National Library of Energy BETA

Sample records for forecast 2 1

  1. Forecast of solar ejecta arrival at 1 AU from radial speed S. Dasso1,2

    E-Print Network [OSTI]

    Dasso, Sergio

    Forecast of solar ejecta arrival at 1 AU from radial speed S. Dasso1,2 , N. Gopalswamy1 and A. Lara of the major requirements to forecast the space weather conditions in the terrestrial envi- ronment. Several properties, such as the background solar wind speed, and the density of the ejecta. However, only a few

  2. Ozone ensemble forecast with machine learning Vivien Mallet,1,2

    E-Print Network [OSTI]

    Mallet, Vivien

    Ozone ensemble forecast with machine learning algorithms Vivien Mallet,1,2 Gilles Stoltz,3; published 13 March 2009. [1] We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system

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

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01

    System (REEPS 2.1) , developed by the Electric Power Research Institute (EPRI), is a forecasting model

  4. 1 Forecasting Greenhouse Gas Emissions from Urban Regions: 2 Microsimulation of Land Use and Transport Patterns in Austin, Texas

    E-Print Network [OSTI]

    Kockelman, Kara M.

    use electricity, natural gas and other energy sources regularly52 for space conditioning and powering1 Forecasting Greenhouse Gas Emissions from Urban Regions: 2 Microsimulation of Land Use 2030 household energy 26 demands and GHG emissions estimates are compared under five different land use

  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.

    1994-05-01

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

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

    SciTech Connect (OSTI)

    Templeton, K.J.

    1996-05-23

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

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

    E-Print Network [OSTI]

    Heinemann, Detlev

    with postprocessing 5) Ciemat, Spain HIRLAM-CI Bias correction AEMET-HIRLAMx - 0.2°x 0.2° - 1 hour 6) Meteotest

  8. 1Bureau of Meteorology | Water Information > INFORMATION SHEET 6 > Flood Forecasting and Warning Services Flood Forecasting

    E-Print Network [OSTI]

    Greenslade, Diana

    SHEET 6 1Bureau of Meteorology | Water Information > INFORMATION SHEET 6 > Flood Forecasting and Warning Services Flood Forecasting and Warning Services The Bureau of Meteorology (the Bureau) is responsible for providing an effective flood forecasting and warning service in each Australian state

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

    E-Print Network [OSTI]

    Appendix A: Fuel Price Forecast Introduction................................................................................................................................. 3 Price Forecasts ............................................................................................................................ 5 U.S. Natural Gas Commodity Prices

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2003-12-01

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

  11. Assimilation of Remote-sensing Soil Moisture in Short-term River Forecasting M. Pan1, E. F. Wood1, W. Crow2, J. Schaake3

    E-Print Network [OSTI]

    Pan, Ming

    Assimilation of Remote-sensing Soil Moisture in Short-term River Forecasting M. Pan1, E. F. Wood1 Hydrology and Remote Sensing Lab, US Department of Agriculture 3 National Weather Service, National Oceanic and Atmospheric Administration 1. Introduction This study focuses on evaluation of hydrologic remote sensing

  12. A global aerosol model forecast for the ACE-Asia field experiment Mian Chin,1,2

    E-Print Network [OSTI]

    Chin, Mian

    layer. We attribute this ``missing'' dust source to desertification regions in the Inner Mongolia forecasting. After incorporating the desertification sources, the model is able to reproduce the observed

  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

    SciTech Connect (OSTI)

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

    1995-12-01

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

  15. Development and Initial Application of the Global-Through-Urban Weather Research1 and Forecasting Model with Chemistry (GU-WRF/Chem)2

    E-Print Network [OSTI]

    Nenes, Athanasios

    1 Development and Initial Application of the Global-Through-Urban Weather Research1 and Forecasting-cloud-radiation-precipitation-climate interactions. In this work, a global-through-urban33 WRF/Chem model (i.e., GU-WRF/Chem) has been developed photolysis rate, near-surface temperature, wind speed at 10-m, planetary boundary layer height,40

  16. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

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

  17. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

    s economy. Demand Forecasts The three energy futures wereto meet the forecast demand in each energy futurE2. e e1£~energy saved through improved appliance efficiencies. Also icit in our demand forecasts

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

    E-Print Network [OSTI]

    Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting SAMUEL RE, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL for the very short-term forecast of fog and low clouds. This forecast system assimilates local observations

  19. 1. Introduction Users of weather forecasts, particularly paying cus-

    E-Print Network [OSTI]

    1. Introduction Users of weather forecasts, particularly paying cus- tomers, are operating within Kingdom out of a total budget of approximately £140 million for winter road maintenance. It is difficult rely on a simple set of statistics provided by the weather service providers. The current guidance

  20. 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

  1. 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

  2. Downscaling Extended Weather Forecasts for Hydrologic Prediction

    SciTech Connect (OSTI)

    Leung, Lai-Yung R.; Qian, Yun

    2005-03-01

    Weather and climate forecasts are critical inputs to hydrologic forecasting systems. The National Center for Environmental Prediction (NCEP) issues 8-15 days outlook daily for the U.S. based on the Medium Range Forecast (MRF) model, which is a global model applied at about 2? spatial resolution. Because of the relatively coarse spatial resolution, weather forecasts produced by the MRF model cannot be applied directly to hydrologic forecasting models that require high spatial resolution to represent land surface hydrology. A mesoscale atmospheric model was used to dynamically downscale the 1-8 day extended global weather forecasts to test the feasibility of hydrologic forecasting through this model nesting approach. Atmospheric conditions of each 8-day forecast during the period 1990-2000 were used to provide initial and boundary conditions for the mesoscale model to produce an 8-day atmospheric forecast for the western U.S. at 30 km spatial resolution. To examine the impact of initialization of the land surface state on forecast skill, two sets of simulations were performed with the land surface state initialized based on the global forecasts versus land surface conditions from a continuous mesoscale simulation driven by the NCEP reanalysis. Comparison of the skill of the global and downscaled precipitation forecasts in the western U.S. showed higher skill for the downscaled forecasts at all precipitation thresholds and increasingly larger differences at the larger thresholds. Analyses of the surface temperature forecasts show that the mesoscale forecasts generally reduced the root-mean-square error by about 1.5 C compared to the global forecasts, because of the much better resolved topography at 30 km spatial resolution. In addition, initialization of the land surface states has large impacts on the temperature forecasts, but not the precipitation forecasts. The improvements in forecast skill using downscaling could be potentially significant for improving hydrologic forecasts for managing river basins.

  3. Motivation Methods Model configuration Results Forecasting Summary & Outlook Retrieving direct and diffuse radiation with the

    E-Print Network [OSTI]

    Heinemann, Detlev

    Motivation Methods Model configuration Results Forecasting Summary & Outlook 1/ 14 Retrieving. 17, 2015 #12;Motivation Methods Model configuration Results Forecasting Summary & Outlook 2/ 14 Motivation Sky Imager based shortest-term solar irradiance forecasts for local solar energy applications

  4. Radiation fog forecasting using a 1-dimensional model 

    E-Print Network [OSTI]

    Peyraud, Lionel

    2001-01-01

    weather patterns known to be favorable for producing fog and once it has formed, to state that it will persist unless the pattern changes. Unfortunately, while such methods have shown some success, many times they have led weather forecasters astray...

  5. Price forecasting for notebook computers 

    E-Print Network [OSTI]

    Rutherford, Derek Paul

    1997-01-01

    of individual features are estimated. A time series analysis is used to forecast and can be used, for example, to forecast (1) notebook computer price at introduction, and (2) rate of price erosion for a notebook's life cycle. Results indicate that this approach...

  6. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

    ....................................................................................................1-16 Energy Consumption Data...............................................1-15 Data Sources for Energy Demand Forecasting ModelsCALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report

  7. Cloudy with a Chance of Breach: Forecasting Cyber Security Incidents Yang Liu1, Armin Sarabi1, Jing Zhang1, Parinaz Naghizadeh1

    E-Print Network [OSTI]

    Liu, Mingyan

    Cloudy with a Chance of Breach: Forecasting Cyber Security Incidents Yang Liu1, Armin Sarabi1, Jing In this study we characterize the extent to which cyber security incidents, such as those referenced by Verizon to understand the extent to which one can forecast if an organization may suffer a cyber security incident

  8. 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,pmilani}@seclab.tuwien.ac performed ei- ther randomly or using techniques focused on avoiding re-analysis of polymorphic malware a machine-learning-based system that uses all statically-available information to predict into which

  9. 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

  10. Real-time forecasting of the April 11, 2012 Sumatra tsunami Dailin Wang,1

    E-Print Network [OSTI]

    Duputel, Zacharie

    Real-time forecasting of the April 11, 2012 Sumatra tsunami Dailin Wang,1 Nathan C. Becker,1 David generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epi- center, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States

  11. NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    income 7 Figure 1.14: United States inflation Rate 8 Figure 1.15: Select United States interest Rates 8 2014 TABLE OF CONTENTS EXECUTiVE SUMMARY 1 CHAPTER 1: THE UNiTED STATES ECONOMY 3 Recent Trends Forecast Summary 2 CHAPTER 1: THE UNiTED STATES ECONOMY Figure 1.1: United States Real GDP Growth 3 Figure

  12. 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

  13. Testing Competing High-Resolution Precipitation Forecasts

    E-Print Network [OSTI]

    Gilleland, Eric

    Testing Competing High-Resolution Precipitation Forecasts Eric Gilleland Research Prediction Comparison Test D1 D2 D = D1 ­ D2 copyright NCAR 2013 Loss Differential Field #12;Spatial Prediction Comparison Test Introduced by Hering and Genton

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

    E-Print Network [OSTI]

    less frequent). Currently there is no effective earthquake prediction programme and we may be fatally-forecasting all damaging earthquakes worldwide Stuart Crampin1,2 , Sergei Zatsepin3 , Chris W. A. Browitt2@jamstec.go.jp. 5 Institute of Earthquake Science, China Earthquake Administration, Beijing 100036, China. E

  15. Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling

    SciTech Connect (OSTI)

    Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao

    2011-09-29

    This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way.

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2005-08-17

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

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

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    that is capable of matching the methane production history and forecast field potential capacity for CO2 injection characterization and simulation process focused on natural gas production and subsequent CO2 injection) are the subject of this pilot CO2 sequestration project. Methane is produced from both coal seams; however CO2

  18. Application of a new phenomenological coronal mass ejection model to space weather forecasting

    E-Print Network [OSTI]

    Howard, Tim

    to space weather forecasting T. A. Howard1 and S. J. Tappin2 Received 15 October 2009; revised 27 April with the Earth. Hence the model can be used for space weather forecasting. We present a preliminary evaluation to fully validate it for integration with existing tools for space weather forecasting. Citation: Howard, T

  19. (1, 0, 2) and (1, 2, 1

    E-Print Network [OSTI]

    2014-09-08

    Quiz 1 solutions, Section ALL. (10 pts.) Find a vector v perpendicular to the plane containing (0, 0, 2), (1, 0, 2) and (1, 2, 1). Solution. Let P = (0, 0, 2), Q = (1, 0, ...

  20. Forecasting phenology under global warming

    E-Print Network [OSTI]

    Silander Jr., John A.

    Forecasting phenology under global warming Ine´s Iba´n~ez1,*, Richard B. Primack2, Abraham J in phenology. Keywords: climate change; East Asia, global warming; growing season, hierarchical Bayes; plant is shifting, and these shifts have been linked to recent global warming (Parmesan & Yohe 2003; Root et al

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

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

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

  2. Short-Term Load Forecasting by Feed-Forward Neural Networks Saied S. Sharif1

    E-Print Network [OSTI]

    Taylor, James H.

    , Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB, CANADA, E3B) is presented for the hourly load forecasting of the coming days. In this approach, 24 independent networks are used for the next day load forecast. Each network is utilized for the prediction of load at a specific

  3. 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

  4. web page: http://w3.pppl.gov/~ zakharov On Real Time Forecasts (RTF) of Tokamak Discharges1

    E-Print Network [OSTI]

    Zakharov, Leonid E.

    structure (Data Base) . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.3 Communication control of code to "yesterday" weather analysis) or predictive codes ("next month" weather predictions), RTF targets a forecast of the plasma regime, e.g., in 0.1 e (like the "next hour" weather predictions). Three components, crucial

  5. web page: http://w3.pppl.gov/~ zakharov On Real Time Forecasts (RTF) of Tokamak Discharges 1

    E-Print Network [OSTI]

    Zakharov, Leonid E.

    structure (Data Base) . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.3 Communication control of code to "yesterday" weather analysis) or predictive codes ("next month" weather predictions), RTF targets a forecast of the plasma regime, e.g., in 0.1 # e (like the "next hour" weather predictions). Three components, crucial

  6. ................................................................... 1 ....................................................... 2

    E-Print Network [OSTI]

    Takada, Shoji

    [] #12;23 30 40-50 15 OA LAN () http://sph.med.kyoto-u.ac.jp/syllabus.html;25 10 WEB #12;26 19 20 3 13 FD WEB-QME 20 20 11 20 SPH SPH SPH WEB ( 5 7 ) 80% 23 (7 ) 1.0-1.9 2.0-2.9 3.0-3.9 4.0-4.9 5.0-5.4 5.5-5.9 6

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

    E-Print Network [OSTI]

    Mosier, Richard Matthew

    2011-02-22

    -derived Products....26 1.6 Thesis Objectives and Hypothesis...........................................................................27 2. DATA AND METHODOLOGY..................................................................................29 2.1 Radar............................................................................................42 2.4.4 Storm Cell Position Forecast............................................................................44 2.5 Lightning Correlation..............................................................................................45 2.6 CG...

  8. Using Climate Predictions in Great Lakes Hydrologic Forecasts T. E. Croley II1

    E-Print Network [OSTI]

    Lakes water levels cause extensive flooding, erosion, and damage to shorelines, shipping, and hydropower the forecasting system utility to decision makers and potential impacts in practical applications. CLIMATE EFFECTS

  9. Improving Inventory Control Using Forecasting

    E-Print Network [OSTI]

    Balandran, Juan

    2005-12-16

    and encouragement. I am very grateful to Lucille and Michael Hobbs for their friendship, understanding and financial support. Finally, thank you to Tom Decker, Pat Jackson and Brian Zellar for all their contributions and hard work on this project... below: 1. Na?ve 2. Linear Regression 3. Moving Average 4. Exponential 5. Double exponential The na?ve forecasting method assumes that more recent data values are the best predictors of future values. The model is ? t+1 = Y t . Where ? t...

  10. Solar Forecasting

    Broader source: Energy.gov [DOE]

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

  11. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01

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

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

    E-Print Network [OSTI]

    to the electricity price forecast. This resource mix is used to forecast the fuel consumption and carbon dioxide (CO2Wholesale Electricity Price Forecast This appendix describes the wholesale electricity price forecast of the Fifth Northwest Power Plan. This forecast is an estimate of the future price of electricity

  13. Web 2.0 vs. the Semantic Web: A Philosophical Assessment Luciano Floridi1, 2

    E-Print Network [OSTI]

    Floridi, Luciano

    Web 2.0 vs. the Semantic Web: A Philosophical Assessment Luciano Floridi1, 2 1 Research Chair at the development of the so-called Semantic Web and Web 2.0 from this perspective and try to forecast their future failed in the past. Regarding Web 2.0, I argue that, although it is a rather ill-defined project, which

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

    SciTech Connect (OSTI)

    McNamara, Laura A.

    2010-08-01

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

  15. FORECASTING THE ROLE OF RENEWABLES IN HAWAII

    E-Print Network [OSTI]

    Sathaye, Jayant

    2013-01-01

    FORECASTING THE ROLE OF RENEWABLES IN HAWAII Jayant SathayeFORECASTING THE ROLF OF RENEWABLES IN HAWAII J Sa and Henrythe Conservation Role of Renewables November 18, 1980 Page 2

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

    SciTech Connect (OSTI)

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

    2012-08-15

    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.

  17. "FLIGHT PLAN" FORECASTS SEATTLE/TACOMA AND

    E-Print Network [OSTI]

    ASSESSMENT OF THE "FLIGHT PLAN" FORECASTS FOR SEATTLE/TACOMA AND REGIONAL AIRPORTS TOGETHER 1. Introduction 5 2. Airport Planning Process 7 Traditional Master Planning Application to Seattle/Tacoma. Uncertainty about Capacity 27 A Fuzzy Concept Assessment Factors Application to Seattle/Tacoma 7. Assessment

  18. FORECAST OF ATLANTIC SEASONAL HURRICANE ACTIVITY AND LANDFALL STRIKE PROBABILITY FOR 2014

    E-Print Network [OSTI]

    Connors, Daniel A.

    1 FORECAST OF ATLANTIC SEASONAL HURRICANE ACTIVITY AND LANDFALL STRIKE PROBABILITY FOR 2014 We are higher than normal, and vertical wind shear throughout the Atlantic basin has been much stronger than-period average values. (as of 31 July 2014) By Philip J. Klotzbach1 and William M. Gray2 This forecast as well

  19. 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

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

    SciTech Connect (OSTI)

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

    1997-01-07

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

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

    SciTech Connect (OSTI)

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

    1991-01-01

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

  2. 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)

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios (with spatial and/or temporal dependence). Statistical approaches to uncertainty forecasting basically consist of estimating the uncertainty based on observed forecasting errors. Quantile regression (QR) is currently a commonly used approach in uncertainty forecasting. In Chapter 3, we propose new statistical approaches to the uncertainty estimation problem by employing kernel density forecast (KDF) methods. We use two estimators in both offline and time-adaptive modes, namely, the Nadaraya-Watson (NW) and Quantilecopula (QC) estimators. We conduct detailed tests of the new approaches using QR as a benchmark. One of the major issues in wind power generation are sudden and large changes of wind power output over a short period of time, namely ramping events. In Chapter 4, we perform a comparative study of existing definitions and methodologies for ramp forecasting. We also introduce a new probabilistic method for ramp event detection. The method starts with a stochastic algorithm that generates wind power scenarios, which are passed through a high-pass filter for ramp detection and estimation of the likelihood of ramp events to happen. The report is organized as follows: Chapter 2 presents the results of the application of ITL training criteria to deterministic WPF; Chapter 3 reports the study on probabilistic WPF, including new contributions to wind power uncertainty forecasting; Chapter 4 presents a new method to predict and visualize ramp events, comparing it with state-of-the-art methodologies; Chapter 5 briefly summarizes the main findings and contributions of this report.

  3. 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EA Jump to:ofEnia SpAFlex Fuels Energy JumpVyncke Jump to:Forecast

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

    SciTech Connect (OSTI)

    BARCOT, R.A.

    2000-08-31

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

  5. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    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:

  6. A comparison study of data assimilation algorithms for ozone forecasts

    E-Print Network [OSTI]

    Mallet, Vivien

    A comparison study of data assimilation algorithms for ozone forecasts Lin Wu,1,2 V. Mallet,1,2 M assimilation schemes with the aim of designing suitable assimilation algorithms for short- range ozone but stable systems with high uncertainties (e.g., over 20% for ozone daily peaks (Hanna et al., 1998; Mallet

  7. 1. 2 , 4 2 ( Times

    E-Print Network [OSTI]

    Kaplan, Alexander

    format with double line spacing (font Times New Roman, 12 pt; margins: left and up -- 30 mm, right , . 20. , , . 1. 2 , 4 2 ( Times New Roman, 12 pt

  8. Price forecasting for U.S. cattle feeders: which technique to apply? 

    E-Print Network [OSTI]

    Hicks, Geoff Cody

    1997-01-01

    the following:1. FAPRI3. AO S5. Univariate Time Series7. Composite 2. WASDE4. Futures Market6. Multivariate Time Series The characteristics of each of the aforementioned forecast techniques are explained within the appropriate chapter. Furthermore, it should...

  9. 1 Introduction 1 2 The Fight 1

    E-Print Network [OSTI]

    Lee, Carl

    Contents 1 Introduction 1 2 The Fight 1 1 Introduction 'Twas brillig, and the slithy toves did gyre by the Tumtum tree, and stood awhile in thought. 2 The Fight And, as in uffish thought he stood, the Jabberwock

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

    SciTech Connect (OSTI)

    1995-04-01

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

  11. HOW ACCURATE ARE WEATHER MODELS IN ASSISTING AVALANCHE FORECASTERS? M. Schirmer, B. Jamieson

    E-Print Network [OSTI]

    Jamieson, Bruce

    HOW ACCURATE ARE WEATHER MODELS IN ASSISTING AVALANCHE FORECASTERS? M. Schirmer, B. Jamieson and decision makers strongly rely on Numerical Weather Prediction (NWP) models, for example on the forecasted on forecasted precipitation. KEYWORDS: Numerical weather prediction models, validation, precipitation 1

  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

    2015-06-23

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

  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, Final Report

    SciTech Connect (OSTI)

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

    2014-04-30

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

  14. DOE Releases Latest Report on Energy Savings Forecast of Solid...

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

    Latest Report on Energy Savings Forecast of Solid-State Lighting DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting September 12, 2014 - 2:06pm Addthis...

  15. Generalized Cost Function Based Forecasting for Periodically Measured Nonstationary Traffic

    E-Print Network [OSTI]

    Zeng, Yong - Department of Mathematics and Statistics, University of Missouri

    1 Generalized Cost Function Based Forecasting for Periodically Measured Nonstationary Traffic true value. However, such a forecast- ing function is not directly applicable for applications potentially result in insufficient allocation of bandwidth leading to short term data loss. To facilitate

  16. An Improved Model To Forecast Co2 Leakage Rates Along A Wellbore | Open

    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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EAandAmminex A S Jump to: navigation,In The Artesian-City Area, IdahoEnergy

  17. Weather and Forecasting EARLY ONLINE RELEASE

    E-Print Network [OSTI]

    Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary PDF of the author, Guangzhou 510301, China9 2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological10, China20 21 22 23 24 Submitted to Weather and Forecasting25 2014. 12. 2826 27 Corresponding author: Dr

  18. 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

  19. Weather and Forecasting EARLY ONLINE RELEASE

    E-Print Network [OSTI]

    Johnson, Richard H.

    Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary PDF of the author Fort Collins, Colorado7 October 20128 (submitted to Weather and Forecasting)9 1 Corresponding author address: Rebecca D. Adams-Selin, HQ Air Force Weather Agency 16th Weather Squadron, 101 Nelson Dr., Offutt

  20. A 110-Day Ensemble Forecasting Scheme for the Major River Basins of Bangladesh: Forecasting Severe Floods of 200307*

    E-Print Network [OSTI]

    Webster, Peter J.

    A 1­10-Day Ensemble Forecasting Scheme for the Major River Basins of Bangladesh: Forecasting Severe of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1­10-day horizons are presented

  1. Volume 15, number 1 February 2010 markets products analysis research Forecasts

    E-Print Network [OSTI]

    projects have been identified, with the bulk being cogeneration, wood pellets and cellulosic ethanol. · u to the historical raw material supply for the pulp and paper/composite panelboard industries. t Wood Pellet markets/trends growth of the global Wood Pellet industry ...continued What's InsIde global prIce trends analysis 1 Wood

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

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

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

  3. Forecasting Market Demand for New Telecommunications Services: An Introduction

    E-Print Network [OSTI]

    McBurney, Peter

    Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc to redress this situation by presenting a discussion of the issues involved in demand forecasting for new or consultancy clients. KEYWORDS: Demand Forecasting, New Product Marketing, Telecommunica­ tions Services. 1 #12

  4. Neural Network forecasts of the tropical Pacific sea surface temperatures

    E-Print Network [OSTI]

    Hsieh, William

    Neural Network forecasts of the tropical Pacific sea surface temperatures Aiming Wu, William W Tang Jet Propulsion Laboratory, Pasadena, CA, USA Neural Networks (in press) December 11, 2005 title: Forecast of sea surface temperature 1 #12;Neural Network forecasts of the tropical Pacific sea

  5. 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

  6. 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, Germany Corresponding author: anja.drews@forwind.de Offshore wind parks in different stages.Green- in op - unknown Source:Siemens Cross-section of a GIL (one of three phases) 1 Future offshore wind power What

  7. Forecasting Random Walks Under Drift Instability

    E-Print Network [OSTI]

    Pesaran, M Hashem; Pick, Andreas

    will yield a biased forecast but will continue to have the least variance. On the other hand a forecast based on the sub-sample {yTi , yTi+1, . . . , yT }, where Ti > 1 is likely to have a lower bias but could be inefficient due to a higher variance... approach considered in Pesaran and Timmermann (2007) is to use different sub-windows to forecast and then average the outcomes, either by means of cross-validated weights or by simply using equal weights. To this end consider the sample {yTi , yTi+1...

  8. Wind Power Forecasting Data

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

    Operations Call 2012 Retrospective Reports 2012 Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email...

  9. Forecasting Water Quality & Biodiversity

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

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability Platform Review Principle Investigator: Dr. Henriette I. Jager Organization: Oak Ridge National...

  10. An Intelligent Solar Powered Battery Buffered EV Charging Station with Solar Electricity Forecasting and EV Charging Load Projection Functions

    E-Print Network [OSTI]

    Zhao, Hengbing; Burke, Andrew

    2014-01-01

    power source from inherent intermittent solar PV power.B. Solar PV Electricity Forecasting Fig. 1. Charging stationForecasting Power Output of Solar Photovoltaic System Using

  11. 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

  12. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

  13. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P. Oglesby Executive

  14. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

    high economic/demographic growth, relatively low electricity and natural gas rates, and relatively low CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION

  15. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    incorporates relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert P

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

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01

    This paper proposes a methodology to include the wind power forecasting ability, or 'forecastability,' of a site as a design criterion in wind resource assessment and wind power plant design stages. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology is adopted to maximize the capacity factor of a wind power plant. The 1-hour-ahead persistence wind power forecasting method is used to characterize the forecastability of a potential wind power plant, thereby partially quantifying the integration cost. A trade-off between the maximum capacity factor and the forecastability is investigated.

  17. 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 turbines. This second effect is the primary use of the fuel price forecast for the Council's Fifth Power

  18. Weather Forecasting Spring 2014

    E-Print Network [OSTI]

    Hennon, Christopher C.

    ATMS 350 Weather Forecasting Spring 2014 Professor : Dr. Chris Hennon Office : RRO 236C Phone : 232 of atmospheric physics and the ability to include this understanding into modern numerical weather prediction agencies, forecast tools, numerical weather prediction models, model output statistics, ensemble

  19. 1.1.1.1. 1) [2,3].

    E-Print Network [OSTI]

    Joo, Su-Chong

    . , , , , . . , . , . , . . , , . #12;- 94 - ( 1) DOGF / / , . DBMS.2 . . :· :· history :· history : , ,· select, insert, update , SQL , , SQL History . Select * from SensorNode_Infor ID, , . SQL SQL

  20. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  1. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

    /demographic growth, relatively low electricity and natural gas rates, and relatively low efficiency program CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity Manager Bill Junker Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY

  2. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

    relatively high economic/demographic growth, relatively low electricity and natural gas rates CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand Gough Office Manager DEMAND ANALYSIS OFFICE Sylvia Bender Deputy Director ELECTRICITY SUPPLY ANALYSIS

  3. U238 (1) (2)

    E-Print Network [OSTI]

    Hong, Deog Ki

    Silver 4.21 x 101 2.75 x 103 4.16 x 101 Cadmium 4.75 x 101 5.95 x 101 2.13 x 10-1 Indium 1.09 3.57 x 10 . - , , , . . . Ferrous sulfamate, Hydroxylamine nitrate Pu(IV) Pu(III) Pu

  4. Page 1 of 2

    Office of Environmental Management (EM)

    Instructions. 1. 2. 3. 4. Questions concerning this Flash should be directed Attachments cc: FAAC POLICY FLASH 2005-08 to Trudy W0;9d at (202) 287-1336. d -' Michael P....

  5. An Investigation of the Limitations in Plume Rise Models used in Air Quality Forecast Systems

    E-Print Network [OSTI]

    Collins, Gary S.

    are important for predicting pollutants regulated by National Ambient Air Quality Standards (NAAQS). NAAQS pollutants, include CO, NO2, PM2.5, PM10, O3, and SO2, are considered deleterious to public health and airAn Investigation of the Limitations in Plume Rise Models used in Air Quality Forecast Systems 1

  6. Solar thematic maps for space weather operations E. Joshua Rigler,1,2

    E-Print Network [OSTI]

    , it presents results from validation experiments designed to ascertain the robustness of the technique Weather, 10, S08009, doi:10.1029/2012SW000780. 1. Introduction [2] Forecasters at the NOAA Space Weather algorithms on powerful computers. Sometimes first-principle physics are used to convert raw measurements

  7. Solar Forecast Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  8. [1]. [2, 3, 4]. , -

    E-Print Network [OSTI]

    Zadkov, Victor

    ), eX, eY , CHk = nX · nHk , SHk = nY · nHk , nX, nY -- X, Y , nHk -- O­Hk, k = 1, 2 = nX · O(n OOH1 , H)O(nO, k)O(n nX nO , /2 - nXnO)nX, SHk = nY · O(n OOH2 , H)O(nO, k)O(n nX nO , /2 - nXnO)nX nX,Y , a) nX /2 - nXnO nXnO nO, , ) - nX O--O k ) nX

  9. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center for X-Ray2.1 Print

  10. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center for X-Ray2.1

  11. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center for X-Ray2.12.1

  12. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center forBeamline 2.1

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center forBeamline2.1

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center forBeamline2.12.1

  15. 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

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUV optics1 Print1

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUV optics1 Print12.1 Print

  18. FORECAST COMBINATION IN REVENUE MANAGEMENT DEMAND FORECASTING

    E-Print Network [OSTI]

    Fernandez, Thomas

    Demandness in Rewriting and Narrowing Sergio Antoy1 and Salvador Lucas2 1 Computer Science by a strategy to compute a step. The notion of demandness provides a suitable framework for pre- senting that the notion of demandness is both atomic and fundamental to the study of strategies. 1 Introduction Modern

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

    SciTech Connect (OSTI)

    Valero, O.J.

    1996-10-03

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

  20. Draft for Public Comment Appendix A. Demand Forecast

    E-Print Network [OSTI]

    in the forecast of electricity consumption for those years has been less than one half of a percent. Figure A-1 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

  1. Probabilistic forecasting of solar flares from vector magnetogram data

    E-Print Network [OSTI]

    Barnes, Graham

    Probabilistic forecasting of solar flares from vector magnetogram data G. Barnes,1 K. D. Leka,1 E to solar flare forecasting, adapted to provide the probability that a measurement belongs to either group, the groups in this case being solar active regions which produced a flare within 24 hours and those

  2. Improving automotive battery sales forecast

    E-Print Network [OSTI]

    Bulusu, Vinod

    2015-01-01

    Improvement in sales forecasting allows firms not only to respond quickly to customers' needs but also to reduce inventory costs, ultimately increasing their profits. Sales forecasts have been studied extensively to improve ...

  3. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

    Demand 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 of any forecast of electricity demand and developing ways to reduce the risk of planning errors

  4. ESTIMATING POTENTIAL SEVERE WEATHER SOCIETAL IMPACTS USING PROBABILISTIC FORECASTS ISSUED BY THE NWS STORM PREDICTION CENTER

    E-Print Network [OSTI]

    effort to estimate potential severe weather societal impacts based on a combination of probabilistic forecasts and high resolution population data. For equal severe weather threat, events that occur over1 ESTIMATING POTENTIAL SEVERE WEATHER SOCIETAL IMPACTS USING PROBABILISTIC FORECASTS ISSUED

  5. 1 Introduction 1 2 Invariants 5

    E-Print Network [OSTI]

    Huntbach, Matthew

    4 4 mÃ?n mÃ?n mÃ?n 4 m 2 n 2 m n 2 m 2 n 2 4 #12;mÃ?n m 2 n 2 b w d l d, b ,w := d+1 ,b+3 , w+1 . l c p-c p ,c := p+1 , c+1 E E m n m, n := m+3 ,n-1 m+3Ã?n m+ 3Ã?n = (m+3) +3Ã?(n-1) . m 3 n 1 m+3Ã?n E ls := rs E[ls := rs] E ls rs (p-c)[p ,c := p+1 ,c+1] = (p+1) -(c+1) (m+ 3Ã?n)[m, n := m+3 ,n-1] = (m+3) +3

  6. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center for X-Ray

  7. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center for

  8. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center forBeamline

  9. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    Rutledge, Steven

    AMERICAN METEOROLOGICAL SOCIETY Weather and Forecasting EARLY ONLINE RELEASE This is a preliminary microbursts than in many previously documented microbursts. Alignment of Doppler radar data to reports of wind-related damage to electrical power infrastructure in Phoenix allowed a comparison of microburst wind damage

  10. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

    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

  11. 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

  12. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    2.1.2 European Solar Radiation Atlas (ESRA)synthetic hourly radiation,” Solar Energy, vol. 49, pp. 67–for supplementing solar radiation network data,” Final

  13. 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

    2007-06-01

    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.

  14. Making Forecasts for Chaotic Physical Processes Christopher M. Danforth* and James A. Yorke

    E-Print Network [OSTI]

    Maryland at College Park, University of

    Making Forecasts for Chaotic Physical Processes Christopher M. Danforth* and James A. Yorke of years into the future [1], as well as the evolution of galactic clusters [2]. Plasma phys- icists use is followed. Given this limitation, the modeler's goal is that some linear combination of ensemble members

  15. 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

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

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    markets could aid in the design of appropriate price forecasting tools for such markets. Scenario1 Scenario Generation for Price Forecasting in Restructured Wholesale Power Markets Qun Zhou, restructured wholesale power markets, scenario generation, ARMA model, moment-matching method I. INTRODUCTION

  17. Human Trajectory Forecasting In Indoor Environments Using Geometric Context

    E-Print Network [OSTI]

    . In addressing this problem, we have built a model to estimate the occupancy behavior of humans based enhancement in the accuracy of trajectory forecasting by incorporating the occupancy behavior model. Keywords Trajectory forecasting, human occupancy behavior, 3D ge- ometric context 1. INTRODUCTION Given a human

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

    E-Print Network [OSTI]

    Hwang, Kai

    1 Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids Yogesh Simmhan. One of the characteristic applications of Smart Grids is demand response optimization (DR). The goal of DR is to use the power consumption time series data to reliable forecast the future consumption

  19. Solar Forecasting System and Irradiance Variability Characterization

    E-Print Network [OSTI]

    solar forecasting system based on numerical weather prediction plus satellite and ground-based data.1 Photovoltaic Systems: Report 3 Development of data base allowing managed access to statewide PV and insolation Based Data 13 Summary 14 References 14 #12;List of Figures Figure Number and Title Page # 1. Topography

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

    Reports and Publications (EIA)

    2010-01-01

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

  1. Marc & Mentat / Dytran version 1.2

    E-Print Network [OSTI]

    Marc & Mentat / Dytran 2015.04 version 1.2 #12; Marc & Mentat / Dytran 1 1. 1 1.1 1 1.2 1 1.3 1 1.4 ID 1 2. Marc & Mentat / Dytran 1 2.1 1 (1) TSUBAME 1 (2) 2 (3.1) Marc 2 (3.2) Mentat 2 (2.3) Dytran 3 2.2 3 3 #12;Marc & Mentat / Dytran 1. Marc & Mentat, Dytran TSUBAME TSUBAME TSUBAME

  2. CONTENTS 2 1 Motivation 4

    E-Print Network [OSTI]

    Gross, Rudolf

    1 #12;CONTENTS 2 Contents 1 Motivation 4 2 Circuit QED and Two-Resonator Circuit-QED 6 2.1 Coupled Fabrication Parameters 94 D E-beam Sample Holder 96 E Danksagungen 103 #12;1 MOTIVATION 4 1 Motivation

  3. Detrending Daily Natural Gas Consumption Series to Improve Short-Term Forecasts

    E-Print Network [OSTI]

    Povinelli, Richard J.

    Detrending Daily Natural Gas Consumption Series to Improve Short-Term Forecasts Ronald H. Brown1 that allows long-term natural gas demand signals to be used effect- ively to generate high quality short-term natural gas demand forecasting models. Short data sets in natural gas forecasting inadequately represent

  4. GENETIC ALGORITHM FORECASTING FOR TELECOMMUNICATIONS PRODUCTS

    E-Print Network [OSTI]

    Havlicek, Joebob

    available economic indicators such as Disposable Personal Income and New Housing Starts as independent exhibiting maximal fitness achieved RMS forecast errors below the the average two-week sales figure. 1 (Holland, 1975), (Packard, 1990), (Koza, 1992), (Bäck, et al., 1997), (Mitchell, 1998). For example, Meyer

  5. Impact of a Revised Convective Triggering Mechanism on CAM2 Model Simulations: Results from Short-Range Weather Forecasts

    SciTech Connect (OSTI)

    Xie, S; Boyle, J S; Cederwall, R T; Potter, G L; Zhang, M; Lin, W

    2004-02-19

    This study implements a revised convective triggering condition in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM2) model to reduce its excessive warm season daytime precipitation over land. The new triggering mechanism introduces a simple dynamic constraint on the initiation of convection that emulates the collective effects of lower level moistening and upward motion of the large-scale circulation. It requires a positive contribution from the large-scale advection of temperature and moisture to the existing positive Convective Available Potential Energy (CAPE) for model convection to start. In contrast, the original convection triggering function in CAM2 assumes that convection is triggered whenever there is positive CAPE, which results in too frequent warm season convection over land arising from strong diurnal variation of solar radiation. We examine the impact of the new trigger on CAM2 simulations by running the climate model in Numerical Weather Prediction (NWP) mode so that more available observations and high-frequency NWP analysis data can be used to evaluate model performance. We show that the modified triggering mechanism has led to considerable improvements in the simulation of precipitation, temperature, moisture, clouds, radiations, surface temperature, and surface sensible and latent heat fluxes when compared to the data collected from the Atmospheric Radiation Measurement (ARM) program at its South Great Plains (SGP) site. Similar improvements are also seen over other parts of the globe. In particular, the surface precipitation simulation has been significantly improved over both the continental United States and around the globe; the overestimation of high clouds in the equatorial tropics has been substantially reduced; and the temperature, moisture, and zonal wind are more realistically simulated. Results from this study also show that some systematic errors in the CAM2 climate simulations can be detected in the early stage of model integration. Examples are the extremely overestimated high clouds in the tropics in the vicinity of ITCZ and the spurious precipitation maximum in the east of the Rockies. This has important implications in studies of these model errors since running the climate model in NWP mode allows us to perform a more in-depth analysis during a short time period where more observations are available and different model errors from various processes have not compensated for the systematic errors.

  6. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01

    This presentation describes the importance of good forecasting for variable generation, the different approaches used by industry, and the importance of validated high-quality data.

  7. 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 

  8. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power AdministrationRobust,Field-effectWorking WithTelecentricNCubicthe FOIA?ResourceMeasurement BuoyForecasting Sign

  9. Online short-term solar power forecasting

    SciTech Connect (OSTI)

    Bacher, Peder; Madsen, Henrik [Informatics and Mathematical Modelling, Richard Pedersens Plads, Technical University of Denmark, Building 321, DK-2800 Lyngby (Denmark); Nielsen, Henrik Aalborg [ENFOR A/S, Lyngsoe Alle 3, DK-2970 Hoersholm (Denmark)

    2009-10-15

    This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model. (author)

  10. Multivariate Forecast Evaluation And Rationality Testing

    E-Print Network [OSTI]

    Komunjer, Ivana; OWYANG, MICHAEL

    2007-01-01

    Economy, 95(5), 1062—1088. MULTIVARIATE FORECASTS Chaudhuri,Notion of Quantiles for Multivariate Data,” Journal of thePress, United Kingdom. MULTIVARIATE FORECASTS Kirchgässner,

  11. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    Supplement — 2. I E Update ENERGY E N D USES A N D D 0 E -Supplement — 2.1E Update ENERGY METERS IN PLANT IntroductionRates Supplement — 2.1E Update ENERGY-CHG accepts a numeric

  12. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    PLANT-PARAMETERS P - HEAT-RECOVERY P - LOAD-ASSIGNMENT P -1 / 3 SPACE-CONDITIONS SUPPLY-1 S-l P - HEAT-RECOVERY . 5 FSUPPLY-2 S-2 P - HEAT-RECOVERY . 5 F 1 V.66 V.66 SUPPLY-5 S-

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

  14. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

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

  15. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

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

  16. 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

  17. 1.2.3. internal event and

    E-Print Network [OSTI]

    Ladkin, Peter B.

    3.1.1.3.1.1.3 low weight on each main gear wheel 3.1.1.3.2.2 braking system's logical design 3 3.1.1.3.1.1.3 low weight on each main gear wheel 3.1.1.3.2.2 braking system's logical design 3.1.1.3.2.3 divergence between consequences of design and behaviour expected by CRW 3.1.1.3.2.3.1 behavior expected

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /1.2 Print1.2 Print1.2

  19. Two techniques for forecasting clear air turbulence 

    E-Print Network [OSTI]

    Arbeiter, Randolph George

    1977-01-01

    result in only mild annoyance or discomfort (air sickness) to crew and passengers. As it becomes moderate, difficulty may be experienced in moving about inside the airplane and the crew may momentarily lose control. Severe CAT can result in injury... successfully used by the Air Force Clobal Heather Central (Barnett, 1970) for oper" tional forecasting on a day-to-day basis. Furthermore, its usefulness 1' or supersonic aircraft in the stratosphere v;as successfully demonstrated by Scoggins et H. (1975...

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01

    Numerical Weather Prediction (NWP), Solar Forecasting  1.   to more accurate prediction of solar  irradiance, given a to create daily solar electricity predictions accurate to 

  1. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

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

  2. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01

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

  3. Hickey -TT136 casts 1-2 Hickey -TT136,Casts 1-2, page 1

    E-Print Network [OSTI]

    Hickey, Barbara

    Hickey -TT136 casts 1-2 Hickey -TT136,Casts 1-2, page 1 CTD001 #12;Hickey -TT136 casts 1-2 Hickey -TT136,Casts 1-2, page 2 CTD001 #12;Hickey -TT136 casts 1-2 Hickey -TT136,Casts 1-2, page 3 CTD001 #12;Hickey -TT136 casts 1-2 Hickey -TT136,Casts 1-2, page 4 CTD001 #12;Hickey -TT136 casts 1-2 Hickey -TT136

  4. 1.2.3. internal event and

    E-Print Network [OSTI]

    Ladkin, Peter B.

    main gear wheel 3.1.1.3.2.2 braking system's logical design 3.1.1.3.2.3 divergence between consequences of design and behaviour expected by CRW 3.1.1.3.2.3.1 behavior expected by CRW 3.1.1.3.2.3.1.1 `normal

  5. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center 29-ID2.1 Print2.1

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

    SciTech Connect (OSTI)

    John Zack; Deborah Hanley; Dora Nakafuji

    2012-07-15

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

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

    E-Print Network [OSTI]

    Jiang, Jiancheng

    Export Control Certification Form J-1, B-1,B-2, B-1/B-2 Visas v.082014 / Rev. 02.2015 Page 1 of 3 EXPORT CONTROL CERTIFICATION for J-1 Visiting Scholar or B-1, B-2, or B-1/B-2 Combination Tourist Visa: __________________________________________________________ It is now mandatory that you complete the "US Export Control Regulations" CITI Program training module

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

    E-Print Network [OSTI]

    Goto, Susumu

    2007-01-01

    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 ...

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

    SciTech Connect (OSTI)

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

    1995-05-01

    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.

  10. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    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

  11. (1) THORPEX Interactive Grand Global Ensemble (TIGGE) References

    E-Print Network [OSTI]

    Froude, Lizzie

    Weather Review, submitted. Hodges, K. I., 1995: Feature tracking on the unit-sphere. Monthly Weather (UK) CPTEC (Brazil) ECMWF Analysis 0 1 2 3 4 5 6 7 Forecast Lead Time (days) 0 0.2 0.4 0.6 0.8 1 1.2 1) JMA (Japan) KMA (Korea) NCEP (USA) UKMO (UK) CPTEC (Brazil) 0 1 2 3 4 5 Forecast Lead Time (days) -5

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

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

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

  13. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /1.2 Print Center for1.2

  14. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /1.2 Print Center1.2

  15. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /1.2 Print1.2 Print

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01

    weather prediction solar irradiance forecasts in the US.2013: Review of solar irradiance forecasting methods and asatellite-derived irradiances: Description and validation.

  17. 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

  18. Table S1. Fuel Properties. JP-8 Blend-1 FT-1 Blend-2 FT-2

    E-Print Network [OSTI]

    Meskhidze, Nicholas

    1 Table S1. Fuel Properties. JP-8 Blend-1 FT-1 Blend-2 FT-2 Feedstock Petroleum Petroleum & Natural Gas Natural Gas Petroleum & Coal Coal Sulfur (ppm by mass) 1148 699 19 658 22 Alkanes (% vol.) 50

  19. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center 29-ID2.1 Print

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center 29-ID2.1

  1. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center 29-ID2.12.1 Print

  2. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center 29-ID2.12.1

  3. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print0.2 Print Surface3.31 Print1

  4. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUVBeamline2.21 Print121.2

  5. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline1 Print1

  6. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUV optics1 Print12.112.1

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUV optics1 Print12.112.12.1

  8. 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 Plasma Science Workshop II 1-3 May 2001 San Diego, California #12;SLM 2/29/2000 WAH 2 May 2001 2WAH 2 May-mode trigger ELMS with reduced magnitude and duration compared with LFS injected pellets #12;SLM 2/29/2000 WAH

  9. 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

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

    E-Print Network [OSTI]

    Mallet, Vivien

    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

  11. DEGREE DAYS AND WEATHER NOTES Weather Forecast: Chance of showers and storms through

    E-Print Network [OSTI]

    Isaacs, Rufus

    1 DEGREE DAYS AND WEATHER NOTES Weather Forecast: Chance of showers and storms through Thursday by ~225. Complete weather summaries and forecasts are at available enviroweather.msu.edu GDD (from March 1.isaacslab.ent.msu.edu/blueberryscout/blueberryscout.htm Contents · Crop Stages · Weather Notes · Disease Update · Scouting the Major Diseases of Highbush

  12. 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 ...

  13. 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 ...

  14. Forecasting consumer products using prediction markets

    E-Print Network [OSTI]

    Trepte, Kai

    2009-01-01

    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 ...

  15. Modeling and Forecasting Electric Daily Peak Loads

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    for the same data. Two methods are described for forecasting daily peak loads up to one week ahead through, including generator unit commitment, hydro-thermal coordination, short-term maintenance, fuel allocation forecasting accuracies. STLF forecasting covers the daily peak load, total daily energy, and daily load curve

  16. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /1.2 Print Center for

  17. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /1.2 Print Center

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /1.2 Print

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUVBeamline2.21

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUVBeamline2.21Beamline

  1. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print0.2 Print Surface3.31 Print

  2. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print0.2 Print Surface3.31

  3. , 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

  4. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline1 Print

  5. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline1 Print11

  6. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline1 Print111

  7. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline1 Print1111

  8. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline1

  9. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline12.1

  10. 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

  11. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Klein, Stephen

    2008-01-15

    Dataset contains the NCAR CAM3 (Collins et al., 2004) and GFDL AM2 (GFDL GAMDT, 2004) forecast data at locations close to the ARM research sites. These data are generated from a series of multi-day forecasts in which both CAM3 and AM2 are initialized at 00Z every day with the ECMWF reanalysis data (ERA-40), for the year 1997 and 2000 and initialized with both the NASA DAO Reanalyses and the NCEP GDAS data for the year 2004. The DOE CCPP-ARM Parameterization Testbed (CAPT) project assesses climate models using numerical weather prediction techniques in conjunction with high quality field measurements (e.g. ARM data).

  12. 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).

  13. 2 Incidence 2.1 Incidence Axioms

    E-Print Network [OSTI]

    Lee, Carl

    of Theorem 2.4.2 can be found in the book. 9 #12;2.3 A Tetrahedron Model The Incidence Axioms do not force to drag the picture to di erent orientations. 10 #12;2.4 Geometrical Worlds Here are some geometrical to draw on, such as very smooth tennis balls, ping-pong balls, oranges or L en art spheres. Also, consider

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

    SciTech Connect (OSTI)

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

    2011-10-01

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

  15. 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

  16. 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

  17. MM5 Contrail Forecasting in Alaska Martin Stuefer, Xiande Meng and Gerd Wendler

    E-Print Network [OSTI]

    Stuefer, Martin

    MM5 Contrail Forecasting in Alaska Martin Stuefer, Xiande Meng and Gerd Wendler Geophysical Institute, University of Alaska, Fairbanks 1. Abstract Fifth-generation mesoscale model (MM5) is being used air. Algorithm input data are MM5 forecasted temperature and humidity values at defined pressure

  18. Next Generation Short-Term Forecasting of Wind Power Overview of the ANEMOS Project.

    E-Print Network [OSTI]

    Boyer, Edmond

    1 Next Generation Short-Term Forecasting of Wind Power ­ Overview of the ANEMOS Project. G outperform current state-of-the-art methods, for onshore and offshore wind power forecasting. Advanced and evaluation at a local, regional and national scale. Finally, the project demonstrates the value of wind

  19. Seasonal Sensitivity on COBEL-ISBA Local Forecast System for Fog and Low Clouds

    E-Print Network [OSTI]

    Seasonal Sensitivity on COBEL-ISBA Local Forecast System for Fog and Low Clouds STEVIE ROQUELAURE of uncertainty that lead to dispersion. Key words: Local numerical forecast system, fog and low clouds, seasonal prediction system. 1. Introduction Accurate prediction of fog and low clouds is one of the main issues

  20. A Novel Forecasting System for Solar Particle Events and Flares (FORSPEF)

    E-Print Network [OSTI]

    Anastasiadis, Anastasios

    A Novel Forecasting System for Solar Particle Events and Flares (FORSPEF) A Papaioannou1 Energetic Particles (SEPs) result from intense solar eruptive events such as solar flares and coronal mass. In this work, we present FORSPEF (Forecasting Solar Particle Events and Flares), a novel dual system, designed

  1. Hourly Temperature Forecasting Using Abductive Networks R. E. Abdel-Aal

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Review Copy 1 Hourly Temperature Forecasting Using Abductive Networks R. E. Abdel-Aal Center temperatures, Artificial intelligence. Dr. R. E. Abdel-Aal, P. O. Box 1759, KFUPM, Dhahran 31261 Saudi Arabia e; Khotanzad, Afkhami-Rohani & Maratukulam, 1998; Sharif & Taylor, 2000; Xu & Chen, 1999). Such forecasts

  2. Tiree Energy Pulse: Exploring Renewable Energy Forecasts on the Edge of the Grid

    E-Print Network [OSTI]

    MacDonald, Mark

    Tiree Energy Pulse: Exploring Renewable Energy Forecasts on the Edge of the Grid Will Simm1 , Maria energy consumption with supply, and together built a prototype renewable energy forecast display. A num local renewable energy was expected to be available, despite having no financial in- centive to do so

  3. Data Assimilation in Weather Forecasting: A Case Study in PDE-Constrained Optimization

    E-Print Network [OSTI]

    Nocedal, Jorge

    Data Assimilation in Weather Forecasting: A Case Study in PDE-Constrained Optimization M. Fisher J weather prediction centers to produce the initial conditions for 7- to 10-day weather fore- casts, with particular reference to the system in operation at the European Centre for Medium-Range Weather Forecasts. 1

  4. 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

  5. . .E1 < E2 , .1 . N n ? ()

    E-Print Network [OSTI]

    Rabani, Eran

    . , : ,Lx = Ly = L .3 nm (x, y) = 2 L sin nx L sin my L |nm | = nm unm |nm .(unm ) . (x, y () : , | = nm unm |nm (x, y) = nm unmnm (x, y) = nm unm 2 L sin nx L sin my L , unm = ¢ L 0 ¢ L 0 nm (x, y) (x, y) dxdy = ¢ L 0 ¢ L 0 2 L sin nx L sin my L 4 3L sin 2x L sin2 y L dxdy = 2 L ¢ L 0 sin nx L sin

  6. Assessing forecast uncertainties in a VECX model for Switzerland: an exercise in forecast combination across models and observation windows

    E-Print Network [OSTI]

    Assenmacher-Wesche, Katrin; Pesaran, M. Hashem

    horizons of up to eight quarters ahead since this is the rele- vant time horizon for central banks when setting interest rates. Table 6 shows the RMSFE, the bias and the hit rate of forecasts based on the VECX*(2,2) model for the longest estimation window...

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print National Center 29-ID

  8. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline12.11 Print

  9. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print3.3 Beamline0.1 Beamline12.11

  10. RANDOM REALS AND 1 (1, ( : ))2 J. TATCH MOORE

    E-Print Network [OSTI]

    Moore, Justin Tatch

    the results of J. Barnett and S. Todorcevi´c concerning the influence MA1 has on random graphs. I of random reals. 1. Introduction The focus of this note is to extend J. Barnett's result in [2] that 1 (1 of MA1 . Also, in [1] Barnett has shown that the stronger partition relation 1 (1, ( : 1))2 holds

  11. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01

    the cloud index,” Solar Energy, vol. 81, no. 2, pp. 280 –Cover Indices,” ASME Journal of Solar Energy Engineering (inHorizontal Irradiance,” submitted to Solar Energy, 2012.

  12. RTL Hardware Design Chapter 2 1

    E-Print Network [OSTI]

    Chu, Pong P.

    1 RTL Hardware Design by P. Chu Chapter 2 1 Hardware Description Language RTL Hardware Design by P. Chu Chapter 2 2 Outline 1. Overview on hardware description language 2. Basic VHDL Concept via an example 3. VHDL in development flow RTL Hardware Design by P. Chu Chapter 2 3 1. Overview on hardware

  13. The extended Baryon Oscillation Spectroscopic Survey (eBOSS): a cosmological forecast

    E-Print Network [OSTI]

    Zhao, Gong-Bo; Ross, Ashley J; Shandera, Sarah; Percival, Will J; Dawson, Kyle S; Kneib, Jean-Paul; Myers, Adam D; Brownstein, Joel R; Comparat, Johan; Delubac, Timothée; Gao, Pengyuan; Hojjati, Alireza; Koyama, Kazuya; McBride, Cameron K; Meza, Andrés; Newman, Jeffrey A; Palanque-Delabrouille, Nathalie; Pogosian, Levon; Prada, Francisco; Rossi, Graziano; Schneider, Donald P; Seo, Hee-Jong; Tao, Charling; Wang, Dandan; Yèche, Christophe; Zhang, Hanyu; Zhang, Yuecheng; Zhou, Xu; Zhu, Fangzhou; Zou, Hu

    2015-01-01

    We present a science forecast for the eBOSS survey, part of the SDSS-IV project, which is a spectroscopic survey using multiple tracers of large-scale structure, including luminous red galaxies (LRGs), emission line galaxies (ELGs) and quasars (both as a direct probe of structure and through the Ly-$\\alpha$ forest). Focusing on discrete tracers, we forecast the expected accuracy of the baryonic acoustic oscillation (BAO), the redshift-space distortion (RSD) measurements, the $f_{\\rm NL}$ parameter quantifying the primordial non-Gaussianity, the dark energy and modified gravity parameters. We also use the line-of-sight clustering in the Ly-$\\alpha$ forest to constrain the total neutrino mass. We find that eBOSS LRGs ($0.60.6$), ELGs ($0.61.2$) and Clustering Quasars (CQs) ($0.62.2$) can achieve a precision of 1%, 2.2% and 1.6% precisions, respectively, for spherically averaged BAO distance measurements. Using the same samples, the constraint on $f\\sigma_8$ is expected to be 2.5%, 3.3% and 2.8...

  14. Forecasting wind speed financial return

    E-Print Network [OSTI]

    D'Amico, Guglielmo; Prattico, Flavio

    2013-01-01

    The prediction of wind speed is very important when dealing with the production of energy through wind turbines. In this paper, we show a new nonparametric model, based on semi-Markov chains, to predict wind speed. Particularly we use an indexed semi-Markov model that has been shown to be able to reproduce accurately the statistical behavior of wind speed. The model is used to forecast, one step ahead, wind speed. In order to check the validity of the model we show, as indicator of goodness, the root mean square error and mean absolute error between real data and predicted ones. We also compare our forecasting results with those of a persistence model. At last, we show an application of the model to predict financial indicators like the Internal Rate of Return, Duration and Convexity.

  15. 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

  16. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    Integrated Desiccant Cooling Supplement — 2. I E Update ForIntegrated Desiccant Cooling Supplement — 2. I E Update Then updates the D O E - 2 Supplement f r o m version 2.ID t o

  17. 1. Director's Report 1 2. Highlights 3

    E-Print Network [OSTI]

    New South Wales, University of

    -Film, Third Generation and Hybrid Devices 33 Program Package 3. Optics/Characterisation 71 Program Package 4-quality training opportunities for the next generation of photovoltaic researchers, particularly through enhanced Activities 101 7. Financial Summary 113 8. Publications 115 #12;1 1. Photovoltaics involves the direct

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

    E-Print Network [OSTI]

    Kulkarni, Siddhivinayak

    2009-01-01

    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...

  19. 1 Introduction 1 1.1 Balancing Autonomy and Strategic Management . . . . . . . . . . . 2

    E-Print Network [OSTI]

    #12;#12;Contents 1 Introduction 1 1.1 Balancing Autonomy and Strategic Management Autonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3 Strategic Management of Multiagent . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5 Tactical Autonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6 Strategic

  20. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01

    Various factors influence the development of the voluntary 'green' power market--the market in which consumers purchase or produce power from non-polluting, renewable energy sources. These factors include climate policies, renewable portfolio standards (RPS), renewable energy prices, consumers' interest in purchasing green power, and utilities' interest in promoting existing programs and in offering new green options. This report presents estimates of voluntary market demand for green power through 2015 that were made using historical data and three scenarios: low-growth, high-growth, and negative-policy impacts. The resulting forecast projects the total voluntary demand for renewable energy in 2015 to range from 63 million MWh annually in the low case scenario to 157 million MWh annually in the high case scenario, representing an approximately 2.5-fold difference. The negative-policy impacts scenario reflects a market size of 24 million MWh. Several key uncertainties affect the results of this forecast, including uncertainties related to growth assumptions, the impacts that policy may have on the market, the price and competitiveness of renewable generation, and the level of interest that utilities have in offering and promoting green power products.

  1. Utilize cloud computing to support dust storm forecasting Qunying Huanga

    E-Print Network [OSTI]

    Chen, Songqing

    storm forecasting operational system should support a disruptive fashion by scaling up to enable high to save energy and costs. With the capability of providing a large, elastic, and virtualized pool and property damages since 1995 (Figure 1). Deaths and injuries are usually caused by car accidents, because

  2. ARM Processes and Their Modeling and Forecasting Methodology Benjamin Melamed

    E-Print Network [OSTI]

    Chapter 73 ARM Processes and Their Modeling and Forecasting Methodology Benjamin Melamed Abstract The class of ARM (Autoregressive Modular) processes is a class of stochastic processes, defined by a non- linear autoregressive scheme with modulo-1 reduction and additional transformations. ARM processes

  3. 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

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

    E-Print Network [OSTI]

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

    2005-01-01

    index.html. Appendix A.1 Natural Gas Price Data for FuturesError STEO Error A.1 Natural Gas Price Data for Futuresof forecasts for natural gas prices as reported by the

  5. RTL Hardware Design Chapter 2 1

    E-Print Network [OSTI]

    Chu, Pong P.

    RTL Hardware Design by P. Chu Chapter 2 1 Hardware Description Language #12;RTL Hardware Design by P. Chu Chapter 2 2 Outline 1. Overview on hardware description language 2. Basic VHDL Concept via an example 3. VHDL in development flow #12;RTL Hardware Design by P. Chu Chapter 2 3 1. Overview on hardware

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

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01

    Heating Electric Furnace Gas Furnace LPG Furnace Oil Furnaceliquid petroleum gas (LPG), and wood Availability ofHeating Electric Furnace Gas Furnace LPG Furnace Oil Furnace

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

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01

    Fuels = Oil and Gas, Other = LPG and Misc. (3) Sources: 1990includes homes heated with LPG or with miscellaneous fuelssegmentation, homes with LPG heating are included in the

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

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01

    data. Residential primary energy use is expected to growmat the overall primary energy intensity per household ofby Stock Equipment (Primary Energy, Trillion Btu) Table B .

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

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01

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

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

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01

    LPG Furnace Oil Furnace Electric Heat Pump Gas BoilerOil Boiler Electric Room Heater Gas Room Heater Wood Stove (Electric Heat Pump Gas Boiler Oil Boiler Electric Room Gas

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

    E-Print Network [OSTI]

    Johnson, F.X.

    2010-01-01

    on data for hot water boilers. Electric room (E RM) costsEIA 1993). Table B.5 Oil Boilers Stock Data Vintage Blockoil furnaces. Figure E3b: Gas Boiler Installed Price by

  12. 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 consumption in residences (EIA 1993). This report is primarily methodological in nature, taking the reader

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

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01

    US DOE. 1995a. Annual Energy Outlook 1995, with ProjectionsELA) 1995 Annual Energy Outlook (AEO); 1990 Residentialof Energy's Annual Energy Outlook ( US DOE 1995a). A l l

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

    E-Print Network [OSTI]

    Koomey, Jonathan G.

    2010-01-01

    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

  15. Optimal combined wind power forecasts using exogeneous variables

    E-Print Network [OSTI]

    Optimal combined wind power forecasts using exogeneous variables Fannar ¨Orn Thordarson Kongens to the Klim wind farm using three WPPT forecasts based on different weather forecasting systems. It is shown of the thesis is combined wind power forecasts using informations from meteorological forecasts. Lyngby, January

  16. Weather Forecasts are for Wimps: Why Water Resource Managers Do Not Use Climate Forecasts

    E-Print Network [OSTI]

    Rayner, Steve; Lach, Denise; Ingram, Helen

    2005-01-01

    and Winter, S. G. : 1960, Weather Information and EconomicThe ENSO Signal 7, 4–6. WEATHER FORECASTS ARE FOR WIMPSWEATHER FORECASTS ARE FOR WIMPS ? : WHY WATER RESOURCE

  17. Marc Schipper1* , Udo Ernst2

    E-Print Network [OSTI]

    Kreiter, Andreas K.

    Marc Schipper1* , Udo Ernst2 and Manfred Fahle1 1 Institute for Human Neurobiology, Germany 2 Nov 2008. * Correspondence: Marc Schipper, Institute for Human Neurobiology, Bremen, Germany, schipper.marc

  18. 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

  19. The Preservation of Physical Fashion Forecasts

    E-Print Network [OSTI]

    Kosztowny, Alexander John

    2015-01-01

    schools and their libraries, which use trend forecastingin archives and libraries would be that the trend forecastsin a library or archive, not exclusively to trend forecasts.

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

    Energy Savers [EERE]

    R&D translates into improved performance and reduced costs for energy technologies. Motivation Technological forecasts, which plot the anticipated performance and costs of...

  1. Promotional forecasting in the grocery retail business

    E-Print Network [OSTI]

    Koottatep, Pakawkul

    2006-01-01

    Predicting customer demand in the highly competitive grocery retail business has become extremely difficult, especially for promotional items. The difficulty in promotional forecasting has resulted from numerous internal ...

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

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

    that take place in complex terrain, this funding opportunity will improve foundational weather models by developing short-term wind forecasts for use by industry professionals,...

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

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

    processes that take place in complex terrain, this funding would improve foundational weather models by developing short-term wind forecasts for use by industry professionals,...

  4. Characterization of 1 and 2-matrices Rafael Bru1

    E-Print Network [OSTI]

    Bru, Rafael

    Characterization of 1 and 2-matrices Rafael Bru1 Ljiljana Cvetkovi2 Vladimir Kosti2 Francisco-matrices. In particular, new characterizations of 1 and of 2-matrices are given. Considering these characterizations some Introduction In this paper we give characterizations of subclasses of H-matrices which are being studied

  5. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power Administration wouldMass map shines lightGeospatial ToolkitSMARTS -BeingFuture forForecasting NREL researchers

  6. Forecasting Market Demand for New Telecommunications Services: An Introduction

    E-Print Network [OSTI]

    Parsons, Simon

    Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc in demand forecasting for new communication services. Acknowledgments: The writing of this paper commenced employers or consultancy clients. KEYWORDS: Demand Forecasting, New Product Marketing, Telecommunica- tions

  7. 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

  8. Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts

    E-Print Network [OSTI]

    Raftery, Adrian

    Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts VERONICA ensembles that generates calibrated probabilistic forecast products for weather quantities at indi- vidual perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather

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

    E-Print Network [OSTI]

    Dickson, Donald

    2013-01-01

    stream_source_info SCN 71 1&2.pdf.txt stream_content_type text/plain stream_size 178618 Content-Encoding ISO-8859-1 stream_name SCN 71 1&2.pdf.txt Content-Type text/plain; charset=ISO-8859...-1 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 SCN, an o?cial organ of the Milton Society...

  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. INTELLIGENT HANDLING OF WEATHER FORECASTS Stephan Kerpedjiev

    E-Print Network [OSTI]

    , discourse and semantic. They are based on a conceptual model underlying weather forecasts as well situations represented in the form of texts in NL, weather maps, data tables or combined information objectsINTELLIGENT HANDLING OF WEATHER FORECASTS Stephan Kerpedjiev I n s t i t u t e of Mathematics Acad

  12. Smooth Calibration, Leaky Forecasts, and Finite Recall

    E-Print Network [OSTI]

    Hart, Sergiu

    Smooth Calibration, Leaky Forecasts, and Finite Recall Sergiu Hart October 2015 SERGIU HART c 2015 ­ p. #12;Smooth Calibration, Leaky Forecasts, and Finite Recall Sergiu Hart Center for the Study of Rationality Dept of Mathematics Dept of Economics The Hebrew University of Jerusalem hart@huji.ac.il http://www.ma.huji.ac.il/hart

  13. Multivariate Time Series Forecasting in Incomplete Environments

    E-Print Network [OSTI]

    Roberts, Stephen

    Multivariate Time Series Forecasting in Incomplete Environments Technical Report PARG 08-03 Seung of Oxford December 2008 #12;Seung Min Lee and Stephen J. Roberts Technical Report PARG 08-03 Multivariate missing observations and forecasting future values in incomplete multivariate time series data. We study

  14. 2.1E Sample Run Book

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    REPORT- BEPS BUILDING ENERGY PERFORMANCE SUMMARY DOE-2.1E-REPORT- BEPU BUILDING ENERGY PERFORMANCE SLIIO4ARY (UTILITYREPORT- BEPS BUILDING ENERGY PERFORMANCE SUMMARY DOE-2.1E-

  15. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    basis for the energy cost calculations. Block costs computedi n 2 . I E T h e energy cost calculation ( E C O N O M I Cu m p s . T h e calculation of energy costs has been shifted

  16. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    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

  17. 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...

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01

    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.  

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast / Femtosecond20.221.2

  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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast / Femtosecond20.221.21.2

  1. 2.1E Supplement

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    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

  2. 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]

    /1 A modeling study of the impact of mesoscale air sea interactions over the Gulf Stream on weather and climate

  3. Greetings 1 Research Highlights 2

    E-Print Network [OSTI]

    Wikswo, John

    is to build a functioning PS1-silicon solar cell using this new design. Jennings has an Environmental with silicon, the material used in solar cells, in a fashion that produces substantially more electrical current than has been reported by previous "biohybrid" solar cells. The research was reported in Journal

  4. , Gerard Parr2 1. 361005

    E-Print Network [OSTI]

    Fu, Xiaoming

    #12; A/D D/A (DAQ)(ZMAQ) ISA ()[5] ( GPIB) ISA DSP PCI CPU 32 64 [6] PCI ISA 132~264 MB/S PCI (plug&play) DSP PCI 1997 9 1 NI PXI[7] PXI PCI (PCI eXtensions for Instrumentation) Compact PCI PCI , PXI VXI PCI VXI PCI PXI PXI PC PC USB(General Serial

  5. Forecasting Agriculturally Driven Global Environmental

    E-Print Network [OSTI]

    Thomas, David D.

    production doubled, greatly reducing food shortages, but at high environ- mental cost (1­5). In addition climate change in environ- mental and societal impacts (2, 8). Population size and per capita consumption capita 1 Department of Ecology, Evolution and Behavior, Uni- versity of Minnesota, 1987 Upper Buford

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

    SciTech Connect (OSTI)

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

    2013-06-15

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

  7. GFDL Laboratory Review, June 30 July 2, 2009 Technology Transferred (1b) U.S.DOC/NOAA/OAR/GFDL 6/30/2009-7/2/2009 Page 1

    E-Print Network [OSTI]

    of National Centers for Environmental Prediction's (NCEP's) coupled Climate Forecast System (CFS) and Global Ocean Data Assimilation System (GODAS) since that time. The CFS became operational in 2004. NCEP's Environmental Modeling Center (EMC) has developed and is testing a new version of CFS (CFS-v2

  8. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.11 Print Ultrafast /

  9. Earthquake Forecast via Neutrino Tomography

    E-Print Network [OSTI]

    Bin Wang; Ya-Zheng Chen; Xue-Qian Li

    2011-03-29

    We discuss the possibility of forecasting earthquakes by means of (anti)neutrino tomography. Antineutrinos emitted from reactors are used as a probe. As the antineutrinos traverse through a region prone to earthquakes, observable variations in the matter effect on the antineutrino oscillation would provide a tomography of the vicinity of the region. In this preliminary work, we adopt a simplified model for the geometrical profile and matter density in a fault zone. We calculate the survival probability of electron antineutrinos for cases without and with an anomalous accumulation of electrons which can be considered as a clear signal of the coming earthquake, at the geological region with a fault zone, and find that the variation may reach as much as 3% for $\\bar \

  10. Round Time Court 1 (Indoor) Court 2 (Indoor) Round Time Court 1 (Indoor) Court 2 (Indoor)

    E-Print Network [OSTI]

    Wagner, Stephan

    :00 Play-Offs (A) A1 (Super) Pos 5 vs A2 (Prem) Pos 2 Play-Offs (B) A1 (Super) Pos 6 vs A2 (Prem) Pos 1 4 13:00 Semi-Final (F) A1 (Super) Pos 2 vs A1 (Super) Pos 3 Play-Offs (H) A2 (Prem) Pos 4 vs A2 (Prem) Pos 5 5 14:30 Semi-Final (G) A1 (Super) Pos 1 vs A1 (Super) Pos 4 Play-Offs (I) A2 (Prem) Pos 3 vs A2

  11. Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States

    E-Print Network [OSTI]

    Zhang, Yang

    States Ming-Tung Chuang a , Yang Zhang a,*, Daiwen Kang b a Air Quality Forecasting Lab, North Carolina on a three-dimensional air quality model provides a powerful tool to forecast air quality and advise, inaccuracies in simulated meteorological variables such as 2-m temperature, 10-m wind speed, and precipitation

  12. 1754-5692(2009)2:2;1-3 Environmental Science

    E-Print Network [OSTI]

    2009-01-01

    energy crisis. In particular, using MFCs to convert organics and inorganic matter into electricity1754-5692(2009)2:2;1-3 Energy& Environmental Science www.rsc.org/ees Volume 2 | Number 2 | February to global warming, air pollution, and energy security ISSN 1754-5692 #12;Exploring the use

  13. 2 --October 12, 2007 2 2CO (g) CO (aq) (1)

    E-Print Network [OSTI]

    , 1 . (, ) , , (5 ). 3. Fugacity fugacity (mole fraction) - x(CO2)·p 3 3HCO (aq) H (aq) CO (aq)+ - + (4) (g), (l), (aq) , , . CO2(aq) H2CO3(aq) . CO2(aq) H2CO3(aq) CO2 * (aq) ( CO2 * (aq) H2CO3 * ). #12;October 12, 2007 2 -- 6 (1), (2) (3

  14. RANDOM REALS AND ! 1 ! (! 1 ; ( : )) 2 J. TATCH MOORE

    E-Print Network [OSTI]

    Moore, Justin Tatch

    will be to extend the results of J. Barnett and S. Todor#20;cevi#19;c concerning the in uence MA@1 has on random is to extend J. Barnett's result in [2] that ! 1 ! (! 1 ; (#11; : n)) 2 holds for all #11; Barnett has shown

  15. V1 1 2 3 4 5 6 First Name

    E-Print Network [OSTI]

    Alekseenko, Alexander

    ) Determine whether the function is odd, even, or neither. f(x) = x2 + 1. b) (5pt) Determine whether the function is odd, even, or neither. g(x) = x3 + 1 x . c) (5pt) Determine whether the function is odd, even, or neither. h(x) = x2 - 1. #12;

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

    SciTech Connect (OSTI)

    Not Available

    2008-06-01

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

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

    SciTech Connect (OSTI)

    Not Available

    2008-06-01

    The U.S. Department of Energy's Wind Powering America Program is committed to educating state-level policy makers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in New York. We forecast the cumulative economic benefits from 1000 MW of development in New York to be $1.3 billion, annual CO2 reductions are estimated at 2.5 million tons, and annual water savings are 1,230 million gallons.

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

    SciTech Connect (OSTI)

    Not Available

    2008-06-01

    The U.S. Department of Energy's Wind Powering America Program is committed to educating state-level policy makers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Virginia. We forecast the cumulative economic benefits from 1000 MW of development in Virginia to be $1.2 billion, annual CO2 reductions are estimated at 3.0 million tons, and annual water savings are 1,600 million gallons.

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

    SciTech Connect (OSTI)

    Not Available

    2008-06-01

    The U.S. Department of Energy's Wind Powering America Program is committed to educating state-level policy makers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Arkansas. We forecast the cumulative economic benefits from 1000 MW of development in Arkansas to be $1.15 billion, annual CO2 reductions are estimated at 2.7 million tons, and annual water savings are 1,507 million gallons.

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

    SciTech Connect (OSTI)

    Not Available

    2008-06-01

    The U.S. Department of Energy's Wind Powering America Program is committed to educating state-level policy makers and other stakeholders about the economic, CO2 emissions, and water conservation impacts of wind power. This analysis highlights the expected impacts of 1000 MW of wind power in Kansas. We forecast the cumulative economic benefits from 1000 MW of development in Kansas to be $1.08 billion, annual CO2 reductions are estimated at 3.2 million tons, and annual water savings are 1,816 million gallons.

  1. 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)

    1993-08-01

    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.

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

    SciTech Connect (OSTI)

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

    2011-10-01

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

  3. Wind-Wave Probabilistic Forecasting based on Ensemble

    E-Print Network [OSTI]

    Wind-Wave Probabilistic Forecasting based on Ensemble Predictions Maxime FORTIN Kongens Lyngby 2012.imm.dtu.dk IMM-PhD-2012-86 #12;Summary Wind and wave forecasts are of a crucial importance for a number weather forecasts and do not take any possible correlation into ac- count. Since wind and wave forecasts

  4. Metrics for Evaluating the Accuracy of Solar Power Forecasting (Presentation)

    SciTech Connect (OSTI)

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

    2013-10-01

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

  5. 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 networks, Neural networks, Modeling, Forecasting, Energy demand, Time series forecasting, Power system demand time series based only on data for six years to forecast the demand for the seventh year. Both

  6. 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

  7. Chapter 5 & 6 1. Evaluate the iterated integral ? 3 ? 2 xy (x2 + y2 ...

    E-Print Network [OSTI]

    2015-04-06

    Chapter 5 & 6. 1. Evaluate the iterated integral. ? 3. 1. ? 2. 1 xy. (x2 + y2). 3/2 dxdy. 2. Find the volume bounded by the graph of f(x, y)=1+2x + 3y, the rectangle

  8. 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

  9. Lenguaje Java Avanzado 1 Presentacin..................................................................................................................2

    E-Print Network [OSTI]

    Escolano, Francisco

    Lenguaje Java Avanzado Índice 1 Presentación herramientas útiles para probar y depurar aplicaciones Java y Java EE. Por último, se estudiará el acceso de sesión Materiales 1. Introducción al lenguaje Java apuntes traspas ejercicios 2. Marco de

  10. Substituted 1,2-azaborine heterocycles

    DOE Patents [OSTI]

    Liu, Shih-Yuan; Lamm, Ashley

    2014-12-30

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

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

    SciTech Connect (OSTI)

    Not Available

    1994-03-01

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

  12. 2.1E BDL Summary

    E-Print Network [OSTI]

    Winkelmann, F.C.

    2010-01-01

    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) •

  13. Math 530 Exam 2 1. Compute ? ? cos x x2 + 1 dx and justify your ...

    E-Print Network [OSTI]

    2011-04-12

    Math 530. Exam 2. 1. Compute. ? ?. 0 cos x x2 + 1 dx and justify your calculations. 2. Prove that there are no polynomials of the form. P(z) = zn + an?

  14. 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

  15. 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....

  16. Load Forecast For use in Resource Adequacy

    E-Print Network [OSTI]

    forecast of 4) Calculate Hourly Load Allocation Factor s for each day for 2019 For use in RA analysis as a function ofthe load for electricity in the region as a function of cyclical, weather and economic variables

  17. Wind Speed Forecasting for Power System Operation 

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22

    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...

  18. New product forecasting in volatile markets

    E-Print Network [OSTI]

    Baldwin, Alexander (Alexander Lee)

    2014-01-01

    Forecasting demand for limited-life cycle products is essentially projecting an arc trend of demand growth and decline over a relatively short time horizon. When planning for a new limited-life product, many marketing and ...

  19. 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 ...

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

    SciTech Connect (OSTI)

    Vrugt, Jasper A; Wohling, Thomas

    2008-01-01

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

  1. QUN NI1, SHOUHUAI XU2, ELISA BERTINO1, RAVI SANDHU2, AND WEILI HAN3

    E-Print Network [OSTI]

    Sandhu, Ravi

    Healthcare Example Diabetic adult patient - first visit in calendar year HBA1c lab result Eye exam Blood.record Output.id Timestamp ID Name Role 1 Alice 1 1 null registration Register 1 1/23/2009 6:00 1 Jame Nurse 2_exam 4 1/26/2009 6:43 4 David Nurse 3 1 Yes 5 5 null HBA1c test HBA1c 7 1/27/2009 6:57 5 Tom Practitioner

  2. Science Park 2 EG S 1A Sonderrume 1A

    E-Print Network [OSTI]

    Jüttler, Bert

    Ausgang Eingang gegenüber S ZD Sonderräume ZD RECENDt ­ Research Center for Non Destructive Testing Gmb ­ Research Center for Non Destructive Testing GmbH DI Dr. Peter BURGHOLZER 2C Lehrlabor Kunststofftechnik 2D Prof. Dr. Zoltán MAJOR S 1D Sonderräume 1D Institute of Polymeric Materials and Testing O

  3. Hadronic transitions ?(2S)??(1S)

    E-Print Network [OSTI]

    Ammar, Raymond G.; Baringer, Philip S.; Bean, Alice; Besson, David Zeke; Coppage, Don; Darling, C.; Davis, Robin E. P.; Kotov, S.; Kravchenko, I.; Kwak, Nowhan; Zhou, L.

    1998-08-07

    Using a 73.6pb(-1) data sample of ?(2S) events collected with the CLEO II detector at the Cornell Electron Storage Ring, we have investigated the hadronic transitions between the ?(2S) and the ?(1S). The dipion transition ...

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

    SciTech Connect (OSTI)

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

    2011-09-13

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

  5. Homo-and Cross-[2+2]-Cycloaddition of 1,1-Diphenylsilene and 1,1-Diphenylgermene. Absolute

    E-Print Network [OSTI]

    Leigh, William J.

    , McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4M1 Canada Received June 10, 1999 always rela- tively facile and occurs at rates in excess of ca. 107 M-1 s-1 in derivatives bearing simple.1,2 The head-to-head regiochemistry is common with derivatives bearing sterically bulky substituents

  6. ,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 TaggedJets

  7. Page 1 of 2 Technology Transfer &

    E-Print Network [OSTI]

    Chapman, Michael S.

    Page 1 of 2 Technology Transfer & Business Development Incoming Material Transfer Request Form & Academic Collaborations Team (iACT) Technology Transfer & Business Development MTA@ohsu.edu (503) 494 the provider? No Yes If yes, please attach a copy to your MTA submission email. #12;Page 2 of 2 Technology

  8. PSYCHOLOGY MAJORS --1 PSYCHOLOGY MAJORS --2

    E-Print Network [OSTI]

    Sanders, Matthew

    PSYCHOLOGY MAJORS -- 1 #12;PSYCHOLOGY MAJORS -- 2 Handbook for Undergraduate Psychology Majors......................................................................................................................................2 A. Psychology Program Goals and Purpose B. Declaration of Major C. History of Marquette University D. Facilities E. Graduate Program in Clinical Psychology 2. Department Faculty and Staff

  9. Detiding DART buoy data for real-time extraction of source coefficients for operational tsunami forecasting

    E-Print Network [OSTI]

    Percival, Donald B; Eble, Marie C; Gica, Edison; Huang, Paul Y; Mofjeld, Harold O; Spillane, Michael C; Titov, Vasily V; Tolkova, Elena I

    2014-01-01

    U.S. Tsunami Warning Centers use real-time bottom pressure (BP) data transmitted from a network of buoys deployed in the Pacific and Atlantic Oceans to tune source coefficients of tsunami forecast models. For accurate coefficients and therefore forecasts, tides at the buoys must be accounted for. In this study, five methods for coefficient estimation are compared, each of which accounts for tides differently. The first three subtract off a tidal prediction based on (1) a localized harmonic analysis involving 29 days of data immediately preceding the tsunami event, (2) 68 pre-existing harmonic constituents specific to each buoy, and (3) an empirical orthogonal function fit to the previous 25 hrs of data. Method (4) is a Kalman smoother that uses method (1) as its input. These four methods estimate source coefficients after detiding. Method (5) estimates the coefficients simultaneously with a two-component harmonic model that accounts for the tides. The five methods are evaluated using archived data from eleven...

  10. Intra-hour Direct Normal Irradiance solar forecasting using genetic programming

    E-Print Network [OSTI]

    Queener, Benjamin Daniel

    2012-01-01

    guideline for Solar Power Forecasting Performance . . 46 viof forecasting techniques for solar power production with noand A. Pavlovski, “Solar power forecasting performance

  11. A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting

    E-Print Network [OSTI]

    Mathiesen, Patrick; Collier, Craig; Kleissl, Jan

    2013-01-01

    of the WRF model solar irradiance forecasts in Andalusia (Beyer, H. , 2009.    Irradiance forecasting for the power dependent probabilistic irradiance  forecasts for coastal 

  12. Mathematics Of Ice To Aid Global Warming Forecasts Mathematics Of Ice To Aid Global Warming Forecasts

    E-Print Network [OSTI]

    Golden, Kenneth M.

    Mathematics Of Ice To Aid Global Warming Forecasts Mathematics Of Ice To Aid Global Warming forecasts of how global warming will affect polar icepacks. See also: Earth & Climate q Global Warming q the effects of climate warming, and its presence greatly reduces solar heating of the polar oceans." "Sea ice

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

    SciTech Connect (OSTI)

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

    2014-05-01

    The variable and uncertain nature of wind generation presents a new concern to power system operators. One of the biggest concerns associated with integrating a large amount of wind power into the grid is the ability to handle large ramps in wind power output. Large ramps can significantly influence system economics and reliability, on which power system operators place primary emphasis. The Wind Forecasting Improvement Project (WFIP) was performed to improve wind power forecasts and determine the value of these improvements to grid operators. This paper evaluates the performance of improved short-term wind power ramp forecasting. The study is performed for the Electric Reliability Council of Texas (ERCOT) by comparing the experimental WFIP forecast to the current short-term wind power forecast (STWPF). Four types of significant wind power ramps are employed in the study; these are based on the power change magnitude, direction, and duration. The swinging door algorithm is adopted to extract ramp events from actual and forecasted wind power time series. The results show that the experimental short-term wind power forecasts improve the accuracy of the wind power ramp forecasting, especially during the summer.

  14. Forecasting Prices andForecasting Prices and Congestion forCongestion for

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    Goal: Design nodal price and grid congestion forecasting tools for market operators and market Traders To facilitate scenario-conditioned planning Price forecasting for Market Participants (MPs) To manage short for portfolio management by power market participants Conclusion #12;Project OverviewProject Overview Project

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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

  3. 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

  4. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01

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

  5. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print5.3.2.1 Print5.3.2.1 Print Scanning

  6. Version 1.2.1 -April 1, 1999 The OpenGLR

    E-Print Network [OSTI]

    Wismath, Stephen

    - etary to Silicon Graphics, Inc. Any copying, adaptation, distribution, public performance, or public . . . . . . . . . . . . . . . . . . . . . . . 43 i #12;Version 1.2.1 - April 1, 1999 ii CONTENTS 2.13.1 Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.13.2 Lighting Parameter Speci cation . . . . . . . . . . . . 49 2.13.3 Color

  7. Effects of aerosols on precipitation from orographic clouds Barry Lynn,1,2

    E-Print Network [OSTI]

    Daniel, Rosenfeld

    humidity, and magnitude of the dominating wind shear. Since urban zones affect both thermal stability; published 31 May 2007. [1] Spectral (bin) microphysics was coupled to the Weather Research Forecast model simulations with clean and dirty air were obtained when ice microphysical processes were included in the model

  8. Page __1_ of _2__ University of Florida

    E-Print Network [OSTI]

    Slatton, Clint

    Page __1_ of _2__ University of Florida Example CHP Standard Operating Procedures Principle Investigator Dr. Al E. Gator Department UF Research Building The Swamp Rooms All Phone 392-GATR This SOP must

  9. 1 2/19/2014 Human Resources

    E-Print Network [OSTI]

    1 2/19/2014 Human Resources Consulting Services Dave Lett, Manager (Acting) Energy, Team Lead Nuclear Science and Engineering Michelle Mazerolle, Team Lead Physical Sciences Michele Lusk Manager* Facilities and Operations Katie Waldrop, Team Lead Business Services; Environment, Safety

  10. CELLULAR GEOGRAPHY1 W. R. TOBLER2

    E-Print Network [OSTI]

    Tobler, Waldo

    CELLULAR GEOGRAPHY1 W. R. TOBLER2 Captain Ahab, in the film version of Moby Dick, searches realistic variable- number-of-neighbors case but the insight is more easily gained in the cellular case. I

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

    DOE Patents [OSTI]

    Lee, K.Y.

    1985-03-05

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

  12. The NUHM2 after LHC Run 1

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

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

    2014-12-17

    We make a frequentist analysis of the parameter space of the NUHM2, in which the soft supersymmetry (SUSY)-breaking contributions to the masses of the two Higgs multiplets, m2Hu,d, vary independently from the universal soft SUSY-breaking contributions m20 to the masses of squarks and sleptons. Our analysis uses the MultiNest sampling algorithm with over 4 × 10? points to sample the NUHM2 parameter space. It includes the ATLAS and CMS Higgs mass measurements as well as the ATLAS search for supersymmetric jets + /ET signals using the full LHC Run 1 data, the measurements of BR(Bs?????) by LHCb and CMS togethermore »with other B-physics observables, electroweak precision observables and the XENON100 and LUX searches for spin-independent dark-matter scattering. We find that the preferred regions of the NUHM2 parameter space have negative SUSY-breaking scalar masses squared at the GUT scale for squarks and sleptons, m20 2Hu 2Hd 2 = 32.5 with 21 degrees of freedom (dof) in the NUHM2, to be compared with ?2/dof = 35.0/23 in the CMSSM, and ?2/dof = 32.7/22 in the NUHM1. We find that the one-dimensional likelihood functions for sparticle masses and other observables are similar to those found previously in the CMSSM and NUHM1.« less

  13. 1997 ## ### 1 ## No.3 (1) NG(H)g1NG(K) = NG(H)g2NG(K) ###, g1 2 NG(H)g2NG(K) ###, h 2 NG(H) #, k 2

    E-Print Network [OSTI]

    Yamashita, Hiroshi

    ## ___ |_1|_ (1) NG(H)g1NG(K) = NG(H)g2NG(K) ###, g1 2 NG(H)g2NG(K) ###, h 2 NG(H) #, k 2 NG(K) #####, g1

  14. 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)

    1993-11-01

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

  15. James Banfield1 Srikanth Allu2

    E-Print Network [OSTI]

    Mihaila, Bogdan

    Performance code [1] for uranium-dioxide (UO2) fuel in a heavy boiling-water reactor and is an extension reactor, which is a Heavy-Boiling Water Reactor (HBWR) with UO2 fuel. There are three rods presented to fixed temperature of 513 K, which is the saturation temperature of the heavy water in the Halden reactor

  16. 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

  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. Mark Wilkinson1 , R. Stuart Haszeldine2

    E-Print Network [OSTI]

    Haszeldine, Stuart

    Mark Wilkinson1 , R. Stuart Haszeldine2 , Tony E. Fallick3 (1) Edinburgh University, Edinburgh Sandstones Cementation sequences for reservoir sandstones from the UK North Sea normally show hydrocarbon filling as a single diagenetic event. However, chemical evidence shows that reservoirs often fill

  19. 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

  20. 6.2. DESIGN EXAMPLE 1 81 6.2 Design Example 1

    E-Print Network [OSTI]

    Helton, J. William

    6.2. DESIGN EXAMPLE 1 81 6.2 Design Example 1 The system is a slide drive, driven by a DC motor the gear train which steps the speed down to ! 2 (t). The gear train is connected to a table which slides of the table very precisely. w 2 LOAD TABLE RAIL GEAR TRAIN w 1 DC MOTOR + ­ U Figure 6.5: Slide drive

  1. 0 0.5 1 1.5 2 2.5 3 Normalizedreactivity

    E-Print Network [OSTI]

    Snyder, Robin

    species 50% connected, 4 species 2 groups of 2 species (b) Continuous time Figure 1: Reactivity as a proportion of the distance from lower bound to upper bound for the hierarchical interactions food web models 50% connected, 4 species 2 groups of 2, 4 species (b) Continuous time Figure 2: Reactivity vs mean

  2. Ethyl iodide decomposition on Cu(1 1 1) and Cu(2 2 1) Dougyong Sung, Andrew J. Gellman *

    E-Print Network [OSTI]

    Gellman, Andrew J.

    of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA Received 18 and adsorbed iodine atoms. The ethyl groups decompose by b-hydride elimination to desorb as ethylene leaving adsorbed iodine atoms. The kinetics of b-hydride elimination on the Cu(2 2 1) surface are similar to those

  3. An Improved RNS Reverse Converter for the {22n+1 -1,2n,2n -1} Moduli Set

    E-Print Network [OSTI]

    Cotofana, Sorin

    An Improved RNS Reverse Converter for the {22n+1 -1,2n,2n -1} Moduli Set K.A. Gbolagade1,2, R Residue Number Systems (RNS) have significant ad- vantages over conventional binary number systems tolerance. RNS have been widely applied in Digital Signal Pro- cessing (DSP) applications [5]. However

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

    E-Print Network [OSTI]

    Raftery, Adrian

    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

  5. New directions for forecasting air travel passenger demand

    E-Print Network [OSTI]

    Garvett, Donald Stephen

    1974-01-01

    While few will disagree that sound forecasts are an essential prerequisite to rational transportation planning and analysis, the making of these forecasts has become a complex problem with the broadening of the scope and ...

  6. The effect of multinationality on management earnings forecasts 

    E-Print Network [OSTI]

    Runyan, Bruce Wayne

    2005-08-29

    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...

  7. Market perceptions of efficiency and news in analyst forecast errors 

    E-Print Network [OSTI]

    Chevis, Gia Marie

    2004-11-15

    Financial analysts are considered inefficient when they do not fully incorporate relevant information into their forecasts. In this dissertation, I investigate differences in the observable efficiency of analysts' earnings forecasts between firms...

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

    E-Print Network [OSTI]

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

    2005-01-01

    Forecasts Using NEMS and GIS National Climatic Data Center.with Changing Boundaries." Use of GIS to Understand Socio-Forecasts Using NEMS and GIS Appendix A. Map Results Gallery

  9. OPERATIONAL EARTHQUAKE FORECASTING State of Knowledge and Guidelines for Utilization

    E-Print Network [OSTI]

    .................................................................................................................................... 323 II. SCIENCE OF EARTHQUAKE FORECASTING AND PREDICTION 325 A. Definitions and Concepts....................................................................................................................................... 325 B. Research on Earthquake PredictabilityOPERATIONAL EARTHQUAKE FORECASTING State of Knowledge and Guidelines for Utilization Report

  10. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry NaturalPrices1 Table 1.10 CoolingNotes &* j o n5 1:2:1 Crack

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

    SciTech Connect (OSTI)

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

    2007-12-01

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

  12. CAPTULO III VISUALIZAO E APLICAES GRFICAS 3D.............................2 1-TRANSFORMAES DE VISUALIZAO .................................................................2

    E-Print Network [OSTI]

    Lewiner, Thomas (Thomas Lewiner)

    ................................................................................................................15 2.1­ Criando Fontes de Luz ...............................................................................16 2.2.1- Luz Ambiente Global

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

    SciTech Connect (OSTI)

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

    2010-04-01

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

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

    SciTech Connect (OSTI)

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

    2010-04-15

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

  15. 1,2-Aryl and 1,2-Hydride Migration in Transition Metal Complex Catalyzed

    E-Print Network [OSTI]

    Wang, Jianbo

    1,2-Aryl and 1,2-Hydride Migration in Transition Metal Complex Catalyzed Diazo Decomposition of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Department of Chemistry, Peking Uni,2-hydride migration was studied. A reaction mechanism involving a "bridged" phenonium ion is proposed

  16. Imperial College London EEE 1L1 Autumn 2009 E2.2 Analogue Electronics E2.2 Analogue Electronics

    E-Print Network [OSTI]

    Papavassiliou, Christos

    Imperial College London ­ EEE 1L1 Autumn 2009 E2.2 Analogue Electronics E2.2 Analogue Electronics Autumn 2009 E2.2 Analogue Electronics What analogue electronics is · Engineering, i.e. the analysis ­ EEE 3L1 Autumn 2009 E2.2 Analogue Electronics analogue electronics is not only · CMOS integrated

  17. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print5.3.2.1 Print Scanning Transmission

  18. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print5.3.2.1 Print Scanning

  19. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print5.3.2.1 Print ScanningBeamline

  20. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print5.3.2.1 Print

  1. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUVBeamline 5.0.20.35.3.2.1

  2. Managerial Career Concerns and Earnings Forecasts SARAH SHAIKH

    E-Print Network [OSTI]

    Tipple, Brett

    's aversion to risk, I find that a CEO is less likely to issue an earnings forecast in periods of stricter non is more pronounced for a CEO who has greater concern for his reputation, faces more risk in forecasting the provision of earnings forecasts. Literature has long recognized that the labor market provides distinct

  3. Managing Wind Power Forecast Uncertainty in Electric Brandon Keith Mauch

    E-Print Network [OSTI]

    i Managing Wind Power Forecast Uncertainty in Electric Grids Brandon Keith Mauch Co Paulina Jaramillo Doctor Paul Fischbeck 2012 #12;ii #12;iii Managing Wind Power Forecast Uncertainty generated from wind power is both variable and uncertain. Wind forecasts provide valuable information

  4. Forecasting Uncertainty Related to Ramps of Wind Power Production

    E-Print Network [OSTI]

    Boyer, Edmond

    Forecasting 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. This paper presents two methods focusing on forecasting large and sharp variations in power output of a wind

  5. SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS

    E-Print Network [OSTI]

    Heinemann, Detlev

    SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS Detlev Heinemann Oldenburg in irradiance forecasting have been presented more than twenty years ago (Jensenius and Cotton, 1981), when or progress with respect to the development of solar irradiance forecasting methods. Heck and Takle (1987

  6. Choosing Words in Computer-Generated Weather Forecasts

    E-Print Network [OSTI]

    Reiter, Ehud

    to communicate numeric weather data. A corpus-based analysis of how humans write forecasts showed that there wereTime- Mousam weather-forecast generator to use consistent data-to-word rules, which avoided words which were weather forecast texts from numerical weather pre- diction data (SumTime-Mousam in fact is used

  7. Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging

    E-Print Network [OSTI]

    Raftery, Adrian

    Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging J. MCLEAN 2011, in final form 26 May 2012) ABSTRACT Probabilistic forecasts of wind vectors are becoming critical with univariate quantities, statistical approaches to wind vector forecasting must be based on bivariate

  8. Accuracy of near real time updates in wind power forecasting

    E-Print Network [OSTI]

    Heinemann, Detlev

    Accuracy of near real time updates in wind power forecasting with regard to different weather October 2007 #12;EMS/ECAM 2007 ­ Nadja Saleck Outline · Study site · Wind power forecasting - method #12;EMS/ECAM 2007 ­ Nadja Saleck Wind power forecast data observed wind power input (2004 ­ 2006

  9. Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

    E-Print Network [OSTI]

    Raftery, Adrian

    Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging J. Mc in the context of wind power, where under- forecasting and overforecasting carry different financial penal- ties, calibrated and sharp probabilistic forecasts can help to make wind power a more financially competitive alter

  10. Forecasting Building Occupancy Using Sensor Network James Howard

    E-Print Network [OSTI]

    Hoff, William A.

    Forecasting Building Occupancy Using Sensor Network Data James Howard Colorado School of Mines@mines.edu ABSTRACT Forecasting the occupancy of buildings can lead to signif- icant improvement of smart heating throughout a building, we perform data mining to forecast occupancy a short time (i.e., up to 60 minutes

  11. Weather Forecasting -Predicting Performance for Streaming Video over Wireless LANs

    E-Print Network [OSTI]

    Claypool, Mark

    Weather Forecasting - Predicting Performance for Streaming Video over Wireless LANs Mingzhe Li, "weather forecasts" are created such that selected wireless LAN performance indicators might be used to evaluate the effec- tiveness of individual weather forecasts. The paper evaluates six distinct weather

  12. Weather Forecasting Predicting Performance for Streaming Video over Wireless LANs

    E-Print Network [OSTI]

    Claypool, Mark

    Weather Forecasting ­ Predicting Performance for Streaming Video over Wireless LANs Mingzhe Li, ``weather forecasts'' are created such that selected wireless LAN performance indicators might be used to evaluate the e#ec­ tiveness of individual weather forecasts. The paper evaluates six distinct weather

  13. 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

  14. Preprints, 15th AMS Conference on Weather Analysis and Forecasting

    E-Print Network [OSTI]

    Doswell III, Charles A.

    ) models have substantially improved forecast skill. Recent and planned changes along these lines (e to delivering two kinds of weather products. The first is a day-to-day forecast of weather elements, e by the private sector. Improvements in automated techniques for the forecasting of basic weather elements

  15. Influences of soil moisture and vegetation on convective precipitation forecasts

    E-Print Network [OSTI]

    Robock, Alan

    Influences of soil moisture and vegetation on convective precipitation forecasts over the United and vegetation on 30 h convective precipitation forecasts using the Weather Research and Forecasting model over, the complete removal of vegetation produced substantially less precipitation, while conversion to forest led

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01

    of Solar 2011, American Solar Energy Society, Raleigh, NC.Description and validation. Solar Energy, 73 (5), 307-317.forecast database. Solar Energy, Perez, R. , S. Kivalov, J.

  17. A NOD2NALP1 complex mediates caspase-1-dependent IL-1 secretion in response to

    E-Print Network [OSTI]

    Nizet, Victor

    A NOD2­NALP1 complex mediates caspase-1-dependent IL-1 secretion in response to Bacillus anthracis February 20, 2008) NOD2, a NOD-like receptor (NLR), is an intracellular sensor of bacterial muramyl, the molecular mechanism by which NOD2 can stimulate IL-1 secretion, and its biological significance were

  18. INDEX TO ALGORITHMS AND THEOREMS Algorithm 1.1E, 2, 4.

    E-Print Network [OSTI]

    Pratt, Vaughan

    APPENDIX C INDEX TO ALGORITHMS AND THEOREMS Algorithm 1.1E, 2, 4. Algorithm 1.1F, 466. Algorithm 1.2.1E, 13{14. Algorithm 1.2.1I, 11{12. Algorithm 1.2.2E, 470. Algorithm 1.2.2L, 26. Law 1.2.4A, 40. Law, 81{82. Theorem 1.2.10A, 101. Algorithm 1.2.10M, 96. Theorem 1.2.11.3A, 119. Algorithm 1.3.2E, 160

  19. Math Concepts: L1 and L2 

    E-Print Network [OSTI]

    Irby, Beverly J.; Lara-Alecio, Rafael

    2010-10-22

    stream_source_info Math Concepts(2005).pdf.txt stream_content_type text/plain stream_size 3793 Content-Encoding ISO-8859-1 stream_name Math Concepts(2005).pdf.txt Content-Type text/plain; charset=ISO-8859-1 425 10011... 0010 1010 1101 0001 0100 1011 Math Concepts: L1 and L2 Beverly J. Irby, Ed.D. Professor and Chair Educational Leadership and Counseling Sam Houston State University Rafael Lara-Alecio, Ph.D. Professor and Director Bilingual Programs Educational...

  20. Stockpile Stewardship Quarterly, Volume 2, Number 1

    National Nuclear Security Administration (NNSA)

    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 Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield Municipal GasAdministration Medal01 Sandia4) August 20123/%2A en Signature ofSebStarting1 |1,21,1 *

  1. TableHC2.1.xls

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988Prices, Sales Volumes &15.14.2 7.6

  2. TableHC2.1.xls

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988Prices, Sales Volumes &15.14.2 7.6

  3. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    1998-07-01

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

  4. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    1996-08-01

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

  5. Forecasting Hot Water Consumption in Residential Houses

    E-Print Network [OSTI]

    MacDonald, Mark

    and technological advancement in energy-intensive applications are causing fast electric energy consumption growth and consumption of electricity [8], as long as there is no significant correlation between intermittent energyArticle Forecasting Hot Water Consumption in Residential Houses Linas Gelazanskas * and Kelum A

  6. GOES Aviation Products Aviation Weather Forecasting

    E-Print Network [OSTI]

    Kuligowski, Bob

    GOES Aviation Products · The GOES aviation forecast products are based on energy measured in different characteristics #12;GOES Aviation Products Quiz · What is a geostationary satellite? · What generates energy received by the satellite in the visible band? · What generates energy received by the satellite

  7. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Reiter, Ehud

    summarisation. We found three alternative ways in which we could model data summarisation. One approach is based turbines. In the domain of meteorology, time series data produced by numerical weather prediction (NWP) models is summarised as weather forecast texts. In the domain of gas turbines, sensor data from

  8. 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

  9. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27

    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.

  10. Stochastic Weather Generator Based Ensemble Streamflow Forecasting

    E-Print Network [OSTI]

    Stochastic Weather Generator Based Ensemble Streamflow Forecasting by Nina Marie Caraway B of Civil Engineering 2012 #12;This thesis entitled: Stochastic Weather Generator Based Ensemble Streamflow mentioned discipline. #12;iii Caraway, Nina Marie (M.S., Civil Engineering) Stochastic Weather Generator

  11. Forecasting of preprocessed daily solar radiation time series using neural networks

    SciTech Connect (OSTI)

    Paoli, Christophe; Muselli, Marc; Nivet, Marie-Laure [University of Corsica, CNRS UMR SPE, Corte (France); Voyant, Cyril [University of Corsica, CNRS UMR SPE, Corte (France); Hospital of Castelluccio, Radiotherapy Unit, Ajaccio (France)

    2010-12-15

    In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad hoc time series pre-processing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE {proportional_to} 21% and RMSE {proportional_to} 3.59 MJ/m{sup 2}. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data pre-processing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayesian inference. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41 55'N, 8 44'E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination..) allow to predict the best daily DC PV power production at horizon d + 1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data (R{sup 2} > 0.99 and nRMSE < 2%). (author)

  12. 1. Introduction 1 2. Noncommutative Bundle Theory 5

    E-Print Network [OSTI]

    Lott, John

    Superconnections 23 5.1. Partially Flat Superconnections 24 5.2. A Finite-Dimensional Index Theorem 29 5 Superconnections 41 6.3. Small Time Limits 44 6.4. Index Theorems 48 6.5. The Analytic Torsion Form II 49 7

  13. Yuki Endo1, 2 Lihua Zhang1, 3

    E-Print Network [OSTI]

    Barron, Annelise E.

    Introduction The sequence of the human genome has been almost completed in early 2001 [1, 2], and the Human Genome Project (HGP) already enters the post-genome-sequenc- ing era [3­6]. During this period and Technology Corporation, Chiba, Japan 3 Furuno Electric Co., LTD., Nishinomiya, Japan 4 Institute of Genome

  14. Photoluminescence spectroscopy of carbon nanotubes Yutaka Ohno1,2

    E-Print Network [OSTI]

    Maruyama, Shigeo

    ) Eii 4. PL PL [12] #12;6 PL [13] 4(a) 2 (50:50) f1f2 (f1+f2)(f1-f2) PL 4(b)1wt,2 , Shigeo Maruyama3 , and Takashi Mizutani1,4 1 Department of Quantum Engineering, Nagoya University 2 PRESTO, Japan Science and Technology Agency 3 Department of Mechanical Engineering, The University

  15. 1. Noah's Bagels 2. Pete's Coffee & Tea

    E-Print Network [OSTI]

    Weinreb, Sander

    1. Noah's Bagels 2. Pete's Coffee & Tea 3. Pie 'N Burger 4. Noda Sushi 5. Subway 6. Madres 7. Coffee Bean & Tea Leaf 14. Cold Stone Creamery 15. Macy's 16. Red Brick Pizza 17. Trader Joe's 18. Green Street Restaurant 42. Radhika's Cuisine of India 43. Crocodile Cafe 44. California Skewers 45

  16. 1,2,3-triazolium ionic liquids

    DOE Patents [OSTI]

    Luebke, David; Nulwala, Hunaid; Tang, Chau

    2014-12-09

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

  17. Kimberly Colas,1 Arthur Motta,2

    E-Print Network [OSTI]

    Motta, Arthur T.

    Kimberly Colas,1 Arthur Motta,2 Mark R. Daymond,3 and Jonathan Almer4 Mechanisms of Hydride., and Almer, Jonathan, "Mechanisms of Hydride Reorientation in Zircaloy-4 Studied in Situ," Zirconium Zirconium hydride platelet reorientation in fuel cladding during dry storage and transportation of spent

  18. July 2, 2007 1 Optimal Transmission Switching

    E-Print Network [OSTI]

    Mangasarian, Olvi L.

    . INTRODUCTION n large electric networks, transmission is traditionally characterized as a static systemJuly 2, 2007 1 Optimal Transmission Switching Emily Bartholomew Fisher, Student Member, IEEE an optimal generation dispatch and transmission topology to meet a specific inflexible load as a mixed

  19. Page 1 of 2 UNIVERSAL WASTE

    E-Print Network [OSTI]

    Jia, Songtao

    -Cadmium (Ni-Cd) Nickel Metal Hydride (Ni-MH) Lithium Ion (Li-ion) Large or Small sealed lead acid (Pb) MercuryPage 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

  20. version 1.11 2. TSUBAME 3

    E-Print Network [OSTI]

    )AMBER4.1Ewald ( Ewald Ewald () ) SANDERNMR-NOE GIBBS 2(Free Energy Pertubation) 5 NMODE ROAR "Penn 11(Bugfix 20) $ export AMBERHOME="/usr/apps/isv/amber/amber11_bugfix20_cuda41/" $ export PATH}/bin/:${PATH}" 12(Bugfix 15) $ export AMBERHOME="/usr/apps/isv/amber/amber12_bugfix15/amber12" $ export PATH

  1. 1 Zoology Building 2 Cruickshank Building

    E-Print Network [OSTI]

    Neri, Peter

    1 Zoology Building 2 Cruickshank Building 3 23 St Machar Drive 4 King's Museum (Old Town House) 5 The Hub 6 St Mary's 7 Fraser Noble Building 8 Elphinstone Road Halls 9 The Sir Duncan Rice Library 10 Meston Building 11 Chaplaincy Centre 12 Confucius Institute 13 Security Office/Mailroom 14 Counselling

  2. Review for Test 2 1. Branching Processes

    E-Print Network [OSTI]

    Shier, Douglas R.

    (SA n state X(t), Markov property parameters vi, Pij, transition rates qij = vi failure rate sum of n exponential() variables is a Gamma(n, ) minimum of X1, X2, . . . , Xn in (0, t); is the average number of events per unit time independent and stationary increments, N

  3. Page 1 of 2 Exporting Encryption Software

    E-Print Network [OSTI]

    Bordenstein, Seth

    Page 1 of 2 Exporting Encryption Software Sharing, shipping, transmission or transfer (exporting) of almost all encryption software in either source code or object code is subject to US export regulations Exception TSU (Technology and Software - Unrestricted). The TSU exception requires the exporter to provide

  4. Cuticular Hydrocarbon Research1 Marion Page2

    E-Print Network [OSTI]

    Cuticular Hydrocarbon Research1 Marion Page2 We have been studying existing taxonomies of forest in the utility of cuticular (surface) hydrocarbons as taxonomic characters (Haverty and others 1988, 1989, Page to be genetically fixed. Because the insects studied so far synthesize all or most of their hydrocarbon components

  5. 1. AGRICULTURE BUILDING 2. AGRICULTURE GREENHOUSE

    E-Print Network [OSTI]

    Hubbard, Keith

    1. AGRICULTURE BUILDING 2. AGRICULTURE GREENHOUSE 3. AGRICULTURE MECHANICS SHOP 4. ALUMNI ASSOCIATION (TRACIE D. PEARMAN) 5. APARTMENTS (UNIVERSITY WOODS) 6. ART BUILDING 7. ART STUDIO 8. AUSTIN BUILDING 9. BIOLOGY GREENHOUSE 10. BOYNTON BUILDING 11. BUSINESS BUILDING (R. E. MCGEE) 12. CHEMISTRY

  6. DATE A DAtabase of TIM Barrel 2.1 Introduction......................................................................................

    E-Print Network [OSTI]

    Babu, M. Madan

    24 DATE ­ A DAtabase of TIM Barrel Enzymes 2.1 Introduction...................................................................................... 2.2 Objective and salient features of the database .................................... 2.2.1 Choice on the database............................................... 2.4 Features

  7. LANL JOWOG 31 2012 Forecast

    SciTech Connect (OSTI)

    Vidlak, Anton J. II [Los Alamos National Laboratory

    2012-08-08

    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.

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

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

  9. txH2O: Volume 2, Number 1 (Complete) 

    E-Print Network [OSTI]

    Texas Water Resources Institute

    2006-01-01

    Otx volume 1 | number 2 A Publication of the Texas Water Resources Institute Texas Water Resources Institute | Texas Agricultural Experiment Station | Texas Cooperative Extension In This Issue: ? TEXAS? NATURAL LAKE ? PANHANDLE AGRIPARTNERS... Water Resources Institute Steven Keating Art Director Agricultural Communications Visit our Web site at http://twri.tamu.edu for more information. On the cover: Caddo Lake Photo courtesy of Texas Parks and Wildlife Department ? 2005 Message from...

  10. Annual Spring Experiments aim to accelerate the transfer of promising new concepts and tools from research to operations through intensive real-time forecasts and evaluations.

    E-Print Network [OSTI]

    Xue, Ming

    research to operations through intensive real-time forecasts and evaluations. B ackground. Each spring research to operations, while inspiring new initiatives for operationally relevant research, through a combined forecast and research area situated between the SPC and OUN operations rooms (Fig. 1

  11. 1993 Pacific Northwest Loads and Resources Study, Technical Appendix: Volume 2, Book 1, Energy.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1993-12-01

    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.

  12. Bet and Energy -From Load Forecasting to Demand Response in a Web of Things

    E-Print Network [OSTI]

    Beigl, Michael

    Bet and Energy - From Load Forecasting to Demand Response in a Web of Things Yong Ding TECO (DSM) [7, 19]. Within DSM, mainly two principal activities i.e. load shifting (demand response programs) and load reduction (energy efficiency and conser- vation programs) can be realized [4]. 1.1 Demand Response

  13. 100.2 Building Evacuation Policy Page 1 of 1 100.2 Building Evacuation Policy

    E-Print Network [OSTI]

    Yang, Eui-Hyeok

    100.2 Building Evacuation Policy Page 1 of 1 100.2 Building Evacuation Policy Policy Name: Building leadership to students and others by directing all persons to promptly vacate a building upon the activation safety of members of the University community, adherence to building evacuation procedures protects

  14. PARENT & FAMILY ORIENTATION HANDBOOK 2 0 1 5 / 2 0 1 6

    E-Print Network [OSTI]

    PARENT & FAMILY ORIENTATION HANDBOOK 2 0 1 5 / 2 0 1 6 w w w . u w i n d s o r. c a #12;As the new and Family Handbook 2015 Table of Contents Orientation Head Start Parent and Family Schedule of the Day

  15. Combinatorial Evolution and Forecasting of Communication Protocol ZigBee

    E-Print Network [OSTI]

    Levin, Mark Sh; Kistler, Rolf; Klapproth, Alexander

    2012-01-01

    The article addresses combinatorial evolution and forecasting of communication protocol for wireless sensor networks (ZigBee). Morphological tree structure (a version of and-or tree) is used as a hierarchical model for the protocol. Three generations of ZigBee protocol are examined. A set of protocol change operations is generated and described. The change operations are used as items for forecasting based on combinatorial problems (e.g., clustering, knapsack problem, multiple choice knapsack problem). Two kinds of preliminary forecasts for the examined communication protocol are considered: (i) direct expert (expert judgment) based forecast, (ii) computation of the forecast(s) (usage of multicriteria decision making and combinatorial optimization problems). Finally, aggregation of the obtained preliminary forecasts is considered (two aggregation strategies are used).

  16. 1. DON'T confuse integral with derivative: ? x 2 dx = x ?3 ? 1 + C ...

    E-Print Network [OSTI]

    2012-02-18

    Common Error to Quiz 5. 1. DON'T confuse integral with derivative: ? x. ?3. 2 dx = x. ?3. 2. ?1. ?3. 2. ? 1. + C = ?. 2. 5 x. ?5. 2 + C. Instead,. ? x. ?3. 2 dx = x. ?3.

  17. Himalayan Journal of Sciences Volume 1, Issue 2, July 2003

    E-Print Network [OSTI]

    Mainali, Kumar P

    2003-01-01

    ??#%2?? ?%?**??*?)? #*?)'??????*.!?*?)??)'?? ?%?#!?????#?*#$ ,????+??!????????.?????"?!?!? ????#??1?9??)??? ??#?)??? ? ?%?#!?? ?8+?..??? "???? *????) %?**+)?%#???)??8+?.*?)?1?3??????+%?+?? #)?? +)%???)?)'?? ?#)??$.?#%??)'?+)?????????$ ?%??0???#?????(?'?)??#!!(??)0?!0???) ??*#)?? "???"?????)%??.?#%????????*??!0??1??+%? .??.!???#0... ??#)? 9?!?!? ????)???0#???)????H?1??D?. ??R F?@9?1?????1?????? ????? ?????? ... S+).+&!????????.???T1?L#??*#)?+A?F?.#??*?)? ? ??#???)#!?@#?2??#)??9?!?!? ????)???0#???)? ??H?1?I?. G?R ?????(#!1?????1???.#!???#.???1???)A??????????#? 3...

  18. An Efficient RNS to Binary Converter Using the Moduli Set {2n + 1, 2n, 2n -1}

    E-Print Network [OSTI]

    Cotofana, Sorin

    An Efficient RNS to Binary Converter Using the Moduli Set {2n + 1, 2n, 2n - 1} Kazeem Alagbe System (RNS) to decimal conversion which is an important issue concerning the utilization of RNS numbers System (RNS) is an unweighted number system with inherent parallel characteristics, which supports carry

  19. Myc regulates embryonic vascular permeability and remodeling Enik Kokai (1), Florian Voss (2), Frank Fleischer (2), Sybille Kempe (1), Dragan

    E-Print Network [OSTI]

    Schmidt, Volker

    ), Frank Fleischer (2), Sybille Kempe (1), Dragan Marinkovic (1), Hartwig Wolburg (3), Frank Leithäuser (4

  20. Name of TMO ODSS1 ODSS2

    E-Print Network [OSTI]

    Joo, Su-Chong

    _TMO Window _TMO Light _TMOFan _TMO Air-Conditioner _TMO LAN Home Gateway Home Server _TMO Heater_TMO Camera. Logical multicast channels, and I/O devices #12;LAN Home Gateway Home Server _TMO Heater_TMO Camera_TMO Window _TMO Light _TMOFan _TMO Air-Conditioner _TMO RMMCRMMC OS+TM OSM OS+TM OSM Site1 OS+TM OSM Site2 OS

  1. Herwig++ 2.1 Release Note

    E-Print Network [OSTI]

    M. Bahr; S. Gieseke; M. Gigg; D. Grellscheid; K. Hamilton; O. Latunde-Dada; S. Platzer; P. Richardson; M. H. Seymour; A. Sherstnev; B. R. Webber

    2007-11-20

    A new release of the Monte Carlo program Herwig++ (version 2.1) is now available. This version includes a number of significant improvements including: an eikonal multiple parton-parton scattering model of the underlying event; the inclusion of Beyond the Standard Model physics; and a new hadronic decay model tuned to LEP data. This version of the program is now fully ready for the simulation of events in hadron-hadron collisions.

  2. Thermodynamics of (2+1)-flavor QCD

    E-Print Network [OSTI]

    C. Schmidt; T. Umeda

    2006-09-21

    We report on the status of our QCD thermodynamics project. It is performed on the QCDOC machine at Brookhaven National Laboratory and the APEnext machine at Bielefeld University. Using a 2+1 flavor formulation of QCD at almost realistic quark masses we calculated several thermodynamical quantities. In this proceeding we show the susceptibilites of the chiral condensate and the Polyakov loop, the static quark potential and the spatial string tension.

  3. 6C2R-2.009. Parking and Traffic Regulations. (1)-(2) No Change

    E-Print Network [OSTI]

    McQuade, D. Tyler

    illegally on University property to include but not limited to: no permit in restricted lots, parking for space, parking in a restricted or reserved lot, improper parking in a loading zone, parking on lawns1 6C2R-2.009. Parking and Traffic Regulations. (1)- (2) No Change (3) Parking Fees and Penalties

  4. Logistic Model Trees Niels Landwehr 1,2 , Mark Hall 2 , and Eibe Frank 2

    E-Print Network [OSTI]

    Frank, Eibe

    Logistic Model Trees Niels Landwehr 1,2 , Mark Hall 2 , and Eibe Frank 2 1 Department of Computer problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way

  5. Obesity-Blocking Neurons in Drosophila Bader Al-Anzi,1,* Viveca Sapin,1 Christopher Waters,1 Kai Zinn,1,* Robert J. Wyman,2 and Seymour Benzer1

    E-Print Network [OSTI]

    Zinn, Kai

    Waters,1 Kai Zinn,1,* Robert J. Wyman,2 and Seymour Benzer1 1Division of Biology, California Institute

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01

    electric grid. Chapter 2 Marine Layer Meteorology 2.1 Marine Layer Stratocumulus Overview In coastal California, the dominant weather

  7. 2hiu Insulin 1gcn Glucagon

    E-Print Network [OSTI]

    Mekalanos, John

    Deoxyribonuclease 1smdAmylase 1poe Phospholipase 5rsa Ribonuclease Cholesterol Phospholipid 1pth Cyclooxygenase 1prc

  8. M. Petremand (1,2), Ch. Collet (1), M. Louys (1), and F. Bonnarel (2) Introduction

    E-Print Network [OSTI]

    of Strasbourg I, FRANCE (2) Observatoire de Strasbourg, CDS, UMR 7050 CNRS, University of Strasbourg I, FRANCE

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

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

    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 on Google Bookmark EERE: AlternativeMonthly","10/2015"Monthly","10/2015" ,"Release7Cubicthroughthe PriceThousanda Oxygenated55,453.9151 and N1.2.N2.1

  10. Bell gems naturally split dynamics information from $SU(2^{2d}) \\rightarrow U(1)^{2^{2d-1}-1} \\times SU(2)^{2^{2d-1}}$

    E-Print Network [OSTI]

    Francisco Delgado

    2015-09-25

    Quantum Computation and Quantum Information are continuously growing research areas which are based on nature and resources of quantum mechanics, as superposition and entanglement. In its gate array version, the use of convenient and appropriate gates is essential. But while those proposed gates adopt convenient forms for computational algorithms, in the practice, their design depends on specific quantum systems and stuff being used. Gates design is restricted to properties and limitations of interactions and physical elements being involved, where Quantum Control plays a deep role. Quantum complexity of multipartite systems and their interactions requires a tight control to manipulate their quantum states, either local and non-local ones, but still a reducibility procedure should be addressed. This work shows how a general $2d$-partite two level spin system in $SU(2d)$ could be decomposed in $2^{n-1}$ subsystems on $SU(2)$, letting establish control operations. In particular, it is shown that Bell gems basis is a set of natural states on which decomposition happen naturally under some interaction restrictions. Thus, alternating the direction of local interaction terms in the Hamiltonian, this procedure states a universal exchange semantics on those basis. The structure developed could be understood as a splitting of the $2d$ information channels into $2^{2d-1}$ pairs of $2$ level information subsystems.

  11. 1) Ethylene glycol (1.2 eq), cat. CSA, (EtO)3CH, CH2Cl2

    E-Print Network [OSTI]

    of the reaction Step 1: Draw the structure of CSA. How is it prepared? O SO3H O camphor OH SO3 H2SO4 Ac2O N OMe

  12. Nation 2007 2008 2009 2010 2011 2012 Afghanistan 2 1 1 2 1 0 -100%

    E-Print Network [OSTI]

    Fernandez, Eduardo

    % Bangladesh 24 27 26 26 30 33 38% Barbados 7 7 4 8 9 10 43% Bassas da India 0 1 1 0 0 0 0% Belarus 3 9 13 10

  13. Development of a next-generation regional weather research and forecast model.

    SciTech Connect (OSTI)

    Michalakes, J.; Chen, S.; Dudhia, J.; Hart, L.; Klemp, J.; Middlecoff, J.; Skamarock, W.

    2001-02-05

    The Weather Research and Forecast (WRF) project is a multi-institutional effort to develop an advanced mesoscale forecast and data assimilation system that is accurate, efficient, and scalable across a range of scales and over a host of computer platforms. The first release, WRF 1.0, was November 30, 2000, with operational deployment targeted for the 2004-05 time frame. This paper provides an overview of the project and current status of the WRF development effort in the areas of numerics and physics, software and data architecture, and single-source parallelism and performance portability.

  14. 1.1 Introduction 2 1.2 Classes of Computers 5

    E-Print Network [OSTI]

    Hexsel, Roberto A

    , lowered the cost and risk of bringing out a new architecture. These changes made it possible to develop in Technology 17 1.5 Trends in Power and Energy in Integrated Circuits 21 1.6 Trends in Cost 27 1 in the technology used to build com- puters and from innovations in computer design. Although technological

  15. 1.25-1.75GHZ 1.5 -2.0 GHZ

    E-Print Network [OSTI]

    . SHEET 4) FP FP -10 -10 RP RP 8-T0-1 SWITCH, POL A (SIGNAL SELECTOR FOR PWR METER 2a) 8-T0-1 SWITCH, POL B (SIGNAL SELECTOR FOR PWR METER 2b) FROM AMPS 1-8 POL. B FROM AMPS 1-8 POL. A COUGAR AC582C AMPS 20 RACK 5 COMPUTER READ OUT NOT USED RACK 5 RIGHT HAND PWR METER DUAL-CHANNEL POWER METER #2 HP E4419B

  16. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

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

    2014-12-30

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

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

    SciTech Connect (OSTI)

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

    2015-08-05

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

  18. Solar Wind Forecasting with Coronal Holes

    E-Print Network [OSTI]

    S. Robbins; C. J. Henney; J. W. Harvey

    2007-01-09

    An empirical model for forecasting solar wind speed related geomagnetic events is presented here. The model is based on the estimated location and size of solar coronal holes. This method differs from models that are based on photospheric magnetograms (e.g., Wang-Sheeley model) to estimate the open field line configuration. Rather than requiring the use of a full magnetic synoptic map, the method presented here can be used to forecast solar wind velocities and magnetic polarity from a single coronal hole image, along with a single magnetic full-disk image. The coronal hole parameters used in this study are estimated with Kitt Peak Vacuum Telescope He I 1083 nm spectrograms and photospheric magnetograms. Solar wind and coronal hole data for the period between May 1992 and September 2003 are investigated. The new model is found to be accurate to within 10% of observed solar wind measurements for its best one-month periods, and it has a linear correlation coefficient of ~0.38 for the full 11 years studied. Using a single estimated coronal hole map, the model can forecast the Earth directed solar wind velocity up to 8.5 days in advance. In addition, this method can be used with any source of coronal hole area and location data.

  19. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

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

    2011-02-23

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

  20. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect (OSTI)

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

    2014-11-13

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

  1. Global disease monitoring and forecasting with Wikipedia

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

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

    2014-11-13

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

  2. M353 Hw 1 (S. Zhang) 0.2, 0.3 0. 0.2: 1ab, 2ab, 3abc, 5, 6, 7cde

    E-Print Network [OSTI]

    Zhang, Shangyou

    -51 + 2-52 + 2-54 )) - 1 (b)(1 + (2-51 + 2-52 + 2-60 )) - 1 ans: (a) Note 1 + = 1 if 2-53 . (but/8 in the binary form. ans: 1 8 (×2)(whole) (fraction) ¨ r 0 1/4 ¨ r 0 1/2 ¨ r 1 0 d d d d 1 8 = .0012 2. (0.2:6) Find the first 15 bits in the binary representation of e. ans: e = 2.718281828 = 2 + .718281828 2 (/2

  3. Lecture Notes 2. 1 Positive Dimensional Varieties

    E-Print Network [OSTI]

    's Nullstellensatz, we have that g = 0 strictly follows from h1, . . . , hn if and only if m 1 such that gm h1 such that gm h1, . . . , ht if and only if 1 h1, . . . , ht, 1 - yg k[x1, . . . , xn, y]. Proof. To prove the equivalence, on one hand, if gm h1, . . . , hn then 1 = ym gm + (1 - ym gm ) = ym gm + (1 - yg)(1 + yg

  4. Quantum Topology Change in (2 + 1)d

    E-Print Network [OSTI]

    A. P. Balachandran; E. Batista; I. P. Costa e Silva; P. Teotonio-Sobrinho

    1999-10-26

    The topology of orientable (2 + 1)d spacetimes can be captured by certain lumps of non-trivial topology called topological geons. They are the topological analogues of conventional solitons. We give a description of topological geons where the degrees of freedom related to topology are separated from the complete theory that contains metric (dynamical) degrees of freedom. The formalism also allows us to investigate processes of quantum topology change. They correspond to creation and annihilation of quantum geons. Selection rules for such processes are derived.

  5. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsiclouddenDVA N C E D B L O OLaura| National2.1 Print

  6. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News PublicationsAudits & Inspections AuditsBarbara2.0.1 Print EUVBeamline 5.0.20.3

  7. Book2.xls?attach=1

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustmentsShirleyEnergyTher i n c i p a l De pEnergy Industrial LocalAprilstaff areVENTO:Units 1&2

  8. Bidding wind energy exploiting wind speed forecasts Antonio Giannitrapani, Simone Paoletti,

    E-Print Network [OSTI]

    Garulli, Andrea

    -ahead generation profile for a wind power producer by exploiting wind speed forecasts provided by a meteorological service. In the con- sidered framework, the wind power producer is called to take part integration in the grid is causing serious problems to transmission and distribution system operators [2]. One

  9. EUROBRISA: A EURO-BRazilian Initiative for improving South American seasonal forecasts

    E-Print Network [OSTI]

    Estudos Climáticos (CPTEC/INPE), Brazil, 2. Universidade de São Paulo (USP), Brazil 3.Universidade Federal do Paraná (UFPR), Brazil, 4. Instituto Nacional de Meteorologia (INMET), Brazil, 5. European Centre for Medium-Range and Weather Forecasts (ECMWF), 6. United Kingdom Met Office (UKMO), UK, 7. University

  10. 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

  11. Improving Tropical Cyclogenesis Statistical Model Forecasts through the Application of a Neural Network Classifier

    E-Print Network [OSTI]

    Marzban, Caren

    /National Hurricane Center 11691 SW 17th Street Miami, FL 33165 Email: Christopher.Hennon@noaa.gov #12;2 ABSTRACT networks are able to detect nonlinear patterns in data and can be a very powerful tool for forecasting applications if they are designed and used properly. Although they are a more recent innovation than

  12. 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

    E-Print Network [OSTI]

    Glowinski, Roland

    Energy Research Park 1 ERP1 E-10 402 UH Energy Research Park 2 ERP2 D-10 403 UH Energy Research Park 3 ERP3 D-10 404 UH Energy Research Park 4 ERP4 D-10 405 UH Energy Research Park 5 ERP5 E-9 406 UH Energy Research Park 6 ERP6 E-9 407 UH Energy Research Park 7 ERP7 D-9 408 UH Energy Research Park 8 ERP8 D-9 409

  13. Aero III/IV Sheet 2 Solutions 1 A. G. Walton 5 ' % n +c + 52 n 25 ' E% n +2 n 2E% n + ' %2 +2 n 2% n E2+ n 2%+

    E-Print Network [OSTI]

    Walton, Andrew G

    Aero III/IV Sheet 2 Solutions 1 A. G. Walton 1(i) 5 ' % n +c + 52 n 25 ' E% n +2 n 2E% n + ' %2 +2 #12;Aero III/IV Sheet 2 Solutions 2 A. G. Walton and let @ ' Z*2 (inside Then, U ' 2ZsE@ ' 2Ze 3Z*2

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

    SciTech Connect (OSTI)

    Not Available

    2009-03-01

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

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

    Energy Savers [EERE]

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

  16. Short-term Forecasting of Offshore Wind Farm Production Developments of the Anemos Project

    E-Print Network [OSTI]

    Heinemann, Detlev

    for the sum of on- and offshore production in Germany with a total capacity of 50GW would benefit fromShort-term Forecasting of Offshore Wind Farm Production ­ Developments of the Anemos Project J , R. A. Brownsword5 , I. Waldl6 1 ForWind ­ Center for Wind Energy Research, Institute of Physics

  17. Testing Automated Solar Flare Forecasting With 13 Years of MDI Synoptic Magnetograms

    E-Print Network [OSTI]

    Hoeksema, Todd

    becomes more technologically dependent on complex global systems, the potential risk posedTesting Automated Solar Flare Forecasting With 13 Years of MDI Synoptic Magnetograms J.P. Mason1 is statistically associated with changes in several characteris- tics of the line-of-sight magnetic field in solar

  18. Fitting and forecasting non-linear coupled dark energy

    E-Print Network [OSTI]

    Casas, Santiago; Baldi, Marco; Pettorino, Valeria; Vollmer, Adrian

    2015-01-01

    We consider cosmological models in which dark matter feels a fifth force mediated by the dark energy scalar field, also known as coupled dark energy. Our interest resides in estimating forecasts for future surveys like Euclid when we take into account non-linear effects, relying on new fitting functions that reproduce the non-linear matter power spectrum obtained from N-body simulations. We obtain fitting functions for models in which the dark matter-dark energy coupling is constant. Their validity is demonstrated for all available simulations in the redshift range $z=0-1.6$ and wave modes below $k=10 \\text{h/Mpc}$. These fitting formulas can be used to test the predictions of the model in the non-linear regime without the need for additional computing-intensive N-body simulations. We then use these fitting functions to perform forecasts on the constraining power that future galaxy-redshift surveys like Euclid will have on the coupling parameter, using the Fisher matrix method for galaxy clustering (GC) and w...

  19. 1 2 3 4 5 6 1 2 3 4 5 6

    E-Print Network [OSTI]

    Gilbert, Matthew

    Foreign Languages Bldg (D4) 64 Freer Hall (D5) G 201 Garage/Car Pool (F1) 128 Geological Survey Lab (D1;Engineering Bldg (A4) 15 Engineering Hall (B4) 162 Engineering Sr Design Studio (B6) 174 Engineering Sciences Bldg (B5) 1209 Engineering Student Project Lab (B5) 44 English Bldg (C4) 1095 Enterprise Works (G2) 213

  20. 2 May 2000 1 Burning Plasmas Physics Issues

    E-Print Network [OSTI]

    2 May 2000 1 Burning Plasmas Physics Issues Illustrated by FIRE Simulations W.A. Houlberg ORNL Workshop on Physics Issues for FIRE 1-3 May 2000 Princeton, NJ #12;2 May 2000 2 Outline q WHIST simulations-mode q Conclusions #12;2 May 2000 3 1-1/2-D Time-Dependent Transport Modeling q 1-1/2-D time

  1. Algebraic Bethe Ansatz solutions for the $sl(2|1)^{(2)}$ and $osp(2|1)$ models with boundary terms

    E-Print Network [OSTI]

    V. Kurak; A. Lima-Santos

    2004-10-01

    This work is concerned with the formulation of the graded quantum inverse scattering method for a class oflattice models with reflecting boundary conditions. The $sl(2|1)^{(2)}$ and $osp(2|1)$ models are considered with their diagonal reflections in BFB grading. This allowed us to derive the eigenvalues and eigenvectors for the corresponding transfer matrices as well as explicit expressions for the Bethe Ansatz equations.

  2. Problem Sheet 1 of 8 Problem 1. Let P1 = (1, 0, 0), P2 = (0, 1, 0) and P3 = (0, 0, 1).

    E-Print Network [OSTI]

    Argerami, Martin

    Problem Sheet 1 of 8 Problem 1. Let P1 = (1, 0, 0), P2 = (0, 1, 0) and P3 = (0, 0, 1). (a) Show that P1, P2, P3 are not aligned. (b) Find a vector parametric equation for the plane P passing through P1, P2, P3. (c) Find normal and standard equation for P. (d) Find a vector parametric equation

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

    SciTech Connect (OSTI)

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

    2013-10-01

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

  4. Fallout forecasting: 1945-1962

    SciTech Connect (OSTI)

    Kennedy, W.R. Jr.

    1986-03-01

    The delayed hazards of fallout from the detonations of nuclear devices in the atmosphere have always been the concern of those involved in the Test Program. Even before the Trinity Shot (TR-2) of July 16, 1945, many very competent, intelligent scientists and others from all fields of expertise tried their hand at the prediction problems. This resume and collection of parts from reports, memoranda, references, etc., endeavor to chronologically outline prediction methods used operationally in the field during Test Operations of nuclear devices fired into the atmosphere.

  5. OPTIMIZAO E ALGORITMOS Exame final -1 e 2 data

    E-Print Network [OSTI]

    Instituto de Sistemas e Robotica

    OPTIMIZA��O E ALGORITMOS Exame final - 1ª e 2ª data Nº Nome 1ª data 2ª data 31684 RICARDO ANDR� 13

  6. EMPORA 1 + 2 EMobile Power Austria (Smart Grid Project) (Salzburg...

    Open Energy Info (EERE)

    EMPORA 1 + 2 EMobile Power Austria (Smart Grid Project) (Salzburg, Austria) Jump to: navigation, search Project Name EMPORA 1 + 2 EMobile Power Austria Country Austria Headquarters...

  7. 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 on Google Bookmark EERE: AlternativeMonthly","10/2015"Monthly","10/2015" ,"Release7Cubicthroughthe PriceThousanda Oxygenated55,453.9151 andN4.1S2.1 and

  8. Take & Complete USMLE No Step 1 84 T2C

    E-Print Network [OSTI]

    Leistikow, Bruce N.

    Take & Complete USMLE No Step 1 84 T2C Start Clerkship #1 ' Yes __. / . ·- Complete No Clerkship #1 #3) T2C: Transition to Clerkships T & C: Take & Complete CSP: Defer Clerkship #1 AND T & C Step 1 prior to Clerkship #2 OR restart 3rd year Complete Clerkship #2 Continue on with 3rd Year No credit

  9. Global warming and its implications for conservation. 1. Overview.

    E-Print Network [OSTI]

    Creel, Scott

    Global warming and its implications for conservation. 1. Overview. The IPCC (Intergovernmental Panel on Climate Change) consensus forecast for climate is an increase in global temperature by 2 - 5o C of atmospheric CO2 would yield an increase in global mean temperature of T2X = 3.5o C (6.7 o F, with 95

  10. The Commission Forecast 1992 Report: Important Resource Planning Issues 

    E-Print Network [OSTI]

    Adib, P.

    1992-01-01

    FORECAST 1992 REPORT: IMPORTANT RESOURCE PLANNING ISSUES PARVIZ ADIB MANAGER, ECONOMIC ANALYSIS SECTION ELECTRIC DIVISION PUBLIC UTILITY COMMISSION OF TEXAS ABSTRACT There is a general agreement among experts in the electric utility industry... there are many important issues in the preparation of a utility's electric resource plan, the Commission staff will address a few important ones in the next Commission Forecast Report (Forecast '92). In particular, the Commission staff will insure...

  11. 1.2 CORE INTERPRETATION AND SEDIMENTOLOGICAL ANALYSIS 1.2.1 Generalized Core Description

    E-Print Network [OSTI]

    Schechter, David S.

    reservoir quality. Carbonate rocks are not very common in the analyzed cores. Lithofacies L2: Shales. Shale infrequently; the black color of these shales is a function of their high organic carbon content. Organics siltstone. This lithofacies is characterized by alternations of light and dark colored fine laminae, each

  12. Pink1, Parkin, DJ-1 and mitochondrial dysfunction in Parkinson's Mark W Dodson1,2

    E-Print Network [OSTI]

    Guo, Ming

    Pink1, Parkin, DJ-1 and mitochondrial dysfunction in Parkinson's disease Mark W Dodson1,2 and Ming forms and some sporadic cases of Parkinson's disease. Recent work on these genes underscores the central importance of mitochondrial dysfunction and oxidative stress in Parkinson's disease. In particular, pink1

  13. Theory of Rayleigh scattering from metallic carbon nanotubes Ermin Mali,1,* Matthias Hirtschulz,1 Frank Milde,1 Yang Wu,2 Janina Maultzsch,2 Tony F. Heinz,2

    E-Print Network [OSTI]

    Heinz, Tony F.

    Theory of Rayleigh scattering from metallic carbon nanotubes Ermin Mali,1,* Matthias Hirtschulz,1, 10623 Berlin, Germany 2 Department of Physics and Department of Electrical Engineering, Columbia

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

  15. The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss

    E-Print Network [OSTI]

    Auffhammer, Maximilian

    2005-01-01

    function. The forecasts of oil, coal and gas prices as wellforecasts for natural gas consumption, electricity sales, coal and electricity prices,

  16. Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures

    E-Print Network [OSTI]

    Richard A. Berk; Brian Kriegler; Jong-Ho Baek

    2011-01-01

    Forecasting Dangerous Inmate Misconduct: An Applications ofof Term Length more dangerous than other inmates servingIV beds or moving less dangerous Level IV inmates to Level

  17. Forecasting Dangerous Inmate Misconduct: An Applications of Ensemble Statistical Procedures

    E-Print Network [OSTI]

    Berk, Richard; Kriegler, Brian; Baek, Jong-Ho

    2005-01-01

    Forecasting Dangerous Inmate Misconduct: An Applications ofof Term Length more dangerous than other inmates servingIV beds or moving less dangerous Level IV inmates to Level

  18. Electric Grid - Forecasting system licensed | ornl.gov

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

    Electric Grid - Forecasting system licensed Location Based Technologies has signed an agreement to integrate and market an Oak Ridge National Laboratory technology that provides...

  19. Ramping Effect on Forecast Use: Integrated Ramping (Presentation...

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

    the shift from ramping. * the benefits - better use of forecast values (load or net load) - reduce the amount of variability that the regulation reserve must accommodate...

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

    SciTech Connect (OSTI)

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

    2015-12-08

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