Sample records for forecast 2 1

  1. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    End-Use Forecasting with EPRI-REEPS 2.1. Lawrence BerkeleyEnd-Use Forecasting with EPRI-REEPS 2.1. Lawrence BerkeleyPower Research Institute. EPRI Research Project Meier, Alan

  2. Page 1 of 2 2005-2006 MLOG Thesis Proposal 10/2/2005 Working Title Improved New Product Forecasting through Visualization of Spatial Diffusion

    E-Print Network [OSTI]

    Brock, David

    Page 1 of 2 2005-2006 MLOG Thesis Proposal 10/2/2005 Working Title Improved New Product Forecasting. Schuster, Stuart J. Allen, and David L. Brock Company Contact Several companies are interested in this area to implement an operational system for improving new product forecasting in practice. Is · Innovative thinking

  3. Benchmarking of different approaches to forecast solar irradiance Elke Lorenz1, Wolfgang Traunmller2, Gerald Steinmaurer2, Christian Kurz3,

    E-Print Network [OSTI]

    Heinemann, Detlev

    Benchmarking of different approaches to forecast solar irradiance Elke Lorenz1, Wolfgang and operation strategies. Due to the strong increase of solar power generation the prediction of solar yields and companies have been developing different methods to forecast irradiance as a basis for respective power

  4. Web 2.0, Cloud Computing, and Earthquake Forecasting Geoffrey Fox1,2

    E-Print Network [OSTI]

    ) introduces several important parallel programming models relevant to data-mining. Web 2.0 provides's EC2 and S3 online services, the Google App Engine, and Microsoft's SkyDrive) outsources basic technologies such as VMWare, Xen, and OpenVZ. Programming models for clouds such as MapReduce-based Hadoop

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

    SciTech Connect (OSTI)

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

    1994-05-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Templeton, K.J.

    1996-05-23T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-04-23T23:59:59.000Z

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

  8. Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1

    E-Print Network [OSTI]

    Levinson, David M.

    Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1 David Levinson 2 February, the assumed networks to the actual in-place networks and other travel behavior assumptions that went

  9. What constrains spread growth in forecasts ini2alized from

    E-Print Network [OSTI]

    Hamill, Tom

    1 What constrains spread growth in forecasts ini2alized from ensemble Kalman filters? Tom from manner in which ini2al condi2ons are generated, some due to the model (e.g., stochas2c physics as error; part of spread growth from manner in which ini2al condi2ons are generated, some due

  10. The Galactic Center Weather Forecast M. Moscibrodzka1

    E-Print Network [OSTI]

    Gammie, Charles F.

    The Galactic Center Weather Forecast M. Mo´scibrodzka1 , H. Shiokawa2 , C. F. Gammie2,3 , J*. The > 3M cloud will #12;­ 2 ­ interact strongly with gas near nominal pericenter at rp 300AU 8000GM/c2 transient phase while the flow circularizes-- accompanied by transient emission--it is natural to think

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2003-12-01T23:59:59.000Z

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

  12. Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1

    E-Print Network [OSTI]

    1 Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1 1 Great Lakes forecasts in operational hydrology builds a sample of possibilities for the future, of climate series from-parametric method can be extended into a new weighted parametric hydrological forecasting technique to allow

  13. J2.6 A SPATIAL DATA MINING APPROACH FOR VERIFICATION AND UNDERSTANDING OF ENSEMBLE PRECIPITATION FORECASTING

    E-Print Network [OSTI]

    Gruenwald, Le

    FORECASTING Xuechao Yu* 1,2 and Ming Xue 2,3 1 NOAA/NWS/WDTB Cooperative Institute for Mesoscale is placed on meso- scale ensemble forecasting in recent years [e.g., the Storm and Mesoscale Ensemble complicated for mesoscale quantitative precipitation forecast (QPF), since QPF is a discontinuous field. Em

  14. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    Natural Gas Oil Lighting 0-1 hrs 1-2 his 2-3 hrs Usage levelNatural gas Oil Dishwasher End-Use Lighting 0-1 hrs 1-2 hrs UsageNatural gas Oil Dishwasher End-Use Lighting 0-1 hrs 1-2 hrs Usage

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

    SciTech Connect (OSTI)

    Koomey, J.G.; Brown, R.E.; Richey, R. [and others

    1995-12-01T23:59:59.000Z

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

  16. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

    2.1.2 European Solar Radiation Atlas (ESRA)2.4 Evaluation of Solar Forecasting . . . . . . . . .2.4.1 Solar Variability . . . . . . . . . . . . .

  17. RESIDENTIAL SECTOR END-USE FORECASTING WITH EPRI-REEPS 2.1: SUMMARY INPUT ASSUMPTIONS AND RESULTS

    E-Print Network [OSTI]

    of Energy. We use the Electric Power Research Institute's (EPRI's) REEPS model, as reconfigured to reflect was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building ....................................................................................................1 2. OVERVIEW OF THE REEPS MODEL..............................................................1

  18. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    A H A M . 1991. Room Air Conditioner Data. Association of7. FUTURE WORK 7.1 Room Air Conditioners 7.2. Common heatingShipments of Unitary Air Conditioners and Heat Pumps. Air-

  19. Introducing the Canadian Crop Yield Forecaster Aston Chipanshi1

    E-Print Network [OSTI]

    Miami, University of

    for crop yield forecasting and risk analysis. Using the Census Agriculture Region (CAR) as the unit Climate Decision Support and Adaptation, Agriculture and Agri-Food Canada, 1011, Innovation Blvd, Saskatoon, SK S7V 1B7, Canada The Canadian Crop Yield Forecaster (CCYF) is a statistical modelling tool

  20. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    Administration. April. EPRI. 1982. Residential End-UseInstitute. EA-2512. July. EPRI. 1990. REEPS 2.0 HVAC ModelInstitute. October 11. EPRI, Electric Power Research

  1. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    Efficiency Choice 6.3 New Home HVAC System Choice 6.4. NewJuly. EPRI. 1990. REEPS 2.0 HVAC Model Logic, prepared by1990. Review of Equipment HVAC Choice Parameters. Cambridge

  2. WIND POWER ENSEMBLE FORECASTING Henrik Aalborg Nielsen1

    E-Print Network [OSTI]

    WIND POWER ENSEMBLE FORECASTING Henrik Aalborg Nielsen1 , Henrik Madsen1 , Torben Skov Nielsen1. In this paper we address the problems of (i) transforming the mete- orological ensembles to wind power ensembles the uncertainty which follow from historical (climatological) data. However, quite often the actual wind power

  3. Consensus Coal Production Forecast for

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    Consensus Coal Production Forecast for West Virginia 2009-2030 Prepared for the West Virginia Summary 1 Recent Developments 2 Consensus Coal Production Forecast for West Virginia 10 Risks References 27 #12;W.Va. Consensus Coal Forecast Update 2009 iii List of Tables 1. W.Va. Coal Production

  4. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 2 AUGUST 15, 2013

    E-Print Network [OSTI]

    Gray, William

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  5. Forecasting U.S. Hurricanes 6 Months in Advance James B. Elsner,1

    E-Print Network [OSTI]

    Elsner, James B.

    . A hurricane can make more than one landfall as hurricane Andrew did in striking southeast FloridaForecasting U.S. Hurricanes 6 Months in Advance James B. Elsner,1 Richard J. Murnane,2 Thomas H correspondence should be addressed; E-mail: jelsner@garnet.fsu.edu. [1] Hurricanes are a serious social

  6. Forecasting U.S. hurricanes 6 months in advance J. B. Elsner,1

    E-Print Network [OSTI]

    Elsner, James B.

    . A hurricane can make more than one landfall as hurricane Andrew did in striking southeast FloridaForecasting U.S. hurricanes 6 months in advance J. B. Elsner,1 R. J. Murnane,2 and T. H. Jagger1 is a grim reminder of the serious social and economic threat that hurricanes pose to the United States

  7. 1 Ozone pollution forecasting 3 Herve Cardot, Christophe Crambes and Pascal Sarda.

    E-Print Network [OSTI]

    Crambes, Christophe

    Contents 1 Ozone pollution forecasting 3 Herv´e Cardot, Christophe Crambes and Pascal Sarda. 1;1 Ozone pollution forecasting using conditional mean and conditional quantiles with functional covariates Herv´e Cardot, Christophe Crambes and Pascal Sarda. 1.1 Introduction Prediction of Ozone pollution

  8. 11.1 DEVELOPMENT OF AN IMMERSED BOUNDARY METHOD TO RESOLVE COMPLEX TERRAIN IN THE WEATHER RESEARCH AND FORECASTING MODEL

    E-Print Network [OSTI]

    Chow, Fotini Katopodes

    11.1 DEVELOPMENT OF AN IMMERSED BOUNDARY METHOD TO RESOLVE COMPLEX TERRAIN IN THE WEATHER RESEARCH AND FORECASTING MODEL Katherine A. Lundquist1 , Fotini K. Chow 2 , Julie K. Lundquist 3 , and Jeffery D. Mirocha 3 in urban areas are profoundly influenced by the presence of build- ings which divert mean flow, affect

  9. Weather Research and Forecasting Model 2.2 Documentation

    E-Print Network [OSTI]

    Sadjadi, S. Masoud

    : Javier Munoz, Diego Lopez, and David Villegas Undergraduate REU Students: Javier Figueroa, Xabriel J International University (FIU) 11200 SW 8th St., Miami, Florida 33199, USA #12;2 Contents Project Motivation

  10. Advanced Numerical Weather Prediction Techniques for Solar Irradiance Forecasting : : Statistical, Data-Assimilation, and Ensemble Forecasting

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01T23:59:59.000Z

    Forecasting and Resource Assessment, 1 st Edition, Editors:Forecasting and Resource Assessment, 1 st Edition, Editors:Forecasting and Resource Assessment, 1 st Ed.. Editor: Jan

  11. Radiation fog forecasting using a 1-dimensional model

    E-Print Network [OSTI]

    Peyraud, Lionel

    2001-01-01T23:59:59.000Z

    The importance of fog forecasting to the aviation community, to road transportation and to the public at large is irrefutable. The deadliest aviation accident in history was in fact partly a result of fog back on 27 March 1977. This has, along...

  12. ASSESSING THE QUALITY AND ECONOMIC VALUE OF WEATHER AND CLIMATE FORECASTS

    E-Print Network [OSTI]

    Katz, Richard

    INFORMATION SYSTEM · Forecast -- Conditional probability distribution for event Z = z indicates forecast tornado #12;(1.2) FRAMEWORK · Joint Distribution of Observations & Forecasts Observed Weather = Forecast probability p (e.g., induced by Z) · Reliability Diagram Observed weather: = 1 (Adverse weather occurs) = 0

  13. FORECAST Skimming off the Malware Cream Matthias Neugschwandtner1

    E-Print Network [OSTI]

    Kruegel, Christopher

    , Gregoire Jacob2 , and Christopher Kruegel2 1 Vienna University of Technology, {mneug and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post

  14. Forecasting OctoberNovember Caribbean hurricane days Philip J. Klotzbach1

    E-Print Network [OSTI]

    Gray, William

    Forecasting October­November Caribbean hurricane days Philip J. Klotzbach1 Received 22 April 2011; revised 1 July 2011; accepted 11 July 2011; published 30 September 2011. [1] October­November Caribbean. Largescale climate parameters associated with active late seasons in the Caribbean are investigated

  15. III.C. 2. Plastics and Competing Materials by 1985: A Delphi Forecasting Study

    E-Print Network [OSTI]

    Bieber, Michael

    189 III.C. 2. Plastics and Competing Materials by 1985: A Delphi Forecasting Study SELWYN ENZER The application of Delphi to the identification and assessment of possible developments in plastics and competing. The ability to tailor-make plastics for various applications, enhanced by growth in understanding of organic

  16. PROBCAST: A Web-Based Portal to Mesoscale Probabilistic Forecasts Clifford Mass1

    E-Print Network [OSTI]

    Mass, Clifford F.

    1 PROBCAST: A Web-Based Portal to Mesoscale Probabilistic Forecasts Clifford Mass1 , Susan Joslyn over the Pacific Northwest. PROBCAST products are derived from the output of a mesoscale ensemble-processing of mesoscale, short-range ensembles. The NAS report also noted current deficiencies in the communication

  17. Sustainable Energy Options for Kosovo January 19, 2012 Page: 1 of 50 http://rael.berkeley.edu

    E-Print Network [OSTI]

    Kammen, Daniel M.

    ---------------------------------------------------------------------------------- 12 1.2 Forecast of Demand and Generation, 2010-2020 ------------------------------------------------------ 13 1.2.1 Demand Forecast --------------------------------------------------------------------------------------------------- 17 2.1.2 Oil & Natural Gas

  18. 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.

    1994-02-01T23:59:59.000Z

    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.

  19. Short-term Wind Power Forecasting Using Advanced Statistical T.S. Nielsen1

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    , in order to be able to absorb a large fraction of wind power in the electrical systems reliable short from refer- ence MET forecasts to the actual wind farm, wind farm power curve models, dynamical models of art wind power prediction system is outlined in Section 2. Numerical Weather Prediction (NWP

  20. Online Forecast Combination for Dependent Heterogeneous Data

    E-Print Network [OSTI]

    Sancetta, Alessio

    the single individual forecasts. Several studies have shown that combining forecasts can be a useful hedge against structural breaks, and forecast combinations are often more stable than single forecasts (e.g. Hendry and Clements, 2004, Stock and Watson, 2004... in expectations. Hence, we have the following. Corollary 4 Suppose maxt?T kl (Yt, hwt,Xti)kr ? A taking expectation on the left hand side, adding 2A ? T and setting ? = 0 in mT (?), i.e. TX t=1 E [lt (wt)? lt (ut...

  1. Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting

    E-Print Network [OSTI]

    Ribes, Aurélien

    Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting SAMUEL RE significant. This led to the implementation of an ensemble Kalman filter (EnKF) within COBEL-ISBA. The new by using an ensemble Kalman filter (EnKF; Evensen 1994, 2003). Theoreti- cally, ensemble filters

  2. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    Energy Commission's final forecasts for 2012­2022 electricity consumption, peak, and natural gas demand Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand

  3. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    Energy Commission staff's revised forecasts for 2012­2022 electricity consumption, peak, and natural Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2005-08-17T23:59:59.000Z

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

  5. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    the California Energy Commission staff's revised forecasts for 2012­2022 electricity consumption, peak Electricity, demand, consumption, forecast, weather normalization, peak, natural gas, self generation REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand

  6. Wind Power Forecasting: State-of-the-Art 2009

    E-Print Network [OSTI]

    Kemner, Ken

    Wind Power Forecasting: State-of-the-Art 2009 ANL/DIS-10-1 Decision and Information Sciences about Argonne and its pioneering science and technology programs, see www.anl.gov. #12;Wind Power................................................ 14 2.2.3 Critical Processes for Wind Forecast

  7. Technology Forecasting Scenario Development

    E-Print Network [OSTI]

    Technology Forecasting and Scenario Development Newsletter No. 2 October 1998 Systems Analysis was initiated on the establishment of a new research programme entitled Technology Forecasting and Scenario and commercial applica- tion of new technology. An international Scientific Advisory Panel has been set up

  8. Rainfall-River Forecasting

    E-Print Network [OSTI]

    US Army Corps of Engineers

    ;2Rainfall-River Forecasting Joint Summit II NOAA Integrated Water Forecasting Program · Minimize losses due management and enhance America's coastal assets · Expand information for managing America's Water Resources, Precipitation and Water Quality Observations · USACE Reservoir Operation Information, Streamflow, Snowpack

  9. RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN

    E-Print Network [OSTI]

    Manry, Michael

    a mixture of hydroelectric and non- hydroelectric power, the economics of the hydroelectric plants depend, and to economically allocate the load between various non-hydroelectric plants. Neural networks provide an attractive technology for inflow forecasting, because of (1) their success in power load forecasting 1- 6 , and (2

  10. 9A.2 FORECAST CHALLENGES AT THE NWS STORM PREDICTION CENTER RELATING TO THE FREQUENCY OF FAVORABLE SEVERE STORM ENVIRONMENTS

    E-Print Network [OSTI]

    to the performance of convective watches issued by the NOAA Storm Prediction Center (SPC layer bulk wind shear, are linked to SPC convective watches and verification metrics forecast performance. 2. SPC CONVECTIVE WATCHES A convective watch is issued by the SPC

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

    SciTech Connect (OSTI)

    Not Available

    1994-12-01T23:59:59.000Z

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

  12. 13.2 A REPORT AND FEATURE-BASED VERIFICATION STUDY OF THE CAPS 2008 STORM-SCALE ENSEMBLE FORECASTS FOR SEVERE CONVECTIVE WEATHER

    E-Print Network [OSTI]

    of computing power, innovative numerical systems, and assimilation of observations at high spatial and temporal system as a means by which model error and uncertainty can be quantified in the forecast. Employing13.2 A REPORT AND FEATURE-BASED VERIFICATION STUDY OF THE CAPS 2008 STORM-SCALE ENSEMBLE FORECASTS

  13. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 1 SEPTEMBER 14, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all are not developing any new tropical cyclones after Earl and Fiona. We expect Earl to generate large amounts of ACE This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting

  14. ...................................................................................................... 1 (1) ........................................................................ 2

    E-Print Network [OSTI]

    Kouroupetroglou, Georgios

    , - ...................................................................................................... 1 - (1) ........................................................................ 2 . //98546 (1) - , - . : 1. : ) 81 . 3057/2002 « . 2725/1999, - » ( 239/2003 « - » ( 146). 2. ' . 8595/12.10.2007 - . 3. - , : 1 28 . 2121/1993 ( ' 25

  15. Hurricane Modeling R. Saravanan 1, Jenshan Hsieh 1, Jaison Kurian 2, Zhao Xu 2

    E-Print Network [OSTI]

    Hurricane Modeling R. Saravanan 1, Jenshan Hsieh 1, Jaison Kurian 2, Zhao Xu 2 Christina M Image: Hurricane Katrina from NOAA GOES 8/28/2005, 1545z #12;Why model hurricanes? · Forecast the track and intensity of individual hurricanes · Outlook for the next hurricane season · Projections of changes

  16. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01T23:59:59.000Z

    This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

  17. Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast

    E-Print Network [OSTI]

    Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast Role of the Economic Forecast..................................................................................................................................... 2 Economic Growth Assumptions

  18. Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

    SciTech Connect (OSTI)

    Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

    2012-08-15T23:59:59.000Z

    We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

  19. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand The California Energy Demand 2014 ­ 2024 Revised Forecast, Volume 2: Electricity Demand by Utility Planning Area Energy Policy Report. The forecast includes three full scenarios: a high energy demand case, a low

  20. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (TO APPEAR, 2014) 1 Electricity Market Forecasting

    E-Print Network [OSTI]

    Giannakis, Georgios

    to spatially-varying energy prices, known as locational marginal prices (LMPs) [24], [17]. Schemes inference. Day-ahead price forecast- ing is cast as a low-rank kernel learning problem. Uniquely exploiting- temporally varying prices. Through a novel nuclear norm-based regularization, kernels across pricing nodes

  1. 24-hours ahead global irradiation forecasting using Multi-Layer Cyril Voyant1*

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ; it is shown that the most relevant is based on a multi- output MLP using endogenous and exogenous input data ahead. In the case of solar plants, the driving factor is the global solar irradiation (sum of direct and diffuse solar radiation projected on a plane (Wh/m²)). This paper focuses on the 24-hours ahead forecast

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

    SciTech Connect (OSTI)

    McNamara, Laura A.

    2010-08-01T23:59:59.000Z

    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.

  3. A Feasibility Study for Probabilistic Convection Initiation Forecasts Based on Explicit Numerical Guidance2

    E-Print Network [OSTI]

    Lakshmanan, Valliappa

    , Steven J. Weiss2 , Fanyou Kong4,5 , Ming Xue4,5 , Ryan A. Sobash1,4 , Andrew R. Dean2 , Israel L. Jirak2 ,6 and Christopher J. Melick2,3 8 1 NOAA/National Severe Storms Laboratory, Norman, OK10 2 NOAA/Storm Prediction Center, Norman, OK 3 Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK12 4

  4. 1.1.1.1.1.1 AP 1.1.1.1.2.1

    E-Print Network [OSTI]

    Ladkin, Peter B.

    . Ladkin, K. Loer 0 AC crashes into landing zone near E1 taxiway 1 AC stalls since 2 CRW unable to recover stall 1.1.1.1.1.1 AP engaged 1.1.1.1.2.1 F/O (PF) triggers GA­lever 1.1.1.1.2.1.2 F/O moves hand on throttles 1.1.1.1.2.1.1 position of GA­lever 1.1.1.1.2.2.1.1 F/O (PF) tries to go direct into LAND mode 1.1.1.1

  5. 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Tanaka, Jiro

    25 #12;2 3 1 2 #12;1 1 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 3 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.1

  6. Verification of hourly forecasts of wind turbine power output

    SciTech Connect (OSTI)

    Wegley, H.L.

    1984-08-01T23:59:59.000Z

    A verification of hourly average wind speed forecasts in terms of hourly average power output of a MOD-2 was performed for four sites. Site-specific probabilistic transformation models were developed to transform the forecast and observed hourly average speeds to the percent probability of exceedance of an hourly average power output. (This transformation model also appears to have value in predicting annual energy production for use in wind energy feasibility studies.) The transformed forecasts were verified in a deterministic sense (i.e., as continuous values) and in a probabilistic sense (based upon the probability of power output falling in a specified category). Since the smoothing effects of time averaging are very pronounced, the 90% probability of exceedance was built into the transformation models. Semiobjective and objective (model output statistics) forecasts were made compared for the four sites. The verification results indicate that the correct category can be forecast an average of 75% of the time over a 24-hour period. Accuracy generally decreases with projection time out to approx. 18 hours and then may increase due to the fairly regular diurnal wind patterns that occur at many sites. The ability to forecast the correct power output category increases with increasing power output because occurrences of high hourly average power output (near rated) are relatively rare and are generally not forecast. The semiobjective forecasts proved superior to model output statistics in forecasting high values of power output and in the shorter time frames (1 to 6 hours). However, model output statistics were slightly more accurate at other power output levels and times. Noticeable differences were observed between deterministic and probabilistic (categorical) forecast verification results.

  7. Natural Priors, CMSSM Fits and LHC Weather Forecasts

    E-Print Network [OSTI]

    Allanach, B C; Cranmer, Kyle; Lester, Christopher G; Weber, Arne M

    2007-08-07T23:59:59.000Z

    ar X iv :0 70 5. 04 87 v3 [ he p- ph ] 5 J ul 20 07 Preprint typeset in JHEP style - HYPER VERSION DAMTP-2007-18 Cavendish-HEP-2007-03 MPP-2007-36 Natural Priors, CMSSM Fits and LHC Weather Forecasts Benjamin C Allanach1, Kyle Cranmer2... ’s likely discoveries. There are big differences between nature of the questions answered by a forecast, and the ques- tions that will be answered by the experiments themselves when they have acquired compelling data. A weather forecast predicting “severe...

  8. SIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL

    E-Print Network [OSTI]

    Gambardella, Luca Maria

    SIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL Luca Maria Gambardella1 , Gianluca@idsia.ch 2 LCST, La Spezia Container Terminal, La Spezia (IT) 3 DSP, Data System & Planning sa, Manno (CH working in intermodal container terminals. INTRODUCTION The amount of work a container terminal deals

  9. 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Tanaka, Jiro

    2011 2 #12;#12;1 1 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 3 2.1

  10. Consensus Coal Production And Price Forecast For

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    Consensus Coal Production And Price Forecast For West Virginia: 2011 Update Prepared for the West December 2011 © Copyright 2011 WVU Research Corporation #12;#12;W.Va. Consensus Coal Forecast Update 2011 i Table of Contents Executive Summary 1 Recent Developments 3 Consensus Coal Production And Price Forecast

  11. 1.1.1.1. 1) [2,3].

    E-Print Network [OSTI]

    Joo, Su-Chong

    - 93 - 1.1.1.1. 1) [1]. u- [2.2.2.2. [4] . 1 / , . o *, *, **, * e-mail : {cslee99 o . , , , , . . , . , . , . . , , . #12;- 94 - ( 1) DOGF / / , . DBMS

  12. 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Toronto, University of

    15 2 12 10408 #12;1 1 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 2 2.1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3 5 4 Musex 1 6 5 Musex 1 9 6 Musex 1 11 6.1

  13. 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Tanaka, Jiro

    25 #12;GPS #12;1 1 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.5 . . . . . . . . . . . . . . . . . . . 3 1

  14. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering P : 10.1016/j.jweia.2008.03.013 #12;2 Abstract This paper studies the application of Kalman filtering forecasts. The application of Kalman filter to these data leads to the elimination of any possible

  15. Model bias correction for dust storm forecast using ensemble Kalman filter

    E-Print Network [OSTI]

    Model bias correction for dust storm forecast using ensemble Kalman filter Caiyan Lin,1,2 Jiang Zhu Kalman filter (EnKF) assimilation targeting heavy dust episodes during the period of 15­24 March 2002. Wang (2008), Model bias correction for dust storm forecast using ensemble Kalman filter, J. Geophys

  16. Advanced statistical methods for shortterm wind power forecasting Research proposal draft

    E-Print Network [OSTI]

    Barnett, Alex

    Barnett July 2001 1 Background Over the last decade wind power has become a cost­effective alternative at a turbine) using linear or nonlinear time­series analysis (Alex­ iadis 1999), or 2) forecasting windAdvanced statistical methods for short­term wind power forecasting Research proposal draft Alex

  17. 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. (Decision and Information Sciences); (INESC Porto)

    2011-12-06T23:59:59.000Z

    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.

  18. 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Tanaka, Jiro

    17 , , , #12;#12;1 1 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 3 2.1.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3 9 3.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.1

  19. 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Tanaka, Jiro

    2012 2 #12;15 #12;1 1 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 4 2.1

  20. Solar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting Questionnaire As someone who is familiar with solar energy issues, we hope that you will tak

    E-Print Network [OSTI]

    Islam, M. Saif

    Page 1 Solar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting QuestionnaireSolar Resource and Forecasting Questionnaire As someone who is familiar with solar energy issues, we hope that you will take a few moments to answer this short survey

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

    SciTech Connect (OSTI)

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

    1997-01-07T23:59:59.000Z

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

  2. GenForecast(26yr)(avg).PDF

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

    SLCAIP Historical & Forecast Generation at Plant Total Range of Hydrology 0 2,000,000,000 4,000,000,000 6,000,000,000 8,000,000,000 10,000,000,000 12,000,000,000 1 9 7 0 1 9 7 2 1...

  3. 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Tanaka, Jiro

    25 LED #12;Kinect Kinect #12;1 1 1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 3 2.1

  4. 1.1 . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . 1

    E-Print Network [OSTI]

    Tanaka, Jiro

    22 #12;1 #12;1 1 1.1 . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.4 . . . . . . . . . . . . . . 2 1.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1

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

    SciTech Connect (OSTI)

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

    1991-01-01T23:59:59.000Z

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

  6. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

    and forecasting of solar radiation data: a review,”forecasting of solar- radiation data,” Solar Energy, vol.sequences of global solar radiation data for isolated sites:

  7. 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1

    E-Print Network [OSTI]

    Martinis, John M.

    1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 Ch S B B S Ch Ch Ch S Ch 1 2 S B · dS, B B = 0 S Hr/2 = 10 Tf = 600 HZ HX |H| = Hr HS F f = 2 Hr y sin y y Ch = 1 ± 0.05 Ch H = 0 S Ch = 1 Ch S #12;y Ch H0 = Hr z H0/Hr = 1.2 H0/Hr = 0 K K Ch H0/Hr HG(kx, ky) = vF (kxx + kyy ) + (m0 - mt)z , vF m

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2000-08-31T23:59:59.000Z

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

  9. 1.1. : 1.2.

    E-Print Network [OSTI]

    Borissova, Daniela

    1 V-1 1 1. 1.1. : - (-) 1.2. : . . . I . - ­ - 1.3. : 1. - - (- ) 2. ". " - () 3. " " - () 1.4. : 1.5. : BG051PO001/07/3.3-02/7/17.06.08. 1.6. : 17.06.2008. 14.11.2008 . 1.7. () (): 1.8. , (. ): : 1. 2. 3. 4. 5

  10. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand, EndUser Natural Gas Demand, and Energy Efficiency SEPTEMBER 2013 CEC2002013004SDV1REV CALIFORNIA The California Energy Demand 2014 ­ 2024 Revised Forecast, Volume 1: Statewide Electricity Demand and Methods

  11. air pollution forecast: Topics by E-print Network

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

    air pollution forecast First Page Previous Page 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 ENVIRONMENTAL INFORMATION SYSTEM...

  12. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

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

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

    2015-08-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2014-04-30T23:59:59.000Z

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

  14. 1.1. : 1.2.

    E-Print Network [OSTI]

    Borissova, Daniela

    4 1. 1.1. : - (-) 1.2. : . . . II . - , 1.3. : 1. - - (-) 2. ". " - () 3. " " - () 1.4. : 1.5. : BG051PO001/07/3.3-02/7/17.06.08. 1.6. : 05.11.2009 . 23.02.2010 . 1.7. /: 17.06.2008 . 17.06.2010 . 1.8. () (): - , 1

  15. A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION

    E-Print Network [OSTI]

    Boyer, Edmond

    1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving. The very first results show an improvement brought by this approach. 1. INTRODUCTION Solar radiation

  16. Simulations of Arctic Mixed-Phase Clouds in Forecasts with CAM3 and AM2 for M-PACE

    SciTech Connect (OSTI)

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

    2008-02-29T23:59:59.000Z

    Simulations of mixed-phase clouds in short-range forecasts with the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) and the Geophysical Fluid Dynamics Laboratory (GFDL) climate model (AM2) for the Mixed-Phase Arctic Cloud Experiment (M-PACE) are performed under the DOE CCPP-ARM Parameterization Testbed (CAPT), which initializes the climate models with analysis data produced from numerical weather prediction (NWP) centers. It is shown that CAM3 significantly underestimates the observed boundary layer mixed-phase clouds and cannot realistically simulate the variations with temperature and cloud height of liquid water fraction in the total cloud condensate based an oversimplified cloud microphysical scheme. In contrast, AM2 reasonably reproduces the observed boundary layer clouds while its clouds contain much less cloud condensate than CAM3 and the observations. Both models underestimate the observed cloud top and base for the boundary layer clouds. The simulation of the boundary layer mixed-phase clouds and their microphysical properties is considerably improved in CAM3 when a new physically based cloud microphysical scheme is used. The new scheme also leads to an improved simulation of the surface and top of the atmosphere longwave radiative fluxes in CAM3. It is shown that the Bergeron-Findeisen process, i.e., the ice crystal growth by vapor deposition at the expense of coexisting liquid water, is important for the models to correctly simulate the characteristics of the observed microphysical properties in mixed-phase clouds. Sensitivity tests show that these results are not sensitive to the analysis data used for model initializations. Increasing model horizontal resolution helps capture the subgrid-scale features in Arctic frontal clouds but does not help improve the simulation of the single-layer boundary layer clouds. Ice crystal number density has large impact on the model simulated mixed-phase clouds and their microphysical properties and needs to be accurately represented in climate models.

  17. Alternative methods for forecasting GDP Dominique Gugan

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    analysis. Better forecast performance for macroeconomic variables will lead to Paris School of Economics the speed of computers that can develop search algorithms from appropriate selection criteria, Devroye. 1 Introduction Forecasting macroeconomic variables such as GDP and inflation play an important role

  18. 2013 Midyear Economic Forecast Sponsorship Opportunity

    E-Print Network [OSTI]

    de Lijser, Peter

    2013 Midyear Economic Forecast Sponsorship Opportunity Thursday, April 18, 2013, ­ Hyatt Regency Irvine 11:30 a.m. ­ 1:30 p.m. Dr. Anil Puri presents his annual Midyear Economic Forecast addressing and Economics at California State University, Fullerton, the largest accredited business school in California

  19. Nonparametric models for electricity load forecasting

    E-Print Network [OSTI]

    Genève, Université de

    Electricity consumption is constantly evolving due to changes in people habits, technological innovations1 Nonparametric models for electricity load forecasting JANUARY 23, 2015 Yannig Goude, Vincent at University Paris-Sud 11 Orsay. His research interests are electricity load forecasting, more generally time

  20. Taking out 1 billion tons of CO2: The magic of China's 11th Five-Year Plan?

    E-Print Network [OSTI]

    Lin, Jiang

    2008-01-01T23:59:59.000Z

    E. I.. 1996. China's Energy A forecast to 2015, U.S. DOEChina’s energy/GDP elasticity remains at 1 and economic growth unfolds as forecast,

  1. Table of Contents Page 2National High Magnetic Field Laboratory and Its Forecasted Impact on the Florida Economy

    E-Print Network [OSTI]

    Weston, Ken

    Impact on the Florida Economy History and Evaluation of the Economic Impact of the Magnet Lab Forecasted Impact on the Florida Economy The National Science Foundation (NSF) awarded the National High generated by Magnet Lab activities across the broader statewide economy. Since 1990, the Magnet Lab has

  2. 1 Worldwide Computing Middleware 1 1.1 Middleware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

    E-Print Network [OSTI]

    Varela, Carlos

    Contents 1 Worldwide Computing Middleware 1 1.1 Middleware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Asynchronous Communication . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Higher-level Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.3 Virtual Machines

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

    SciTech Connect (OSTI)

    Not Available

    1993-12-01T23:59:59.000Z

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

  4. P2.3 DEVELOPMENT OF A COMPREHENSIVE SEVERE WEATHER FORECAST VERIFICATION SYSTEM AT THE STORM PREDICTION CENTER

    E-Print Network [OSTI]

    : Andrew R. Dean, CIMMS, Univ. of Oklahoma, National Weather Center, Suite 2300, Norman, OK 73072-7268; e PREDICTION CENTER Andrew R. Dean*1,2 , Russell S. Schneider 2 , and Joseph T. Schaefer 2 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 2 NOAA/NWS Storm

  5. A High-Fidelity Temperature Distribution Forecasting System for Data Centers Jinzhu Chen1

    E-Print Network [OSTI]

    consumption in many data centers [3]. Various thermal management schemes for improving the energy efficiency Michigan State University, USA; 2 Ohio State University, USA Abstract--Data centers have become a critical are essential for preventing overheating- induced server shutdowns and improving a data center's energy

  6. LOAD FORECASTING Eugene A. Feinberg

    E-Print Network [OSTI]

    Feinberg, Eugene A.

    , regression, artificial intelligence. 1. Introduction Accurate models for electric power load forecasting to make important decisions including decisions on pur- chasing and generating electric power, load for different operations within a utility company. The natures 269 #12;270 APPLIED MATHEMATICS FOR POWER SYSTEMS

  7. RACORO Forecasting

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's Possible forPortsmouth/Paducah47,193.70COMMUNITYResponses: QuestionDaniel Hartsock CIMMS,

  8. Forecasted Opportunities

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville Power AdministrationField8,Dist.New MexicoFinancingProofWorkingEnergyGo modelP eForForAForecasted

  9. PHYSICAL REVIEW E 85, 056220 (2012) Forecasting synchronizability of complex networks from data

    E-Print Network [OSTI]

    Lai, Ying-Cheng

    2012-01-01T23:59:59.000Z

    PHYSICAL REVIEW E 85, 056220 (2012) Forecasting synchronizability of complex networks from data Ri-Qi Su,1 Xuan Ni,1 Wen-Xu Wang,1,2 and Ying-Cheng Lai1,3 1 School of Electrical, Computer, and Energy of Management and Center for Complexity Research, Beijing Normal University, Beijing 100875, China 3 Department

  10. Radar-Derived Forecasts of Cloud-to-Ground Lightning Over Houston, Texas

    E-Print Network [OSTI]

    Mosier, Richard Matthew

    2011-02-22T23:59:59.000Z

    .1.6 Comparison to Previous Studies......................................................................59 3.2 VII Forecast Method...............................................................................................61 3.2.1 Percentile Value...) percentile values for the entire dataset (1997-2006) when considering only cells with a minimum track count of 2.......................................................................... 117 3.5 Same as Figure 3.4 for the POD values...

  11. Forecastability as a Design Criterion in Wind Resource Assessment: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01T23:59:59.000Z

    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.

  12. PROACTIVE ENERGY MANAGEMENT FOR NEXT-GENERATION BUILDING Victor M. Zavala1, Jianhui Wang2, Sven Leyffer1

    E-Print Network [OSTI]

    Anitescu, Mihai

    PROACTIVE ENERGY MANAGEMENT FOR NEXT-GENERATION BUILDING SYSTEMS Victor M. Zavala1, Jianhui Wang2 S Cass Ave, Argonne, IL 60439 ABSTRACT We present a proactive energy management framework that integrates predictive dynamic building models and day-ahead forecasts of disturbances affecting efficiency and costs

  13. ,-// ' 0* & 1 $ & ,--0 ' 2* 1 $ 3 1 %

    E-Print Network [OSTI]

    Solka, Jeff

    #12;! " # ! # $ $ # % & # '! () #12;% #12;* ) ) $ ) + ,-. ,-// ' 0* & 1 $ & ,--0 ' 2* 1 $ 3 1 % ,--/ ' 4* 1 $ (5 ) 3 63 7+ # ! $ * 8 ' 9 $ $ # # : * *77 $ ' $ 7 $ 7$ 7 $ 7 7 7 $ #12 # # $ $ # # # * # " # 1 ) 3 73 & # # ) 7 EE ) (5 7 B 7 $ $ #12;! & # #* & $ @$ A 7 * $ & # * $ ) $ & # #12;B # ; *77 ' F

  14. Forecasting project progress and early warning of project overruns with probabilistic methods

    E-Print Network [OSTI]

    Kim, Byung Cheol

    2008-10-10T23:59:59.000Z

    , the critical path method (CPM) and earned value management (EVM) are deterministic and fail to account for the inherent uncertainty in forecasting and project performance. The objective of this dissertation is to improve the predictive capabilities....4.1 Earned Value Management ................................................... 14 2.4.2 CPM ...................................................................................... 20 2.4.3 Monte Carlo Simulation...

  15. Econometric model and futures markets commodity price forecasting

    E-Print Network [OSTI]

    Just, Richard E.; Rausser, Gordon C.

    1979-01-01T23:59:59.000Z

    Versus CCll1rnercial Econometric M:ldels." Uni- versity ofWorking Paper No. 72 ECONOMETRIC ! 'econometric forecasts with the futures

  16. Weather Forecast Data an Important Input into Building Management Systems

    E-Print Network [OSTI]

    Poulin, L.

    2013-01-01T23:59:59.000Z

    it can generate as much or more energy that it needs ? Building activities need N kWhrs per day (solar panels, heating, etc) ? Harvested from solar panels & passive solar. Amount depends on weather ? NWP models forecast DSWRF @ surface (MJ/m2...://collaboration.cmc.ec.gc.ca/cmc/cmoi/SolarScribe/SolarScribe/ CMC NWP datasets for Day 2 Forecasts ? Regional Deterministic Prediction System (RDPS) ? RDPS raw model data ? 10 km resolution, North America, 000-054 forecasts ? Data at: http...

  17. NREL: Transmission Grid Integration - Forecasting

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's Possible for Renewable Energy: Grid IntegrationReport AvailableForecasting NREL researchers use

  18. 1 1 1 1 1 1 2 2 Network of offshore wind farms connected by

    E-Print Network [OSTI]

    Heinemann, Detlev

    2 33 3 3 1 1 1 1 1 1 2 2 Network of offshore wind farms connected by gas insulated transmission of connecting these offshore wind farms by gas in- sulated transmission lines (GIL) is investigated. Aim, Germany Corresponding author: anja.drews@forwind.de Offshore wind parks in different stages.Green- in op

  19. Probabilistic manpower forecasting

    E-Print Network [OSTI]

    Koonce, James Fitzhugh

    1966-01-01T23:59:59.000Z

    PROBABILISTIC MANPOWER FORECASTING A Thesis JAMES FITZHUGH KOONCE Submitted to the Graduate College of the Texas ASSAM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May, 1966 Major Subject...: Computer Science and Statistics PROBABILISTIC MANPOWER FORECASTING A Thesis By JAMES FITZHUGH KOONCE Submitted to the Graduate College of the Texas A@M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May...

  20. A1 = (b1 + m1y1)y1 = (2 + 2 1)(1) = 4 m2 A2 = (b2 + m2y2)y2 = (2.5 + 2 1)(1) = 4.5 m2

    E-Print Network [OSTI]

    Bowen, James D.

    .79)(8.4)(0.147) = 12.1 kW where w = 9.79 kN/m3 at 20C. 3.24. The Darcy-Weisbach equation can be written as hf = ¯fL D ¯V 2 2g Defining S = hf L and ¯R = D 4 and substituting into the Darcy-Weisbach equation gives S = ¯f = Q A1 = 8.4 4 = 2.10 m/s V2 = Q A2 = 8.4 4.5 = 1.87 m/s Substituting into the energy equation gives 1

  1. UPF Forecast | Y-12 National Security Complex

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

    Uranium Processing Facility UPF Forecast UPF Forecast UPF Procurement provides the following forecast of subcontracting opportunities. Keep in mind that these requirements may be...

  2. Long Term Forecast ofLong Term Forecast of TsunamisTsunamis

    E-Print Network [OSTI]

    : ImproveImprove NOAANOAA''ss understandingunderstanding and forecast capabilityand forecast capability inin

  3. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2Beamline 2.1 Print2.1

  4. 2.1 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

    E-Print Network [OSTI]

    Iye, Yasuhiro

    - ^ ^ d ^ g ^S ^Sd ^Sg #12;2 10 GaAs/AlGaAs2 - yx xx (SCBA) 2.7 [5] 0 Sxx Sxy ^ 2.10 ^ T " " 2.8 0 0 Sxx S N Sxx = S eN [6] #12;2 11 2.7: SCBA 2.8: #12;12 3 3.1 Coplanar Waveguide, CPW 3.1 3.1: CPW [14] 3

  5. Steam System Forecasting and Management

    E-Print Network [OSTI]

    Mongrue, D. M.; Wittke, D. O.

    1982-01-01T23:59:59.000Z

    '. This and the complex and integrated nature of the plants energy balance makes steam system forecasting and management essential for optimum use of the plant's energy. This paper discusses the method used by Union carbide to accomplish effective forecasting...

  6. Improving Inventory Control Using Forecasting

    E-Print Network [OSTI]

    Balandran, Juan

    2005-12-16T23:59:59.000Z

    EMGT 835 FIELD PROJECT: Improving Inventory Control Using Forecasting By Juan Mario Balandran jmbg@hotmail.com Master of Science The University of Kansas Fall Semester, 2005 An EMGT Field Project report submitted...............................................................................................................................................10 Current Inventory Forecast Process ...........................................................................................10 Development of Alternative Forecast Process...

  7. timber quality Modelling and forecasting

    E-Print Network [OSTI]

    Forest and timber quality in Europe Modelling and forecasting yield and quality in Europe Forest and timber quality in Europe Modelling and forecasting yield and quality in Europe M E F Y Q U E #12;Valuing and the UK ­ are working closely together to develop a model to help forecast timber growth, yield, quality

  8. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

    electricity demand forecast means that the region's electricity needs would grow by 5,343 average megawattsDemand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required in electricity demand is, of course, crucial to determining the need for new electricity resources and helping

  9. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 16 AUGUST 29, 2013

    E-Print Network [OSTI]

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  10. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST, and utilities. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption STAFFFINALREPORT NOVEMBER 2007 CEC-200-2007-015-SF2 Arnold Schwarzenegger, Governor #12;CALIFORNIA ENERGY

  11. 1992 five year battery forecast

    SciTech Connect (OSTI)

    Amistadi, D.

    1992-12-01T23:59:59.000Z

    Five-year trends for automotive and industrial batteries are projected. Topic covered include: SLI shipments; lead consumption; automotive batteries (5-year annual growth rates); industrial batteries (standby power and motive power); estimated average battery life by area/country for 1989; US motor vehicle registrations; replacement battery shipments; potential lead consumption in electric vehicles; BCI recycling rates for lead-acid batteries; US average car/light truck battery life; channels of distribution; replacement battery inventory end July; 2nd US battery shipment forecast.

  12. 4. P1,P2 (P1), (P2) () SUSY()?

    E-Print Network [OSTI]

    P1, P2 1. 2. P1 3. P2 4. P1,P2 (P1), (P2) 11 #12; () =SUSY = () SUSY()? CP CP ...... LHC-ATLASK 22002 2008 #12;P1: I + () Maxwell () (2kV) #12;( · ­ #12;5 2009Lamb shift ­ QEDloop-) ­ 0 Lamb shift Dirac 212p s1 212s QED 232p 212p s1 212s 232p #12; RF H2

  13. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2 Beamline2.1 Print

  14. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2 Beamline2.1

  15. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2 Beamline2.12.1 Print

  16. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2 Beamline2.12.1

  17. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2Beamline 2.1 Print

  18. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2Beamline 2.1

  19. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor4 Print22 Print2.212 Print2.1

  20. Multiagent Bayesian Forecasting of Structural Time-Invariant Dynamic Systems with

    E-Print Network [OSTI]

    Xiang, Yang

    . Alternatively, time series are represented by state-space models, also referred to as multivariate dynamic and science. We study forecasting of stochastic, dynamic systems based on observations from multivariate time to a discrete time, multivariate time series [1, 2]. The primary inference that we address is one- step

  1. Time series modeling and large scale global solar radiation forecasting from geostationary satellites data

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 Time series modeling and large scale global solar radiation forecasting from geostationary global solar radiation. In this paper, we use geostationary satellites data to generate 2-D time series of solar radiation for the next hour. The results presented in this paper relate to a particular territory

  2. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    AMERICAN METEOROLOGICAL SOCIETY Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary and interpretation of information from National Weather Service watches and warnings by10 decision makers such an outlier to the regional severe weather climatology. An analysis of the synoptic and13 mesoscale

  3. Fuel Price Forecasts INTRODUCTION

    E-Print Network [OSTI]

    Fuel Price Forecasts INTRODUCTION Fuel prices affect electricity planning in two primary ways and water heating, and other end-uses as well. Fuel prices also influence electricity supply and price because oil, coal, and natural gas are potential fuels for electricity generation. Natural gas

  4. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

    Quantifying PV power output variability,” Solar Energy, vol.each solar sen at node i, P(t) the total power output of theSolar Forecasting Historically, traditional power generation technologies such as fossil and nu- clear power which were designed to run in stable output

  5. Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa

    E-Print Network [OSTI]

    Revised 1997 Retail Electricity Price Forecast March 1998 Principal Author: Ben Arikawa Electricity 1997 FORE08.DOC Page 1 CALIFORNIA ENERGY COMMISSION ELECTRICITY ANALYSIS OFFICE REVISED 1997 RETAIL ELECTRICITY PRICE FORECAST Introduction The Electricity Analysis Office of the California Energy Commission

  6. Draft for Public Comment Appendix A. Demand Forecast

    E-Print Network [OSTI]

    in the planning process. Electricity demand is forecast to grow from 20,080 average megawatts in 2000 to 25 forecast of electricity demand is a required component of the Council's Northwest Regional Conservation and Electric Power Plan.1 Understanding growth in electricity demand is, of course, crucial to determining

  7. Forecasting Uncertainty Related to Ramps of Wind Power Production

    E-Print Network [OSTI]

    Boyer, Edmond

    - namic reserve quantification [8], for the optimal oper- ation of combined wind-hydro power plants [5, 1Forecasting Uncertainty Related to Ramps of Wind Power Production Arthur Bossavy, Robin Girard - The continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power

  8. Forecasting Building Occupancy Using Sensor Network James Howard

    E-Print Network [OSTI]

    Hoff, William A.

    of the forecasting algorithm for the different conditions. 1. INTRODUCTION According to the U.S. Department of Energy could take advantage of times when electricity cost is lower, to chill a cold water storage tankForecasting Building Occupancy Using Sensor Network Data James Howard Colorado School of Mines

  9. Chapter 2 -History Louden, Chapter 2/Scott, Chapter 1 1

    E-Print Network [OSTI]

    Allan, Vicki H.

    of a language j d d?judged? Louden, Chapter 2/Scott, Chapter 1 2 #12;2 Example of Babylonian "Programming" (p1 Chapter 2 - History Louden, Chapter 2/Scott, Chapter 1 1 Thought questions · When (decade Louden, Chapter 2/Scott, Chapter 1 3 Divide 6 into 120, obtaining 20. Divide 5 into 20, obtaining

  10. Page 1 of 2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn April 23, 2014, an OHA Administrative Judge issuedEnergy1of Page This finalof Page 2

  11. Evaluation of Mixed-Phase Cloud Parameterizations in Short-Range Weather Forecasts with CAM3 and AM2 for Mixed-Phase Arctic Cloud Experiment

    SciTech Connect (OSTI)

    Xie, S; Boyle, J; Klein, S; Liu, X; Ghan, S

    2007-06-01T23:59:59.000Z

    By making use of the in-situ data collected from the recent Atmospheric Radiation Measurement Mixed-Phase Arctic Cloud Experiment, we have tested the mixed-phase cloud parameterizations used in the two major U.S. climate models, the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) and the Geophysical Fluid Dynamics Laboratory climate model (AM2), under both the single-column modeling framework and the U.S. Department of Energy Climate Change Prediction Program-Atmospheric Radiation Measurement Parameterization Testbed. An improved and more physically based cloud microphysical scheme for CAM3 has been also tested. The single-column modeling tests were summarized in the second quarter 2007 Atmospheric Radiation Measurement metric report. In the current report, we document the performance of these microphysical schemes in short-range weather forecasts using the Climate Chagne Prediction Program Atmospheric Radiation Measurement Parameterizaiton Testbest strategy, in which we initialize CAM3 and AM2 with realistic atmospheric states from numerical weather prediction analyses for the period when Mixed-Phase Arctic Cloud Experiment was conducted.

  12. Dominik Jain2 Michael Beetz2 1

    E-Print Network [OSTI]

    1O Dominik Jain2 Michael Beetz2 1 moheo@bi.snu.ac.kr jain@cs.tum.edu michael.beetz of Their Configuration Spaces and a Conversion Method Min-Oh Heo1O Dominik Jain2 Michael Beetz2 Byoung-Tak Zhang1 School

  13. Facebook IPO updated valuation and user forecasting

    E-Print Network [OSTI]

    Facebook IPO ­ updated valuation and user forecasting Based on: Amendment No. 6 to Form S-1 (May 9. Peter Cauwels and Didier Sornette, Quis pendit ipsa pretia: facebook valuation and diagnostic Extreme Growth JPMPaper Cauwels and Sornette 840 1110 1820 S1- filing- May 9 2012 1006 1105 1371 Facebook

  14. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand, EndUser Natural Gas Demand, and Energy Efficiency DECEMBER 2013 CEC2002013004SFV1 CALIFORNIA and expertise of numerous California Energy Commission staff members in the Demand Analysis Office. In addition

  15. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

    2.1.2 European Solar Radiation Atlas (ESRA)for supplementing solar radiation network data,” FinalEstimating incident solar radiation at the surface from geo-

  16. 1 2 1 3 4 3 2 2 5 7

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    for the site identification. èéæçëíæçëíéæèæêç 2.1 Material Data from a nuclear pressure vessel steel similar such as reactor pressure vessels depends on the material resistance against brittle fracture. Cleavage which orientation (L)). It should be noted that the CT specimens were tested without side­grooves. 2.3 Specimen

  17. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 15 SEPTEMBER 28, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  18. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 27 OCTOBER 10, 2013

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  19. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 3 AUGUST 16, 2012

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  20. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 2012

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  1. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 13 SEPTEMBER 26, 2013

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  2. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 18 AUGUST 31, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  3. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2012

    E-Print Network [OSTI]

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  4. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 17 AUGUST 30, 2012

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  5. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 4 AUGUST 17, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  6. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 29 OCTOBER 12, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  7. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 11 SEPTEMBER 24, 2014

    E-Print Network [OSTI]

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  8. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 30 SEPTEMBER 12, 2013

    E-Print Network [OSTI]

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  9. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2011

    E-Print Network [OSTI]

    Birner, Thomas

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the third year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  10. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 28 SEPTEMBER 10, 2014

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  11. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 13 OCTOBER 26, 2010

    E-Print Network [OSTI]

    Gray, William

    with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

  12. Forecast of the electricity consumption by aggregation of specialized experts; application to Slovakian and French

    E-Print Network [OSTI]

    Forecast of the electricity consumption by aggregation of specialized experts; application-term forecast of electricity consumption based on ensemble methods. That is, we use several possibly independent´erieure and CNRS. hal-00484940,version1-19May2010 #12;Forecast of the electricity consumption by aggregation

  13. 2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA

    E-Print Network [OSTI]

    Perez, Richard R.

    2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA J models 1 INTRODUCTION Solar radiation and PV production forecasts are becoming increasingly important/) three teams of experts are benchmarking their solar radiation forecast against ground truth data

  14. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2 Beamline 12.3.222.1

  15. Forecasting oilfield economic performance

    SciTech Connect (OSTI)

    Bradley, M.E. (Univ. of Chicago, IL (United States)); Wood, A.R.O. (BP Exploration, Anchorage, AK (United States))

    1994-11-01T23:59:59.000Z

    This paper presents a general method for forecasting oilfield economic performance that integrates cost data with operational, reservoir, and financial information. Practices are developed for determining economic limits for an oil field and its components. The economic limits of marginal wells and the role of underground competition receive special attention. Also examined is the influence of oil prices on operating costs. Examples illustrate application of these concepts. Categorization of costs for historical tracking and projections is recommended.

  16. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Sripada, Yaji

    for generating textual summaries. Our algorithm has been implemented in a weather forecast generation system. 1 presentation, aid human understanding of the underlying data sets. SUMTIME is a research project aiming turbines. In the domain of meteorology, time series data produced by numerical weather prediction (NWP

  17. Forecasting sudden changes in environmental pollution patterns

    E-Print Network [OSTI]

    Olascoaga, Maria Josefina

    Forecasting sudden changes in environmental pollution patterns María J. Olascoagaa,1 and George of Mexico in 2010. We present a methodology to predict major short-term changes in en- vironmental River's mouth in the Gulf of Mexico. The resulting fire could not be extinguished and the drilling rig

  18. 2.1 Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 . . . . . . . . . . . . . 4

    E-Print Network [OSTI]

    Tanaka, Jiro

    17 #12;Web Web Web Web Navi #12;1 1 2 Web 3 2.1 Web.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2.1 Web.2.3 . . . . . . . . . . . . . . . . . . . . . 15 4.3 Web . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.3.1 Web

  19. ) " (2010: 1. KI + I2 .

    E-Print Network [OSTI]

    Maoz, Shahar

    (. .") (Sr2+ . Sr2+ + CrO4 2- SrCrO4 3.2.)Ca +2()Na2CO3.( .")Ca+2 (. . 4.1.,Na2CO3.CaCO3-SrCO3, Na+ , Cl- , CO3 2- ., .) .( 4.2.,SrCO3-CaCO3,-HCl)CO2( CaCl2-SrCl2.,crown ether ,: 4.3.HCl,tungstosilicic acid,18/L)ppm( ).( . ),,,( . - . ., . - . . . . - , . .,,) ,CaCO3 #12;[CaCO3(s) + CO2(g) + H2O(l) Ca2+ (aq) + 2HCO3 - (aq)](,. . - ,. . , , . : 1. 2.)(Zn+2

  20. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2 Beamline

  1. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2

  2. Beamline 2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor4 Print22 Print2.212

  3. Characterization and Simulation of ECBM: History Matching of Forecasting CO2 Sequestration in Marshal County, West Virginia.

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    . Bromhal, National Energy Technology Laboratory Copyright 2010, Society of Petroleum Engineers This paper injection. Introduction Sustain economic growth by providing sufficient energy and controlling the CO2 emissions as byproduct of producing Energy from fossil fuels is one of the most challenging tasks

  4. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01T23:59:59.000Z

    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.

  5. ELECTRICITY DEMAND FORECAST COMPARISON REPORT

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005.................................................................................................................................3 PACIFIC GAS & ELECTRIC PLANNING AREA ........................................................................................9 Commercial Sector

  6. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

    Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN Manager Joseph O' Hagan Project Manager Kelly Birkinshaw Program Area Manager ENERGY-RELATED ENVIRONMENTAL

  7. 1 2 2 2 3 4 Zuttomo Zuttomo Zuttomo

    E-Print Network [OSTI]

    Tanaka, Jiro

    Friends index screen. Zuttomo Zuttomo GPS 2. Zuttomo Zuttomo 2.1 Zuttomo Facebook Zuttomo Facebook Facebook Zuttomo Facebook Zuttomo 2.2 Zuttomo 2.3 Zuttomo 2.4 Zuttomo 1 2.5 ping ping ping 2 ping ping 3. Zuttomo iOS5 WebAPI 3 3.1 3.1.1 Facebook Facebook Facebook SDK SDK Facebook API Facebook Facebook #12

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    2 2. Annual Energy Outlook (Administration’s Annual Energy Outlook forecasted price (of Energy, Annual Energy Outlook 2004 with Projections to

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

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade Year-0E (2001) -heatingintensityArea: U.S. Eastdieseldiesel

  10. NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    · NATIONAL AND GLOBAL FORECASTS · WEST VIRGINIA PROFILES AND FORECASTS · ENERGY · HEALTHCARE Research West Virginia University College of Business and Economics P.O. Box 6527, Morgantown, WV 26506 EXPERT OPINION PROVIDED BY Keith Burdette Cabinet Secretary West Virginia Department of Commerce

  11. Conservation The Northwest ForecastThe Northwest Forecast

    E-Print Network [OSTI]

    & Resources Creating Mr. Toad's Wild Ride for the PNW's Energy Efficiency InCreating Mr. Toad's Wild RideNorthwest Power and Conservation Council The Northwest ForecastThe Northwest Forecast ­­ Energy EfficiencyEnergy Efficiency Dominates ResourceDominates Resource DevelopmentDevelopment Tom EckmanTom Eckman

  12. A methodology for forecasting carbon dioxide flooding performance

    E-Print Network [OSTI]

    Marroquin Cabrera, Juan Carlos

    1998-01-01T23:59:59.000Z

    A methodology was developed for forecasting carbon dioxide (CO2) flooding performance quickly and reliably. The feasibility of carbon dioxide flooding in the Dollarhide Clearfork "AB" Unit was evaluated using the methodology. This technique is very...

  13. africa conditional forecasts: Topics by E-print Network

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

    forecasts had the potential to improve resource management but instead played only a marginal role in real-world decision making. 1 A widespread perception that the quality of the...

  14. accident risk forecasting: Topics by E-print Network

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

    forecasts had the potential to improve resource management but instead played only a marginal role in real-world decision making. 1 A widespread perception that the quality of the...

  15. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400. Hall Deputy Director Energy Efficiency and Demand Analysis Division Scott W. Matthews Acting Executive

  16. Mathematical Forecasting Donald I. Good

    E-Print Network [OSTI]

    Boyer, Robert Stephen

    Mathematical Forecasting Donald I. Good Technical Report 47 September 1989 Computational Logic Inc the physical behavior of computer programs can reduce these risks for software engineering in the same way that it does for aerospace and other fields of engineering. Present forecasting capabilities for computer

  17. Regional-seasonal weather forecasting

    SciTech Connect (OSTI)

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01T23:59:59.000Z

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  18. Weather Forecast Data an Important Input into Building Management Systems 

    E-Print Network [OSTI]

    Poulin, L.

    2013-01-01T23:59:59.000Z

    GEPS 21 members ? Provides probabilistic forecasts ? Can give useful outlooks for longer term weather forecasts ? Scribe matrix from GDPS ? includes UMOS post processed model data ? Variables like Temperature, humidity post processed by UMOS ? See...://collaboration.cmc.ec.gc.ca/cmc/cmoi/cmc-prob-products/ ? Link to experimental 3-day outlook of REPS quilts ? http://collaboration.cmc.ec.gc.ca/cmc/cmoi/cmc-prob-products.reps Users can also make their own products from ensemble forecast data? Sample ascii matrix of 2m temperature could be fed...

  19. 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.

    2012-09-01T23:59:59.000Z

    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.

  20. U238 (1) (2)

    E-Print Network [OSTI]

    Hong, Deog Ki

    , . . 6.1. 6. 1 . 3.3% (PWR) 33,000MWd/MT 150 . . 50MWe PWR 33,000MWd/MT, 32.5% 0.8 3.3% 27,271 . 26,011 #12; 0.83%. 6. 1 (PWR

  1. Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework

    E-Print Network [OSTI]

    Arumugam, Sankar

    . Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser, forecast consumers, water managers and reservoir operators, have difficulty interpreting such products in a risk management framework A. Sankarasubramanian,1 Upmanu Lall,2 Francisco Assis Souza Filho,3

  2. MPX V1.2

    Energy Science and Technology Software Center (OSTI)

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

  3. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    cooling system simulation in DOE-2.1D was developed with the support and collaboration of the Gas Research

  4. Page 1 of 2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn April 23, 2014, an OHA Administrative Judge issuedEnergy1of Page This finalof Page

  5. Page 1 of 2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn April 23, 2014, an OHA Administrative Judge issuedEnergy1of Page This finalof

  6. Page 1 of 2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn April 23, 2014, an OHA Administrative Judge issuedEnergy1of Page This finalofa new

  7. Page 1 of 2

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn April 23, 2014, an OHA Administrative Judge issuedEnergy1of Page This finalofa

  8. Page 1 of 2

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  9. Page 1 of 2

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  10. Page 1 of 2

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  11. Page 1 of2

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  12. Page 1 of2

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

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  13. A first large-scale flood inundation forecasting model

    SciTech Connect (OSTI)

    Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.

    2013-11-04T23:59:59.000Z

    At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode revealed that it is crucial to account for basin-wide hydrological response time when assessing lead time performances notwithstanding structural limitations in the hydrological model and possibly large inaccuracies in precipitation data.

  14. Page 1 of 2

    Energy Savers [EERE]

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  15. Page 1 of 2

    Energy Savers [EERE]

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  16. Page 1 of2

    Energy Savers [EERE]

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  17. AUTHORS.......................................................................... 1 ACKNOWLEDGMENTS............................................................. 2

    E-Print Network [OSTI]

    of the world's oil consumption,2 and at least 50% of U.S. air pollution is caused by motor vehicle emissions.3 and producer of pollution. A significant amount of this energy is used in the transportation sector has much room for improvement in reducing the energy used and pollution produced by transportation

  18. Award_1_2

    Office of Environmental Management (EM)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 1112011AT&T, Inc.'s ReplyApplication ofTribal Renewable EnergyFuel EconomyPrescribedof

  19. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07T23:59:59.000Z

    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.

  20. 1.2 Solutions

    E-Print Network [OSTI]

    In other words, the graph of the ... coef?cient p0 — (k/r) of the exponential term in Eq. (19). 1f p0 > Mr ..... be other solutions, then perhaps we should continue to search for them. .... Then the tensile force in the rod does not enter the equation.

  1. 1 Introduction 2 2 Preliminary results 4

    E-Print Network [OSTI]

    -torsion in Br(E) was made in the case of a local field k and all pro . . . . . . . . . . . . . . . . . . . . . * *. . . . . . . . . 5 2.4 Torsion, 1999 1 Introduction Let k be a field, char(k) 6= 2, 3, and let X be a smooth projective

  2. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

    Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN with primary contributions in the area of decision support for reservoir planning and management Commission Energy-Related Environmental Research Joseph O' Hagan Contract Manager Joseph O' Hagan Project

  3. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

    Arnold Schwarzenegger Governor INTEGRATED FORECAST AND RESERVOIR MANAGEMENT (INFORM) FOR NORTHERN: California Energy Commission Energy-Related Environmental Research Joseph O' Hagan Contract Manager Joseph O' Hagan Project Manager Kelly Birkinshaw Program Area Manager ENERGY-RELATED ENVIRONMENTAL RESEARCH Martha

  4. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01T23:59:59.000Z

    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.

  5. Weather forecasting : the next generation : the potential use and implementation of ensemble forecasting

    E-Print Network [OSTI]

    Goto, Susumu

    2007-01-01T23:59:59.000Z

    This thesis discusses ensemble forecasting, a promising new weather forecasting technique, from various viewpoints relating not only to its meteorological aspects but also to its user and policy aspects. Ensemble forecasting ...

  6. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    F 1 2 .1E- 8 1 / 3 EXT-FUEL-BTU/HR s - PLANT-ASSIGNMENT . 28 1 / 3 ZIE- 8 1 / 3 PROCESS-CHW-BTU/HR PROCESS-CHW-POWERPROCESS-CHW-SCH PROCESS-HW-BTU/HR s - PLANT-ASSIGNMENT s -

  7. Traffic congestion forecasting model for the INFORM System. Final report

    SciTech Connect (OSTI)

    Azarm, A.; Mughabghab, S.; Stock, D.

    1995-05-01T23:59:59.000Z

    This report describes a computerized traffic forecasting model, developed by Brookhaven National Laboratory (BNL) for a portion of the Long Island INFORM Traffic Corridor. The model has gone through a testing phase, and currently is able to make accurate traffic predictions up to one hour forward in time. The model will eventually take on-line traffic data from the INFORM system roadway sensors and make projections as to future traffic patterns, thus allowing operators at the New York State Department of Transportation (D.O.T.) INFORM Traffic Management Center to more optimally manage traffic. It can also form the basis of a travel information system. The BNL computer model developed for this project is called ATOP for Advanced Traffic Occupancy Prediction. The various modules of the ATOP computer code are currently written in Fortran and run on PC computers (pentium machine) faster than real time for the section of the INFORM corridor under study. The following summarizes the various routines currently contained in the ATOP code: Statistical forecasting of traffic flow and occupancy using historical data for similar days and time (long term knowledge), and the recent information from the past hour (short term knowledge). Estimation of the empirical relationships between traffic flow and occupancy using long and short term information. Mechanistic interpolation using macroscopic traffic models and based on the traffic flow and occupancy forecasted (item-1), and the empirical relationships (item-2) for the specific highway configuration at the time of simulation (construction, lane closure, etc.). Statistical routine for detection and classification of anomalies and their impact on the highway capacity which are fed back to previous items.

  8. Analysis and forecasting of gas well performanc: a rigorous approach using decline curve analysis

    E-Print Network [OSTI]

    Palacio Uran, Juan Carlos

    1993-01-01T23:59:59.000Z

    . . . . . . . . . . . . 146 Normalized Flow Rate Profile versus Material Balance Pseudotimes for Well C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 138 139 6. 35 6, 36 6. 37 A-1 A-2 A-3 149 186 Type Curve Match...ANALYSIS AND FORECASTING OF GAS WELL PERFORMANCE A RIGOROUS APPROACH USING DECLINE CURVE ANALYSIS A Thesis by JUAN CARLOS PALACIO URAN Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment...

  9. PI Research Organisation Project Title NE/J024678/1 Dr Christopher Davis University of Reading Driving space weather forecasts with real data

    E-Print Network [OSTI]

    University of Southampton NE/J021075/1 Where did all the CO2 go? Insights from boron isotopes in deep University of Leeds NE/J02371X/1 Did the Southern Ocean drive deglacial atmospheric CO2 rise?Dr Raja of Leeds NE/J023310/1 Spectrally High resolution Infrared measurements for the characterisation of Volcanic

  10. Optimal combined wind power forecasts using exogeneous variables

    E-Print Network [OSTI]

    Optimal combined wind power forecasts using exogeneous variables Fannar ¨Orn Thordarson Kongens of the thesis is combined wind power forecasts using informations from meteorological forecasts. Lyngby, January

  11. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    GENERATOR Introduction Gas Turbine Steam Turbine SIMULATIONSModes 1: Chillers, Gas Turbine, and Boiler 2: Chillers,O R SIMULATIONS Introduction Gas Turbine Steam Turbine PLANT

  12. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    ECONOMICS LITCHG = = ELECTRICTY = M2 MONTH—CHGS ENERGY—CHGElectric rate (not lights) ELECTRICTY M1 $ Default meter E N

  13. Volatility Forecasts in Financial Time Series with HMM-GARCH Models

    E-Print Network [OSTI]

    Chen, Yiling

    Volatility Forecasts in Financial Time Series with HMM-GARCH Models Xiong-Fei Zhuang and Lai {xfzhuang,lwchan}@cse.cuhk.edu.hk Abstract. Nowadays many researchers use GARCH models to generate of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH

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

    SciTech Connect (OSTI)

    John Zack; Deborah Hanley; Dora Nakafuji

    2012-07-15T23:59:59.000Z

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

  15. 1. P1,P2 (P1), (P2) Relativistic Quantum Field Theory

    E-Print Network [OSTI]

    1. P1,P2 (P1), (P2) 2. 3. P1 4. P2 1 #12;P1 P2 " " Relativistic Quantum Field Theory Dirac 212p s1 212s QED 232p 212p s1 212s 232p #12;7 2009 2010 2S 2010 2011 2012 #12;8 2009 2010://tabletop.icepp.s.u- tokyo.ac.jp/Tabletop_experiments/HFS_measurement_with _quantum_oscillation.html P2(?) P1 #12

  16. Ensemble-based air quality forecasts: A multimodel approach applied to ozone

    E-Print Network [OSTI]

    Boyer, Edmond

    Ensemble-based air quality forecasts: A multimodel approach applied to ozone Vivien Mallet1 21 September 2006. [1] The potential of ensemble techniques to improve ozone forecasts ozone-monitoring networks. We found that several linear combinations of models have the potential

  17. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 2011

    E-Print Network [OSTI]

    Gray, William

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone (TC) activity starting in early August. We have decided to discontinue our individual for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  18. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2011

    E-Print Network [OSTI]

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  19. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2012

    E-Print Network [OSTI]

    Gray, William

    that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

  20. Beamline 3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2Beamline2.1 Print2.1

  1. Forecasting consumer products using prediction markets

    E-Print Network [OSTI]

    Trepte, Kai

    2009-01-01T23:59:59.000Z

    Prediction Markets hold the promise of improving the forecasting process. Research has shown that Prediction Markets can develop more accurate forecasts than polls or experts. Our research concentrated on analyzing Prediction ...

  2. Massachusetts state airport system plan forecasts.

    E-Print Network [OSTI]

    Mathaisel, Dennis F. X.

    This report is a first step toward updating the forecasts contained in the 1973 Massachusetts State System Plan. It begins with a presentation of the forecasting techniques currently available; it surveys and appraises the ...

  3. Management Forecast Quality and Capital Investment Decisions

    E-Print Network [OSTI]

    Goodman, Theodore H.

    Corporate investment decisions require managers to forecast expected future cash flows from potential investments. Although these forecasts are a critical component of successful investing, they are not directly observable ...

  4. Forecast and Funding Arrangements - Hanford Site

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC) Environmental Assessments (EA)Budget(DANCE) Target 1Annual Waste Forecast and Funding

  5. Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations

    E-Print Network [OSTI]

    Kemner, Ken

    forecasting methods and better integration of advanced wind power forecasts into system and plant operations and wind power plants) ­ Review and assess current practices Propose and test new and improved approachesWind Power Forecasting andWind Power Forecasting and Electricity Market Operations Audun Botterud

  6. 1995 shipment review & five year forecast

    SciTech Connect (OSTI)

    Fetherolf, D.J. Jr. [East Penn Manufacturing Co., Inc., Lyon Station, PA (United States)

    1996-01-01T23:59:59.000Z

    This report describes the 1995 battery shipment review and five year forecast for the battery market. Historical data is discussed.

  7. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    Modes 1: Chillers, Gas Turbine, and Boiler 2: Chillers,the operation of gas turbine and diesel engine, etc 10:compression chiller, a gas turbine, and a boiler. Subsequent

  8. Classification of Commodity Price Forecast With Random Forests and Bayesian

    E-Print Network [OSTI]

    de Freitas, Nando

    economy. Commodity prices are key economical20 drivers in the market. Raw products such as oil, gold 15 1 Introduction16 17 1.1 Forecasting the commodities market18 The commodities market focuses of prices in both the short and long-term view25 point to help market participants gage a greater

  9. 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).

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

    SciTech Connect (OSTI)

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

    2011-10-01T23:59:59.000Z

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

  11. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.21.2 Print Center for6.1.2

  12. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.21.2 Print Center for6.1.21.2

  13. Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching

    E-Print Network [OSTI]

    Genton, Marc G.

    Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime at a wind energy site and fits a conditional predictive model for each regime. Geographically dispersed was applied to 2-hour-ahead forecasts of hourly average wind speed near the Stateline wind energy center

  14. Calculator simplifies field production forecasting

    SciTech Connect (OSTI)

    Bixler, B.

    1982-05-01T23:59:59.000Z

    A method of forecasting future field production from an assumed average well production schedule and drilling schedule has been programmed for the HP-41C hand-held programmable computer. No longer must tedious row summations be made by hand for staggered well production schedules. Details of the program are provided.

  15. Arantxa Alonso1, Oriol Batiste1, Edgar Knobloch2 and Isabel Mercader1 1 Introduction

    E-Print Network [OSTI]

    Knobloch, Edgar

    Convectons Arantxa Alonso1, Oriol Batiste1, Edgar Knobloch2 and Isabel Mercader1 1 Introduction of California, Berkeley CA 94720, USA 1 #12;2 Arantxa Alonso1, Oriol Batiste1, Edgar Knobloch2 and Isabel

  16. Renewable Forecast Min-Max2020.xls

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiation Protection Technical s o Freiberge s 3 c/)RenewableRenewable EnergyForecast of

  17. Extendedrange seasonal hurricane forecasts for the North Atlantic with a hybrid dynamicalstatistical model

    E-Print Network [OSTI]

    Webster, Peter J.

    Extendedrange seasonal hurricane forecasts for the North Atlantic with a hybrid 20 September 2010; published 9 November 2010. [1] A hybrid forecast model for seasonal hurricane between the number of seasonal hurricane and the large scale variables from ECMWF hindcasts. The increase

  18. he long-term economic forecast calls for the continuation of the

    E-Print Network [OSTI]

    Hemmers, Oliver

    T he long-term economic forecast calls for the continuation of the economic recovery in 2014 predicts a steady economic recovery for Southern Nevada from 2014 onward. The Las Vegas economy-Term Economic Forecast Figure 1: Total Employment (1990-2050) Source: Center for Business and Economic Research

  19. Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines

    E-Print Network [OSTI]

    Cañizares, Claudio A.

    1 Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines H. In this paper, the MARS technique is applied to forecast the hourly Ontario energy price (HOEP). The MARS models values of the latest pre- dispatch price and demand information, made available by the Ontario

  20. 6.9 A NEW APPROACH TO FIRE WEATHER FORECASTING AT THE TULSA WFO

    E-Print Network [OSTI]

    6.9 A NEW APPROACH TO FIRE WEATHER FORECASTING AT THE TULSA WFO Sarah J. Taylor* and Eric D. Howieson NOAA/National Weather Service Tulsa, Oklahoma 1. INTRODUCTION The modernization of the National then providesthemeteorologistanopportunitytoadjustmodel forecasts for local biases and terrain effects. The Tulsa, Oklahoma WFO has been a test office

  1. ANN-based Short-Term Load Forecasting in Electricity Markets

    E-Print Network [OSTI]

    Cañizares, Claudio A.

    ANN-based Short-Term Load Forecasting in Electricity Markets Hong Chen Claudio A. Ca~nizares Ajit1 Abstract--This paper proposes an Artificial Neu- ral Network (ANN)-based short-term load forecasting, electricity markets, spot prices, Artificial Neural Networks (ANN) I. Introduction Short

  2. Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 Optimization of an artificial neural network dedicated to the multivariate forecasting of daily Ajaccio, France Abstract. This paper presents an application of Artificial Neural Networks (ANNs Artificial Neural Networks (ANNs) which are a popular artificial intelligence technique in the forecasting

  3. THE PREV AIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE; APPLICATIONS

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    THE PREV AIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE Author ABSTRACT Since Summer 2003, the PREV'AIR system has been delivering through the Internet1 daily air quality forecasts over Europe. This is the visible part of a wider collaborative project

  4. M353 Hw 2 (S. Zhang) 1.1, 1.2, 1.4 0. 1.1: 2ab, 4ab, 5

    E-Print Network [OSTI]

    Zhang, Shangyou

    = -0.875 i a, f(a) c, f(c) b, f(b) 0 -1, 0.158 -0.5, -2.47 0, -5 1 -1, 0.158 -0.75, -1.18 -0.5, -2.47 2 -1, 0.158 -0.875 -0.75, -1.18 2. (1.1:5) Find an interval of length one that contains a root. x4 = x3 (r) = 27 ei+1 e2 i M = f (r) 2f (r) = 2 We have a quadratic convergence. For r = 1: f (r) = 0 (m > 1

  5. Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging

    E-Print Network [OSTI]

    Washington at Seattle, University of

    February 24, 2006 1J. McLean Sloughter is Graduate Research Assistant, Adrian E. Raftery is BlumsteinProbabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging J. McLean Sloughter, Adrian E. Raftery and Tilmann Gneiting 1 Department of Statistics, University of Washington

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

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01T23:59:59.000Z

    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.

  7. Beamline 3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2Beamline2.1 Print

  8. Beamline 3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2Beamline2.1

  9. Beamline 3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This Return12.0.13.2Beamline2.12.1 Print

  10. SLM 2/29/2000 WAH 2 May 2001 1WAH 2 May 2001 1

    E-Print Network [OSTI]

    SLM 2/29/2000 WAH 2 May 2001 1WAH 2 May 2001 1 Experimental Evidence of the Effects of Pellet and the DIII-D Experimental Team Burning Plasma Science Workshop II 1-3 May 2001 San Diego, California #12;SLM 2/29/2000 WAH 2 May 2001 2WAH 2 May 2001 2 OverviewOverviewOverview · Pellet enhanced performance

  11. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.2 Beamline5.4.1320.221.2

  12. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.2 Beamline5.4.1320.221.21.2

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

    SciTech Connect (OSTI)

    KETUSKY, EDWARD

    2005-10-31T23:59:59.000Z

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

  14. Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices

    E-Print Network [OSTI]

    Kulkarni, Siddhivinayak

    2009-01-01T23:59:59.000Z

    This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the cr...

  15. Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales

    E-Print Network [OSTI]

    Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales Alec N. Kercheval describe how the histori- cal data can first be GARCH filtered and then used to calibrate parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Data and Stylized Facts . . . . . . . . . . . . . . . . . . . . . . . 16 3.3 GARCH Filter

  16. Development and Deployment of an Advanced Wind Forecasting Technique

    E-Print Network [OSTI]

    Kemner, Ken

    findings. Part 2 addresses how operators of wind power plants and power systems can incorporate advanced the output of advanced wind energy forecasts into decision support models for wind power plant and power and applications of power market simulation models around the world. Argonne's software tools are used extensively

  17. 1 Basic Notions N = {1, 2, . . . }, Z = {0, 1, 2, . . . } Z+ = {0, 1, 2, . . . } = N {0}

    E-Print Network [OSTI]

    Ramakrishnan, Dinakar

    Induction (PMI): A statement P about Z+ is true if (i) P holds for n = 0; and (ii) If P holds for all m problems than hard ones.) 1 #12;Remark: In fact, PMI and WOA are equivalent. Try to show PMI WOA. Theorem: Every n N is uniquely written as n = r i=1 pmi i , with each pi prime and mi > 0. Proof of unique

  18. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.2 Beamline5.4.1320.22

  19. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.2

  20. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.21.2 Print Center for X-Ray

  1. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.21.2 Print Center for

  2. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.23.2.21.2 Print Center

  3. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor4 Print2225.3.2.12.21121.2

  4. , Web . Web Google[1] CLEVER[2] ,

    E-Print Network [OSTI]

    Shirai, Kiyoaki

    Web 1 , Web , Web , Web . Web , Web , Web . , , . , , , . , , 1 . , . , 1 , . , , Web . , , . Web . Google[1] CLEVER[2] , . , 1 , [3]. , . Amitay , HTML , . , , [4]. , Web . , [5]. , HTML Amitay . , , , 1: Web . 2 , · Web . , , 200 . , . , 10 . , . , 1 . , . , . 2.1 1: Web 14 7 386 296 27.6 42.3 #12

  5. Olga Bilenko2 Dmitri Gavrilov1

    E-Print Network [OSTI]

    Luryi, Serge

    Olga Bilenko2 Dmitri Gavrilov1 Boris Gorbovitski2 Vera Gorfinkel1 Michael Gouzman1 Georgy Gudkov1 Vyacheslav Khozikov1 Olga Khozikov1 Olga Kosobokova1 Nadia Lifshitz2 Serge Luryi1 Andrew Stepoukhovitch1 are important for this discussion, here and elsewhere in this paper we present as-received software

  6. StackOverview 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

    E-Print Network [OSTI]

    Tanaka, Jiro

    17 Web #12;Web Web 81% Web Web Web Web StackOverview #12;1 1 2 Web 3 2.2.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Web . . . . . . . . . . . . . . . . . . . . . . 4 . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Web 6 3.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3

  7. Beamline 3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor4 Print22 Print2.21212.1

  8. Scenario Generation for Price Forecasting in Restructured Wholesale Power Markets

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    1 Scenario Generation for Price Forecasting in Restructured Wholesale Power Markets Qun Zhou--In current restructured wholesale power markets, the short length of time series for prices makes are fitted between D&O and wholesale power prices in order to obtain price scenarios for a specified time

  9. Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract

    E-Print Network [OSTI]

    Dalang, Robert C.

    Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract] Nicolas Gast EPFL, IC generation. The use of energy storage compensates to some extent these negative effects; it plays a buffer role between demand and production. We revisit a model of real storage proposed by Bejan et al.[1]. We

  10. URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA

    E-Print Network [OSTI]

    Boyer, Edmond

    Perceptron; Ozone concentration. 1. Introduction Tropospheric ozone is a major air pollution problem, both, Ajaccio, France, e-mail: balu@univ-corse.fr Abstract: Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air

  11. Exploiting weather forecasts for sizing photovoltaic energy bids

    E-Print Network [OSTI]

    Giannitrapani, Antonello

    1 Exploiting weather forecasts for sizing photovoltaic energy bids Antonio Giannitrapani, Simone for a photovoltaic (PV) power producer taking part into a competitive electricity market characterized by financial set from an Italian PV plant. Index Terms--Energy market, bidding strategy, photovoltaic power

  12. Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids

    E-Print Network [OSTI]

    Prasanna, Viktor K.

    1 Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids Yogesh Simmhan, prasanna}@usc.edu I. INTRODUCTION Smart Power Grids exemplify an emerging class of Cyber Physical-on paradigm to support operational needs. Smart Grids are an outcome of instrumentation, such as Phasor

  13. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01T23:59:59.000Z

    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.

  14. 1 Introduction 2 2 Preliminary results 4

    E-Print Network [OSTI]

    ] and [V97] a complete description of the 2-torsion in Br(E) was made in the case of a local field k.4 Torsion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 be a field, char(k) ¡= 2, 3, and let X be a smooth projective geometrically integral curve over k. Assume

  15. 1 Introduction 2 2 Preliminary results 4

    E-Print Network [OSTI]

    ] and [V97] a complete description of the 2­torsion in Br(E) was made in the case of a local field k.4 Torsion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 be a field, char(k) #= 2, 3, and let X be a smooth projective geometrically integral curve over k. Assume

  16. 1 Basic Notions N = {1, 2, . . . }, Z = {0, 1, 2, . . . } # Z+ = {0, 1, 2, . . . } = N # {0}

    E-Print Network [OSTI]

    Ramakrishnan, Dinakar

    Induction (PMI): A statement P about Z+ is true if (i) P holds for n = 0; and (ii) If P holds for all m often apply it to infinte sets; this is accepting the WOA. Lemma: WOA#PMI (for Z+ ). Proof: Suppose problems than hard ones.) 1 #12; Remark: In fact, PMI and WOA are equivalent. Try to show PMI# WOA. Theorem

  17. 1 Algebra 2 2 Algebra 3

    E-Print Network [OSTI]

    May, J. Peter

    and Fourier Analysis 15 15 Probability 16 16 Probability 17 17 Apprentice/Undecided 18 18 Apprentice/Undecided 19 19 Apprentice/Undecided 20 1 #12;1 Algebra Mentors · Saravanan Thiyagarajan (avan

  18. EWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology The Anemos Wind Power Forecasting Platform Technology -

    E-Print Network [OSTI]

    Boyer, Edmond

    the fluctuating output from wind farms into power plant dispatching and energy trading, wind power predictionsEWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology 1 The Anemos Wind Power a professional, flexible platform for operating wind power prediction models, laying the main focus on state

  19. 1 , 230031 2 430074; zhigangzeng@163.com

    E-Print Network [OSTI]

    Hefei Institute of Intelligent Machines

    > ,/|)||(|21 1 * +++ = iij n j iji cbax },,2,1{ li L (8) -+-- = kkj n j kjk cbax /|)||(|21 1 * },,1{ nlk L

  20. Homework (sections 2.1-2.4)

    E-Print Network [OSTI]

    Input—output tables for this function are shown below. The table on the ... stored in glucose or other organic compounds.1 In the process, oxygen is produced.

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

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

    collects data on a variety of physical processes that impact the wind forecasts used by wind farms, system operators and other industry professionals. By having access to...

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

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

    collects data on a variety of physical processes that impact the wind forecasts used by wind farms, system operators and other industry professionals. By having access to...

  3. Solid low-level waste forecasting guide

    SciTech Connect (OSTI)

    Templeton, K.J.; Dirks, L.L.

    1995-03-01T23:59:59.000Z

    Guidance for forecasting solid low-level waste (LLW) on a site-wide basis is described in this document. Forecasting is defined as an approach for collecting information about future waste receipts. The forecasting approach discussed in this document is based solely on hanford`s experience within the last six years. Hanford`s forecasting technique is not a statistical forecast based upon past receipts. Due to waste generator mission changes, startup of new facilities, and waste generator uncertainties, statistical methods have proven to be inadequate for the site. It is recommended that an approach similar to Hanford`s annual forecasting strategy be implemented at each US Department of Energy (DOE) installation to ensure that forecast data are collected in a consistent manner across the DOE complex. Hanford`s forecasting strategy consists of a forecast cycle that can take 12 to 30 months to complete. The duration of the cycle depends on the number of LLW generators and staff experience; however, the duration has been reduced with each new cycle. Several uncertainties are associated with collecting data about future waste receipts. Volume, shipping schedule, and characterization data are often reported as estimates with some level of uncertainty. At Hanford, several methods have been implemented to capture the level of uncertainty. Collection of a maximum and minimum volume range has been implemented as well as questionnaires to assess the relative certainty in the requested data.

  4. Geothermal wells: a forecast of drilling activity

    SciTech Connect (OSTI)

    Brown, G.L.; Mansure, A.J.; Miewald, J.N.

    1981-07-01T23:59:59.000Z

    Numbers and problems for geothermal wells expected to be drilled in the United States between 1981 and 2000 AD are forecasted. The 3800 wells forecasted for major electric power projects (totaling 6 GWe of capacity) are categorized by type (production, etc.), and by location (The Geysers, etc.). 6000 wells are forecasted for direct heat projects (totaling 0.02 Quads per year). Equations are developed for forecasting the number of wells, and data is presented. Drilling and completion problems in The Geysers, The Imperial Valley, Roosevelt Hot Springs, the Valles Caldera, northern Nevada, Klamath Falls, Reno, Alaska, and Pagosa Springs are discussed. Likely areas for near term direct heat projects are identified.

  5. The Value of Wind Power Forecasting

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

    Wind Power Forecasting Preprint Debra Lew and Michael Milligan National Renewable Energy Laboratory Gary Jordan and Richard Piwko GE Energy Presented at the 91 st American...

  6. U-M Construction Forecast December 15, 2011 U-M Construction Forecast

    E-Print Network [OSTI]

    Kamat, Vineet R.

    U-M Construction Forecast December 15, 2011 U-M Construction Forecast Spring ­ Fall 2012 As of December 15, 2011 Prepared by AEC Preliminary & Advisory #12;U-M Construction Forecast December 15, 2011 Overview · Campus by campus · Snapshot in time ­ Not all projects · Construction coordination efforts

  7. Wind Power Forecasting

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del SolStrengtheningWildfires may contribute more to global

  8. Wind Power Forecasting Data

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron SpinPrincetonUsingWhat is abig world of tinyWind Industry

  9. Scuttlebutt Volume 2, No. 1

    E-Print Network [OSTI]

    2008-01-01T23:59:59.000Z

    , of the USS Genesis. To CPT Kieran Bock and his crew, the officers and crew of the Southern Cross send their very best wishes and congratulations, and we wish them fair seas and LOTS of stars to follow. The ScuttleButt Volume 2, Issue 1 5 USS Atlantis... will be forwarded separately to each individual member. Please forward any enquiries to the Intel Officer - ENS Ashley Walker on alwalk78@optusnet.com.au. ENS Ashley Walker Operations Intelligence Officer Welcoming New Crewmembers Savannah Clark Daniel...

  10. Beamline 3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor4 Print22 Print2.2121

  11. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor4 Print2225.3.2.12.2112

  12. 1. Objetivo 2 2. O que o ABNTColeo 2

    E-Print Network [OSTI]

    Floeter, Sergio Ricardo

    . Visualizando os dados da norma 11 7.1 Visualização da norma 11 7.2. Referências 12 7.3. Colaboração 12 7 que há de mais moderno, com base na plataforma Microsoft.NET que oferece, entre outras vantagens período de publicação #12;11 7. Visualizando dados da norma Ao clicar no link aparecerão suas principais

  13. 1-10 nM E2 E2 30 E2

    E-Print Network [OSTI]

    Kawato, Suguru

    076 1. E2 E2 E2 E2 2. E2 E2 2 E2 1 1-10 nM E2 5), 7) E2 30 E2 7) E2 512076-0792011 Modulation of Learning and Memory slowly but also rapidly. Slow actions of estradiol (E2) occur via nuclear receptors (ER), while rapid E2

  14. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

    has developed longterm forecasts of transportation energy demand as well as projected ranges of transportation fuel and crude oil import requirements. The transportation energy demand forecasts makeCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY

  15. Dynamic Filtering and Mining Triggers in Mesoscale Meteorology Forecasting

    E-Print Network [OSTI]

    Plale, Beth

    Dynamic Filtering and Mining Triggers in Mesoscale Meteorology Forecasting Nithya N. Vijayakumar {rramachandran, xli}@itsc.uah.edu Abstract-- Mesoscale meteorology forecasting as a data driven application Triggers, Data Mining, Stream Processing, Meteorology Forecasting I. INTRODUCTION Mesoscale meteorologists

  16. Steam System Forecasting and Management 

    E-Print Network [OSTI]

    Mongrue, D. M.; Wittke, D. O.

    1982-01-01T23:59:59.000Z

    process unit is not operating (down) is specified. If the process will be operating for the entire period, the word "LP" is displayed. PRODUCTION SCHEDULE 2/20/92 - 5/20/82 UNIT 1 A UP 2 B UP D 2./20-3./15 D 4.19. TO- 3 C D 2./20-3./15 D 4..../9. TO- 4 D 5 E D 4./15 TO- UP 6 F D 2./20-2./21 7 G UP 9 I UP 8 H D 2./20-3./13 D 3./27 TO- 10 J 11 K D 2./20 TO- 12 L UP 13 M UP UP 15 0 14 N UP D 2./20-2./24 D 3./7.-3./17 D 3./28 TO- 16 P 17 G D 4./1. TO- 19 R II 3...

  17. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    modeling framework of the Residential End-Use Energy Plamiing System (REEPS) developed for the Electric

  18. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    US DOE. 1995a. Annual Energy Outlook 1995, with ProjectionsAdministration (ELA) 1995 Annual Energy Outlook (AEO); 1990of Energy's Annual Energy Outlook ( US DOE 1995a). A l l

  19. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    kBtuh Output (4) Non-linear regression curve based on theprice-size data and a non-linear regression for the price-provided for the non-linear regression because unlike linear

  20. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    of electric or gas water heater EFFIC Average householdfreezers, clothes dryers, water heaters, clothes washers,Freezers Refrigerators Water Heaters Dishwashers Clothes

  1. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    E3c: Gas Boilers Installed Price by Size and Efficiency AFUEE3b: Gas Boiler Installed Price by Efficiency Figure E-3a:E.4c: Oil Boiler Installed Price by Size and Efficiency AFUE

  2. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    Description Prices for oil, gas, electricity, liquidElectric Electric Electric Gas Oil Electric ElectricElectric Gas Electric Gas Oil Electric Electric Gas Oil

  3. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    Dubin, Rivers Associates. EIA. 1989. Housing CharacteristicsU.S. Dept. of Energy, Washington, DC. DOE/EIA- 0314(87).May. EIA. 1990. Energy Consumption and Conservation

  4. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    30% of electricity consumption, 70% of natural gas consumption and 90% of oil consumption in the U by the Electric Power Research Institute (McMenamin et al. 1992). This modeling framework treats space consumption in residences (EIA 1993). This report is primarily methodological in nature, taking the reader

  5. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    Type Equipment Market Shares Index Heating ElecFurnace GasType Equipment Market Shares Index Heating Elec Furnace Gas

  6. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    Manufactured Home Room Heating Market Shares". Lawrenceset based on the market share of heating equipment in newMarket for Energy Efficiency in Residential Appliances Including Heating and

  7. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    technologies. The heating technologies are: natural gasThe combination of a heating technology, cooling technologyCharacteristics End-Use Heating Technology Efficiency Units

  8. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    there are 10 primary heating technologies and two primaryThe combination of a heating technology, cooling technology,defined by a heating technology, cooling technology, and

  9. Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    was developed by the Electric Power Research Institute (McMenamin et al. 1992). In this modeling framework the modeling framework of the Residential End-Use Energy Planning System (REEPS) developed for the Electric provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which

  10. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    of contractors in the HVAC market will certainly have anational version of the HVAC market share decision model,equipment 4.5. HVAC Equipment Market Shares We now define

  11. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    HVAC equipment as constrained by efficiency standards and marketand HVAC equipment as a result of the market; accounts foror HVAC system (by fuel type). New home market shares data

  12. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    2010, while electricity demand is projected to grow at aboutrates. Electricity demand is projected to grow at about 0.7%

  13. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    residential home heating equipment, depending on product class and size. Figure E.6b: Electric Heat Pump

  14. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    equipment (EIA 1993), and a compilation of utility surveySurvey (RECS) conducted by the Energy Information Administration (EIAMay. EIA. 1993. 1990 Residential Energy Consumption Survey (

  15. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    assessing future trends in energy consumption at the end-usedetermine the basic trend of energy consumption and are used

  16. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    ~ unitary central air and heat pumps — and secondary ~ roomSystem MH SF MF Central Air Heat Pump No Central Air Source:MF SSF LSF North Central Air Heat Pump No Central Air South

  17. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    non-central residential home heating equipment (GAMA1992). (AFUE for residential home heating equipment, depending onManufactured Home Room Heating Market Shares". Lawrence

  18. Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01T23:59:59.000Z

    $/household 10e3 Site Energy Prices Electricity ElectricityAverage electricity price Average household disposableAverage price of electricity Average household disposable

  19. Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01T23:59:59.000Z

    Central Air, Fuels = Oil and Gas, Other = LPG and Misc. (3)Central Air, Fuels = Oil and Gas, LPG and Misc. (3) Sources:Central Air, Fuels = Oil and Gas, Other = LPG and Misc. (3)

  20. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluating A PotentialJumpGermanFife Energy Park atFisiaFlorida:Forecast Energy Jump to:

  1. Section 2: Biomechanics 1 Section 2: Biomechanics

    E-Print Network [OSTI]

    Kohlenbach, Ulrich

    Ricken S1|03­23 Multiphasic Modelling of Human Brain Tissue for Intracranial Drug Infusion Studies Arndt Wagner, Wolfgang Ehlers (Universität Stuttgart) A direct intracranial infusion of a therapeutic solution allows the computational study of several conditions influencing the irregular distribution of infused

  2. Export Control Certification Form J-1, B-1,B-2, B-1/B-2 Visas

    E-Print Network [OSTI]

    Howitt, Ivan

    Export Control Certification Form J-1, B-1,B-2, B-1/B-2 Visas v.082014 Page 1 of 3 EXPORT CONTROL the information available on the export control website before answering the below questions in advance of preparing the visa petition. Certification must be made by the University's Export Control Officer

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

    Office of Environmental Management (EM)

    The Wind Forecast Improvement Project (WFIP): A PublicPrivate Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations The...

  4. Forecasting of Solar Radiation Detlev Heinemann, Elke Lorenz, Marco Girodo

    E-Print Network [OSTI]

    Heinemann, Detlev

    Forecasting of Solar Radiation Detlev Heinemann, Elke Lorenz, Marco Girodo Oldenburg University have been presented more than twenty years ago (Jensenius, 1981), when daily solar radiation forecasts

  5. Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01T23:59:59.000Z

    and validation.   Solar Energy.   73:5, 307? Perez, R. , irradiance forecasts for solar energy applications based on using satellite data.   Solar Energy 67:1?3, 139?150.  

  6. 3 DMC 1 -4 DMC 2 -

    E-Print Network [OSTI]

    Hong, Deog Ki

    . 5. 8 1. 2. Modeling of Reactive Distillation Column 3. Simulation Results of RD system for DMC-phosgene process. [ 2-1 ] (Stoichiometric lines) [ 2-2 ] (Distillation lines) [ 2-3 ] [ 2-4 ] a + b 2c control PI control [ 8-15 ] Feed ratio control PI control [ 8-16 ] MPC #12;[ 8

  7. A NEW APPROACH FOR EVALUATING ECONOMIC FORECASTS

    E-Print Network [OSTI]

    Vertes, Akos

    APPROACH FOR EVALUATING ECONOMIC FORECASTS Tara M. Sinclair , H.O. Stekler, and Warren Carnow Department of Economics The George Washington University Monroe Hall #340 2115 G Street NW Washington, DC 20052 JEL Codes, Mahalanobis Distance Abstract This paper presents a new approach to evaluating multiple economic forecasts

  8. Dynamic Algorithm for Space Weather Forecasting System

    E-Print Network [OSTI]

    Fischer, Luke D.

    2011-08-08T23:59:59.000Z

    /effective forecasts, and we have performed preliminary benchmarks on this algorithm. The preliminary benchmarks yield surprisingly effective results thus far?forecasts have been made 8-16 hours into the future with significant magnitude and trend accuracy, which is a...

  9. memphis.edu/law A 2 0 1 2 -2 0 1 3

    E-Print Network [OSTI]

    Dasgupta, Dipankar

    memphis.edu/law A 2 0 1 2 - 2 0 1 3 #12;B www.memphis.edu/law The University of Memphis Cecil C are considering the Cecil C. Humphreys School of Law as you plan for a legal career. We are building upon our 3 The Cecil C. Humphreys School of Law: Preparation for Tomorrow The Cecil C. Humphreys School

  10. Vo l . 4 6 , N o s . 2 & 3 , 2 0 1 3 c o n t e n t s

    E-Print Network [OSTI]

    Pennycook, Steve

    potential f e a t u r e s 2 Multi-faceted forecasting 6 3D printing rises to the occasion 10 Tag-team R&D 12

  11. Isothermal vapor-liquid equilibria for 2-methyl-2-butanol + 2-methyl-1-butanol + 1-pentanol

    SciTech Connect (OSTI)

    Aucejo, A.; Burguet, M.C.; Monton, J.B.; Munoz, R.; Sanchotello, M.; Vazquez, M.I. (Univ. of Valencia (Spain). Dept. de Ingenieria Quimica)

    1994-07-01T23:59:59.000Z

    Vapor-liquid equilibria (VLE) for 2-methyl-2-butanol + 2-methyl-1-butanol and 2-methyl-2-butanol + 2-methyl-1-butanol + 1-pentanol have been measured at 373.15 K. The binary VLE results have been correlated by different liquid-phase activity coefficient models. The binary interaction parameters obtained from Wilson, NRTL, and UNIQUAC models in this and a previously study are used to predict the VLE data for the ternary system. Vapor-liquid equilibrium (VLE) data are necessary for the design of distillation processes.

  12. Pressure Normalization of Production Rates Improves Forecasting Results

    E-Print Network [OSTI]

    Lacayo Ortiz, Juan Manuel

    2013-08-07T23:59:59.000Z

    reservoir conditions, psi 2/cp ?wf Pseudopressure at flowing conditions, psi 2/cp ? Characteristic time parameter for SEPD model, D ?g Gas viscosity, cp ?o Oil viscosity, cp Acronyms BDF Boundary-Dominated Flow DCA Decline Curve Analysis EUR..., as the advanced analytical and numerical models depend on copious inputs, there is a high probability that different combinations of those parameters could generate equivalent and acceptable history matches, but different production forecasts and EUR...

  13. 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. HIGH-ACCURACY LASER POWER AND ENERGY METER CALIBRATION SYSTEM . . . . . . . . 2

    E-Print Network [OSTI]

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. HIGH-ACCURACY LASER POWER AND ENERGY METER CALIBRATION SYSTEM . . . . . . . . 2 2.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1 High-Accuracy Calibration System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 Basic cryogenic radiometer operating principal

  14. SLM 2/29/2000 WAH 1 May 2001 1WAH 1 May 2001 1

    E-Print Network [OSTI]

    SLM 2/29/2000 WAH 1 May 2001 1WAH 1 May 2001 1 Effects of Pellet Injection on Density Profiles of FIRE W. A. Houlberg ORNL Burning Plasma Science Workshop II 1-3 May 2001 San Diego, California #12;SLM 2/29/2000 WAH 1 May 2001 2WAH 1 May 2001 2 High Field Side (HFS 45°) Pellet Injection on DIII

  15. 1 function fe2d_r 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    E-Print Network [OSTI]

    Garvie, Marcus R

    _ki = triangle_area*s1/(h3*h1); 100 K_ik = K_ki; 101 K_kj = triangle_area*s2/(h3*h2); 102 K_jk = K_kj; 103 K

  16. 1 function fe2dx_d 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    E-Print Network [OSTI]

    Garvie, Marcus R

    _ki = triangle_area*s1/(h3*h1); 100 K_ik = K_ki; 101 K_kj = triangle_area*s2/(h3*h2); 102 K_jk = K_kj; 103 K

  17. 1 function fe2d_n 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    E-Print Network [OSTI]

    Garvie, Marcus R

    _ki = triangle_area*s1/(h3*h1); 98 K_ik = K_ki; 99 K_kj = triangle_area*s2/(h3*h2); 100 K_jk = K_kj; 101 K

  18. 1 function fe2dx_n 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    E-Print Network [OSTI]

    Garvie, Marcus R

    _ki = triangle_area*s1/(h3*h1); 98 K_ik = K_ki; 99 K_kj = triangle_area*s2/(h3*h2); 100 K_jk = K_kj; 101 K

  19. 1 function fe2dx_r 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    E-Print Network [OSTI]

    Garvie, Marcus R

    _ki = triangle_area*s1/(h3*h1); 100 K_ik = K_ki; 101 K_kj = triangle_area*s2/(h3*h2); 102 K_jk = K_kj; 103 K

  20. Rev. 1 Page 1 of 2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiation Protection TechnicalResonant Soft X-Ray ScatteringBenefits »Reuben Sarkar

  1. Beispiel 19. Sei v L2([-1, 1]) eine verrauschte Funktion. Sei u eine entrauschte Abschatzung von v mit u L2([-1, 1]) und u L2([-1, 1]), die durch die Minimierung von

    E-Print Network [OSTI]

    Keeling, Stephen L.

    , da die folgende Gleichung gilt: J(u)(a1w1 + a2w2) = 2 +1 -1 {[a1w1(x) + a2w2(x)][u(x) - v(x)] + µ[a1w

  2. SLM 2/29/2000 WAH 2 May 2001 1WAH 2 May 2001 1

    E-Print Network [OSTI]

    .8 1.9 2.0 Time (s) Ne-lineavg 10 6 0 0.0 0.2 0.1 4 Lower Divertor (PHD03) D-alpha LFS 760 m/s V+1 474 m/s 2 0.3 HFS 45 169 m/s DIII-D 99474 H-mode 9.5 MW 0.4 8 2.11.7 Direct Comparison in H-mode - HFS Injection on ELMs Experimental Evidence of the EffectsExperimental Evidence of the Effects of Pellet

  3. An analysis of winter precipitation in the northeast and a winter weather precipitation type forecasting tool for New York City 

    E-Print Network [OSTI]

    Gordon, Christopher James

    1999-01-01T23:59:59.000Z

    1's accuracy in forecasting frozen precipitation. . . . 60 23 25 Same as FIG. 22 except for model 2 . . . . Same as FIG. 22 except for model 3 . . . . Same as FIG. 22 except for model 4 . . . . 61 62 26 Histogram of responses for snow cases... to the logistic regression analysis of snow cases versus rain cases for model 1. 64 FIGURE Page 27 Histogram of responses for rain cases to the logistic regression analysis of snow cases versus rain cases for model 1 28 Histogram of responses for snow cases...

  4. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST

    E-Print Network [OSTI]

    procurement process at the California Public Utilities Commission. This forecast was produced with the Energy Commission demand forecast models. Both the staff draft energy consumption and peak forecasts are slightly and commercial sectors. Keywords Electricity demand, electricity consumption, demand forecast, weather

  5. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    and water pumping sectors. Mark Ciminelli forecasted energy for transportation, communication and utilities. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data at the California Public Utilities Commission. This forecast was produced with the Energy Commission demand forecast

  6. Distribution of Wind Power Forecasting Errors from Operational Systems (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Ela, E.; Milligan, M.

    2011-10-01T23:59:59.000Z

    This presentation offers new data and statistical analysis of wind power forecasting errors in operational systems.

  7. 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.

    2013-10-01T23:59:59.000Z

    This presentation proposes a suite of metrics for evaluating the performance of solar power forecasting.

  8. PSO (FU 2101) Ensemble-forecasts for wind power

    E-Print Network [OSTI]

    PSO (FU 2101) Ensemble-forecasts for wind power Analysis of the Results of an On-line Wind Power Ensemble- forecasts for wind power (FU2101) a demo-application producing quantile forecasts of wind power correct) quantile forecasts of the wind power production are generated by the application. However

  9. 1993 Solid Waste Reference Forecast Summary

    SciTech Connect (OSTI)

    Valero, O.J.; Blackburn, C.L. [Westinghouse Hanford Co., Richland, WA (United States); Kaae, P.S.; Armacost, L.L.; Garrett, S.M.K. [Pacific Northwest Lab., Richland, WA (United States)

    1993-08-01T23:59:59.000Z

    This report, which updates WHC-EP-0567, 1992 Solid Waste Reference Forecast Summary, (WHC 1992) forecasts the volumes of solid wastes to be generated or received at the US Department of Energy Hanford Site during the 30-year period from FY 1993 through FY 2022. The data used in this document were collected from Westinghouse Hanford Company forecasts as well as from surveys of waste generators at other US Department of Energy sites who are now shipping or plan to ship solid wastes to the Hanford Site for disposal. These wastes include low-level and low-level mixed waste, transuranic and transuranic mixed waste, and nonradioactive hazardous waste.

  10. table1.2_02

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Building Floorspace (Square Feet) 1,001 to 5,00064,7834)NewHeat Pumps Furnaces12

  11. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor41 Print0.1 Print1 Print1

  12. ,Jet)TE( 0 0.5 1 1.5 2 2.5 3

    E-Print Network [OSTI]

    Quigg, Chris

    #12;1b _ 1b g g g 0 1 0 1 _ _ b b b b _ q q #12;#12;#12;#12;,Jet)TE( 0 0.5 1 1.5 2 2.5 3 TaggedJets 0 5 10 15 20 ,Jet)TE( 0 0.5 1 1.5 2 2.5 3 TaggedJets 0 5 10 15 20 Data QCD-multijet Top W/Z+jets,Diboson -1 CDF Run II Preliminary, 156pb b1b ~ Search for Gluino Jet) st ,1TE( 0 0.5 1 1.5 2 2.5 3 Tagged

  13. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    version added HERM-CENT-COND-PWR HERM-CENT-COND-TYPE P -QUAD V 4 , 3 HERM-REC-COND-PWR P - PLANT-PARAMETERS V 2 . .FT DEFROST-CAP-FT DEFROST-PWR-FT HPDefrst HPDefrst HPDefrst

  14. DOE-2, BDL summary. Version 2.1E

    SciTech Connect (OSTI)

    Winkelmann, F.C.; Birdsall, B.E.; Buhl, W.F.; Ellington, K.L.; Erdem, A.E. [Lawrence Berkeley Lab., CA (United States); Hirsch, J.J.; Gates, S. [Hirsch & Associates, Camarillo, CA (United States)

    1993-11-01T23:59:59.000Z

    This document contains summary information on all commands and keywords in the DOE-2 Building Description Language (BDL). It also contains supplementary tables and maps. The fundamentals of BDL are discussed in Chapter II of the Reference Manual (2.1A); detailed descriptions of the commands and keywords summarized here can be found in the Reference Manual (2.1A) and in the Supplement (2.1E).

  15. Volume 68, Numbers 1&2 (Complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2010-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2010 Vol. 68 Nos. 1&2 Including THE NEO-LATIN NEWS Vol. 58, Nos. 1&2 Seventeenth-Century newS VOLUME 68, Nos. 1&2 SPRING-SUMMER, 2010 SCN... of the Lake University E. Joe Johnson, Clayton State University EDITORIAL ASSISTANTS Jacob Schornick, Texas A&M University contents volume 68, nos. 1&2 ............................... spring-summer, 2010 John Considine, Dictionaries in Early Modern Europe...

  16. Volume 60, Numbers 1&2 (Complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2002-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2002 Vol. 60 Nos. 1&2 budleafswbudleafse Including THE NEO-LATIN NEWS Vol. 50, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 60, Nos. 1&2 SPRING-SUMMER, 2002 SCN, an official organ of the Milton Society... Meter, Texas A&M University CONTENTS VOLUME 60, Nos. 1&2 SPRING-SUMMER, 2002 REVIEWS ?Vocation Homily, George Herbert, and the Cultural Criticism: A Review Essay? by JONATHAN NAUMAN...

  17. E=1/2Mv2 Pf Pb(Pf>Pb)

    E-Print Network [OSTI]

    Hong, Deog Ki

    /2Mv2 , ( 3.1). 3.1 UF6 , Pf Pb(Pf>Pb) . i J2 = V1P1f V2P2f = M2 M1 1/2 x (1-x) . = y/(1-y) x/(1-x) = (M2/M1)1/2 UF6 = (M2/M1)1/2 =(238 UF6/235 UF6)1/2 = (352/349)1/2 = 1.00429 1 . 5% 900

  18. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.11 Print1

  19. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.112.1

  20. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor41 Print0.1 Print1 Print

  1. Section D. Anisotropic rock physics and related studies D4-1 GEMS: the opportunity for stress-forecasting all damaging earthquakes

    E-Print Network [OSTI]

    geophysical instruments; and a better understanding of the evolution of the interior of the solid Earth. Key. The largest earthquakes, such as the 2004, Mw = 9.2, Sumatra-Andaman earthquake, whose tsunami killed over 250

  2. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    supervised data preparation. Steven Mac and Keith O'Brien prepared the historical energy consumption data. Nahid Movassagh forecasted consumption for the agriculture and water pumping sectors. Cynthia Rogers generation, conservation, energy efficiency, climate zone, investorowned, public, utilities, additional

  3. Wind Speed Forecasting for Power System Operation 

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

    In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system...

  4. STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES 2005 TO 2018 Mignon Marks Principal Author Mignon Marks Project Manager David Ashuckian Manager ELECTRICITY ANALYSIS OFFICE Sylvia Bender Acting Deputy Director ELECTRICITY SUPPLY DIVISION B.B. Blevins Executive Director

  5. Wind Speed Forecasting for Power System Operation

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

    In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system...

  6. Potential Economic Value of Seasonal Hurricane Forecasts

    E-Print Network [OSTI]

    Emanuel, Kerry Andrew

    This paper explores the potential utility of seasonal Atlantic hurricane forecasts to a hypothetical property insurance firm whose insured properties are broadly distributed along the U.S. Gulf and East Coasts. Using a ...

  7. Text-Alternative Version LED Lighting Forecast

    Broader source: Energy.gov [DOE]

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

  8. Essays in International Macroeconomics and Forecasting

    E-Print Network [OSTI]

    Bejarano Rojas, Jesus Antonio

    2012-10-19T23:59:59.000Z

    This dissertation contains three essays in international macroeconomics and financial time series forecasting. In the first essay, I show, numerically, that a two-country New-Keynesian Sticky Prices model, driven by monetary and productivity shocks...

  9. Video Textures Arno Schodl1,2

    E-Print Network [OSTI]

    Meenakshisundaram, Gopi

    Video Textures Arno Sch¨odl1,2 Richard Szeliski2 David H. Salesin2,3 Irfan Essa1 1 Georgia type of medium, called a video texture, which has qualities somewhere between those of a photograph and a video. A video texture provides a continuous infinitely varying stream of images. While the individual

  10. 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

    2011-09-13T23:59:59.000Z

    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.

  11. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    ECONO-LOW-LIMIT and ECONO-LOCKOUT, described below. FIXED be2. i E Update ECONO—LOCKOUT accepts code-words Y E S and NL B — L I M I T = 78 ECONO-LOCKOUT =YES M I N — H G B — R A

  12. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    I T Y - R A T E Command Cogeneration B L O C K - C H A R G Econsultant for the P l a n t Cogeneration a n d t h e R e fa n d chillers for cogeneration, (2) simpler f u n c t i o n

  13. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.11 Print

  14. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.11 Print11

  15. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.11 Print111

  16. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.11 Print1111

  17. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.11

  18. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.112.11

  19. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.112.111

  20. Beamline 8.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like3.3 Print Small- and0.1 Print0.112.1111

  1. Jacob D. Jaffe1, 2 Howard C. Berg2, 3

    E-Print Network [OSTI]

    Church, George M.

    Jacob D. Jaffe1, 2 Howard C. Berg2, 3 George M. Church1 1 Harvard Medical School Dept. of Genetics. Using recent advances in prote- omics, we set out to predict the set of ORFs for an organism based direct molecular evidence of the existence of the protein in the living cell. As well, advances

  2. Structure and Dynamics of CO2 on Rutile TiO2(110)-1×1....

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

    Structure and Dynamics of CO2 on Rutile TiO2(110)-1×1. Abstract: Adsorption, binding, and diffusion of CO2 molecules on rutile TiO2(110) model surfaces was...

  3. Comparison of AEO 2009 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01T23:59:59.000Z

    gas price forecasts with contemporaneous natural gas pricesreference-case natural gas price forecast, and that have notof AEO 2009 Natural Gas Price Forecast to NYMEX Futures

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    Gas Price Forecast W ith natural gas prices significantlyof AEO 2006 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEO

  5. Comparing Price Forecast Accuracy of Natural Gas Models and Futures Markets

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    to accurately forecast natural gas prices. Many policyseek alternative methods to forecast natural gas prices. Thethe accuracy of forecasts for natural gas prices as reported

  6. Comparison of AEO 2008 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01T23:59:59.000Z

    gas price forecasts with contemporaneous natural gas pricesreference-case natural gas price forecast, and that have notof AEO 2008 Natural Gas Price Forecast to NYMEX Futures

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

    the base-case natural gas price forecast, but to alsogas price forecasts with contemporaneous natural gas pricesof AEO 2010 Natural Gas Price Forecast to NYMEX Futures

  8. Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX Futures Prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01T23:59:59.000Z

    Natural Gas Price Forecast Although natural gas prices areof AEO 2007 Natural Gas Price Forecast to NYMEX Futurescase long-term natural gas price forecasts from the AEO

  9. Alcohol Services Category #1 # Permit Applica2on Category #2

    E-Print Network [OSTI]

    Powers, Robert

    Alcohol Services Category #1 # Permit Applica2on Category #2 Category #3 If an outdoor event, a;ach UNL Police approved licensed area plan to Permit Request ADMINISTRATOR TO OBTAIN THE REQUIRED ALCOHOL SERVICES PERMIT FOR EACH EVENT

  10. 0 0.5 1 1.5 2 2.5 3 Normalizedreactivity

    E-Print Network [OSTI]

    Snyder, Robin

    0 0.2 0.4 0.6 0.8 1 0 0.5 1 1.5 2 2.5 3 Normalizedreactivity Mean Jacobian element size u.b. l;-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Reactivity Mean Jacobian element size

  11. TTW 2-1-10

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over Our InstagramStructureProposedPAGE Creating a Geologic Play-13, 2004OUR5,0,

  12. Beamline 6.1.2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplex historian ...BESFor4

  13. Ramp Forecasting Performance from Improved Short-Term Wind Power Forecasting: Preprint

    SciTech Connect (OSTI)

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

    2014-05-01T23:59:59.000Z

    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.

  14. Radiation in (2+1)-dimensions

    E-Print Network [OSTI]

    Mauricio Cataldo; Alberto A. García

    2014-05-15T23:59:59.000Z

    In this paper we discuss the radiation equation of state $p=\\rho/2$ in (2+1)-dimensions. In (3+1)-dimensions the equation of state $p=\\rho/3$ may be used to describe either actual electromagnetic radiation (photons) as well as a gas of massless particles in a thermodynamic equilibrium (for example neutrinos). In this work it is shown that in the framework of (2+1)-dimensional Maxwell electrodynamics the radiation law $p=\\rho/2$ takes place only for plane waves, i.e. for $E = B$. Instead of the linear Maxwell electrodynamics, to derive the (2+1)-radiation law for more general cases with $E \

  15. Certified Bitcoins Giuseppe Ateniese1,2

    E-Print Network [OSTI]

    Certified Bitcoins Giuseppe Ateniese1,2 , Antonio Faonio1 , Bernardo Magri1 , and Breno de Medeiros University, USA ateniese@cs.jhu.edu 3 Google, Inc. breno@google.com Abstract. Bitcoin is a peer-to-peer (p2p (called the Blockchain). A critical component of Bitcoin's success is the decentralized nature of its

  16. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01T23:59:59.000Z

    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. 4. P1,P2 (P1), (P2) 2009Lamb shift

    E-Print Network [OSTI]

    1. 2. P1 3. P2 4. P1,P2 (P1), (P2) 1 #12; =SUSY = () SUSY()? CP CP ...... LHC () 2011 2012 #12; #12;2009Lamb shift QEDloop-) 0 6 Lamb shift Dirac 212p s1 212s QED 232p 212p s1 212s 232p #12;7 2009 W M o Cu #12;8 2010 () #12;9 2011 9 RF #12; RF H2 H+H 1S+e 2S1

  18. Section 1.2 of Penney

    E-Print Network [OSTI]

    (In the battery symbol, positive is the larger line.) 1.2.2 Applications to Circuit Theory. Electricity is a kind of energy created by the ?ow of electrons. The force of

  19. Volume 67, Numbers 1 & 2 (Complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2009-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2009 Vol. 67 Nos. 1&2 Including THE NEO-LATIN NEWS Vol. 57, Nos. 1&2 Seventeenth-Century newS VOLUME 67, Nos. 1&2 SPRING-SUMMER, 2009 SCN... that the emergence of regional natural histories in Europe was a product of the rapidly changing commercial and political landscape of the early modern period. Based on a compelling analysis of a vast array of natural history texts, manuscripts...

  20. CLIM 2.2 User Guide 1 -1 version 2.2

    E-Print Network [OSTI]

    Wilkins, David E.

    as specified in DOD FAR 52.227- 7013 (c) (1) (ii). Allegro CL and Allegro Composer are registered trademarks of Franz inc. Allegro Common Windows, Allegro Presto, Allegro Runtime, and Allegro Matrix are trademarks of Franz inc. Unix is a trademark of AT&T. The Allegro CL software as provided may contain material

  1. Volume 62, Numbers 1&2 (Complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2004-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2004 Vol. 62 Nos. 1&2 budleafswbudleafse Including THE NEO-LATIN NEWS Vol. 52, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 62, Nos. 1&2 SPRING-SUMMER, 2004 SCN, an official organ of the Milton Society... ASSISTANTS Christopher E. Garrett, Texas A&M University Christopher L. Morrow, Texas A&M University Larry A. Van Meter, Texas A&M University CONTENTS VOLUME 62, NOS. 1&2 SPRING-SUMMER, 2004 REVIEWS D. Loewenstein and J. Mueller, eds., The Cambridge History...

  2. Volume 63, Numbers 1&2 (Complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2005-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2005 Vol. 63 Nos. 1&2 Including THE NEO-LATIN NEWS Vol. 53, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 63, Nos. 1&2 SPRING-SUMMER, 2005 SCN, an official organ of the Milton Society of America... Christopher E. Garrett, Texas A&M University Michael Mattair, Texas A&M University CONTENTS VOLUME 63, NOS. 1&2 SPRING-SUMMER, 2005 REVIEWS William A. Dyrness, Reformed Theology and Visual Culture: the Protestant Imagination from Calvin to Edwards. Review...

  3. Substituted 1,2-azaborine heterocycles

    DOE Patents [OSTI]

    Liu, Shih-Yuan; Lamm, Ashley

    2014-12-30T23:59:59.000Z

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

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

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

    2 Even while Building 9201-1 (Alpha 1) was being constructed, the first experimental Alpha unit (XAX) in Building 9731 successfully operated, after a week of start up efforts, on...

  5. 2.1E BDL Summary

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01T23:59:59.000Z

    TOWERAIR) ' OPEN-CENT-COND-PWR(0.3;0.0 to 1.0 Btu/Btu) •changed i n 2.1E OPEN-REC-COND-PWR(0.03;0.0 to 1.0Btu/Btu) HERM-CENT-COND-PWR(0.3;0.0 to 1.0 Btu/Btu) •

  6. Chapter 1 -Introduction to SPICE 1 1.1. Fast Start 2

    E-Print Network [OSTI]

    Wilamowski, Bogdan Maciej

    circuit 17 1.4. Transient Analysis 20 Example 1. Simple RC circuit 21 Example 2. Voltage quadrupler circuit 22 1.5. Subcircuits 24 Example 1. Ring oscillator 24 1.6. Device models and Monte Carlo analysis-precision diode circuit 93 Example 7. Astable multivibrator 94 Example 8. Piecewise linear transfer function 95

  7. 2.0 Diode Applications 1 of 31 2.1 Introduction

    E-Print Network [OSTI]

    Allen, Gale

    2.0 Diode Applications 1 of 31 2.1 Introduction 2.2 Load-Line Analysis #12;2.0 Diode Applications 2 of 31 Drawing the load line and finding the point of operation. Drawing the load line. 1) Redraw the circuit with the diode on the right. 2) Remove the diode and find a couple of points on the curve of VD vs

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

    SciTech Connect (OSTI)

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

    2007-12-01T23:59:59.000Z

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

  9. Appendix A: Fuel Price Forecast Introduction..................................................................................................................................... 1

    E-Print Network [OSTI]

    (LNG) imports. With the higher natural gas prices of recent years and technological improvements in drilling, nonconventional supplies of natural gas have expanded rapidly. A significant amount of LNG import

  10. Page 1 of 2 Dissertation Milestone Form

    E-Print Network [OSTI]

    Gilchrist, James F.

    Page 1 of 2 Dissertation Milestone Form (effective Fall 2008) Name: Date (when form filled into one chapter Will have an accepted dissertation proposal (chapters 1 and 2 plus Methods chapter) Will complete data collection Will have an accepted final copy of the dissertation #12;

  11. Viability, Development, and Reliability Assessment of Coupled Coastal Forecasting Systems

    E-Print Network [OSTI]

    Singhal, Gaurav

    2012-10-19T23:59:59.000Z

    disaster, Cook Inlet (CI) and Prince William Sound (PWS) are regions that suffer from a lack of accurate wave forecast information. This dissertation develops high- resolution integrated wave forecasting schemes for these regions in order to meet...

  12. Potential to Improve Forecasting Accuracy: Advances in Supply Chain Management

    E-Print Network [OSTI]

    Datta, Shoumen

    2008-07-31T23:59:59.000Z

    Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view of the great strides made by research and the increasing abundance of data made possible by automatic ...

  13. Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output Perturbation

    E-Print Network [OSTI]

    Washington at Seattle, University of

    Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output. This is typically not feasible for mesoscale weather prediction carried out locally by organizations without by simulating realizations of the geostatistical model. The method is applied to 48-hour mesoscale forecasts

  14. The effect of multinationality on management earnings forecasts

    E-Print Network [OSTI]

    Runyan, Bruce Wayne

    2005-08-29T23:59:59.000Z

    This study examines the relationship between a firm??s degree of multinationality and its managers?? earnings forecasts. Firms with a high degree of multinationality are subject to greater uncertainty regarding earnings forecasts due...

  15. Volume 66, Numbers 1&2 (Complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2008-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2008 Vol. 66 Nos. 1&2 Including THE NEO-LATIN NEWS Vol. 56, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 66, Nos. 1&2 SPRING-SUMMER, 2008 SCN, an official organ of the Milton Society of America...; or that of Margaret Maurer and Ernest W. Sullivan on the prose letters; or indeed many of the non-British scholarly developments described recently by John R. Roberts in the John Donne Journal (vol. 23, 2004, pp. 1-24). Stubbs even prints a citation of The Variorum...

  16. Volume 61, Numbers 1&2 (Complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2003-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2003 Vol. 61 Nos. 1&2 budleafswbudleafse Including THE NEO-LATIN NEWS Vol. 51, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 61, Nos. 1&2 SPRING-SUMMER, 2003 SCN, an official organ of the Milton Society... of the distemper,? which might be considered sat- ire, he later characterized as Satira Seria, ?serious satire? (Works 1.730, 5.18). While the satirist of the Marprelate and Anti- Marprelate stamp sought ?by wit to deride and traduce much of that which is good...

  17. Volume 71, Numbers 1 & 2 (complete)

    E-Print Network [OSTI]

    Dickson, Donald

    2013-01-01T23:59:59.000Z

    EVENTEENTH- ENTURY EWS SPRING - SUMMER 2012 Vol. 71 Nos. 1&2 Including THE NEO-LATIN NEWS Vol. 61, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 71, Nos. 1&2 SPRING-SUMMER, 2013...-begot, self-rais?d / By [their] own quickn?ing power? (PL 5.860-1), and St. Hilaire engages in an il- luminating and extended close reading of this passage. As elsewhere in the poem, Satan speaks in this passage primarily in questions, and in a nice turn...

  18. Alec Jacobson1! Daniele Panozzo2!

    E-Print Network [OSTI]

    Grinspun, Eitan

    Alec Jacobson1! Daniele Panozzo2! Oliver Glauser2! Cédric Pradalier3! Otmar Hilliges2! Olga Sorkine! #12;...but in general our software fixes disparities! 29! #12;30! #12;We can match skeletons;Acknowledgements...! · Ladislav Kavan · Gilles Caprari (gctronic.com) · Marco Attene's MESHFIX · Olga Diamanti

  19. B1 -pag 1 di 2 1 -PRODOTTI BASE

    E-Print Network [OSTI]

    Di Pillo, Gianni

    Activ Plus 1,30 bevande lattina 330 ml prodotti autorizzati nella fascia di prezzo: PEPSI COLA S. PELLEGRINO GUARANITO bevande PET 330 ml prodotti autorizzati nella fascia di prezzo: COCA COLA PEPSI COLA

  20. Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01T23:59:59.000Z

    This presentation presents some statistical analysis of wind power forecast errors and error distributions, with examples using ERCOT data.

  1. Sixth Northwest Conservation & Electric Power Plan Draft Wholesale Power Price Forecasts

    E-Print Network [OSTI]

    Higher Coal Prices Medium Long-term Trend Forecasts for PNW Zones 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1 Mexico Arizona Utah Nevada North Alberta Baja California North Nevada South PNW Westside 10 Northwest

  2. 3:2:1 Crack Spread

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade Year-0E (2001)gasoline prices4 Oil demand8)Commercial5 1:2:1 Crack

  3. Mee-Jung Han1 Sang Yup Lee1, 2

    E-Print Network [OSTI]

    Review Mee-Jung Han1 Sang Yup Lee1, 2 1 Metabolic and Biomolecular Engineering National Research, a family of biodegradable polymers. . . . . . . . 2320 3 Concluding remarks for the analysis of biological data, metabolic Correspondence: Dr. Sang Yup Lee, Department of Chemical

  4. FORECASTING SOLAR RADIATION PRELIMINARY EVALUATION OF AN APPROACH

    E-Print Network [OSTI]

    Perez, Richard R.

    FORECASTING SOLAR RADIATION -- PRELIMINARY EVALUATION OF AN APPROACH BASED UPON THE NATIONAL, and undertake a preliminary evaluation of, a simple solar radiation forecast model using sky cover predictions forecasts is 0.05o in latitude and longitude. Solar Radiation model: The model presented in this paper

  5. AUTOMATION OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S.

    E-Print Network [OSTI]

    Povinelli, Richard J.

    AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand of the energy demand forecasting are achieved by integrating nonlinear transformations within the models

  6. Univariate Modeling and Forecasting of Monthly Energy Demand Time Series

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural dedicated models to forecast the 12 individual months directly. Results indicate better performance is superior to naïve forecasts based on persistence and seasonality, and is better than results quoted

  7. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

    requirements. The transportation energy demand forecasts make assumptions about fuel price forecastsCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY POLICY ENERGY COMMISSION Gordon Schremp, Jim Page, and Malachi Weng-Gutierrez Principal Authors Jim Page Project

  8. PSO (FU 2101) Ensemble-forecasts for wind power

    E-Print Network [OSTI]

    PSO (FU 2101) Ensemble-forecasts for wind power Wind Power Ensemble Forecasting Using Wind Speed the problems of (i) transforming the meteorological ensembles to wind power ensembles and, (ii) correcting) data. However, quite often the actual wind power production is outside the range of ensemble forecast

  9. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance Setting It is the policy of the U, and local environmental protection laws, regulations, compliance agreements and decrees, settlement in compliance with the letter and intent of applicable environmental statutes. The protection of the public

  10. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance Setting It is DOE-ORO and DOE National, state, and local environmental protection laws, regulations, compliance agreements and decrees operations in compliance with the letter and intent of applicable environmental statutes. The protection

  11. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance It is DOE-ORO and NNSA policy to conduct its operations in compliance with federal, state, and local environmental protection laws, regulations operations in compliance with the letter and intent of applicable environmental statutes. The protection

  12. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance It is DOE-ORO and NNSA policy to conduct operations in compliance with federal, state, and local environmental protection laws, regulations operations in compliance with the letter and intent of applicable environmental statutes. The protection

  13. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance Abstract It is the policy of the U, state, and local environmental protection laws, regulations, compliance agreements and decrees operations in compliance with the letter and intent of applicable environmental statutes. The protection

  14. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance It is DOE-ORO and DOE National Nuclear, and local environmental protection laws, regulations, compliance agreements and decrees, settlement in compliance with the letter and intent of applicable environmental statutes. The protection of the public

  15. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance It is DOE-ORO and National Nuclear environmental protection laws, regulations, compliance agree- ments and decrees, settlement agreements and intent of applicable environmental statutes. The protection of the public, personnel, and the environment

  16. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance H. M. Braunstein, L. V. Hamilton, L. W. Mc to conduct its operations in compliance with federal, state, and local environmental protection laws environmental statutes. The protection of the public, personnel, and the environment is of paramount importance

  17. Environmental Compliance 2-1 2. Environmental Compliance

    E-Print Network [OSTI]

    Pennycook, Steve

    Environmental Compliance 2-1 2. Environmental Compliance It is DOE Oak Ridge Operations Office with federal, state, and local environmental protection laws, regulations, compliance agreements and decrees in compliance with the letter and intent of applicable environmental statutes. The protection of the public

  18. Data:7146d5fb-ec7c-48ac-98a8-118f52ea32e2 | Open Energy Information

    Open Energy Info (EERE)

    charge calculated as follows: forecast capacity rate (1.87) + reactive supply (0.046) + regulation and frequency response (0.1897) Energy charge calculated as follows: forecast...

  19. Forecasting Random Walks Under Drift Instability

    E-Print Network [OSTI]

    Pesaran, M Hashem; Pick, Andreas

    .0154 RMSFE 0.3185 0.3200 0.3193 0.3230 0.3193 0.3199 0.3187 SW 1.000 ?0.6774 ?0.4170 ?1.3892 ?0.5368 ?0.7610 ?0.2390 AveW(0.1) 1.0046 1.8402 ?2.4394 0.9874 0.2780 0.9164 AveW(0.2) 1.0025 ?2.3431 ?0.0142 ?1.1110 0.5311 ExpW(0.95) 1.0140 2.0605 2.0922 1...

  20. Forecasting the Market Penetration of Energy Conservation Technologies: The Decision Criteria for Choosing a Forecasting Model

    E-Print Network [OSTI]

    Lang, K.

    1982-01-01T23:59:59.000Z

    capital requirements and research and development programs in the alum inum industry. : CONCLUSIONS Forecasting the use of conservation techndlo gies with a market penetration model provides la more accountable method of projecting aggrega...

  1. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    , Gary Occhiuzzo, and Keith O'Brien prepared the historical energy consumption data. Nahid Movassagh forecasted consumption for the agriculture and water pumping sectors. Don Schultz and Doug Kemmer developed. California Energy Commission, Electricity Supply Analysis Division. Publication Number: CEC2002012001CMFVI

  2. Modeling of Uncertainty in Wind Energy Forecast

    E-Print Network [OSTI]

    regression and splines are combined to model the prediction error from Tunø Knob wind power plant. This data of the thesis is quantile regression and splines in the context of wind power modeling. Lyngby, February 2006Modeling of Uncertainty in Wind Energy Forecast Jan Kloppenborg Møller Kongens Lyngby 2006 IMM-2006

  3. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    NONE

    1998-07-01T23:59:59.000Z

    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. New Concepts in Wind Power Forecasting Models

    E-Print Network [OSTI]

    Kemner, Ken

    New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, João Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind characteristics (mainly speed and direction) in wind parks connected to a power grid. Renyi's Entropy is combined

  5. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    NONE

    1996-08-01T23:59:59.000Z

    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.

  6. Forecast Technical Document Felling and Removals

    E-Print Network [OSTI]

    of local investment and business planning. Timber volume production will be estimated at sub. Planning of operations. Control of the growing stock. Wider reporting (under UKWAS). The calculation fellings and removals are handled in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan

  7. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27T23:59:59.000Z

    This paper presents a nonparametric diffusion modeling approach for forecasting partially observed noisy turbulent modes. The proposed forecast model uses a basis of smooth functions (constructed with the diffusion maps algorithm) to represent probability densities, so that the forecast model becomes a linear map in this basis. We estimate this linear map by exploiting a previously established rigorous connection between the discrete time shift map and the semi-group solution associated to the backward Kolmogorov equation. In order to smooth the noisy data, we apply diffusion maps to a delay embedding of the noisy data, which also helps to account for the interactions between the observed and unobserved modes. We show that this delay embedding biases the geometry of the data in a way which extracts the most predictable component of the dynamics. The resulting model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and in the observation noise limit. We will show numerical examples on a wide-range of well-studied turbulent modes, including the Fourier modes of the energy conserving Truncated Burgers-Hopf (TBH) model, the Lorenz-96 model in weakly chaotic to fully turbulent regimes, and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. In these examples, forecasting skills of the nonparametric diffusion model are compared to a wide-range of stochastic parametric modeling approaches, which account for the nonlinear interactions between the observed and unobserved modes with white and colored noises.

  8. Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price forecast of the Fifth Northwest Power

    E-Print Network [OSTI]

    Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price as traded on the wholesale, short-term (spot) market at the Mid-Columbia trading hub. This price represents noted. BASE CASE FORECAST The base case wholesale electricity price forecast uses the Council's medium

  9. Assignment 1 1. Since kf n 0k 2 =

    E-Print Network [OSTI]

    Olver, Peter

    ) = #6; n+1 k=0 c nk P k (t) (2) where c nk = htP n ; P k i (3) It follows from the fact that P n is orthogonal to every polynomial of order less than n, that c nk = htP n ; P k i = hP n ; tP k i = 0 (4) 1 #12 (t) (6) where a n = htP n ; P n+1 i = hP n ; tP n+1 i = htP n+1 ; P n i; and c n = htP n ; P n 1 i

  10. Page 1 of 2 UNIVERSAL WASTE

    E-Print Network [OSTI]

    Jia, Songtao

    (laboratories should follow hazardous waste procedures) or thorough central battery recycling receptaclesPage 1 of 2 UNIVERSAL WASTE and OTHER ENVIRONMENTALLY DELETERIOUS PRODUCTS Batteries All Universal Waste Batteries generated in laboratories must be collected through the hazardous waste program

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18T23:59:59.000Z

    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.

  12. DOE-2 supplement: Version 2.1E

    SciTech Connect (OSTI)

    Winkelmann, F.C.; Birdsall, B.E.; Buhl, W.F.; Ellington, K.L.; Erdem, A.E. [Lawrence Berkeley Lab., CA (United States); Hirsch, J.J.; Gates, S. [Hirsch (James J.) and Associates, Camarillo, CA (United States)

    1993-11-01T23:59:59.000Z

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

  13. Page 1 of 2 MATERIALS ENGINEERING SCIENCE

    E-Print Network [OSTI]

    Mukhopadhyay, Sharmila M.

    Page 1 of 2 ME 370/570 MATERIALS ENGINEERING SCIENCE Fall 2011 TEXT: W. D. Callister, Materials: This is the first course where most of you will be introduced to Materials Science & Engineering. All engineers need) is normally the first part of a 2-course sequence: ME 370: Materials Engineering Science - Introduction ME 371

  14. Realized Stock Volatility 2.1 Introduction

    E-Print Network [OSTI]

    Niebur, Ernst

    Chapter 2 Realized Stock Volatility 2.1 Introduction Financial market volatility is indispensable for asset and derivative pricing, asset allocation, and risk management. As volatility is not a directly is to calculate the daily volatility from the sample variance of intraday returns, the `realized' volatility

  15. How Modern Displays Push Conventional Colorimetry to its Limit Abhijit Sarkar1,2, Laurent Blond1, Patrick Le Callet2, Florent Autrusseau2, Jrgen Stauder1,

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 How Modern Displays Push Conventional Colorimetry to its Limit Abhijit Sarkar1,2, Laurent Blondé1

  16. TOWARD RELIABLE BENCHMARKING OF SOLAR FLARE FORECASTING METHODS

    SciTech Connect (OSTI)

    Bloomfield, D. Shaun; Higgins, Paul A.; Gallagher, Peter T. [Astrophysics Research Group, School of Physics, Trinity College Dublin, College Green, Dublin 2 (Ireland); McAteer, R. T. James, E-mail: shaun.bloomfield@tcd.ie [Department of Astronomy, New Mexico State University, Las Cruces, NM 88003-8001 (United States)

    2012-03-10T23:59:59.000Z

    Solar flares occur in complex sunspot groups, but it remains unclear how the probability of producing a flare of a given magnitude relates to the characteristics of the sunspot group. Here, we use Geostationary Operational Environmental Satellite X-ray flares and McIntosh group classifications from solar cycles 21 and 22 to calculate average flare rates for each McIntosh class and use these to determine Poisson probabilities for different flare magnitudes. Forecast verification measures are studied to find optimum thresholds to convert Poisson flare probabilities into yes/no predictions of cycle 23 flares. A case is presented to adopt the true skill statistic (TSS) as a standard for forecast comparison over the commonly used Heidke skill score (HSS). In predicting flares over 24 hr, the maximum values of TSS achieved are 0.44 (C-class), 0.53 (M-class), 0.74 (X-class), 0.54 ({>=}M1.0), and 0.46 ({>=}C1.0). The maximum values of HSS are 0.38 (C-class), 0.27 (M-class), 0.14 (X-class), 0.28 ({>=}M1.0), and 0.41 ({>=}C1.0). These show that Poisson probabilities perform comparably to some more complex prediction systems, but the overall inaccuracy highlights the problem with using average values to represent flaring rate distributions.

  17. License Plate 1 License Plate 2

    E-Print Network [OSTI]

    Wagner, Diane

    License Plate 1 License Plate 2 License Plate 3 License Plate 4 Single Vehicle $254 Multi Vehicle $264 ( up to 4 vehicles) Annual Decal Valid: 09/01/12 - 08/31/13 Fall /Spring Decal (Pick date range , then Single or Multi Vehicle) Multi Vehicles $200 (up to 4 vehicles) Select date range Valid 9/1/12 - 5

  18. Availability Page 1 of 2 AVAILABILITY ANALYSIS

    E-Print Network [OSTI]

    Oliver, Douglas L.

    Availability Page 1 of 2 AVAILABILITY ANALYSIS Section 46a-68-39 This section was found to be in compliance in the previous submission, and there were no proposals/recommendations. Availability analyses, the following were consulted in determining availability computations: 1. Employment figures (immediate labor

  19. Draft Fourth Northwest Conservation and Electric Power Plan, Appendix C FUEL PRICE FORECASTS

    E-Print Network [OSTI]

    . Figure C-1 illustrates this for world oil prices, and similar patterns apply to natural gas. The last. Figure C-1 World Oil Prices Have Been Following the 1991 Plan Low Forecast 0.00 5.00 10.00 15.00 20.00 25 and earlier Council plans, natural gas prices were dependent on the assumptions about world oil prices

  20. Evaluation of Advanced Wind Power Forecasting Models Results of the Anemos Project

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 Evaluation of Advanced Wind Power Forecasting Models ­ Results of the Anemos Project I. Martí1.kariniotakis@ensmp.fr Abstract An outstanding question posed today by end-users like power system operators, wind power producers or traders is what performance can be expected by state-of-the-art wind power prediction models. This paper

  1. LANL JOWOG 31 2012 Forecast

    SciTech Connect (OSTI)

    Vidlak, Anton J. II [Los Alamos National Laboratory

    2012-08-08T23:59:59.000Z

    Joint Working Group (JOWOG) 31, Nuclear Weapons Engineering, has a particularly broad scope of activities within its charter which emphasizes systems engineering. JOWOG 31 brings together experts from AWE and the national laboratories to address engineering issues associated with warhead design and certification. Some of the key areas of interaction, as addressed by the HOCWOGs are: (1) Engineering Analysis, (2) Hydrodynamic Testing, (3) Environmental Testing, and (4) Model Based Integrated Toolkit (MBIT). Gas Transfer Systems and Condition Monitoring interaction has been moved back to JOWOG 31. The regularly scheduled JOWOG 31 activities are the General Sessions, Executive Sessions, Focused Exchanges and HOCWOGs. General Sessions are scheduled every 12-18 months and are supported by the four design laboratories (AWE, LANL, LLNL, and SNL). Beneficial in educating the next generation of weapons engineers and establishing contacts between AWE and the US laboratory personnel. General Sessions are based on a blend of presentations and workshops centered on various themed subjects directly related to Stockpile Stewardship. HOCWOG meetings are more narrowly focused than the General Sessions. They feature presentations by experts in the field with a greater emphasis on round table discussions. Typically about 20 people attend. Focused exchanges are generally the result of interactions within JOWOG general sessions or HOCWOG meetings. They generally span a very specific topic of current interest within the US and UK.

  2. CLUster Matching and Permutation Program Version 1.1.2

    E-Print Network [OSTI]

    Rosenberg, Noah

    for Computational Medicine and Biology Department of Human Genetics University of Michigan Noah A. Rosenberg Center for Computational Medicine and Biology Department of Human Genetics University of Michigan October 10, 2007 (version 1.1.2 May 31, 2009) The CLUMPP software is available at http://rosenberglab.bioinformatics

  3. Beamline 5.3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.2 Print310.3 Print15.3.2.15.3.2.1

  4. TableHC2.1.xls

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly Download:Stocks2.14.2 7.6 16.68

  5. TableHC2.1.xls

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly Download:Stocks2.14.2 7.6 16.68

  6. Advanced Technology for Railway Hydraulic Hazard Forecasting

    E-Print Network [OSTI]

    Huff, William Edward 1988-

    2012-12-05T23:59:59.000Z

    Page 1.1 Map of Total Railway Hydraulic Hazard Events from 1982-2011 ............ 2 1.2 90 mi Effective Radar Coverage for Reliable Rainfall Rate Determination ....................................................................... 5 3... Administration (FRA) for the period of 1982-2011. This data was compiled from the FRA Office of Safety Analysis website (FRA, 2011). A map of the railway hydraulic hazard events over the same time period is displayed in Figure 1.1. Table 1.1. U.S. Railway...

  7. JAIME HORTA RANGEL1,2) , WITOLD BROSTOW2),*)

    E-Print Network [OSTI]

    North Texas, University of

    resembling elongated fullerenes comprised of interconnected hexa- gons of carbon atoms spanning the entire of the nanotubes, at the 276 POLIMERY 2013, 58, nr 4 1) State University of Queretaro, Queretaro, Mexico. 2 Autonomous University of Mexico, Mexico. *) Corresponding author: wbrostow@yahoo.com #12;nanoscale, has

  8. Test application of a semi-objective approach to wind forecasting for wind energy applications

    SciTech Connect (OSTI)

    Wegley, H.L.; Formica, W.J.

    1983-07-01T23:59:59.000Z

    The test application of the semi-objective (S-O) wind forecasting technique at three locations is described. The forecasting sites are described as well as site-specific forecasting procedures. Verification of the S-O wind forecasts is presented, and the observed verification results are interpreted. Comparisons are made between S-O wind forecasting accuracy and that of two previous forecasting efforts that used subjective wind forecasts and model output statistics. (LEW)

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghurajiConventionalMississippi"site.1 Relative Standard Errors for Table 1.1;"21 and N6.2.3S2.1

  10. Beamline 5.3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.2 Print310.3 Print15.3.2.1 Print

  11. Beamline 5.3.2.1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041clothAdvanced Materials Advanced Materials Find More Like This3.3.2 Print310.3 Print15.3.2.1

  12. 1993 Pacific Northwest Loads and Resources Study, Technical Appendix: Volume 2, Book 1, Energy.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1993-12-01T23:59:59.000Z

    The 1993 Pacific Northwest Loads and Resources Study establishes the Bonneville Power Administration`s (BPA) planning basis for supplying electricity to BPA customers. The Loads and Resources Study is presented in three documents: (1) this technical appendix detailing loads and resources for each major Pacific and Northwest generating utility, (2) a summary of Federal system and Pacific Northwest region loads and resources, and (3) a technical appendix detailing forecasted Pacific Northwest economic trends and loads. This analysis updates the 1992 Pacific Northwest Loads and Resources Study Technical Appendix published in December 1992. This technical appendix provides utility-specific information that BPA uses in its long-range planning. It incorporates the following for each utility (1) Electrical demand firm loads; (2) Generating resources; and (3) Contracts both inside and outside the region. This document should be used in combination with the 1993 Pacific Northwest Loads and Resources Study, published in December 1993, because much of the information in that document is not duplicated here.

  13. Grid Technology Overview and Status Geoffrey Fox1,2

    E-Print Network [OSTI]

    Grid Technology Overview and Status Geoffrey Fox1,2 , Alex Ho2 , Marlon Pierce1 1 Community Grids...................................................................................................................... 1 2 What is a Grid? ................................................................................................................ 1 3 Grid Technologies and Capabilities

  14. European Wind Energy Conference -Brussels, Belgium, April 2008 Data mining for wind power forecasting

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    European Wind Energy Conference - Brussels, Belgium, April 2008 Data mining for wind power-term forecasting of wind energy produc- tion up to 2-3 days ahead is recognized as a major contribution the improvement of predic- tion systems performance is recognised as one of the priorities in wind energy research

  15. Gaussian Processes for Short-Horizon Wind Power Forecasting Joseph Bockhorst, Chris Barber

    E-Print Network [OSTI]

    Bockhorst, Joseph

    on this task, and attention has shifted to statistical and machine learning approaches. Among the challenges of wind energy into electrical trans- mission systems. The importance of wind forecasts for wind energy throughout a power system must be nearly in balance at all times, 2) because it depends strongly on wind

  16. Using multi-layer models to forecast gas flow rates in tight gas reservoirs 

    E-Print Network [OSTI]

    Jerez Vera, Sergio Armando

    2007-04-25T23:59:59.000Z

    , and (2) to use the single-layer match to demonstrate the error that can occur when forecasting long-term gas production for such complex gas reservoirs. A finite-difference reservoir simulator was used to simulate gas production from various layered tight...

  17. Solar Power Forecasting at UC San Diego Jan Kleissl, Dept of Mechanical & Aerospace Engineering, UCSD

    E-Print Network [OSTI]

    Fainman, Yeshaiahu

    show 2 cloud layers. Vaisala Fig. 4: Observed solar power output (black line) and simulation (Fig. 4). Tier 3: Power output forecast As cloud related solar radiation reductions are observed algorithm to determine actual expected solar power output at each PV array over the hour ahead. #12;

  18. Addressing model bias and uncertainty in three dimensional groundwater transport forecasts for a physical aquifer experiment

    E-Print Network [OSTI]

    Vermont, University of

    Addressing model bias and uncertainty in three dimensional groundwater transport forecasts, and D. M. Rizzo (2008), Addressing model bias and uncertainty in three dimensional groundwater transport. Introduction [2] Eigbe et al. [1998] provide an excellent review of groundwater applications of the linear

  19. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

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

    2014-12-30T23:59:59.000Z

    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.

  20. Fusion Rules for Affine sl(2|1;C) at Fractional Level k=-1/2

    E-Print Network [OSTI]

    Gavin Johnstone

    2001-06-05T23:59:59.000Z

    We calculate fusion rules for the admissible representations of the affine superalgebra sl(2|1;C) at fractional level k=-1/2 in the Ramond sector. By representing 3-point correlation functions involving a singular vector as the action of differential operators on the sl(2|1;C) invariant 3-point function, we obtain conditions on permitted quantum numbers involved. We find that in this case the primary fields close under fusion.