National Library of Energy BETA

Sample records for module forecasts consumption

  1. 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

  2. Forecasting Hot Water Consumption in Dwellings Using Artifitial Neural Networks

    E-Print Network [OSTI]

    MacDonald, Mark

    electricity consumption in time. This paper investigates the ability on Artificial Neural Networks to predict shift electric energy. Keywords--Hot Water Consumption; Forecasting; Artifitial Neural Networks; SmartForecasting Hot Water Consumption in Dwellings Using Artifitial Neural Networks Linas Gelazanskas

  3. Energy consumption in optical modulators for interconnects

    E-Print Network [OSTI]

    Miller, David A. B.

    Energy consumption in optical modulators for interconnects David A. B. Miller* Ginzton Laboratory@ee.stanford.edu Abstract: We analyze energy consumption in optical modulators operated in depletion and intended for low, even conceivably being more energy-efficient than an ideal loss-less modulator. ©2012 Optical Society

  4. 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

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

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

    the metrics and performance measurement tools used in Earned Value. This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices....

  6. Energy consumption and expenditure projections by population group on the basis of the annual energy outlook 1999 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Balsley, J.H.

    2000-01-07

    This report presents an analysis of the relative impact of the base-case scenario used in Annual Energy Outlook 1999 on different population groups. Projections of energy consumption and expenditures, as well as energy expenditure as a share of income, from 1996 to 2020 are given. The projected consumption of electricty, natural gas, distillate fuel, and liquefied petroleum gas during this period is also reported for each population group. In addition, this report compares the findings of the Annual Energy Outlook 1999 report with the 1998 report. Changes in certain indicators and information affect energy use forecasts, and these effects are analyzed and discussed.

  7. Short-Term Energy Outlook Model Documentation: Motor Gasoline Consumption Model

    Reports and Publications (EIA)

    2011-01-01

    The motor gasoline consumption module of the Short-Term Energy Outlook (STEO) model is designed to provide forecasts of total U.S. consumption of motor gasolien based on estimates of vehicle miles traveled and average vehicle fuel economy.

  8. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Climate Zonea for Non-Mall Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,,,"Total Floorspace of...

  9. Consumption

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

    A. Electricity Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,,,"Total Floorspace of...

  10. Consumption

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

    A. Electricity Consumption and Conditional Energy Intensity by Building Size for All Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of...

  11. Consumption

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

    3. Electricity Consumption and Conditional Energy Intensity, 1999" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of Buildings Using Electricity (million square...

  12. Consumption

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

    A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace...

  13. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Building Size for Non-Mall Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of...

  14. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Division for Non-Mall Buildings, 2003: Part 1" ,"Total Electricity Consumption (billion kWh)",,,"Total...

  15. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Division for Non-Mall Buildings, 2003: Part 2" ,"Total Electricity Consumption (billion kWh)",,,"Total...

  16. Consumption

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

    9A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace...

  17. Consumption

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

    Electricity Consumption and Conditional Energy Intensity by Census Region, 1999" ,"Total Electricity Consumption (billion kWh)",,,,"Total Floorspace of Buildings Using Electricity...

  18. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Region for Non-Mall Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,,"Total Floorspace of...

  19. Consumption

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

    A. Electricity Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,,"Total Floorspace of...

  20. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Year Constructed for Non-Mall Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of...

  1. Consumption

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

    4. Electricity Consumption and Conditional Energy Intensity by Year Constructed, 1999" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of Buildings Using...

  2. Consumption

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

    A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace...

  3. Consumption

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

    A. Electricity Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003" ,"Total Electricity Consumption (billion kWh)",,,"Total Floorspace of...

  4. Consumption

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

    . Electricity Consumption and Conditional Energy Intensity by Census Division for Non-Mall Buildings, 2003: Part 3" ,"Total Electricity Consumption (billion kWh)",,,"Total...

  5. Consumption

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

    5. Fuel Oil Consumption and Conditional Energy Intensity by Census Region for Non-Mall Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,"Total Floorspace of...

  6. Consumption

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

    3. Fuel Oil Consumption and Conditional Energy Intensity by Census Region, 1999" ,"Total Fuel Oil Consumption (million gallons)",,,,"Total Floorspace of Buildings Using Fuel Oil...

  7. Consumption

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

    A. Fuel Oil Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,"Total Floorspace of Buildings...

  8. 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

  9. Consumption

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

    . Consumption and Gross Energy Intensity by Building Size for Sum of Major Fuels for Non-Mall Buildings, 2003" ,"Sum of Major Fuel Consumption (trillion Btu)",,,"Total Floorspace...

  10. Short-Term Energy Outlook Model Documentation: Other Petroleum Products Consumption Model

    Reports and Publications (EIA)

    2011-01-01

    The other petroleum product consumption module of the Short-Term Energy Outlook (STEO) model is designed to provide U.S. consumption forecasts for 6 petroleum product categories: asphalt and road oil, petrochemical feedstocks, petroleum coke, refinery still gas, unfinished oils, and other miscvellaneous products

  11. Energy consumption and expenditure projections by income quintile on the basis of the Annual Energy Outlook 1997 forecast

    SciTech Connect (OSTI)

    Poyer, D.A.; Allison, T.

    1998-03-01

    This report presents an analysis of the relative impacts of the base-case scenario used in the Annual Energy Outlook 1997, published by the US Department of Energy, Energy Information Administration, on income quintile groups. Projected energy consumption and expenditures, and projected energy expenditures as a share of income, for the period 1993 to 2015 are reported. Projected consumption of electricity, natural gas, distillate fuel, and liquefied petroleum gas over this period is also reported for each income group. 33 figs., 11 tabs.

  12. Design of a Solid-State Fast Voltage Compensator for klystron modulators requiring constant AC power consumption

    E-Print Network [OSTI]

    Davide, Aguglia; Philippe, Viarouge; Jerome, Cros

    2015-01-01

    This paper proposes a novel topological solution for klystron modulators integrating a Fast Voltage Compensator which allows an operation at constant power consumption from the utility grid. This kind of solution is mandatory for the CLIC project under study, which requires several hundreds of synchronously operated klystron modulators for a total pulsed power of 39 GW. The topology is optimized for the challenging CLIC specifications, which require a very precise output voltage flat-top as well as fast rise and fall times (3µs). The Fast Voltage Compensator is integrated in the modulator such that it only has to manage the capacitor charger current and a fraction of the charging voltage. Consequently, its dimensioning power and cost is minimized.

  13. A microbial functional group-based module for simulating methane production and consumption: Application to an incubated permafrost soil

    SciTech Connect (OSTI)

    Xu, Xiaofeng; Elias, Dwayne A.; Graham, David E.; Phelps, Tommy J.; Carroll, Sue L.; Wullschleger, Stan D.; Thornton, Peter E.

    2015-07-23

    In this study, accurately estimating methane (CH4) flux is critically important for investigating and predicting the biogeochemistry-climate feedback. Better simulating CH4 flux requires explicit representations of microbial processes on CH4 dynamics because all processes for CH4 production and consumption are actually carried out by microbes. A microbial functional group based module was developed and tested against an incubation experiment. The module considers four key mechanisms for CH4 production and consumption: methanogenesis from acetate or single-carbon compounds and CH4 oxidation using molecular oxygen or other inorganic electron acceptors. These four processes were carried out by four microbial functional groups: acetoclastic methanogens, hydrogenotrophic methanogens, aerobic methanotrophs, and anaerobic methanotrophs. This module was then linked with the decomposition subroutine of the Community Land Model, and was further used to simulate dynamics of carbon dioxide (CO2) and CH4 concentrations from an incubation experiment with permafrost soils. The results show that the model could capture the dynamics of CO2 and CH4 concentrations in microcosms with top soils, mineral layer soils and permafrost soils under natural and saturated moisture conditions and a temperature gradient of -2°C, 3°C, and 5°C. Sensitivity analysis confirmed the importance of acetic acid's direct contribution as substrate and indirect effects through pH feedback on CO2 and CH4 production and consumption. This study suggests that representing the microbial mechanisms is critical for modeling CH4 production and consumption; it is urgent to incorporate microbial mechanisms into Earth system models for better predicting the behavior of the climate system.

  14. A microbial functional group-based module for simulating methane production and consumption: Application to an incubated permafrost soil

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

    Xu, Xiaofeng; Elias, Dwayne A.; Graham, David E.; Phelps, Tommy J.; Carroll, Sue L.; Wullschleger, Stan D.; Thornton, Peter E.

    2015-07-23

    In this study, accurately estimating methane (CH4) flux is critically important for investigating and predicting the biogeochemistry-climate feedback. Better simulating CH4 flux requires explicit representations of microbial processes on CH4 dynamics because all processes for CH4 production and consumption are actually carried out by microbes. A microbial functional group based module was developed and tested against an incubation experiment. The module considers four key mechanisms for CH4 production and consumption: methanogenesis from acetate or single-carbon compounds and CH4 oxidation using molecular oxygen or other inorganic electron acceptors. These four processes were carried out by four microbial functional groups: acetoclastic methanogens,more »hydrogenotrophic methanogens, aerobic methanotrophs, and anaerobic methanotrophs. This module was then linked with the decomposition subroutine of the Community Land Model, and was further used to simulate dynamics of carbon dioxide (CO2) and CH4 concentrations from an incubation experiment with permafrost soils. The results show that the model could capture the dynamics of CO2 and CH4 concentrations in microcosms with top soils, mineral layer soils and permafrost soils under natural and saturated moisture conditions and a temperature gradient of -2°C, 3°C, and 5°C. Sensitivity analysis confirmed the importance of acetic acid's direct contribution as substrate and indirect effects through pH feedback on CO2 and CH4 production and consumption. This study suggests that representing the microbial mechanisms is critical for modeling CH4 production and consumption; it is urgent to incorporate microbial mechanisms into Earth system models for better predicting the behavior of the climate system.« less

  15. Electricity Demand and Energy Consumption Management System

    E-Print Network [OSTI]

    Sarmiento, Juan Ojeda

    2008-01-01

    This project describes the electricity demand and energy consumption management system and its application to the Smelter Plant of Southern Peru. It is composted of an hourly demand-forecasting module and of a simulation component for a plant electrical system. The first module was done using dynamic neural networks, with backpropagation training algorithm; it is used to predict the electric power demanded every hour, with an error percentage below of 1%. This information allows management the peak demand before this happen, distributing the raise of electric load to other hours or improving those equipments that increase the demand. The simulation module is based in advanced estimation techniques, such as: parametric estimation, neural network modeling, statistic regression and previously developed models, which simulates the electric behavior of the smelter plant. These modules allow the proper planning because it allows knowing the behavior of the hourly demand and the consumption patterns of the plant, in...

  16. 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

  17. A Novel Active Bouncer System for Klystron Modulators with Constant AC Power Consumption

    E-Print Network [OSTI]

    Cabaleiro Magallanes, F; Viarouge, P; Cros, J; De Almeida Martins, C

    2014-01-01

    This paper presents the principles and design methodologies of a novel active bouncer system, to be implemented in a transformer-based klystron modulator, which is able to meet two different objectives: 1. Regulate the output pulse voltage flattop, and 2. Attenuate the power fluctuation withdrawn from the AC network. This solution allows the utilization of a standard constant voltage / constant current power supply as a capacitor charger. The solution consists of a 4-quadrant switching converter placed in series with the main capacitor bank (forming a unique element in parallel with the capacitor charger), controlled with specific feed-back loops to achieve the two objectives. The complete design method, including a numerical optimization, of the whole system, is presented in the paper. Analyses of the compromises between the active bouncer specifications and the other modulator sub-components design is presented as well.

  18. Model documentation coal market module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1997-02-01

    This report documents the objectives and the conceptual and methodological approach used in the development of the Coal Production Submodule (CPS). It provides a description of the CPS for model analysts and the public. The Coal Market Module provides annual forecasts of prices, production, and consumption of coal.

  19. 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,

  20. 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

  1. 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....

  2. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    . Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data Office. Andrea Gough ran the summary energy model and supervised data preparation. Glen Sharp prepared models. Both the staff revised energy consumption and peak forecasts are slightly higher than

  3. 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

  4. 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:

  5. 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

  6. Comparison of Bottom-Up and Top-Down Forecasts: Vision Industry Energy Forecasts with ITEMS and NEMS 

    E-Print Network [OSTI]

    Roop, J. M.; Dahowski, R. T

    2000-01-01

    Comparisons are made of energy forecasts using results from the Industrial module of the National Energy Modeling System (NEMS) and an industrial economic-engineering model called the Industrial Technology and Energy Modeling System (ITEMS), a model...

  7. 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.

  8. Renewable Fuels Module - NEMS Documentation

    Reports and Publications (EIA)

    2014-01-01

    This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the Annual Energy Outlook forecasts.

  9. 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...

  10. 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 . . . . . . . . . . . . .

  11. 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...

  12. 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

  13. 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

  14. 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

  15. 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...

  16. Neural network based short-term load forecasting using weather compensation

    SciTech Connect (OSTI)

    Chow, T.W.S.; Leung, C.T. [City Univ. of Hong Kong, Kowloon (Hong Kong). Dept. of Electronic Engineering] [City Univ. of Hong Kong, Kowloon (Hong Kong). Dept. of Electronic Engineering

    1996-11-01

    This paper presents a novel technique for electric load forecasting based on neural weather compensation. The proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. The weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error.

  17. 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 ...

  18. 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

  19. 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

  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. 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.

  2. 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

  3. 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

  4. Module Handbook Module title

    E-Print Network [OSTI]

    . Students learn about selected topics from inorganic chemistry, biochemistry, materials chemistryModule Handbook Module title Module title in English Credits Degree of compulsion Level Learning Theory of Functional Materials 6 Compulsory Basic A systematic foundation for quantum physics

  5. ZBB EnerStore(tm): Deep Discharge Zinc-Bromine Battery Module...

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

    Battery Module Long-Lasting Electrical Energy Storage Module Allows Off-Peak Power Generation Electricity consumption during peak demand can overload utilities, forcing them to...

  6. GREET Pretreatment Module

    SciTech Connect (OSTI)

    Adom, Felix K.; Dunn, Jennifer B.; Han, Jeongwoo

    2014-09-01

    A wide range of biofuels and biochemicals can be produced from biomass via different pretreatment technologies that yield sugars. This report documents the material and energy flows that occur when fermentable sugars from four lignocellulosic feedstocks (corn stover, miscanthus, switchgrass, and poplar) are produced via dilute acid pretreatment and ammonia fiber expansion. These flows are documented for inclusion in the pretreatment module of the Greenhouses Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. Process simulations of each pretreatment technology were developed in Aspen Plus. Material and energy consumption data from Aspen Plus were then compiled in the GREET pretreatment module. The module estimates the cradle-to-gate fossil energy consumption (FEC) and greenhouse gas (GHG) emissions associated with producing fermentable sugars. This report documents the data and methodology used to develop this module and the cradle-to-gate FEC and GHG emissions that result from producing fermentable sugars.

  7. Classification of Energy Consumption in Buildings with Outlier Detection

    E-Print Network [OSTI]

    Yao, Xin

    1 Classification of Energy Consumption in Buildings with Outlier Detection Xiaoli Li, Chris P is to enable a building management system to be used for forecasting and detection of abnormal energy use. First, an outlier detection method is proposed to identify abnormally high or low energy use in building

  8. 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.

  9. Factors of material consumption

    E-Print Network [OSTI]

    Silva Díaz, Pamela Cristina

    2012-01-01

    Historic consumption trends for materials have been studied by many researchers, and, in order to identify the main drivers of consumption, special attention has been given to material intensity, which is the consumption ...

  10. The Effects of External Temperature on the Energy Consumption of Household Refrigerator-Freezers and Freezers 

    E-Print Network [OSTI]

    Burgess, Tiffany

    2015-06-30

    to improve efficiency and reduce consumption, it is important to understand how a unit behaves outside the design conditions. The forecasted annual energy consumption as published on the EnergyGuide sticker is determined by testing the unit at a specified...

  11. 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

  12. Introduction to the Buildings Sector Module of SEDS

    SciTech Connect (OSTI)

    DeForest, Nicholas; Bonnet, Florence; Stadler, Michael; Marnay, Chris

    2010-12-31

    SEDS is a stochastic engineering-economics model that forecasts economy-wide energy consumption in the U.S. to 2050. It is the product of multi-laboratory collaboration among the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), and Lumina Decision Systems. Among national energy models, SEDS is unique, as it is the only model written to explicitly incorporate uncertainty in its inputs and outputs. The primary purpose of SEDS is to estimate the impact of various US Department of Energy (DOE)R&D and policy programs on the performance and subsequent adoption rates of technologies relating to every energy consuming sector of the economy (shown below). It has previously been used to assist DOE in complying with the Government Performance and Results Act of 1993 (GPRA). The focus of LBNL research has been exclusively on develop the buildings model (SBEAM), which is capable of running as a stand-alone forecasting model, or as a part of SEDS as a whole. The full version of SEDS, containing all sectors and interaction is also called the 'integrated' version and is managed by NREL. Forecasts from SEDS are often compared to those coming from National Energy Modeling System (NEMS). The intention of this document is to present new users and developers with a general description of the purpose, functionality and structure of the buildings module within the Stochastic Energy Deployment System (SEDS). The Buildings module, which is capable of running as a standalone model, is also called the Stochastic Buildings Energy and Adoption Model (SBEAM). This document will focus exclusively on SBEAM and its interaction with other major sector modules present within SEDS. The methodologies and major assumptions employed in SBEAM will also be discussed. The organization of this report will parallel the organization of the model itself, being divided into major submodules. As the description progresses, the nature of modules will change from broad, easily understood concepts to lower-level data manipulation. Because SBEAM contains dozens of submodules and hundreds of variables, it would not be relevant or useful to describe each and every one. Rather, the investigation will focus more generally on the operations performed throughout the model. This manual is by no means a complete description of SBEAM; however it should provide the foundation for an introductory understanding of the model. The manual assumes a basic level of understating of Analytica{reg_sign}, the platform on which SEDS and SBEAM have been developed.

  13. 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

  14. ,"Total Fuel Oil Consumption

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

    0. Fuel Oil Consumption (gallons) and Energy Intensities by End Use for Non-Mall Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,,"Fuel Oil Energy Intensity...

  15. ,"Total Fuel Oil Consumption

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

    A. Fuel Oil Consumption (gallons) and Energy Intensities by End Use for All Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,,"Fuel Oil Energy Intensity...

  16. Model documentation, Coal Market Module of the National Energy Modeling System

    SciTech Connect (OSTI)

    NONE

    1998-01-01

    This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The international area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.

  17. 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...

  18. 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,

  19. 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.

  20. 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.

  1. 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 ...

  2. Reduces electric energy consumption

    E-Print Network [OSTI]

    BENEFITS · Reduces electric energy consumption · Reduces peak electric demand · Reduces natural gas consumption · Reduces nonhazardous solid waste and wastewater generation · Potential annual savings products for the automotive industry, electrical equipment, and miscellaneous other uses nationwide. ALCOA

  3. & CONSUMPTION US HYDROPOWER PRODUCTION

    E-Print Network [OSTI]

    ENERGY PRODUCTION & CONSUMPTION US HYDROPOWER PRODUCTION In the United States hydropower supplies the NAO. ENERGY CONSUMPTION AND PRODUCTION IN NORWAY AND THE NAO The demand for heating oil in Norway Average Winter Temperature NORWAY kilotonsofoilmillibars°Cmmofrainfall Annual Heating Oil Consumption

  4. Population, Consumption & the Environment

    E-Print Network [OSTI]

    Columbia University

    consumes the 77 trillion barrels of oil energy equivalent per year ­ Fossil fuel consumption (oil, coal12/11/2009 1 Population, Consumption & the Environment Alex de Sherbinin Center for International of carbon in 2001 · The ecological footprint, a composite measure of consumption measured in hectares

  5. 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...

  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

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

  7. Connected Consumption: The hidden networks of consumption

    E-Print Network [OSTI]

    Reed, David P.

    In this paper, we present the Connected Consumption Network (CCN) that allows a community of consumers to collaboratively sense the market from a mobile device, enabling more informed financial decisions in geo-local ...

  8. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect (OSTI)

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

  9. 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 ...

  10. 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 ...

  11. 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 ...

  12. 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

  13. 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

  14. 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

  15. 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

  16. ,"Total Natural Gas Consumption

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

    Gas Consumption (billion cubic feet)",,,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"Total ","Space Heating","Water Heating","Cook- ing","Other","Total ","Space...

  17. 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

  18. 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

  19. Module 6 - Metrics, Performance Measurements and Forecasting | Department

    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 GasAdjustmentsShirleyEnergy AEnergy Managing SwimmingMicrosoft Word1 2 - 2

  20. Short-Term Energy Outlook Model Documentation: Petroleum Product Prices Module

    Reports and Publications (EIA)

    2015-01-01

    The petroleum products price module of the Short-Term Energy Outlook (STEO) model is designed to provide U.S. average wholesale and retail price forecasts for motor gasoline, diesel fuel, heating oil, and jet fuel.

  1. Changes in Natural Gas Monthly Consumption Data Collection and the Short-Term Energy Outlook

    Reports and Publications (EIA)

    2010-01-01

    Beginning with the December 2010 issue of the Short-Term Energy Outlook (STEO), the Energy Information Administration (EIA) will present natural gas consumption forecasts for the residential and commercial sectors that are consistent with recent changes to the Form EIA-857 monthly natural gas survey.

  2. 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.

  3. 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...

  4. Consumption conundrums unravelled

    E-Print Network [OSTI]

    Horrell, Sara; Humphries, Jane; Sneath, Ken

    2014-12-17

    revolution’.16 Indeed, some economic historians have been sceptical about the extent to which tastes could drive consumption, arguing instead that improved technology reduced costs so that increased consumption of new items was a passive (and standard... that is entirely consistent with the idea of a systematic sifting of property for it attractiveness and value. Thieves are described as well-equipped and organised, often starting on the top floor of premises and working their way through the building, pausing...

  5. 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

  6. 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

  7. 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.

  8. 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...

  9. 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 ...

  10. 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,...

  11. 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,...

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. Module Configuration

    DOE Patents [OSTI]

    Oweis, Salah (Ellicott City, MD); D'Ussel, Louis (Bordeaux, FR); Chagnon, Guy (Cockeysville, MD); Zuhowski, Michael (Annapolis, MD); Sack, Tim (Cockeysville, MD); Laucournet, Gaullume (Paris, FR); Jackson, Edward J. (Taneytown, MD)

    2002-06-04

    A stand alone battery module including: (a) a mechanical configuration; (b) a thermal management configuration; (c) an electrical connection configuration; and (d) an electronics configuration. Such a module is fully interchangeable in a battery pack assembly, mechanically, from the thermal management point of view, and electrically. With the same hardware, the module can accommodate different cell sizes and, therefore, can easily have different capacities. The module structure is designed to accommodate the electronics monitoring, protection, and printed wiring assembly boards (PWAs), as well as to allow airflow through the module. A plurality of modules may easily be connected together to form a battery pack. The parts of the module are designed to facilitate their manufacture and assembly.

  19. Estimation of food consumption

    SciTech Connect (OSTI)

    Callaway, J.M. Jr.

    1992-04-01

    The research reported in this document was conducted as a part of the Hanford Environmental Dose Reconstruction (HEDR) Project. The objective of the HEDR Project is to estimate the radiation doses that people could have received from operations at the Hanford Site. Information required to estimate these doses includes estimates of the amounts of potentially contaminated foods that individuals in the region consumed during the study period. In that general framework, the objective of the Food Consumption Task was to develop a capability to provide information about the parameters of the distribution(s) of daily food consumption for representative groups in the population for selected years during the study period. This report describes the methods and data used to estimate food consumption and presents the results developed for Phase I of the HEDR Project.

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  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. 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...

  7. AVLIS: a technical and economic forecast

    SciTech Connect (OSTI)

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

    1986-01-01

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

  8. & CONSUMPTION US HYDROPOWER PRODUCTION

    E-Print Network [OSTI]

    ENERGY PRODUCTION & CONSUMPTION US HYDROPOWER PRODUCTION In the United States hydropower supplies 12% of the nation's electricity. Hydropower produces more than 90,000 megawatts of electricity, which is enough to meet the needs of 28.3 million consumers. Hydropower accounts for over 90% of all electricity

  9. Understanding environmentally significant consumption

    E-Print Network [OSTI]

    Integration and Sustainability, Michigan State University, East Lansing, MI 48824 Consumer choices can have emissions by 7% through modest policies to encourage more efficient household energy consumption (1 that consumers also misunderstood household energy use (5, 6). Respondents underestimated the amount of energy

  10. 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 \

  11. 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.

  12. 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...

  13. 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.

  14. Health Care Buildings: Consumption Tables

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

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

  15. A Java Reinforcement Learning Module for the Recursive Porous

    E-Print Network [OSTI]

    Tesfatsion, Leigh

    A Java Reinforcement Learning Module for the Recursive Porous Agent Simulation Toolkit Facilitating Evaluation of free java-libraries for social- scientific agent based simulation, Tobias and Hoffman, 2004 in Repast Genetic Algorithm demo model OpenForecast demo model Java Object Oriented Neural Engine (JOONE

  16. Model documentation: Electricity Market Module, Electricity Fuel Dispatch Submodule

    SciTech Connect (OSTI)

    Not Available

    1994-04-08

    This report documents the objectives, analytical approach and development of the National Energy Modeling System Electricity Fuel Dispatch Submodule (EFD), a submodule of the Electricity Market Module (EMM). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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....

  10. 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

  11. 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...

  12. 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

  13. 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 ...

  14. 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 ...

  15. Resource demand growth and sustainability due to increased world consumption

    SciTech Connect (OSTI)

    Balatsky, Alexander V.; Balatsky, Galina I.; Borysov, Stanislav S.

    2015-03-20

    The paper aims at continuing the discussion on sustainability and attempts to forecast the impossibility of the expanding consumption worldwide due to the planet’s limited resources. As the population of China, India and other developing countries continue to increase, they would also require more natural and financial resources to sustain their growth. We coarsely estimate the volumes of these resources (energy, food, freshwater) and the gross domestic product (GDP) that would need to be achieved to bring the population of India and China to the current levels of consumption in the United States. We also provide estimations for potentially needed immediate growth of the world resource consumption to meet this equality requirement. Given the tight historical correlation between GDP and energy consumption, the needed increase of GDP per capita in the developing world to the levels of the U.S. would deplete explored fossil fuel reserves in less than two decades. These estimates predict that the world economy would need to find a development model where growth would be achieved without heavy dependence on fossil fuels.

  16. Resource demand growth and sustainability due to increased world consumption

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

    Balatsky, Alexander V.; Balatsky, Galina I.; Borysov, Stanislav S.

    2015-03-20

    The paper aims at continuing the discussion on sustainability and attempts to forecast the impossibility of the expanding consumption worldwide due to the planet’s limited resources. As the population of China, India and other developing countries continue to increase, they would also require more natural and financial resources to sustain their growth. We coarsely estimate the volumes of these resources (energy, food, freshwater) and the gross domestic product (GDP) that would need to be achieved to bring the population of India and China to the current levels of consumption in the United States. We also provide estimations for potentially neededmore »immediate growth of the world resource consumption to meet this equality requirement. Given the tight historical correlation between GDP and energy consumption, the needed increase of GDP per capita in the developing world to the levels of the U.S. would deplete explored fossil fuel reserves in less than two decades. These estimates predict that the world economy would need to find a development model where growth would be achieved without heavy dependence on fossil fuels.« less

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

    SciTech Connect (OSTI)

    Eisenberg, Joel F. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2005-10-31

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

  18. 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

  19. 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 

  20. Using Electricity",,,"Electricity Consumption",,,"Electricity...

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

    . Total Electricity Consumption and Expenditures, 2003" ,"All Buildings* Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number of Buildings...

  1. 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

  2. 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.

  3. 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

  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. Model documentation: Electricity Market Module, Electricity Capacity Planning submodule

    SciTech Connect (OSTI)

    Not Available

    1994-04-07

    The National Energy Modeling System (NEMS) is a computer modeling system developed by the Energy Information Administration (EIA). The NEMS produces integrated forecasts for energy markets in the United States by achieving a general equilibrium solution for energy supply and demand. Currently, for each year during the period from 1990 through 2010, the NEMS describes energy supply, conversion, consumption, and pricing. The Electricity Market Module (EMM) is the electricity supply component of the National Energy Modeling System (NEMS). The supply of electricity is a conversion activity since electricity is produced from other energy sources (e.g., fossil, nuclear, and renewable). The EMM represents the generation, transmission, and pricing of electricity. The EMM consists of four main submodules: Electricity Capacity Planning (ECP), Electricity Fuel Dispatching (EFD), Electricity Finance and Pricing (EFP), and Load and Demand-Side Management (LDSM). The ECP evaluates changes in the mix of generating capacity that are necessary to meet future demands for electricity and comply with environmental regulations. The EFD represents dispatching (i.e., operating) decisions and determines how to allocate available capacity to meet the current demand for electricity. Using investment expenditures from the ECP and operating costs from the EFD, the EFP calculates the price of electricity, accounting for state-level regulations involving the allocation of costs. The LDSM translates annual demands for electricity into distributions that describe hourly, seasonal, and time-of-day variations. These distributions are used by the EFD and the ECP to determine the quantity and types of generating capacity that are required to insure reliable and economical supplies of electricity. The EMM also represents nonutility suppliers and interregional and international transmission and trade. These activities are included in the EFD and the ECP.

  6. Thermionic modules

    DOE Patents [OSTI]

    King, Donald B. (Albuquerque, NM); Sadwick, Laurence P. (Salt Lake City, UT); Wernsman, Bernard R. (Clairton, PA)

    2002-06-18

    Modules of assembled microminiature thermionic converters (MTCs) having high energy-conversion efficiencies and variable operating temperatures manufactured using MEMS manufacturing techniques including chemical vapor deposition. The MTCs incorporate cathode to anode spacing of about 1 micron or less and use cathode and anode materials having work functions ranging from about 1 eV to about 3 eV. The MTCs also exhibit maximum efficiencies of just under 30%, and thousands of the devices and modules can be fabricated at modest costs.

  7. Module No: 420244International Humanitarian Module Title

    E-Print Network [OSTI]

    Module No: 420244International Humanitarian law Module Title: Co-requisite:public international law 1Pre-requisite: Module Type: specialization requirementModule level: Second year Evening Study-mailOffice Number Office Phone Academic rank Instructor Name E-mailOffice Number Office Phone Academic rank Module

  8. module.h

    E-Print Network [OSTI]

    /* OS-9 module header definitions */ /* Executable memory module */ typedef struct { unsigned m_sync, /* sync bytes ($87cd) */ m_size, /* module size ...

  9. 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

  10. 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 ...

  11. 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

  12. 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...

  13. 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...

  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. 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

  16. 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

  17. NOAAINMFS Developments Shrimp 1980: Consumption

    E-Print Network [OSTI]

    NOAAINMFS Developments Shrimp 1980: Consumption Is Up in a Difficult Year Table 1.-Supplles and uses 01 all shrimp, 1974-78 s.ersge, 1979, anil 1980, hesds oil weight. Data are preliminary with the slight increase in landings easily offset the drop in im- ports. Increased supplies went to consumption

  18. 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.

  19. 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)

  20. World energy consumption

    SciTech Connect (OSTI)

    NONE

    1995-12-01

    Historical and projected world energy consumption information is displayed. The information is presented by region and fuel type, and includes a world total. Measurements are in quadrillion Btu. Sources of the information contained in the table are: (1) history--Energy Information Administration (EIA), International Energy Annual 1992, DOE/EIA-0219(92); (2) projections--EIA, World Energy Projections System, 1994. Country amounts include an adjustment to account for electricity trade. Regions or country groups are shown as follows: (1) Organization for Economic Cooperation and Development (OECD), US (not including US territories), which are included in other (ECD), Canada, Japan, OECD Europe, United Kingdom, France, Germany, Italy, Netherlands, other Europe, and other OECD; (2) Eurasia--China, former Soviet Union, eastern Europe; (3) rest of world--Organization of Petroleum Exporting Countries (OPEC) and other countries not included in any other group. Fuel types include oil, natural gas, coal, nuclear, and other. Other includes hydroelectricity, geothermal, solar, biomass, wind, and other renewable sources.

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  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 effec- tiveness of individual weather forecasts. The paper evaluates six distinct weather

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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)

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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

  5. "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

  6. Forecast Technical Document Felling and Removals

    E-Print Network [OSTI]

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

  7. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27

    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.

  8. 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

  9. Thermoelectric module

    DOE Patents [OSTI]

    Kortier, William E. (Columbus, OH); Mueller, John J. (Columbus, OH); Eggers, Philip E. (Columbus, OH)

    1980-07-08

    A thermoelectric module containing lead telluride as the thermoelectric mrial is encapsulated as tightly as possible in a stainless steel canister to provide minimum void volume in the canister. The lead telluride thermoelectric elements are pressure-contacted to a tungsten hot strap and metallurgically bonded at the cold junction to iron shoes with a barrier layer of tin telluride between the iron shoe and the p-type lead telluride element.

  10. 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

  11. 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.

  12. 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.

  13. Technology data characterizing water heating in commercial buildings: Application to end-use forecasting

    SciTech Connect (OSTI)

    Sezgen, O.; Koomey, J.G.

    1995-12-01

    Commercial-sector conservation analyses have traditionally focused on lighting and space conditioning because of their relatively-large shares of electricity and fuel consumption in commercial buildings. In this report we focus on water heating, which is one of the neglected end uses in the commercial sector. The share of the water-heating end use in commercial-sector electricity consumption is 3%, which corresponds to 0.3 quadrillion Btu (quads) of primary energy consumption. Water heating accounts for 15% of commercial-sector fuel use, which corresponds to 1.6 quads of primary energy consumption. Although smaller in absolute size than the savings associated with lighting and space conditioning, the potential cost-effective energy savings from water heaters are large enough in percentage terms to warrant closer attention. In addition, water heating is much more important in particular building types than in the commercial sector as a whole. Fuel consumption for water heating is highest in lodging establishments, hospitals, and restaurants (0.27, 0.22, and 0.19 quads, respectively); water heating`s share of fuel consumption for these building types is 35%, 18% and 32%, respectively. At the Lawrence Berkeley National Laboratory, we have developed and refined a base-year data set characterizing water heating technologies in commercial buildings as well as a modeling framework. We present the data and modeling framework in this report. The present commercial floorstock is characterized in terms of water heating requirements and technology saturations. Cost-efficiency data for water heating technologies are also developed. These data are intended to support models used for forecasting energy use of water heating in the commercial sector.

  14. Photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan; Graves, Simon; Lenox, Carl J. S.; Culligan, Matthew; Danning, Matt

    2013-08-27

    A photovoltaic (PV) module including a PV device and a frame, The PV device has a PV laminate defining a perimeter and a major plane. The frame is assembled to and encases the laminate perimeter, and includes leading, trailing, and side frame members, and an arm that forms a support face opposite the laminate. The support face is adapted for placement against a horizontal installation surface, to support and orient the laminate in a non-parallel or tilted arrangement. Upon final assembly, the laminate and the frame combine to define a unitary structure. The frame can orient the laminate at an angle in the range of 3.degree.-7.degree. from horizontal, and can be entirely formed of a polymeric material. Optionally, the arm incorporates integral feature(s) that facilitate interconnection with corresponding features of a second, identically formed PV module.

  15. Photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan (El Cerrito, CA); Graves, Simon (Berkeley, CA); Lenox, Carl J. S. (Oakland, CA); Culligan, Matthew (Berkeley, CA); Danning, Matt (Oakland, CA)

    2012-07-17

    A photovoltaic (PV) module including a PV device and a frame. The PV device has a PV laminate defining a perimeter and a major plane. The frame is assembled to and encases the laminate perimeter, and includes leading, trailing, and side frame members, and an arm that forms a support face opposite the laminate. The support face is adapted for placement against a horizontal installation surface, to support and orient the laminate in a non-parallel or tilted arrangement. Upon final assembly, the laminate and the frame combine to define a unitary structure. The frame can orient the laminate at an angle in the range of 3.degree.-7.degree. from horizontal, and can be entirely formed of a polymeric material. Optionally, the arm incorporates integral feature(s) that facilitate interconnection with corresponding features of a second, identically formed PV module.

  16. 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).

  17. Per Capita Consumption The NMFS calculation of per capita consumption is

    E-Print Network [OSTI]

    capita consumption of fresh and frozen products was 11.8 pounds, 0.4 pound more than 2003. Fresh in the world. #12;Per Capita Consumption 79 U.S. Consumption Annual per capita consumption of seafood productsPer Capita Consumption 78 The NMFS calculation of per capita consumption is based

  18. Per Capita Consumption The NMFS calculation of per capita consumption is

    E-Print Network [OSTI]

    capita consumption of fresh and frozen products was 11.6 pounds, 0.2 pound less than 2004. Fresh in the world. #12;Per Capita Consumption 74 U.S. Consumption Annual per capita consumption of seafood productsPer Capita Consumption 73 The NMFS calculation of per capita consumption is based

  19. Progressive consumption : strategic sustainable excess

    E-Print Network [OSTI]

    Bonham, Daniel J. (Daniel Joseph MacLeod)

    2007-01-01

    Trends in the marketplace show that urban dwellers are increasingly supporting locally produced foods. This thesis argues for an architecture that responds to our cultures consumptive behaviors. Addressing the effects of ...

  20. Manufacturing consumption of energy 1991

    SciTech Connect (OSTI)

    Not Available

    1994-12-01

    This report provides estimates on energy consumption in the manufacturing sector of the US economy. These estimates are based on data from the 1991 Manufacturing Energy Consumption Survey (MECS). This survey--administered by the Energy End Use and Integrated Statistics Division, Office of Energy Markets and End Use, Energy Information Administration (EIA)--is the most comprehensive source of national-level data on energy-related information for the manufacturing industries.

  1. Energy Information Administration - Commercial Energy Consumption...

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

    4A. Electricity Consumption and Expenditure Intensities for All Buildings, 2003 Electricity Consumption Electricity Expenditures per Building (thousand kWh) per Square Foot (kWh)...

  2. Energy Information Administration - Commercial Energy Consumption...

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

    5A. Electricity Consumption and Conditional Energy Intensity by Census Region for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings...

  3. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    9A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 3 Total Electricity Consumption (billion kWh) Total Floorspace of...

  4. Energy Information Administration - Commercial Energy Consumption...

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

    2A. Electricity Consumption and Conditional Energy Intensity by Year Constructed for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings...

  5. Using Electricity",,,"Electricity Consumption",,,"Electricity...

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

    A. Total Electricity Consumption and Expenditures for All Buildings, 2003" ,"All Buildings Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number of...

  6. Energy Information Administration - Commercial Energy Consumption...

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

    1A. Electricity Consumption and Conditional Energy Intensity by Building Size for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings...

  7. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    8A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 2 Total Electricity Consumption (billion kWh) Total Floorspace of...

  8. Energy Information Administration - Commercial Energy Consumption...

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

    Table C13. Total Electricity Consumption and Expenditures for Non-Mall Buildings, 2003 All Buildings* Using Electricity Electricity Consumption Electricity Expenditures Number of...

  9. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    7A. Electricity Consumption and Conditional Energy Intensity by Census Division for All Buildings, 2003: Part 1 Total Electricity Consumption (billion kWh) Total Floorspace of...

  10. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    0A. Electricity Consumption and Conditional Energy Intensity by Climate Zonea for All Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace of Buildings...

  11. Energy Information Administration - Commercial Energy Consumption...

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

    Table C22. Electricity Consumption and Conditional Energy Intensity by Year Constructed for Non-Mall Buildings, 2003 Total Electricity Consumption (billion kWh) Total Floorspace...

  12. Electricity",,,"Electricity Consumption",,,"Electricity Expenditures...

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

    C9. Total Electricity Consumption and Expenditures, 1999" ,"All Buildings Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number of Buildings...

  13. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    3A. Total Electricity Consumption and Expenditures for All Buildings, 2003 All Buildings Using Electricity Electricity Consumption Electricity Expenditures Number of Buildings...

  14. Electricity",,,"Electricity Consumption",,,"Electricity Expenditures...

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

    DIV. Total Electricity Consumption and Expenditures by Census Division, 1999" ,"All Buildings Using Electricity",,,"Electricity Consumption",,,"Electricity Expenditures" ,"Number...

  15. ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures"

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

    4. Fuel Oil Consumption and Expenditure Intensities for Non-Mall Buildings, 2003" ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures" ,"per Building (gallons)","per Square Foot...

  16. ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures"

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

    2. Fuel Oil Consumption and Expenditure Intensities, 1999" ,"Fuel Oil Consumption",,,"Fuel Oil Expenditures" ,"per Building (gallons)","per Square Foot (gallons)","per Worker...

  17. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    A. Consumption and Gross Energy Intensity by Climate Zonea for All Buildings, 2003 Sum of Major Fuel Consumption (trillion Btu) Total Floorspace of Buildings (million square feet)...

  18. New York: Weatherizing Westbeth Reduces Energy Consumption |...

    Office of Environmental Management (EM)

    York: Weatherizing Westbeth Reduces Energy Consumption New York: Weatherizing Westbeth Reduces Energy Consumption August 21, 2013 - 12:00am Addthis The New York State Homes and...

  19. The Impact of Using Derived Fuel Consumption Maps to Predict...

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

    The Impact of Using Derived Fuel Consumption Maps to Predict Fuel Consumption The Impact of Using Derived Fuel Consumption Maps to Predict Fuel Consumption Poster presented at the...

  20. The new Athens Center applied to Space Weather Forecasting

    SciTech Connect (OSTI)

    Mavromichalaki, H.; Sarlanis, C.; Souvatzoglou, G.; Mariatos, G.; Gerontidou, M.; Plainaki, C.; Papaioannou, A.; Tatsis, S. [University of Athens, Physics Department, Section of Nuclear and Particle Physics, Zografos 15771 Athens (Greece); Belov, A.; Eroshenko, E.; Yanke, V. [IZMIRAN, Russian Academy of Science, 1420092 Moscow (Russian Federation)

    2006-08-25

    The Sun provides most of the initial energy driving space weather and modulates the energy input from sources outside the solar system, but this energy undergoes many transformations within the various components of the solar-terrestrial system, which is comprised of the solar wind, magnetosphere and radiation belts, the ionosphere, and the upper and lower atmospheres of Earth. This is the reason why an Earth's based neutron monitor network can be used in order to produce a real time forecasting of space weather phenomena.Since 2004 a fully functioned new data analysis Center in real-time is in operation in Neutron Monitor Station of Athens University (ANMODAP Center) suitable for research applications. It provides a multi sided use of twenty three neutron monitor stations distributing in all world and operating in real-time given crucial information on space weather phenomena. In particular, the ANMODAP Center can give a preliminary alert of ground level enhancements (GLEs) of solar cosmic rays which can be registered around 20 to 30 minutes before the main part of lower energy particles. Therefore these energetic solar cosmic rays provide the advantage of forth warning. Moreover, the monitoring of the precursors of cosmic rays gives a forehand estimate on that kind of events should be expected (geomagnetic storms and/or Forbush decreases)

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. Supported PV module assembly

    SciTech Connect (OSTI)

    Mascolo, Gianluigi; Taggart, David F.; Botkin, Jonathan D.; Edgett, Christopher S.

    2013-10-15

    A supported PV assembly may include a PV module comprising a PV panel and PV module supports including module supports having a support surface supporting the module, a module registration member engaging the PV module to properly position the PV module on the module support, and a mounting element. In some embodiments the PV module registration members engage only the external surfaces of the PV modules at the corners. In some embodiments the assembly includes a wind deflector with ballast secured to a least one of the PV module supports and the wind deflector. An array of the assemblies can be secured to one another at their corners to prevent horizontal separation of the adjacent corners while permitting the PV modules to flex relative to one another so to permit the array of PV modules to follow a contour of the support surface.

  8. Module Embedding Atanas Radenski

    E-Print Network [OSTI]

    Radenski, Atanas

    Module Embedding 1 Atanas Radenski Computer Science Department UNC-WSSU, P. O. Box 19479 Winston module embedding that enables the building of new modules from existing ones through inheritance for this mechanism. Module embedding is beneficial when modules and classes are used in combination and need

  9. Working with Modules within Python

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

    Using Modules within Python The EnvironmentModules python package gives access to the module system from within python. The EnvironmentModules python package has a single...

  10. 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.

  11. Short-Term Energy Outlook Model Documentation: Petroleum Products Supply Module

    Reports and Publications (EIA)

    2013-01-01

    The Petroleum Products Supply Module of the Short-Term Energy Outlook (STEO) model provides forecasts of petroleum refinery inputs (crude oil, unfinished oils, pentanes plus, liquefied petroleum gas, motor gasoline blending components, and aviation gasoline blending components) and refinery outputs (motor gasoline, jet fuel, distillate fuel, residual fuel, liquefied petroleum gas, and other petroleum products).

  12. Severe Weather on the Web: Computer Lab for WEST Severe Weather Module

    E-Print Network [OSTI]

    Jiang, Haiyan

    Severe Weather on the Web: Computer Lab for WEST Severe Weather Module Summary: Students Weather Service-- National Weather Hazards Website: http://www.weather.gov/view/largemap.php --This termforecasts in the lower 48 USstates. Definitions Forecast--The prediction of what the weather

  13. 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...

  14. Monitoring Energy Consumption of Smartphones

    E-Print Network [OSTI]

    Ding, Fangwei; Zhang, Wei; Zhao, Xuhai; Ma, Chengchuan

    2012-01-01

    With the rapid development of new and innovative applications for mobile devices like smartphones, advances in battery technology have not kept pace with rapidly growing energy demands. Thus energy consumption has become a more and more important issue of mobile devices. To meet the requirements of saving energy, it is critical to monitor and analyze the energy consumption of applications on smartphones. For this purpose, we develop a smart energy monitoring system called SEMO for smartphones using Android operating system. It can profile mobile applications with battery usage information, which is vital for both developers and users.

  15. 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.

  16. Per Capita Consumption The NM.S calculation of per capita consumption is

    E-Print Network [OSTI]

    consumed in 2001. Per capita consumption of fresh and frozen products was 11.0 pounds, 0.7 pound more than catfish. Consumption of canned fishery products was 4.3 pounds per capita in 2002, 0.1 pound more than in the world. #12;Per Capita Consumption 86 U.S. Consumption Annual per capita consumption of seafood products

  17. Wind power forecasting : state-of-the-art 2009.

    SciTech Connect (OSTI)

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

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and uncertainty in wind power and to more efficiently operate power systems with large wind power penetrations. Moreover, in a market environment, the wind power contribution to the generation portofolio becomes important in determining the daily and hourly prices, as variations in the estimated wind power will influence the clearing prices for both energy and operating reserves. With the increasing penetration of wind power, WPF is quickly becoming an important topic for the electric power industry. System operators (SOs), generating companies (GENCOs), and regulators all support efforts to develop better, more reliable and accurate forecasting models. Wind farm owners and operators also benefit from better wind power prediction to support competitive participation in electricity markets against more stable and dispatchable energy sources. In general, WPF can be used for a number of purposes, such as: generation and transmission maintenance planning, determination of operating reserve requirements, unit commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and energy trading. The objective of this report is to review and analyze state-of-the-art WPF models and their application to power systems operations. We first give a detailed description of the methodologies underlying state-of-the-art WPF models. We then look at how WPF can be integrated into power system operations, with specific focus on the unit commitment problem.

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

    SciTech Connect (OSTI)

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

    2012-09-01

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

  19. 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

  20. 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

  1. 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...

  2. 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...

  3. 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.

  4. Weather-based yield forecasts developed for 12 California crops

    E-Print Network [OSTI]

    Lobell, David; Cahill, Kimberly Nicholas; Field, Christopher

    2006-01-01

    RESEARCH ARTICLE Weather-based yield forecasts developed fordepend largely on the weather, measurements from existingpredictions. We developed weather-based models of statewide

  5. Nuclear Theory Helps Forecast Neutron Star Temperatures | U.S...

    Office of Science (SC) Website

    Nuclear Theory Helps Forecast Neutron Star Temperatures Nuclear Physics (NP) NP Home About Research Facilities Science Highlights Benefits of NP Funding Opportunities Nuclear...

  6. Energy Consumption Profile for Energy

    E-Print Network [OSTI]

    Langendoen, Koen

    ...................................................................................318 12.2.1 Motivations for Energy Harvesting...............................................319 12 the example of a "smart application'' assisted by a decision engine that transforms itself into an "energy317 Chapter 12 Energy Consumption Profile for Energy Harvested WSNs T. V. Prabhakar, R Venkatesha

  7. EIA lowers forecast for summer gasoline prices

    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 Natural GasNatural GasEIA lowers forecast for summer gasoline prices

  8. 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

  9. ECMWF analyses and forecasts of 500 mb synoptic-scale activity during wintertime blocking 

    E-Print Network [OSTI]

    Matson, David Michael

    1993-01-01

    An observational study of 500 mb atmospheric blocking is conducted based on an European Centre for Medium-Range Weather Forecasts (ECMWF) wintertime analysis and forecast dataset during dynamic extended range forecasting ...

  10. Comparison of AEO 2005 natural gas price forecast to NYMEX futures prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01

    revisions to the EIA’s natural gas price forecasts in AEOsolely on the AEO 2005 natural gas price forecasts willComparison of AEO 2005 Natural Gas Price Forecast to NYMEX

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01

    to estimate the base-case natural gas price forecast, but toComparison of AEO 2010 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

  12. Reducing the demand forecast error due to the bullwhip effect in the computer processor industry

    E-Print Network [OSTI]

    Smith, Emily (Emily C.)

    2010-01-01

    Intel's current demand-forecasting processes rely on customers' demand forecasts. Customers do not revise demand forecasts as demand decreases until the last minute. Intel's current demand models provide little guidance ...

  13. Comparison of AEO 2005 natural gas price forecast to NYMEX futures prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01

    revisions to the EIA’s natural gas price forecasts in AEOon the AEO 2005 natural gas price forecasts will likely onceComparison of AEO 2005 Natural Gas Price Forecast to NYMEX

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01

    to estimate the base-case natural gas price forecast, but toComparison of AEO 2010 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from the AEO

  15. 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

  16. Ballasted photovoltaic module and module arrays

    DOE Patents [OSTI]

    Botkin, Jonathan (El Cerrito, CA); Graves, Simon (Berkeley, CA); Danning, Matt (Oakland, CA)

    2011-11-29

    A photovoltaic (PV) module assembly including a PV module and a ballast tray. The PV module includes a PV device and a frame. A PV laminate is assembled to the frame, and the frame includes an arm. The ballast tray is adapted for containing ballast and is removably associated with the PV module in a ballasting state where the tray is vertically under the PV laminate and vertically over the arm to impede overt displacement of the PV module. The PV module assembly can be installed to a flat commercial rooftop, with the PV module and the ballast tray both resting upon the rooftop. In some embodiments, the ballasting state includes corresponding surfaces of the arm and the tray being spaced from one another under normal (low or no wind) conditions, such that the frame is not continuously subjected to a weight of the tray.

  17. Shield Module Design Considerations

    E-Print Network [OSTI]

    McDonald, Kirk

    Shield Module Design Considerations Adam Carroll Van Graves July 3, 2014 #12;2 Managed by UT-Battelle for the U.S. Department of Energy Shield Module Design Considerations 3 July 2014 Overview · Capability to remotely remove and reinstall the shield modules is required · Shield module concept is He-cooled tungsten

  18. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01

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

  19. Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010

    SciTech Connect (OSTI)

    Jacobs, David E. . E-mail: dejacobs@starpower.net; Nevin, Rick

    2006-11-15

    We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels {>=}10 {mu}g/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

  20. 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

  1. Using Neural Networks to Forecast Stock Market Prices Ramon Lawrence

    E-Print Network [OSTI]

    Lawrence, Ramon

    Using Neural Networks to Forecast Stock Market Prices Ramon Lawrence Department of Computer Science on the application of neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more

  2. Impact of PV forecasts uncertainty in batteries management in microgrids

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Impact of PV forecasts uncertainty in batteries management in microgrids Andrea Michiorri Arthur-based battery schedule optimisation in microgrids in presence of network constraints. We examine a specific case production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size

  3. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

    of transportation fuel and crude oil import requirements. The transportation energy demand forecasts make. The transportation fuel and crude oil import requirement assessments build on assumptions about California crude oil forecasts, transportation energy, gasoline, diesel, jet fuel, crude oil production, fuel imports, crude oil

  4. A Deep Hybrid Model for Weather Forecasting Aditya Grover

    E-Print Network [OSTI]

    Horvitz, Eric

    @microsoft.com ABSTRACT Weather forecasting is a canonical predictive challenge that has depended primarily on model-based methods. We ex- plore new directions with forecasting weather as a data- intensive challenge that involves the joint statistics of a set of weather-related vari- ables. We show how the base model can be enhanced

  5. Hydrological Forecasting Improvements Primary Investigator: Thomas Croley -NOAA GLERL (Emeritus)

    E-Print Network [OSTI]

    multiple data streams in a near-real-time manner and incorporate them into the AHPS data base, run for matching weather forecasts with historical data, and prepare extensive forecasts of hydrology probabilities maximum use of all available information and be based on efficient and true hydrological process models

  6. DEEP COMPREHENSION, GENERATION AND TRANSLATION OF WEATHER FORECASTS (WEATHRA)

    E-Print Network [OSTI]

    in a data base and graphic representation with tile standard meteorological icons on a map, e.g. iconsDEEP COMPREHENSION, GENERATION AND TRANSLATION OF WEATHER FORECASTS (WEATHRA) by BENGT SIGURD, Sweden E-mail: linglund@gemini.ldc.lu.se FAX:46-(0)46 104210 Introduction and abstract Weather forecasts

  7. 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

  8. 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

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

    E-Print Network [OSTI]

    Singhal, Gaurav

    2012-10-19

    Real-time wave forecasts are critical to a variety of coastal and offshore opera- tions. NOAA’s global wave forecasts, at present, do not extend into many coastal regions of interest. Even after more than two decades of the historical Exxon Valdez...

  10. 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

  11. Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center

    E-Print Network [OSTI]

    Washington at Seattle, University of

    Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime at wind energy sites are becoming paramount. Regime-switching space-time (RST) models merge meteorological forecast regimes at the wind energy site and fits a conditional predictive model for each regime

  12. MAINTENANCE, UPGRADE AND VERIFICATION OF OPERATIONAL FORECASTS OF

    E-Print Network [OSTI]

    MAINTENANCE, UPGRADE AND VERIFICATION OF OPERATIONAL FORECASTS OF CLOUD COVER AND WATER VAPOUR Purchase Order 58311/ODG/99/8362/GWI/LET #12;i PREFACE Starting in August 1998, operational forecasts satellite imagery from the Co-operative Institute for Research in the Atmosphere (CIRA) and upper

  13. THE DESIRE TO ACQUIRE: FORECASTING THE EVOLUTION OF HOUSEHOLD

    E-Print Network [OSTI]

    THE DESIRE TO ACQUIRE: FORECASTING THE EVOLUTION OF HOUSEHOLD ENERGY SERVICES by Steven Groves BASc of Research Project: The Desire to Acquire: Forecasting the Evolution of Household Energy Services Report No, and gasoline. A fixed effects panel model was used to examine the relationship of demand for energy

  14. Airplanes Aloft as a Sensor Network for Wind Forecasting

    E-Print Network [OSTI]

    Horvitz, Eric

    Airplanes Aloft as a Sensor Network for Wind Forecasting Ashish Kapoor, Zachary Horvitz, Spencer for observing weather phenomena at a continental scale. We focus specifically on the problem of wind forecasting with the sensed winds. The experiments show the promise of using airplane in flight as a large-scale sensor

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

    E-Print Network [OSTI]

    Freitas, Nando de

    on the sentiment of price39 forecasts and reports for commodities such as gold, natural gas or most commonly oil or natural gas can impact everything from the21 critical business decisions made within nationsClassification of Commodity Price Forecast Sentiment With Random Forests and Bayesian Optimization

  16. Solar irradiance forecasting at multiple time horizons and novel methods to evaluate uncertainty

    E-Print Network [OSTI]

    Marquez, Ricardo

    2012-01-01

    114 Solar Irradiance And Power Output Variabilityand L. Bangyin. Online 24-h solar power forecasting based onNielsen. Online short-term solar power forecasting. Solar

  17. 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 numerical weather prediction solar irradiance forecasts numerical weather prediction model for solar irradiance weather prediction for intra?day solar  forecasting in the 

  18. Building Electricity Load Forecasting via Stacking Ensemble Learning Method with Moving Horizon Optimization

    E-Print Network [OSTI]

    Burger, Eric M.; Moura, Scott J.

    2015-01-01

    K. W. Yau, “Predicting electricity energy con- sumption: Afor building-level electricity load forecasts,” Energy andannealing algorithms in electricity load forecasting,”

  19. Sixth Northwest Conservation and Electric Power Plan Appendix A: Fuel Price Forecast

    E-Print Network [OSTI]

    Sixth Northwest Conservation and Electric Power Plan Appendix A: Fuel Price Forecast Introduction................................................................................................................................. 3 Price Forecasts ............................................................................................................................ 5 U.S. Natural Gas Commodity Prices

  20. Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA

    SciTech Connect (OSTI)

    Hou, Zhangshuan; Etingov, Pavel V.; Makarov, Yuri V.; Samaan, Nader A.

    2014-10-27

    In this paper, we introduce a new approach without implying normal distributions and stationarity of power generation forecast errors. In addition, it is desired to more accurately quantify the forecast uncertainty by reducing prediction intervals of forecasts. We use automatically coupled wavelet transform and autoregressive integrated moving-average (ARIMA) forecasting to reflect multi-scale variability of forecast errors. The proposed analysis reveals slow-changing “quasi-deterministic” components of forecast errors. This helps improve forecasts produced by other means, e.g., using weather-based models, and reduce forecast errors prediction intervals.

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01

    Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01

    Comparison of AEO 2008 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01

    Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01

    Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXcase long-term natural gas price forecasts from theto contemporaneous natural gas prices that can be locked in

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01

    to  predict daily solar radiation.   Agriculture and Forest and Chuo, S.   2008.  Solar radiation forecasting using Short?term forecasting of solar radiation:   A statistical 

  6. User-needs study for the 1993 residential energy consumption survey

    SciTech Connect (OSTI)

    Not Available

    1993-09-24

    During 1992, the Energy Information Administration (EIA) conducted a user-needs study for the 1993 Residential Energy Consumption Survey (RECS). Every 3 years, the RECS collects information on energy consumption and expenditures for various classes of households and residential buildings. The RECS is the only source of such information within EIA, and one of only a few sources of such information anywhere. EIA sent letters to more than 750 persons, received responses from 56, and held 15 meetings with users. Written responses were also solicited by notices published in the April 14, 1992 Federal Register and in several energy-related publications. To ensure that the 1993 RECS meets current information needs, EIA made a specific effort to get input from policy makers and persons needing data for forecasting efforts. These particular needs relate mainly to development of the National Energy Modeling System and new energy legislation being considered at the time of the user needs survey.

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

    SciTech Connect (OSTI)

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

    2012-07-01

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

  8. Software-related Energy Footprint of a Wireless Broadband Module

    E-Print Network [OSTI]

    Software-related Energy Footprint of a Wireless Broadband Module Mikael Asplund mikael Keywords 3G, Energy footprint, Power consumption, Wireless broad- band 1. INTRODUCTION The battery lifetime Linköping, Sweden ABSTRACT Energy economy in mobile devices is becoming an increas- ingly important factor

  9. Consumption

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

    Using Natural Gas (million square feet)",,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"North- east","Mid- west","South","West","North- east","Mid-...

  10. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"West South Central","Moun- tain","Pacific","West South Central","Moun-...

  11. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"1959 or Before","1960 to 1989","1990 to 2003","1959 or Before","1960 to...

  12. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"West North Central","South Atlantic","East South Central","West North...

  13. Consumption

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

    Using Natural Gas (million square feet)",,,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"Zone 1","Zone 2","Zone 3","Zone 4","Zone 5","Zone 1","Zone 2","Zone 3","Zone...

  14. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"1959 or Before","1960 to 1989","1990 to 2003","1959 or Before","1960 to...

  15. Consumption

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

    (million square feet)",,,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"North- east","Mid- west","South","West","North- east","Mid-...

  16. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"New England","Middle Atlantic","East North Central","New England","Middle...

  17. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"West North Central","South Atlantic","East South Central","West North...

  18. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"1,001 to 10,000 Square Feet","10,001 to 100,000 Square Feet","Over 100,000...

  19. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"1,001 to 10,000 Square Feet","10,001 to 100,000 Square Feet","Over 100,000...

  20. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"New England","Middle Atlantic","East North Central","New England","Middle...

  1. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"1,001 to 10,000 Square Feet","10,001 to 100,000 Square Feet","Over 100,000...

  2. Consumption

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

    (million square feet)",,,,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"Zone 1","Zone 2","Zone 3","Zone 4","Zone 5","Zone 1","Zone 2","Zone 3","Zone...

  3. Consumption

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

    Using Natural Gas (million square feet)",,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"1959 or Before","1960 to 1989","1990 to 1999","1959 or Before","1960 to...

  4. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btu square foot)" ,"West South Central","Moun- tain","Pacific","West South Central","Moun-...

  5. Consumption

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

    (million square feet)",,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"1959 or Before","1960 to 1989","1990 to 1999","1959 or Before","1960 to...

  6. Consumption

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

    (million square feet)",,,,"Energy Intensity for Sum of Major Fuels (thousand Btusquare foot)" ,"North- east","Mid- west","South","West","North- east","Mid-...

  7. Consumption

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

    90,1024,3251,1511,"Q",106.6,97.3,100.6 "Office ...",305,325,329,175,3012,2989,3782,2425,101.2,108.8,87,72.1 "Public Assembly ...",93,103,109,64,1048,...

  8. Consumption

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

    9,60,56.7,43.1,31.4,22.1 "1990 to 1999 ...",69,87,51,93,34,1735,1988,1202,3012,1267,40,43.8,42.4,30.9,26.9 "2000 to 2003 ...",23,40,"Q",28,15,693,1086,7...

  9. Manufacturing consumption of energy 1994

    SciTech Connect (OSTI)

    1997-12-01

    This report provides estimates on energy consumption in the manufacturing sector of the U.S. economy based on data from the Manufacturing Energy Consumption Survey. The sample used in this report represented about 250,000 of the largest manufacturing establishments which account for approximately 98 percent of U.S. economic output from manufacturing, and an expected similar proportion of manufacturing energy use. The amount of energy use was collected for all operations of each establishment surveyed. Highlights of the report include profiles for the four major energy-consuming industries (petroleum refining, chemical, paper, and primary metal industries), and an analysis of the effects of changes in the natural gas and electricity markets on the manufacturing sector. Seven appendices are included to provide detailed background information. 10 figs., 51 tabs.

  10. 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.

  11. State energy data report 1992: Consumption estimates

    SciTech Connect (OSTI)

    Not Available

    1994-05-01

    This is a report of energy consumption by state for the years 1960 to 1992. The report contains summaries of energy consumption for the US and by state, consumption by source, comparisons to other energy use reports, consumption by energy use sector, and describes the estimation methodologies used in the preparation of the report. Some years are not listed specifically although they are included in the summary of data.

  12. Jrgen Richter Consumption and Production: Transformational Pro-

    E-Print Network [OSTI]

    Hartmann, Robert

    Jürgen Richter Consumption and Production: Transformational Pro- cesses in the Upper Levels as cores (1) Only one example of a "migrating core" was aested: Jürgen Richter Consumption and Production, also imported for bifacial production. Conclusion: Consumption of lithics Unit VI, level 1 delivered

  13. Mathematical models of natural gas consumption

    E-Print Network [OSTI]

    Scitovski, Rudolf

    tolerance interval. Previous research in the area of energy consumption (gas or electricity) revealsMathematical models of natural gas consumption Kristian Sabo, Rudolf Scitovski, Ivan of natural gas consumption Kristian Sabo, Rudolf Scitovski, Ivan Vazler , Marijana Zeki-Susac ksabo

  14. Energy Consumption ESPRIMO E7935 E80+

    E-Print Network [OSTI]

    Ott, Albrecht

    Computers is also taking significant effort to reduce the energy consumption in data centres by providingEnergy Consumption ESPRIMO E7935 E80+ White Paper Issue: September 2008 In order to strengthen all important energy information about their products. With the publication of energy consumption

  15. Ethanol Consumption by Rat Dams During Gestation,

    E-Print Network [OSTI]

    Galef Jr., Bennett G.

    Ethanol Consumption by Rat Dams During Gestation, Lactation and Weaning Increases Ethanol examined effects of ethanol consumption in rat dams during gestation, lactation, and weaning on voluntary ethanol consumption by their adolescent young. We found that exposure to an ethanol-ingesting dam

  16. Consumption-based accounting of CO2 emissions

    E-Print Network [OSTI]

    Davis, S. J; Caldeira, K.

    2010-01-01

    separation of production and consumption complicates theto compare production- and consumption-based emissions2008) From production-based to consumption-based national

  17. Residential Energy Consumption Survey Results: Total Energy Consumptio...

    Open Energy Info (EERE)

    Residential Energy Consumption Survey Results: Total Energy Consumption, Expenditures, and Intensities (2005) The Residential Energy Consumption Survey (RECS) is a national survey...

  18. Solar Adoption and Energy Consumption in the Residential Sector

    E-Print Network [OSTI]

    McAllister, Joseph Andrew

    2012-01-01

    49 3.3.3. Pre-installation electricity consumption of CSIE. Kahn (2011). Electricity Consumption and Durable Housing:on Electricity Consumption .

  19. Modelling the impact of user behaviour on heat energy consumption

    E-Print Network [OSTI]

    Combe, Nicola Miss; Harrison, David Professor; Way, Celia Miss

    2011-01-01

    strategies impact on energy consumption in residentialBEHAVIOUR ON HEAT ENERGY CONSUMPTION Nicola Combe 1 ,2 ,nearly 60% of domestic energy consumption and 27% of total

  20. TV Energy Consumption Trends and Energy-Efficiency Improvement Options

    E-Print Network [OSTI]

    Park, Won Young

    2011-01-01

    and Low Power Mode Energy Consumption”, Energy Efficiency inEnergy Consumption ..26 3.1.3. 3D TV Energy Consumption and Efficiency

  1. Solar Adoption and Energy Consumption in the Residential Sector

    E-Print Network [OSTI]

    McAllister, Joseph Andrew

    2012-01-01

    Solar Adoption and Energy Consumption in the ResidentialFall 2012 Solar Adoption and Energy Consumption in theAbstract Solar Adoption and Energy Consumption in the

  2. TV Energy Consumption Trends and Energy-Efficiency Improvement Options

    E-Print Network [OSTI]

    Park, Won Young

    2011-01-01

    technology. Power consumption of self-emissive displays suchtechnology. Power consumption of self-emissive displays suchof PDP Power Consumption PDPs are self-emissive displays

  3. Per Capita Consumption The NM.S calculation of per capita consumption is

    E-Print Network [OSTI]

    capita consumption of fresh and frozen products was 10.5 pounds, 0.1 pound more than 1999. .resh in the world. Fish and Fishery Products Apparent Consumption Average 1995-1997 (LiveWeightEquivalent) 3,400 3Per Capita Consumption 85 The NM.S calculation of per capita consumption is based

  4. Per Capita Consumption The NM.S calculation of per capita consumption is

    E-Print Network [OSTI]

    capita consumption of fresh and frozen products was 10.3 pounds, 0.1 pound more than 2000. .resh of the consumption. PER CAPITA USE. Per capita use is based on the supply of fishery products, both edible and non.S. Consumption Annual per capita consumption of seafood products represents the pounds of edible meat consumed

  5. Per Capita Consumption The NMFS calculation of per capita consumption is

    E-Print Network [OSTI]

    consumption of fresh and frozen products was 11.8 pounds, 0.3 pound less than in 2007. Fresh and frozen consumption of seafood products represents the pounds of edible meat consumed from domesticallyPer Capita Consumption 73 The NMFS calculation of per capita consumption is based

  6. Per Capita Consumption The NMFS calculation of per capita consumption is

    E-Print Network [OSTI]

    consumption of fresh and frozen products was 12.1 pounds, 0.2 pound less than in 2006. Fresh and frozen consumption of seafood products represents the pounds of edible meat consumed from domesticallyPer Capita Consumption 73 The NMFS calculation of per capita consumption is based

  7. Electrical appliance energy consumption control methods and electrical energy consumption systems

    DOE Patents [OSTI]

    Donnelly, Matthew K. (Kennewick, WA); Chassin, David P. (Pasco, WA); Dagle, Jeffery E. (Richland, WA); Kintner-Meyer, Michael (Richland, WA); Winiarski, David W. (Kennewick, WA); Pratt, Robert G. (Kennewick, WA); Boberly-Bartis, Anne Marie (Alexandria, VA)

    2008-09-02

    Electrical appliance energy consumption control methods and electrical energy consumption systems are described. In one aspect, an electrical appliance energy consumption control method includes providing an electrical appliance coupled with a power distribution system, receiving electrical energy within the appliance from the power distribution system, consuming the received electrical energy using a plurality of loads of the appliance, monitoring electrical energy of the power distribution system, and adjusting an amount of consumption of the received electrical energy via one of the loads of the appliance from an initial level of consumption to an other level of consumption different than the initial level of consumption responsive to the monitoring.

  8. Electrical appliance energy consumption control methods and electrical energy consumption systems

    DOE Patents [OSTI]

    Donnelly, Matthew K. (Kennewick, WA); Chassin, David P. (Pasco, WA); Dagle, Jeffery E. (Richland, WA); Kintner-Meyer, Michael (Richland, WA); Winiarski, David W. (Kennewick, WA); Pratt, Robert G. (Kennewick, WA); Boberly-Bartis, Anne Marie (Alexandria, VA)

    2006-03-07

    Electrical appliance energy consumption control methods and electrical energy consumption systems are described. In one aspect, an electrical appliance energy consumption control method includes providing an electrical appliance coupled with a power distribution system, receiving electrical energy within the appliance from the power distribution system, consuming the received electrical energy using a plurality of loads of the appliance, monitoring electrical energy of the power distribution system, and adjusting an amount of consumption of the received electrical energy via one of the loads of the appliance from an initial level of consumption to an other level of consumption different than the initial level of consumption responsive to the monitoring.

  9. 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.

  10. Essays in International Macroeconomics and Forecasting 

    E-Print Network [OSTI]

    Bejarano Rojas, Jesus Antonio

    2012-10-19

    the industrialized countries’ in ation even though their cross-country correlation in money growth rate is negligible. The structure of this model generates cross-country correlations of in ation, output and consumption that appear to closely correspond... to the data. Additionally, this model can explain the internal correlation between in ation and output observed in the data. The second essay presents two important results. First, gains from monetary policy cooperation are di erent from zero when...

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

    SciTech Connect (OSTI)

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

    2005-07-01

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

  12. Science and Engineering of an Operational Tsunami Forecasting System

    SciTech Connect (OSTI)

    Gonzalez, Frank

    2009-04-06

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

  13. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema (OSTI)

    Gonzalez, Frank

    2010-01-08

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

  14. Full Consumption Report.indd

    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 NaturalPrices1Markets 9,WhyConsumption6Fuel Oil andF2:

  15. CloudCast: Cloud Computing for Short-term Mobile Weather Forecasts

    E-Print Network [OSTI]

    Shenoy, Prashant

    of Massachusetts Amherst Abstract--Since today's weather forecasts only cover large regions every few hours algorithm for generating accurate short-term weather forecasts. We study CloudCast's design space, which One useful application is mobile weather forecasting, which provides hour-to-hour forecasts

  16. Smard Grid Software Applications for Distribution Network Load Forecasting Eugene A. Feinberg, Jun Fei

    E-Print Network [OSTI]

    Feinberg, Eugene A.

    of the distribution network. Keywords: load forecasting, feeder, transformer, load pocket, SmartGrid I. INTRODUCTION

  17. Solar irradiance forecasting at multiple time horizons and novel methods to evaluate uncertainty

    E-Print Network [OSTI]

    Marquez, Ricardo

    2012-01-01

    Solar irradiance data . . . . . . . . . . . . .Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . .4 Forecasting Solar Irradiance With GOES-West Satellite

  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. Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction

    E-Print Network [OSTI]

    Raftery, Adrian

    Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction LE proposes an effective bias correction technique for wind direction forecasts from numerical weather forecasts. These techniques are applied to 48-h forecasts of surface wind direction over the Pacific

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

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  1. Solar irradiance forecasting at multiple time horizons and novel methods to evaluate uncertainty

    E-Print Network [OSTI]

    Marquez, Ricardo

    2012-01-01

    Solar irradiance data . . . . . . . . . . . . .Accuracy . . . . . . . . . . . . . . . . . Solar Resourcev Uncertainty In Solar Resource: Forecasting

  2. USING BOX-JENKINS MODELS TO FORECAST FISHERY DYNAMICS: IDENTIFICATION, ESTIMATION, AND CHECKING

    E-Print Network [OSTI]

    ~ is illustrated by developing a model that makes monthly forecasts of skipjack tuna, Katsuwonus pelamis, catches

  3. Improved forecasts of extreme weather events by future space borne Doppler wind lidar

    E-Print Network [OSTI]

    Marseille, Gert-Jan

    of forecast failures, in particular those with large socio economic impact. Forecast failures of high- impact on their ability to improve meteorological analyses and subsequently reduce the probability of forecast failures true atmospheric state. This was generated by the European Centre for Medium-Range Weather Forecasts

  4. Advanced silicon photonic modulators

    E-Print Network [OSTI]

    Sorace, Cheryl M

    2010-01-01

    Various electrical and optical schemes used in Mach-Zehnder (MZ) silicon plasma dispersion effect modulators are explored. A rib waveguide reverse biased silicon diode modulator is designed, tested and found to operate at ...

  5. Modelling the impact of user behaviour on heat energy consumption

    E-Print Network [OSTI]

    Combe, Nicola Miss; Harrison, David Professor; Way, Celia Miss

    2011-01-01

    3. Actual heating and hot water energy consumption of theon-site energy consumption for heating and hot water. The

  6. Modulating lignin in plants

    DOE Patents [OSTI]

    Apuya, Nestor; Bobzin, Steven Craig; Okamuro, Jack; Zhang, Ke

    2013-01-29

    Materials and methods for modulating (e.g., increasing or decreasing) lignin content in plants are disclosed. For example, nucleic acids encoding lignin-modulating polypeptides are disclosed as well as methods for using such nucleic acids to generate transgenic plants having a modulated lignin content.

  7. Solar Trackers Market Forecast | OpenEI Community

    Open Energy Info (EERE)

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

  8. Grid-scale Fluctuations and Forecast Error in Wind Power

    E-Print Network [OSTI]

    G. Bel; C. P. Connaughton; M. Toots; M. M. Bandi

    2015-03-29

    The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_{\\tau}$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_{\\zeta}$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_{\\tau}$) and the scaling error ($e_{\\zeta}$).

  9. 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.

  10. Forecasting and Risk Analysis in Supply Chain Management

    E-Print Network [OSTI]

    Hilmola, Olli-Pekka

    Forecasting is an underestimated field of research in supply chain management. Recently advanced methods are coming into use. Initial results are encouraging, but often require changes in policies for collaboration and ...

  11. Improving the Accuracy of Solar Forecasting Funding Opportunity

    Broader source: Energy.gov [DOE]

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

  12. PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022

    E-Print Network [OSTI]

    low electricity and natural gas rates, and relatively low efficiency program and self Deputy Director ELECTRICITY SUPPLY ANALYSIS DIVISION Robert Oglesby Executive Director DISCLAIMER Staff for electric vehicles. #12;ii #12;iii ABSTRACT The Preliminary California Energy Demand Forecast 2012

  13. Optimally Controlling Hybrid Electric Vehicles using Path Forecasting

    E-Print Network [OSTI]

    Kolmanovsky, Ilya V.

    The paper examines path-dependent control of Hybrid Electric Vehicles (HEVs). In this approach we seek to improve HEV fuel economy by optimizing charging and discharging of the vehicle battery depending on the forecasted ...

  14. Multidimensional approaches to performance evaluation of competing forecasting models 

    E-Print Network [OSTI]

    Xu, Bing

    2009-01-01

    The purpose of my research is to contribute to the field of forecasting from a methodological perspective as well as to the field of crude oil as an application area to test the performance of my methodological contributions ...

  15. Grid-scale Fluctuations and Forecast Error in Wind Power

    E-Print Network [OSTI]

    Bel, G; Toots, M; Bandi, M M

    2015-01-01

    The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_{\\tau}$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_{\\zeta}$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_{\\tau}$) and the scaling error ($e_{\\zeta}$).

  16. Optimally controlling hybrid electric vehicles using path forecasting

    E-Print Network [OSTI]

    Katsargyri, Georgia-Evangelina

    2008-01-01

    Hybrid Electric Vehicles (HEVs) with path-forecasting belong to the class of fuel efficient vehicles, which use external sensory information and powertrains with multiple operating modes in order to increase fuel economy. ...

  17. Mesoscale predictability and background error convariance estimation through ensemble forecasting 

    E-Print Network [OSTI]

    Ham, Joy L

    2002-01-01

    Over the past decade, ensemble forecasting has emerged as a powerful tool for numerical weather prediction. Not only does it produce the best estimate of the state of the atmosphere, it also could quantify the uncertainties ...

  18. Forecasting and strategic inventory placement for gas turbine aftermarket spares

    E-Print Network [OSTI]

    Simmons, Joshua T. (Joshua Thomas)

    2007-01-01

    This thesis addresses the problem of forecasting demand for Life Limited Parts (LLPs) in the gas turbine engine aftermarket industry. It is based on work performed at Pratt & Whitney, a major producer of turbine engines. ...

  19. Dispersion in analysts' forecasts: does it make a difference? 

    E-Print Network [OSTI]

    Adut, Davit

    2004-09-30

    Financial analysts are an important group of information intermediaries in the capital markets. Their reports, including both earnings forecasts and stock recommendations, are widely transmitted and have a significant impact on stock prices (Womack...

  20. 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...

  1. Pressure Normalization of Production Rates Improves Forecasting Results 

    E-Print Network [OSTI]

    Lacayo Ortiz, Juan Manuel

    2013-08-07

    reliable production forecasting technique suited to interpret unconventional wells in specific situations such as unstable operating conditions, limited availability of production data (short production history) and high-pressure, rate-restricted wells...

  2. Forecasting Stock Market Volatility: Evidence from Fourteen Countries. 

    E-Print Network [OSTI]

    Balaban, Ercan; Bayar, Asli; Faff, Robert

    2002-01-01

    This paper evaluates the out-of-sample forecasting accuracy of eleven models for weekly and monthly volatility in fourteen stock markets. Volatility is defined as within-week (within-month) standard deviation of continuously ...

  3. Adaptive sampling and forecasting with mobile sensor networks

    E-Print Network [OSTI]

    Choi, Han-Lim

    2009-01-01

    This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information ...

  4. FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007

    E-Print Network [OSTI]

    ......................................................................... 11 3. Demand Side Management (DSM) Program Impacts................................... 13 4. Demand Sylvia Bender Manager DEMAND ANALYSIS OFFICE Scott W. Matthews Chief Deputy Director B.B. Blevins Forecast Methods and Models ....................................................... 14 5. Demand-Side

  5. Forecasting the probability of forest fires in Northeast Texas 

    E-Print Network [OSTI]

    Wadleigh, Stuart Allen

    1972-01-01

    FORECASTING THE PROBABILITY OF FOREST FIRES IN NORTHEAST TEXAS A Thesis by STUART ALLEN WADLEIGH Submit ted to the Graduate College of Texas ASM University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE... December 1972 Major Subject: Meteorology FORECASTING THE PROBABILITY OF FOREST FIRES IN NORTHEAST TEXAS A Thesis by STUART ALLEN WADLEIGH Approved as to style and content by: ( irman of ee) (Head of Depar nt) (Member) (Member) December 1972 c...

  6. Forecasting potential project risks through leading indicators to project outcome 

    E-Print Network [OSTI]

    Choi, Ji Won

    2007-09-17

    for the degree of MASTER OF SCIENCE May 2007 Major Subject: Civil Engineering FORECASTING POTENTIAL PROJECT RISKS THROUGH LEADING INDICATORS TO PROJECT OUTCOME A Thesis by JI WON CHOI... Guikema Head of Department, David Rosowsky May 2007 Major Subject: Civil Engineering iii ABSTRACT Forecasting Potential Project Risks through Leading Indicators to Project Outcome. (May 2007) Ji Won Choi, B.S., Han-Yang University...

  7. Point-trained models in a grid environment: Transforming a potato late blight risk forecast for use with the National Digital Forecast Database

    E-Print Network [OSTI]

    Douches, David S.

    Point-trained models in a grid environment: Transforming a potato late blight risk forecast for use with the National Digital Forecast Database Kathleen Baker a, , Paul Roehsner a , Thomas Lake b , Douglas Rivet

  8. Weather-based forecasts of California crop yields

    SciTech Connect (OSTI)

    Lobell, D B; Cahill, K N; Field, C B

    2005-09-26

    Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over the 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.

  9. Household energy consumption and expenditures 1993

    SciTech Connect (OSTI)

    1995-10-05

    This presents information about household end-use consumption of energy and expenditures for that energy. These data were collected in the 1993 Residential Energy Consumption Survey; more than 7,000 households were surveyed for information on their housing units, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information represents all households nationwide (97 million). Key findings: National residential energy consumption was 10.0 quadrillion Btu in 1993, a 9% increase over 1990. Weather has a significant effect on energy consumption. Consumption of electricity for appliances is increasing. Houses that use electricity for space heating have lower overall energy expenditures than households that heat with other fuels. RECS collected data for the 4 most populous states: CA, FL, NY, TX.

  10. Trends in Renewable Energy Consumption and Electricity

    Reports and Publications (EIA)

    2012-01-01

    Presents a summary of the nation’s renewable energy consumption in 2010 along with detailed historical data on renewable energy consumption by energy source and end-use sector. Data presented also includes renewable energy consumption for electricity generation and for non-electric use by energy source, and net summer capacity and net generation by energy source and state. The report covers the period from 2006 through 2010.

  11. Energy consumption in thermomechanical pulping

    SciTech Connect (OSTI)

    Marton, R.; Tsujimoto, N.; Eskelinen, E.

    1981-08-01

    Various components of refining energy were determined experimentally and compared with those calculated on the basis of the dimensions of morphological elements of wood. The experimentally determined fiberization energy of spruce was 6 to 60 times larger than the calculated value and that of birch 3 to 15 times larger. The energy consumed in reducing the Canadian standard freeness of isolated fibers from 500 to 150 ml was found to be approximately 1/3 of the total fiber development energy for both spruce and birch TMP. Chip size affected the refining energy consumption; the total energy dropped by approximately 30% when chip size was reduced from 16 mm to 3 mm in the case of spruce and approximately 40% for birch. 6 refs.

  12. Energy: a historical perspective and 21st century forecast

    SciTech Connect (OSTI)

    Salvador, Amos [University of Texas, Austin, TX (United States)

    2005-07-01

    Contents are: Preface; Chapter 1: introduction, brief history, and chosen approach; Chapter 2: human population and energy consumption: the future; Chapter 4: sources of energy (including a section on coal); Chapter 5: electricity: generation and consumption; and Chapter 6: energy consumption and probable energy sources during the 21st century.

  13. CSUF Economic Outlook and Forecasts Midyear Update, April 2011

    E-Print Network [OSTI]

    de Lijser, Peter

    and government support) to fundamental forces (business and consumption spending) has reached a more advanced, (4) sustained elevated oil prices, and (5) financial shocks from the European sovereign debt crisis, reflecting continued but moderate expansion. Consumption Spending. Real personal consumption expenditure fell

  14. Commercial Buildings Energy Consumption and Expenditures 1992

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

    Appendix I Related EIA Publications on Energy Consumption For information about how to obtain these publi- cations, see the inside cover of this report. Please note that the...

  15. Commercial Buildings Energy Consumption and Expenditures 1992

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

    Appendix A How the Survey Was Conducted Introduction The Commercial Buildings Energy Consumption Survey (CBECS) is conducted by the Energy Information Administration (EIA) on a...

  16. Commercial Buildings Energy Consumption and Expenditures 1992

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

    Distribution Category UC-950 Commercial Buildings Energy Consumption and Expenditures 1992 April 1995 Energy Information Adminstration Office of Energy Markets and End Use U.S....

  17. Commercial Buildings Energy Consumption and Expenditures 1992

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

    in this report were based on monthly billing records submitted by the buildings' energy suppliers. The section, "Annual Consumption and Expenditures" provide a detailed...

  18. Energy Information Administration - Commercial Energy Consumption...

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

    2003 Fuel Oil Consumption Fuel Oil Expenditures per Building (gallons) per Square Foot (gallons) per Building (thousand dollars) per Square Foot (dollars) per Gallon...

  19. Energy Information Administration - Commercial Energy Consumption...

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

    Gas Consumption Natural Gas Expenditures per Building (thousand cubic feet) per Square Foot (cubic feet) Distribution of Building-Level Intensities (cubic feetsquare foot) 25th...

  20. Energy Information Administration - Commercial Energy Consumption...

    Gasoline and Diesel Fuel Update (EIA)

    Heat Consumption District Heat Expenditures per Building (million Btu) per Square Foot (thousand Btu) per Building (thousand dollars) per Square Foot (dollars) per Thousand...

  1. ,"Maine Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Maine Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  2. ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; GREENHOUSES...

    Office of Scientific and Technical Information (OSTI)

    fuel-fired peak heating for geothermal greenhouses Rafferty, K. 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; GREENHOUSES; AUXILIARY HEATING; CAPITALIZED COST; OPERATING...

  3. Displacing Natural Gas Consumption and Lowering Emissions

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

    for Refinery and Chemical Plant Process Heaters ADVANCED MANUFACTURING OFFICE Displacing Natural Gas Consumption and Lowering Emissions By enabling process heaters to utilize...

  4. ,"Washington Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Washington Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  5. ,"Natural Gas Consumption",,,"Natural Gas Expenditures"

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

    Census Division, 1999" ,"Natural Gas Consumption",,,"Natural Gas Expenditures" ,"per Building (thousand cubic feet)","per Square Foot (cubic feet)","per Worker (thousand cubic...

  6. Commercial Buildings Energy Consumption and Expenditures 1995...

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

    fuel oil, and district heat consumption and expenditures for commercial buildings by building characteristics. Previous Page Arrow Separater Bar File Last Modified: January 29,...

  7. ,"Hawaii Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Hawaii Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  8. ,"Texas Natural Gas Vehicle Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Vehicle Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  9. ,"Texas Natural Gas Lease Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Lease Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  10. ,"Texas Natural Gas Plant Fuel Consumption (MMcf)"

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

    ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Plant Fuel Consumption (MMcf)",1,"Annual",2014 ,"Release Date:","930...

  11. 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.

  12. Minimizing energy consumption for handheld computers in Moby Dick Paul J.M. Havinga, Gerard J.M. Smit

    E-Print Network [OSTI]

    Havinga, Paul J.M.

    be careful not to waste the scarce energy resources in their batteries. Even though battery technology. Even though battery technology, processors and displays are improving continuously in terms of power to reduce energy consumption for mobile comput- ers. We use extra dedicated low-power modules to cut

  13. Results from the Second Forum on the Future Role of the Human in the Forecast Process. Part II: Cognitive Psychological Aspects of Expert Weather Forecasters

    E-Print Network [OSTI]

    Schultz, David

    : Cognitive Psychological Aspects of Expert Weather Forecasters NEIL A. STUART* NOAA/National Weather Service of Applied Research Associates, Fairborn, Ohio In Preparation for Submission to Forecasters Forum, Weather and Forecasting 30 June 2006 Corresponding author address: Neil A. Stuart, National Weather Service, 10009 General

  14. Coal supply/demand, 1980 to 2000. Task 3. Resource applications industrialization system data base. Final review draft. [USA; forecasting 1980 to 2000; sector and regional analysis

    SciTech Connect (OSTI)

    Fournier, W.M.; Hasson, V.

    1980-10-10

    This report is a compilation of data and forecasts resulting from an analysis of the coal market and the factors influencing supply and demand. The analyses performed for the forecasts were made on an end-use-sector basis. The sectors analyzed are electric utility, industry demand for steam coal, industry demand for metallurgical coal, residential/commercial, coal demand for synfuel production, and exports. The purpose is to provide coal production and consumption forecasts that can be used to perform detailed, railroad company-specific coal transportation analyses. To make the data applicable for the subsequent transportation analyses, the forecasts have been made for each end-use sector on a regional basis. The supply regions are: Appalachia, East Interior, West Interior and Gulf, Northern Great Plains, and Mountain. The demand regions are the same as the nine Census Bureau regions. Coal production and consumption in the United States are projected to increase dramatically in the next 20 years due to increasing requirements for energy and the unavailability of other sources of energy to supply a substantial portion of this increase. Coal comprises 85 percent of the US recoverable fossil energy reserves and could be mined to supply the increasing energy demands of the US. The NTPSC study found that the additional traffic demands by 1985 may be met by the railways by the way of improved signalization, shorter block sections, centralized traffic control, and other modernization methods without providing for heavy line capacity works. But by 2000 the incremental traffic on some of the major corridors was projected to increase very significantly and is likely to call for special line capacity works involving heavy investment.

  15. Forecasting the 2013–2014 influenza season using Wikipedia

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

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

    2015-05-14

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

  16. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect (OSTI)

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

    2015-05-14

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

  17. Monitoring Energy Consumption In Wireless Sensor Networks

    E-Print Network [OSTI]

    Turau, Volker

    Monitoring Energy Consumption In Wireless Sensor Networks Matthias Witt, Christoph Weyer to monitor the consump- tion of energy in wireless sensor networks based on video streams com- posed from energy consumption in both standby and active modes is the basis of wireless networks. Energy preserving

  18. COMPUTER POWER CONSUMPTION BENCHMARKING FOR GREEN COMPUTING

    E-Print Network [OSTI]

    Way, Thomas

    COMPUTER POWER CONSUMPTION BENCHMARKING FOR GREEN COMPUTING A Thesis Presented to the Faculty Computing Sciences Full Title of Thesis Computer Power Consumption Benchmarking for Green Computing Dr would like to thank my friend and colleague Christopher Continanza for our past collaborations on power

  19. Energy Consumption of Minimum Energy Coding in

    E-Print Network [OSTI]

    Johansson, Karl Henrik

    Energy Consumption of Minimum Energy Coding in CDMA Wireless Sensor Networks Benigno Zurita Ares://www.ee.kth.se/control Abstract. A theoretical framework is proposed for accurate perfor- mance analysis of minimum energy coding energy consumption is analyzed for two coding schemes proposed in the literature: Minimum Energy coding

  20. EIA model documentation: Electricity market module - electricity fuel dispatch

    SciTech Connect (OSTI)

    1997-01-01

    This report documents the National Energy Modeling System Electricity Fuel Dispatch Submodule (EFD), a submodule of the Electricity Market Module (EMM) as it was used for EIA`s Annual Energy Outlook 1997. It replaces previous documentation dated March 1994 and subsequent yearly update revisions. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This document serves four purposes. First, it is a reference document providing a detailed description of the model for reviewers and potential users of the EFD including energy experts at the Energy Information Administration (EIA), other Federal agencies, state energy agencies, private firms such as utilities and consulting firms, and non-profit groups such as consumer and environmental groups. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports. Third, it facilitates continuity in model development by providing documentation which details model enhancements that were undertaken for AE097 and since the previous documentation. Last, because the major use of the EFD is to develop forecasts, this documentation explains the calculations, major inputs and assumptions which were used to generate the AE097.

  1. Power consumption monitoring using additional monitoring device

    SciTech Connect (OSTI)

    Tru?c?, M. R. C. Albert, ?. Tudoran, C. Soran, M. L. F?rca?, F.; Abrudean, M.

    2013-11-13

    Today, emphasis is placed on reducing power consumption. Computers are large consumers; therefore it is important to know the total consumption of computing systems. Since their optimal functioning requires quite strict environmental conditions, without much variation in temperature and humidity, reducing energy consumption cannot be made without monitoring environmental parameters. Thus, the present work uses a multifunctional electric meter UPT 210 for power consumption monitoring. Two applications were developed: software which carries meter readings provided by electronic and programming facilitates remote device and a device for temperature monitoring and control. Following temperature variations that occur both in the cooling system, as well as the ambient, can reduce energy consumption. For this purpose, some air conditioning units or some computers are stopped in different time slots. These intervals were set so that the economy is high, but the work's Datacenter is not disturbed.

  2. Developing a tool to estimate water withdrawal and consumption in electricity generation in the United States.

    SciTech Connect (OSTI)

    Wu, M.; Peng, J.

    2011-02-24

    Freshwater consumption for electricity generation is projected to increase dramatically in the next couple of decades in the United States. The increased demand is likely to further strain freshwater resources in regions where water has already become scarce. Meanwhile, the automotive industry has stepped up its research, development, and deployment efforts on electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Large-scale, escalated production of EVs and PHEVs nationwide would require increased electricity production, and so meeting the water demand becomes an even greater challenge. The goal of this study is to provide a baseline assessment of freshwater use in electricity generation in the United States and at the state level. Freshwater withdrawal and consumption requirements for power generated from fossil, nonfossil, and renewable sources via various technologies and by use of different cooling systems are examined. A data inventory has been developed that compiles data from government statistics, reports, and literature issued by major research institutes. A spreadsheet-based model has been developed to conduct the estimates by means of a transparent and interactive process. The model further allows us to project future water withdrawal and consumption in electricity production under the forecasted increases in demand. This tool is intended to provide decision makers with the means to make a quick comparison among various fuel, technology, and cooling system options. The model output can be used to address water resource sustainability when considering new projects or expansion of existing plants.

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2012-04-01

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

  4. Bracket for photovoltaic modules

    DOE Patents [OSTI]

    Ciasulli, John; Jones, Jason

    2014-06-24

    Brackets for photovoltaic ("PV") modules are described. In one embodiment, a saddle bracket has a mounting surface to support one or more PV modules over a tube, a gusset coupled to the mounting surface, and a mounting feature coupled to the gusset to couple to the tube. The gusset can have a first leg and a second leg extending at an angle relative to the mounting surface. Saddle brackets can be coupled to a torque tube at predetermined locations. PV modules can be coupled to the saddle brackets. The mounting feature can be coupled to the first gusset and configured to stand the one or more PV modules off the tube.

  5. Minimize oil field power consumption

    SciTech Connect (OSTI)

    Harris, B.; Ennis, P.

    1999-08-01

    Though electric power is a major operating cost of oil production, few producers have systematically evaluated their power consumption for ways to be more efficient. There is significant money to be saved by doing so, and now is a good time to make an evaluation because new power options are at hand. They range from small turbo generators that can run on casing head gas and power one or two lift pumps, to rebuilt major turbines and ram-jet powered generators that can be set in a multi-well field and deliver power at bargain prices. Power industry deregulation is also underway. Opportunities for more advantageous power contracts from competitive sources are not far off. This two-part series covers power efficiency and power options. This article reviews steps you can take to evaluate the efficiency of your power use and go about improving it. Part 2 will discuss opportunities for use of distributed power and changes you can expect from decentralized power.

  6. Module No: 410336Personal Statutes for Non-Module Title

    E-Print Network [OSTI]

    Module No: 410336Personal Statutes for Non- Muslims Module Title: Co-requisite:Introduction of Islamic Jurisprudence Pre-requisite: Module Type: specialization requirementModule level: Third Year Academic rank Module coordinator 307384Assistant Professor Dr. Fuad Sartawi ResearchTutorial Guidance

  7. Module No: 410319Copyrights and Neighboring Module Title

    E-Print Network [OSTI]

    Module No: 410319Copyrights and Neighboring Rights Module Title: Co-requisite:Effects of ObligationsPre-requisite: Module Type: specialization elective requirementModule level: Third Year Evening Academic rank Module coordinator e-bataineh@philadelphia.edu.joAssistant Professor Dr. Iyad Bataineh

  8. Approved Module Information for PD2003, 2014/5 Module Title/Name: Engineering Principles 2 Module Code: PD2003

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PD2003, 2014/5 Module Title/Name: Engineering Principles 2 Module Code: PD2003 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module? No Module Dependancies Pre-requisites: Engineering Principles (PD1803). Co-requisites: None Specified Module

  9. Approved Module Information for BF2210, 2014/5 Module Title/Name: Making Managerial Decisions Module Code: BF2210

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF2210, 2014/5 Module Title/Name: Making Managerial Decisions Module Code: BF2210 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Florian Gebreiter Email Address gebreif1

  10. Approved Module Information for LPM040, 2014/5 Module Title/Name: Rethinking European Integration Module Code: LPM040

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LPM040, 2014/5 Module Title/Name: Rethinking European Integration Module Code: LPM040 School: Languages and Social Sciences Module Type: Standard Module New Module? Yes Module Credits: 20 Module Management Information Module Leader Name Nathaniel Copsey Email Address n

  11. Approved Module Information for LEM039, 2014/5 Module Title/Name: Grammar Module Code: LEM039

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LEM039, 2014/5 Module Title/Name: Grammar Module Code: LEM039 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 20 Module Management Information Module Leader Name Urszula Clark Email Address u

  12. Approved Module Information for LS3006, 2014/5 Module Title/Name: Hispanic Film Module Code: LS3006

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LS3006, 2014/5 Module Title/Name: Hispanic Film Module Code: LS3006 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 10 Module Management Information Module Leader Name Raquel Medina Email Address r

  13. Approved Module Information for LT2102, 2014/5 Module Title/Name: Inventory Control Module Code: LT2102

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT2102, 2014/5 Module Title/Name: Inventory Control Module Code: LT2102 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name James Stone Email Address j

  14. Approved Module Information for PY2217, 2014/5 Module Title/Name: Personality Practical (JH) Module Code: PY2217

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY2217, 2014/5 Module Title/Name: Personality Practical (JH) Module Code: PY2217 School: Life and Health Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Ed Walford Email Address e

  15. Approved Module Information for BS3347, 2014/5 Module Title/Name: Economics of Entrepreneurship Module Code: BS3347

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BS3347, 2014/5 Module Title/Name: Economics of Entrepreneurship Module Code: BS3347 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Anna Rebmann Email Address rebmanna

  16. Approved Module Information for PY3351, 2014/5 Module Title/Name: Child Development Module Code: PY3351

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY3351, 2014/5 Module Title/Name: Child Development Module Code: PY3351 School: Life and Health Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Claire Farrow Email Address farrowc

  17. Approved Module Information for CE2110, 2014/5 Module Title/Name: Process Laboratory Module Code: CE2110

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE2110, 2014/5 Module Title/Name: Process Laboratory Module Code: CE2110 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name John Brammer Email Address brammejg

  18. Approved Module Information for LK2004, 2014/5 Module Title/Name: Global Society Module Code: LK2004

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LK2004, 2014/5 Module Title/Name: Global Society Module Code: LK2004 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Demelza Jones Email Address jonesd4@aston

  19. Approved Module Information for CH3117, 2014/5 Module Title/Name: Literature Research Project Module Code: CH3117

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CH3117, 2014/5 Module Title/Name: Literature Research Project Module Code: CH3117 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Andrew James Sutherland Email Address

  20. Approved Module Information for LE1008, 2014/5 Module Title/Name: Grammar & Meaning Module Code: LE1008

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LE1008, 2014/5 Module Title/Name: Grammar & Meaning Module Code: LE1008 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 10 Module Management Information Module Leader Name Jack Grieve Email Address grievej1

  1. Approved Module Information for BN3385, 2014/5 Module Title/Name: Effective Project Delivery Module Code: BN3385

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BN3385, 2014/5 Module Title/Name: Effective Project Delivery Module Code: BN3385 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Panagiotis Petridis Email Address petridip

  2. Approved Module Information for BS1163, 2014/5 Module Title/Name: Introduction to Microeconomics Module Code: BS1163

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BS1163, 2014/5 Module Title/Name: Introduction to Microeconomics Module Code: BS1163 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Morris Email Address morrisd5@aston

  3. Approved Module Information for LEM016, 2014/5 Module Title/Name: Methodology Module Code: LEM016

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LEM016, 2014/5 Module Title/Name: Methodology Module Code: LEM016 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Muna Morris-Adams Email Address adamsmm

  4. Approved Module Information for BF2251, 2014/5 Module Title/Name: Financial Management Module Code: BF2251

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF2251, 2014/5 Module Title/Name: Financial Management Module Code: BF2251 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Colin Chapman Email Address chapmac1@aston

  5. Approved Module Information for LT2315, 2014/5 Module Title/Name: Rail Transport Module Code: LT2315

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT2315, 2014/5 Module Title/Name: Rail Transport Module Code: LT2315 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name P Connor Email Address connorp

  6. Approved Module Information for ME2018, 2014/5 Module Title/Name: Quality Engineering Module Code: ME2018

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for ME2018, 2014/5 Module Title/Name: Quality Engineering Module Code: ME2018 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Upton Email Address uptondp

  7. Approved Module Information for LI2008, 2014/5 Module Title/Name: Communication across Cultures Module Code: LI2008

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LI2008, 2014/5 Module Title/Name: Communication across Cultures Module Code: LI2008 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Olga Castro Email Address o

  8. Approved Module Information for CE4018, 2014/5 Module Title/Name: Advanced Particle Processing Module Code: CE4018

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE4018, 2014/5 Module Title/Name: Advanced Particle Processing Module Code: CE4018 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Mark Leaper Email Address m

  9. Approved Module Information for SE4031, 2014/5 Module Title/Name: Extended Integrative Option Module Code: SE4031

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for SE4031, 2014/5 Module Title/Name: Extended Integrative Option Module Code: SE4031 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 30 Module Management Information Module Leader Name Trevor Oliver Email Address t

  10. Approved Module Information for LT1312, 2014/5 Module Title/Name: Literature Review Project Module Code: LT1312

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT1312, 2014/5 Module Title/Name: Literature Review Project Module Code: LT1312 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Carpenter Email Address d

  11. Approved Module Information for LS2017, 2014/5 Module Title/Name: Contemporary Latin America Module Code: LS2017

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LS2017, 2014/5 Module Title/Name: Contemporary Latin America Module Code: LS2017 School: Languages and Social Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Stephanie Panichelli-Batalla Email Address

  12. Approved Module Information for CE3013, 2014/5 Module Title/Name: Particle Processing Module Code: CE3013

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE3013, 2014/5 Module Title/Name: Particle Processing Module Code: CE3013 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Mark Leaper Email Address m

  13. Approved Module Information for LE2057, 2014/5 Module Title/Name: Computer Mediated Communication Module Code: LE2057

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LE2057, 2014/5 Module Title/Name: Computer Mediated Communication Module Code: LE2057 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 20 Module Management Information Module Leader Name Nur Hooton Email Address n

  14. Approved Module Information for BS3325, 2014/5 Module Title/Name: Competition Policy -Theory Module Code: BS3325

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BS3325, 2014/5 Module Title/Name: Competition Policy - Theory Module Code: BS3325 School: Aston Business School Module Type: Standard Module New Module? Yes Module Credits: 10 Module Management Information Module Leader Name Matt Olczak Email Address olczakm

  15. Approved Module Information for BL1179, 2014/5 Module Title/Name: Accounting for Law Module Code: BL1179

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BL1179, 2014/5 Module Title/Name: Accounting for Law Module Code: BL1179 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Angela Stanhope Email Address a

  16. Approved Module Information for BFM120, 2014/5 Module Title/Name: Investment Management Module Code: BFM120

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BFM120, 2014/5 Module Title/Name: Investment Management Module Code: BFM120 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 15 Module Management Information Module Leader Name Colin Chapman Email Address chapmac1@aston

  17. Approved Module Information for PY2216, 2014/5 Module Title/Name: Neuroscience Practicals Module Code: PY2216

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY2216, 2014/5 Module Title/Name: Neuroscience Practicals Module Code: PY2216 School: Life and Health Sciences Module Type: Standard Module New Module? Yes Module Credits: 10 Module Management Information Module Leader Name Ed Walford Email Address e

  18. Approved Module Information for BHM348, 2014/5 Module Title/Name: Employee Relations & Counselling Module Code: BHM348

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BHM348, 2014/5 Module Title/Name: Employee Relations & Counselling Module Code: BHM348 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 15 Module Management Information Module Leader Name M Carter Email Address cartermr

  19. Approved Module Information for LT1307, 2014/5 Module Title/Name: Principles of Economics Module Code: LT1307

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT1307, 2014/5 Module Title/Name: Principles of Economics Module Code: LT1307 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name David Carpenter Email Address d

  20. Approved Module Information for ME2050, 2014/5 Module Title/Name: Dynamics and Control Module Code: ME2050

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for ME2050, 2014/5 Module Title/Name: Dynamics and Control Module Code: ME2050 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Xianghong Ma Email Address max

  1. Approved Module Information for BHM328, 2014/5 Module Title/Name: Strategic Business Sustainability Module Code: BHM328

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BHM328, 2014/5 Module Title/Name: Strategic Business Sustainability Module Code: BHM328 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 15 Module Management Information Module Leader Name H Borland Email Address borlanhm

  2. Approved Module Information for BF2244, 2014/5 Module Title/Name: Strategic Finance Module Code: BF2244

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF2244, 2014/5 Module Title/Name: Strategic Finance Module Code: BF2244 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Colin Chapman Email Address chapmac1@aston

  3. Approved Module Information for CE3102, 2014/5 Module Title/Name: Reaction Engineering Module Code: CE3102

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE3102, 2014/5 Module Title/Name: Reaction Engineering Module Code: CE3102 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Feroz Kabir Email Address kabirf

  4. Approved Module Information for LE2053, 2014/5 Module Title/Name: Variations of English Module Code: LE2053

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LE2053, 2014/5 Module Title/Name: Variations of English Module Code: LE2053 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 20 Module Management Information Module Leader Name Jack Grieve Email Address grievej1

  5. Approved Module Information for BF3314, 2014/5 Module Title/Name: Derivatives Module Code: BF3314

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BF3314, 2014/5 Module Title/Name: Derivatives Module Code: BF3314 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Winifred Huang-Meier Email Address w

  6. Approved Module Information for PY3472, 2014/5 Module Title/Name: Autistic Spectrum Module Code: PY3472

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PY3472, 2014/5 Module Title/Name: Autistic Spectrum Module Code: PY3472 School: Life and Health Sciences Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Gina Rippon Email Address rippong@aston.ac.uk Telephone

  7. Approved Module Information for CE3112, 2014/5 Module Title/Name: Nanomaterials Module Code: CE3112

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE3112, 2014/5 Module Title/Name: Nanomaterials Module Code: CE3112 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Qingchun Yuan Email Address q.yuan@aston.ac.uk Telephone

  8. Approved Module Information for CE1002, 2014/5 Module Title/Name: Design and Build Module Code: CE1002

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for CE1002, 2014/5 Module Title/Name: Design and Build Module Code: CE1002 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Paul Andrew Tack Email Address tackpa

  9. Approved Module Information for LG2018, 2014/5 Module Title/Name: Metropolis Berlin Module Code: LG2018

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LG2018, 2014/5 Module Title/Name: Metropolis Berlin Module Code: LG2018 School: Languages and Social Sciences Module Type: Standard Module New Module? Not Specified Module Credits: 10 Module Management Information Module Leader Name Uwe Schütte Email Address u

  10. Approved Module Information for PD2002, 2014/5 Module Title/Name: Commercial Practice Module Code: PD2002

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for PD2002, 2014/5 Module Title/Name: Commercial Practice Module Code: PD2002 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 20 Module Management Information Module Leader Name Jon Hewitt Email Address Not Specified

  11. Approved Module Information for BN2290, 2014/5 Module Title/Name: Operational Research Techniques Module Code: BN2290

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for BN2290, 2014/5 Module Title/Name: Operational Research Techniques Module Code: BN2290 School: Aston Business School Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name Ozren Despic Email Address o

  12. Approved Module Information for LT1319, 2014/5 Module Title/Name: Air Transport Module Code: LT1319

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    Approved Module Information for LT1319, 2014/5 Module Title/Name: Air Transport Module Code: LT1319 School: Engineering and Applied Science Module Type: Standard Module New Module? No Module Credits: 10 Module Management Information Module Leader Name James Stone Email Address j.stone@aston.ac.uk Telephone

  13. Encapsulation of High Temperature Thermoelectric Modules | Department...

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

    Encapsulation of High Temperature Thermoelectric Modules Encapsulation of High Temperature Thermoelectric Modules Presents concept for hermetic encapsulation of TE modules...

  14. Membrane module assembly

    DOE Patents [OSTI]

    Kaschemekat, J.

    1994-03-15

    A membrane module assembly is described which is adapted to provide a flow path for the incoming feed stream that forces it into prolonged heat-exchanging contact with a heating or cooling mechanism. Membrane separation processes employing the module assembly are also disclosed. The assembly is particularly useful for gas separation or pervaporation. 2 figures.

  15. Membrane module assembly

    DOE Patents [OSTI]

    Kaschemekat, Jurgen (Palo Alto, CA)

    1994-01-01

    A membrane module assembly adapted to provide a flow path for the incoming feed stream that forces it into prolonged heat-exchanging contact with a heating or cooling mechanism. Membrane separation processes employing the module assembly are also disclosed. The assembly is particularly useful for gas separation or pervaporation.

  16. Module Safety Issues (Presentation)

    SciTech Connect (OSTI)

    Wohlgemuth, J.

    2012-02-01

    Description of how to make PV modules so that they are less likely to turn into safety hazards. Making modules inherently safer with minimum additional cost is the preferred approach for PV. Safety starts with module design to ensure redundancy within the electrical circuitry to minimize open circuits and proper mounting instructions to prevent installation related ground faults. Module manufacturers must control the raw materials and processes to ensure that that every module is built like those qualified through the safety tests. This is the reason behind the QA task force effort to develop a 'Guideline for PV Module Manufacturing QA'. Periodic accelerated stress testing of production products is critical to validate the safety of the product. Combining safer PV modules with better systems designs is the ultimate goal. This should be especially true for PV arrays on buildings. Use of lower voltage dc circuits - AC modules, DC-DC converters. Use of arc detectors and interrupters to detect arcs and open the circuits to extinguish the arcs.

  17. Module Handbook Fernstudium

    E-Print Network [OSTI]

    Berns, Karsten

    Module Handbook Fernstudium postgradual Nanotechnology (Master of Science) Infineon Technology, Munich Distance studies postgraduate #12;Module name Fundamentals of Quantum Mechanics Lecturer apl. Prof is to present the fundamental concepts of quantum physics in a way that a clear understanding of the theoretical

  18. State Energy Data Report, 1991: Consumption estimates

    SciTech Connect (OSTI)

    Not Available

    1993-05-01

    The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to the Government, policy makers, and the public; and (2) to provide the historical series necessary for EIA`s energy models.

  19. Residential Energy Consumption Survey: Quality Profile

    SciTech Connect (OSTI)

    1996-03-01

    The Residential Energy Consumption Survey (RECS) is a periodic national survey that provides timely information about energy consumption and expenditures of U.S. households and about energy-related characteristics of housing units. The survey was first conducted in 1978 as the National Interim Energy Consumption Survey (NIECS), and the 1979 survey was called the Household Screener Survey. From 1980 through 1982 RECS was conducted annually. The next RECS was fielded in 1984, and since then, the survey has been undertaken at 3-year intervals. The most recent RECS was conducted in 1993.

  20. State energy data report 1993: Consumption estimates

    SciTech Connect (OSTI)

    1995-07-01

    The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public; and (2) to provide the historical series necessary for EIA`s energy models.

  1. 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).

  2. 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).

  3. Impacts of Improved Day-Ahead Wind Forecasts on Power Grid Operations: September 2011

    SciTech Connect (OSTI)

    Piwko, R.; Jordan, G.

    2011-11-01

    This study analyzed the potential benefits of improving the accuracy (reducing the error) of day-ahead wind forecasts on power system operations, assuming that wind forecasts were used for day ahead security constrained unit commitment.

  4. Status of Centralized Wind Power Forecasting in North America: May 2009-May 2010

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2010-04-01

    Report surveys grid wind power forecasts for all wind generators, which are administered by utilities or regional transmission organizations (RTOs), typically with the assistance of one or more wind power forecasting companies.

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01

    Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01

    Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01

    Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01

    late January 2008, extend its natural gas futures strip anComparison of AEO 2008 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

  9. Comparison of AEO 2005 natural gas price forecast to NYMEX futures prices

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01

    the AEO 2005 reference case oil price forecast and NYMEX oibasis-adjusted NYMEX crude oil futures con tracts fo r 2010more than the reference case oil price forecast for that

  10. Forecasting 65+ travel : an integration of cohort analysis and travel demand modeling

    E-Print Network [OSTI]

    Bush, Sarah, 1973-

    2003-01-01

    Over the next 30 years, the Boomers will double the 65+ population in the United States and comprise a new generation of older Americans. This study forecasts the aging Boomers' travel. Previous efforts to forecast 65+ ...

  11. 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

  12. Generating day-of-operation probabilistic capacity scenarios from weather forecasts

    E-Print Network [OSTI]

    Buxi, Gurkaran

    2012-01-01

    0400Z on the 18 th the wind is forecast at 15Knots blowingforecast for the day for the quarter-hour period , representing the windthe forecast is valid. The TAF predicts the wind speed, wind

  13. Earnings Management Pressure on Audit Clients: Auditor Response to Analyst Forecast Signals 

    E-Print Network [OSTI]

    Newton, Nathan J.

    2013-06-26

    This study investigates whether auditors respond to earnings management pressure created by analyst forecasts. Analyst forecasts create an important earnings target for management, and professional standards direct auditors to consider how...

  14. Error growth in poor ECMWF forecasts over the contiguous United States 

    E-Print Network [OSTI]

    Modlin, Norman Ray

    1993-01-01

    Successive improvements to the European Center for Medium-range Weather Forecasting model have resulted in improved forecast performance over the Contiguous United States (CONUS). While the overall performance of the model ...

  15. Improving Groundwater Predictions Utilizing Seasonal Precipitation Forecasts from General Circulation Models

    E-Print Network [OSTI]

    Arumugam, Sankar

    Improving Groundwater Predictions Utilizing Seasonal Precipitation Forecasts from General. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater

  16. A supply forecasting model for Zimbabwe's corn sector: a time series and structural analysis 

    E-Print Network [OSTI]

    Makaudze, Ephias

    1993-01-01

    Board's financial resource needs. Thus, the corn supply forecasts are important information used by the government for contingency planning, decision-making, policy-formulation and implementation. As such, the need for accurate forecasts is obvious...

  17. Application of the Stretched Exponential Production Decline Model to Forecast Production in Shale Gas Reservoirs 

    E-Print Network [OSTI]

    Statton, James Cody

    2012-07-16

    . This study suggests a type curve is most useful when 24 months or less is available to forecast. The SEPD model generally provides more conservative forecasts and EUR estimates than Arps' model with a minimum decline rate of 5%....

  18. Distributed quantitative precipitation forecasts combining information from radar and numerical weather prediction model outputs

    E-Print Network [OSTI]

    Ganguly, Auroop Ratan

    2002-01-01

    Applications of distributed Quantitative Precipitation Forecasts (QPF) range from flood forecasting to transportation. Obtaining QPF is acknowledged to be one of the most challenging areas in hydrology and meteorology. ...

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01

    Figure 9: Two Alternative Price Forecasts (denoted by openComparison of AEO 2007 Natural Gas Price Forecast toNYMEX Futures Prices Date: December 6, 2006 Introduction On

  20. Solar irradiance forecasting at multiple time horizons and novel methods to evaluate uncertainty

    E-Print Network [OSTI]

    Marquez, Ricardo

    2012-01-01

    and forecasting of solar radiation data: a review. Int. J.beam and global solar radiation data. Solar Energy , 81:768–forecasting of solar radiation data: a review. International