Sample records for forecasts million short

  1. Short term forecasting of solar radiation based on satellite data

    E-Print Network [OSTI]

    Heinemann, Detlev

    Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer University, D-26111 Oldenburg Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources into existing energy supply structures. Fluctuations of solar irradiance

  2. A model for short term electric load forecasting

    E-Print Network [OSTI]

    Tigue, John Robert

    1975-01-01T23:59:59.000Z

    A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE, III Submitted to the Graduate College of Texas ASM University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE May 1975 Major... Subject: Electrical Engineering A MODEL FOR SHORT TERM ELECTRIC LOAD FORECASTING A Thesis by JOHN ROBERT TIGUE& III Approved as to style and content by: (Chairman of Committee) (Head Depart t) (Member) ;(Me r (Member) (Member) May 1975 ABSTRACT...

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

    SciTech Connect (OSTI)

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

    2014-05-01T23:59:59.000Z

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

  4. Evaluation of forecasting techniques for short-term demand of air transportation

    E-Print Network [OSTI]

    Wickham, Richard Robert

    1995-01-01T23:59:59.000Z

    Forecasting is arguably the most critical component of airline management. Essentially, airlines forecast demand to plan the supply of services to respond to that demand. Forecasts of short-term demand facilitate tactical ...

  5. Short-term planning and forecasting for petroleum. Master's thesis

    SciTech Connect (OSTI)

    Elkins, R.D.

    1988-06-01T23:59:59.000Z

    The Defense Fuel Supply Center (DFSC) has, in recent past, been unable to adequately forecast for short-term petroleum requirements. This has resulted in inaccurate replenishment quantities and required short-notice corrections, which interrupted planned resupply methods. The relationship between the annual CINCLANTFLT DFM budget and sales from the the Norfolk Defense Fuel Support Point (DFSP) is developed and the past sales data from the Norfolk DFSP is used to construct seasonality indices. Finally, the budget/sales relationship is combined with the seasonality indices to provide a new forecasting model. The model is then compared with the current one for FY-88 monthly forecasts. The comparison suggests that the new model can provide accurate, timely requirements data and improve resupply of the Norfolk Defense Fuel Support Point.

  6. Short-Term Solar Energy Forecasting Using Wireless Sensor Networks

    E-Print Network [OSTI]

    Cerpa, Alberto E.

    Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Stefan Achleitner, Tao Liu an advantage for output power prediction. Solar Energy Prediction System Our prediction model is based variability of more then 100 kW per minute. For practical usage of solar energy, predicting times of high

  7. SHORT-TERM FORECASTING OF SOLAR RADIATION BASED ON SATELLITE DATA WITH STATISTICAL METHODS

    E-Print Network [OSTI]

    Heinemann, Detlev

    SHORT-TERM FORECASTING OF SOLAR RADIATION BASED ON SATELLITE DATA WITH STATISTICAL METHODS Annette governing the insolation, forecasting of solar radiation makes the description of development of the cloud

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

    E-Print Network [OSTI]

    Cañizares, Claudio A.

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

  9. Probabilistic wind power forecasting -European Wind Energy Conference -Milan, Italy, 7-10 May 2007 Probabilistic short-term wind power forecasting

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Probabilistic wind power forecasting - European Wind Energy Conference - Milan, Italy, 7-10 May 2007 Probabilistic short-term wind power forecasting based on kernel density estimators J´er´emie Juban jeremie.juban@ensmp.fr; georges.kariniotakis@ensmp.fr Abstract Short-term wind power forecasting tools

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

    SciTech Connect (OSTI)

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

    2012-09-01T23:59:59.000Z

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

  11. Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market

    SciTech Connect (OSTI)

    Tripathi, M.M.; Upadhyay, K.G.; Singh, S.N.

    2008-11-15T23:59:59.000Z

    For the economic and secure operation of power systems, a precise short-term load forecasting technique is essential. Modern load forecasting techniques - especially artificial neural network methods - are particularly attractive, as they have the ability to handle the non-linear relationships between load, weather temperature, and the factors affecting them directly. A test of two different ANN models on data from Australia's Victoria market is promising. (author)

  12. VALIDATION OF SHORT AND MEDIUM TERM OPERATIONAL SOLAR RADIATION FORECASTS IN THE US

    E-Print Network [OSTI]

    Perez, Richard R.

    VALIDATION OF SHORT AND MEDIUM TERM OPERATIONAL SOLAR RADIATION FORECASTS IN THE US Richard Perez This paper presents an initial validation of a solar radiation service that provides historical, as well observed solar radiation conditions based on immediate measured history: The position and impact of future

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

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Short-term Forecasting of Offshore Wind Farm Production ­ Developments of the Anemos Project J to the large dimensions of offshore wind farms, their electricity production must be known well in advance networks) models were calibrated on power data from two offshore wind farms: Tunoe and Middelgrunden

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

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

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

  15. Research on Short-term Load Forecasting of the Thermoelectric Boiler Based on a Dynamic RBF Neural Network 

    E-Print Network [OSTI]

    Dai, W.; Zou, P.; Yan, C.

    2006-01-01T23:59:59.000Z

    As thermal inertia is the key factor for the lag of thermoelectric utility regulation, it becomes very important to forecast its short-term load according to running parameters. In this paper, dynamic radial basis function (RBF) neural network...

  16. Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)

    Reports and Publications (EIA)

    1998-01-01T23:59:59.000Z

    The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

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

    SciTech Connect (OSTI)

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

    2007-12-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2014-04-30T23:59:59.000Z

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

  19. Short Term Hourly Load Forecasting Using Abductive Networks R. E. Abdel-Aal

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Physical Sciences, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran, Saudi for forecasting next-day hourly loads have been developed. Evaluated on data for the 6th year, the models give. INTRODUCTION Accurate load forecasting is a key requirement for the planning and economic and secure operation

  20. Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Lew, D.; Milligan, M.

    2013-01-01T23:59:59.000Z

    Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of the day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.

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

    E-Print Network [OSTI]

    Boyer, Edmond

    of difficulties to the power system operation. This is due to the fluctuating nature of wind generation to the management of wind generation. Accurate and reliable forecasting systems of the wind production are widely

  2. Freeway Short-Term Traffic Flow Forecasting by Considering Traffic Volatility Dynamics and Missing Data Situations

    E-Print Network [OSTI]

    Zhang, Yanru

    2012-10-19T23:59:59.000Z

    , assuming constant variance when perform forecasting. This method does not consider the volatility nature of traffic flow data. This paper demonstrated that the variance part of traffic flow data is not constant, and dependency exists. A volatility model...

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

    SciTech Connect (OSTI)

    Finley, Cathy [WindLogics

    2014-04-30T23:59:59.000Z

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times. A comprehensive analysis of wind energy forecast errors for the various model-based power forecasts was presented for a suite of wind energy ramp definitions. The results compiled over the year-long study period showed that the power forecasts based on the research models (ESRL_RAP, HRRR) more accurately predict wind energy ramp events than the current operational forecast models, both at the system aggregate level and at the local wind plant level. At the system level, the ESRL_RAP-based forecasts most accurately predict both the total number of ramp events and the occurrence of the events themselves, but the HRRR-based forecasts more accurately predict the ramp rate. At the individual site level, the HRRR-based forecasts most accurately predicted the actual ramp occurrence, the total number of ramps and the ramp rates (40-60% improvement in ramp rates over the coarser resolution forecast

  4. Gaussian Processes for Short-Horizon Wind Power Forecasting Joseph Bockhorst, Chris Barber

    E-Print Network [OSTI]

    Bockhorst, Joseph

    on this task, and attention has shifted to statistical and machine learning approaches. Among the challenges of wind energy into electrical trans- mission systems. The importance of wind forecasts for wind energy throughout a power system must be nearly in balance at all times, 2) because it depends strongly on wind

  5. Short-term forecasting through intermittent assimilation of data from Taiwan and mainland China coastal radars

    E-Print Network [OSTI]

    Xue, Ming

    ; published 29 March 2012. [1] Radial velocity (Vr) and reflectivity (Z) data from eight coastal operational, but the benefit is mostly lost within the first hour of forecast. Assimilating data from a single Doppler radar with a good coverage of the typhoon inner core region is also quite effective, but it takes one more cycle

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

    SciTech Connect (OSTI)

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

    2012-09-01T23:59:59.000Z

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

  7. Short-Term Energy Outlook Supplement: Uncertainties in the Short-Term Global Petroleum and Other Liquids Supply Forecast

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over Our Instagram Secretary Moniz9 SeptemberSettingUncertainties in the Short-Term

  8. A comparison of water vapor quantities from model short-range forecasts and ARM observations

    SciTech Connect (OSTI)

    Hnilo, J J

    2006-03-17T23:59:59.000Z

    Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the 'Merged-sounding' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.

  9. Prediction of wind speed profiles for short-term forecasting in the offshore environment R.J. Barthelmie and G. Giebel

    E-Print Network [OSTI]

    in planning of maintenance visits to offshore wind farms. In most cases the basis for the predictionPrediction of wind speed profiles for short-term forecasting in the offshore environment R wind farms. The main effects considered here are: wind speed gradients in the coastal zone, vertical

  10. Seismic Activity of the Earth, the Cosmological Vectorial Potential And Method of a Short-term Earthquakes Forecasting

    E-Print Network [OSTI]

    Yu. A. Baurov; Yu. A. Baurov; Yu. A. Baurov Jr.; A. A. Spitalnaya; A. A. Abramyan; V. A. Solodovnikov

    2008-08-20T23:59:59.000Z

    To the foundation of a principally new short-term forecasting method there has been laid down a theory of surrounding us world's creation and of physical vacuum as a result of interaction of byuons - discrete objects. The definition of the byuon contains the cosmological vector-potential A_g - a novel fundamental vector constant. This theory predicts a new anisotropic interaction of nature objects with the physical vacuum. A peculiar "tap" to gain new energy (giving rise to an earthquake) are elementary particles because their masses are proportional to the modulus of some summary potential A_sum that contains potentials of all known fields. The value of A_sum cannot be larger than the modulus of A_g. In accordance with the experimental results a new force associated with A_sum ejects substance from the area of the weakened A_sum along a conical formation with the opening of 100 +- 10 and the axis directed along the vector A_sum. This vector has the following coordinates in the second equatorial coordinate system: right ascension alpha = 293 +- 10, declination delta = 36 +- 10. Nearly 100% probability of an earthquake (earthquakes of 6 points strong and more by the Richter scale) arises when in the process of the earth rotation the zenith vector of a seismically dangerous region and/or the vectorial potential of Earth's magnetic fields are in a certain way oriented relative to the vector A_g. In the work, basic models and standard mechanisms of earthquakes are briefly considered, results of processing of information on the earthquakes in the context of global spatial anisotropy caused by the existence of the vector A_g, are presented, and an analysis of them is given.

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

    E-Print Network [OSTI]

    Arumugam, Sankar

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

  12. Research on Short-term Load Forecasting of the Thermoelectric Boiler Based on a Dynamic RBF Neural Network

    E-Print Network [OSTI]

    Dai, W.; Zou, P.; Yan, C.

    2006-01-01T23:59:59.000Z

    is proposed based on the RBF neural network with the associated parameters of sample deviation and partial sample deviation, which are defined for the purpose of effective judgment of new samples. Also, in order to forecast the load of sample with large...

  13. Short and Long-Term Perspectives: The Impact on Low-Income Consumers of Forecasted Energy Price Increases in 2008 and A Cap & Trade Carbon Policy in 2030

    SciTech Connect (OSTI)

    Eisenberg, Joel Fred [ORNL

    2008-01-01T23:59:59.000Z

    The Department of Energy's Energy Information Administration (EIA) recently released its short-term forecast for residential energy prices for the winter of 2007-2008. The forecast indicates increases in costs for low-income consumers in the year ahead, particularly for those using fuel oil to heat their homes. 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 report provides an update of bill estimates provided in a previous study, "The Impact Of Forecasted Energy Price Increases On Low-Income Consumers" (Eisenberg, 2005). The statistics are intended for use by 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 2008 fiscal year. In addition to providing expenditure forecasts for the year immediately ahead, this analysis uses a similar methodology to give policy makers some insight into one of the major policy debates that will impact low-income energy expenditures well into the middle decades of this century and beyond. There is now considerable discussion of employing a cap-and-trade mechanism to first limit and then reduce U.S. emissions of carbon into the atmosphere in order to combat the long-range threat of human-induced climate change. The Energy Information Administration has provided an analysis of projected energy prices in the years 2020 and 2030 for one such cap-and-trade carbon reduction proposal that, when integrated with the RECS 2001 database, provides estimates of how low-income households will be impacted over the long term by such a carbon reduction policy.

  14. 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...

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

    SciTech Connect (OSTI)

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

    2007-06-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Islam, M. Saif

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

  18. Solar Variability and Forecasting Jan Kleissl, Chi Chow, Matt Lave, Patrick Mathiesen,

    E-Print Network [OSTI]

    Homes, Christopher C.

    Forecasting Benefits Use of state-of-art wind and solar forecasts reduces WECC operating costs by up to 14/MWh of wind and solar generation). WECC operating costs could be reduced by an additional $500 million

  19. analytical energy forecasting: Topics by E-print Network

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

    COMMISSION Tom Gorin Lynn Marshall Principal Author Tom Gorin Project 11 Short-Term Solar Energy Forecasting Using Wireless Sensor Networks Computer Technologies and...

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

    E-Print Network [OSTI]

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

  1. " Million Housing Units, Final...

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

    3 Appliances in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950...

  2. " Million Housing Units, Final...

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

    3 Televisions in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to 1949","1950...

  3. " Million Housing Units, Final...

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

    4 Televisions in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More...

  4. " Million Housing Units, Final...

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

    6 Space Heating in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold","Mixed- Humid","Mixed-Dry"...

  5. " Million Housing Units, Final...

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

    7 Space Heating in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Space...

  6. " Million Housing Units, Final...

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

    4 Space Heating in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More...

  7. " Million Housing Units, Final...

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

    3 Space Heating in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to...

  8. " Million Housing Units, Final...

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

    6 Televisions in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold","Mixed- Humid","Mixed-Dry"...

  9. " Million Housing Units, Preliminary"

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

    Computers and Other Electronics in U.S. Homes, By Number of Household Members, 2009" " Million Housing Units, Preliminary" ,,"Number of Household Members" ,"Total U.S.1 (millions)"...

  10. " Million Housing Units, Final...

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

    3 Computers and Other Electronics in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before...

  11. " Million Housing Units, Final...

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

    7 Computers and Other Electronics in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South"...

  12. " Million Housing Units, Final"

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

    5 Computers and Other Electronics in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,"Below Poverty...

  13. " Million Housing Units, Final...

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

    7 Water Heating in U.S. Homes, by Census Region, 2009" " Million Housing Units, Final" ,,"Census Region" ,"Total U.S.1 (millions)" ,,"Northeast","Midwest","South","West" "Water...

  14. " Million Housing Units, Final...

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

    3 Water Heating in U.S. Homes, by Year of Construction, 2009" " Million Housing Units, Final" ,,"Year of Construction" ,"Total U.S.1 (millions)" ,,"Before 1940","1940 to...

  15. " Million Housing Units, Final...

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

    4 Water Heating in U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" ,,,,,,"5 or More...

  16. " Million Housing Units, Final...

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

    6 Water Heating in U.S. Homes, by Climate Region, 2009" " Million Housing Units, Final" ,,"Climate Region2" ,"Total U.S.1 (millions)" ,,"Very Cold","Mixed- Humid","Mixed-Dry"...

  17. " Million Housing Units, Final...

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

    5 Water Heating in U.S. Homes, by Household Income, 2009" " Million Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,"Below Poverty Line2" ,,"Less than...

  18. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

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

  19. Short-term energy outlook, annual supplement 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-01T23:59:59.000Z

    The Short-Term Energy Outlook Annual Supplement (Supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

  20. Short-term energy outlook annual supplement, 1993

    SciTech Connect (OSTI)

    NONE

    1993-08-06T23:59:59.000Z

    The Short-Term Energy Outlook Annual Supplement (supplement) is published once a year as a complement to the Short-Term Energy Outlook (Outlook), Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts.

  1. " Million Housing Units, Final...

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

    Used and End Uses in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East...

  2. " Million Housing Units, Final...

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

    Used and End Uses in Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,"Pacific...

  3. " Million Housing Units, Final...

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

    Used and End Uses in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census...

  4. " Million Housing Units, Final...

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

    1 Space Heating in U.S. Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,"Pacific...

  5. " Million Housing Units, Final...

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

    0 Space Heating in U.S. Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East...

  6. " Million Housing Units, Final...

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

    9 Space Heating in U.S. Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" " ",,,"East North Central Census...

  7. " Million Housing Units, Final...

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

    8 Space Heating in U.S. Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census...

  8. " Million Housing Units, Final...

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

    1 Computers and Other Electronics in Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census...

  9. " Million Housing Units, Final...

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

    0 Computers and Other Electronics in Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census...

  10. " Million Housing Units, Final...

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

    9 Computers and Other Electronics in Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census...

  11. " Million Housing Units, Final...

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

    8 Computers and Other Electronics in Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census...

  12. " Million Housing Units, Final...

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

    8 Water Heating in U.S. Homes in Northeast Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Northeast Census Region" ,,,"New England Census...

  13. " Million Housing Units, Final...

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

    11 Water Heating in U.S. Homes in West Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"West Census Region" ,,,"Mountain Census Division",,,"Pacific...

  14. " Million Housing Units, Final...

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

    9 Water Heating in U.S. Homes in Midwest Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"Midwest Census Region" ,,,"East North Central Census...

  15. " Million Housing Units, Final...

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

    0 Water Heating in U.S. Homes in South Region, Divisions, and States, 2009" " Million Housing Units, Final" ,,"South Census Region" ,,,"South Atlantic Census Division",,,,,,"East...

  16. Short-Term Energy Outlook

    Gasoline and Diesel Fuel Update (EIA)

    day Forecast -1.0 2012 2013 2014 OPEC countries North America Russia and Caspian Sea Latin America North Sea Other Non-OPEC Source: Short-Term Energy Outlook, November 2013 -1 0...

  17. " Million Housing Units, Final...

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

    Televisions in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total U.S.1...

  18. " Million Housing Units, Final...

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

    2 Fuels Used and End Uses in U.S. Homes, by OwnerRenter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings...

  19. " Million Housing Units, Final...

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

    2 Space Heating in U.S. Homes, by OwnerRenter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With"...

  20. " Million Housing Units, Final...

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

    Space Heating in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total...

  1. " Million Housing Units, Final...

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

    Computers and Other Electronics in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings...

  2. " Million Housing Units, Final...

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

    2 Computers and Other Electronics in U.S. Homes, by OwnerRenter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in...

  3. " Million Housing Units, Final...

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

    2 Water Heating in U.S. Homes, by OwnerRenter Status, 2009" " Million Housing Units, Final" ,,,,"Housing Unit Type" ,,,,"Single-Family Units",,,,"Apartments in Buildings With"...

  4. " Million Housing Units, Final...

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

    Water Heating in U.S. Homes, by Housing Unit Type, 2009" " Million Housing Units, Final" ,,"Housing Unit Type" ,,"Single-Family Units",,"Apartments in Buildings With" ,"Total...

  5. " Million Housing Units, Final"

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

    Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,"Below Poverty Line2" ,,"Less than 20,000","20,000 to 39,999","40,000 to 59,999","60,000 to...

  6. " Million Housing Units, Final...

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

    Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,"Below Poverty Line2" ,,"Less than 20,000","20,000 to 39,999","40,000 to 59,999","60,000 to...

  7. 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 meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts acknowledges the support of PPM Energy, Inc. The data used in this work were obtained from Oregon State

  8. Short-term energy outlook quarterly projections. First quarter 1994

    SciTech Connect (OSTI)

    Not Available

    1994-02-07T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short- term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets.

  9. Forecasting sudden changes in environmental pollution patterns

    E-Print Network [OSTI]

    Olascoaga, Maria Josefina

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

  10. Sea-floor spreading during the past 10 million years on the East Pacific Rise between 35 p0 sS and 53 p0 sS, and the identification of short period pole reversal events

    E-Print Network [OSTI]

    Woodward, Robert Joseph

    1974-01-01T23:59:59.000Z

    the East Pacific Rise between 35 S 0 and 53 S, and the Identification of Short Period Pole Reversal Events (Nay 1974) Robert Joseph Woodward, B. S. Florida State University Chairman of Advisory Committee: Dr. James N. Shapiro Twelve magnetic anomaly... available for discussion when he was needed. Without the magnetic anomaly profiles, this research could not have been done. Therefore, the author would like to thank Dr. Stuart Smith of Scripps Institute of Oceanography, Dr. Ellen Herron of Lamont...

  11. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

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

  12. Short-Term Energy Outlook September 2013

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

    day Forecast -0.9 2012 2013 2014 OPEC countries North America Russia and Caspian Sea Latin America North Sea Other Non-OPEC Source: Short-Term Energy Outlook, September 2013 -1...

  13. Technology Forecasting Scenario Development

    E-Print Network [OSTI]

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

  14. Rainfall-River Forecasting

    E-Print Network [OSTI]

    US Army Corps of Engineers

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

  15. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2011-10-01T23:59:59.000Z

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

  18. Probabilistic manpower forecasting

    E-Print Network [OSTI]

    Koonce, James Fitzhugh

    1966-01-01T23:59:59.000Z

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

  19. UPF Forecast | Y-12 National Security Complex

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

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

  20. Long Term Forecast ofLong Term Forecast of TsunamisTsunamis

    E-Print Network [OSTI]

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

  1. LOW CARBON & 570 million GVA

    E-Print Network [OSTI]

    Wrigley, Stuart

    LOW CARBON & RENEWABLES #12;£570 million GVA THE SECTOR COMPRISES 326 COMPANIES EMPLOYING 12- tor comprises 326 companies, employing approximately 12,240 people and contributing £570 million nuclear, wind, solar, geo-thermal and tidal power. The total market value of the low carbon environmental

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

    E-Print Network [OSTI]

    Barnett, Alex

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

  3. Short-term energy outlook. Quarterly projections, Third quarter 1995

    SciTech Connect (OSTI)

    NONE

    1995-08-02T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent projections with those of other forecasting services, and discusses current topics related to the short-term energy markets. The forecast period for this issue of the Outlook extends from the third quarter of 1995 through the fourth quarter of 1996. Values for the second quarter of 1995, however, are preliminary EIA estimates.

  4. Steam System Forecasting and Management

    E-Print Network [OSTI]

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

    1982-01-01T23:59:59.000Z

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

  5. 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

  6. Improving Inventory Control Using Forecasting

    E-Print Network [OSTI]

    Balandran, Juan

    2005-12-16T23:59:59.000Z

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

  7. timber quality Modelling and forecasting

    E-Print Network [OSTI]

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

  8. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

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

  9. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

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

  10. Fuel Price Forecasts INTRODUCTION

    E-Print Network [OSTI]

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

  11. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Tesfatsion, Leigh

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

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

    E-Print Network [OSTI]

    de Freitas, Nando

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

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01T23:59:59.000Z

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

  15. " Million U.S. Housing Units"

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

    0 Home Appliances Usage Indicators by Year of Construction, 2005" " Million U.S. Housing Units" ,,"Year of Construction" ,"Housing Units (millions)" ,,"Before 1940","1940 to...

  16. " Million U.S. Housing Units"

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

    5 Space Heating Usage Indicators by Type of Housing Unit, 2005" " Million U.S. Housing Units" ,,"Type of Housing Unit" ,"Housing Units (millions)","Single-Family...

  17. " Million U.S. Housing Units"

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

    5 Space Heating Usage Indicators by Number of Household Members, 2005" " Million U.S. Housing Units" ,,"Number of Households With --" ,"Housing Units (millions)" ,,"1 Member","2...

  18. " Million U.S. Housing Units"

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

    5 Space Heating Usage Indicators by Year of Construction, 2005" " Million U.S. Housing Units" ,,"Year of Construction" ,"Housing Units (millions)" ,,"Before 1940","1940 to...

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

    SciTech Connect (OSTI)

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-12-06T23:59:59.000Z

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

  20. ON THE IMPACT OF SUPER RESOLUTION WSR-88D DOPPLER RADAR DATA ASSIMILATION ON HIGH RESOLUTION NUMERICAL MODEL FORECASTS

    SciTech Connect (OSTI)

    Chiswell, S

    2009-01-11T23:59:59.000Z

    Assimilation of radar velocity and precipitation fields into high-resolution model simulations can improve precipitation forecasts with decreased 'spin-up' time and improve short-term simulation of boundary layer winds (Benjamin, 2004 & 2007; Xiao, 2008) which is critical to improving plume transport forecasts. Accurate description of wind and turbulence fields is essential to useful atmospheric transport and dispersion results, and any improvement in the accuracy of these fields will make consequence assessment more valuable during both routine operation as well as potential emergency situations. During 2008, the United States National Weather Service (NWS) radars implemented a significant upgrade which increased the real-time level II data resolution to 8 times their previous 'legacy' resolution, from 1 km range gate and 1.0 degree azimuthal resolution to 'super resolution' 250 m range gate and 0.5 degree azimuthal resolution (Fig 1). These radar observations provide reflectivity, velocity and returned power spectra measurements at a range of up to 300 km (460 km for reflectivity) at a frequency of 4-5 minutes and yield up to 13.5 million point observations per level in super-resolution mode. The migration of National Weather Service (NWS) WSR-88D radars to super resolution is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current operational mesoscale model domains utilize grid spacing several times larger than the legacy data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of super resolution reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution is investigated here to determine the impact of the improved data resolution on model predictions.

  1. Forecasting oilfield economic performance

    SciTech Connect (OSTI)

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

    1994-11-01T23:59:59.000Z

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

  2. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J. (Decision and Information Sciences); (INESC Porto)

    2011-02-23T23:59:59.000Z

    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.

  3. Short-term energy outlook. Volume 2. Methodology

    SciTech Connect (OSTI)

    Not Available

    1982-05-01T23:59:59.000Z

    This volume updates models and forecasting methodologies used and presents information on new developments since November 1981. Chapter discusses the changes in forecasting methodology for motor gasoline demand, electricity sales, coking coal, and other petroleum products. Coefficient estimates, summary statistics, and data sources for many of the short-term energy models are provided. Chapter 3 evaluates previous short-term forecasts for the macroeconomic variables, total energy, petroleum supply and demand, coal consumption, natural gas, and electricity fuel shares. Chapter 4 reviews the relationship of total US energy consumption to economic activity between 1960 and 1981.

  4. Short-term energy outlook. Quarterly projections, Third quarter 1994

    SciTech Connect (OSTI)

    Not Available

    1994-08-02T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202). The feature article for this issue is Demand, Supply and Price Outlook for Reformulated Gasoline, 1995.

  5. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01T23:59:59.000Z

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

  6. ELECTRICITY DEMAND FORECAST COMPARISON REPORT

    E-Print Network [OSTI]

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

  7. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

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

  8. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

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

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

    2015-08-01T23:59:59.000Z

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

  9. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

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

  10. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

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

  11. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

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

  12. NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS

    E-Print Network [OSTI]

    Mohaghegh, Shahab

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

  13. Conservation The Northwest ForecastThe Northwest Forecast

    E-Print Network [OSTI]

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

  14. European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. Forecasting of Regional Wind Generation by a Dynamic

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    European Wind Energy Conference & Exhibition EWEC 2003, Madrid, Spain. Forecasting of Regional Wind. Abstract-Short-term wind power forecasting is recognized nowadays as a major requirement for a secure and economic integration of wind power in a power system. In the case of large-scale integration, end users

  15. Paper presented at EWEC 2008, Brussels, Belgium (31 March-03 April) Uncertainty Estimation of Wind Power Forecasts

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    -Antipolis, France Abstract--Short-term wind power forecasting tools providing "single-valued" (spot) predictions associated to the future wind power produc- tion for performing more efficiently functions such as reserves and modelling architec- tures for probabilistic wind power forecasting. Then, a comparison is carried out

  16. Earth: 15 Million Years Ago

    E-Print Network [OSTI]

    Masataka Mizushima

    2008-10-13T23:59:59.000Z

    In Einstein's general relativity theory the metric component gxx in the direction of motion (x-direction) of the sun deviates from unity due to a tensor potential caused by the black hole existing around the center of the galaxy. Because the solar system is orbiting around the galactic center at 200 km/s, the theory shows that the Newtonian gravitational potential due to the sun is not quite radial. At the present time, the ecliptic plane is almost perpendicular to the galactic plane, consistent with this modification of the Newtonian gravitational force. The ecliptic plane is assumed to maintain this orientation in the galactic space as it orbits around the galactic center, but the rotational angular momentum of the earth around its own axis can be assumed to be conserved. The earth is between the sun and the galactic center at the summer solstice all the time. As a consequence, the rotational axis of the earth would be parallel to the axis of the orbital rotation of the earth 15 million years ago, if the solar system has been orbiting around the galactic center at 200 km/s. The present theory concludes that the earth did not have seasons 15 million years ago. Therefore, the water on the earth was accumulated near the poles as ice and the sea level was very low. Geological evidence exists that confirms this effect. The resulting global ice-melting started 15 million years ago and is ending now.

  17. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

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

  18. Mathematical Forecasting Donald I. Good

    E-Print Network [OSTI]

    Boyer, Robert Stephen

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

  19. Regional-seasonal weather forecasting

    SciTech Connect (OSTI)

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

    1980-08-01T23:59:59.000Z

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

  20. Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

    SciTech Connect (OSTI)

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

    2013-05-01T23:59:59.000Z

    Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

  1. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

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

  2. Arnold Schwarzenegger INTEGRATED FORECAST AND

    E-Print Network [OSTI]

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

  3. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Goto, Susumu

    2007-01-01T23:59:59.000Z

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

  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 of the thesis is combined wind power forecasts using informations from meteorological forecasts. Lyngby, January

  6. Energy Department Announces $2.5 Million to Improve Wind Forecasting |

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsNovember 13,Statement | DepartmentBlog Energy BlogDeployment |Geothermal

  7. Paper accepted for presentation at 2003 IEEE Bologna PowerTech Conference, June 23-26, Bologna, Italy Wind Power Forecasting using Fuzzy Neural Networks

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    , Italy Wind Power Forecasting using Fuzzy Neural Networks Enhanced with On-line Prediction Risk) as input, to predict the power production of wind park8 48 hours ahead. The prediction system integrates of the numerical weather predictions. Index Term-Wind power, short-term forecasting, numerical weather predictions

  8. Modification of the pattern informatics method for forecasting large earthquake events using complex eigenvectors

    E-Print Network [OSTI]

    Holliday, J R; Klein, B; Rundle, J B; Tiampo, K F

    2006-01-01T23:59:59.000Z

    Recent studies have shown that real-valued principal component analysis can be applied to earthquake fault systems for forecasting and prediction. In addition, theoretical analysis indicates that earthquake stresses may obey a wave-like equation, having solutions with inverse frequencies for a given fault similar to those that characterize the time intervals between the largest events on the fault. It is therefore desirable to apply complex principal component analysis to develop earthquake forecast algorithms. In this paper we modify the Pattern Informatics method of earthquake forecasting to take advantage of the wave-like properties of seismic stresses and utilize the Hilbert transform to create complex eigenvectors out of measured time series. We show that Pattern Informatics analyses using complex eigenvectors create short-term forecast hot-spot maps that differ from hot-spot maps created using only real-valued data and suggest methods of analyzing the differences and calculating the information gain.

  9. Forecasting consumer products using prediction markets

    E-Print Network [OSTI]

    Trepte, Kai

    2009-01-01T23:59:59.000Z

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

  10. 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 ...

  11. 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 ...

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

    E-Print Network [OSTI]

    Kemner, Ken

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

  13. Million Species EXTINCTION RISK FROM CLIMATE CHANGE

    E-Print Network [OSTI]

    Poff, N. LeRoy

    Saving Million Species EXTINCTION RISK FROM CLIMATE CHANGE Edited by Lee Hannah ISLANDPRESS-in-Publication Data Saving a million species : extinction risk from climate change / edited by LeeHannah. p. cm. ISBN, extinction, extinction risk, biodiversity,freshwater, marine, biology, coral bleaching, species area

  14. 1995 shipment review & five year forecast

    SciTech Connect (OSTI)

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

    1996-01-01T23:59:59.000Z

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

  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. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

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

  17. 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

  18. Calculator simplifies field production forecasting

    SciTech Connect (OSTI)

    Bixler, B.

    1982-05-01T23:59:59.000Z

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

  19. Short-term energy outlook: Quarterly projections. Second quarter 1995

    SciTech Connect (OSTI)

    NONE

    1995-05-02T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent projections with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the second quarter of 1995 through the fourth quarter of 1996. Values for the first quarter of 1995, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the second quarter 1995 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service.

  20. EWEC 2006 Scientific Track Advanced Forecast Systems for the Grid Integration of 25 GW

    E-Print Network [OSTI]

    Heinemann, Detlev

    forecasts, smoothing effects Abstract The economic success of offshore wind farms in liberalised electricity of offshore wind farms, their electricity production must be known well in advance to allow an efficient Oldenburg, Germany Key words: Offshore wind power, grid integration, short-term prediction, regional

  1. Skill forecasting from ensemble predictions of wind power P. Pinson,a

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Skill forecasting from ensemble predictions of wind power P. Pinson,a , H.Aa. Nielsena , H. Madsena with the commonly provided short-term wind power point predictions. Alternative approaches for the use uncertainty (and potential energy imbalances). Wind power ensemble predictions are derived from the conversion

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

    E-Print Network [OSTI]

    Mass, Clifford F.

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

  3. Short-term energy outlook, Annual supplement 1995

    SciTech Connect (OSTI)

    NONE

    1995-07-25T23:59:59.000Z

    This supplement is published once a year as a complement to the Short- Term Energy Outlook, Quarterly Projections. The purpose of the Supplement is to review the accuracy of the forecasts published in the Outlook, make comparisons with other independent energy forecasts, and examine current energy topics that affect the forecasts. Chap. 2 analyzes the response of the US petroleum industry to the recent four Federal environmental rules on motor gasoline. Chap. 3 compares the EIA base or mid case energy projections for 1995 and 1996 (as published in the first quarter 1995 Outlook) with recent projections made by four other major forecasting groups. Chap. 4 evaluates the overall accuracy. Chap. 5 presents the methology used in the Short- Term Integrated Forecasting Model for oxygenate supply/demand balances. Chap. 6 reports theoretical and empirical results from a study of non-transportation energy demand by sector. The empirical analysis involves the short-run energy demand in the residential, commercial, industrial, and electrical utility sectors in US.

  4. " Million U.S. Housing Units,...

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

    Housing Units, Final" ,,"Household Income" ,"Total U.S.1 (millions)",,,"Below Poverty Line2" "Structural and Geographic Characteristics",,"Less than 20,000","20,000 to...

  5. NREL: Transmission Grid Integration - Forecasting

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

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

  6. Short-term energy outlook. Quarterly projections, second quarter 1996

    SciTech Connect (OSTI)

    NONE

    1996-04-01T23:59:59.000Z

    The Energy Information Administration prepares quarterly, short-term energy supply, demand, and price projections. The forecasts in this issue cover the second quarter of 1996 through the fourth quarter of 1997. Changes to macroeconomic measures by the Bureau of Economic Analysis have been incorporated into the STIFS model used.

  7. Secretary Chu Announces $30 Million for Research Competition...

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

    0 Million for Research Competition to Develop Next Generation Energy Storage Technologies Secretary Chu Announces 30 Million for Research Competition to Develop Next Generation...

  8. Energy Department Awards $5 Million to Spur Local Clean Energy...

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

    5 Million to Spur Local Clean Energy Development, Energy Savings Energy Department Awards 5 Million to Spur Local Clean Energy Development, Energy Savings October 14, 2014 -...

  9. Obama Administration Announces $12 Million i6 Green Investment...

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

    12 Million i6 Green Investment to Promote Clean Energy Innovation and Job Creation Obama Administration Announces 12 Million i6 Green Investment to Promote Clean Energy...

  10. Energy Department Awards More Than $7 Million for Innovative...

    Office of Environmental Management (EM)

    More Than 7 Million for Innovative Hydrogen Storage Technologies in Fuel Cell Electric Vehicles Energy Department Awards More Than 7 Million for Innovative Hydrogen Storage...

  11. Energy Department Announces $35 Million to Advance Fuel Cell...

    Energy Savers [EERE]

    Energy Department Announces 35 Million to Advance Fuel Cell and Hydrogen Technologies Energy Department Announces 35 Million to Advance Fuel Cell and Hydrogen Technologies March...

  12. Energy Department Invests Over $7 Million to Commercialize Cost...

    Energy Savers [EERE]

    Over 7 Million to Commercialize Cost-Effective Hydrogen and Fuel Cell Technologies Energy Department Invests Over 7 Million to Commercialize Cost-Effective Hydrogen and Fuel Cell...

  13. Department of Energy Awards Nearly $7 Million to Advance Fuel...

    Office of Environmental Management (EM)

    Million to Advance Fuel Cell and Hydrogen Storage Systems Research Department of Energy Awards Nearly 7 Million to Advance Fuel Cell and Hydrogen Storage Systems Research August...

  14. Obama Administration Awards More than $96 Million for State Energy...

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

    96 Million for State Energy Programs in Ohio, Oregon, Virginia and West Virginia Obama Administration Awards More than 96 Million for State Energy Programs in Ohio, Oregon,...

  15. DOE Announces Over $30 Million to Help Universities Train the...

    Office of Environmental Management (EM)

    30 Million to Help Universities Train the Next Generation of Industrial Energy Efficiency Experts DOE Announces Over 30 Million to Help Universities Train the Next Generation of...

  16. ARPA-E Announces $43 Million for Transformational Energy Storage...

    Energy Savers [EERE]

    43 Million for Transformational Energy Storage Projects to Advance Electric Vehicle and Grid Technologies ARPA-E Announces 43 Million for Transformational Energy Storage Projects...

  17. Energy Secretary Chu Announces $384 Million in Recovery Act Funding...

    Energy Savers [EERE]

    384 Million in Recovery Act Funding for Environmental Cleanup in New Mexico Energy Secretary Chu Announces 384 Million in Recovery Act Funding for Environmental Cleanup in New...

  18. Energy Department Announces $15 Million to Help Communities Boost...

    Office of Environmental Management (EM)

    Energy Department Announces 15 Million to Help Communities Boost Solar Deployment Energy Department Announces 15 Million to Help Communities Boost Solar Deployment April 17, 2014...

  19. Energy Department Invests More Than $55 Million to Advance Efficient...

    Energy Savers [EERE]

    Invests More Than 55 Million to Advance Efficient Vehicle Technologies Energy Department Invests More Than 55 Million to Advance Efficient Vehicle Technologies August 15, 2014 -...

  20. Energy Department Announces $11 Million to Advance Renewable...

    Office of Environmental Management (EM)

    1 Million to Advance Renewable Carbon Fiber Production from Biomass Energy Department Announces 11 Million to Advance Renewable Carbon Fiber Production from Biomass July 30, 2014...

  1. Energy Department Announces $6 Million to Accelerate Alternative...

    Office of Environmental Management (EM)

    6 Million to Accelerate Alternative Fuel Vehicle Market Growth Energy Department Announces 6 Million to Accelerate Alternative Fuel Vehicle Market Growth March 9, 2015 - 11:20am...

  2. DOE and USCAR Announce $70 Million Project to Accelerate Development...

    Energy Savers [EERE]

    Announce 70 Million Project to Accelerate Development of Lightweight, High-Strength Materials DOE and USCAR Announce 70 Million Project to Accelerate Development of Lightweight,...

  3. USDA, DOE Announce $18 Million Solicitation for Biomass Research...

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

    Bodman & Johanns Kick Off Renewable Energy Conference with 17.5 Million for Biofuels Research & Development Grants USDA-DOE Make Available 4 Million for Biomass Genomics Research...

  4. Energy Department Announces $32 Million to Boost Solar Workforce...

    Energy Savers [EERE]

    Announces 32 Million to Boost Solar Workforce Training, Drive Solar Energy Innovation Energy Department Announces 32 Million to Boost Solar Workforce Training, Drive Solar Energy...

  5. Department of Energy Finalizes Partial Guarantee for $852 Million...

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

    Partial Guarantee for 852 Million Loan to Support California Concentrating Solar Power Plant Department of Energy Finalizes Partial Guarantee for 852 Million Loan to Support...

  6. Energy Department Invests $6 Million to Increase Energy Efficiency...

    Office of Environmental Management (EM)

    6 Million to Increase Energy Efficiency of Schools, Offices, Stores and other U.S. Buildings Energy Department Invests 6 Million to Increase Energy Efficiency of Schools, Offices,...

  7. Secretary of Energy Announces Nearly $24 Million in Grants for...

    Office of Environmental Management (EM)

    Nearly 24 Million in Grants for Carbon Sequestration Research Secretary of Energy Announces Nearly 24 Million in Grants for Carbon Sequestration Research October 23, 2006 -...

  8. Interior Department to Open 190 Million Acres to Geothermal Power...

    Energy Savers [EERE]

    Interior Department to Open 190 Million Acres to Geothermal Power Interior Department to Open 190 Million Acres to Geothermal Power October 29, 2008 - 3:56pm Addthis...

  9. President Obama Announces Over $467 Million in Recovery Act Funding...

    Office of Environmental Management (EM)

    Over 467 Million in Recovery Act Funding for Geothermal and Solar Energy Projects President Obama Announces Over 467 Million in Recovery Act Funding for Geothermal and Solar...

  10. Energy Department Announces $18 Million for Innovative Projects...

    Energy Savers [EERE]

    Energy Department Announces 18 Million for Innovative Projects to Advance Geothermal Energy Energy Department Announces 18 Million for Innovative Projects to Advance Geothermal...

  11. President Obama Announces Over $467 Million in Recovery Act Funding...

    Energy Savers [EERE]

    President Obama Announces Over 467 Million in Recovery Act Funding for Geothermal and Solar Energy Projects President Obama Announces Over 467 Million in Recovery Act Funding for...

  12. DOE Offers $15 Million Geothermal Heat Recovery Opportunity ...

    Office of Environmental Management (EM)

    15 Million Geothermal Heat Recovery Opportunity DOE Offers 15 Million Geothermal Heat Recovery Opportunity August 25, 2010 - 11:11am Addthis Photo of geothermal power plant....

  13. The Geothermal Technologies Office Invests $18 Million for Innovative...

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

    The Geothermal Technologies Office Invests 18 Million for Innovative Projects The Geothermal Technologies Office Invests 18 Million for Innovative Projects The McGuiness Hills...

  14. Energy Department Announces Up to $31 Million for Initial Phases...

    Office of Environmental Management (EM)

    Up to 31 Million for Initial Phases of Enhanced Geothermal Systems Field Observatory Energy Department Announces Up to 31 Million for Initial Phases of Enhanced Geothermal...

  15. Energy Department Announces $10 million for Wave Energy Demonstration...

    Energy Savers [EERE]

    10 million for Wave Energy Demonstration at Navy's Hawaii Test Site Energy Department Announces 10 million for Wave Energy Demonstration at Navy's Hawaii Test Site April 28, 2014...

  16. Energy Department Finalizes $737 Million Loan Guarantee to Tonopah...

    Energy Savers [EERE]

    Finalizes 737 Million Loan Guarantee to Tonopah Solar Energy for Nevada Project Energy Department Finalizes 737 Million Loan Guarantee to Tonopah Solar Energy for Nevada Project...

  17. Energy Secretary Announces $170 Million Solicitation for Solar...

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

    70 Million Solicitation for Solar Energy Technologies Energy Secretary Announces 170 Million Solicitation for Solar Energy Technologies June 28, 2006 - 2:36pm Addthis Key Element...

  18. Obama Administration Delivers More than $304 Million for Weatherizatio...

    Energy Savers [EERE]

    304 Million for Weatherization Programs in Georgia, Illinois and New York Obama Administration Delivers More than 304 Million for Weatherization Programs in Georgia, Illinois and...

  19. Energy Department Finalizes $337 Million Loan Guarantee to Mesquite...

    Energy Savers [EERE]

    337 Million Loan Guarantee to Mesquite Solar 1 for Innovative Solar Power Plant Energy Department Finalizes 337 Million Loan Guarantee to Mesquite Solar 1 for Innovative Solar...

  20. DOE Moab Site Cost-Effectively Eliminates 200 Million Gallons...

    Office of Environmental Management (EM)

    Site Cost-Effectively Eliminates 200 Million Gallons of Contaminated Ground Water DOE Moab Site Cost-Effectively Eliminates 200 Million Gallons of Contaminated Ground Water July...

  1. Energy Secretary Chu Announces $108 Million in Recovery Act Funding...

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

    cleanup efforts in the state: Moab (108 million) - Accelerate removal of uranium mill tailings away from the Colorado River and dispose of an additional two million tons of...

  2. Department of Energy to Invest Nearly $18 Million for Advanced...

    Energy Savers [EERE]

    Department of Energy to Invest Nearly 18 Million for Advanced Biofuels User Facility Department of Energy to Invest Nearly 18 Million for Advanced Biofuels User Facility March...

  3. Department of Energy Announces up to $12 Million in Investments...

    Energy Savers [EERE]

    up to 12 Million in Investments to Support Development and Production of Drop-In Biofuels Department of Energy Announces up to 12 Million in Investments to Support...

  4. Energy Secretary Moniz Unveils More Than $55 Million to Advance...

    Office of Environmental Management (EM)

    Moniz Unveils More Than 55 Million to Advance Fuel Efficient Vehicle Technologies Energy Secretary Moniz Unveils More Than 55 Million to Advance Fuel Efficient Vehicle...

  5. Obama Administration Launches $130 Million Building Energy Efficiency...

    Energy Savers [EERE]

    Administration Launches 130 Million Building Energy Efficiency Effort Obama Administration Launches 130 Million Building Energy Efficiency Effort February 12, 2010 - 12:00am...

  6. Energy Department to Award $6 Million to State Partnerships to...

    Energy Savers [EERE]

    to Award 6 Million to State Partnerships to Increase Energy Efficiency Energy Department to Award 6 Million to State Partnerships to Increase Energy Efficiency September 19, 2006...

  7. DOE Awards $3 Million Contract to Oak Ridge Associated Universities...

    Office of Environmental Management (EM)

    Million Contract to Oak Ridge Associated Universities for Expert Review of Yucca Mountain Work DOE Awards 3 Million Contract to Oak Ridge Associated Universities for Expert...

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

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

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

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

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

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

  10. Solid low-level waste forecasting guide

    SciTech Connect (OSTI)

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

    1995-03-01T23:59:59.000Z

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

  11. Geothermal wells: a forecast of drilling activity

    SciTech Connect (OSTI)

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

    1981-07-01T23:59:59.000Z

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

  12. Online Forecast Combination for Dependent Heterogeneous Data

    E-Print Network [OSTI]

    Sancetta, Alessio

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

  13. The Value of Wind Power Forecasting

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

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

  14. Extreme wave events during hurricanes can seriously jeopardize the integrity and safety of offshore oil and gas operations in the Gulf of Mexico. Validation of wave forecast for

    E-Print Network [OSTI]

    oil and gas operations in the Gulf of Mexico. Validation of wave forecast for significant wave heights of Mexico. Before the storm, it produced 148,000 barrels of oil equivalent per day and 160 million cubic over the warm Gulf of Mexico water between 26 and 28 August, and became a category 5 hurricane by 1200

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

    E-Print Network [OSTI]

    Kamat, Vineet R.

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

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

    SciTech Connect (OSTI)

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

    1995-05-01T23:59:59.000Z

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

  17. Wyoming Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (MillionAdjustments (Million CubicCubic2009 2010Decade

  18. Wyoming Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (MillionAdjustments (Million CubicCubic2009

  19. Wyoming Natural Gas Processed (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade1(MillionExtensionsThousand Cubic%perYearBarrels) Reserves(Million

  20. Oklahoma Natural Gas Repressuring (Million Cubic Feet)

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear Jan Feb Mar Apr May JunFeet) (MillionRepressuring (Million

  1. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

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

  2. 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

  3. 1992 five year battery forecast

    SciTech Connect (OSTI)

    Amistadi, D.

    1992-12-01T23:59:59.000Z

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

  4. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

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

  5. Models for Millions Department of Statistics

    E-Print Network [OSTI]

    Stine, Robert A.

    Models for Millions Bob Stine Department of Statistics The Wharton School, UniversityDepartment of Statistics Introduction #12;WhartonDepartment of Statistics WhartonDepartment of Statistics Statistics in the News Hot topics Big Data Business Analytics Data Science Are the authors talking about statistics

  6. Million U.S. Housing Units Total...............................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    13.2 1.3 3.5 3.0 3.0 2.5 Table HC9.10 Home Appliances Usage Indicators by Climate Zone, 2005 Housing Units (millions) Greater than 7,000 HDD 5,500 to 7,000 HDD 4,000...

  7. Team Surpasses 1 Million Hours Safety Milestone

    Broader source: Energy.gov [DOE]

    NISKAYUNA, N.Y. – Vigilance and dedication to safety led the EM program’s disposition project team at the Separations Process Research Unit (SPRU) to achieve a milestone of one million hours — over two-and-a-half-years — without injury or illness resulting in time away from work.

  8. Update on the Million Solar Roofs Initiative

    SciTech Connect (OSTI)

    Herig, C.

    1999-05-09T23:59:59.000Z

    The Million Solar Roofs Initiative, announced by the President in June of 1997, spans a period of twelve years and intends to increase domestic deployment of solar technologies. This paper presents an overview of the development of the initiative and significant activities to date.

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

    E-Print Network [OSTI]

    Heinemann, Detlev

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

  10. Alternative methods for forecasting GDP Dominique Gugan

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

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

  11. A NEW APPROACH FOR EVALUATING ECONOMIC FORECASTS

    E-Print Network [OSTI]

    Vertes, Akos

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

  12. 2013 Midyear Economic Forecast Sponsorship Opportunity

    E-Print Network [OSTI]

    de Lijser, Peter

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

  13. Dynamic Algorithm for Space Weather Forecasting System

    E-Print Network [OSTI]

    Fischer, Luke D.

    2011-08-08T23:59:59.000Z

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

  14. 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

  15. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

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

  16. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01T23:59:59.000Z

    This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

  17. Essays on financial analysts' forecasts

    E-Print Network [OSTI]

    Rodriguez, Marius del Giudice

    2006-01-01T23:59:59.000Z

    for Caterpillar (CAT) and The Coca-Cola Company (KO) duringJohnson & Johnson The Coca-Cola Company McDonald’sfor Caterpillar and Coca Cola. Variations in the short

  18. Texas Natural Gas Processed (Million Cubic Feet)

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear JanYear Jan Feb Mar Apr May Jun1 (Million

  19. Pennsylvania Natural Gas Repressuring (Million Cubic Feet)

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear Jan Feb Mar Apr MayYearAdditionsLiquidsRepressuring (Million

  20. Space-time forecasting and evaluation of wind speed with statistical tests for comparing accuracy of spatial predictions

    E-Print Network [OSTI]

    Hering, Amanda S.

    2010-10-12T23:59:59.000Z

    High-quality short-term forecasts of wind speed are vital to making wind power a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information...

  1. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST

    E-Print Network [OSTI]

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

  2. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

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

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

    SciTech Connect (OSTI)

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

    2011-10-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2013-10-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

  6. 1993 Solid Waste Reference Forecast Summary

    SciTech Connect (OSTI)

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

    1993-08-01T23:59:59.000Z

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

  7. Short-Term Energy Outlook: Quarterly projections. Fourth quarter 1993

    SciTech Connect (OSTI)

    Not Available

    1993-11-05T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the fourth quarter of 1993 through the fourth quarter of 1994. Values for the third quarter of 1993, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications.

  8. Short-term energy outlook quarterly projections: First quarter 1993

    SciTech Connect (OSTI)

    Not Available

    1993-02-03T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.). The forecast period for this issue of the Outlook extends from the first quarter of 1993 through the fourth quarter of 1994. Values for the fourth quarter of 1992, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding.

  9. Short-term energy outlook: Quarterly projections, Third quarter 1992

    SciTech Connect (OSTI)

    Not Available

    1992-08-01T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The principal users of the Outlook are managers and energy analysts in private industry and government. The forecast period for this issue of the Outlook extends from the third quarter of 1992 through the fourth quarter of 1993. Values for the second quarter of 1992, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding.

  10. Short-term energy outlook, Quarterly projections. Third quarter 1993

    SciTech Connect (OSTI)

    NONE

    1993-08-04T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). An annual supplement analyzes the performance of previous forecasts, compares recent cases with those of other forecasting services, and discusses current topics related to the short-term energy markets. (See Short-Term Energy Outlook Annual Supplement, DOE/EIA-0202.) The forecast period for this issue of the Outlook extends from the third quarter of 1993 through the fourth quarter of 1994. Values for the second quarter of 1993, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data are EIA data published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding.

  11. Forecasting future oil production in Norway and the UK: a general improved methodology

    E-Print Network [OSTI]

    Fievet, Lucas; Cauwels, Peter; Sornette, Didier

    2014-01-01T23:59:59.000Z

    We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the standard methodology that tends to underestimate remaining oil reserves, our method gives a better description of future oil production, as validated by our back-tests starting in 2008. Specifically, we predict remaining reserves extractable until 2030 to be 188 +/- 10 million barrels for Norway and 98 +/- 10 million barrels for the UK, which are respectively 45% and 66% above the predictions using the standard methodology.

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

    E-Print Network [OSTI]

    Kulkarni, Siddhivinayak

    2009-01-01T23:59:59.000Z

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

  13. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

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

  14. Wind Speed Forecasting for Power System Operation 

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

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

  15. 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

  16. Wind Speed Forecasting for Power System Operation

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

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

  17. 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 ...

  18. 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....

  19. Essays in International Macroeconomics and Forecasting

    E-Print Network [OSTI]

    Bejarano Rojas, Jesus Antonio

    2012-10-19T23:59:59.000Z

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

  20. Better Buildings Challenge Saves $840 Million in Energy Costs...

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

    Saves 840 Million in Energy Costs, Adds New Water Savings Goal Better Buildings Challenge Saves 840 Million in Energy Costs, Adds New Water Savings Goal May 27, 2015 - 10:08am...

  1. Department of Energy Announces more than $18 Million to Strengthen...

    Office of Environmental Management (EM)

    more than 18 Million to Strengthen Nuclear Education at U.S. Universities and Colleges Department of Energy Announces more than 18 Million to Strengthen Nuclear Education at U.S....

  2. VOLUME & VALUE OF CATCH BY REGIONS 1970 Million Pounds

    E-Print Network [OSTI]

    .7 million; in 1969, $580.8 million. There were record packs of tuna, shrimp, and animal (pet) food. Recorded, and retail. In 1970, demand for fiShery products was strong. Both consumption and prices rose. On the average

  3. Department of Energy Announces $40 Million to Develop the Next...

    Office of Environmental Management (EM)

    0 Million to Develop the Next Generation Nuclear Plant Department of Energy Announces 40 Million to Develop the Next Generation Nuclear Plant March 8, 2010 - 12:00am Addthis...

  4. Energy Department Announces Up to $7 Million to Expand Clean...

    Energy Savers [EERE]

    Up to 7 Million to Expand Clean Energy and Energy Efficiency on Tribal Lands Energy Department Announces Up to 7 Million to Expand Clean Energy and Energy Efficiency on Tribal...

  5. Department of Energy Offers $102 Million Conditional Commitment...

    Office of Environmental Management (EM)

    Offers 102 Million Conditional Commitment for Loan Guarantee to U.S. Geothermal Inc. Department of Energy Offers 102 Million Conditional Commitment for Loan Guarantee to U.S....

  6. Department of Energy Offers $102 Million Conditional Commitment...

    Office of Environmental Management (EM)

    02 Million Conditional Commitment for Loan Guarantee to U.S. Geothermal, Inc. Department of Energy Offers 102 Million Conditional Commitment for Loan Guarantee to U.S. Geothermal,...

  7. Energy Department Announces $7 Million to Reduce Non-Hardware...

    Office of Environmental Management (EM)

    7 Million to Reduce Non-Hardware Costs of Solar Energy Systems Energy Department Announces 7 Million to Reduce Non-Hardware Costs of Solar Energy Systems November 15, 2011 -...

  8. EM Completes Salt Waste Disposal Units $8 Million under Budget...

    Office of Environmental Management (EM)

    EM Completes Salt Waste Disposal Units 8 Million under Budget at Savannah River Site EM Completes Salt Waste Disposal Units 8 Million under Budget at Savannah River Site February...

  9. Energy Department Announces $13.4 Million to Develop Advanced...

    Office of Environmental Management (EM)

    .4 Million to Develop Advanced Biofuels and Bioproducts Energy Department Announces 13.4 Million to Develop Advanced Biofuels and Bioproducts October 9, 2014 - 11:48am Addthis The...

  10. Secretary Chu Announces $93 Million from Recovery Act to Support...

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

    93 Million from Recovery Act to Support Wind Energy Projects Secretary Chu Announces 93 Million from Recovery Act to Support Wind Energy Projects April 29, 2009 - 12:00am Addthis...

  11. Energy Department Invests Over $7 Million to Deploy Tribal Clean...

    Energy Savers [EERE]

    Invests Over 7 Million to Deploy Tribal Clean Energy Projects Energy Department Invests Over 7 Million to Deploy Tribal Clean Energy Projects November 14, 2013 - 10:00am Addthis...

  12. Energy Department Invests Over $10 Million to Improve Grid Reliability...

    Energy Savers [EERE]

    10 Million to Improve Grid Reliability and Resiliency Energy Department Invests Over 10 Million to Improve Grid Reliability and Resiliency June 11, 2014 - 6:20pm Addthis NEWS...

  13. Kentucky Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

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

  14. Kentucky Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) Kenai,Feet)Year Jan Feb Mar Apr May

  15. Louisiana Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 0 0 0 1569 02,208,9204.49 4.65 4.15

  16. Louisiana Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 0 0 0 1569 02,208,9204.49 4.65 4.15Year

  17. Maryland Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0 0.0 0.05.03 5.68 4.61 5.60

  18. Maryland Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0 0.0 0.05.03 5.68 4.61 5.60Year Jan

  19. Michigan Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3Exports (NoYear Jan2009 2010 2011Decade

  20. Michigan Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3Exports (NoYear Jan2009 2010

  1. Mississippi Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic Feet) PriceLiquids, Proved2009Decade

  2. Mississippi Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

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

  3. Missouri Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic Feet)SameThousand CubicDecade Year-0

  4. Missouri Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic Feet)SameThousand CubicDecade

  5. Montana Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic32,876 10,889Decade03 4.83 4.53 4.34

  6. Montana Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic32,876 10,889Decade03 4.83 4.53 4.34Year Jan

  7. Colorado Natural Gas Processed (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40Coal Stocks at CommercialDecadeReservesYear JanDecadeDecadeYear(Million Cubic

  8. Kentucky Natural Gas Processed (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40Coal StocksProved Reserves (Billion Cubic Feet) DecadeYear(Million Cubic Feet)

  9. Colorado Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321Spain (MillionFeet)2008 2009 2010Decade

  10. Colorado Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321Spain (MillionFeet)2008 2009 2010DecadeYear

  11. Ohio Natural Gas Processed (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade1 Source: Office(BillionYear JanYear Jan Feb Mar AprProcessed (Million

  12. Oklahoma Natural Gas Processed (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade1 Source: Office(BillionYear JanYear JanYear Jan(Million Cubic Feet)

  13. Arizona Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40Coal Stocks at CommercialDecade Year-0 Year-1Year Jan FebRepressuring (Million

  14. Arkansas Natural Gas Processed (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40Coal Stocks at CommercialDecade Year-0 Year-1Year% ofInputYear(Million Cubic

  15. Arkansas Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40Coal Stocks at CommercialDecade Year-0 Year-1Year% ofInputYear(Million

  16. Arkansas Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40Coal Stocks at CommercialDecade Year-0 Year-1Year% ofInputYear(MillionYear Jan

  17. Virginia Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17 34 44Year JanDecade Year-0 Year-1 Year-2 (MillionDecade

  18. Virginia Natural Gas Repressuring (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17 34 44Year JanDecade Year-0 Year-1 Year-2 (MillionDecadeYear Jan Feb

  19. Michigan Natural Gas Processed (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

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

  20. Mississippi Natural Gas Processed (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade1 Source: Office of Fossil Energy,off) Shale% ofElements)(Million

  1. Montana Natural Gas Processed (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for On-Highway4,1,50022,3,,,,6,1,9,1,50022,3,,,,6,1,Decade1 Source: Office of FossilFoot) Year Jan Feb(Million Cubic Feet)

  2. Illinois Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,12803 TableTotal Consumption (Million CubicRepressuring

  3. Nebraska Natural Gas Processed (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,12803andYearWithdrawals (Million Cubic Feet)2009 2010

  4. Nebraska Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,12803andYearWithdrawals (Million Cubic Feet)2009

  5. Nebraska Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,12803andYearWithdrawals (Million Cubic Feet)2009Repressuring

  6. Nevada Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,12803andYearWithdrawals (MillionYearNADecadeand2009 2010Decade

  7. Nevada Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,12803andYearWithdrawals (MillionYearNADecadeand2009

  8. Nevada Natural Gas Wellhead (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,12803andYearWithdrawalsYear Jan Feb Mar Apr May Jun(Million

  9. Oklahoma Natural Gas Repressuring (Million Cubic Feet)

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear Jan Feb Mar Apr May JunFeet) (MillionRepressuring

  10. Louisiana Natural Gas Processed (Million Cubic Feet)

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office of Coal,Cubic Feet)FuelDecade Year-0Input (Million Cubic2009

  11. Pennsylvania Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial Consumers (NumberThousand CubicFuel Consumption (Million2008Year

  12. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01T23:59:59.000Z

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

  19. Short-term energy outlook, January 1999

    SciTech Connect (OSTI)

    NONE

    1999-01-01T23:59:59.000Z

    The Energy Information Administration (EIA) prepares the Short-Term Energy Outlook (energy supply, demand, and price projections) monthly. The forecast period for this issue of the Outlook extends from January 1999 through December 2000. Data values for the fourth quarter 1998, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the January 1999 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 28 figs., 19 tabs.

  20. Short-term energy outlook, July 1998

    SciTech Connect (OSTI)

    NONE

    1998-07-01T23:59:59.000Z

    The Energy Information Administration (EIA) prepares The Short-Term Energy Outlook (energy supply, demand, and price projections) monthly for distribution on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. In addition, printed versions of the report are available to subscribers in January, April, July and October. The forecast period for this issue of the Outlook extends from July 1998 through December 1999. Values for second quarter of 1998 data, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the July 1998 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. 28 figs., 19 tabs.

  1. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01T23:59:59.000Z

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

  2. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

    SciTech Connect (OSTI)

    Brown, C. W.; Hood, Raleigh R.; Long, Wen; Jacobs, John M.; Ramers, D. L.; Wazniak, C.; Wiggert, J. D.; Wood, R.; Xu, J.

    2013-09-01T23:59:59.000Z

    The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Singhal, Gaurav

    2012-10-19T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Datta, Shoumen

    2008-07-31T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Washington at Seattle, University of

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

  7. The effect of multinationality on management earnings forecasts

    E-Print Network [OSTI]

    Runyan, Bruce Wayne

    2005-08-29T23:59:59.000Z

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

  8. Exploration of Short Reads Genome Mapping in Hardware Edward Fernandez, Walid Najjar

    E-Print Network [OSTI]

    Lonardi, Stefano

    Exploration of Short Reads Genome Mapping in Hardware Edward Fernandez, Walid Najjar Department, such as Illumina/Solexa Genome Analyzer and ABI SOLiD, can generate hundreds of millions of short DNA "reads" from a single run. These reads must be matched against a reference genome to identify their original location

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

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01T23:59:59.000Z

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

  10. 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)

    2005-10-31T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Boyer, Edmond

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

  13. FORECASTING SOLAR RADIATION PRELIMINARY EVALUATION OF AN APPROACH

    E-Print Network [OSTI]

    Perez, Richard R.

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

  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. Univariate Modeling and Forecasting of Monthly Energy Demand Time Series

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

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

  16. TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

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

  18. RETI Phase 1B Final Report Update NET SHORT RECALCULATION AND NEW PV ASSUMPTIONS

    E-Print Network [OSTI]

    RETI Phase 1B Final Report Update NET SHORT RECALCULATION AND NEW PV ASSUMPTIONS With Revisions distributed photovoltaic (PV) installations in the Report is unclear and perhaps misleading. At the direction-generation is required. The CEC forecast assumed that 1,082 GWh will be self-generated by consumers from new PV

  19. SHORT TERM PREDICTIONS FOR THE POWER OUTPUT OF ENSEMBLES OF WIND TURBINES AND PV-GENERATORS

    E-Print Network [OSTI]

    Heinemann, Detlev

    SHORT TERM PREDICTIONS FOR THE POWER OUTPUT OF ENSEMBLES OF WIND TURBINES AND PV-GENERATORS Hans the state of the art of power predictios for wind and solar power plants.with a time horizon of several market there is a need for a forecast of the power production of wind and solar generators with time

  20. Labor Department Offers $500 Million for Clean Energy Job Training...

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

    five grant competitions, totaling 500 million, to fund projects that prepare workers for green jobs in the energy efficiency and renewable energy industries. Four of the...

  1. Energy Department Invests $7 Million to Commercialize Fuel Cells...

    Energy Savers [EERE]

    than 7 million for projects that will help bring cost-effective, advanced hydrogen and fuel cell technologies online faster. This investment-across four projects in Georgia,...

  2. Department of Energy Announces $64 Million in Hydrogen Research...

    Office of Environmental Management (EM)

    of over 64 million in research and development projects aimed at making hydrogen fuel cell vehicles and refueling stations available, practical and affordable for American...

  3. Secretary Chu Announces More than $155 Million for Industrial...

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

    Dayton University of Delaware University of Louisiana at Lafayette University of Michigan West Virginia University State Agencies (3.84 million total, approximately 350,000...

  4. LOW-HIGH VALUES FOR PETROLEUM AVERAGE INVENTORY RANGES (MILLION...

    Gasoline and Diesel Fuel Update (EIA)

    ENERGY INFORMATION ADMINISTRATION LOW-HIGH VALUES FOR PETROLEUM AVERAGE INVENTORY RANGES (MILLION BARRELS) FILE UPDATED April 2004 Line Month Low High Number Product Name Geography...

  5. $787 Million Total in Small Business Contract Funding Awarded...

    National Nuclear Security Administration (NNSA)

    787 Million Total in Small Business Contract Funding Awarded in FY2009 by DOE Programs in Oak Ridge | National Nuclear Security Administration Facebook Twitter Youtube Flickr RSS...

  6. Energy Department Announces $10 Million for Innovative Commercial...

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

    Articles Laying the Foundation for Energy Efficient Commercial Buildings Daylighting Basics Energy Department Announces 10 Million for Full-Scale Wave Energy Device Testing...

  7. Energy Department Finalizes $150 Million Loan Guarantee to 1366...

    Office of Environmental Management (EM)

    for a Loan Guarantee to Support Breakthrough Solar Manufacturing Process The Reality of Solar Panels at 50% Cost Department of Energy Finalizes 197 Million Loan Guarantee to...

  8. $60 Million to Fund Projects Advancing Concentrating Solar Power

    Broader source: Energy.gov [DOE]

    The SunShot initiative announces a $60 million funding opportunity (FOA) to advance concentrating solar power in the United States.

  9. Energy Department Announces $10 Million to Speed Enhanced Geothermal...

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

    to Speed Enhanced Geothermal Systems into the Market Energy Department Announces 10 Million to Speed Enhanced Geothermal Systems into the Market February 24, 2014 - 11:46am...

  10. Energy Department Announces $3 Million to Support Clean Energy...

    Office of Environmental Management (EM)

    Businesses and Entrepreneurs Energy Department Announces Over 12 Million to Spur Solar Energy Innovation Geothermal Home About the Geothermal Technologies Office Enhanced...

  11. Secretary Chu Announces Nearly $15 Million for Next Generation...

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

    the full spectrum of research, development, and deployment for solid-state lighting (SSL) technologies and will leverage an additional 4 million in private sector funding....

  12. DOE to Award $100 Million for Energy Frontier Research Centers...

    Office of Science (SC) Website

    to Award 100 Million for Energy Frontier Research Centers Energy Frontier Research Centers (EFRCs) EFRCs Home Centers Research Science Highlights News & Events EFRC News EFRC...

  13. Energy Department Announces $5 Million for Residential Building...

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

    opportunity, the Department will make 1 million available through its annual Buildings University Innovators and Leaders Development (BUILD) funding opportunity to support...

  14. Department of Energy Awards $338 Million to Accelerate Domestic...

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

    to 338 million in Recovery Act funding for the exploration and development of new geothermal fields and research into advanced geothermal technologies. These grants will support...

  15. Energy Department Announces $3 Million to Identify New Geothermal...

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

    U.S. Department of Energy today announced 3 million to spur geothermal energy development using play fairway analysis. This technique identifies prospective geothermal resources...

  16. President Requests $842.1 Million for Fossil Energy Programs...

    Energy Savers [EERE]

    commercial storage. In FY 2012, NEHHOR converted to a 1 million barrel configuration of Ultra Low Sulfur Diesel (ULSD) stored in the Northeast terminals, to meet new Northeast...

  17. ,"Sherwood, ND Natural Gas Pipeline Exports to Canada (Million...

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

    Exports to Canada (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data...

  18. ,"Warroad, MN Natural Gas Pipeline Exports to Canada (Million...

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

    Exports to Canada (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data...

  19. ,"Grand Island, NY Natural Gas Pipeline Exports to Canada (Million...

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

    Exports to Canada (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data...

  20. ,"Calais, ME Natural Gas Pipeline Exports to Canada (Million...

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

    Exports to Canada (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data...

  1. ,"Massena, NY Natural Gas Pipeline Exports to Canada (Million...

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

    Exports to Canada (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data...

  2. ,"Waddington, NY Natural Gas Pipeline Exports to Canada (Million...

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

    Exports to Canada (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data...

  3. Energy Department Announces $9 Million to Lower Costs, Increase...

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

    Lower Costs, Increase Performance of Solar Energy Systems Energy Department Announces 9 Million to Lower Costs, Increase Performance of Solar Energy Systems December 2, 2014 -...

  4. Small Business Innovation Research Announces $1.15 Million to...

    Energy Savers [EERE]

    million funding opportunity for small businesses to expand U.S. markets for geothermal electricity production or direct-use applications (not including ground source heat...

  5. Secretary Chu Announces Nearly $80 Million Investment for Advanced...

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

    of nearly 80 million under the American Recovery and Reinvestment Act for advanced biofuels research and fueling infrastructure that will help support the development of a clean...

  6. Secretary Chu Announces up to $62 Million for Concentrating Solar...

    Office of Environmental Management (EM)

    to 62 million over five years to research, develop, and demonstrate Concentrating Solar Power (CSP) systems capable of providing low-cost electrical power. This funding will...

  7. ,"New York Natural Gas Gross Withdrawals from Shale Gas (Million...

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

    ,,"(202) 586-8800",,,"2262015 9:43:21 AM" "Back to Contents","Data 1: New York Natural Gas Gross Withdrawals from Shale Gas (Million Cubic Feet)"...

  8. Energy Department Announces $3 Million to Identify New Geothermal...

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

    Addthis The U.S. Department of Energy today announced 3 million to spur geothermal energy development using play fairway analysis. This technique identifies prospective...

  9. Department of Energy Offers Severstal Dearborn, LLC a $730 Million...

    Office of Environmental Management (EM)

    Department of Energy Finalizes a 967 Million Loan Guarantee to Support the Agua Caliente Solar Project Department of Energy Conditional Loan Guarantee Commitment to Support the...

  10. Obama Administration Announces Nearly $100 Million for Smart...

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

    in communities across the country. Secretary Chu made the announcement while visiting a Pepco engineering and service center in Rockville, Maryland that is receiving 4.4 million...

  11. Secretary Chu Announces $620 Million for Smart Grid Demonstration...

    Office of Environmental Management (EM)

    620 Million for Smart Grid Demonstration and Energy Storage Projects: Recovery Act Funding Will Upgrade the Electrical Grid, Save Energy, and Create Jobs Secretary Chu Announces...

  12. Energy Department Announces $12 Million for Technologies to Produce...

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

    and national laboratory partners on a balanced portfolio of research in biomass feedstocks and conversion technologies. Addthis Related Articles DOE Offers 12 Million for...

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

    E-Print Network [OSTI]

    Lang, K.

    1982-01-01T23:59:59.000Z

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

  14. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

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

  15. Facebook IPO updated valuation and user forecasting

    E-Print Network [OSTI]

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

  16. Modeling of Uncertainty in Wind Energy Forecast

    E-Print Network [OSTI]

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

  17. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    NONE

    1998-07-01T23:59:59.000Z

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

  18. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Sripada, Yaji

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

  19. New Concepts in Wind Power Forecasting Models

    E-Print Network [OSTI]

    Kemner, Ken

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

  20. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

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

  1. SIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL

    E-Print Network [OSTI]

    Gambardella, Luca Maria

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

  2. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    NONE

    1996-08-01T23:59:59.000Z

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

  3. 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

  4. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27T23:59:59.000Z

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

  5. Short-term energy outlook, April 1999

    SciTech Connect (OSTI)

    NONE

    1999-04-01T23:59:59.000Z

    The forecast period for this issue of the Outlook extends from April 1999 through December 2000. Data values for the first quarter 1999, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the April 1999 version of the Short-Term Integrated forecasting system (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the STIFS model. 25 figs., 19 tabs.

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

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01T23:59:59.000Z

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

  7. Short-term energy outlook. Quarterly projections, first quarter 1995

    SciTech Connect (OSTI)

    Not Available

    1995-02-01T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Short-Term Energy Outlook (Outlook). The forecast period for this issue of the Outlook extends from the first quarter of 1995 through the fourth quarter of 1996. Values for the fourth quarter of 1994, however, are preliminary EIA estimates or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the first quarter 1995 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The cases are produced using the Short-Term Integrated Forecasting System (STIFS). The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. The EIA model is available on computer tape from the National Technical Information Service.

  8. Test application of a semi-objective approach to wind forecasting for wind energy applications

    SciTech Connect (OSTI)

    Wegley, H.L.; Formica, W.J.

    1983-07-01T23:59:59.000Z

    The test application of the semi-objective (S-O) wind forecasting technique at three locations is described. The forecasting sites are described as well as site-specific forecasting procedures. Verification of the S-O wind forecasts is presented, and the observed verification results are interpreted. Comparisons are made between S-O wind forecasting accuracy and that of two previous forecasting efforts that used subjective wind forecasts and model output statistics. (LEW)

  9. Short-Term Load Forecasting This paper discusses the state of the art in short-term load fore-

    E-Print Network [OSTI]

    Gross, George

    spectrum of time intervals. In therange of seconds, when load variationsare small and random, the automatic by a number of generation control functions such as hydro scheduling, unit commitment, hydro-ther- mal present, functions such as fuel, hydro, and maintenance scheduling are performed to ensure that the load

  10. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

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

    2014-12-30T23:59:59.000Z

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

  11. Tenneco raises $75 million for independents' E and P

    SciTech Connect (OSTI)

    Not Available

    1992-07-20T23:59:59.000Z

    Tenneco Gas's ventures group, Houston, has raised $75 million to invest in gas exploration and production by independent operations on the U.S. Gulf Coast. Institutional investors committed $50 million to the fund and a group of industrial investors $25 million. Tenneco the the fund will expand to accommodate additional investors through this year. This paper reports that the company's ventures group is evaluating acquisition and drilling opportunities with independents. Ventures group capital will be invested in independent exploratory, development, and producing properties.

  12. Ashland outlines $261 million in refinery unit construction

    SciTech Connect (OSTI)

    Not Available

    1992-08-31T23:59:59.000Z

    This paper reports that Ashland Petroleum Co. has spelled out $261 million in projects completed, under way, or planned to produce cleaner fuel and further reduce emissions at two U.S. refineries. The company: Started up at $13 million pollution control system at its 213,400 b/cd Catlettsburg, Ky., plant. Started construction on six projects at its 67,100 b/cd St. Paul Park, Minn., refinery that will cost about $114 million and enable the plant to produce cleaner burning diesel fuel and further reduce emissions.

  13. Massachusetts Natural Gas Underground Storage Withdrawals (Million Cubic

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0Feet) (Million(MillionFeet)

  14. Colorado Natural Gas Plant Liquids Production Extracted in Kansas (Million

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321Spain (MillionFeet) (Million(Million

  15. Colorado Natural Gas Plant Liquids Production Extracted in Utah (Million

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321Spain (MillionFeet) (Million(MillionCubic

  16. Wisconsin Natural Gas Underground Storage Withdrawals (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(MillionYear JanThousand(Million

  17. Wisconsin Natural Gas Vehicle Fuel Consumption (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(MillionYear JanThousand(MillionDecade

  18. Wyoming Natural Gas Plant Liquids Production (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (MillionAdjustments (Million Cubic Feet)Fuel(Million

  19. Short-term energy outlook: Quarterly projections, fourth quarter 1997

    SciTech Connect (OSTI)

    NONE

    1997-10-14T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections for printed publication in January, April, July, and October in the Short-Term Energy Outlook. The details of these projections, as well as monthly updates on or about the 6th of each interim month, are available on the internet at: www.eia.doe.gov/emeu/steo/pub/contents.html. The forecast period for this issue of the Outlook extends from the fourth quarter of 1997 through the fourth quarter of 1998. Values for the fourth quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the fourth quarter 1997 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. 19 tabs.

  20. 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.

    2013-10-01T23:59:59.000Z

    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.

  1. Minnesota Company 3M Awarded $3 Million by Energy Department...

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

    of 3 million to 3M Company in St. Paul, Minnesota, to lower the cost of advanced fuel cell systems by developing cost-effective, durable, and highly efficient fuel cell...

  2. Energy Department Announces $2 Million to Develop Supply Chain...

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

    today announced up to 2 million to develop the domestic supply chain for hydrogen and fuel cell technologies and study the competitiveness of U.S. hydrogen and fuel cell system...

  3. Hanford Landfill Reaches 15 Million Tons Disposed - Waste Disposal...

    Energy Savers [EERE]

    ERDF comprises a series disposal areas called cells. Each pair of cells is 70 feet deep, 500 feet wide and 1,000 feet long at the base - large enough to hold about three million...

  4. DOE Awards $15 Million in Technical Assistance to Support Major...

    Office of Environmental Management (EM)

    of Energy (DOE) today announced the first phase of awards, valued at 15 million, for the Net-Zero Energy Commercial Building Initiative (CBI). Twenty-one companies, which will...

  5. DOE Announces Over $8 Million to Increase Use and Availability...

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

    Announces Over 8 Million to Increase Use and Availability of Alternative Fuels WASHINGTON, DC -Today, U.S. Department of Energy (DOE) Secretary Samuel W. Bodman announced 8.6...

  6. Energy Department Announces $3 Million to Lower Cost of Geothermal...

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

    Lower Cost of Geothermal Energy and Boost U.S. Supply of Critical Materials Energy Department Announces 3 Million to Lower Cost of Geothermal Energy and Boost U.S. Supply of...

  7. Department of Energy Closes $400 Million Loan Guarantee for State...

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

    Chu announced today that a 400 million loan guarantee has been finalized for Abound Solar Manufacturing, LLC to manufacture state-of-the-art thin-film solar panels. The Abound...

  8. NNSA Provides More Than $290 Million in Small Business Contract...

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

    Provides More Than ... NNSA Provides More Than 290 Million in Small Business Contract Obligations in FY 2012 Posted: December 18, 2012 - 11:45am In recognition of its commitment...

  9. Department of Energy Offers $90.6 Million Conditional Commitment...

    Office of Environmental Management (EM)

    invested 17.5 million in seven companies in its first round of funding -- and those companies have gone on to attract more than 1.6 billion of private financing as they...

  10. Oak Ridge: Approaching 4 Million Safe Work Hours

    Broader source: Energy.gov [DOE]

    Workers at URS | CH2M Oak Ridge (UCOR), the prime contractor for EM’s Oak Ridge cleanup, are approaching a milestone of 4 million safe work hours without a lost time away incident.

  11. Energy Department Awards More Than $1 Million to Three States...

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

    2013 - 1:12pm Addthis In support of President Obama's goal of doubling U.S. energy productivity by 2030, the Energy Department today announced new awards of more than 1 million...

  12. Department of Energy's Paducah Site Reaches Million-Hour Safety...

    Office of Environmental Management (EM)

    a core value" attitude. "Our team adheres to the concept that we will only achieve productivity through safety," LATA Kentucky Project Manager Mark Duff said. "The million-hour...

  13. President Requests $881.6 Million for Fossil Energy Programs

    Broader source: Energy.gov [DOE]

    President Obama's FY 2010 budget seeks $881.6 million for the Office of Fossil Energy to support improved energy security and rapid development of climate-oriented technology.

  14. President Requests $760.4 Million for Fossil Energy Programs

    Broader source: Energy.gov [DOE]

    President Obama's FY 2011 budget seeks $760.4 million for the Office of Fossil Energy to support improved energy security and rapid development of climate-oriented technology.

  15. Secretary Chu Announces Closing of $117 Million Loan Guarantee...

    Office of Environmental Management (EM)

    Power Project Secretary Chu Announces Closing of 117 Million Loan Guarantee for Kahuku Wind Power Project July 27, 2010 - 12:00am Addthis Washington D.C. --- Energy Secretary...

  16. ,"New York Dry Natural Gas Production (Million Cubic Feet)"

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

    ,,"(202) 586-8800",,,"2262015 9:22:39 AM" "Back to Contents","Data 1: New York Dry Natural Gas Production (Million Cubic Feet)" "Sourcekey","NA1160SNY2"...

  17. Obama Administration Offers $59 Million in Conditional Loan Guarantees...

    Energy Savers [EERE]

    expansion of its assembly plant in Pocatello, Idaho, to produce its one megawatt wind turbine. Beacon Power, an energy storage company, has been offered 43 million to support the...

  18. Energy Department Announces $180 Million for Ambitious New Initiative...

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

    Secretary Steven Chu today announced the start of an ambitious initiative to capture the potential of wind energy off American coasts. As part of a planned six-year 180 million...

  19. Short-term energy outlook: Quarterly projections, second quarter 1997

    SciTech Connect (OSTI)

    NONE

    1997-04-01T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly short-term energy supply, demand, and price projections for publication in January, April, July, and October in the Outlook. The forecast period for this issue of the Outlook extends from the second quarter of 1997 through the fourth quarter of 1998. Values for the first quarter of 1997, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in EIA`s Weekly Petroleum Status Report) or are calculated from model simulations that use the latest exogenous information available (for example, electricity sales and generation are simulated by using actual weather data). The historical energy data, compiled in the second quarter 1997 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook. By varying the assumptions, alternative cases are produced by using the Short-Term Integrated Forecasting System (STIFS). 34 figs., 19 tabs.

  20. Short-term energy outlook. Quarterly projections, first quarter 1996

    SciTech Connect (OSTI)

    NONE

    1996-02-01T23:59:59.000Z

    The Energy Information Administration (EIA) prepares quarterly, short-term energy supply, demand, and price projections for publication in February, May, August, and November in the Outlook. The forecast period for this issue of the Outlook extends from the first quarter of 1996 through the fourth quarter of 1997. Values for the fourth quarter of 1995, however, are preliminary EIA estimates (for example, some monthly values for petroleum supply and disposition are derived in part from weekly data reported in the Weekly Petroleum Status Report) or are calculated from model simulations using the latest exogenous information available (for example, electricity sales and generation are simulated using actual weather data). The historical energy data, compiled into the first quarter 1996 version of the Short-Term Integrated Forecasting System (STIFS) database, are mostly EIA data regularly published in the Monthly Energy Review, Petroleum Supply Monthly, and other EIA publications. Minor discrepancies between the data in these publications and the historical data in this Outlook are due to independent rounding. The STIFS database is archived quarterly and is available from the National Technical Information Service. The cases are produced using the Short-Term Integrated Forecasting System (STIFS). The STIFS model is driven principally by three sets of assumptions or inputs: estimates of key macroeconomic variables, world oil price assumptions, and assumptions about the severity of weather. Macroeconomic estimates are produced by DRI/McGraw-Hill but are adjusted by EIA to reflect EIA assumptions about the world price of crude oil, energy product prices, and other assumptions which may affect the macroeconomic outlook.

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

    E-Print Network [OSTI]

    Poulin, L.

    2013-01-01T23:59:59.000Z

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

  2. Forecasting model of the PEPCO service area economy. Volume 3

    SciTech Connect (OSTI)

    Not Available

    1984-03-01T23:59:59.000Z

    Volume III describes and documents the regional economic model of the PEPCO service area which was relied upon to develop many of the assumptions of future values of economic and demographic variables used in the forecast. The PEPCO area model is mathematically linked to the Wharton long-term forecast of the U.S. Volume III contains a technical discussion of the structure of the regional model and presents the regional economic forecast.

  3. 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.

    2012-09-01T23:59:59.000Z

    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.

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

    Office of Environmental Management (EM)

    Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report.pdf More Documents & Publications Computational Advances in Applied...

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

    E-Print Network [OSTI]

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

    2011-01-01T23:59:59.000Z

    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

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    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

  7. Forecasting the underlying potential governing climatic time series

    E-Print Network [OSTI]

    Livina, V N; Mudelsee, M; Lenton, T M

    2012-01-01T23:59:59.000Z

    We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for `potential analysis' of climatic tipping points which altogether serves anticipating, detecting and forecasting climate transitions and bifurcations using several independent techniques of time series analysis.

  8. Sandia National Laboratories: Solar Energy Forecasting and Resource...

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

    & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource Assessment, provides an authoritative voice on the...

  9. Econometric model and futures markets commodity price forecasting

    E-Print Network [OSTI]

    Just, Richard E.; Rausser, Gordon C.

    1979-01-01T23:59:59.000Z

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

  10. Optimization Online - Data Assimilation in Weather Forecasting: A ...

    E-Print Network [OSTI]

    M. Fisher

    2007-02-14T23:59:59.000Z

    Feb 14, 2007 ... Data Assimilation in Weather Forecasting: A Case Study in PDE-Constrained Optimization. M. Fisher(Mike.Fisher ***at*** ecmwf.int)

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

    E-Print Network [OSTI]

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

    2006-01-01T23:59:59.000Z

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

  12. Using Customers' Reported Forecasts to Predict Future Sales

    E-Print Network [OSTI]

    Gordon, Geoffrey J.

    Using Customers' Reported Forecasts to Predict Future Sales Nihat Altintas , Alan Montgomery , Michael Trick Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213. nihat

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

    E-Print Network [OSTI]

    Smith, Emily (Emily C.)

    2010-01-01T23:59:59.000Z

    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 ...

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    of two methods to forecast natural gas prices: using theof two methods to forecast natural gas prices is performed:accurate average forecast of natural gas prices than the

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01T23:59:59.000Z

    Gas Price Forecast With natural gas prices significantlyto the EIA’s natural gas price forecasts in AEO 2004 and AEOon the AEO 2005 natural gas price forecasts will likely once

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01T23:59:59.000Z

    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

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

    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

  18. Experimental short-term climate forecasting procedure for US winter temperatures and its verification

    SciTech Connect (OSTI)

    Navato, A.R.

    1981-01-01T23:59:59.000Z

    A linear multivariate regression model for continental United States Januray surface air temperatures is presented. The predictors enter the regression equation at more than one lag if their effects on the predictand tend to be spread over several months rather than be completed in one month. The monthly mean surface tempertures for 12 cities were taken to be representative of temperatures for the regions around them. The predictand series then consisted of annual January values for each representative city. A separate regression equation was derived for the annual departures from the long term means for January for each city. The procedure therefore predicts the inter-annual variability for January. The regression equations for inter-annual variability were able to explain a much higher percentage of variance compared to the corresponding regression equations for monthly variability. The same procedure can be applied to obtain prediction equations for other months. The higher percentage is to be expected if it is considered that there must be differences in the operation of temperature-controlling physical mechanisms which are dependent on the march of the seasons.

  19. Development of Short-term Demand Forecasting Model Application in Analysis of Resource Adequacy

    E-Print Network [OSTI]

    calculated. Peak load and energy load for all months were ranked and top 5th percentile load were used

  20. Short- and Long-Term Earthquake Forecasts for California and Nevada

    E-Print Network [OSTI]

    Kagan, Y. Y.; Jackson, D. D.

    2010-01-01T23:59:59.000Z

    Cent. , http://neic.usgs.gov/neis/epic/epic.html and http://neic. usgs.gov/neis/epic/code_catalog.html. S CHORLEMMER ,

  1. Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty

    Reports and Publications (EIA)

    2009-01-01T23:59:59.000Z

    It is often noted that energy prices are quite volatile, reflecting market participants' adjustments to new information from physical energy markets and/or markets in energy-related financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the marketclearing process for risk transfer can be used to generate "price bands" around observed futures prices for crude oil, natural gas, and other commodities.

  2. Renewable Forecast Min-Max2020.xls

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

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

  3. Forecast and Funding Arrangements - Hanford Site

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

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

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

    SciTech Connect (OSTI)

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

    2014-10-27T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

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

  6. A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

    E-Print Network [OSTI]

    Hansens, Jim

    A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size Andrew. R.Lawrence@ecmwf.int #12;Abstract An ensemble-based data assimilation approach is used to transform old en- semble. The impact of the transformations are propagated for- ward in time over the ensemble's forecast period

  7. RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN

    E-Print Network [OSTI]

    Manry, Michael

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

  8. Introducing the Canadian Crop Yield Forecaster Aston Chipanshi1

    E-Print Network [OSTI]

    Miami, University of

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

  9. Wind-Wave Probabilistic Forecasting based on Ensemble

    E-Print Network [OSTI]

    have to be jointly taken into account in some decision-making problems, e.g. offshore wind farmWind-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

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

    E-Print Network [OSTI]

    Kemner, Ken

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

  11. PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022

    E-Print Network [OSTI]

    PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022 AUGUST 2011 CEC-200-2011-011-SD CALIFORNIA for electric vehicles. #12;ii #12;iii ABSTRACT The Preliminary California Energy Demand Forecast 2012 includes three full scenarios: a high energy demand case, a low energy demand case, and a mid energy demand

  12. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

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

  13. CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Manager Kae Lewis Acting Manager Demand Analysis Office Valerie T. Hall Deputy Director Energy Efficiency Demand Forecast report is the product of the efforts of many current and former California Energy

  14. Distribution Based Data Filtering for Financial Time Series Forecasting

    E-Print Network [OSTI]

    Bailey, James

    recent past. In this paper, we address the challenge of forecasting the behavior of time series using@unimelb.edu.au Abstract. Changes in the distribution of financial time series, particularly stock market prices, can of stock prices, which aims to forecast the future values of the price of a stock, in order to obtain

  15. 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 for the modeled wind- CAES system would not cover annualized capital costs. We also estimate market prices-ahead market is roughly $100, with large variability due to electric power prices. Wind power forecast errors

  16. Draft for Public Comment Appendix A. Demand Forecast

    E-Print Network [OSTI]

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

  17. FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS

    E-Print Network [OSTI]

    Keller, Arturo A.

    resources resulting in water stress. Effective water management ­ a solution Supply side management Demand side management #12;Developing a regression equation based on cluster analysis for forecasting waterFORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil

  18. Forecasting Uncertainty Related to Ramps of Wind Power Production

    E-Print Network [OSTI]

    Boyer, Edmond

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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    -- Photovoltaic systems, Batteries, Forecasting I. INTRODUCTION This paper presents first results of a study Energies and Energy Systems Sophia Antipolis, France andrea.michiorri@mines-paristech.fr Abstract production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size

  20. Forecasting Building Occupancy Using Sensor Network James Howard

    E-Print Network [OSTI]

    Hoff, William A.

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

  1. Energy Department Awards $2.6 Million to Boost Combustion Efficiency...

    Office of Environmental Management (EM)

    2.6 Million to Boost Combustion Efficiency in Industrial Boilers Energy Department Awards 2.6 Million to Boost Combustion Efficiency in Industrial Boilers September 26, 2005 -...

  2. Energy Department Accepting Applications for a $3.6 Million Hydroelect...

    Office of Environmental Management (EM)

    Accepting Applications for a 3.6 Million Hydroelectric Production Incentive Program Energy Department Accepting Applications for a 3.6 Million Hydroelectric Production Incentive...

  3. Department of Energy Finalizes $96.8 Million Loan Guarantee for...

    Office of Environmental Management (EM)

    Finalizes 96.8 Million Loan Guarantee for Oregon Geothermal Project Department of Energy Finalizes 96.8 Million Loan Guarantee for Oregon Geothermal Project February 24, 2011 -...

  4. DOE Announces $27 Million to Reduce Costs of Solar Energy Projects...

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

    7 Million to Reduce Costs of Solar Energy Projects, Streamline Permitting and Installations DOE Announces 27 Million to Reduce Costs of Solar Energy Projects, Streamline...

  5. ARPA-E Announces Projects Have Attracted Over $450 Million in...

    Office of Environmental Management (EM)

    Million in Private Sector Funding, Spurred Start-up Company Formation and Fostered ARPA-E Announces Projects Have Attracted Over 450 Million in Private Sector Funding, Spurred...

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

    SciTech Connect (OSTI)

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

    2012-07-01T23:59:59.000Z

    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.

  7. Development of a real-time quantitative hydrologic forecasting model

    E-Print Network [OSTI]

    Bell, John Frank

    1986-01-01T23:59:59.000Z

    data made in each domain. Fit3ur e 1. Lieatner Radar Carr ection Procedures (by drtmatni ( Bussel I et a 1 . , (97EI ) (Weeks and Hebbert, 1980) and the Boughton Model (Weeks and Hebbert, 1980) are but a few. Models range from sophisticated... of hydroelectric power with existing facilities, $73 million, e) benefits to navigation, $2 million and f) more efFective use of recreational facilities and wildlife habitat, $3 million. Total $485 million The resulting expected benefits in 1983 dollars...

  8. Saudi production capacity climbing to 10 million b/d

    SciTech Connect (OSTI)

    Not Available

    1994-07-11T23:59:59.000Z

    Saudi Arabia this year is completing its expansion of production capacity and developing recent discoveries to enhance export flexibility. The 3 million b/d capacity expansion to 10 million b/d, announced in 1989, is on target for completion by year end 1994. Most of the effort involves restoration of mothballed production equipment and installation of several gas-oil separation plants (GOSPs) in existing fields. But Saudi Arabian Oil Co. (Saudi Aramco) also this year will start up production of extra-light oil from a new field in the central part of the kingdom. Start-up of Hawtah area production demonstrates success of an oil search Aramco began after receiving exclusive exploration rights to nearly all of Saudi Arabia's prospective area in 1986. From new fields and traditional producing areas, therefore, Saudi Arabia has the potential to expand production capacity beyond 10 million b/d. The paper describes the development of the extra capacity.

  9. Verification of hourly forecasts of wind turbine power output

    SciTech Connect (OSTI)

    Wegley, H.L.

    1984-08-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Templeton, K.J.

    1996-05-23T23:59:59.000Z

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

  11. NatioNal aNd Global Forecasts West VirGiNia ProFiles aNd Forecasts

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    · NatioNal aNd Global Forecasts · West VirGiNia ProFiles aNd Forecasts · eNerGy · Healt Global Insight, paid for by the West Virginia Department of Revenue. 2013 WEST VIRGINIA ECONOMIC OUTLOOKWest Virginia Economic Outlook 2013 is published by: Bureau of Business & Economic Research West

  12. Louisiana Natural Gas LNG Storage Additions (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 0 0 0 1569 0 0Year JanAdditions (Million

  13. Maryland Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0 0.0 0.0 0.0YearCommercial (Million

  14. Massachusetts Natural Gas LNG Storage Withdrawals (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0 0.04,0009,929Withdrawals (Million

  15. Massachusetts Natural Gas Pipeline and Distribution Use (Million Cubic

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0Feet) (Million Cubic Feet)

  16. Massachusetts Natural Gas Residential Consumption (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0Feet) (Million CubicperDecade

  17. Massachusetts Natural Gas Total Consumption (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3 00.0Feet) (Million

  18. Michigan Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3Exports (NoYear Jan (Million Cubic Feet)

  19. Michigan Natural Gas Plant Fuel Consumption (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3Exports (NoYear Jan (Million CubicFuel

  20. Michigan Natural Gas Plant Liquids Production (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3Exports (NoYear Jan (Million

  1. Michigan Natural Gas Plant Liquids, Proved Reserves (Million Barrels)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet) 3Exports (NoYear Jan (MillionProved

  2. Minnesota Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million Cubic Feet)Commercial Consumers (Number of (Million

  3. Mississippi Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic Feet) Price AllFuelCommercial (Million

  4. Montana Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic32,876 10,889Decade Year-0and (Million Cubic

  5. Montana Natural Gas Plant Fuel Consumption (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto China (Million CubicCubic32,876 10,889Decade Year-0and (MillionFuel

  6. South Carolina Natural Gas LNG Storage Withdrawals (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial ConsumersThousand CubicCubicIndia (Million2,116CubicWithdrawals (Million

  7. California Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321 2,590Fuel Consumption (Million (Million Cubic

  8. California Natural Gas Plant Fuel Consumption (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321 2,590Fuel Consumption (Million (MillionFuel

  9. Colorado Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321Spain (MillionFeet) (Million Cubic Feet)

  10. Colorado Natural Gas Plant Fuel Consumption (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321Spain (MillionFeet) (Million CubicFuel

  11. Colorado Natural Gas Plant Liquids Production (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40CoalLease(Billion2,128 2,469 2,321Spain (MillionFeet) (Million

  12. Washington Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58 810 0CubicFeet) Lease (Million

  13. West Virginia Natural Gas Plant Liquids Production (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million Cubic Feet) West Virginia

  14. West Virginia Natural Gas Plant Liquids, Proved Reserves (Million Barrels)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million Cubic Feet) WestProved

  15. West Virginia Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million Cubic Feet)Nov-14

  16. West Virginia Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million Cubic Feet)Nov-14Repressuring

  17. West Virginia Natural Gas Residential Consumption (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million CubicDecade Year-0 Year-1

  18. West Virginia Natural Gas Total Consumption (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million CubicDecade Year-0 Year-1Total

  19. West Virginia Natural Gas Underground Storage Capacity (Million Cubic Feet)

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million CubicDecade Year-0

  20. West Virginia Natural Gas Underground Storage Net Withdrawals (Million

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,397 125 Q 69 (Million Cubic58(Million CubicDecade Year-0Cubic