sensors by collar size for each manufacturer. Following the Quick Reference Guide are the comparison tables, which list general, directional, gamma ray, resistivity, density and neutron data for each tool. The MWD Tables should only be used as a reference source. System specifications frequently change as tools are refined and developed. Consult MWD marketing representatives prior to making final tool selections. A contact key for all the companies listed in the tables appears on the last page.

  10. A = 9 General Tables

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

    The General Table for 9Li is subdivided into the following categories: Shell Model Cluster Model Theoretical Ground State Properties Special States Other Model Calculations...

  11. 5Li General Tables

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

    Table for 5Li is subdivided into the folowing categories: Ground State Properties Cluster Model Shell Model Special States Model Calculations Model Discussions Complex...

  12. 10N General Tables

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

    subdivided into the following categories: Reviews Ground-State Properties Shell Model Cluster Model Other Theoretical Work These General Tables correspond to "Energy Levels of...

  13. SECTION J - TABLE OF CONTENTS

    National Nuclear Security Administration (NNSA)

    Conformed to Mod 0108 DE-NA0000622 Section J Page i PART III - LIST OF DOCUMENTS, EXHIBITS, AND OTHER ATTACHMENTS SECTION J LIST OF APPENDICES TABLE OF CONTENTS Appendix A Statement of Work (Replaced by Mod 002; Modified Mod 016; Replaced Mod 029) Appendix B Performance Evaluation Plan (Replaced by Mods 002, 016, 020, 029, 0084) Appendix C Contractor's Transition Plan Appendix D Sensitive Foreign Nations Control Appendix E Performance Guarantee Agreement(s) Appendix F National Work Breakdown

  14. 5He General Tables

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

    He General Table The General Table for 5He is subdivided into the following categories: Ground State Properties Theoretical Special States Model Discussions Shell Model Cluster Model Complex Reactions Astrophysics Model Calculations Light-ion and Neutron Induced Reactions Electron Scattering Muon Catalyzed Fusion Other Fusion Photodisintegration Polarization Pions Hypernuclei

  15. 6He General Tables

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

    He General Table The General Table for 6He is subdivided into the following categories: Ground State Properties Theoretical Special States Shell Model Cluster and alpha-particle Models Other Models Model Calculations Complex Reactions Involving 6He Electromagnetic Transitions Muon and Neutrino Capture and Reactions Reactions Involving pions, Other Mesons and Baryon States Photodisintegration Astrophysics Hypernuclei

  16. 6Li General Tables

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

    Li General Table The General Table for 6Li is subdivided into the following categories: Ground State Properties of 6Li Special States Theoretical Shell Model Cluster Models Complex Reactions Involving 6Li Model Calculations Electromagnetic Transitions Muon and Neutrino Capture and Reactions Reactions Involving Pions, Other Mesons and Baryon States Light-ion and Neutron Induced Reactions Pions Hypernuclei Reactions Involving Antiprotons Astrophysics

  17. 7Be General Tables

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

    Be General Table The General Table for 7Be is subdivided into the following categories: Reviews Experimental Work Shell Model Cluster Model Other Theoretical Work Model Calculations Projectile Fragmentation and Multifragmentation Astrophysical b Decay Astrophysical Neutrinos Hypernuclei, Mesons and Other Exotic Particles Applications

  18. 7Li General Tables

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

    Li General Table The General Table for 7Li is subdivided into the following categories: Reviews Ground State Properties Shell Model Cluster Model Other Theoretical Work Model Calculations Photodisintegration Polarization Fission and Fusion Elastic and Inelastic Scattering Projectile Fragmentation and Multifragmentation Astrophysical Hyperfine Structure b-decay Muons Hypernuclei and Mesons Hypernuclei and Baryons Pion, Kaon and Eta-Mesons Other Work Applications

  19. 8B General Tables

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

    B General Tables The General Table for 8B is subdivided into the following categories: Reviews Ground State Properties Shell Model Cluster Model Other Models Photodisintegration and Coulomb Dissociation Elastic and Inelastic Scattering Fragmentation Reactions Astrophysical b Decay Nucleon Spatial Distribution

  20. 8Li General Tables

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

    Li General Tables The General Table for 8Li is subdivided into the following categories: Reviews Ground State Properties Shell Model Cluster Model Other Models Photodissociation Fusion and Fission Elastic and Inelastic Scattering Fragmentation Reactions Astrophysical b Decay Hypernuclei Pions, Kaons and h-mesons

  1. 9Li General Tables

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

    Li General Table The General Table for 9Li is subdivided into the following categories: Shell Model Cluster Model Theoretical Ground State Properties Special States Other Model Calculations Complex Reactions Beta-Decay Pions Muons Photodisintegration Elastic and Inelastic Scattering Electromagnetic Transitions Astrophysical

  2. 1996 MWD comparison tables

    SciTech Connect (OSTI)

    Gastineau, J.

    1996-05-01

    Petroleum Engineer International`s ninth annual Measurement While Drilling Tables compare the different operating capabilities of survey and logging tools from 13 MWD vendors. This year`s MWD Comparison Tables include a Quick Reference Guide listing MWD sensors by collar size for each manufacturer. Following the Quick Reference Guide are the comparison tables, listing general, directional, gamma ray, resistivity, density and neutron data for each tool. The MWD Tables should serve only as a reference source. System specifications can change rapidly as tools are refined and developed. Consultation with MWD marketing representatives before making a final tool selection is recommended. A contact key for all of the companies listed in the tables is provided.

  3. 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:...

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

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

    6 - Metrics, Performance Measurements and Forecasting Module 6 - Metrics, Performance Measurements and Forecasting This module focuses on the metrics and performance measurement ...

  5. 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.

  6. 1995 CECS C&E Tables

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

    Fuel Oil Tables (10 pages, 58 kb) CONTENTS PAGES Table 26. Total Fuel Oil Consumption and Expenditures, 1995 Table 27. Fuel Oil Consumption and Expenditure Intensities, 1995 Table...

  7. Office of Cyber Security Evaluations Appraisal Process Guide...

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

    Office of Cyber Security Evaluations Appraisal Process Guide Table of Contents April 2008 ii Table of Contents Acronyms......

  8. CBECS Buildings Characteristics --Revised Tables

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

    Geographic Location Tables (24 pages, 136kb) CONTENTS PAGES Table 3. Census Region, Number of Buildings and Floorspace, 1995 Table 4. Census Region and Division, Number of Buildings, 1995 Table 5. Census Region and Division, Floorspace, 1995 Table 6. Climate Zone, Number of Buildings and Floorspace, 1995 Table 7. Metropolitan Status, Number of Buildings and Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey

  9. All Consumption Tables.vp

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

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

  10. Prod_Tables_2013.indd

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

    State Energy Production Estimates 1960 Through 2013 2013 Summary Tables U.S. Energy Information Administration | State Energy Data 2013: Production 1 Table P1. Energy Production ...

  11. 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

  12. Table_of_Contents

    Office of Environmental Management (EM)

    1: Points of Entry/Exit and Transporters Table 1: Points of Entry/Exit and Transporters PDF icon POEE List.pdf More Documents & Publications EA-262-A TransCanada Power Marketing Ltd EA-262-C TransCanada Power Marketing Ltd EA-262-B TransCanada Power Marketing Ltd

    2: U.S. Geographic Areas and Census Regions Table 2: U.S. Geographic Areas and Census Regions PDF icon Table 2: U.S. Geographic Areas and Census Regions More Documents & Publications Memorandum Summarizing Ex Parte

  13. Tables of Energy Levels

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

    Tables of Energy Levels The Image Map below will direct you to the table of energy levels [PDF format only] for that particular nuclide from the most recent publication found within the TUNL and FAS publications of "Energy Levels of Light Nuclei" for A = 4 - 20. If your browser does not support image maps or you would like the choice of PostScript and PDF formats for the tables, please view the list below. Click on the button corresponding to the nucleus for which you would like to

  14. Table G3

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

    May 28, 2010 Voluntary Reporting of Greenhouse Gases 14 Table G3. Decision Chart for a ... Form EIA-1605 Voluntary Reporting of Greenhouse Gases Form Approved OMB No. 1905-0194 ...

  15. TABLE OF CONTENTS

    Energy Savers [EERE]

    008 High Temperature Superconductivity for Electric Systems Peer Review Final Report i TABLE OF CONTENTS High Temperature Superconductivity for Electric Systems Program Overview ...... 1 The Peer Review................................................................................................................ 3 Review Criteria ................................................................................................................. 5 Guidelines

  16. Table_of_Contents

    Energy Savers [EERE]

    Table of Contents 1. Physical Security .............................................................................................................................. 1-1 101. Headquarters Security Badges ........................................................................................ 101-1 102. HSPD-12 Badges and the PIV Process ........................................................................... 102-1 103. Prohibited Articles

  17. TABLE OF CONTENTS

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

    3, Revision 0 i TABLE OF CONTENTS 1.0 Summary .............................................................................................................................. 1 2.0 Introduction .......................................................................................................................... 1 3.0 Discussion ............................................................................................................................ 4 3.1 Selection of Tanks for Level Decrease

  18. TABLE OF CONTENTS

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

    4, Revision 0 i TABLE OF CONTENTS 1.0 Summary .............................................................................................................................. 1 2.0 Introduction .......................................................................................................................... 1 3.0 Discussion ............................................................................................................................ 4 3.1 Selection of Tanks for Level Decrease

  19. TABLE OF CONTENTS

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

    5, Revision 0 i TABLE OF CONTENTS 1.0 Summary .............................................................................................................................. 1 2.0 Introduction .......................................................................................................................... 1 3.0 Discussion ............................................................................................................................ 4 3.1 Selection of Tanks for Level Decrease

  20. table11.xls

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

    ... 14.1 NA 17.9 18.3 19.6 20.1 Table 11. Fuel Economy, Selected Survey Years (Miles Per Gallon) Survey Years Page A-1 of A-5 1983 1985 1988...

  1. National Targets Table

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

    Nov 2011 For instructions on how to use the table and footnotes, see page 2 Education 144 63% 58 K-12 School CollegeUniversity (campus level) 244 63% 104 Food Sales 570 86% 193 ...

  2. Building Materials Property Table

    SciTech Connect (OSTI)

    2010-04-16

    This information sheet describes a table of some of the key technical properties of many of the most common building materials taken from ASHRAE Fundamentals - 2001, Moisture Control in Buildings, CMHC, NRC/IRC, IEA Annex 24, and manufacturer data.

  3. 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. File Acquisition-Forecast-2016-05-06.xlsx More Documents & Publications Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment Small Business Program Manager Directory

  4. FY 2007 State Table

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

    state tables preliminary Department of Energy FY 2007 Congressional Budget Request February 2006 Printed with soy ink on recycled paper Office of Chief Financial Officer state tables preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other

  5. FY 2008 Laboratory Table

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

    Laboratory Table Preliminary Department of Energy FY 2008 Congressional Budget Request February 2007 Office of Chief Financial Officer Laboratory Table Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other

  6. FY 2008 State Table

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

    State Table Preliminary Department of Energy FY 2008 Congressional Budget Request February 2007 Office of Chief Financial Officer State Table Preliminary Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, uses of prior year balances, deferrals, rescissions, or other adjustments

  7. FY 2009 State Table

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

    State Tables Preliminary February 2008 Office of Chief Financial Officer Department of Energy FY 2009 Congressional Budget Request State Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE

  8. FY 2010 Laboratory Table

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

    Laboratory Tables Preliminary May 2009 Office of Chief Financial Officer FY 2010 Congressional Budget Request Laboratory Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by

  9. FY 2010 State Table

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

    State Tables Preliminary May 2009 Office of Chief Financial Officer FY 2010 Congressional Budget Request State Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the

  10. FY 2011 Laboratory Table

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

    Laboratory Tables Department of Energy FY 2011 Congressional Budget Request DOE/CF-0055 March 2010 Office of Chief Financial Officer Laboratory Tables Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments

  11. FY 2011 State Table

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

    State Tables Department of Energy FY 2011 Congressional Budget Request DOE/CF-0054 March 2010 Office of Chief Financial Officer State Tables Printed with soy ink on recycled paper The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated

  12. FY 2012 State Table

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

    6 Department of Energy FY 2012 Congressional Budget Request State Tables P li i Preliminary February 2012 Office of Chief Financial Officer DOE/CF-0066 Department of Energy FY 2012 Congressional Budget Request State Tables P li i Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They displayed. The figures include both the discretionary and

  13. FY 2013 State Table

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

    9 Department of Energy FY 2013 Congressional Budget Request State Tables P li i Preliminary February 2012 Office of Chief Financial Officer DOE/CF-0079 Department of Energy FY 2013 Congressional Budget Request State Tables P li i Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They displayed. The figures include both the discretionary and

  14. CBECS Buildings Characteristics --Revised Tables

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

    Structure Tables (16 pages, 93 kb) CONTENTS PAGES Table 8. Building Size, Number of Buildings, 1995 Table 9. Building Size, Floorspace, 1995 Table 10. Year Constructed, Number of Buildings, 1995 Table 11. Year Constructed, Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of commercial buildings sponsored by the Energy Information Administration, that provides information on the use of energy in commercial

  15. CBECS Buildings Characteristics --Revised Tables

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

    Percentage Tables (16 pages, 92 kb) CONTENTS PAGES Table 29. Percent of Floorspace Heated, Number of Buildings and Floorspace, 1995 Table 30. Percent of Floorspace Cooled, Number of Buildings and Floorspace, 1995 Table 31. Percent of Floorspace Lit when Open, Number of Buildings and Floorspace, 1995 Table 32. Heated, Cooled, and Lit Buildings, Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of commercial

  16. CBECS Buildings Characteristics --Revised Tables

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

    Conservation Tables (16 pages, 86 kb) CONTENTS PAGES Table 41. Energy Conservation Features, Number of Buildings and Floorspace, 1995 Table 42. Building Shell Conservation Features, Number of Buildings, 1995 Table 43. Building Shell Conservation Features, Floorspace, 1995 Table 44. Reduction in Equipment Use During Off Hours, Number of Buildings and Floorspace, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of

  17. Selected papers on fuel forecasting and analysis

    SciTech Connect (OSTI)

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

    1983-05-01

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

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

    SciTech Connect (OSTI)

    Finley, Cathy

    2014-04-30

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

  19. T-583: Linux Kernel OSF Partition Table Buffer Overflow Lets Local Users Obtain Information

    Broader source: Energy.gov [DOE]

    A local user can create a storage device with specially crafted OSF partition tables. When the kernel automatically evaluates the partition tables, a buffer overflow may occur and data from kernel heap space may leak to user-space.

  20. 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 PDF icon Wind Forecast Improvement Project ...

  1. Uncertainty Reduction in Power Generation Forecast Using Coupled...

    Office of Scientific and Technical Information (OSTI)

    quantify the forecast uncertainty by reducing prediction intervals of forecasts. ... means, e.g., using weather-based models, and reduce forecast errors prediction intervals. ...

  2. FY 2012 Laboratory Table

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

    5 Department of Energy FY 2012 Congressional Budget Request Laboratory Tables y Preliminary February 2012 Office of Chief Financial Officer DOE/CF-0065 Department of Energy FY 2012 Congressional Budget Request Laboratory Tables P li i Preliminary h b d i d i hi d h l l f b d h i f h The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider

  3. FY 2013 Laboratory Table

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

    8 Department of Energy FY 2013 Congressional Budget Request Laboratory Tables y Preliminary February 2012 Office of Chief Financial Officer DOE/CF-0078 Department of Energy FY 2013 Congressional Budget Request Laboratory Tables P li i Preliminary h b d i d i hi d h l l f b d h i f h The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include both the discretionary and mandatory funding in the budget. They do not consider

  4. 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

  5. Forecasting the 2013–2014 influenza season using Wikipedia

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

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

    2015-05-14

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

  6. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect (OSTI)

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

    2015-05-14

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

  7. TABLE OF CONTENTS

    National Nuclear Security Administration (NNSA)

    AC05-00OR22800 TABLE OF CONTENTS Contents Page # TOC - i SECTION A - SOLICITATION/OFFER AND AWARD ......................................................................... A-i SECTION B - SUPPLIES OR SERVICES AND PRICES/COSTS ........................................................ B-i B.1 SERVICES BEING ACQUIRED ....................................................................................B-2 B.2 TRANSITION COST, ESTIMATED COST, MAXIMUM AVAILABLE FEE, AND AVAILABLE FEE (Modification 295,

  8. FY 2006 Statistical Table

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2004 FY 2005 FY 2006 Comparable Comparable Request to FY 2006 vs. FY 2005 Approp Approp Congress Discretionary Summary By Appropriation Energy And Water Development Appropriation Summary: Energy Programs Energy supply Operation and maintenance................................................. 787,941 909,903 862,499 -47,404 -5.2% Construction......................................................................... 6,956

  9. FY 2007 Statistical Table

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2005 FY 2006 FY 2007 Current Current Congressional Approp. Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy supply and conservation Operation and maintenance............................................ 1,779,399 1,791,372 1,917,331 +125,959 +7.0%

  10. FY 2008 Statistical Table

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2006 FY 2007 FY 2008 Current Congressional Congressional Approp. Request Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy supply and conservation Operation and maintenance........................................... 1,781,242 1,917,331 2,187,943 +270,612 +14.1%

  11. Table of Contents

    Energy Savers [EERE]

    COMMUNICATIONS REQUIREMENTS OF SMART GRID TECHNOLOGIES October 5, 2010 i Table of Contents I. Introduction and Executive Summary.......................................................... 1 a. Overview of Smart Grid Benefits and Communications Needs................. 2 b. Summary of Recommendations .................................................................... 5 II. Federal Government Smart Grid Initiatives ................................................ 7 a. DOE Request for Information

  12. FY 2013 Statistical Table

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2011 FY 2012 FY 2013 Current Enacted Congressional Approp. Approp. * Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy........................................ 1,771,721 1,809,638 2,337,000 +527,362 +29.1% Electricity delivery and energy reliability.........................................

  13. 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. ...

  14. 1995 CECS C&E Tables

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

    Electricity Tables (35 pages, 218 kb) CONTENTS PAGES Table 9. Total Electricity Consumption and Expenditures, 1995 Table 10. Electricity Consumption and Expenditure Intensities,...

  15. 1995 CECS C&E Tables

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

    kb) CONTENTS PAGES Table 1. Total Energy Consumption by Major Fuel, 1995 Table 9. Total Electricity Consumption and Expenditures, 1995 Table 20. Total Natural Gas Consumption and...

  16. 1995 CECS C&E Tables

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

    pages, 95 kb) CONTENTS PAGES Table 3. Consumption for Sum of Major Fuels, 1995 Table 10. Electricity Consumption and Expenditure Intensities, 1995 Table 21. Natural Gas...

  17. 1995 CECS C&E Tables

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

    kb) CONTENTS PAGES Table 2. Total Energy Expenditures by Major Fuel, 1995 Table 9. Total Electricity Consumption and Expenditures, 1995 Table 20. Total Natural Gas Consumption and...

  18. 1995 CECS C&E Tables

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

    pages, 95 kb) CONTENTS PAGES Table 4. Expenditures for Sum of Major Fuels, 1995 Table10. Electricity Consumption and Expenditure Intensities, 1995 Table 21. Natural Gas...

  19. EJ and EK Pay Table

    Broader source: Energy.gov [DOE]

    The 2014 EJ and EK pay table excludes locality pay.  Refer to the General Schedule Complete Set of Locality Pay Tables to determine the locality pay for your applicable geographic area.   

  20. Usage by Job Size Table

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

    Usage by Job Size Table Usage by Job Size Table page loading animation Usage Query Interface System All Hopper Edison Cori Carver Planck Matgen Franklin Hopper 1 Magellan Dirac...

  1. 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

  2. Advanced Vehicle Technologies Awards Table

    Broader source: Energy.gov [DOE]

    The table contains a listing of the applicants, their locations, the amounts of the awards, and description of each project.

  3. Energy.gov Data Tables

    Broader source: Energy.gov [DOE]

    Follow these guidelines for creating Section 508-compliant data tables in the Energy.gov Drupal environment.

  4. NREL: Resource Assessment and Forecasting Home Page

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

    NREL's resource assessment and forecasting research supports industry, government, and academia by providing renewable energy resource measurements, models, maps, and support services. These resources are used to plan and develop renewable energy technologies and support climate change research. Learn more about NREL's resource assessment and forecasting research: Capabilities Facilities Research staff Data and resources. Resource assessment and forecasting research is primarily performed at

  5. 2003 CBECS Detailed Tables: Summary

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

    c32.pdf c32.xls c32.html Fuel Oil (Tables C33-C36) set12-pdf Table C33. Total Fuel Oil Consumption and Expenditures c33-pdf c33.xls c33.html Table C34. Fuel Oil Consumption...

  6. FY 2009 Statistical Table

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2007 FY 2008 FY 2009 Current Current Congressional Op. Plan Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy.......................... -- 1,722,407 1,255,393 -467,014 -27.1% Electricity delivery and energy reliability........................... -- 138,556 134,000 -4,556 -3.3% Nuclear

  7. FY 2010 Statistical Table

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2008 FY 2009 FY 2009 FY 2010 Current Current Current Congressional Approp. Approp. Recovery Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy....................................... 1,704,112 2,178,540 16,800,000 2,318,602 +140,062 +6.4% Electricity delivery and energy

  8. FY 2012 Statistical Table

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

    2Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2010 FY 2011 FY 2011 FY 2012 Current Congressional Annualized Congressional Approp. Request CR Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy....................................... 2,216,392 2,355,473 2,242,500 3,200,053 +983,661 +44.4% Electricity delivery and energy

  9. Description of Energy Intensity Tables (12)

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

    3. Description of Energy Intensity Data Tables There are 12 data tables used as references for this report. Specifically, these tables are categorized as tables 1 and 2 present...

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

    SciTech Connect (OSTI)

    Thomas, L.C.

    1994-10-01

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

  11. compare_tables.xlsx

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

    Current Forecast: May 10, 2016; Previous Forecast: April 12, 2016 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2014 2015 2016 2017 2014-2015 2015-2016 2016-2017 U.S. Energy Supply U.S. Crude Oil Production (million barrels per day) Current 9.48 9.50 9.43 9.32 9.13 8.78 8.27 8.23 8.23 8.20 8.07 8.26 8.71 9.43 8.60 8.19 8.3% -8.8% -4.8% Previous 9.48 9.50 9.43 9.31 9.11 8.79 8.29 8.21 8.16 8.07 7.89 8.05 8.71 9.43 8.60 8.04 8.3% -8.8% -6.5% Percent Change 0.0% 0.0% 0.0% 0.0% 0.2% -0.2% -0.3% 0.3% 0.8% 1.6%

  12. International Program Action Table - October 2012 | Department...

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

    Communication & Engagement International Programs International Program Action Table - October 2012 International Program Action Table - October 2012 International Program ...

  13. QTR table of respondents | Department of Energy

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

    table of respondents QTR table of respondents PDF icon QTR_RFI_Comments_Table _V2.pdf More Documents & Publications Table of QTR comments in response to Federal Register RFI Table of QTR comments in response to Federal Register RFI Table of QTR comments in response to Federal Register RFI

  14. TABLE OF CONTENTS

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

    through December 2001 2 TABLE OF CONTENTS Page A. Project Summary 1. Technical Progress 3 2. Cost Reporting 4 B. Detailed Reports 1.1 Magnets & Supports 7 1.2 Vacuum System 9 1.3 Power Supplies 13 1.4 RF System 16 1.5 Instrumentation & Controls 17 1.6 Cable Plant 18 1.9 Installation 19 2.0 Accelerator Physics 20 3 A. SPEAR 3 PROJECT SUMMARY 1. Technical Progress In the magnet area, the production of all major components (dipoles, quadrupoles, and sextupoles) has been completed on

  15. TABLE OF CONTENTS

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

    2 TABLE OF CONTENTS Page A. Project Summary 1. Technical Progress 3 2. Cost Reporting 5 B. Detailed Reports 1.1 Magnets & Supports 8 1.2 Vacuum System 12 1.3 Power Supplies 14 1.4 RF System 16 1.5 Instrumentation & Controls 17 1.6 Cable Plant 18 1.7 Beam Line Front Ends 19 1.8 Facilities 19 1.9 Installation 20 2.1 Accelerator Physics 21 2 A. SPEAR 3 PROJECT SUMMARY 1. Technical Progress The progress and highlights of each major technical system are summarized below. Additional details

  16. TABLE OF CONTENTS

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

    through June 2001 2 TABLE OF CONTENTS Page A. Project Summary 1. Technical Progress 3 2. Cost Reporting 4 B. Detailed Reports 1.1 Magnets & Supports 9 1.2 Vacuum System 16 1.3 Power Supplies 21 1.4 RF System 25 1.5 Instrumentation & Controls 26 1.6 Cable Plant 28 1.8 Facilities 28 2.0 Accelerator Physics 29 2.1 ES&H 31 3 A. SPEAR 3 PROJECT SUMMARY 1. Technical Progress Magnet System - The project has received three shipments of magnets from IHEP. A total of 55 dipole, quadrupole and

  17. TABLE OF CONTENTS

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

    through September 2001 2 TABLE OF CONTENTS Page A. Project Summary 1. Technical Progress 3 2. Cost Reporting 4 B. Detailed Reports 1.1 Magnets & Supports 9 1.2 Vacuum System 14 1.3 Power Supplies 21 1.4 RF System 24 1.5 Instrumentation & Controls 26 1.6 Cable Plant 27 1.7 Beam Line Front Ends 28 1.8 Facilities 29 2.0 Accelerator Physics 30 2.1 ES&H 32 3 A. SPEAR 3 PROJECT SUMMARY 1. Technical Progress Summary - The SPEAR 3 project is near the 50% completion mark in terms of

  18. Sandia Unstructured Triangle Table Generator

    Energy Science and Technology Software Center (OSTI)

    2013-09-16

    The software generates data tables for thermodynamic and transport properties of materials as described by a set of input models. For each input model parameterization, an associated table is created on an unstructured triangular grid. These grids all conform to the same topology. A statistical accuracy guarantee is provided for the tabular representation of the model. Details of the model and table specification are given in a XML input deck.

  19. CBECS Buildings Characteristics --Revised Tables

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

    Summary Tables (12 pages, 59 kb) CONTENTS PAGES 1. Summary Table: Totals and Means of Floorspace, Number of Workers, and Hours of Operation, 1995 2. Summary Table: Totals and Medians of Floorspace, Number of Workers, Hours of Operation, and Age of Building, 1995 These data are from the 1995 Commercial Buildings Energy Consumption Survey (CBECS), a national probability sample survey of commercial buildings sponsored by the Energy Information Administration, that provides information on the use of

  20. MECS 1991 Publications and Tables

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

    Capability To Switch Fuels Appendices Appendix A. Detailed Tables Appendix B. Survey Design, Implementation, and Estimates (file size 141,211 bytes) pages: 22. Appendix C....

  1. Table 1. Crude Oil Prices

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

    from Table 24. Refiner acquisition costs -- Energy Information Administration, Form FEA-P110-M-1, "Refiners' Monthly Cost Allocation Report," January 1978 through June 1978;...

  2. Health Care Buildings: Subcategories Table

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

    Subcategories Table Selected Data by Type of Health Care Building Number of Buildings (thousand) Percent of Buildings Floorspace (million square feet) Percent of Floorspace Square...

  3. Health Care Buildings: Equipment Table

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

    Equipment Table Buildings, Size and Age Data by Equipment Types for Health Care Buildings Number of Buildings (thousand) Percent of Buildings Floorspace (million square feet)...

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

    SciTech Connect (OSTI)

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

    2011-03-28

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

  5. 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

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

    SciTech Connect (OSTI)

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

    2005-02-09

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

  7. Solar Forecast Improvement Project | Department of Energy

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

    NOAA also will provide advanced satellite products. INNOVATIONS NOAA is providing numerical weather prediction (NWP) modeling with new information that will help solar forecasts. ...

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

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

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

  9. 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...

  10. 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...

  11. Microsoft Word - table_02

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

    5 Created on: 4/27/2016 10:43:40 AM Table 2. Natural gas consumption in the United States, 2011-2016 (billion cubic feet, or as indicated) Year and Month Lease and Plant Fuel a Pipeline and Distribution Use b Delivered to Consumers Total Consumption Heating Value c (Btu per cubic foot) Residential Commercial Industrial Electric Power Vehicle Fuel Total 2011 Total 1,323 688 4,714 3,155 6,994 7,574 30 22,467 24,477 1,022 2012 Total 1,396 731 4,150 2,895 7,226 9,111 30 23,411 25,538 1,024 2013

  12. Microsoft Word - table_03

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

    7 Created on: 4/27/2016 10:43:51 AM Table 3. Selected national average natural gas prices, 2011-2016 (dollars per thousand cubic feet, except where noted) Year and Month NGPL Composite Spot Price a Natural Gas Spot Price b Citygate Price Delivered to Consumers Electric Power Price d Residential Commercial Industrial Price % of Total c Price % of Total c Price % of Total c 2011 Annual Average 15.12 4.00 5.63 11.03 96.2 8.91 67.3 5.13 16.3 4.89 2012 Annual Average 10.98 2.75 4.73 10.65 95.8 8.10

  13. Table of Contents

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

    U U . . S S . . D D E E P P A A R R T T M M E E N N T T O O F F E E N N E E R R G G Y Y O O F F F F I I C C E E O O F F I I N N S S P P E E C C T T O O R R G G E E N N E E R R A A L L Semiannual Report toCongress DOE/IG-0065 April 1 - September 30, 2013 TABLE OF CONTENTS From the Desk of the Inspector General ..................................................... 2 Impacts Key Accomplishments ............................................................................................... 3

  14. 1999 Commercial Building Characteristics--Detailed Tables--Principal...

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

    Principal Building Activities > Detailed Tables-Principal Building Activities Complete Set of 1999 CBECS Detailed Tables Detailed Tables-Principal Building Activities Table B1....

  15. 1999 Commercial Building Characteristics--Detailed Tables--Year...

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

    Year Constructed > Detailed Tables-Year Constructed Complete Set of 1999 CBECS Detailed Tables Detailed Tables-Year Constructed Table B8. Year Constructed, Number of Buildings...

  16. 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 ...

  17. Study forecasts disappearance of conifers due to climate change

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

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

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

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

    Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits ... Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits ...

  19. Modeling and forecasting the distribution of Vibrio vulnificus...

    Office of Scientific and Technical Information (OSTI)

    Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay Citation Details In-Document Search Title: Modeling and forecasting the distribution of Vibrio ...

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

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

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

  1. Annual Energy Outlook (AEO) 2006 - Supplemental Tables - All Tables

    SciTech Connect (OSTI)

    2009-01-18

    Tables describing regional energy consumption and prices by sector; residential, commercial, and industrial demand sector data; transportation demand sector; electricity and renewable fuel; and petroleum, natural gas, and coal data.

  2. 1995 CECS C&E Tables

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

    Category (6 pages, 36 kb) CONTENTS PAGES Table 17. Peak Electricity Demand Category, Number of Buildings, 1995 Table 18. Peak Electricity Demand Category, Floorspace, 1995 These...

  3. Commerial Buildings Characteristics, 1995 (Table of Contents...

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

    Number of Buildings and Relative Standard Errors, 1995 Table I.2. Participation in Energy Conservation Programs, Floorspace and Relative Standard Errors, 1995 Table J.1....

  4. Trends in Commercial Buildings--Table

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

    Home > Trends in Commercial Buildings > Energy Consumption - Part 1> Site Energy Consumption Tables Table 1. Total site energy consumption, relative standard errors, and 95%...

  5. Appendix B: Technical Projection Tables, Bioenergy Technologies...

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

    Tables B-2 Last updated: November 2014 Table B-2: Terrestrial Feedstock Supply and Logistics Costs to Supply Feedstock to a Pyrolysis Conversion Process Processing Area Cost...

  6. Precision Flow Table | Open Energy Information

    Open Energy Info (EERE)

    Table Jump to: navigation, search Basic Specifications Facility Name Flow Table Overseeing Organization United States Army Corp of Engineers (ERDC) Hydrodynamic Testing Facility...

  7. 1995 CECS C&E Tables

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

    Building Level Intensities (percentile) (6 pages, 39 kb) CONTENTS PAGES Table 10. Electricity Consumption and Expenditure Intensities, 1995 Table 21. Natural Gas Consumption and...

  8. 1995 CECS C&E Tables

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

    and Gross Energy Intensity by Census Region for Sum of Major Fuels, 1995 Table 11. Electricity Consumption and Conditional Energy Intensity by Census Region, 1995 Table 22....

  9. 1995 CECS C&E Tables

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

    and Gross Energy Intensity by Year Constructed for Sum of Major Fuels, 1995 Table 14. Electricity Consumption and Conditional Energy Intensity by Year Constructed, 1995 Table...

  10. 1995 CECS C&E Tables

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

    and Gross Energy Intensity by Building Size for Sum of Major Fuels, 1995 Table13. Electricity Consumption and Conditional Energy Intensity by Building Size, 1995 Table 24....

  11. FY 2006 Control Table by Appropriation

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

    ... Page 1 Department of Energy FY 2006 Control Table by Appropriation (dollars in thousands - ... Page 2 300 Department of Energy FY 2006 Control Table by Appropriation (dollars in ...

  12. FY 2006 Control Table by Organization

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

    ... Page 1 Department of Energy FY 2006 Control Table by Organization (dollars in ... Page 2 Department of Energy FY 2006 Control Table by Organization (dollars in ...

  13. FY 2005 Control Table by Appropriation

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

    ... 13 Department of Energy FY 2005 Control Table by Appropriation (dollars in ... FY 2004 Page 1 Department of Energy FY 2005 Control Table by Appropriation (dollars in ...

  14. Table of tables: A database design tool for SYBASE

    SciTech Connect (OSTI)

    Brown, B.C.; Coulter, K.; Glass, H.D.; Glosson, R.; Hanft, R.W.; Harding, D.J.; Trombly-Freytag, K.; Walbridge, D.G.C.; Wallis, D.B. ); Allen, M.E. )

    1991-01-04

    The Table of Tables' application system captures in a set of SYBASE tables the basic design specification for a database schema. Specification of tables, columns (including the related defaults and rules for the stored values) and keys is provided. The feature which makes this application specifically useful for SYBASE is the ability to automatically generate SYBASE triggers. A description field is provided for each database object. Based on the data stored, SQL scripts for creating complete schema including the tables, their defaults and rules, their indexes, and their SYBASE triggers, are written by TOT. Insert, update and delete triggers are generated from TOT to guarantee integrity of data relations when tables are connected by single column foreign keys. The application is written in SYBASE's APT-SQL and includes a forms based data entry system. Using the features of TOT we can create a complete database schema for which the data integrity specified by our design is guaranteed by the SYBASE triggers generated by TOT. 3 refs.

  15. Microsoft Word - table_26.doc

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

    Fueled Vehicles"; state agencies; Form EIA-23, "Annual Survey of Domestic Oil and Gas Reserves"; PointLogic Energy; DI; Ventyz; and EIA estimates based on historical data. Table 27...

  16. Health Care Buildings: Consumption Tables

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

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

  17. 2012 NISE Awards Summary Table

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

    Awards » 2012 NISE Summary Table 2012 NISE Awards Summary Table Investigator NERSC repo Hours awarded DOE Office Project Title Gilbert Compo, University of Colorado at Boulder m958 10,000,000 BER Climate Research Ocean-Atmosphere Reanalysis for Climate Applications (OARCA) 1850-2013 Silvia Crivelli, Lawrence Berkeley National Laboratory m1532 1,550,000 BER Biological Systems Science WeFold: A collaborative effort for protein structure prediction Thomas Hamill, National Oceanic & Atmospheric

  18. 2013 NISE Awards Summary Table

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

    Awards » 2013 NISE Summary Table 2013 NISE Awards Summary Table Investigator NERSC repo Hours awarded DOE Office Project Title Katie Antypas, Lawrence Berkeley National Laboratory m1759 250,000 ASCR Applied Mathematical Sciences NERSC Application Readiness for Future Architectures Inez Fung, University of California Berkeley m189 750,000 BER Climate and Environmental Sciences Carbon Data Assimilation with a Coupled Ensemble Kalman Filter Thomas Hamill, National Oceanic & Atmospheric

  19. 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.

  20. 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.

  1. Offshore Lubricants Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  2. 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...

  3. 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....

  4. 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...

  5. Forecast of transportation energy demand through the year 2010

    SciTech Connect (OSTI)

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

    1991-04-01

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

  6. EERE Program Management Guide - Cover and Table of Contents | Department of

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

    Energy Guide - Cover and Table of Contents EERE Program Management Guide - Cover and Table of Contents Table of contents includes an introduction and eight chapters on the program's background, management, outreach planning, budget formulation, implementation, analysis and evaluation, and information/business management. Also includes appendices. PDF icon pmguide_toc.pdf More Documents & Publications EERE Program Management Guide - Chapter 4 EERE Program Management Guide - Chapter 6

  7. NREL: Resource Assessment and Forecasting - Research Staff

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

    Research Staff NREL's resource assessment and forecasting research staff provides expertise in renewable energy measurement and instrumentation through NREL's Power Systems Engineering Center. Photo not available Linda Crow - Administrative Associate B.S. Environmental Studies, The Evergreen State College Linda currently works for the Resource Assessment and Forecasting group as their administrative support. She has worked with scientists at the Office of Science at the Air Force Academy and at

  8. Sandia National Labs: PCNSC: IBA Table

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

    Departments Radiation, Nano Materials, & Interface Sciences > Radiation & Solid Interactions > Nanomaterials Sciences > Surface & Interface Sciences Semiconductor & Optical Sciences Energy Sciences Small Science Cluster Business Office News Partnering Research Ion Beam Analysis (IBA) Periodic Table (HTML) IBA Table (HTML) | IBA Table (135KB GIF) | IBA Table (1.2MB PDF) | IBA Table (33MB TIF) | Heavy Ion Backscattering Spectrometry (HIBS) | Virtual Lab Tour (6MB) The

  9. 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.

  10. Microsoft Word - BL SP3 Table 11-03 v19 - final1.doc

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

    Are Product Spreads Useful for Forecasting Oil Prices? An Empirical Evaluation of the Verleger Hypothesis Christiane Baumeister Lutz Kilian Xiaoqing Zhou Bank of Canada University of Michigan University of Michigan CEPR EIA 2014 Workshop on Financial and Physical Oil Market Linkages October 6, 2014 The views expressed in this presentation, or in my remarks, are my own, and do not necessarily represent those of the Bank of Canada. Background  Oil price forecasts affect the economic outlook of

  11. Microsoft Word - table_08.doc

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

    3 Table 8 Created on: 4/26/2016 5:59:09 PM Table 8. Underground natural gas storage - all operators, 2011-2016 (volumes in billion cubic feet) Year and Month Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Base Gas Working Gas Total a Volume Percent Injections Withdrawals Net Withdrawals b 2011 Total c -- -- -- -- -- 3,422 3,074 -348 2012 Total c -- -- -- -- -- 2,825 2,818 -7 2013 Total c -- -- -- -- -- 3,156 3,702 546

  12. Microsoft Word - table_09.doc

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

    5 Table 9 Created on: 4/26/2016 5:59:19 PM Table 9. Underground natural gas storage - by season, 2014-2016 (volumes in billion cubic feet) Year, Season, and Month Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Base Gas Working Gas Total Volume Percent Injections Withdrawals Net Withdrawals a 2014 Heating Season November 4,366 3,605 7,971 -194 -5.1 155 366 211 December 4,365 2,890 7,255 -523 -15.3 94 808 714 January 4,363

  13. Microsoft Word - table_11.doc

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

    27 Table 11 Created on: 4/26/2016 5:59:40 PM Table 11. Underground natural gas storage - storage fields other than salt caverns, 2011-2016 (volumes in billion cubic feet) Year and Month Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Base Gas Working Gas Total Volume Percent Injections Withdrawals Net Withdrawals a 2011 Total b -- -- -- -- -- 2,889 2,634 -255 2012 Total b -- -- -- -- -- 2,360 2,373 12 2013 Total b -- -- --

  14. Microsoft Word - table_13.doc

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

    33 Table 13 Table 13. Activities of underground natural gas storage operators, by state, February 2016 (volumes in million cubic feet) State Field Count Total Storage Capacity Working Gas Storage Capacity Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Base Gas Working Gas Total Volume Percent Injections Withdrawals Alabama 2 43,600 33,150 9,640 20,669 30,309 13,768 199.5 3,081 2,367 Alaska a 5 83,592 67,915 14,197 24,790

  15. 2011 NISE Awards Summary Table

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

    Awards » 2011 NISE Summary Table 2011 NISE Awards Summary Table Investigator NERSC Repo Hours Awarded DOE Office Project Title Dmitri Babikov, Marquette University m409 1,450,000 BES Chemistry New potential energy surface for ozone molecule Connor Balance, Auburn University m41 600,000 Fusion Energy Hybrid OpenMP/MPI approach to R-matrix scattering Amitava Bhattacharjee, University of New Hampshire m148 1,000,000 Fusion Energy Global Effects on the Dynamics of Plasmoids and Flux Ropes during

  16. 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.

  17. Radioactive decay data tables

    SciTech Connect (OSTI)

    Kocher, D.C.

    1981-01-01

    The estimation of radiation dose to man from either external or internal exposure to radionuclides requires a knowledge of the energies and intensities of the atomic and nuclear radiations emitted during the radioactive decay process. The availability of evaluated decay data for the large number of radionuclides of interest is thus of fundamental importance for radiation dosimetry. This handbook contains a compilation of decay data for approximately 500 radionuclides. These data constitute an evaluated data file constructed for use in the radiological assessment activities of the Technology Assessments Section of the Health and Safety Research Division at Oak Ridge National Laboratory. The radionuclides selected for this handbook include those occurring naturally in the environment, those of potential importance in routine or accidental releases from the nuclear fuel cycle, those of current interest in nuclear medicine and fusion reactor technology, and some of those of interest to Committee 2 of the International Commission on Radiological Protection for the estimation of annual limits on intake via inhalation and ingestion for occupationally exposed individuals.

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

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

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

  19. EA-1909: South Table Wind Farm Project, Kimball County, Nebraska

    Broader source: Energy.gov [DOE]

    DOE’s Western Area Power Administration is preparing this EA to evaluate the environmental impacts of interconnecting the proposed South Table Wind Project, which would generate approximately 60 megawatts from about 40 turbines, to Western’s existing Archer-Sidney 115-kV Transmission Line in Kimball County, Nebraska.

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

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

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

  1. FY 2014 Budget Request Statistical Table | Department of Energy

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

    Statistical Table FY 2014 Budget Request Statistical Table PDF icon Stats Table FY2014.pdf More Documents & Publications FY 2009 Environmental Management Budget Request to Congress ...

  2. FY 2014 Budget Request Summary Table | Department of Energy

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

    PDF icon Summary Table by Appropriations PDF icon Summary Table by Organization More Documents & Publications FY 2014 Budget Request Statistical Table FY 2014 Budget Justification ...

  3. CBECS 1992 - Building Characteristics, Detailed Tables

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

    major topics of each table. Directions for calculating an approximate relative standard error (RSE) for each estimate in the tables are presented in Figure A1, "Use of RSE Row...

  4. FY 2013 Control Table by Organization

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

    3 Summary Control Table by Organization (dollars in thousands - OMB Scoring) FY 2011 FY ... FY 2013 vs. FY 2012 Control table by Organization Page 1 FY 2013 Congressional Budget ...

  5. FY 2012 Control Table by Organization

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

    2 Summary Control Table by Organization (dollars in thousands - OMB Scoring) FY 2010 FY ... FY10 Current Approp for all programs including NNSA Control table by organization Page 1 ...

  6. FY 2010 Control Table by Organization

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

    0 Control Table by Organization (dollars in thousands - OMB Scoring) FY 2008 FY 2009 FY ... FY 2010 vs. FY 2009 Control table by organization 1 562009 9:54 AM Department of Energy ...

  7. FY 2011 Control Table by Organization

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

    1 Summary Control Table by Organization (dollars in thousands - OMB Scoring) FY 2009 FY ... FY 2011 vs. FY 2010 Control table by Organization Page 1 of 10 312010 8:16 AM Department ...

  8. FY 2007 Control Table by Organization

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

    Control Table by Organization (dollars in thousands - OMB Scoring) FY 2005 FY 2006 FY 2007 ... FY 2006 Page 1 Department of Energy FY 2007 Control Table by Organization (dollars in ...

  9. Code Tables | National Nuclear Security Administration

    National Nuclear Security Administration (NNSA)

    Code Tables U.S. Department of Energy / U.S. Nuclear Regulatory Commission Nuclear Materials Management & Safeguards System Code Tables Action Code The action code identifies the type of activity being reported in a transaction. The Action Code table shows the valid action codes. Nature of Transaction (TI) Code The financial code signifies the nature of the financial or contractual activity that is involved in the transaction. The Nature of Transaction (TI) Code table shows the valid action

  10. 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 ...

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

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

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

  12. RADIOACTIVE ELEMENTS IN THE STANDARD ATOMIC WEIGHTS TABLE

    SciTech Connect (OSTI)

    Holden, N.E.; Holden, N.; Holden,N.E.

    2011-07-27

    In the 1949 Report of the Atomic Weights Commission, a series of new elements were added to the Atomic Weights Table. Since these elements had been produced in the laboratory and were not discovered in nature, the atomic weight value of these artificial products would depend upon the production method. Since atomic weight is a property of an element as it occurs in nature, it would be incorrect to assign an atomic weight value to that element. As a result of that discussion, the Commission decided to provide only the mass number of the most stable (or longest-lived) known isotope as the number to be associated with these entries in the Atomic Weights Table. As a function of time, the mass number associated with various elements has changed as longer-lived isotopes of a particular element has been found in nature, or as improved half-life values of an element's isotopes might cause a shift in the longest-lived isotope from one mass to another. In the 1957 Report of the Atomic Weights Commission, it was decided to discontinue the listing of the mass number in the Atomic Weights Table on the grounds that the kind of information supplied by the mass number is inconsistent with the primary purpose of the Table, i.e., to provide accurate values of 'these constants' for use in various chemical calculations. In addition to the Table of Atomic Weights, the Commission included an auxiliary Table of Radioactive Elements for the first time, where the entry would be the isotope of that element which was the most stable, i.e., the one with the longest known half-life. In their 1973 Report, the Commission noted that the users of the main Table of Atomic Weights were dissatisfied with the omission of values for some elements in that Table and it was decided to reintroduce the mass number for the radioactive elements into the main Table. In their 1983 Report, the Commission decided that radioactive elements were considered to lack a characteristic terrestrial isotopic composition, from which an atomic weight value could be calculated to five or more figure accuracy, without prior knowledge of the sample involved. These elements were again listed in the Atomic Weights Table with no further information, i.e., with no mass number or atomic weight value. For the elements, which have no stable characteristic terrestrial isotopic composition, the data on the half-lives and the relative atomic masses for the nuclides of interest for those elements have been evaluated. The values of the half-lives with their uncertainties are listed in the table. The uncertainties are given for the last digit quoted of the half-life and are given in parentheses. A half-life entry for the Table having a value and an uncertainty of 7 {+-} 3 is listed in the half-life column as 7 (3). The criteria to include data in this Table, is to be the same as it has been for over sixty years. It is the same criteria, which are used for all data that are evaluated for inclusion in the Standard Table of Atomic Weights. If a report of data is published in a peer-reviewed journal, that data is evaluated and considered for inclusion in the appropriate table of the biennial report of the Atomic Weights Commission. As better data becomes available in the future, the information that is contained in either of the Tables of Standard Atomic Weights or in the Table of Radioactive Elements may be modified. It should be noted that the appearance of any datum in the Table of the Radioactive Elements is merely for the purposes of calculating an atomic mass value for any sample of a radioactive material, which might have a variety of isotopic compositions and it has no implication as to the priority for claiming discovery of a given element and is not intended to. The atomic mass values have been taken primarily from the 2003 Atomic Mass Table. Mass values for those radioisotopes that do not appear in the 2003 Atomic mass Table have been taken from preliminary data of the Atomic Mass Data Center. Most of the quoted half-lives.

  13. Microsoft Word - table_01.doc

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

    3 Table 1 Created on: 4/27/2016 11:07:44 AM Table 1. Summary of natural gas supply and disposition in the United States, 2011-2016 (billion cubic feet) Year and Month Gross Withdrawals Marketed Production NGPL Production a Dry Gas Production b Supplemental Gaseous Fuels c Net Imports Net Storage Withdrawals d Balancing Item e Consumption f 2011 Total 28,479 24,036 1,134 22,902 60 1,963 -354 -94 24,477 2012 Total 29,542 25,283 1,250 24,033 61 1,519 -9 -66 25,538 2013 Total 29,523 25,562 1,357

  14. FY 2014 Budget Request Laboratory Table | Department of Energy

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

    Laboratory Table FY 2014 Budget Request Laboratory Table PDF icon Lab Table FY2014.pdf More Documents & Publications FY 2014 Budget Request State Table Fiscal Year 2013 President's Budget Request Fiscal Year 2013 President's

  15. FY 2014 Budget Request State Table | Department of Energy

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

    State Table FY 2014 Budget Request State Table PDF icon State Table FY2014.pdf More Documents & Publications FY 2014 Budget Request Laboratory Table FY 2007 Congressional Budget Request FY 2007 Congressional

  16. 1999 Commercial Building Characteristics--Detailed Tables--Census...

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

    Census Region > Detailed Tables-Census Region Complete Set of 1999 CBECS Detailed Tables Detailed Tables-Census Region Table B3. Census Region, Number of Buildings and Floorspace...

  17. 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...

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

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

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

  19. 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.

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

    SciTech Connect (OSTI)

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

    2010-04-01

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

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

    SciTech Connect (OSTI)

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

    2010-04-15

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

  2. Uncertainty Reduction in Power Generation Forecast Using Coupled

    Office of Scientific and Technical Information (OSTI)

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

  3. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

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

  4. 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 includes the characterization of patient-specific response with implications for treatment management and research study design.

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

    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 Preprint Jie Zhang 1 , Bri-Mathias Hodge 1 , Siyuan Lu 2 , Hendrik F. Hamann 2 , Brad Lehman 3 , Joseph Simmons 4 , Edwin Campos 5 , and Venkat Banunarayanan 6 1 National Renewable Energy Laboratory 2 IBM TJ Watson Research Center 3 Northeastern University 4 University of Arizona 5 Argonne National Laboratory 6 U.S. Department of Energy Presented at the IEEE Power and Energy Society General Meeting Denver,

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

    SciTech Connect (OSTI)

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

    2015-10-30

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

  7. TableBuster V1.0

    Energy Science and Technology Software Center (OSTI)

    2003-06-06

    Brief Description:TableBuster enables Telelogic DOORS users to export tables with split merged cells from Microsoft Word into DOORS. Practical Application: Users of Telelogic DOORS will be more easily able to track and manage requirements that are initally defined in Microsoft Word tables containing split or merged cells. Method of Solution: TableSplitter contains two procedures. The Setup subroutine unlinks all Word fields in the active Word document. It next counts all the tables in the documentmore » and then calls the SplitCells subroutine. SplitCells splits the appropriate cells for each table, so a n row by m column table actually has n by m cells that DOORS can import.« less

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

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

  9. 1999 Commercial Buildings Energy Consumption Survey Detailed Tables

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

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

  10. Table

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

    1.0 fs 5, 7, 9, 14, 15, 19, 20, 23, 24, 25 5.2409 0.3 5 2 + 3.25 0.30 ps 4, 5, 6, 7, 9, 14, 15, 18, 19, 20, 23, 24, 25, 27 g +0.248 0.026 6.1763 1.7 3 2 -...

  11. Table

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

    5.4 from (1991AJ01): Energy levels of 15 N a E x J ; T m or Decay Reactions (MeV keV) c.m. (keV) 0 1 2 - ; 1 2 - stable 3, 4, 5, 6, 13, 14, 16, 17, 18, 19, 20, 24, 25,...

  12. Table

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

    from (2012KE01): Energy Levels of 11 Li E x (MeV ± keV) J π ; T T 1 2 or Γ Decay Reactions g.s. 3 2 - ; 5 2 T 1 2 = 8.75 ± 0.14 ms β - 1, 2, 4, 5, 6, 8, 9 1.220 ± 40 Γ = 0.53 ± 0.15 MeV n 2, 6, 7, 9, 10 2.420 ± 50 Γ = 1.26 ± 0.30 MeV n 2, 4, 6, 7, 9, 10 3.700 ± 130 Γ < 200 keV n 7 4.860 ± 60 Γ < 100 keV n 2, 4, 9 6.230 ± 60 Γ < 100 keV n 2, 4, 9 11.300 n 2 1

  13. Table

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

    4 from (2012KE01): Energy levels of 11 Be E x (MeV ± keV) J π ; T T 1 2 or Γ c.m. (keV) Decay Reactions 0 1 2 + ; 3 2 T 1 2 = 13.76 ± 0.07 s β - 1, 3, 4, 5, 6, 8, 9, 10, 12, 14, 16, 17, 19, 23, 24, 25, 26, 27, 28, 30, 31, 32 0.32004 ± 0.1 1 2 - T 1 2 = 115 ± 10 fs γ 4, 5, 6, 8, 9, 10, 14, 15, 16, 17, 19, 21, 22, 23, 26, 28, 29, 30, 33 1.783 ± 4 5 2 + Γ = 100 ± 10 n 4, 5, 6, 9, 10, 14, 23, 26, 28 2.654 ± 10 3 2 - a 206 ± 8 n 5, 6, 9, 10, 15, 16, 21, 22, 23, 28, 29 3.40 ± 6 ( 3 2 - ,

  14. Table

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

    8 from (2012KE01): Energy levels of 11 B E x J π ; T Γ cm (keV) Decay Reactions (MeV ± keV) 0 3 2 - ; 1 2 stable 2, 3, 7, 8, 11, 15, 16, 17, 18, 19, 22, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 67, 68, 69, 70, 71, 72, 73, 74 2.124693 ± 0.027 1 2 - 0.117 ± 0.004 eV γ 2, 7, 8, 11, 15, 16, 17, 18, 19, 26, 27, 28, 30, 32, 33, 35, 36, 37, 39, 40, 42, 44, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 67, 68, 69,

  15. Table

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

    38 from (2012KE01): Energy levels of 11 C a E x in 11 C J π ; T T 1 2 or Γ cm Decay Reactions (MeV ± keV) 0 3 2 - ; 1 2 T 1 2 = 20.364 ± 0.014 min β + 1, 2, 6, 7, 10, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 43, 44 2.0000 ± 0.4 1 2 - T 1 2 = 7.1 ± 0.5 fs γ 2, 3, 6, 7, 10, 16, 17, 18, 19, 21, 22, 26, 28, 30, 31, 32, 33, 38, 39, 44 4.3188 ± 1.2 5 2 - < 8.3 fs γ 2, 3, 6, 7, 10, 16, 17, 19, 21, 22, 26, 28, 30, 31, 34, 38, 39, 44 4.8042

  16. Table

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

    5 from (2012KE01): Energy levels of 11 N E res (MeV ± keV) E x (MeV ± keV) J π ; T Γ (keV) Decay Reactions 1.49 ± 60 0 1 2 + ; 3 2 830 ± 30 p 1, 2, 3, 6 2.22 ± 30 0.73 ± 70 1 2 - 600 ± 100 p 1, 2, 3, 5, 6 3.06 ± 80 (1.57 ± 80) < 100 p 3 3.69 ± 30 2.20 ± 70 5 2 + 540 ± 40 p 1, 3, 5, 6 4.35 ± 30 2.86 ± 70 3 2 - 340 ± 40 p 1, 3, 5, 6 5.12 ± 80 (3.63 ± 100) ( 5 2 - ) < 220 p 5 5.91 ± 30 4.42 ± 70 ( 5 2 - ) p 3, 5, 6 6.57 ± 100 5.08 ± 120 ( 3 2 - ) 100 ± 60 p 3, 6 1

  17. Table

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

    6.13 from (1993TI07): Energy Levels of 16 O a E x (MeV ± keV) J π ; T K π Γ c.m. or τ m (keV) Decay Reactions 0 0 + ; 0 stable 5, 7, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 30, 32, 33, 34, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82 6.0494 ± 1.0 0 + ; 0 0 + τ m = 96 ± 7 psec π 5, 7, 11, 12, 13, 15, 17, 19, 21, 23, 30, 32, 33, 34, 38, 39, 43, 44,

  18. Table

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

    transitions in A 18-19 nuclei a Nucleus E xi E xf J i J f b Mult. S (MeV) (eV) (W.u.) 18 O c 1.98 0 2 + 0 + (2.35 0.06) 10 -4 E2 3.32 ...

  19. Table

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

    - 17 a Nucleus E xi E xf J i (T i ) J f (T f ) (eV) Branching ratio Mult. S (W.u.) (MeV) (%) 16 N b 0.12 0 0 - (1) 2 - (1) (8.7 0.1) 10 -11 100 E2...

  20. Table

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

    transitions in A 5 - 10 a Nucleus E xi E xf (MeV) J i J f b (eV) Mult. S (W.u.) 5 He 16.75 0 3 2 + 3 2 - 2.1 0.4 E1 (2.3 0.4) 10 -3 5 Li...

  1. Table

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

    transitions in A 11 - 12 a Nucleus E xi E xf J i J f b Mult. S (MeV) (eV) (W.u.) 11 Be 0.32 0 1 2 - 1 2 + (3.97 0.36) 10 -3 E1 0.360...

  2. Table

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

    transitions in A 20 nuclei a Nucleus E xi E xf J i J f b Mult. S (MeV) (eV) (W.u.) 20 O c 1.67 0 2 + 0 + (6.28 0.24) 10 -5 E2 1.80 ...

  3. Table

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

    transitions in A 5 - 7 Nucleus E xi E xf J i J f a (eV) Mult. S (W.u.) b (MeV) 5 He 16.84 0 3 2 + 3 2 - 2.1 0.4 E1 (2.2 0.4) 10 -3...

  4. Table

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

    in A 13 - 15 a Nucleus E xi E xf J i (T i ) J i (T f ) Mult. S (MeV) (eV) (W.u.) 13 C b 3.09 0 1 2 + 1 2 - 0.43 0.04 E1 (3.9 0.4) ...

  5. Table

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

    transitions in A 18 - 20 a Nucleus E xi E xf J i J f b Mult. S (MeV) (eV) (W.u.) 18 O c 1.98 0 2 + 0 + (2.35 0.06) 10 -4 E2 3.32 ...

  6. Table

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

    electromagnetic transitions in A 11 Nucleus E xi E xf J i J f Mult. W (MeV) (eV) (W.u.) 11 Be 0.32 0 1 2 - 1 2 + (3.97 0.35) 10 -3...

  7. 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.

  8. Microsoft Word - table_15.doc

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

    0 Table 15. Consumption of natural gas by state, 2010-2014 (million cubic feet) a Lease fuel quantities were estimated by assuming that the proportions of onsystem production used as lease fuel by respondents to the Form EIA-176, "Annual Report of Natural and Supplemental Gas Supply and Disposition," were the same as the proportions of gross withdrawals as reported on Form EIA-895, "Annual Quantity and Value of Natural Gas Production Report," used as lease by all operators.

  9. Microsoft Word - table_21.doc

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

    9 Table 21. Number of natural gas commercial consumers by type of service and state, 2013-2014 R Revised data. Note: Totals may not equal sum of components due to independent rounding. Source: Energy Information Administration (EIA), Form EIA-176, "Annual Report of Natural and Supplemental Gas Supply and Disposition." Please see the cautionary note regarding the number of residential and commercial customers located on the second page of Appendix A of this report. Alabama 67,006 130

  10. Microsoft Word - table_10.doc

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

    6 Created on: 4/26/2016 5:59:29 PM Table 10. Underground natural gas storage - salt cavern storage fields, 2011-2016 (volumes in billion cubic feet) Year and Month Natural Gas in Underground Storage at End of Period Change in Working Gas from Same Period Previous Year Storage Activity Base Gas Working Gas Total Volume Percent Injections Withdrawals Net Withdrawals a 2011 Total b -- -- -- -- -- 533 440 -92 2012 Total b -- -- -- -- -- 465 445 -20 2013 Total b -- -- -- -- -- 492 521 29 2014 January

  11. Table-top job analysis

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

    The purpose of this Handbook is to establish general training program guidelines for training personnel in developing training for operation, maintenance, and technical support personnel at Department of Energy (DOE) nuclear facilities. TTJA is not the only method of job analysis; however, when conducted properly TTJA can be cost effective, efficient, and self-validating, and represents an effective method of defining job requirements. The table-top job analysis is suggested in the DOE Training Accreditation Program manuals as an acceptable alternative to traditional methods of analyzing job requirements. DOE 5480-20A strongly endorses and recommends it as the preferred method for analyzing jobs for positions addressed by the Order.

  12. Evaluation

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

    Savings Portfolio (122013) Energy Smart Grocer Impact Evaluation (102013) Energy Smart Industrial - Energy Management Pilot Impact Evaluation (22013) Clark PUD Home...

  13. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

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

    2014-12-30

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

  14. Global disease monitoring and forecasting with Wikipedia

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

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

    2014-11-13

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

  15. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

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

    2014-11-13

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

  16. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

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

    2011-02-23

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

  17. TableHC2.12.xls

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

    Table HC2.12 Home Electronics Usage Indicators by Type of Housing Unit, 2005 5 or More Units Mobile Homes Type of Housing Unit Housing Units (millions) Single-Family Units Apartments in Buildings With-- Home Electonics Usage Indicators Detached Attached 2 to 4 Units Energy Information Administration: 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing Units Table HC2.12 Home Electronics Usage Indicators by Type of Housing Unit, 2005 5 or

  18. Environmental Regulatory Update Table, November 1991

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

    1991-12-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  19. Environmental Regulatory Update Table, October 1990

    SciTech Connect (OSTI)

    Houlberg, L.M.; Noghrei-Nikbakht, P.A.; Salk, M.S.

    1990-11-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  20. Environmental regulatory update table, July 1991

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

    1991-08-01

    This Environmental Regulatory Update Table (July 1991) provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  1. Environmental Regulatory Update Table, December 1989

    SciTech Connect (OSTI)

    Houlbert, L.M.; Langston, M.E. ); Nikbakht, A.; Salk, M.S. )

    1990-01-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  2. Environmental Regulatory Update Table, October 1991

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

    1991-11-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  3. Environmental regulatory update table, March 1989

    SciTech Connect (OSTI)

    Houlberg, L.; Langston, M.E.; Nikbakht, A.; Salk, M.S.

    1989-04-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  4. Environmental Regulatory Update Table, September 1991

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

    1991-10-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  5. Environmental Regulatory Update Table, August 1991

    SciTech Connect (OSTI)

    Houlberg, L.M., Hawkins, G.T.; Salk, M.S.

    1991-09-01

    This Environmental Regulatory Update Table (August 1991) provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  6. Environmental Regulatory Update Table, April 1989

    SciTech Connect (OSTI)

    Houlberg, L.; Langston, M.E.; Nikbakht, A.; Salk, M.S.

    1989-05-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  7. Community Leaders Round Table | Argonne National Laboratory

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

    Community Leaders Round Table The Round Table consists of citizens with regional constituencies, including elected officials on the village, city, township, county and state levels; leaders of school districts, environmental boards and other agencies; and officers of labor unions and home owners associations. The Argonne National Laboratory/U.S. Department of Energy Community Leaders Round Table provides an informal and convenient forum for sharing information about Argonne plans and activities

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

    SciTech Connect (OSTI)

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

    2015-08-05

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

  9. Program Evaluation: EERE Planned and Completed Evaluations | Department of

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

    Energy Evaluation » Information Resources » Program Evaluation: EERE Planned and Completed Evaluations Program Evaluation: EERE Planned and Completed Evaluations This section provides a list of past and planned evaluation reports for the U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE). Many of the listed reports are available as downloads. The table below tells you which offices have reports accessible as downloads. A checked box in the table -

  10. Public Notice Applicability Table | Open Energy Information

    Open Energy Info (EERE)

    http:crossref.org Citation Retrieved from "http:en.openei.orgwindex.php?titlePublicNoticeApplicabilityTable&oldid792160" Feedback Contact needs updating Image...

  11. Continuous Learning Points Credit Assignment Table | Department...

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

    PDF icon Microsoft Word - CLPCreditAssignmentTable More Documents & Publications PMCDP Curriculum Learning Map Microsoft Word - AL2006-07.doc PMCDP Certification and Equivalency ...

  12. TABLES1.CHP:Corel VENTURA

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

    Energy Information AdministrationPetroleum Supply Monthly, September 2004 2 Table S1. Crude Oil and Petroleum Products Overview, 1988 - Present (Continued) (Thousand Barrels...

  13. Action Codes Table | National Nuclear Security Administration

    National Nuclear Security Administration (NNSA)

    Blog Home About Us Our Programs Defense Nuclear Security Nuclear Materials Management & Safeguards System NMMSS ... Action Codes Table U.S. Department of Energy ...

  14. 1995 CECS C&E Tables

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

    reported for fewer than 20 buildings. Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding RSE column and RSE row factors. * See Glossary for...

  15. FY 2005 Control Table by Organization

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

    Organization (dollars in thousands - OMB Scoring) Table of Contents Summary...................................................................................................... 1 Mandatory Funding....................................................................................... 2 National Nuclear Security Administration..................................................... 3 Energy Efficiency and Renewable Energy.................................................... 4 Electric Transmission

  16. TABLE53.CHP:Corel VENTURA

    Gasoline and Diesel Fuel Update (EIA)

    Table 53. Movements of Crude Oil and Petroleum Products by Pipeline, Tanker, and Barge Between July 2004 Crude Oil ... 0 383 0...

  17. TABLE54.CHP:Corel VENTURA

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

    Administration (EIA) Forms EIA-812, "Monthly Product Pipeline Report," and EIA-813, Monthly Crude Oil Report." Table 54. Movements of Crude Oil and Petroleum Products by Pipeline...

  18. 1995 CECS C&E Tables

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

    (3 pages, 20 kb) CONTENTS PAGES Table 19. Distribution of Peak Watts per Square Foot and Load Factors, 1995 These data are from the 1995 Commercial Buildings Energy...

  19. Summary Statistics Table 1. Crude Oil Prices

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

    from Table 24. Refiner acquisition costs -- Energy Information Administration, Form FEA-P110-M-1, "Refiners' Monthly Cost Allocation Report," January 1978 through June 1978;...

  20. 1995 CECS C&E Tables

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

    15. Season of Peak Electricity Demand, Number of Buildings and Floorspace, 1995 Table 16. Electricity Consumption and Conditional Energy Intensity by Season of Peak Demand, 1995...

  1. Health Care Buildings : Basic Characteristics Tables

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

    Basic Characteristics Tables Buildings and Size Data by Basic Characteristics for Health Care Buildings Number of Buildings (thousand) Percent of Buildings Floorspace (million...

  2. TABLE11.CHP:Corel VENTURA

    Gasoline and Diesel Fuel Update (EIA)

    (Thousand Barrels) Table 11. PAD District II-Year-to-Date Supply, Disposition, and Ending Stocks of Crude Oil and Petroleum January-July 2004 Products, Crude Oil...

  3. TABLE15.CHP:Corel VENTURA

    Gasoline and Diesel Fuel Update (EIA)

    Table 15. PAD District III-Year-to-Date Supply, Disposition, and Ending Stocks of Crude Oil and Petroleum (Thousand Barrels) January-July 2004 Products, Crude Oil...

  4. TABLE19.CHP:Corel VENTURA

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

    Table 19. PAD District IV-Year-to-Date Supply, Disposition, and Ending Stocks of Crude Oil and Petroleum (Thousand Barrels) January-July 2004 Products, Crude Oil...

  5. Microsoft Word - SEC J_Table of Contents

    National Nuclear Security Administration (NNSA)

    J Page i PART III - LIST OF DOCUMENTS, EXHIBITS, AND OTHER ATTACHMENTS SECTION J LIST OF APPENDICES TABLE OF CONTENTS Appendix A Statement of Work Appendix B Performance Evaluation Plan Appendix C Contractor's Transition Plan Appendix D Sensitive Foreign Nations Control Appendix E Performance Guarantee Agreement(s) Appendix F National Work Breakdown Structure Appendix G RESERVED - Governance Appendix H RESERVED Appendix I Personnel Appendix Appendix J Key Personnel Appendix K Small Business

  6. NREL: Resource Assessment and Forecasting - Metrology Laboratory

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

    Metrology Laboratory Photo of Solar Radiation Research Laboratory researchers inspecting radiometers mounted to calibration tables at the outside test site. Researchers at the Solar Radiation Research Laboratory use pyranometers, pyrheliometers, pyrgeometers, photometers, and spectroradiometers to provide the solar resource information necessary for renewable energy research and development. Metrology, the science of measurement, is a critical part of providing accurate and repeatable data.

  7. Tables of E2 transition probabilities from the first 2+ states in even-even nuclei [B(E2) evaluation for 0+1 → 2+1 transitions in even-even nuclei

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

    Pritychenko, B.; Birch, M.; Singh, B.; Horoi, M.

    2015-11-03

    A complete B(E2)↑ evaluation and compilation for even-even nuclei has been presented. The present paper is a continuation of P.H. Stelson and L. Grodzins, and S. Raman et al. nuclear data evaluations and was motivated by a large number of new measurements. It extends the list of evaluated nuclides from 328 to 452, includes an extended list of nuclear reaction kinematics parameters and comprehensive shell model analysis. Evaluation policies for analysis of experimental data have been discussed and conclusions are given. Moreover, future plans for B(E2)↑ systematics and experimental technique analyses of even-even nuclei are outlined.

  8. Tool Support for Software Lookup Table Optimization

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

    Wilcox, Chris; Strout, Michelle Mills; Bieman, James M.

    2011-01-01

    A number of scientific applications are performance-limited by expressions that repeatedly call costly elementary functions. Lookup table (LUT) optimization accelerates the evaluation of such functions by reusing previously computed results. LUT methods can speed up applications that tolerate an approximation of function results, thereby achieving a high level of fuzzy reuse. One problem with LUT optimization is the difficulty of controlling the tradeoff between performance and accuracy. The current practice of manual LUT optimization adds programming effort by requiring extensive experimentation to make this tradeoff, and such hand tuning can obfuscate algorithms. In this paper we describe a methodology andmore » tool implementation to improve the application of software LUT optimization. Our Mesa tool implements source-to-source transformations for C or C++ code to automate the tedious and error-prone aspects of LUT generation such as domain profiling, error analysis, and code generation. We evaluate Mesa with five scientific applications. Our results show a performance improvement of 3.0× and 6.9× for two molecular biology algorithms, 1.4× for a molecular dynamics program, 2.1× to 2.8× for a neural network application, and 4.6× for a hydrology calculation. We find that Mesa enables LUT optimization with more control over accuracy and less effort than manual approaches.« less

  9. Microsoft Word - table_03.doc

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

    9 Table 3. Gross withdrawals and marketed production of natural gas by state and the Gulf of Mexico, 2010-2014 (million cubic feet) 2010 Total 13,247,498 5,834,703 1,916,762 5,817,122 26,816,085 3,431,587 165,928 836,698 22,381,873 1,066,366 21,315,507 2011 Total 12,291,070 5,907,919 1,779,055 8,500,983 28,479,026 3,365,313 209,439 867,922 24,036,352 1,134,473 22,901,879 2012 Total 12,504,227 4,965,833 1,539,395 10,532,858 29,542,313 3,277,588 212,848 768,598 25,283,278 1,250,012 24,033,266 2013

  10. Microsoft Word - table_04.doc

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

    2 Table 4. Offshore gross withdrawals of natural gas by state and the Gulf of Mexico, 2010-2014 (million cubic feet) 2010 Total 234,236 341,365 575,601 1,701,665 598,679 2,300,344 2,875,945 Alabama 101,487 0 101,487 NA NA NA 101,487 Alaska 42,034 328,114 370,148 0 0 0 370,148 California 71 5,483 5,554 1,757 39,444 41,200 46,755 Gulf of Mexico 0 0 0 1,699,908 559,235 2,259,144 2,259,144 Louisiana 63,222 6,614 69,836 NA NA NA 69,836 Texas 27,421 1,153 28,574 NA NA NA 28,574 2011 Total 208,970

  11. Microsoft Word - table_08.doc

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

    5 Table 8. Summary of U.S. natural gas imports, 2010-2014 Imports Volume (million cubic feet) Pipeline Canada a 3,279,752 3,117,081 2,962,827 2,785,427 2,634,375 Mexico 29,995 2,672 314 1,069 1,426 Total Pipeline Imports 3,309,747 3,119,753 2,963,140 2,786,496 2,635,801 LNG by Truck Canada 0 0 0 555 132 LNG by Vessel Egypt 72,990 35,120 2,811 0 0 Nigeria 41,733 2,362 0 2,590 0 Norway 26,014 15,175 6,212 5,627 5,616 Peru 16,045 16,620 0 0 0 Qatar 45,583 90,972 33,823 7,320 0 Trinidad/Tobago

  12. Microsoft Word - table_13.doc

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

    3 Table 13. Additions to and withdrawals from gas storage by state, 2014 (million cubic feet) Alabama 34,286 28,683 5,603 1,664 1,869 -206 5,397 Alaska 11,675 6,523 5,152 0 0 0 5,152 Arkansas 3,398 3,866 -468 56 42 14 -453 California 280,516 235,181 45,335 83 82 1 45,336 Colorado 72,510 70,692 1,818 0 0 0 1,818 Connecticut 0 0 0 1,032 1,359 -327 -327 Delaware 0 0 0 157 128 29 29 Georgia 0 0 0 7,130 4,046 3,085 3,085 Idaho 0 0 0 64 740 -676 -676 Illinois 270,831 260,100 10,730 61 503 -442 10,288

  13. Microsoft Word - table_17.doc

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

    4 Table 17. Natural gas delivered to residential consumers for the account of others by state, 2010-2014 (volumes in million cubic feet) Alabama 0 -- 0 -- 0 -- 0 -- 0 -- Alaska 0 -- 0 -- 0 -- 0 -- 0 -- Arizona 0 -- 2 < 2 < 3 < 2 < Arkansas 0 -- 0 -- 0 -- 0 -- 0 -- California 7,205 1.5 8,769 1.7 12,108 2.5 18,795 3.9 20,703 5.2 Colorado 21 < 18 < 16 < 19 < 18 < Connecticut 1,156 2.7 1,438 3.2 1,364 3.3 2,199 4.7 2,096 4.1 Delaware 0 -- 0 -- 0 -- 0 -- 0 -- District of

  14. Microsoft Word - table_20.doc

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

    8 Table 20. Number of natural gas residential consumers by type of service and state, 2013-2014 Alabama 765,957 0 765,957 769,418 0 769,418 Alaska 124,411 0 124,411 126,416 0 126,416 Arizona 1,171,997 6 1,172,003 1,186,788 6 1,186,794 Arkansas R 549,764 0 R 549,764 549,034 0 549,034 California 10,471,814 283,094 10,754,908 10,372,973 408,747 10,781,720 Colorado 1,672,307 5 1,672,312 1,690,576 5 1,690,581 Connecticut 512,110 1,382 513,492 521,460 1,198 522,658 Delaware 155,627 0 155,627 158,502 0

  15. Microsoft Word - table_22.doc

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

    0 Table 22. Number of natural gas industrial consumers by type of service and state, 2013-2014 Alabama 2,876 267 3,143 2,973 271 3,244 Alaska 2 1 3 1 0 1 Arizona 257 126 383 256 130 386 Arkansas 513 507 1,020 531 478 1,009 California 32,662 5,334 37,996 32,266 5,282 37,548 Colorado 946 6,347 7,293 986 6,837 7,823 Connecticut 3,360 1,094 4,454 3,340 877 4,217 Delaware 28 110 138 28 113 141 Florida 166 362 528 165 355 520 Georgia 984 1,258 2,242 887 1,594 2,481 Hawaii 22 0 22 23 0 23 Idaho 109 R

  16. Microsoft Word - table_25.doc

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

    72 Table 25. Average price of natural gas delivered to residential and commercial sector consumers by local distribution and marketers in selected states, 2013-2014 (dollars per thousand cubic feet) Georgia 11.86 15.04 14.60 13.9 12.38 14.79 14.45 14.0 New York 12.24 13.07 12.49 70.3 12.15 13.46 12.54 70.5 Ohio 9.20 9.52 9.46 19.8 10.15 10.16 10.16 20.0 Residential State 2013 2014 Local Distribution Company Average Price a Marketer Average Price b Combined Average Price c Percent Sold by Local

  17. Evaluation of Hose in Hose Transfer Line Service Life for Hanford's Interim Stabilization Program

    SciTech Connect (OSTI)

    TORRES, T.D.

    2000-08-24

    RPP-6153, Engineering Task Plan for Hose-in-Hose Transfer System for the Interim Stabilization Program, defines the programmatic goals, functional requirements, and technical criteria for the development and subsequent installation of transfer line equipment to support Hanford's Interim Stabilization Program. RPP-6028, Specification for Hose in Hose Transfer Lines for Hanford's Interim Stabilization Program, has been issued to define the specific requirements for the design, manufacture, and verification of transfer line assemblies for specific waste transfer applications. Included in RPP-6028 are tables defining the chemical constituents of concern to which transfer lines will be exposed. Current Interim Stabilization Program planning forecasts that the at-grade transfer lines will be required to convey pumpable waste for as much as three years after commissioning. Prudent engineering dictates that the equipment placed in service have a working life in excess of this forecasted time period, with some margin to allow for future adjustments to the planned schedule. This document evaluates the effective service life of the Hose-in-Hose Transfer Lines, based on information submitted by the manufacturer and published literature. The effective service life of transfer line assemblies is a function of several factors. Foremost among these are process fluid characteristics, ambient environmental conditions, and the manufacturer's stated shelf life. This evaluation examines the manufacturer's certification of shelf life, the manufacturer's certifications of chemical compatibility with waste, and published literature on the effects of exposure to ionizing radiation on the mechanical properties of elastomeric materials to evaluate transfer line service life.

  18. EM International Program Action Table - June 2014 | Department of Energy

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

    Action Table - June 2014 EM International Program Action Table - June 2014 EM International Program Action Table - June 2014 PDF icon EM International Program Action Table - June 2014 More Documents & Publications International Program Action Table - October 2012 Across the Pond Newsletter Issue 9 Across the Pond Newsletter Issue 3

  19. Supplemental Tables to the Annual Energy Outlook

    Reports and Publications (EIA)

    2015-01-01

    The Annual Energy Outlook (AEO) Supplemental tables were generated for the reference case of the AEO using the National Energy Modeling System, a computer-based model which produces annual projections of energy markets. Most of the tables were not published in the AEO, but contain regional and other more detailed projections underlying the AEO projections.

  20. 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.

  1. Industrial end-use forecasting that incorporates DSM and air quality

    SciTech Connect (OSTI)

    Tutt, T.; Flory, J.

    1995-05-01

    The California Energy Commission (CEC) and major enregy utilities in California have generally depended on simple aggregate intensity or economic models to forecast energy use in the process industry sector (which covers large industries employing basic processes to transform raw materials, such as paper mills, glass plants, and cement plants). Two recent trends suggests that the time has come to develop a more disaggregate process industry forecasting model. First, recent efforts to improve air quality, especially by the South Coast Air Quality Management District (SCAQMD), could significantly affect energy use by the process industry by altering the technologies and processes employed in order to reduce emissions. Second, there is a renewed interest in Demand-Side Management (DSM), not only for utility least-cost planning, but also for improving the economic competitiveness and environmental compliance of the pro{minus}cess industries. A disaggregate forecasting model is critical to help the CEC and utilities evaluate both the air quality and DSM impacts on energy use. A crucial obstacle to the development and use of these detailed process industry forecasting models is the lack of good data about disaggregate energy use in the sector. The CEC is nearing completion of a project to begin to overcome this lack of data. The project is testing methds of developing detailed energy use data, collecting an initial database for a large portion of southern California, and providing recommendations and direction for further data collection efforts.

  2. Upcoming Funding Opportunity for Wind Forecasting Improvement Project in

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

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

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

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

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

  4. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical

    Office of Scientific and Technical Information (OSTI)

    Modelling Approach (Journal Article) | SciTech Connect Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach Citation Details In-Document Search Title: Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the

  5. Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Office of Scientific and Technical Information (OSTI)

    (BNL) Field Campaign Report (Technical Report) | SciTech Connect SciTech Connect Search Results Technical Report: Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report Citation Details In-Document Search Title: Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf]

  6. Energy Conservation Program: Data Collection and Comparison with Forecasted

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

    Unit Sales for Five Lamp Types, Notice of Data Availability | Department of Energy Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability This document is the notice of data availability for Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types. PDF icon

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

  8. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    Title: 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory When considering the amount of shortwave radiation incident on a photovoltaic solar array and, ...

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

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

    These projects aim to improve the accuracy of solar forecasting that could increase penetration of solar power by enabling more certainty in power prediction from solar power ...

  10. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic...

    Office of Scientific and Technical Information (OSTI)

    Title: Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates ...

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

    SciTech Connect (OSTI)

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

    2015-12-08

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

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

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

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

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

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

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

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

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

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

  15. NREL: Resource Assessment and Forecasting - Facilities

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

    Printable Version Facilities Photo of two researchers standing on a platform near a solar tracker at the Solar Radiation Research Laboratory. The Solar Radiation Research Laboratory gathers solar radiation and meteorological data on South Table Mountain. NREL's Solar Radiation Research Laboratory (SRRL) has been collecting continuous measurements of basic solar radiation components since 1981. Since then, it has expanded its expertise to include integrated metrology, optics, electronics, and

  16. 1999 Commercial Building Characteristics--Detailed Tables--Size...

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

    Complete Set of 1999 CBECS Detailed Tables Detailed Tables- of Buildings Table B6. Building Size, Number of Buildings b6.pdf (PDF file), b6.xls (Excel spreadsheet file), b6.txt...

  17. 1999 Commercial Buildings Characteristics--Detailed Tables--Conservati...

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

    as rowstubs in most detailed tables. Total buildings, total floorspace, and average building size for these categories are shown in Table B1. The PDF and spreadsheet data tables...

  18. 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.

  19. 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.

  20. Tables and graphs of electron-interaction cross sections from 10 eV to 100 GeV derived from the LLNL Evaluated Electron Data Library (EEDL), Z = 1--100

    SciTech Connect (OSTI)

    Perkins, S.T.; Cullen, D.E. ); Seltzer, S.M. , Gaithersburg, MD . Center for Radiation Research)

    1991-11-12

    Energy-dependent evaluated electron interaction cross sections and related parameters are presented for elements H through Fm (Z = 1 to 100). Data are given over the energy range from 10 eV to 100 GeV. Cross sections and average energy deposits are presented in tabulated and graphic form. In addition, ionization cross sections and average energy deposits for each shell are presented in graphic form. This information is derived from the Livermore Evaluated Electron Data Library (EEDL) as of July, 1991.

  1. Microsoft Word - table_05.doc

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

    3 Table 5. Number of producing gas wells by state and the Gulf of Mexico, December 31, 2010-2014 Alabama 7,026 7,063 6,327 R 6,165 6,118 Alaska 269 277 185 R 159 170 Arizona 5 5 5 5 5 Arkansas 7,397 8,388 8,538 R 9,843 10,150 California 1,580 1,308 1,423 R 1,335 1,118 Colorado 28,813 30,101 32,000 R 32,468 38,346 Gulf of Mexico 1,852 1,559 1,474 R 1,146 1,400 Illinois 50 40 40 R 34 36 Indiana 620 914 819 R 921 895 Kansas 22,145 25,758 24,697 R 23,792 24,354 Kentucky 17,670 14,632 17,936 R 19,494

  2. Microsoft Word - table_14.doc

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

    44 Table 14. Underground natural gas storage capacity by state, December 31, 2014 (million cubic feet) Alabama 1 21,950 30,100 0 0 0 1 11,200 13,500 2 33,150 43,600 Alaska 0 0 0 0 0 0 5 67,915 83,592 5 67,915 83,592 Arkansas 0 0 0 0 0 0 2 12,178 21,853 2 12,178 21,853 California 0 0 0 1 10,000 12,000 13 364,296 587,711 14 374,296 599,711 Colorado 0 0 0 0 0 0 10 63,774 130,186 10 63,774 130,186 Illinois 0 0 0 19 292,544 978,624 9 11,768 25,923 28 304,312 1,004,547 Indiana 0 0 0 12 19,215 80,746

  3. Microsoft Word - table_18.doc

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

    5 Table 18. Natural gas delivered to commercial consumers for the account of others by state, 2010-2014 (volumes in million cubic feet) Alabama 5,494 20.3 5,313 21.1 5,126 23.8 5,935 23.4 5,941 21.6 Alaska 1,951 12.3 2,208 11.4 1,005 5.1 1,022 5.5 980 5.5 Arizona 3,605 11.3 3,988 12.2 4,213 13.4 4,772 14.5 4,743 15.6 Arkansas 17,862 44.4 19,402 48.5 24,772 59.8 26,797 56.3 27,604 54.5 California 113,903 45.9 112,448 45.7 126,571 50.0 127,588 50.1 122,637 51.6 Colorado 3,118 5.4 3,457 6.2 4,061

  4. Microsoft Word - table_19.doc

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

    7 Table 19. Natural gas delivered to industrial consumers for the account of others by state, 2010-2014 (volumes in million cubic feet) Alabama 109,031 75.2 117,277 76.5 133,765 77.9 137,103 76.4 143,849 76.7 Alaska 1,893 29.5 2,657 39.2 0 -- 3,698 91.0 0 -- Arizona 14,343 74.5 16,469 75.8 17,800 78.6 18,486 83.4 19,612 87.2 Arkansas 80,722 97.2 83,671 97.9 80,090 98.2 85,595 98.3 87,179 98.2 California 671,372 95.4 674,420 95.5 704,680 95.8 744,986 96.0 759,369 96.3 Colorado 108,317 94.8 68,813

  5. Microsoft Word - table_23.doc

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

    6 Table 23. Average citygate price of natural gas in the United States, 2010- 2014 (dollars per thousand cubic feet) Alabama 6.46 5.80 5.18 4.65 4.93 Alaska 6.67 6.53 6.14 6.02 6.34 Arizona 6.59 5.91 4.68 4.73 5.20 Arkansas 6.76 6.27 5.36 4.99 5.84 California 4.86 4.47 3.46 4.18 4.88 Colorado 5.26 4.94 4.26 4.76 5.42 Connecticut 6.58 5.92 5.12 5.42 5.61 Delaware 5.67 9.03 7.19 5.67 5.54 Florida 5.49 5.07 3.93 4.44 5.05 Georgia 5.93 5.19 4.35 4.66 5.19 Hawaii 22.94 31.58 32.39 28.45 26.94 Idaho

  6. Microsoft Word - table_24.doc

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

    Table 24. Average price of natural gas delivered to consumers by state and sector, 2014 (dollars per thousand cubic feet) Alabama 14.59 100.0 11.92 78.4 5.49 23.3 4.74 Alaska 9.11 100.0 8.30 94.5 7.97 100.0 5.06 Arizona 17.20 100.0 10.34 84.4 7.52 12.8 5.30 Arkansas 10.39 100.0 7.88 45.5 6.99 1.8 W California 11.51 94.8 9.05 48.4 7.65 3.7 5.23 Colorado 8.89 100.0 8.15 94.5 6.84 7.7 5.49 Connecticut 14.13 95.9 10.24 67.2 8.07 39.4 6.82 Delaware 13.21 100.0 11.42 46.2 10.95 0.3 W District of

  7. Microsoft Word - table_27.doc

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

    8 Table 28. Percent distribution of natural gas delivered to consumers by state, 2014 Alabama 0.8 0.8 2.5 0.6 4.2 Alaska 0.3 0.5 0.1 < 0.4 Arizona 0.6 0.9 0.3 5.8 2.5 Arkansas 0.7 1.5 1.2 0.1 0.9 California 7.8 6.9 10.3 47.0 10.1 Colorado 2.6 1.7 1.0 0.9 1.2 Connecticut 1.0 1.5 0.4 0.2 1.2 Delaware 0.2 0.3 0.4 < 0.6 District of Columbia 0.3 0.5 -- 2.9 -- Florida 0.3 1.8 1.2 0.6 12.9 Georgia 2.6 1.7 2.1 3.3 3.6 Hawaii < 0.1 < < -- Idaho 0.5 0.5 0.4 0.4 0.2 Illinois 9.4 7.1 3.9 1.0

  8. Petroleum Products Table 43. Refiner Motor Gasoline Volumes...

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

    of table. 43. Refiner Motor Gasoline Volumes by Grade, Sales Type, PAD District, and State 262 Energy Information Administration Petroleum Marketing Annual 1997 Table 43....

  9. Petroleum Products Table 43. Refiner Motor Gasoline Volumes...

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

    of table. 43. Refiner Motor Gasoline Volumes by Grade, Sales Type, PAD District, and State 262 Energy Information Administration Petroleum Marketing Annual 1996 Table 43....

  10. Petroleum Products Table 31. Motor Gasoline Prices by Grade...

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

    at end of table. 31. Motor Gasoline Prices by Grade, Sales Type, PAD District, and State 56 Energy Information Administration Petroleum Marketing Annual 1996 Table 31. Motor...

  11. Minimum Efficiency Requirements Tables for Heating and Cooling...

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

    Minimum Efficiency Requirements Tables for Heating and Cooling Product Categories The Federal Energy Management Program (FEMP) created tables that mirror American Society of ...

  12. 1999 CBECS Summary Table for All Building Activities

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

    Tables 1999 Commercial Buildings Consumption Survey SUMMARY TABLES FOR ALL PRINCIPAL BUILDING ACTIVITIES Number of Buildings (thousand) Floorspace (million square feet) Square...

  13. Headquarters Facilities Master Security Plan- Table of Contents

    Broader source: Energy.gov [DOE]

    2016 Headquarters Facilities Master Security Plan - Table of Contents Table of Contents for the 2016 Headquarters Facilities Master Security Plan (HQFMSP).

  14. EIA - Annual Energy Outlook (AEO) 2013 Data Tables

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

    Table 55.2. Electric Power Projections by Electricity Market Module Region - Florida Reliability Coordinating Council XLS Table 55.3. Electric Power Projections by Electricity...

  15. Energy Information Administration - Energy Efficiency-Table 3...

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

    Energy Efficiency > Iron and Steel Manufacturing Energy, 1998 and 2002 > Table 3 Page Last Modified: June 2010 Table 3. Offsite-Produced Fuel Consumption, 1998, 2002, and 2006...

  16. Energy.gov Data Tables in Content Management System | Department...

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

    Data Tables in Content Management System Energy.gov Data Tables in Content Management System For Office of Energy Efficiency and Renewable Energy (EERE) websites, follow these...

  17. 1998 MWD/LWD comparison tables

    SciTech Connect (OSTI)

    1998-05-01

    This year`s comparison tables feature an updated Quick Reference Guide listing MWD sensors by collar size for each manufacturer. Following the Quick Reference Guide are the comparison tables, which list general, directional, gamma ray, resistivity, density and neutron data for each tool. The MWD Tables should only be used as a reference source. System specifications frequently change as tools are refined and developed. Please consult representatives for each company prior to making final tool selections. A contact key for all the companies is included.

  18. EA-1440-S1: National Renewable Energy Laboratory's South Table Mountain Complex, Golden Field Office, National Renewable Energy Laboratory

    Broader source: Energy.gov [DOE]

    ThIs EA evaluates the potential environmental impact of a DOE proposal that consists of three site development projects at the National Renewable Energy Laboratory’s (NREL) South Table Mountain ...

  19. Evaluation

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

    of Array Irradiance Models at Locations across the United States Matthew Lave, Member, IEEE, William Hayes, Andrew Pohl, and Clifford W. Hansen Abstract-We report an evaluation of...

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

    SciTech Connect (OSTI)

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

    2012-07-01

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

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

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

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

  2. ARM - Lesson Plans: Rainfall and Water Table

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

    to show how an increase of rainfall under climate change can affect the water table and soil salinity underground. Materials Each student or group of students will need the...

  3. TABLE25A.CHP:Corel VENTURA

    Gasoline and Diesel Fuel Update (EIA)

    Persian Gulf e ...... 2,409 0 0 0 0 0 0 0 0 0 (Thousand Barrels) Table 25. PAD Districts IV and V-Imports of Crude Oil and Petroleum Products by Country of ...

  4. Table 2. Nuclear power plant data

    Gasoline and Diesel Fuel Update (EIA)

    Revised: February 3, 2016 (revision) Next release date: Late 2018 Table 2. Nuclear power ... (year) Actual retirement (year) Arkansas Nuclear 1 AR PWR B&W 177 1974 2034 Arkansas ...

  5. Table of Contents for Desk Guide

    Energy Savers [EERE]

    September, 2014 U. S. Department of Energy - Real Estate Desk Guide Revised 2014 Real Estate Desk Guide Table of Contents Chapter 1-- Purpose of Desk Guide............................................................................... 1 Chapter 2-- Introduction ................................................................................................. 3 Chapter 3-- Planning Policy ........................................................................................... 9 Chapter 4-- Real

  6. FY 2015 Summary Control Table by Organization

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

    5 Summary Control Table by Organization (dollars in thousands - OMB Scoring) Summary Control by Organization Page 1 FY 2015 Congressional Request FY 2013 FY 2014 FY 2014 FY 2014 FY ...

  7. TableHC9.13.xls

    Gasoline and Diesel Fuel Update (EIA)

    ... Energy-Efficient Bulbs Used...... 11.0 ... Climate Zone 1 Table HC9.13 Lighting Usage Indicators by ... (1971-2000) of the annual heating and cooling degree-days. ...

  8. FY 2015 Statistical Table by Appropriation

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) Statistical Table by Appropriation Page 1 FY 2015 Congressional Request FY 2013 FY 2014 FY 2014 FY 2014 FY 2015 Current Enacted Adjustment Current Congressional Approp. Approp. Approp. Request Discretionary Summary By Appropriation Energy And Water Development And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy............................... 1,691,757 1,900,641 ---- 1,900,641

  9. FY 2015 Statistical Table by Organization

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

    of Energy FY 2015 Statistical Table by Organization (dollars in thousands - OMB Scoring) Statistical Table by Organization Page 1 FY 2015 Congressional Request FY 2013 FY 2014 FY 2014 FY 2014 FY 2015 Current Enacted Adjustments Current Congressional Approp. Approp. Approp. Request Discretionary Summary By Organization National Nuclear Security Administration Weapons Activities........................................................................... 6,966,855 7,781,000 ---- 7,781,000 8,314,902

  10. FY 2015 Summary Control Table by Appropriation

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

    Summary Control Table by Appropriation (dollars in thousands - OMB Scoring) Summary Control Table by Appropriation Page 1 FY 2015 Congressional Request FY 2013 FY 2014 FY 2014 FY 2014 FY 2015 Current Enacted Adjustment Current Congressional Approp. Approp. Approp. Request Discretionary Summary By Appropriation Energy And Water Development And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy................................... 1,691,757 1,900,641 ----

  11. Action Codes Table | National Nuclear Security Administration

    National Nuclear Security Administration (NNSA)

    Action Codes Table U.S. Department of Energy / U.S. Nuclear Regulatory Commission Nuclear Materials Management & Safeguards System Action Codes Table Action codes *U.S.: **IAEA: A - Shipper's original data A B - Receiver's data accepting shipper's weights without measurement W C - Shipper's adjustment or acknowledgement C D - Receiver's adjustment or acknowledgement W, Z E - Receiver's independent measurement or determination W, Z I - Inventory difference explanation data *Historical -

  12. 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.

  13. 3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information

    Open Energy Info (EERE)

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

  14. 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.

  15. "RSE Table N5.1. Relative Standard Errors for Table N5.1;...

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

    ... old and the new basis in bridge tables that allow comparisons" "between the two systems. ... (onsite) mines or wells." "During manufacturing processes, it is possible that the ...

  16. RSE Table N3.1 and N3.2. Relative Standard Errors for Tables...

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

    ... old and the new basis in bridge tables that allow comparisons" "between the two systems. ... (onsite) mines or wells." "During manufacturing processes, it is possible that the ...

  17. RSE Table N4.1 and N4.2. Relative Standard Errors for Tables...

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

    old and the new basis in bridge tables that allow comparisons" "between the two systems. ... Division, Form EIA-846, '1998 Manufacturing" "Energy Consumption Survey,' and ...

  18. RSE Table 8.2 Relative Standard Errors for Table 8.2

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

    for Table 8.2;" " Unit: Percents." " "," ",,"Computer Control of Building Wide Evironment(c)",,,"Computer Control of Processes or Major Energy-Using Equipment(d)",,,"Waste ...

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

    SciTech Connect (OSTI)

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

    2010-04-20

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

  20. Qualified Energy Conservation Bond State-by-State Summary Tables

    Broader source: Energy.gov [DOE]

    Provides a list of qualified energy conservation bond state summary tables. Author: Energy Programs Consortium

  1. Product Guide Product Guide Volumes Category Prices Table Crude...

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

    suppliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -- 49 Product Guide Volumes Category Prices Table Energy Information Administration Petroleum...

  2. Product Guide Product Guide Volumes Category Prices Table Crude...

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

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -- 49 Product Guide Volumes Category Prices Table Energy Information Administration Petroleum Marketing...

  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 (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

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

    SciTech Connect (OSTI)

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

    2005-07-01

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

  5. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema (OSTI)

    Gonzalez, Frank

    2010-01-08

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

  6. Using Economic Input/Output Tables to Predict a Countrys Nuclear Status

    SciTech Connect (OSTI)

    Weimar, Mark R.; Daly, Don S.; Wood, Thomas W.

    2010-07-15

    Both nuclear power and nuclear weapons programs should have (related) economic signatures which are detectible at some scale. We evaluated this premise in a series of studies using national economic input/output (IO) data. Statistical discrimination models using economic IO tables predict with a high probability whether a country with an unknown predilection for nuclear weapons proliferation is in fact engaged in nuclear power development or nuclear weapons proliferation. We analyzed 93 IO tables, spanning the years 1993 to 2005 for 37 countries that are either members or associates of the Organization for Economic Cooperation and Development (OECD). The 2009 OECD input/output tables featured 48 industrial sectors based on International Standard Industrial Classification (ISIC) Revision 3, and described the respective economies in current country-of-origin valued currency. We converted and transformed these reported values to US 2005 dollars using appropriate exchange rates and implicit price deflators, and addressed discrepancies in reported industrial sectors across tables. We then classified countries with Random Forest using either the adjusted or industry-normalized values. Random Forest, a classification tree technique, separates and categorizes countries using a very small, select subset of the 2304 individual cells in the IO table. A nations efforts in nuclear power, be it for electricity or nuclear weapons, are an enterprise with a large economic footprint -- an effort so large that it should discernibly perturb coarse country-level economics data such as that found in yearly input-output economic tables. The neoclassical economic input-output model describes a countrys or regions economy in terms of the requirements of industries to produce the current level of economic output. An IO table row shows the distribution of an industrys output to the industrial sectors while a table column shows the input required of each industrial sector by a given industry.

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

    Broader source: Energy.gov [DOE]

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

  8. Solar Trackers Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

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

    Open Energy Info (EERE)

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

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

    Reports and Publications (EIA)

    2003-01-01

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

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

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

    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...

  12. Recently released EIA report presents international forecasting data

    SciTech Connect (OSTI)

    1995-05-01

    This report presents information from the Energy Information Administration (EIA). Articles are included on international energy forecasting data, data on the use of home appliances, gasoline prices, household energy use, and EIA information products and dissemination avenues.

  13. Value of Improved Short-Term Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2015-02-01

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

  14. Summer gasoline price forecast slightly higher, but drivers still...

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

    In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular grade gasoline will average 2.21 per gallon this summer. While that's 17 ...

  15. 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.

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

    Office of Scientific and Technical Information (OSTI)

    1) To provide profiles of the horizontal wind to be used to test and validate short-term cloud advection forecasts for solar-energy applications and 2) to provide vertical ...

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

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

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

  18. Forecasting the oil-gasoline price relationship: should we care...

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

    (2007, EE) obtain similar results on a panel of 15 OECD countries, with annual data ... Results Point forecasts of the N.Y. gasoline price 26 Panel (a): daily data Model MSFE ...

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

    Office of Environmental Management (EM)

    New Forecasting Tools Enhance Wind Energy Integration in Idaho and Oregon Page 1 Under the ... (RIT) that enables grid operators to use wind energy more cost-effectively to serve ...

  20. Modeling and forecasting the distribution of Vibrio vulnificus in

    Office of Scientific and Technical Information (OSTI)

    Chesapeake Bay (Journal Article) | SciTech Connect Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay Citation Details In-Document Search Title: Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters

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

    Office of Scientific and Technical Information (OSTI)

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

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

    Energy Savers [EERE]

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

  3. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National

    Office of Scientific and Technical Information (OSTI)

    Laboratory (Technical Report) | SciTech Connect 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory Citation Details In-Document Search Title: 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to

  4. Study forecasts disappearance of conifers due to climate change

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

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. December 21, 2015 Los Alamos scientist Nate McDowell discusses how climate change is killing trees with PBS NewsHour reporter Miles O'Brien. Los Alamos scientist Nate McDowell discusses how climate change is

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

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

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

  6. SimTable key tool for preparing, responding to wildfire

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

    SimTable key tool for preparing, responding to wildfire SimTable key tool for preparing, responding to wildfire Camera tracks movement and objects and project them onto a sand table. May 30, 2012 SimTable: Stephen Guerin (L) and Chip Garner (R) with SimTable, a Santa Fe company helping firefighters model and predict where a fire is most likely to spread, received support for their business through Lab economic development programs: VAF, NMSBA, Springboard. SimTable: Stephen Guerin (L) and Chip

  7. AVLIS: a technical and economic forecast

    SciTech Connect (OSTI)

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

    1986-01-01

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

  8. FY 2007 Control Table by Appropriation

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

    Control Table by Appropriation (dollars in thousands - OMB Scoring) FY 2005 FY 2006 FY 2007 Current Current Congressional Approp. Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy supply and Conservation......................................... 1,801,815 1,812,627 1,923,361 +110,734 +6.1% Fossil energy programs Clean coal technology.......................................................

  9. FY 2008 Control Table by Appriopriation

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

    Control Table by Appropriation (dollars in thousands - OMB Scoring) FY 2006 FY 2007 FY 2008 Current Congressional Congressional Approp. Request Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy supply and Conservation..................................... 1,812,397 1,923,361 2,187,943 +264,582 +13.8% Fossil energy programs Clean coal technology...................................................

  10. Table of Contents for Desk Guide

    Energy Savers [EERE]

    May, 2013 U. S. Department of Energy - Real Estate Desk Guide Revised 2013 Real Estate Desk Guide Table of Contents Chapter 1-- Purpose of Desk Guide ........................................................................ 1 Chapter 2-- Introduction ......................................................................................... 3 Chapter 3-- Planning Policy .................................................................................... 7 Chapter 4-- Real Estate Function

  11. FY 2015 Summary Control Table by Organization

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

    5 Summary Control Table by Organization (dollars in thousands - OMB Scoring) Summary Control by Organization Page 1 FY 2015 Congressional Request FY 2013 FY 2014 FY 2014 FY 2014 FY 2015 Current Enacted Adjustments Current Congressional Approp. Approp. Approp. Request Discretionary Summary By Organization Department Of Energy By Organization National Nuclear Security Administration Weapons Activities............................................................................. 6,966,855 7,781,000

  12. Help:Tables | Open Energy Information

    Open Energy Info (EERE)

    on tables 3.2 Attributes on cells 3.3 Attributes on rows 3.4 HTML colspan and rowspan 3.5 With HTML attributes and CSS styles 4 Caveats 4.1 Negative numbers 4.2 CSS vs Attributes...

  13. RSE Table S3.1 and S3.2. Relative Standard Errors for Tables...

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

    39,"Miscellaneous Manufacturing Industries",10,6,21,38,17,27,6,0,34 ... old and the new basis in bridge tables that allow comparisons" "between the two systems. ...

  14. EIA Energy Efficiency-Table 1a. Table 1a. Consumption of Site...

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

    a Page Last Modified: May 2010 Table 1a. Consumption of Energy (Site Energy) for All Purposes (First Use) for Selected Industries, 1998, 2002, and 2006 (Trillion Btu) MECS Survey...

  15. CBECS - Buildings and Energy in the 1980's, Table Titles

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

    for primary or site energy ("p" or "s"). For example, Table R8.90p, shows primary energy data for residential buildings for the 1990 survey year. The tables are arranged into...

  16. Widget:UtilityRateEntryHelperTable | Open Energy Information

    Open Energy Info (EERE)

    UtilityRateEntryHelperTable Jump to: navigation, search This widget displays the utility rate database form. For example: Widget:UtilityRateEntryHelperTable Retrieved from...

  17. OE Budget Control Table TerriL.pdf

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

    To: Hoffman, Patricia Subject: RE: OE Budget Control Table TerriL.pdf Hi Pat, Ifyou have ... Henderson, Robin Subject: FW: OE Budget Control Table TerriL.pdf Ingrid, Robin The ...

  18. Environmental Regulatory Update Table, January/February 1992

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

    1992-03-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action. This table is for January/February 1992.

  19. CORRELATION BETWEEN RAINFALL PATTERNS AND THE WATER TABLE IN THEGENERAL SEPARATIONS AREA OF THE SAVANNAH RIVERSITE

    SciTech Connect (OSTI)

    Smith, C.

    2009-08-10

    The objective of the study was to evaluate rainfall and water table elevation data in search of a correlation that could be used to understand and predict water elevation changes. This information will be useful in placing screen zones for future monitoring wells and operations of groundwater treatment units. Fifteen wells in the General Separations Area (GSA) at Savannah River Site were evaluated from 1986 through 2001. The study revealed that the water table does respond to rainfall with minimal delay. (Water level information was available monthly, which restricted the ability to evaluate a shorter delay period.) Water elevations were found to be related to the cumulative sum (Q-Delta Sum) of the difference between the average rainfall for a specific month and the actual rainfall for that month, calculated from an arbitrary starting point. Water table elevations could also be correlated between wells, but using the right well for correlation was very important. The strongest correlation utilized a quadratic equation that takes into account the rainfall in a specific area and the rainfall from an adjacent area that contributes through a horizontal flow. Specific values vary from well to well as a result of geometry and underground variations. R2's for the best models ranged up to 0.96. The data in the report references only GSA wells but other wells (including confined water tables) on the site have been observed to return similar water level fluctuation patterns.

  20. Minimum Efficiency Requirements Tables for Heating and Cooling Product Categories

    Broader source: Energy.gov [DOE]

    The Federal Energy Management Program (FEMP) created tables that mirror American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 90.1-2013 tables, which include minimum efficiency requirements for FEMP-designated and ENERGY STAR-qualified heating and cooling product categories. Download the tables below to incorporate FEMP and ENERGY STAR purchasing requirements into federal product acquisition documents.

  1. Environmental Regulatory Update Table, January/February 1995

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Bock, R.E.; Mayer, S.J.; Salk, M.S.

    1995-03-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives impacting environmental, health, and safety management responsibilities. the table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2012-04-01

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

  3. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect (OSTI)

    Not Available

    1981-05-27

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

  4. AVLIS documentation overview and tables of contents

    SciTech Connect (OSTI)

    Not Available

    1984-11-15

    Three documents constitute the executive summary series in Data Package III: this document (Documentation Overview and Tables of Contents (E001)) plus the AVLIS Production Plant Executive Summary (E010) and the AVLIS Production Plant Overall Design Report (E020). They provide progressively greater detail on the key information and conclusions contained within the data package. The Executive Summary and Overall Design Report present summaries of each Data Package III document. They are intended to provide a global overview of AVLIS Production Plant deployment including program planning, project management, schedules, engineering design, production, operations, capital cost, and operating cost. The purpose of Overview and Tables of Contents is threefold: to briefly review AVLIS goals for Data Package III documentation, to present an overview of the contents of the data package, and to provide a useful guide to information contained in the numerous documents comprising the package.

  5. FY 2008 Control Table by Organization

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

    Control Table by Organization (dollars in thousands - OMB Scoring) FY 2006 FY 2007 FY 2008 Current Congressional Congressional Approp. Request Request $ % Discretionary Summary By Organization National Security Weapons.............................................................................. 6,355,297 6,407,889 6,511,312 +103,423 +1.6% Defense Nuclear Nonproliferation....................................... 1,619,179 1,726,213 1,672,646 -53,567 -3.1% Naval

  6. FY 2009 Control Table by Appropriation

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

    Control Table by Appropriation (dollars in thousands - OMB Scoring) FY 2007 FY 2008 FY 2009 Current Current Congressional Op. Plan Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy.......................... -- 1,722,407 1,255,393 -467,014 -27.1% Electricity delivery and energy reliability........................... -- 138,556 134,000 -4,556 -3.3% Nuclear

  7. FY 2009 Control Table by Organization

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

    9 Control Table by Organization (dollars in thousands - OMB Scoring) FY 2007 FY 2008 FY 2009 Current Current Congressional Op. Plan Approp. Request $ % Discretionary Summary By Organization National Security Weapons................................................................................. 6,258,583 6,297,466 6,618,079 +320,613 +5.1% Defense Nuclear Nonproliferation........................................... 1,824,202 1,335,996 1,247,048 -88,948 -6.7% Naval

  8. FY 2010 Control Table by Appropriation

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

    Control Table by Appropriation (dollars in thousands - OMB Scoring) FY 2008 FY 2009 FY 2009 FY 2010 Current Current Current Congressional Approp. Approp. Recovery Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy.......................................... 1,704,112 2,178,540 16,800,000 2,318,602 +140,062 +6.4% Electricity delivery and energy

  9. FY 2011 Control Table by Appropriation

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

    Summary Control Table by Appropriation (dollars in thousands - OMB Scoring) FY 2009 FY 2009 FY 2010 FY 2011 Current Current Current Congressional Approp. Recovery Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy........................................... 2,156,865 16,771,907 2,242,500 2,355,473 +112,973 +5.0% Electricity delivery and energy

  10. FY 2011 Statistical Table by Appropriation

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

    Statistical Table by Appropriation (dollars in thousands - OMB Scoring) FY 2009 FY 2009 FY 2010 FY 2011 Current Current Current Congressional Approp. Recovery Approp. Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy........................................... 2,156,865 16,771,907 2,242,500 2,355,473 +112,973 +5.0% Electricity delivery and energy

  11. FY 2012 Control Table by Appropriation

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

    FY 2012 Summary Control Table by Appropriation (dollars in thousands - OMB Scoring) FY 2010 FY 2011 FY 2011 FY 2012 Current Congressional Annualized Congressional Approp. Request CR Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy....................................... 2,216,392 2,355,473 2,242,500 3,200,053 +983,661 +44.4% Electricity delivery and energy

  12. FY 2013 Control Table by Appropriation

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

    Summary Control Table by Appropriation (dollars in thousands - OMB Scoring) FY 2011 FY 2012 FY 2013 Current Enacted Congressional Approp. Approp. * Request $ % Discretionary Summary By Appropriation Energy And Water Development, And Related Agencies Appropriation Summary: Energy Programs Energy efficiency and renewable energy......................................... 1,771,721 1,809,638 2,337,000 +527,362 +29.1% Electricity delivery and energy reliability..........................................

  13. FY 2017 Statistical Table by Organization

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

    Organization (dollars in thousands - OMB Scoring) Statistical Table by Organization Page 1 FY 2017 Congressional Budget Justification FY 2015 FY 2015 FY 2016 FY 2017 Enacted Current Enacted Congressional Approp. Approp. Approp. Request $ % Discretionary Summary By Organization National Nuclear Security Administration Weapons Activities................................................................................. 8,180,359 8,180,609 8,846,948 9,243,147 +396,199 +4.5% Defense Nuclear

  14. EXECUTIVE SUMMARY- Inserted before Table of Contents

    National Nuclear Security Administration (NNSA)

    DRAFT ENVIRONMENTAL ASSESSMENT FOR REMOVAL ACTIONS AT THE TECHNICAL AREA III CLASSIFIED WASTE LANDFILL, SANDIA NATIONAL LABORATORIES, NEW MEXICO DOE/EA-1729 June 2010 National Nuclear Security Administration Sandia Site Office P.O. Box 5400 Albuquerque, New Mexico 87185-5400 DOE/EA-1729: Environmental Assessment for Removal Actions at the Technical Area III June 2010 Classified Waste Landfill, Sandia National Laboratories, New Mexico i TABLE OF CONTENTS Section Page 1.0 PURPOSE AND NEED FOR

  15. SimTable helps firefighters model and predict fire direction

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

    SimTable models and predicts fire path SimTable helps firefighters model and predict fire direction In 2009, SimTable received $100,000 from the LANS Venture Acceleration Fund to improve the user interface and seed firefighting academies with customized set ups. April 3, 2012 Stephen Guerin (L) and Chip Garner (R) with SimTable Stephen Guerin (L), and Chip Garner (R), with SimTable, a Santa Fe company helping firefighters model and predict where a fire is most likely to spread, received support

  16. 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).

  17. 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).

  18. Table of QTR comments in response to Federal Register RFI | Department of

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

    Energy Table of QTR comments in response to Federal Register RFI QTR table of respondents

  19. Waste Treatment Plant Liquid Effluent Treatability Evaluation

    SciTech Connect (OSTI)

    LUECK, K.J.

    2001-06-07

    Bechtel National, Inc. (BNI) provided a forecast of the radioactive, dangerous liquid effluents expected to be generated by the Waste Treatment Plant (WTP). The forecast represents the liquid effluents generated from the processing of 25 distinct batches of tank waste through the WTP. The WTP liquid effluents will be stored, treated, and disposed of in the Liquid Effluent Retention Facility (LERF) and the Effluent Treatment Facility (ETF). Fluor Hanford, Inc. (FH) evaluated the treatability of the WTP liquid effluents in the LERFIETF. The evaluation was conducted by comparing the forecast to the LERFIETF treatability envelope, which provides information on the items that determine if a liquid effluent is acceptable for receipt and treatment at the LERFIETF. The WTP liquid effluent forecast is outside the current LERFlETF treatability envelope. There are several concerns that must be addressed before the WTP liquid effluents can be accepted at the LERFIETF.

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

    Broader source: Energy.gov [DOE]

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

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

    SciTech Connect (OSTI)

    Piwko, R.; Jordan, G.

    2011-11-01

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

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2010-04-01

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

  3. Beyond "Partly Sunny": A Better Solar Forecast | Department of Energy

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

    Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during

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

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

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

  5. Data Collection and Comparison with Forecasted Unit Sales of Five Lamp

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

    Types | Department of Energy 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 and Comparison with Forecasted Unit Sales of Five Lamp Types More Documents & Publications Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability CX-100584 Categorical Exclusion Determination ISSUANCE

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2009-12-01

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

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

    SciTech Connect (OSTI)

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

    2009-03-01

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

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

    Energy Science and Technology Software Center (OSTI)

    2012-05-01

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

  9. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

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

    2011-11-29

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

  10. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect (OSTI)

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

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the flying brick technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.

  11. 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.

  12. 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.

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  14. Microsoft Word - table_A2.doc

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

    195 19 4 Figure A1. Natural gas processing plant capacity in the United States, 2014 2014 Table A2. Natural gas processing plant capacity, by state, 2014 (million cubic feet per day) Alabama 1,459 Arkansas 37 California 898 Colorado 6,130 Florida 90 Illinois 2,102 Kansas 1,664 Kentucky 255 Louisiana 10,870 Michigan 126 Mississippi 1,883 State Plant Capacity Notes: Coverage includes the Lower 48 States (excluding Alaska and Hawaii). Source: Energy Information Administration (EIA), Form EIA-757,

  15. LCLS CDR Appendix A - Parameter Tables

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

    A Parameter Tables A.1 FEL-Physics A.1.1 Performance A.1.1.1 Electron Beam Parameter Name Low Energy High Energy All Energies Unit Electron energy 4.54 14.35 GeV Electron Lorentz factor 8880 28082 Normalized slice emittance 1.2 1.2 µm rad Charge at undulator entrance 1 1 nC Peak current 3400 3400 A Longitudinal pulse form Flat-Top Transverse pulse form Gaussian RMS bunch length 23 23 µm RMS bunch duration 77 77 fs FWHM bunch length 69 69 µm FWHM bunch duration 230 230 fs Slice rms gamma

  16. "RSE Table N5.2. Relative Standard Errors for Table N5.2;...

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

    Standard Errors for Table N5.2;" " Unit: Percents." ,,"S e l e c t e d","W o o d","a n d","W o o d -","R e l a t e d","P r o d u c t s" ,,,,,"B i o m a s s" ,,,,,,"Wood Residues" ...

  17. International energy indicators. [Statistical tables and graphs

    SciTech Connect (OSTI)

    Bauer, E.K.

    1980-05-01

    International statistical tables and graphs are given for the following: (1) Iran - Crude Oil Capacity, Production and Shut-in, June 1974-April 1980; (2) Saudi Arabia - Crude Oil Capacity, Production, and Shut-in, March 1974-Apr 1980; (3) OPEC (Ex-Iran and Saudi Arabia) - Capacity, Production and Shut-in, June 1974-March 1980; (4) Non-OPEC Free World and US Production of Crude Oil, January 1973-February 1980; (5) Oil Stocks - Free World, US, Japan, and Europe (Landed, 1973-1st Quarter, 1980); (6) Petroleum Consumption by Industrial Countries, January 1973-December 1979; (7) USSR Crude Oil Production and Exports, January 1974-April 1980; and (8) Free World and US Nuclear Generation Capacity, January 1973-March 1980. Similar statistical tables and graphs included for the United States include: (1) Imports of Crude Oil and Products, January 1973-April 1980; (2) Landed Cost of Saudi Oil in Current and 1974 Dollars, April 1974-January 1980; (3) US Trade in Coal, January 1973-March 1980; (4) Summary of US Merchandise Trade, 1976-March 1980; and (5) US Energy/GNP Ratio, 1947 to 1979.

  18. Environmental Regulatory Update Table, September/October 1993

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.; Danford, G.S.; Lewis, E.B.

    1993-11-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operation and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  19. EIA - Greenhouse Gas Emissions - Table-Figure Notes and Sources

    Gasoline and Diesel Fuel Update (EIA)

    A1. Notes and Sources Tables Chapter 1: Greenhouse gas emissions overview Table 1. U.S. emissions of greenhouse gases, based on global warming potential, 1990-2009: Sources: Emissions: EIA estimates. Data in this table are revised from the data contained in the previous EIA report, Emissions of Greenhouse Gases in the United States 2008, DOE/EIA-0573(2008) (Washington, DC, December 2009). Global warming potentials: Intergovernmental Panel on Climate Change, Climate Change 2007: The Physical

  20. Environmental Regulatory Update Table, November--December 1993

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.; Danford, G.S.; Lewis, E.B.

    1994-01-01

    The Environmental Regulatory Update Table provides information on regulatory of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  1. Environmental Regulatory Update Table, November--December 1992

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Lewis, E.B.; Salk, M.S.

    1993-01-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly wit information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  2. Environmental sciences division: Environmental regulatory update table July 1988

    SciTech Connect (OSTI)

    Langston, M.E.; Nikbakht, A.; Salk, M.S.

    1988-08-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated each month with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  3. Environmental Regulatory Update Table, March/April 1992

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.

    1992-05-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  4. Environmental regulatory update table, September--October 1992

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Lewis, E.B.; Salk, M.S.

    1992-11-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  5. Environmental Regulatory Update Table, July--August 1992

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Lewis, E.B.; Salk, M.S.

    1992-09-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  6. Environmental Regulatory Update Table, January--February 1993

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.; Danford, G.S.; Lewis, E.B.

    1993-03-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  7. Composite slip table of dissimilar materials for damping longitudinal modes

    DOE Patents [OSTI]

    Gregory, Danny L. (Albuquerque, NM); Priddy, Tommy G. (Albuquerque, NM); Smallwood, David O. (Albuquerque, NM); Woodall, Tommy D. (Albuquerque, NM)

    1991-01-01

    A vibration slip table for use in a vibration testing apparatus. The table s comprised of at least three composite layers of material; a first metal layer, a second damping layer, and a third layer having a high acoustic velocity relative to the first layer. The different acoustic velocities between the first and third layers cause relative shear displacements between the layers with the second layer damping the displacements between the first and third layers to reduce the table longitudinal vibration modes.

  8. Environmental regulatory update table, March--April 1994

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Bock, R.E.; Salk, M.S.

    1994-03-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  9. Environmental regulatory update table November--December 1994

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Bock, R.E.; Mayer, S.J.; Salk, M.S.

    1995-01-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  10. Environmental Regulatory Update Table, January--February 1994

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Salk, M.S.; Danford, G.S.; Lewis, E.B.

    1994-03-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations ad contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  11. Environmental regulatory update table: September/October 1994

    SciTech Connect (OSTI)

    Houlberg, L.M.; Hawkins, G.T.; Bock, R.E.; Salk, M.S.

    1994-11-01

    The Environmental Regulatory Update Table provides information on regulatory initiatives of interest to DOE operations and contractor staff with environmental management responsibilities. The table is updated bi-monthly with information from the Federal Register and other sources, including direct contact with regulatory agencies. Each table entry provides a chronological record of the rulemaking process for that initiative with an abstract and a projection of further action.

  12. Petroleum Products Table 43. Refiner Motor Gasoline Volumes...

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

    1995 Table 43. Refiner Motor Gasoline Volumes by Grade, Sales Type, PAD District, and State (Thousand Gallons per Day) - Continued Geographic Area Month Premium All Grades Sales...

  13. Petroleum Products Table 43. Refiner Motor Gasoline Volumes...

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

    2000 Table 43. Refiner Motor Gasoline Volumes by Grade, Sales Type, PAD District, and State (Thousand Gallons per Day) - Continued Geographic Area Month Premium All Grades Sales...

  14. Petroleum Products Table 31. Motor Gasoline Prices by Grade...

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

    Annual 1995 Table 31. Motor Gasoline Prices by Grade, Sales Type, PAD District, and State (Cents per Gallon Excluding Taxes) - Continued Geographic Area Month Premium All...

  15. Petroleum Products Table 31. Motor Gasoline Prices by Grade...

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

    Annual 2000 Table 31. Motor Gasoline Prices by Grade, Sales Type, PAD District, and State (Cents per Gallon Excluding Taxes) - Continued Geographic Area Month Premium All...

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

    Gasoline and Diesel Fuel Update (EIA)

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

  17. Energy.gov Content Management System Data Tables | Department...

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

    Energy.gov Content Management System Data Tables For Office of Energy Efficiency and Renewable Energy (EERE) websites, follow these guidelines for creating Section 508-compliant ...

  18. First-principles opacity table of warm dense deuterium forinertial...

    Office of Scientific and Technical Information (OSTI)

    ...ial-confinement-fusion applications Citation Details In-Document Search Title: First-principles opacity table of warm dense deuterium for inertial-confinement-fusion applications ...

  19. "Table A52. Nonswitchable Minimum Requirements and Maximum...

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

    ... for which the" "switching status was not ascertained." " Notes: To obtain a RSE percentage for any table cell, multiply the cell's" "corresponding RSE column and RSE row factors. ...

  20. Table 10.1 Nonswitchable Minimum and Maximum Consumption,...

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

    ... is greater than 50 percent." " NANot available." " Notes: To obtain the RSE percentage for any table cell, multiply the cell's" "corresponding RSE column and RSE row factors. ...

  1. Buildings and Energy in the 1980's (TABLES)

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

    than 10 households were sampled. Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. * Because of rounding, data may...

  2. Table 1b. Relative Standard Errors for Effective, Occupied, and...

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

    b.Relative Standard Errors Table 1b. Relative Standard Errors for Effective Occupied, and Vacant Square Footage, 1992 Building Characteristics All Buildings (thousand) Total...

  3. Table III: Technical Targets for Catalyst Coated Membranes (CCMs...

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

    III: Technical Targets for Catalyst Coated Membranes (CCMs): Stationary Table III: Technical Targets for Catalyst Coated Membranes (CCMs): Stationary Technical targets for CCMs in ...

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

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

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

  5. Table 2b. Relative Standard Errors for Electricity Consumption...

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

    2b. Relative Standard Errors for Electricity Table 2b. Relative Standard Errors for Electricity Consumption and Electricity Intensities, per Square Foot, Specific to Occupied and...

  6. Table 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane...

    Gasoline and Diesel Fuel Update (EIA)

    See footnotes at end of table. 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane, and Residual Fuel Oil by PAD District and State 386 Energy Information...

  7. Table 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane...

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

    Marketing Annual 1998 Table 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane, and Residual Fuel Oil by PAD District and State (Thousand Gallons per Day) -...

  8. Table 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane...

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

    Marketing Annual 1995 Table 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane, and Residual Fuel Oil by PAD District and State (Thousand Gallons per Day) -...

  9. Table 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane...

    Gasoline and Diesel Fuel Update (EIA)

    Marketing Annual 1999 Table 49. Prime Supplier Sales Volumes of Aviation Fuels, Propane, and Residual Fuel Oil by PAD District and State (Thousand Gallons per Day) -...

  10. Table 34. Reformulated Motor Gasoline Prices by Grade, Sales...

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

    Information AdministrationPetroleum Marketing Annual 1999 Table 34. Reformulated Motor Gasoline Prices by Grade, Sales Type, PAD District, and Selected States (Cents per...

  11. Table 35. Refiner Motor Gasoline Prices by Grade, Sales Type...

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

    Energy Information Administration Petroleum Marketing Annual 1995 Table 35. Refiner Motor Gasoline Prices by Grade, Sales Type, PAD District, and State (Cents per Gallon...

  12. Table 44. Refiner Motor Gasoline Volumes by Formulation, Sales...

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

    250 Energy Information AdministrationPetroleum Marketing Annual 1999 Table 44. Refiner Motor Gasoline Volumes by Formulation, Sales Type, PAD District, and State (Thousand Gallons...

  13. Table 32. Conventional Motor Gasoline Prices by Grade, Sales...

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

    Information Administration Petroleum Marketing Annual 1995 Table 32. Conventional Motor Gasoline Prices by Grade, Sales Type, PAD District, and State (Cents per Gallon...

  14. Table 44. Refiner Motor Gasoline Volumes by Formulation, Sales...

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

    Energy Information Administration Petroleum Marketing Annual 1995 Table 44. Refiner Motor Gasoline Volumes by Formulation, Sales Type, PAD District, and State (Thousand Gallons...

  15. Table 48. Prime Supplier Sales Volumes of Motor Gasoline by...

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

    Petroleum Marketing Annual 1998 Table 48. Prime Supplier Sales Volumes of Motor Gasoline by Grade, Formulation, PAD District, and State (Thousand Gallons per Day) -...

  16. Table 35. Refiner Motor Gasoline Prices by Grade, Sales Type...

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

    134 Energy Information AdministrationPetroleum Marketing Annual 1998 Table 35. Refiner Motor Gasoline Prices by Grade, Sales Type, PAD District, and State (Cents per Gallon...

  17. Table 35. Refiner Motor Gasoline Prices by Grade, Sales Type...

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

    134 Energy Information AdministrationPetroleum Marketing Annual 1999 Table 35. Refiner Motor Gasoline Prices by Grade, Sales Type, PAD District, and State (Cents per Gallon...

  18. Table 43. Refiner Motor Gasoline Volumes by Grade, Sales Type...

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

    220 Energy Information AdministrationPetroleum Marketing Annual 1998 Table 43. Refiner Motor Gasoline Volumes by Grade, Sales Type, PAD District, and State (Thousand Gallons per...

  19. Table 33. Oxygenated Motor Gasoline Prices by Grade, Sales Type...

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

    - - - - - - - - - - - - See footnotes at end of table. 33. Oxygenated Motor Gasoline Prices by Grade, Sales Type, PAD District, and State 116 Energy Information...

  20. Table 44. Refiner Motor Gasoline Volumes by Formulation, Sales...

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

    - - - - W W - - - - - - See footnotes at end of table. 44. Refiner Motor Gasoline Volumes by Formulation, Sales Type, PAD District, and State 292 Energy...