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1

Annual Energy Outlook Forecast Evaluation - Table 1. Forecast Evaluations:  

Gasoline and Diesel Fuel Update (EIA)

Average Absolute Percent Errors from AEO Forecast Evaluations: Average Absolute Percent Errors from AEO Forecast Evaluations: 1996 to 2000 Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Average Absolute Percent Error Variable 1996 Evaluation: AEO82 to AEO93 1997 Evaluation: AEO82 to AEO97 1998 Evaluation: AEO82 to AEO98 1999 Evaluation: AEO82 to AEO99 2000 Evaluation: AEO82 to AEO2000 Consumption Total Energy Consumption 1.8 1.6 1.7 1.7 1.8 Total Petroleum Consumption 3.2 2.8 2.9 2.8 2.9 Total Natural Gas Consumption 6.0 5.8 5.7 5.6 5.6 Total Coal Consumption 2.9 2.7 3.0 3.2 3.3 Total Electricity Sales 1.8 1.6 1.7 1.8 2.0 Production Crude Oil Production 5.1 4.2 4.3 4.5 4.5

2

Weather Research and Forecasting Model 2.2 Documentation  

E-Print Network [OSTI]

................................................................................................. 20 3.1.2 Integrate's Flow of ControlWeather Research and Forecasting Model 2.2 Documentation: A Step-by-step guide of a Model Run .......................................................................................................................... 19 3.1 The Integrate Subroutine

Sadjadi, S. Masoud

3

12-32021E2_Forecast  

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

FORECAST OF VACANCIES FORECAST OF VACANCIES Until end of 2014 (Issue No. 20) Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff at the P4-P5 levels: * Advanced university degree (or equivalent postgraduate degree); * 7 or 10 years, respectively, of experience in a field of relevance to the post; * Resource management experience; * Strong analytical skills; * Computer skills: standard Microsoft Office software; * Languages: Fluency in English. Working knowledge of other official languages (Arabic, Chinese, French, Russian, Spanish) advantageous; * Ability to work effectively in multidisciplinary and multicultural teams; * Ability to communicate effectively. Professional staff at the P1-P3 levels:

4

Model error in weather forecasting D. Orrell 1,2 , L. Smith 1,3 , J. Barkmeijer 4 , and T. Palmer 4  

E-Print Network [OSTI]

numerical weather prediction mod­ els. A simple law is derived to relate model error to likely shadowingModel error in weather forecasting D. Orrell 1,2 , L. Smith 1,3 , J. Barkmeijer 4 , and T. Palmer 4 in the model, and inac­ curate initial conditions (Bjerknes, 1911). Because weather models are thought

Smith, Leonard A

5

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

SciTech Connect (OSTI)

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.

Templeton, K.J.

1996-05-23T23:59:59.000Z

6

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network [OSTI]

: Technology Forecast, Laws of Technical systems evolution, Analysis of Contradictions. 1. Introduction Let us: If technology forecasting practice remains at the present level, it is necessary to significantly improve to new demands (like Green House Gases - GHG Effect reduction or covering exploded nuclear reactor

Paris-Sud XI, Université de

7

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

SciTech Connect (OSTI)

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

Valero, O.J.

1996-04-23T23:59:59.000Z

8

What constrains spread growth in forecasts ini2alized from  

E-Print Network [OSTI]

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

Hamill, Tom

9

Does Money Matter in Inflation Forecasting? JM Binner 1  

E-Print Network [OSTI]

1 Does Money Matter in Inflation Forecasting? JM Binner 1 P Tino 2 J Tepper 3 R Anderson4 B Jones 5 range of different definitions of money, including different methods of aggregation and different that there exists a long-run relationship between the growth rate of the money supply and the growth rate of prices

Tino, Peter

10

CAPP 2010 Forecast.indd  

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

Forecast, Markets & Pipelines 1 Crude Oil Forecast, Markets & Pipelines June 2010 2 CANADIAN ASSOCIATION OF PETROLEUM PRODUCERS Disclaimer: This publication was prepared by the...

11

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

E-Print Network [OSTI]

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting-uses include Heating, Ventilation and Air Conditioning (HVAC). Our analysis uses the modeling framework provided by the HVAC module in the Residential End-Use Energy Planning System (REEPS), which was developed

12

Solar forecasting review  

E-Print Network [OSTI]

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

Inman, Richard Headen

2012-01-01T23:59:59.000Z

13

Annual Energy Outlook Forecast Evaluation - Tables 2-18  

Gasoline and Diesel Fuel Update (EIA)

Total Energy Consumption: AEO Forecasts, Actual Values, and Total Energy Consumption: AEO Forecasts, Actual Values, and Absolute and Percent Errors, 1985-1999 Publication 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Average Absolute Error (Quadrillion Btu) AEO82 79.1 79.6 79.9 80.8 82.0 83.3 1.8 AEO83 78.0 79.5 81.0 82.4 83.8 84.6 89.5 1.2 AEO84 78.5 79.4 81.2 83.1 85.0 86.4 93.5 1.5 AEO85 77.6 78.5 79.8 81.2 82.6 83.3 84.2 85.2 85.9 86.7 87.7 1.3 AEO86 77.0 78.8 79.8 80.6 81.5 82.9 84.0 84.8 85.7 86.5 87.9 88.4 87.8 88.7 3.6 AEO87 78.9 80.0 81.9 82.8 83.9 85.3 86.4 87.5 88.4 1.5 AEO89 82.2 83.7 84.5 85.4 86.4 87.3 88.2 89.2 90.8 91.4 90.9 91.7 1.8

14

E-Print Network 3.0 - africa conditional forecasts Sample Search...  

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

Search Powered by Explorit Topic List Advanced Search Sample search results for: africa conditional forecasts Page: << < 1 2 3 4 5 > >> 1 COLORADO STATE UNIVERSITY FORECAST...

15

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

E-Print Network [OSTI]

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

Crambes, Christophe

16

Forecast Prices  

Gasoline and Diesel Fuel Update (EIA)

Notes: Notes: Prices have already recovered from the spike, but are expected to remain elevated over year-ago levels because of the higher crude oil prices. There is a lot of uncertainty in the market as to where crude oil prices will be next winter, but our current forecast has them declining about $2.50 per barrel (6 cents per gallon) from today's levels by next October. U.S. average residential heating oil prices peaked at almost $1.50 as a result of the problems in the Northeast this past winter. The current forecast has them peaking at $1.08 next winter, but we will be revisiting the outlook in more detail next fall and presenting our findings at the annual Winter Fuels Conference. Similarly, diesel prices are also expected to fall. The current outlook projects retail diesel prices dropping about 14 cents per gallon

17

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

E-Print Network [OSTI]

Short-term Wind Power Forecasting Using Advanced Statistical Methods T.S. Nielsen1 , H. Madsen1 , H considered in the ANEMOS project for short-term fore- casting of wind power. The total procedure typically in for prediction of wind power or wind speed, estimating the uncertainty of the wind power forecast, and finally

Paris-Sud XI, Université de

18

Radiation fog forecasting using a 1-dimensional model  

E-Print Network [OSTI]

measuring site (Molly Caren), the soil moisture measuring site (Wilmington), and (b) location of the forecast site (Ohio River Basin near Cincinnati including Lunken airport) . . 23 3 An example of a COBEL configuration file for 25 August 1996, showing... measuring site (Molly Caren), the soil moisture measuring site (Wilmington), and (b) location of the forecast site (Ohio River Basin near Cincinnati including Lunken airport) . . 23 3 An example of a COBEL configuration file for 25 August 1996, showing...

Peyraud, Lionel

2012-06-07T23:59:59.000Z

19

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sanchez by Esmeralda Sanchez Errata -(7/14/04) The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors, expressed as the percentage difference between the Reference Case projection and actual historic value, shown for every AEO and for each year in the forecast horizon (for a given variable). The historical data are typically taken from the Annual Energy Review (AER). The last column of Table 1 provides a summary of the most recent average absolute forecast errors. The calculation of the forecast error is shown in more detail in Tables 2 through 18. Because data for coal prices to electric generating plants were not available from the AER, data from the Monthly Energy Review (MER), July 2003 were used.

20

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

Open Energy Info (EERE)

Model To Forecast Co2 Leakage Rates Along A Wellbore Model To Forecast Co2 Leakage Rates Along A Wellbore Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Journal Article: An Improved Model To Forecast Co2 Leakage Rates Along A Wellbore Details Activities (0) Areas (0) Regions (0) Abstract: Large-scale geological storage of CO2 is likely to bring CO2 plumes into contact with a large number of existing wellbores. Wellbores that no longer provide proper zonal isolation establish a primary pathway for a buoyant CO2-rich phase to escape from the intended storage formation. The hazard of CO2 leakage along these pathways will depend on the rate of leakage. Thus a useful component of a risk assessment framework is a model of CO2 leakage. Predicting the flux of CO2 along a leaking wellbore requires a model of fluid properties and of transport along the leakage

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

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

SciTech Connect (OSTI)

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.

United States. Bonneville Power Administration.

1994-02-01T23:59:59.000Z

22

Testing Competing High-Resolution Precipitation Forecasts  

E-Print Network [OSTI]

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

Gilleland, Eric

23

E-Print Network 3.0 - air pollution forecast Sample Search Results  

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

forecast Search Powered by Explorit Topic List Advanced Search Sample search results for: air pollution forecast Page: << < 1 2 3 4 5 > >> 1 DISCOVER-AQ Outlook for Wednesay, July...

24

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation Analysis Papers > Annual Energy Outlook Forecast Evaluation Release Date: February 2005 Next Release Date: February 2006 Printer-friendly version Annual Energy Outlook Forecast Evaluation* Table 1.Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Printer Friendly Version Average Absolute Percent Error Variable AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 AEO82 to AEO2004 Consumption Total Energy Consumption 1.9 2.0 2.1 2.1 2.1 2.1 Total Petroleum Consumption 2.9 3.0 3.1 3.1 3.0 2.9 Total Natural Gas Consumption 7.3 7.1 7.1 6.7 6.4 6.5 Total Coal Consumption 3.1 3.3 3.5 3.6 3.7 3.8 Total Electricity Sales 1.9 2.0 2.3 2.3 2.3 2.4 Production Crude Oil Production 4.5 4.5 4.5 4.5 4.6 4.7

25

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Table 2. Total Energy Consumption, Actual vs. Forecasts Table 3. Total Petroleum Consumption, Actual vs. Forecasts Table 4. Total Natural Gas Consumption, Actual vs. Forecasts Table 5. Total Coal Consumption, Actual vs. Forecasts Table 6. Total Electricity Sales, Actual vs. Forecasts Table 7. Crude Oil Production, Actual vs. Forecasts Table 8. Natural Gas Production, Actual vs. Forecasts Table 9. Coal Production, Actual vs. Forecasts Table 10. Net Petroleum Imports, Actual vs. Forecasts Table 11. Net Natural Gas Imports, Actual vs. Forecasts Table 12. Net Coal Exports, Actual vs. Forecasts Table 13. World Oil Prices, Actual vs. Forecasts Table 14. Natural Gas Wellhead Prices, Actual vs. Forecasts Table 15. Coal Prices to Electric Utilities, Actual vs. Forecasts

26

RACORO Forecasting  

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

Daniel Hartsock CIMMS, University of Oklahoma ARM AAF Wiki page Weather Briefings Observed Weather Cloud forecasting models BUFKIT forecast soundings + guidance...

27

Correspondence among the Correlation, RMSE, and Heidke Forecast Verification Measures; Refinement of the Heidke Score  

Science Journals Connector (OSTI)

The correspondence among the following three forecast verification scores, based on forecasts and their associated observations, is described: 1) the correlation score, 2) the root-mean-square error (RMSE) score, and 3) the Heidke score (based on ...

Anthony G. Barnston

1992-12-01T23:59:59.000Z

28

Products and Service of Center for Weather Forecast and Climate Studies  

E-Print Network [OSTI]

) Seasonal Climate Forecast (1-6 months) #12;Weather Forecast Weather Bulletin PCD SCD1 SCD2 SX6 SatelliteLOG O Products and Service of Center for Weather Forecast and Climate Studies Simone Sievert da AND DEVELOP. DIVISION SATELLITE DIVISION ENVIROM. SYSTEM OPERATIONAL DIVISION CPTEC/INPE Msc / PHD &TRAINING

29

Forecasting wireless communication technologies  

Science Journals Connector (OSTI)

The purpose of the paper is to present a formal comparison of a variety of multiple regression models in technology forecasting for wireless communication. We compare results obtained from multiple regression models to determine whether they provide a superior fitting and forecasting performance. Both techniques predict the year of wireless communication technology introduction from the first (1G) to fourth (4G) generations. This paper intends to identify the key parameters impacting the growth of wireless communications. The comparison of technology forecasting approaches benefits future researchers and practitioners when developing a prediction of future wireless communication technologies. The items of focus will be to understand the relationship between variable selection and model fit. Because the forecasting error was successfully reduced from previous approaches, the quadratic regression methodology is applied to the forecasting of future technology commercialisation. In this study, the data will show that the quadratic regression forecasting technique provides a better fit to the curve.

Sabrina Patino; Jisun Kim; Tugrul U. Daim

2010-01-01T23:59:59.000Z

30

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

31

Annual Energy Outlook with Projections to 2025-Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2004 with Projections to 2025 Forecast Comparisons Index (click to jump links) Economic Growth World Oil Prices Total Energy Consumption Electricity Natural Gas Petroleum Coal The AEO2004 forecast period extends through 2025. One other organization—Global Insight, Incorporated (GII)—produces a comprehensive energy projection with a similar time horizon. Several others provide forecasts that address one or more aspects of energy markets over different time horizons. Recent projections from GII and others are compared here with the AEO2004 projections. Economic Growth Printer Friendly Version Average annual percentage growth Forecast 2002-2008 2002-2013 2002-2025 AEO2003 3.2 3.3 3.1 AEO2004 Reference 3.3 3.2 3.0

32

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

Energy Savers [EERE]

better forecasts, wind energy plant operators and industry professionals can ensure wind turbines operate closer to maximum capacity, leading to lower energy costs for consumers....

33

Consensus Coal Production Forecast for  

E-Print Network [OSTI]

Rate Forecasts 19 5. EIA Forecast: Regional Coal Production 22 6. Wood Mackenzie Forecast: W.V. Steam to data currently published by the Energy Information Administration (EIA), coal production in the state in this report calls for state production to decline by 11.3 percent in 2009 to 140.2 million tons. During

Mohaghegh, Shahab

34

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

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

diesel fuel price forecast to be 1 penny lower this summer diesel fuel price forecast to be 1 penny lower this summer at $3.94 a gallon The retail price of diesel fuel is expected to average $3.94 a gallon during the summer driving season that which runs from April through September. That's close to last summer's pump price of $3.95, according to the latest monthly energy outlook from the U.S. Energy Information Administration. Demand for distillate fuel, which includes diesel fuel, is expected to be up less than 1 percent from last summer. Daily production of distillate fuel at U.S. refineries is forecast to be 70,000 barrels higher this summer. With domestic distillate output exceeding demand, U.S. net exports of distillate fuel are expected to average 830,000 barrels per day this summer. That's down 12 percent from last summer's

35

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations during ETEX 2  

E-Print Network [OSTI]

Evaluating the ability of a numerical weather prediction model to forecast tracer concentrations an operational numerical weather prediction model to forecast air quality are also investigated. These potential a numerical weather prediction (NWP) model independently of the CTM. The NWP output is typically archived

Dacre, Helen

36

Gridded Operational Consensus Forecasts of 2-m Temperature over Australia CHERMELLE ENGEL  

E-Print Network [OSTI]

-resolution grid. Local and in- ternational numerical weather prediction model inputs are found to have coarse by numerical weather prediction (NWP) model forecasts. As NWP models improve, public weather forecasting University of Melbourne, Melbourne, Victoria, Australia ELIZABETH E. EBERT Centre for Australia Weather

Ebert, Beth

37

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities  

Science Journals Connector (OSTI)

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

Carolina Garca-Martos; Julio Rodrguez; Mara Jess Snchez

2013-01-01T23:59:59.000Z

38

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

SciTech Connect (OSTI)

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

Not Available

1994-12-01T23:59:59.000Z

39

Expert Panel: Forecast Future Demand for Medical Isotopes  

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

Expert Panel: Expert Panel: Forecast Future Demand for Medical Isotopes March 1999 Expert Panel: Forecast Future Demand for Medical Isotopes September 25-26, 1998 Arlington, Virginia The Expert Panel ............................................................................................. Page 1 Charge To The Expert Panel........................................................................... Page 2 Executive Summary......................................................................................... Page 3 Introduction ...................................................................................................... Page 4 Rationale.......................................................................................................... Page 6 Economic Analysis...........................................................................................

40

Solid Waste Forecast Database: User`s guide (Version 1.5)  

SciTech Connect (OSTI)

The Solid Waste Forecast Database (SWFD) system is an analytical tool developed by Pacific Northwest Laboratory (PNL) for Westinghouse Hanford Company (WHC) specifically to address Hanford solid waste management issues. This document is one of a set of documents supporting the SWFD system and providing instructions in the use and maintenance of SWFD components. This manual contains instructions for using Version 1.5 of the SWFD, including system requirements and preparation, entering and maintaining data, and performing routine database functions. This document supports only those operations that are specific to SWFD menus and functions and does not provide instruction in the use of Paradox, the database management system in which the SWFD is established.

Bierschbach, M.C.

1994-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis  

Science Journals Connector (OSTI)

The ARIMA(0,1,1) demand model has been analysed extensively by researchers and used widely by forecasting practitioners due to its attractive theoretical properties and empirical evidence in its support. However, no empirical investigations have been conducted in the academic literature to analyse demand forecasting and inventory performance under such a demand model. In this paper, we consider a supply chain formed by a manufacturer and a retailer facing an ARIMA(0,1,1) demand process. The relationship between the forecasting accuracy and inventory performance is analysed along with an investigation on the potential benefits of forecast information sharing between the retailer and the manufacturer. Results are obtained analytically but also empirically by means of experimentation with the sales data related to 329 Stock Keeping Units (SKUs) from a major European superstore. Our analysis contributes towards the development of the current state of knowledge in the areas of inventory forecasting and forecast information sharing and offers insights that should be valuable from the practitioner perspective.

M.Z. Babai; M.M. Ali; J.E. Boylan; A.A. Syntetos

2013-01-01T23:59:59.000Z

42

Review/Verify Strategic Skills Needs/Forecasts/Future Mission...  

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

ReviewVerify Strategic Skills NeedsForecastsFuture Mission Shifts Annual Lab Plan (1-10 yrs) Fermilab Strategic Agenda (2-5 yrs) Sector program Execution Plans (1-3...

43

Forecast of Advanced Technology for Coal Power Generation Towards the Year of 2050 in CO2 Reduction Model of Japan  

Science Journals Connector (OSTI)

Abstract In the fossil fuel, coal is enough to get easily because it has supply and price stability brought about its ubiquitously. Coal is used for power generation as the major fuel in the world. However it is true that control of global warming should be applied to coal power generations. Therefore, many people expect CO2 reduction by technical innovation such as efficiency improvement, Carbon dioxide Capture and Storage (CCS). In case of coal power plant are considered for improving efficiency. Some of them have already put into commercial operation but others are still under R&D stage. Especially, the technical development prospect of the power plant is very important for planning the energy strategy in the resource-importing country. Japan Coal Energy Center (JCOAL) constructed a program to forecast the share of advanced coal fired plants/natural gas power plants towards the year of 2050. Then, we simulated the future prediction about 2 cases (the Japanese scenario and the world scenario). The fuel price and the existence of CCS were considered in the forecast of the technical development of the thermal power generation. Especially in the Japanese scenario, we considered the CO2 reduction target which is 80% reduction in 1990. In the world scenario, coal price had almost no influence on the share of coal fired plant. However, when the gas price increased 1.5% or more, the share of coal fired plant increased. In that case, CO2 emissions increased because coal-fired plant increased. Compared with both cases, the amount of CO2 in 2050 without CCS case was 50% higher than that of with CCS case. In Japanese scenario, achievement of 80% CO2 reduction target is impossible without CCS. If CCS is introduced into all the new establishment coal fired plant, CO2 reduction target can be attained. In the Japanese scenario, the gas price more expensive than a coal price so that the amount of the coal fired plant does not decline. Since the reduction of the amount of CO2 will be needed in all over the world, introductory promotion and technical development of CCS are very important not only Japan but also all over the world.

Takashi Nakamura; Keiji Makino; Kunihiko Shibata; Michiaki Harada

2013-01-01T23:59:59.000Z

44

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

Science Journals Connector (OSTI)

Because poor visibility conditions have a considerable influence on airport traffic, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL)-Interactions between Soil, Biosphere, and Atmosphere (...

Samuel Rmy; Thierry Bergot

2010-05-01T23:59:59.000Z

45

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

E-Print Network [OSTI]

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

Kouroupetroglou, Georgios

46

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

E-Print Network [OSTI]

Lightning Forecasts..........................................................................................45 2.7 First Flash Forecasts and Lead Times.....................................................................47 vii... Cell Number ? 25 August 2000..............................................68 3.4 First Flash Forecast Time........................................................................................70 3.5 Lightning Forecasting Algorithm (LFA) Development...

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

47

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

Solar Energy Market Forecast Solar Energy Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast Agency/Company /Organization: United States Department of Energy Sector: Energy Focus Area: Solar Topics: Market analysis, Technology characterizations Resource Type: Publications Website: giffords.house.gov/DOE%20Perspective%20on%20Solar%20Market%20Evolution References: Solar Energy Market Forecast[1] Summary " Energy markets / forecasts DOE Solar America Initiative overview Capital market investments in solar Solar photovoltaic (PV) sector overview PV prices and costs PV market evolution Market evolution considerations Balance of system costs Silicon 'normalization' Solar system value drivers Solar market forecast Additional resources"

48

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

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

Sathaye, Jayant

2013-01-01T23:59:59.000Z

49

Convective-scale Warn on Forecast: A Vision for 2020 David J. Stensrud1  

E-Print Network [OSTI]

NOAA/NWS/Storm Prediction Center, Norman, Oklahoma 5 NOAA/Earth System Research Laboratory, Boulder (NWS) issues warnings for severe thunderstorms, tornadoes, and flash floods since these phenomena forecast guidance. Since increasing severe thunderstorm, tornado, and flash flood warning lead times

Droegemeier, Kelvin K.

50

Annual Energy Outlook with Projections to 2025 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2005 Forecast Comparisons Table 32. Forecasts of annual average economic growth, 2003-2025 Printer Friendly Version Average annual percentage growth Forecast 2003-2009 2003-2014 2003-2025 AEO2004 3.5 3.2 3.0 AEO2005 Reference 3.4 3.3 3.1 Low growth 2.9 2.8 2.5 High growth 4.1 3.9 3.6 GII 3.4 3.2 3.1 OMB 3.6 NA NA CBO 3.5 3.1 NA OEF 3.5 3.5 NA Only one other organization—Global Insight, Incorporated (GII)—produces a comprehensive energy projection with a time horizon similar to that of AEO2005. Other organizations address one or more aspects of the energy markets. The most recent projection from GII, as well as other forecasts that concentrate on economic growth, international oil prices, energy

51

Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations  

Gasoline and Diesel Fuel Update (EIA)

AEO82 to AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 AEO82 to AEO2004 Total Energy Consumption 1.9 2.0 2.1 2.1 2.1 2.1 Total Petroleum Consumption 2.9 3.0 3.1 3.1 3.0 2.9 Total Natural Gas Consumption 7.3 7.1 7.1 6.7 6.4 6.5 Total Coal Consumption 3.1 3.3 3.5 3.6 3.7 3.8

52

Consensus Coal Production And Price Forecast For  

E-Print Network [OSTI]

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

Mohaghegh, Shahab

53

On Sequential Probability Forecasting  

E-Print Network [OSTI]

at the same time. [Probability, Statistics and Truth, MacMillan 1957. page 11] ... the collective "denotes a collective wherein the attribute of the single event is the number of points thrown. [Probability, StatisticsOn Sequential Probability Forecasting David A. Bessler 1 David A. Bessler Texas A&M University

McCarl, Bruce A.

54

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation by Susan H. Holte In this paper, the Office of Integrated Analysis and Forecasting (OIAF) of the Energy Information Administration (EIA) evaluates the projections published in the Annual Energy Outlook (AEO), (1) by comparing the projections from the Annual Energy Outlook 1982 through the Annual Energy Outlook 2001 with actual historical values. A set of major consumption, production, net import, price, economic, and carbon dioxide emissions variables are included in the evaluation, updating similar papers from previous years. These evaluations also present the reasons and rationales for significant differences. The Office of Integrated Analysis and Forecasting has been providing an

55

The Application of Improved Grey GM(1,1) Model in Power System Load Forecast  

Science Journals Connector (OSTI)

According to existing Grey prediction model GM (1,1) in the data fluctuation, mutation, turning under uncertainty such as the problem of poor prediction accuracy, this paper presents an original data sequence ...

Zhengyuan Jia; Zhou Fan; Chuancai Li

2012-01-01T23:59:59.000Z

56

Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe  

Science Journals Connector (OSTI)

Abstract This article combines and discusses three independent validations of global horizontal irradiance (GHI) multi-day forecast models that were conducted in the US, Canada and Europe. All forecast models are based directly or indirectly on numerical weather prediction (NWP). Two models are common to the three validation efforts the ECMWF global model and the GFS-driven WRF mesoscale model and allow general observations: (1) the GFS-based WRF- model forecasts do not perform as well as global forecast-based approaches such as ECMWF and (2) the simple averaging of models output tends to perform better than individual models.

Richard Perez; Elke Lorenz; Sophie Pelland; Mark Beauharnois; Glenn Van Knowe; Karl Hemker Jr.; Detlev Heinemann; Jan Remund; Stefan C. Mller; Wolfgang Traunmller; Gerald Steinmauer; David Pozo; Jose A. Ruiz-Arias; Vicente Lara-Fanego; Lourdes Ramirez-Santigosa; Martin Gaston-Romero; Luis M. Pomares

2013-01-01T23:59:59.000Z

57

BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts  

Science Journals Connector (OSTI)

Bayesian model averaging (BMA) probability quantitative precipitation forecast (PQPF) models were established by calibrating their parameters using 17-day ensemble forecasts of 24-h accumulated precipitation, and observations from 43 ...

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

58

1.1.1.1.1.1 AP 1.1.1.1.2.1  

E-Print Network [OSTI]

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

Ladkin, Peter B.

59

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

SciTech Connect (OSTI)

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

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

1997-01-07T23:59:59.000Z

60

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

E-Print Network [OSTI]

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

Tanaka, Jiro

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

Forecasting Uncertainty Related to Ramps of Wind Power Production Arthur Bossavy, Robin Girard - The continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power study. Key words : wind power forecast, ramps, phase er- rors, forecasts ensemble. 1 Introduction Most

Boyer, Edmond

62

Forecasting phenology under global warming  

Science Journals Connector (OSTI)

...Forrest Forecasting phenology under global warming Ines Ibanez 1 * Richard B. Primack...and site-specific responses to global warming. We found that for most species...climate change|East Asia, global warming|growing season, hierarchical...

2010-01-01T23:59:59.000Z

63

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

E-Print Network [OSTI]

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

Tanaka, Jiro

64

1.1.1.1. 1) [2,3].  

E-Print Network [OSTI]

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

Joo, Su-Chong

65

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

E-Print Network [OSTI]

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

Toronto, University of

66

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

E-Print Network [OSTI]

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

Tanaka, Jiro

67

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

E-Print Network [OSTI]

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

Tanaka, Jiro

68

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect (OSTI)

We validated one year of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, air pressure) over the entire planet for forecasts extending from zero hours into the future (an analysis) to 36 hours. Approximately 12,000 surface stations world-wide were included in this analysis. Root-Mean-Square- Errors (RMSE) increased as the forecast period increased from zero to 36 hours, but the initial RMSE were almost as large as the 36 hour forecast RMSE for all variables. Typical RMSE were 3 C for air temperature, 2-3mb for sea-level pressure, 3.5 C for dewpoint temperature and 2.5 m/s for wind speed. Approximately 20-40% of the GFS errors can be attributed to a lack of resolution of local features. We attribute the large initial RMSE for the zero hour forecasts to the inability of the GFS to resolve local terrain features that often dominate local weather conditions, e.g., mountain- valley circulations and sea and land breezes. Since the horizontal resolution of the GFS (about 1{sup o} of latitude and longitude) prevents it from simulating these locally-driven circulations, its performance will not improve until model resolution increases by a factor of 10 or more (from about 100 km to less than 10 km). Since this will not happen in the near future, an alternative for the near term to improve surface weather analyses and predictions for specific points in space and time would be implementation of a high-resolution, limited-area mesoscale atmospheric prediction model in regions of interest.

Werth, D.; Garrett, A.

2009-04-15T23:59:59.000Z

69

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

E-Print Network [OSTI]

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

Tanaka, Jiro

70

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Highlights Highlights World energy consumption is projected to increase by 57 percent from 2002 to 2025. Much of the growth in worldwide energy use in the IEO2005 reference case forecast is expected in the countries with emerging economies. Figure 1. World Marketed Energy Consumptiion by Region, 1970-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data In the International Energy Outlook 2005 (IEO2005) reference case, world marketed energy consumption is projected to increase on average by 2.0 percent per year over the 23-year forecast horizon from 2002 to 2025—slightly lower than the 2.2-percent average annual growth rate from 1970 to 2002. Worldwide, total energy use is projected to grow from 412 quadrillion British thermal units (Btu) in 2002 to 553 quadrillion Btu in

71

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

E-Print Network [OSTI]

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

Tanaka, Jiro

72

1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1  

E-Print Network [OSTI]

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

Martinis, John M.

73

FORECASTING THE RESPONSE OF COASTAL WETLANDS TO DECLINING3 WATER LEVELS AND ENVIRONMENTAL DISTURBANCES IN THE GREAT4  

E-Print Network [OSTI]

i 1 2 FORECASTING THE RESPONSE OF COASTAL WETLANDS TO DECLINING3 WATER LEVELS AND ENVIRONMENTALMaster University23 (Biology) Hamilton, Ontario24 TITLE: Forecasting the response of coastal wetlands to declining plants in Lake Ontario coastal36 wetlands while taking into account other factors such as urbanization

McMaster University

74

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)

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.

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

75

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network [OSTI]

1 FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY Rick Katz Institute for Study on Extremes · Emil Gumbel (1891 ­ 1966) -- Pioneer in application of statistics of extremes "Il est impossible que l'improbable n'arrive jamais." #12;3 OUTLINE (1) Motivation (2) Conventional Methods (3) Extreme

Katz, Richard

76

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network [OSTI]

1 FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY Rick Katz Institute for Study ON EXTREMES · Emil Gumbel (1891 ­ 1966) -- Pioneer in application of statistics of extremes "Il est impossible que l'improbable n'arrive jamais." #12;3 OUTLINE (1) Motivation (2) Conventional Methods (3) Extreme

Katz, Richard

77

Operational Rainfall and Flow Forecasting for the Panama Canal Watershed  

Science Journals Connector (OSTI)

An integrated hydrometeorological system was designed for the utilization of data from various sensors in the 3300 km2 Panama Canal Watershed for the purpose of producing ... forecasts. These forecasts are used b...

Konstantine P. Georgakakos; Jason A. Sperfslage

2005-01-01T23:59:59.000Z

78

Building Energy Software Tools Directory: Degree Day Forecasts  

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

Forecasts Forecasts Degree Day Forecasts example chart Quick and easy web-based tool that provides free 14-day ahead degree day forecasts for 1,200 stations in the U.S. and Canada. Degree Day Forecasts charts show this year, last year and three-year average. Historical degree day charts and energy usage forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700. Expertise Required No special expertise required. Simple to use. Users Over 1,000 weekly users. Audience Anyone who needs degree day forecasts (next 14 days) for the U.S. and Canada. Input Select a weather station (1,200 available) and balance point temperature. Output Charts show (1) degree day (heating and cooling) forecasts for the next 14

79

1.1. : 1.2.  

E-Print Network [OSTI]

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

Borissova, Daniela

80

Microsoft Word - fourthqmetric 2.doc  

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

United States Government or the University of California, and shall not be used for advertising or product endorsement purposes. i CONTENTS 1. INTRODUCTION 1 2. FORECASTS 1 a....

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving1 A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION. The very first results show an improvement brought by this approach. 1. INTRODUCTION Solar radiation

Boyer, Edmond

82

UHERO FORECAST PROJECT DECEMBER 5, 2014  

E-Print Network [OSTI]

deficits. After solid 3% growth this year, real GDP growth will recede a bit for the next two years. New household spending. Real GDP will firm above 3% in 2015. · The pace of growth in China has continuedUHERO FORECAST PROJECT DECEMBER 5, 2014 Asia-Pacific Forecast: Press Version: Embargoed Until 2

83

Operational Forecasts of Cloud Cover and Water Vapour  

E-Print Network [OSTI]

of the forecast programme, which involved the additional use of 10.7 µm GOES-8 satellite data and surface weather cirrus cloud cover 15 5. A satellite-derived extinction parameter 17 5.1 Background 17 5.2 Previous work 20 5.3 Continued development of a satellite-derived 22 extinction parameter 6. Suggestions

84

Measuring forecast skill: is it real skill or  

E-Print Network [OSTI]

samples, then many verification metrics will credit a forecast with extra skill it doesn't deserve islands, zero meteorologists Imagine a planet with a global ocean and two isolated islands. Weather three metrics... (1) Brier Skill Score (2) Relative Operating Characteristic (3) Equitable Threat Score

Hamill, Tom

85

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))

1992-02-01T23:59:59.000Z

86

A model for Long-term Industrial Energy Forecasting (LIEF)  

SciTech Connect (OSTI)

The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

1992-02-01T23:59:59.000Z

87

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

Forecasting wave height probabilities with numerical weather prediction models Mark S. Roulstona; Numerical weather prediction 1. Introduction Wave forecasting is now an integral part of operational weather methods for generating such forecasts from numerical model output from the European Centre for Medium

Stevenson, Paul

88

Building Energy Software Tools Directory: Energy Usage Forecasts  

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

Energy Usage Forecasts Energy Usage Forecasts Energy Usage Forecasts Quick and easy web-based tool that provides free 14-day ahead energy usage forecasts based on the degree day forecasts for 1,200 stations in the U.S. and Canada. The user enters the daily non-weather base load and the usage per degree day weather factor; the tool applies the degree day forecast and displays the total energy usage forecast. Helpful FAQs explain the process and describe various options for the calculation of the base load and weather factor. Historical degree day reports and 14-day ahead degree day forecasts are available from the same site. Keywords degree days, historical weather, mean daily temperature, load calculation, energy simulation Validation/Testing Degree day data provided by AccuWeather.com, updated daily at 0700.

89

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

SciTech Connect (OSTI)

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

Not Available

1993-12-01T23:59:59.000Z

90

1.1. : 1.2.  

E-Print Network [OSTI]

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

Borissova, Daniela

91

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Table 1. Comparison of Absolute Percent Errors for Present and Current AEO Forecast Evaluations Average Absolute Percent Error Variable AEO82 to AEO98 AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 AEO82 to AEO2003 Consumption Total Energy Consumption 1.7 1.7 1.8 1.9 1.9 2.1 Total Petroleum Consumption 2.9 2.8 2.9 3.0 2.9 2.9 Total Natural Gas Consumption 5.7 5.6 5.6 5.5 5.5 6.5 Total Coal Consumption 3.0 3.2 3.3 3.5 3.6 3.7 Total Electricity Sales 1.7 1.8 1.9 2.4 2.5 2.4 Production Crude Oil Production 4.3 4.5 4.5 4.5 4.5 4.7 Natural Gas Production 4.8 4.7 4.6 4.6 4.4 4.4 Coal Production 3.6 3.6 3.5 3.7 3.6 3.8 Imports and Exports Net Petroleum Imports 9.5 8.8 8.4 7.9 7.4 7.5 Net Natural Gas Imports 16.7 16.0 15.9 15.8 15.8 15.4

92

Wind Power Forecasting  

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

Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email List Self Supplied Balancing Reserves Dynamic...

93

Wind Power Forecasting  

Science Journals Connector (OSTI)

The National Center for Atmospheric Research (NCAR) has configured a Wind Power Forecasting System for Xcel Energy that integrates high resolution and ensemble...

Sue Ellen Haupt; William P. Mahoney; Keith Parks

2014-01-01T23:59:59.000Z

94

Energy Demand Forecasting  

Science Journals Connector (OSTI)

This chapter presents alternative approaches used in forecasting energy demand and discusses their pros and cons. It... Chaps. 3 and 4 ...

S. C. Bhattacharyya

2011-01-01T23:59:59.000Z

95

Improving Inventory Control Using Forecasting  

E-Print Network [OSTI]

This project studied and analyzed Electronic Controls, Inc.s forecasting process for three high-demand products. In addition, alternative forecasting methods were developed to compare to the current forecast method. The ...

Balandran, Juan

2005-12-16T23:59:59.000Z

96

18 Bureau of Meteorology Annual Report 201314 Hazards, warnings and forecasts  

E-Print Network [OSTI]

and numerical prediction models. #12;19Bureau of Meteorology Annual Report 2013­14 2 Performance Performance programs: · Weather forecasting services; · Flood forecasting and warning services; · Hazard prediction, Warnings and Forecasts portfolio provides a range of forecast and warning services covering weather, ocean

Greenslade, Diana

97

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect (OSTI)

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.

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

98

Project Cost Escalation Standards, document IEAB 2007-2 Page 1 Independent Economic Analysis Board  

E-Print Network [OSTI]

Project Cost Escalation Standards, document IEAB 2007-2 Page 1 Independent Economic Analysis Board Project Cost Escalation Standards Task 115 Council document IEAB 2007-2 March 30, 2007 Summary Project;Project Cost Escalation Standards, document IEAB 2007-2 Page 2 Third, managers are often asked to forecast

99

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

E-Print Network [OSTI]

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

Varela, Carlos

100

1 Introduction 1 1.1 Visual Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  

E-Print Network [OSTI]

Contents 1 Introduction 1 1.1 Visual Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.2 Texture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.1.3 Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.1.4 Stereo

Sebe, Nicu

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

ANL Wind Power Forecasting and Electricity Markets ANL Wind Power Forecasting and Electricity Markets Jump to: navigation, search Logo: Wind Power Forecasting and Electricity Markets Name Wind Power Forecasting and Electricity Markets Agency/Company /Organization Argonne National Laboratory Partner Institute for Systems and Computer Engineering of Porto (INESC Porto) in Portugal, Midwest Independent System Operator and Horizon Wind Energy LLC, funded by U.S. Department of Energy Sector Energy Focus Area Wind Topics Pathways analysis, Technology characterizations Resource Type Software/modeling tools Website http://www.dis.anl.gov/project References Argonne National Laboratory: Wind Power Forecasting and Electricity Markets[1] Abstract To improve wind power forecasting and its use in power system and electricity market operations Argonne National Laboratory has assembled a team of experts in wind power forecasting, electricity market modeling, wind farm development, and power system operations.

102

Valuing Climate Forecast Information  

Science Journals Connector (OSTI)

The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. ...

Steven T. Sonka; James W. Mjelde; Peter J. Lamb; Steven E. Hollinger; Bruce L. Dixon

1987-09-01T23:59:59.000Z

103

Comparing Forecast Skill  

Science Journals Connector (OSTI)

A basic question in forecasting is whether one prediction system is more skillful than another. Some commonly used statistical significance tests cannot answer this question correctly if the skills are computed on a common period or using a common ...

Timothy DelSole; Michael K. Tippett

2014-12-01T23:59:59.000Z

104

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Analysis Papers > Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Download Adobe Acrobat Reader Printer friendly version on our site are provided in Adobe Acrobat Spreadsheets are provided in Excel Actual vs. Forecasts Formats Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF Table 12. World Oil Prices Excel, PDF Table 13. Natural Gas Wellhead Prices

105

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Modeling and Analysis Papers> Annual Energy Outlook Forecast Evaluation>Tables Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) MS Excel Viewer PDF (Acrobat Reader required Download Acrobat Reader ) Adobe Acrobat Reader Logo Table 2. Total Energy Consumption Excel, PDF Table 3. Total Petroleum Consumption Excel, PDF Table 4. Total Natural Gas Consumption Excel, PDF Table 5. Total Coal Consumption Excel, PDF Table 6. Total Electricity Sales Excel, PDF Table 7. Crude Oil Production Excel, PDF Table 8. Natural Gas Production Excel, PDF Table 9. Coal Production Excel, PDF Table 10. Net Petroleum Imports Excel, PDF Table 11. Net Natural Gas Imports Excel, PDF

106

Annual Energy Outlook 2001 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Economic Growth World Oil Prices Total Energy Consumption Residential and Commercial Sectors Industrial Sector Transportation Sector Electricity Natural Gas Petroleum Coal Three other organizations—Standard & Poor’s DRI (DRI), the WEFA Group (WEFA), and the Gas Research Institute (GRI) [95]—also produce comprehensive energy projections with a time horizon similar to that of AEO2001. The most recent projections from those organizations (DRI, Spring/Summer 2000; WEFA, 1st Quarter 2000; GRI, January 2000), as well as other forecasts that concentrate on petroleum, natural gas, and international oil markets, are compared here with the AEO2001 projections. Economic Growth Differences in long-run economic forecasts can be traced primarily to

107

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4  

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

Current Forecast: December 10, 2013; Previous Forecast: November 13, 2013 Current Forecast: December 10, 2013; Previous Forecast: November 13, 2013 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2011 2012 2013 2014 2011-2012 2012-2013 2013-2014 U.S. Energy Supply U.S. Crude Oil Production (million barrels per day) Current 6.22 6.29 6.42 7.02 7.11 7.29 7.61 7.97 8.26 8.45 8.57 8.86 5.65 6.49 7.50 8.54 14.8% 15.6% 13.8% Previous 6.22 6.30 6.43 7.04 7.13 7.30 7.60 7.91 8.22 8.40 8.52 8.80 5.65 6.50 7.49 8.49 15.0% 15.2% 13.3% Percent Change 0.0% -0.1% -0.2% -0.2% -0.3% -0.1% 0.1% 0.7% 0.5% 0.5% 0.6% 0.6% 0.0% -0.1% 0.1% 0.6% U.S. Dry Natural Gas Production (billion cubic feet per day) Current 65.40 65.49 65.76 66.34 65.78 66.50 67.11 67.88 67.99 67.74 67.37 67.70 62.74 65.75 66.82 67.70 4.8% 1.6% 1.3% Previous 65.40 65.49 65.76 66.34 65.78 66.50 67.11 67.30 67.47 67.41 67.04 67.37 62.74 65.75 66.68 67.32

108

Effect of Typhoon Songda (2004) on Remote Heavy Rainfall in Japan Yongqing Wang1,2  

E-Print Network [OSTI]

& Technology, Nanjing, China 2 International Pacific Research Center and Department of Meteorology, University center. The Advanced Weather Research and Forecast (WRF_ARW) model was used to investigate the possible event studied. #12;2 1. Introduction Tropical cyclones (TCs, or typhoons in the western North Pacific

Wang, Yuqing

109

Analysis and forecast improvements from simulated satellite water vapor profiles and rainfall using a global data assimilation system  

SciTech Connect (OSTI)

The potential improvements of analyses and forecasts from the use of satellite-observed rainfall and water vapor measurements from the Defense Meteorological Satellite Program Sensor Microwave (SSM) T-1 and T-2 instruments are investigated in a series of observing system simulation experiments using the Air Force Phillips Laboratory (formerly Air Force Geophysics Laboratory) data assimilation system. Simulated SSM radiances are used directly in a radiance retrieval step following the conventional optimum interpolation analysis. Simulated rainfall rates in the tropics are used in a moist initialization procedure to improve the initial specification of divergence, moisture, and temperature. Results show improved analyses and forecasts of relative humidity and winds compared to the control experiment in the tropics and the Southern Hemisphere. Forecast improvements are generally restricted to the first 1-3 days of the forecast. 27 refs., 11 figs.

Nehrkorn, T.; Hoffman, R.N.; Louis, J.F.; Isaacs, R.G.; Moncet, J.L. (Atmospheric and Environmental Research, Inc., Cambridge, MA (United States))

1993-10-01T23:59:59.000Z

110

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

E-Print Network [OSTI]

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

Solka, Jeff

111

Generating Spatio-Temporal Descriptions in Pollen Forecasts Ross Turner, Somayajulu Sripada and Ehud Reiter  

E-Print Network [OSTI]

Date AreaID Value 27/06/2005 1 (North) 6 27/06/2005 2 (North West) 5 27/06/2005 3 (Central) 5 27/06/2005 4Generating Spatio-Temporal Descriptions in Pollen Forecasts Ross Turner, Somayajulu Sripada al., 1994) and MultiMeteo (Coch, 1998). 2 Knowledge Acquisition Our knowledge acquisition activities

112

U238 (1) (2)  

E-Print Network [OSTI]

;Neodymium 3.51 x 103 9.47 x 101 2.65 x 10-1 Promethium 1.10 x 102 1.00 x 105 9.17 x 101 Samarium 6.96 x 102

Hong, Deog Ki

113

IEEE Trans. on Components and Packaging Technologies, Dec. 2000, pp. 707-717 1 Electronic Part Life Cycle Concepts and Obsolescence Forecasting  

E-Print Network [OSTI]

Cycle Concepts and Obsolescence Forecasting Rajeev Solomon, Peter Sandborn, and Michael Pecht Abstract ­ Obsolescence of electronic parts is a major contributor to the life cycle cost of long- field life systems such as avionics. A methodology to forecast life cycles of electronic parts is presented, in which both years

Sandborn, Peter

114

Sandia National Laboratories: solar forecasting  

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

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

115

Demonstrating the Validity of a Wildfire Craig C. Douglas1,2  

E-Print Network [OSTI]

Demonstrating the Validity of a Wildfire DDDAS Craig C. Douglas1,2 , Jonathan D. Beezley4 , Janice, Boulder, CO 80307-3000, USA. janicec@ucar.edu. 4 University of Colorado at Denver and Health Sciences a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real

Mandel, Jan

116

A suite of metrics for assessing the performance of solar power forecasting  

Science Journals Connector (OSTI)

Abstract Forecasting solar energy generation is a challenging task because of the variety of solar power systems and weather regimes encountered. Inaccurate forecasts can result in substantial economic losses and power system reliability issues. One of the key challenges is the unavailability of a consistent and robust set of metrics to measure the accuracy of a solar forecast. 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, and applications) that were developed as part of the U.S. Department of Energy SunShot Initiatives efforts to improve the accuracy of solar forecasting. 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, sensitivity analysis, and nonparametric statistical testing methods. The three types of forecasting improvements are (i) uniform forecasting improvements when there is not a ramp, (ii) ramp forecasting magnitude improvements, and (iii) ramp forecasting threshold changes. Day-ahead and 1-hour-ahead forecasts for both simulated and actual solar power plants are analyzed. The results show that the proposed metrics can efficiently evaluate the quality of solar forecasts and assess the economic and reliability impacts of improved solar forecasting. Sensitivity analysis results show that (i) all proposed metrics are suitable to show the changes in the accuracy of solar forecasts with uniform forecasting improvements, and (ii) the metrics of skewness, kurtosis, and Rnyi entropy are specifically suitable to show the changes in the accuracy of solar forecasts with ramp forecasting improvements and a ramp forecasting threshold.

Jie Zhang; Anthony Florita; Bri-Mathias Hodge; Siyuan Lu; Hendrik F. Hamann; Venkat Banunarayanan; Anna M. Brockway

2015-01-01T23:59:59.000Z

117

Univariate time-series forecasting of monthly peak demand of electricity in northern India  

Science Journals Connector (OSTI)

This study forecasts the monthly peak demand of electricity in the northern region of India using univariate time-series techniques namely Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) and Holt-Winters Multiplicative Exponential Smoothing (ES) for seasonally unadjusted monthly data spanning from April 2000 to February 2007. In-sample forecasting reveals that the MSARIMA model outperforms the ES model in terms of lower root mean square error, mean absolute error and mean absolute percent error criteria. It has been found that ARIMA (2, 0, 0) (0, 1, 1)12 is the best fitted model to explain the monthly peak demand of electricity, which has been used to forecast the monthly peak demand of electricity in northern India, 15 months ahead from February 2007. This will help Northern Regional Load Dispatch Centre to make necessary arrangements a priori to meet the future peak demand.

Sajal Ghosh

2008-01-01T23:59:59.000Z

118

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

SciTech Connect (OSTI)

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

Valero, O.J.

1996-10-03T23:59:59.000Z

119

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

120

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by Esmeralda Sánchez The Office of Integrated Analysis and Forecasting has produced an annual evaluation of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and the most recent historical year of data. The Forecast Evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute forecast errors for each of the major variables for AEO82 through AEO2003. The average absolute forecast error, which for the purpose of this report will also be referred to simply as "average error" or "forecast error", is computed as the simple mean, or average, of all the absolute values of the percent errors,

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

Annual Energy Outlook Forecast Evaluation Annual Energy Outlook Forecast Evaluation Actual vs. Forecasts Available formats Excel (.xls) for printable spreadsheet data (Microsoft Excel required) PDF (Acrobat Reader required) Table 2. Total Energy Consumption HTML, Excel, PDF Table 3. Total Petroleum Consumption HTML, Excel, PDF Table 4. Total Natural Gas Consumption HTML, Excel, PDF Table 5. Total Coal Consumption HTML, Excel, PDF Table 6. Total Electricity Sales HTML, Excel, PDF Table 7. Crude Oil Production HTML, Excel, PDF Table 8. Natural Gas Production HTML, Excel, PDF Table 9. Coal Production HTML, Excel, PDF Table 10. Net Petroleum Imports HTML, Excel, PDF Table 11. Net Natural Gas Imports HTML, Excel, PDF Table 12. Net Coal Exports HTML, Excel, PDF Table 13. World Oil Prices HTML, Excel, PDF

122

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

SciTech Connect (OSTI)

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.

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

2007-06-01T23:59:59.000Z

123

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

E-Print Network [OSTI]

1 Next Generation Short-Term Forecasting of Wind Power ­ Overview of the ANEMOS Project. G outperform current state-of-the-art methods, for onshore and offshore wind power forecasting. Advanced forecasts for the power system management and market integration of wind power. Keywords: Wind power, short

Boyer, Edmond

124

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

E-Print Network [OSTI]

, and medium term forecasts (up to seven days ahead) from numerical weather prediction models [1]. Forecasts radiation forecasting. One approach relies on numerical weather prediction (NWP) models which can be global modeling of the atmosphere. NWP models cannot, at this stage of their development, predict the exact

Perez, Richard R.

125

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

E-Print Network [OSTI]

Ensemble-based air quality forecasts: A multimodel approach applied to ozone Vivien Mallet1., and B. Sportisse (2006), Ensemble-based air quality forecasts: A multimodel approach applied to ozone, J, the uncertainty in chem- istry transport models is a major limitation of air quality forecasting. The source

Boyer, Edmond

126

Do quantitative decadal forecasts from GCMs provide  

E-Print Network [OSTI]

' · Empirical models quantify our ability to predict without knowing the laws of physics · Climatology skill' model? 2. Dynamic climatology (DC) is a more appropriate benchmark for near- term (initialised) climate forecasts · A conditional climatology, initialised at launch and built from the historical archive

Stevenson, Paul

127

FORECAST OF VACANCIES Until end of 2016  

E-Print Network [OSTI]

#12;FORECAST OF VACANCIES Until end of 2016 (Issue No. 22) #12;Page 2 OVERVIEW OF BASIC REQUIREMENTS FOR PROFESSIONAL VACANCIES IN THE IAEA Education, Experience and Skills: Professional staff the team of professionals. Second half 2015 VACANCY GRADE REQUIREMENTS / ROLE EXPECTED DATE OF VACANCY

128

Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models  

Science Journals Connector (OSTI)

Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in AprilMay provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 23 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in AprilMay that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3tha?1 in June and 0.4tha?1 in April, while performance of three approaches for 2011 was almost the same (0.50.6tha?1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 23 months prior to harvest, while providing minimum requirements to input datasets.

Felix Kogan; Nataliia Kussul; Tatiana Adamenko; Sergii Skakun; Oleksii Kravchenko; Oleksii Kryvobok; Andrii Shelestov; Andrii Kolotii; Olga Kussul; Alla Lavrenyuk

2013-01-01T23:59:59.000Z

129

Price forecasting for notebook computers.  

E-Print Network [OSTI]

??This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a (more)

Rutherford, Derek Paul

2012-01-01T23:59:59.000Z

130

Ensemble Forecasts and their Verification  

E-Print Network [OSTI]

· Ensemble forecast verification ­ Performance metrics: Brier Score, CRPSS · New concepts and developments of weather Sources: Insufficient spatial resolution, truncation errors in the dynamical equations

Maryland at College Park, University of

131

Phase 1 -- 2  

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

2 2 Revised 8/7/02 " "Sample Statement of Work - Standard Service Offerings for Contractor-Identified Project" "Task #","Task Title","Work Scope","Deliverable","Agency Requirements" " " "Phase Two - Initial Project Development" "2-1","DO RFP Development - Direct Support","Based upon interviews Agency/site staff and consultation support, FEMP Services will prepare DO RFP for Agency/site. FEMP Services will provide onsite or telecon review of draft DO RFP with agency staff. FEMP Services will prepare 2nd draft DO RFP based on telecon and written agency review comments and recommendations. ","Draft DO RFP Document. On-site review of draft DO RFP.

132

Phase 1 --2  

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

2 2 Rev 4-01-05 " "Statement of Work - Standard Service Offerings for Contractor-Identified Project at (insert project site)" "Task #","Task Title","Work Scope","Deliverable","Agency Requirements" " " "Phase Two - Initial Project Development" "2-1","DO RFP Development - Direct Support","Based upon interviews Agency/site staff and consultation support, FEMP Services will prepare DO RFP for Agency/site. FEMP Services will provide onsite or telecon review of draft DO RFP with agency staff. FEMP Services will prepare 2nd draft DO RFP based on telecon and written agency review comments and recommendations. ","Draft DO RFP Document. On-site review of draft DO RFP.

133

IPSJ SIG Technical Report 1 1 1 1 2 2 1 1  

E-Print Network [OSTI]

OSCAR OSCAR MTG 4 4 1 1 c 2012 Information Processing Society of Japan 3 Vol.2012-ARC-201 No.22 2012 Processing Society of Japan 1 Vol.2012-ARC-201 No.22 2012/8/2 #12;IPSJ SIG Technical Report 1 C Simulink[1 OSCAR 3. OSCAR OSCAR c 2012 Information Processing Society of Japan 2 Vol.2012-ARC-201 No.22 2012

Kasahara, Hironori

134

BEAMLINE 2-1  

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

1 1 CURRENT STATUS: Open SUPPORTED TECHNIQUES: Powder diffraction Thin film diffraction MAIN SCIENTIFIC DISCIPLINES: Materials / Environmental % TIME GENERAL USE: 100% SCHEDULING: Proposal Submittal and Scheduling Procedures Current SPEAR and Beam Line Schedules SOURCE: 1.3 Tesla Bend Magnet BEAM LINE SPECIFICATIONS: energy range resolution DE/E spot size flux angular acceptance focused 4000-14500 eV ~5 x 10-4 .20 x 0.45 mm 1.5 mrad OPTICS: Bent cylinder, single-crystal Si, Rh-coated mirror Radii: 2900 m (adjustable) x 52 mm Mean angle of incidence: 4.2 milliradians Cut off energy: 14.5 keV, Magnification: 1.1 MONOCHROMATOR: Si(111), Si(220) Si(400), upward reflecting, double-crystal Monochromator Crystal Glitch Library Crystal changes need to be scheduled and coordinated in advance with BL

135

Probabilistic manpower forecasting  

E-Print Network [OSTI]

- ing E. Results- Probabilistic Forecasting . 26 27 Z8 29 31 35 36 38 39 IV. CONCLUSIONS. V. GLOSSARY 42 44 APPENDICES REFERENCES 50 70 LIST OF TABLES Table Page Outline of Job-Probability Matrix Job-Probability Matrix. Possible... Outcomes of Job A Possible Outcomes of Jobs A and B 10 Possible Outcomes of Jobs A, B and C II LIST GF FIGURES Figure Page Binary Representation of Numbers 0 Through 7 12 First Cumulative Probability Table 14 3. Graph of Cumulative Probability vs...

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

136

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Journals Connector (OSTI)

This paper reports on the development of a diagnostic approach that can be used to examine the sources of numerical model forecast error that contribute to degraded tropical cyclone (TC) motion forecasts. Tropical cyclone motion forecasts depend ...

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

137

Genomedata Documentation Release 1.2.2  

E-Print Network [OSTI]

Genomedata Documentation Release 1.2.2 Michael M. Hoffman August 12, 2010 #12;#12;CONTENTS 1 Genomedata 1.2 documentation 3 1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 Indices and tables 17 Python Module Index 19 Index 21 i #12;ii #12;Genomedata Documentation

Noble, William Stafford

138

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

E-Print Network [OSTI]

. . . . . . . . . . . . . . . . . . . . . . . . . 9 4.5 . . . . . . . . . . . . . . . . . . . . 13 4.5.1 Arduino . . . . . . . . . . . . . . . . . 13.5 . . . . . . . . . . . . . . . . . . . . . . . . 15 4.6 Arduino ( :Arduino :Arduino ) . . . . . 15 4.043 0.013 OSHR5161A-QR OS5RKA5B61P 5mm LED 4.2 LED LED PC Arduino Arduino Arduino Mega2560 OS5RKA5B61P

Tanaka, Jiro

139

Online short-term solar power forecasting  

SciTech Connect (OSTI)

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

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

2009-10-15T23:59:59.000Z

140

Project Profile: Forecasting and Influencing Technological Progress...  

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

Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Logos of the University of...

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Forecasting with adaptive extended exponential smoothing  

Science Journals Connector (OSTI)

Much of product level forecasting is based upon time series techniques. However, traditional time series forecasting techniques have offered either smoothing constant adaptability or consideration of various t...

John T. Mentzer Ph.D.

142

Electricity price forecasting in a grid environment.  

E-Print Network [OSTI]

??Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. Market participants rely on price forecasts to decide their bidding strategies, allocate (more)

Li, Guang, 1974-

2007-01-01T23:59:59.000Z

143

Energy Department Forecasts Geothermal Achievements in 2015 ...  

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

Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in...

144

A methodology for forecasting carbon dioxide flooding performance  

E-Print Network [OSTI]

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

Marroquin Cabrera, Juan Carlos

2012-06-07T23:59:59.000Z

145

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Title of Paper Annual Energy Outlook Forecast Evaluation Title of Paper Annual Energy Outlook Forecast Evaluation by Susan H. Holte OIAF has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Natural gas has generally been the fuel with the least accurate forecasts of consumption, production, and prices. Natural gas was the last fossil fuel to be deregulated following the strong regulation of energy markets in the 1970s and early 1980s. Even after deregulation, the behavior

146

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

E-Print Network [OSTI]

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

Kemner, Ken

147

Notes for Session 5, Forcasting, Summer School 2007 T.Seidenfeld 1 Notes for Session 5 Forecasting, wrong and right!  

E-Print Network [OSTI]

point the Atlantic Ocean is deeper than the Indian Ocean. 8. At its deepest point the Indian Ocean.Seidenfeld 2 6. At its deepest point the Pacific Ocean is deeper than the Atlantic Ocean. 7. At its deepest is deeper than the Artic Ocean. 9. At its deepest point the Artic Ocean is deeper than the Mediterrian Sea

Spirtes, Peter

148

Notes for Session 5, Forcasting, Summer School 2009 T.Seidenfeld 1 Notes for Session 5 Forecasting, wrong and right!  

E-Print Network [OSTI]

point the Atlantic Ocean is deeper than the Indian Ocean. 8. At its deepest point the Indian Ocean.Seidenfeld 2 6. At its deepest point the Pacific Ocean is deeper than the Atlantic Ocean. 7. At its deepest is deeper than the Artic Ocean. 9. At its deepest point the Artic Ocean is deeper than the Mediterrian Sea

Spirtes, Peter

149

Page 1 of 2  

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

~ ~ Page 1 of 2 1) This memorandum provides reminders on the proper use of non-DOE contracting vehicles. including Economy Act transactions. Government-wide Acquisition Contracts. and Federal Supply Schedules. The recent discovery of improper use of such contracting vehicles has led to investigations ,:>f procurement activities at the General Services Administration and Department of Defense. Contracting Officers are cautioned to be diligent in the use of other agencies' contracting vehicles. 2) This memorandwn provides an overview of a Biobased Products Procurement Preference Program being developed as a result of the F8ml Security and Rural Development Act of 2002 and a recent rulemaking issued by the U.S. Department of Agriculture. It will highlight

150

Measuring the forecasting accuracy of models: evidence from industrialised countries  

Science Journals Connector (OSTI)

This paper uses the approach suggested by Akrigay (1989), Tse and Tung (1992) and Dimson and Marsh (1990) to examine the forecasting accuracy of stock price index models for industrialised markets. The focus of this paper is to compare the Mean Absolute Percentage Error (MAPE) of three models, that is, the Random Walk model, the Single Exponential Smoothing model and the Conditional Heteroskedastic model with the MAPE of the benchmark Naive Forecast 1 case. We do not evidence that a single model to provide better forecasting accuracy results compared to other models.

Athanasios Koulakiotis; Apostolos Dasilas

2009-01-01T23:59:59.000Z

151

Graduates 0 2 3 2 2 1 2 2 1 1 2 Percent of Graduates with  

E-Print Network [OSTI]

D Completions and Placement, Ten Year Trend 2002-2003 to 2011-2012 French and Italian Placement Category As of 4/2/2013 #12;12 Number of Grads with Placement Info French and Italian, PhD Graduates First First Placement Category by Broad Field Category 77% 14% 18% 60% 11% 68% 36% 18% 3% 13% 42% 11% 3% 4% 7

Grzybowski, Bartosz A.

152

QUARTERLY ECONOMIC COMMENTARY Vol 30 No 2  

E-Print Network [OSTI]

' was not present in the UK and was present in three key sectors: other services, business services & real estate to forecast weaker Scottish growth in 2005 compared to 2004. For our central forecast, we are projecting Scottish GDP growth of 1.8% in 2005, 1.8% in 2006 and 2% in 2007. On the basis of the present published

Mottram, Nigel

153

Correcting and combining time series forecasters  

Science Journals Connector (OSTI)

Combined forecasters have been in the vanguard of stochastic time series modeling. In this way it has been usual to suppose that each single model generates a residual or prediction error like a white noise. However, mostly because of disturbances not ... Keywords: Artificial neural networks hybrid systems, Linear combination of forecasts, Maximum likelihood estimation, Time series forecasters, Unbiased forecasters

Paulo Renato A. Firmino; Paulo S. G. De Mattos Neto; Tiago A. E. Ferreira

2014-02-01T23:59:59.000Z

154

NOAA Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps  

E-Print Network [OSTI]

Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12;Bay Harmful Algal Bloom Operational Forecast System Southwest Florida Forecast Region Maps 0 5 102.5 Miles #12 N Collier N Charlotte S Charlotte NOAA Harmful Algal Bloom Operational Forecast System Southwest

155

1 Introduction. 1 2 Generalities. 5  

E-Print Network [OSTI]

;1 INTRODUCTION. 1 1 Introduction. Let X be a scheme. Then a cycle on X is a formal linear combination of points. . . . . . . . . . . . . 5 2.2 Universally equidimensional closed subschemes. . . . . . . . . 8 2.3 Cycles on Noetherian schemes. . . . . . . . . . . . . . . . . . . 12 3 Relative cycles. 15 3.1 Relative cycles

156

Prediction of Indian summer monsoon onset using dynamical sub-seasonal forecasts: effects of realistic initialization of the atmosphere  

Science Journals Connector (OSTI)

Ensembles of retrospective 2-months dynamical forecasts initiated May 1st are used to predict the onset of the Indian Summer Monsoon (ISM) for the period 1989-2005. The Sub-Seasonal Predictions (SSPs) are based on a Coupled General Circulation ...

Andrea Alessandri; Andrea Borrelli; Annalisa Cherchi; Stefano Materia; Antonio Navarra; June-Yi Lee; Bin Wang

157

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

Forecast Energy Forecast Energy Jump to: navigation, search Name Forecast Energy Address 2320 Marinship Way, Suite 300 Place Sausalito, California Zip 94965 Sector Services Product Intelligent Monitoring and Forecasting Services Year founded 2010 Number of employees 11-50 Company Type For profit Website http://www.forecastenergy.net Coordinates 37.865647°, -122.496315° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":37.865647,"lon":-122.496315,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

158

Price forecasting for notebook computers  

E-Print Network [OSTI]

This paper proposes a four-step approach that uses statistical regression to forecast notebook computer prices. Notebook computer price is related to constituent features over a series of time periods, and the rates of change in the influence...

Rutherford, Derek Paul

2012-06-07T23:59:59.000Z

159

Demand Forecasting of New Products  

E-Print Network [OSTI]

Keeping Unit or SKU) employing attribute analysis techniques. The objective of this thesis is to improve Abstract This thesis is a study into the demand forecasting of new products (also referred to as Stock

Sun, Yu

160

Exponential smoothing with covariates applied to electricity demand forecast  

Science Journals Connector (OSTI)

Exponential smoothing methods are widely used as forecasting techniques in industry and business. Their usual formulation, however, does not allow covariates to be used for introducing extra information into the forecasting process. In this paper, we analyse an extension of the exponential smoothing formulation that allows the use of covariates and the joint estimation of all the unknowns in the model, which improves the forecasting results. The whole procedure is detailed with a real example on forecasting the daily demand for electricity in Spain. The time series of daily electricity demand contains two seasonal patterns: here the within-week seasonal cycle is modelled as usual in exponential smoothing, while the within-year cycle is modelled using covariates, specifically two harmonic explanatory variables. Calendar effects, such as national and local holidays and vacation periods, are also introduced using covariates. [Received 28 September 2010; Revised 6 March 2011, 2 October 2011; Accepted 16 October 2011

José D. Bermúdez

2013-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect (OSTI)

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

Yoo, Wucherl; Sim, Alex

2014-07-07T23:59:59.000Z

163

1 Mo 1 Do 1 Sa 1 Di 1 Fr 1 Fr 1 Mo 1 Mi 1 Sa 1 Mo 1 Do 1 So 2 Di 2 Fr 2 So 2 Mi 2 Sa 2 Sa 2 Di 2 Do 2 So 2 Di 2 Fr 2 Mo  

E-Print Network [OSTI]

1 Mo 1 Do 1 Sa 1 Di 1 Fr 1 Fr 1 Mo 1 Mi 1 Sa 1 Mo 1 Do 1 So 2 Di 2 Fr 2 So 2 Mi 2 Sa 2 Sa 2 Di 2 Do 2 So 2 Di 2 Fr 2 Mo 3 Mi 3 Sa 3 Mo 3 Do 3 So 3 So 3 Mi 3 Fr 3 Mo 3 Mi 3 Sa 3 Di 4 Do 4 So 4 Di 4 Fr 4 Mo 4 Mo 4 Do 4 Sa 4 Di 4 Do P StAU4 So 4 Mi 5 Fr 5 Mo 5 Mi 5 Sa 5 Di 5 Di 5 Fr 5 So 5 Mi 5 Fr 5 Mo

Mayberry, Marty

164

Voluntary Green Power Market Forecast through 2015  

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

158 158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 * www.nrel.gov NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Operated by the Alliance for Sustainable Energy, LLC Contract No. DE-AC36-08-GO28308 Technical Report NREL/TP-6A2-48158 May 2010 Voluntary Green Power Market Forecast through 2015 Lori Bird National Renewable Energy Laboratory Ed Holt Ed Holt & Associates, Inc. Jenny Sumner and Claire Kreycik National Renewable Energy Laboratory

165

1,2 2,1 JST ERATO1  

E-Print Network [OSTI]

(Fi+1) (i) STr(Fi)Ui+1 Tr(Fi+1):= Tr(Fi+1) {S} (ii) S{e} eUi+1 Tr(Fi+1):= Tr;Kavvadias-Stravropoulos (S, i) (S, i+1) SUi+1 SFi := {U1,...,Ui} eUi+1 S{e}-{e'} e'S (S{e}, i+1

Banbara, Mutsunori

166

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

E-Print Network [OSTI]

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 ...

Goto, Susumu

2007-01-01T23:59:59.000Z

167

" East North Central",1.7,1.7,1.8,1.8,1.9,2  

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

" 60 Years or More","NA","NA",1.8,1.8,1.8,2 "Race of Householder1" " White",1.8,1.8,1.8,1.8,1.9,2 " Black ",1.6,1.5,1.5,1.7,1.5,1.7 " Other ",1.9,1.6,1.8,1.7,1.6...

168

1 Introduction. 1 2 Generalities. 5  

E-Print Network [OSTI]

INTRODUCTION. 1 1 Introduction. Let X be a scheme. Then a cycle on X is a formal linear combination of points. . . . . . . . . . . . . 5 2.2 Universally equidimensional closed subschemes. . . . . . . . . 8 2.3 Cycles on Noetherian schemes. . . . . . . . . . . . . . . . . . . 12 3 Relative cycles. 15 3.1 Relative cycles

169

1 Introduction. 1 2 Generalities. 5  

E-Print Network [OSTI]

. 1 1 Introduction. Let X be a scheme. Then a cycle on X is a formal linear combination. . . . . . . . . 8 2.3 Cycles on Noetherian schemes. . . . . . . . . . . . . . . . . . . 12 3 Relative cycles. 15 3.1 Relative cycles. . . . . . . . . . . . . . . . . . . . . . . . . . . * *15 3

170

1. Red Blue 2. GLdirect  

E-Print Network [OSTI]

(ps.solidmode) { case 1: ...// Dodecahedron case 2: ...// Icosahedron case 3: ...// Teapot case 4: ...// Sun & Earth

Ouhyoung, Ming

171

(1) a2 = b2 + c2 ?2bccos? (2)  

E-Print Network [OSTI]

At 2:00 PM, a ship leaves port and travels N40E at the rate of 30 mph. Another ship leaves the same port at 3:00 PM, and travels N75W at the rate of 20 mph.

charlotb

2010-07-02T23:59:59.000Z

172

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

Office of Environmental Management (EM)

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

173

Applying Bayesian Forecasting to Predict New Customers' Heating Oil Demand.  

E-Print Network [OSTI]

??This thesis presents a new forecasting technique that estimates energy demand by applying a Bayesian approach to forecasting. We introduce our Bayesian Heating Oil Forecaster (more)

Sakauchi, Tsuginosuke

2011-01-01T23:59:59.000Z

174

Page 1 of 2  

Energy Savers [EERE]

the DOE Acquisition Guide on project management requirements including the Earned Value Management System. a. Chapter 1, Administrators Project Management and on the Earned Value...

175

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Journals Connector (OSTI)

Ensemble prediction systems produce forecasts that represent the probability distribution of a continuous forecast variable. Most often, the verification problem is simplified by transforming the ensemble forecast into probability forecasts for ...

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

176

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

by by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: * Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the

177

Aggregate vehicle travel forecasting model  

SciTech Connect (OSTI)

This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.

Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)

1995-05-01T23:59:59.000Z

178

MPX V1.2  

Energy Science and Technology Software Center (OSTI)

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

179

Communication of uncertainty in temperature forecasts  

Science Journals Connector (OSTI)

We used experimental economics to test whether undergraduate students presented with a temperature forecast with uncertainty information in a table and bar graph format were able to use the extra information to interpret a given forecast. ...

Pricilla Marimo; Todd R. Kaplan; Ken Mylne; Martin Sharpe

180

Massachusetts state airport system plan forecasts.  

E-Print Network [OSTI]

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 ...

Mathaisel, Dennis F. X.

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Journals Connector (OSTI)

For over 30 years, weather forecasting for the Antarctic continent and adjacent Southern Ocean has relied on weather satellites. Significant advancements in forecasting skill have come via the weather satellite. The advent of the high-resolution ...

Matthew A. Lazzara; Linda M. Keller; Charles R. Stearns; Jonathan E. Thom; George A. Weidner

2003-02-01T23:59:59.000Z

182

Forecasting Water Use in Texas Cities  

E-Print Network [OSTI]

In this research project, a methodology for automating the forecasting of municipal daily water use is developed and implemented in a microcomputer program called WATCAL. An automated forecast system is devised by modifying the previously...

Shaw, Douglas T.; Maidment, David R.

183

Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis  

E-Print Network [OSTI]

analyses of regional mod- eling with Polar WRF have been performed with results compared to selected localEvaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air.1.1 of the Weather Research and Forecasting model (WRF), a highresolution regional scale model, is used to simulate

Howat, Ian M.

184

table1.2_02  

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

2 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; 2 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources and Shipments; Unit: Trillion Btu. Shipments RSE NAICS Net Residual Distillate Natural LPG and Coke and of Energy Sources Row Code(a) Subsector and Industry Total(b) Electricity(c) Fuel Oil Fuel Oil(d) Gas(e) NGL(f) Coal Breeze Other(g) Produced Onsite(h) Factors Total United States RSE Column Factors: 0.9 1 1.2 1.8 1 1.6 0.8 0.9 1.2 0.4 311 Food 1,123 230 13 19 582 5 184 1 89 0 6.8 311221 Wet Corn Milling 217 23 * * 61 * 121 0 11 0 1.1 31131 Sugar 112 2 2 1 22 * 37 1 46 0 0.9 311421 Fruit and Vegetable Canning 47 7 1 1 36 Q 0 0 1 0 11 312 Beverage and Tobacco Products 105 26 2 2 46 1 17 0 11

185

Page 1 of 2  

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

FLASH 2004-02 FLASH 2004-02 February 11, 2004 As discussed in Policy Flash 2003-05, the BPN, an element under the Integrated Acquisition Environment (IAE) 'which is part of the e-government initiative, is a grouping of systems that track vendor data. ()nline Representations and Certifications (ORCA) is another application to the BPN, similar to 1:he Central Contractor Registration (CCR), and the Federal Procurement Data System-Next Cieneration (FPDS-NG). This electronic application replaces the paper based Representations and Certifications (reps. and certs.) process. Also, ORCA is desi;~ed to eliminate the administrative burden on contractors who are required to provide reps. and certs. infonnation to various contracting offices as many times as an otTer is submitted. The use1ulness of the Online Representations and Certifications allows Federal

186

Modeling and Analysis Papers - Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Evaluation > Table 1 Evaluation > Table 1 Table 1. Comparison of Absolute Percent Errors for AEO Forecast Evaluation, 1996 to 2002 Average Absolute Percent Error Variable AEO82 to AEO97 AEO82 to AEO98 AEO82 to AEO99 AEO82 to AEO2000 AEO82 to AEO2001 AEO82 to AEO2002 Consumption Total Energy Consumption 1.6 1.7 1.7 1.8 1.9 1.9 Total Petroleum Consumption 2.8 2.9 2.8 2.9 3.0 2.9 Total Natural Gas Consumption 5.8 5.7 5.6 5.6 5.5 5.5 Total Coal Consumption 2.7 3.0 3.2 3.3 3.5 3.6 Total Electricity Sales 1.6 1.7 1.8 1.9 2.4 2.5 Production Crude Oil Production 4.2 4.3 4.5 4.5 4.5 4.5 Natural Gas Production 5.0 4.8 4.7 4.6 4.6 4.4 Coal Production 3.7 3.6 3.6 3.5 3.7 3.6 Imports and Exports Net Petroleum Imports 10.1 9.5 8.8 8.4 7.9 7.4 Net Natural Gas Imports 17.4 16.7 16.0 15.9 15.8 15.8 Net Coal Exports

187

Energy demand forecasting: industry practices and challenges  

Science Journals Connector (OSTI)

Accurate forecasting of energy demand plays a key role for utility companies, network operators, producers and suppliers of energy. Demand forecasts are utilized for unit commitment, market bidding, network operation and maintenance, integration of renewable ... Keywords: analytics, energy demand forecasting, machine learning, renewable energy sources, smart grids, smart meters

Mathieu Sinn

2014-06-01T23:59:59.000Z

188

Page 1 of 2  

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

The purpose of this memorandum is to revise the discretionary set-aside authority addressed on The purpose of this memorandum is to revise the discretionary set-aside authority addressed on Page 13 of Acquisition Letter 2004-03 from SSO,(XX) to SIOO,(XX). Questions may be addressed to Steve Zvolensky at (202) 287-1307 or St~hen.zvolensk~@hg.doe.£ov Attachment cc: PP AG Members ;k..t ./ /!k~ --'"' Michael P. Fischetti Acting Director Office of Procurement and Assistance Policy Michael Acting I Office 0 Page 2 of 2 MEMORANDUM FOR DISTRIBUTION 6tfJ FROM: RICHARD H. HOPF, DIRECT~ OFFICE OF PROCUREMENT AND ASSIST ANCE ~ Discretionary Set-Aside A:tho} SUBJECT: The purpose of this memorandum is to revise the discretionary set-aside authority addressed on Page 13 of Acquisition Letter 2004-03 from $50,000 to $100,000. The paragraph entitled, Discretionary Set-Asides, is revised to read:

189

Page 1 of2  

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

new product, the E-DEAR, is packaged as a single file in Microsoft Wordc fontlat and is new product, the E-DEAR, is packaged as a single file in Microsoft Wordc fontlat and is fontlatted to be word processor friendly. It is "searchable" in the "edit/find" sense but lacks any sophisticated search al1d list capability. The E-DEAR has bookmarks for each Part, and in Part 970, bookmarks exist for each subpart. After opening the file in WordC, you access the bookmarks by selectmg "insert," then "bookmark." Select the part or subpart you wish to access from the drop down menu and click the "Go To" icon. We have retained the current "read by Subpart" capability and added a link to the Hill AFB FarSite. Also a librar)' of final rules which amended the DEAR is also available. These tools

190

Page 1 of 2  

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

"benchmark compensation amount" is to be used for contractor FY 2004 and subsequent "benchmark compensation amount" is to be used for contractor FY 2004 and subsequent contractor FY s unless and until revised by OFFP. This benchmark compensation amount applies to contract costs incurred after January 1,2004, under covered contracts of both defense and civilian procurement agencies, as specified in Section 808 of Pub. L.I05-85. This "benchmark compensation amount" supersedes the amount cited in Headquarters Policy Flash 2003-19, August 26,2003. Applicability of the FAR to M&O contracts is addressed at 48 CFR 31.205-6(p) and 48CFR 970.3102-O5-6(p), respectively. For questions related to this Flash, contact Terry Sheppard at (202) 586-8193 or via e-mail at terrv.sheQoard@hg.doe.gov Attachment Cc: PP AG Members FLASH 2004.16

191

Generated using version 3.2 of the official AMS LATEX template The Impact of Lightning Data Assimilation on Deterministic and1  

E-Print Network [OSTI]

forecasts. Today, radar reflectivity and short-range23 lightning flash rate observations are assimilatedGenerated using version 3.2 of the official AMS LATEX template The Impact of Lightning Data, Seattle, WA 98195-1640. E-mail: kendixon@atmos.washington.edu 1 #12;ABSTRACT4 A lightning data

Mass, Clifford F.

192

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

Evaluation Evaluation Annual Energy Outlook Forecast Evaluation by Esmeralda Sanchez The Office of Integrated Analysis and Forecasting has been providing an evaluation of the forecasts in the Annual Energy Outlook (AEO) annually since 1996. Each year, the forecast evaluation expands on that of the prior year by adding the most recent AEO and the most recent historical year of data. However, the underlying reasons for deviations between the projections and realized history tend to be the same from one evaluation to the next. The most significant conclusions are: Over the last two decades, there have been many significant changes in laws, policies, and regulations that could not have been anticipated and were not assumed in the projections prior to their implementation. Many of these actions have had significant impacts on energy supply, demand, and prices; however, the impacts were not incorporated in the AEO projections until their enactment or effective dates in accordance with EIA's requirement to remain policy neutral and include only current laws and regulations in the AEO reference case projections.

193

Load Forecasting of Supermarket Refrigeration  

E-Print Network [OSTI]

energy system. Observed refrigeration load and local ambient temperature from a Danish su- permarket renewable energy, is increasing, therefore a flexible energy system is needed. In the present ThesisLoad Forecasting of Supermarket Refrigeration Lisa Buth Rasmussen Kongens Lyngby 2013 M.Sc.-2013

194

Essays on macroeconomics and forecasting  

E-Print Network [OSTI]

explanatory variables. Compared to Stock and Watson (2002)â??s models, the models proposed in this chapter can further allow me to select the factors structurally for each variable to be forecasted. I find advantages to using the structural dynamic factor...

Liu, Dandan

2006-10-30T23:59:59.000Z

195

Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model.  

E-Print Network [OSTI]

?? The short-term (1 to 48 hours) predictability of wind power production from wind power plants in a power system is critical to the value (more)

Boone, Andrew

2005-01-01T23:59:59.000Z

196

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

SciTech Connect (OSTI)

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

John Zack; Deborah Hanley; Dora Nakafuji

2012-07-15T23:59:59.000Z

197

Forecasting-based SKU classification  

Science Journals Connector (OSTI)

Different spare parts are associated with different underlying demand patterns, which in turn require different forecasting methods. Consequently, there is a need to categorise stock keeping units (SKUs) and apply the most appropriate methods in each category. For intermittent demands, Croston's method (CRO) is currently regarded as the standard method used in industry to forecast the relevant inventory requirements; this is despite the bias associated with Croston's estimates. A bias adjusted modification to CRO (SyntetosBoylan Approximation, SBA) has been shown in a number of empirical studies to perform very well and be associated with a very robust behaviour. In a 2005 article, entitled On the categorisation of demand patterns published by the Journal of the Operational Research Society, Syntetos et al. (2005) suggested a categorisation scheme, which establishes regions of superior forecasting performance between CRO and SBA. The results led to the development of an approximate rule that is expressed in terms of fixed cut-off values for the following two classification criteria: the squared coefficient of variation of the demand sizes and the average inter-demand interval. Kostenko and Hyndman (2006) revisited this issue and suggested an alternative scheme to distinguish between CRO and SBA in order to improve overall forecasting accuracy. Claims were made in terms of the superiority of the proposed approach to the original solution but this issue has never been assessed empirically. This constitutes the main objective of our work. In this paper the above discussed classification solutions are compared by means of experimentation on more than 10,000 \\{SKUs\\} from three different industries. The results enable insights to be gained into the comparative benefits of these approaches. The trade-offs between forecast accuracy and other implementation related considerations are also addressed.

G. Heinecke; A.A. Syntetos; W. Wang

2013-01-01T23:59:59.000Z

198

CCPP-ARM Parameterization Testbed Model Forecast Data  

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

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

Klein, Stephen

199

Slide 1  

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

Forecasted CVP Transmission Rates Forecasted CVP Transmission Rates Central Valley Project Transmission Rates Forecast (as of May 2013) Rate or Revenue Requirement Current Non-Binding Forecast FY 2013 FY 2014 FY 2015 FY 2016 FY 2017 Transmission Revenue Requirement (TRR) $39.6 Million $39.5 Million $41.7 Million $44.9 Million $47.2 Million Network Integrated Transmission Service (NITS) $28.4 Million $27.4 Million $29.0 Million $31.3 Million $32.8 Million Point-to-Point Transmission Rate (P-to-P) $/kW Mo. $1.33 $1.44 $1.52 $1.63 $1.72 Transmission Plant Ratio 61.70% 62.24% 63.82% 66.28% 67.45% SNR Rates Group - Customer Meeting, May 23, 2013 Factors used in forecast: 1.O&M based on FY 2012 actual, then inflated 3% per year starting FY 2015. 2. Plant Additions: FY 2014: Elverta Maintenance Facility Rehab ($4.2M); Hurley-Tracy Fiber ($3.3M).

200

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

E-Print Network [OSTI]

and the flow is subcritical . 55 © 2006 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved use of adopters of the book Water-Resources Engineering, Second Edition, by David A. Chin. ISBN 0.5 ? 3) (9.81)(4 ? 3 + 1.5 ? 32)3 = 0.19 hence Fr = 0.45 and the flow is subcritical . 3.27. From

Bowen, James D.

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

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

E-Print Network [OSTI]

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

Genton, Marc G.

202

Analysis of moisture variability in the European Centre for Medium-Range Weather Forecasts 15-year  

E-Print Network [OSTI]

Analysis of moisture variability in the European Centre for Medium-Range Weather Forecasts 15-year Centre for Medium-Range Weather Forecasts 15-year reanalysis (ERA-15) moisture over the tropical oceans. Introduction [2] Because water vapor is the most significant green- house gas and it exhibits a strong

Allan, Richard P.

203

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

E-Print Network [OSTI]

and decision makers strongly rely on Numerical Weather Prediction (NWP) models, for example on the forecasted on forecasted precipitation. KEYWORDS: Numerical weather prediction models, validation, precipitation 1. INTRODUCTION Numerical Weather Prediction (NWP) models are widely used by avalanche practitioners. Their de

Jamieson, Bruce

204

Comparing NWS PoP Forecasts to Third-Party Providers  

Science Journals Connector (OSTI)

In this paper, the authors verify probability of precipitation (PoP) forecasts provided by the National Weather Service (NWS), The Weather Channel (TWC), and CustomWeather (CW). The n-day-ahead forecasts, where n ranges from 1 to 3 for the NWS, ...

J. Eric Bickel; Eric Floehr; Seong Dae Kim

2011-10-01T23:59:59.000Z

205

Coupling and evaluating gas/particle mass transfer treatments for aerosol simulation and forecast  

E-Print Network [OSTI]

Coupling and evaluating gas/particle mass transfer treatments for aerosol simulation and forecast hindcasting and forecasting. The lack of an efficient yet accurate gas/particle mass transfer treatment December 2007; accepted 21 February 2008; published 12 June 2008. [1] Simulating gas/particle mass transfer

Jacobson, Mark

206

1 Di Neujahr 1 Fr 1 Fr 1 Mo Ostermontag 2 Mi 2 Sa 2 Sa 2 Di  

E-Print Network [OSTI]

1 Di Neujahr 1 Fr 1 Fr 1 Mo Ostermontag 2 Mi 2 Sa 2 Sa 2 Di 3 Do 3 So 3 So 3 Mi 4 Fr 4 Mo 4 Mo 4 Do 5 Sa 5 Di 5 Di 5 Fr 6 So 6 Mi 6 Mi 6 Sa 7 Mo 7 Do 7 Do 7 So 8 Di 8 Fr 8 Fr 8 Mo 9 Mi 9 Sa 9 Sa 9 Di 10 Do 10 So 10 So 10 Mi 11 Fr 11 Mo 11 Mo 11 Do 12 Sa 12 Di 12 Di 12 Fr 13 So 13 Mi 13 Mi Power

Grübel, Rudolf

207

2.1E Supplement  

E-Print Network [OSTI]

Coefficients for Curves 1 (Hot gas bypass), 2 (Back pressure valve), andSuction valve). See table below for coefficients. T h e

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

208

. . () 2012 1 / 1 () N = {1,2,...,n} ui  

E-Print Network [OSTI]

2 / 1 #12; i N ( ) t = 0,1,2...,T xi t R, : xi t+1 = xi t +f i (xi t ,ui t), (1) ui t R, xi t f i (·,·) : R?R R, xi t ui t. . . () 2012 3 / 1 #12; i N = n(¯d +1) Amax ¯n ? ¯n : ai,j max = p i,((j-1)modn)+1 j÷n p i,((j-1)modn)+1 a bi,((j-1)modn)+1

Granichin, Oleg

209

A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE  

E-Print Network [OSTI]

A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE Wensheng Zhang1,* , Hongfu Chen1 and excessive fluctuation of agricultural and livestock products price is not only harmful to residents' living, but also affects CPI (Consumer Price Index) values, and even leads to social crisis, which influences

Boyer, Edmond

210

Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics  

E-Print Network [OSTI]

. Over the past two decades, ensembles of numerical weather prediction (NWP) models have been developed and phrases: Continuous ranked probability score; Density forecast; Ensem- ble system; Numerical weather prediction; Heteroskedastic censored regression; Tobit model; Wind energy. 1 #12;1 Introduction Accurate

Washington at Seattle, University of

211

Increasing NOAA's computational capacity to improve global forecast modeling  

E-Print Network [OSTI]

competing numerical weather prediction centers such as the European Center for MediumRange Weather Forecasts (ECMWF). For most sensibleweather metrics, we lag 1 to 1.5 days (i.e., they make a 3.5day of NOAA's current investment in weather satellites. Without a modern data assimilation system

Hamill, Tom

212

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

SciTech Connect (OSTI)

The Middle East will continue to play the dominant role of a petroleum supplier in the world oil market in the year 2000, according to business-as-usual forecasts published by the US Department of Energy. However, interesting trade patterns will emerge as a result of the democratization in the Soviet Union and Eastern Europe. US petroleum imports will increase from 46% in 1989 to 49% in 2000. A significantly higher level of US petroleum imports (principally products) will be coming from Japan, the Soviet Union, and Eastern Europe. Several regions, the Far East, Japan, Latin American, and Africa will import more petroleum. Much uncertainty remains about of the level future Soviet crude oil production. USSR net petroleum exports will decrease; however, the United States and Canada will receive some of their imports from the Soviet Union due to changes in the world trade patterns. The Soviet Union can avoid becoming a net petroleum importer as long as it (1) maintains enough crude oil production to meet its own consumption and (2) maintains its existing refining capacities. Eastern Europe will import approximately 50% of its crude oil from the Middle East.

Das, S.

1991-12-01T23:59:59.000Z

213

Linear Diagnostics to Assess the Performance of an Ensemble Forecast System  

E-Print Network [OSTI]

. The mathematical model we adopt to predict the evolution of uncertainty in a local state estimate (analysis or forecast), xe, is based on the assumption that the error in the state estimate, ? = xe ? xt, (2.1) *Portions of this chapter have been reprinted from... variable. In Equation (2.1) xt is the model representation of the, in practice unknown, true state of the atmosphere. The covariance between the different components of ? is described by the error covariance matrix P`. We employ a K-member ensemble...

Satterfield, Elizabeth A.

2011-10-21T23:59:59.000Z

214

NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS  

E-Print Network [OSTI]

Spending 4 Figure 1.4: United States Total Employment 4 Figure 1.5: United States Unemployment Statistics 5 Virginia's Counties 35 West Virginia's Metropolitan Statistical Areas 38 CHAPTER 5: SPECiAL TOPiCS, HEALTHRGiNiA ECONOMY Figure 2.1: Total Employment 9 Figure 2.2: West Virginia Employment Distribution by Sector (2012

Mohaghegh, Shahab

215

Forecasting wind speed financial return  

E-Print Network [OSTI]

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

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

216

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

SciTech Connect (OSTI)

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

KETUSKY, EDWARD

2005-10-31T23:59:59.000Z

217

Annual Energy Outlook 2001-Appendix G: Major Assumptions for the Forecasts  

Gasoline and Diesel Fuel Update (EIA)

Forecasts Forecasts Summary of the AEO2001 Cases/ Scenarios - Appendix Table G1 bullet1.gif (843 bytes) Model Results (Formats - PDF, ZIP) - Appendix Tables - Reference Case - 1998 to 2020 bullet1.gif (843 bytes) Download Report - Entire AEO2001 (PDF) - AEO2001 by Chapters (PDF) bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts Related Links bullet1.gif (843 bytes) Assumptions to the AEO2001 bullet1.gif (843 bytes) Supplemental Data to the AEO2001 (Only available on the Web) - Regional and more detailed AEO 2001 Reference Case Results - 1998, 2000 to 2020 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Forecast Homepage bullet1.gif (843 bytes) EIA Homepage Appendix G Major Assumptions for the Forecasts Component Modules Major Assumptions for the Annual Energy Outlook 2001

218

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

E-Print Network [OSTI]

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...

Kulkarni, Siddhivinayak

2009-01-01T23:59:59.000Z

219

Development and Deployment of an Advanced Wind Forecasting Technique  

E-Print Network [OSTI]

findings. Part 2 addresses how operators of wind power plants and power systems can incorporate advanced the output of advanced wind energy forecasts into decision support models for wind power plant and power in Porto) Power Systems Unit Porto, Portugal Industry Partners Horizon Wind Energy, LLC Midwest Independent

Kemner, Ken

220

Voluntary Green Power Market Forecast through 2015  

SciTech Connect (OSTI)

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.

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

2010-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA  

E-Print Network [OSTI]

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

Boyer, Edmond

222

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network [OSTI]

1 FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY Rick Katz Institute for Study ON EXTREMES · Emil Gumbel (1891 ­ 1966) -- Pioneer in application of statistics of extremes (Germany, France) Conventional Methods (3) Extreme Value Theory (EVT) (4) Application of EVT to Verification (5) Frost

Katz, Richard

223

Statistical Downscaling Multimodel Forecasts for Seasonal Precipitation and Surface Temperature over the Southeastern United States  

Science Journals Connector (OSTI)

This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern ...

Di Tian; Christopher J. Martinez; Wendy D. Graham; Syewoon Hwang

2014-11-01T23:59:59.000Z

224

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

Lewis Poulin Implementation and Operational Services Section Canadian Meteorological Centre, Dorval, Qc National Prediction Operations Division ICEBO 2013, Montreal, Qc October 10 2013 Version 2013-09-27 Weather Forecast Data An Important... and weather information ? Numerical weather forecast production 101 ? From deterministic to probabilistic forecasts ? Some MSC weather forecast (NWP) datasets ? Finding the appropriate data for the appropriate forecast ? Preparing for probabilistic...

Poulin, L.

2013-01-01T23:59:59.000Z

225

Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts  

Science Journals Connector (OSTI)

The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (...

Felix Fundel; Andre Walser; Mark A. Liniger; Christoph Frei; Christof Appenzeller

2010-01-01T23:59:59.000Z

226

Page 1 of 2 Telecommunications  

E-Print Network [OSTI]

Page 1 of 2 Telecommunications User Agent Telephone Returns Important Note: Before using this form** Color *Most common # of lines: single, 2, 4, 8, 10, 12, 16, and 24. **Optional. Telecommunications, 10, 12, 16, and 24. **Optional. Telecommunications Office Use Only Received by: Date: #12;

227

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

E-Print Network [OSTI]

EWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology 1 The Anemos Wind Power a professional, flexible platform for operating wind power prediction models, laying the main focus on state models from all over Europe are able to work on this platform. Keywords: wind energy, wind power

Boyer, Edmond

228

2.1E Supplement  

E-Print Network [OSTI]

or ROOF INTERIOR-WALL SPACE TROMBE-WALL-V or-NV 2.1B-1/15/83i t y for U . S . Cities TROMBE WALLS General Rules Warningfor U . S . Cities TROMBE WALLS General Rules Warning

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

229

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

H Tables H Tables Appendix H Comparisons With Other Forecasts, and Performance of Past IEO Forecasts for 1990, 1995, and 2000 Forecast Comparisons Three organizations provide forecasts comparable with those in the International Energy Outlook 2005 (IEO2005). The International Energy Agency (IEA) provides “business as usual” projections to the year 2030 in its World Energy Outlook 2004; Petroleum Economics, Ltd. (PEL) publishes world energy forecasts to 2025; and Petroleum Industry Research Associates (PIRA) provides projections to 2015. For this comparison, 2002 is used as the base year for all the forecasts, and the comparisons extend to 2025. Although IEA’s forecast extends to 2030, it does not publish a projection for 2025. In addition to forecasts from other organizations, the IEO2005 projections are also compared with those in last year’s report (IEO2004). Because 2002 data were not available when IEO2004 forecasts were prepared, the growth rates from IEO2004 are computed from 2001.

230

Funding Opportunity Announcement for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

231

Upcoming Funding Opportunity for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

to improved forecasts, system operators and industry professionals can ensure that wind turbines will operate at their maximum potential. Data collected during this field...

232

Huge market forecast for linear LDPE  

Science Journals Connector (OSTI)

Huge market forecast for linear LDPE ... It now appears that the success of the new technology, which rests largely on energy and equipment cost savings, could be overwhelming. ...

1980-08-25T23:59:59.000Z

233

NOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future)  

E-Print Network [OSTI]

conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through are generated every 6 hours and you can step backward in hourly increments to view conditions over the previousNOAA GREAT LAKES COASTAL FORECASTING SYSTEM Forecasts (up to 5 days in the future) and Nowcasts

234

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

235

Ensemble typhoon quantitative precipitation forecasts model in Taiwan  

Science Journals Connector (OSTI)

In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened at a ...

Jing-Shan Hong; Chin-Tzu Fong; Ling-Feng Hsiao; Yi-Chiang Yu; Chian-You Tzeng

236

Research of least squares support vector regression based on differential evolution algorithm in short-term load forecasting model  

Science Journals Connector (OSTI)

To improve the accuracy of short-term load forecasting a differential evolution algorithm (DE) based least squares support vector regression (LSSVR) method is proposed in this paper. Through optimizing the regularization parameter and kernel parameter of the LSSVR by DE a short-term load forecasting model which can take load affected factors such as meteorology weather and date types into account is built. The proposed LSSVR method is proved by implementing short-term load forecasting on the real historical data of Yangquan power system in China. The average forecasting error is less than 1.6% which shows better accuracy and stability than the traditional LSSVR and Support vector regression. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system more efficiently.

2014-01-01T23:59:59.000Z

237

Forecast of geothermal drilling activity  

SciTech Connect (OSTI)

The numbers of each type of geothermal well expected to be drilled in the United States for each 5-year period to 2000 AD are specified. Forecasts of the growth of geothermally supplied electric power and direct heat uses are presented. The different types of geothermal wells needed to support the forecasted capacity are quantified, including differentiation of the number of wells to be drilled at each major geothermal resource for electric power production. The rate of growth of electric capacity at geothermal resource areas is expected to be 15 to 25% per year (after an initial critical size is reached) until natural or economic limits are approached. Five resource areas in the United States should grow to significant capacity by the end of the century (The Geysers; Imperial Valley; Valles Caldera, NM; Roosevelt Hot Springs, UT; and northern Nevada). About 3800 geothermal wells are expected to be drilled in support of all electric power projects in the United States between 1981 and 2000 AD. Half of the wells are expected to be drilled in the Imperial Valley. The Geysers area is expected to retain most of the drilling activity for the next 5 years. By the 1990's, the Imperial Valley is expected to contain most of the drilling activity.

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

1981-10-01T23:59:59.000Z

238

New Concepts in Wind Power Forecasting Models  

E-Print Network [OSTI]

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 for more accurate short term wind power forecasting models has led to solid and impressive development

Kemner, Ken

239

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near-surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

240

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

. (2004) this forecast error was encountered when assimilating satellite measurements of zonal wind speeds between satellite measurements and meteorological forecasts of near­surface ocean winds. This type of covariance enters in assimilation techniques such as Kalman filtering. In all, six residual fields

Malmberg, Anders

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

. Satellite images and Numerical Weather Prediction (NWP) models are used together with the synoptic surfaceAmending Numerical Weather Prediction forecasts using GPS Integrated Water Vapour: a case study to validate the amounts of humidity in Numerical Weather Prediction (NWP) model forecasts. This paper presents

Stoffelen, Ad

242

A Forecasting Support System Based on Exponential Smoothing  

Science Journals Connector (OSTI)

This chapter presents a forecasting support system based on the exponential smoothing scheme to forecast time-series data. Exponential smoothing methods are simple to apply, which facilitates...

Ana Corbern-Vallet; Jos D. Bermdez; Jos V. Segura

2010-01-01T23:59:59.000Z

243

ANL Software Improves Wind Power Forecasting | Department of...  

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

principal investigator for the project. For wind power point forecasting, ARGUS PRIMA trains a neural network using data from weather forecasts, observations, and actual wind...

244

Improved Prediction of Runway Usage for Noise Forecast :.  

E-Print Network [OSTI]

??The research deals with improved prediction of runway usage for noise forecast. Since the accuracy of the noise forecast depends on the robustness of runway (more)

Dhanasekaran, D.

2014-01-01T23:59:59.000Z

245

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

Energy Savers [EERE]

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

246

PBL FY 2002 Third Quarter Review Forecast of Generation Accumulated...  

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

Power Business Line Generation Accumulated Net Revenues Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) FY 2002 Third Quarter Review Forecast in Millions...

247

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Electricity Electricity Electricity consumption nearly doubles in the IEO2005 projection period. The emerging economies of Asia are expected to lead the increase in world electricity use. Figure 58. World Net Electricity Consumption, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 59. World Net Electricity Consumption by Region, 2002-2025 (Billion Kilowatthours). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data The International Energy Outlook 2005 (IEO2005) reference case projects that world net electricity consumption will nearly double over the next two decades.10 Over the forecast period, world electricity demand is projected to grow at an average rate of 2.6 percent per year, from 14,275 billion

248

Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev'Air  

E-Print Network [OSTI]

Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10'Air operational platform. This platform aims at forecasting maps, on a daily basis, for ozone, nitrogen dioxide models, ozone, nitrogen dioxide, particulate matter, threshold exceedance 1. Introduction1 Operational

Mallet, Vivien

249

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

E-Print Network [OSTI]

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

250

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

E-Print Network [OSTI]

Research, Inc. July 25. Hanford, James W. , Jonathan G.Francis X . and James W. Hanford. 1992. Memorandum toA : Ritschard, Ron L. , Jim W. Hanford and A. Osman Sezgen.

Johnson, F.X.

2010-01-01T23:59:59.000Z

251

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

E-Print Network [OSTI]

Richard E. Brown, James W. Hanford, Alan H . Sanstad, andFrancis X . , James W. Hanford, Richard E. Brown, Alan H.place for these end-uses (Hanford et al. 1994, Hwang et al.

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

252

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

E-Print Network [OSTI]

volume) of the equipment (AHAM 1991, ARI1991, G A M A 1992).Energy Factors (SWEFs), (AHAM 1991). b. 1990 RECS (EIAdata for their members (AHAM 1991, ARI1991, G A M A 1992).

Johnson, F.X.

2010-01-01T23:59:59.000Z

253

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

E-Print Network [OSTI]

Imports/Exports Gas Availability Change efficiency choice equation Add technologiestoHVAC model Adjust cost-efficiency parameter Develop HVAC Conversion

Johnson, F.X.

2010-01-01T23:59:59.000Z

254

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

E-Print Network [OSTI]

Conservation and Renewable Energy, Building EquipmentConservation and Renewable Energy, Building EquipmentEnergy Efficiency and Renewable Energy, Building Equipment

Johnson, F.X.

2010-01-01T23:59:59.000Z

255

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

E-Print Network [OSTI]

Heating (Screen HV-6cl) Fuel Price Elasticity Electric Furnace Gas Furnace LPG Furnace OilHeating Price Expense Electric Furnace n/a n/a Gas Furnace Oil

Johnson, F.X.

2010-01-01T23:59:59.000Z

256

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

E-Print Network [OSTI]

Conservation Standards for Consumer Products: Room Air Conditioners, Water Heaters, Direct Heating Equipment, Mobile Home Furnaces, Kitchen Ranges and Ovens, Pool

Johnson, F.X.

2010-01-01T23:59:59.000Z

257

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

E-Print Network [OSTI]

and the size of refrigerators and freezers; for all otherwhile water heating, refrigerator, and freezer end-uses showas projected by REEPS. Refrigerator and freezer percentage

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

258

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

E-Print Network [OSTI]

-76SF00098. #12;#12;i ABSTRACT This paper describes current and projected future energy use by end energy intensity per household of the residential sector is declining, and the electricity intensity per. Sanstad, and Leslie Shown Energy Analysis Program Energy and Environment Division Ernest Orlando Lawrence

259

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

E-Print Network [OSTI]

Consumption and Expenditures 1992. Energy Information Administration, U.S.92). April. US DOE. 1995c. Residential Energy ConsumptionConsumption and Expenditures 1993. EIA, Energy Information Administration, U.S.

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

260

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

E-Print Network [OSTI]

stock, household size, fuel prices and household income.needed by the model. Fuel price projections are implementedand Exogenous Drivers Fuel Prices Income Household Size

Johnson, F.X.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

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

E-Print Network [OSTI]

room, and electric (air source) heat pump. Gas furnaces weresplit-system air-source electric heat pumps. (5) As of Jan.ground source heat pumps, evaporative cooling, ductiess air

Johnson, F.X.

2010-01-01T23:59:59.000Z

262

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

E-Print Network [OSTI]

loans Energy Doctor Energy Audits Incentives to Builders/Developers New building/shell technologies Passive solar

Johnson, F.X.

2010-01-01T23:59:59.000Z

263

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

E-Print Network [OSTI]

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

264

SF424_2_1-V2.1.pdf  

Gasoline and Diesel Fuel Update (EIA)

Number: 4040-0004 Number: 4040-0004 Expiration Date: 03/31/2012 * 1. Type of Submission: * 2. Type of Application: * 3. Date Received: 4. Applicant Identifier: 5a. Federal Entity Identifier: * 5b. Federal Award Identifier: 6. Date Received by State: 7. State Application Identifier: * a. Legal Name: * b. Employer/Taxpayer Identification Number (EIN/TIN): * c. Organizational DUNS: * Street1: Street2: * City: County/Parish: * State: Province: * Country: * Zip / Postal Code: Department Name: Division Name: Prefix: * First Name: Middle Name: * Last Name: Suffix: Title: Organizational Affiliation: * Telephone Number: Fax Number: * Email: * If Revision, select appropriate letter(s): * Other (Specify): State Use Only: 8. APPLICANT INFORMATION: d. Address: e. Organizational Unit: f. Name and contact information of person to be contacted on matters involving this application:

265

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting JASPER A. VRUGT  

E-Print Network [OSTI]

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting JASPER A. VRUGT Earth values must be specified (Table 1). Corresponding author address: Jasper Vrugt, Earth and Envi- ronmental

Vrugt, Jasper A.

266

Are there Gains from Pooling Real-Time Oil Price Forecasts?  

Gasoline and Diesel Fuel Update (EIA)

forecast, with a ratio below 1 indicating a gain in accuracy. There is no valid test for judging the statistical significance of the MSPE reductions in our context, but we...

267

Exploring Variations in Peoples Sources, Uses, and Perceptions of Weather Forecasts  

Science Journals Connector (OSTI)

Past research has shown that individuals vary in their attitudes and behaviors regarding weather forecast information. To deepen knowledge about these variations, this article explores 1) patterns in peoples sources, uses, and perceptions of ...

Julie L. Demuth; Jeffrey K. Lazo; Rebecca E. Morss

2011-07-01T23:59:59.000Z

268

1d5/2-2s1/2 splitting in light nuclei  

Science Journals Connector (OSTI)

Energy differences between 1d5/2 and 2s1/2 states in light nuclei are reviewed and systematized. A simple model accounts for the Coulomb shifts.

H. T. Fortune

1995-10-01T23:59:59.000Z

269

Buildings Energy Data Book: 2.1 Residential Sector Energy Consumption  

Buildings Energy Data Book [EERE]

7 7 Range 10 4 48 Clothes Dryer 359 (2) 4 49 Water Heating Water Heater-Family of 4 40 64 (3) 26 294 Water Heater-Family of 2 40 32 (3) 12 140 Note(s): Source(s): 1) $1.139/therm. 2) Cycles/year. 3) Gallons/day. A.D. Little, EIA-Technology Forecast Updates - Residential and Commercial Building Technologies - Reference Case, Sept. 2, 1998, p. 30 for range and clothes dryer; LBNL, Energy Data Sourcebook for the U.S. Residential Sector, LBNL-40297, Sept. 1997, p. 62-67 for water heating; GAMA, Consumers' Directory of Certified Efficiency Ratings for Heating and Water Heating Equipment, Apr. 2002, for water heater capacity; and American Gas Association, Gas Facts 1998, December 1999, www.aga.org for range and clothes dryer consumption. Operating Characteristics of Natural Gas Appliances in the Residential Sector

270

Beamline 3.2.1  

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

2.1 Print 2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson This e-mail address is being protected from spambots. You need JavaScript enabled to view it Advanced Light Source, Berkeley Lab Phone: (510) 486-5527 Fax: (510) 486-4102 This e-mail address is being protected from spambots. You need JavaScript enabled to view it AXSUN Technology

271

Beamline 3.2.1  

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

2.1 Print 2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson This e-mail address is being protected from spambots. You need JavaScript enabled to view it Advanced Light Source, Berkeley Lab Phone: (510) 486-5527 Fax: (510) 486-4102 This e-mail address is being protected from spambots. You need JavaScript enabled to view it AXSUN Technology

272

Beamline 3.2.1  

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

2.1 Print 2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson This e-mail address is being protected from spambots. You need JavaScript enabled to view it Advanced Light Source, Berkeley Lab Phone: (510) 486-5527 Fax: (510) 486-4102 This e-mail address is being protected from spambots. You need JavaScript enabled to view it AXSUN Technology

273

Beamline 3.2.1  

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

2.1 Print 2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson This e-mail address is being protected from spambots. You need JavaScript enabled to view it Advanced Light Source, Berkeley Lab Phone: (510) 486-5527 Fax: (510) 486-4102 This e-mail address is being protected from spambots. You need JavaScript enabled to view it AXSUN Technology

274

Beamline 3.2.1  

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

2.1 Print 2.1 Print Commercial deep-etch x-ray lithography (LIGA) GENERAL BEAMLINE INFORMATION Operational Yes, but not open to users Source characteristics Bend magnet Energy range 3-12 keV Monochromator None Endstations Hutch with automated scanner Calculated spot size at sample 100 x 10 mm Sample format 3- and 4-in. wafer format; x-ray mask and LIGA substrate Sample environment Ambient, air Scientific disciplines Applied science Scientific applications Deep-etch x-ray lithography (LIGA) Spokesperson This e-mail address is being protected from spambots. You need JavaScript enabled to view it Advanced Light Source, Berkeley Lab Phone: (510) 486-5527 Fax: (510) 486-4102 This e-mail address is being protected from spambots. You need JavaScript enabled to view it AXSUN Technology

275

Effect of random perturbations on adaptive observation techniques M. J. Hossen1, I. M. Navon2,, and D. N. Daescu3  

E-Print Network [OSTI]

-1212, Bangladesh 2Department of Scientific Computing, Florida State University, Tallahassee, FL 32306 number of additional observational resources must be deployed to improve a specific forecast aspect, see

Navon, Michael

276

Beamline 8.2.1  

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

1 Print 1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Proposal cycle Proposals for Structural Biology Beamlines (2-month cycle) Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max at sample) 3.0 (h) x 0.5 (v) mrad Measured spot size (FWHM) 100 µm Endstations Minihutch Detectors 3x3 CCD array (ADSC Q315R) Sample format Single crystals of biological molecules Sample preparation Support labs available Sample environment Ambient or ~100 K

277

Beamline 8.2.1  

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

1 Print 1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Proposal cycle Proposals for Structural Biology Beamlines (2-month cycle) Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max at sample) 3.0 (h) x 0.5 (v) mrad Measured spot size (FWHM) 100 µm Endstations Minihutch Detectors 3x3 CCD array (ADSC Q315R) Sample format Single crystals of biological molecules Sample preparation Support labs available Sample environment Ambient or ~100 K

278

Beamline 8.2.1  

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

1 Print 1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Proposal cycle Proposals for Structural Biology Beamlines (2-month cycle) Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max at sample) 3.0 (h) x 0.5 (v) mrad Measured spot size (FWHM) 100 µm Endstations Minihutch Detectors 3x3 CCD array (ADSC Q315R) Sample format Single crystals of biological molecules Sample preparation Support labs available Sample environment Ambient or ~100 K

279

Beamline 8.2.1  

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

1 Print 1 Print Berkeley Center for Structural Biology (BCSB) Multiple-Wavelength Anomalous Diffraction (MAD) and Macromolecular Crystallography (MX) Scientific discipline: Structural biology GENERAL BEAMLINE INFORMATION Operational Yes Proposal cycle Proposals for Structural Biology Beamlines (2-month cycle) Source characteristics Superbend magnet (5.0 T, single pole) Energy range 5-16 keV Monochromator Double crystal, Si(111) Measured flux (1.9 GeV, 400 mA) 3.0 x 1011 photons/sec Resolving power (E/ΔE) 7,000 Divergence (max at sample) 3.0 (h) x 0.5 (v) mrad Measured spot size (FWHM) 100 µm Endstations Minihutch Detectors 3x3 CCD array (ADSC Q315R) Sample format Single crystals of biological molecules Sample preparation Support labs available Sample environment Ambient or ~100 K

280

Environmental Chemistry 2.1 INTRODUCTION  

E-Print Network [OSTI]

I CHAPTER 2 Environmental Chemistry 2.1 INTRODUCTION 2.2 STOICHIOMETRY 2.3 ENTHALPY IN CHEMICAL SYSTEMS 2.4 CHEMICAL EQUILIBRIA 2.S ORGANIC CHEMISTRY 2.6 NUCLEAR CHEMISTRY PROBLEMS REFERENCES It often #12;40 Chapter 2 Environmental Chemistry TABLE 2.1 Atomic Numbers andAtomic Weights Atomic Atomic

Kammen, Daniel M.

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

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

SciTech Connect (OSTI)

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

Xu, Lilai, E-mail: llxu@iue.ac.cn [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021 (China); Xiamen Key Lab of Urban Metabolism, Xiamen 361021 (China); Gao, Peiqing, E-mail: peiqing15@yahoo.com.cn [Xiamen City Appearance and Environmental Sanitation Management Office, 51 Hexiangxi Road, Xiamen 361004 (China); Cui, Shenghui, E-mail: shcui@iue.ac.cn [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021 (China); Xiamen Key Lab of Urban Metabolism, Xiamen 361021 (China); Liu, Chun, E-mail: xmhwlc@yahoo.com.cn [Xiamen City Appearance and Environmental Sanitation Management Office, 51 Hexiangxi Road, Xiamen 361004 (China)

2013-06-15T23:59:59.000Z

282

1993 Solid Waste Reference Forecast Summary  

SciTech Connect (OSTI)

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.

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

283

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

E-Print Network [OSTI]

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. HIGH-ACCURACY LASER POWER AND ENERGY METER CALIBRATION SYSTEM . . . . . . . . 2 2.1 Calibration

284

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

SciTech Connect (OSTI)

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

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

2013-10-01T23:59:59.000Z

285

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

286

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

and number of countries withforeign subsidiaries) are significantly positively related to more optimistic management earnings forecasts....

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

287

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

SciTech Connect (OSTI)

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

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

2011-10-01T23:59:59.000Z

288

Homework (sections 2.1-2.4)  

E-Print Network [OSTI]

Photosynthesis is the conversion of light energy to chemical energy that is stored in glucose or other organic compounds.1 In the process, oxygen is produced.

289

) " (2010: 1. KI + I2 .  

E-Print Network [OSTI]

."'.' . ,'., ,.' '. '., ,) '(. . ­ ? ,, .-. - - . . ! 2. : SrCl2 NaCl CaCl2 . . ? . ("-Mass Spectrum: : NaCl ­ 58,60 CaCl2 ­ 110- , CO3 2- ., .) .( 4.2.,SrCO3-CaCO3,-HCl)CO2( CaCl2-SrCl2.,crown ether ,: 4.3.HCl,tungstosilicic acid,18

Maoz, Shahar

290

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

E-Print Network [OSTI]

recently. In 2005, total energy consumption reached 2,225unfolds as forecast, total energy consumption in 2010 wouldthereby reducing total energy consumption from 2,833 Mtce to

Lin, Jiang

2008-01-01T23:59:59.000Z

291

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

E-Print Network [OSTI]

J.B. , 2004: Probabilistic wind power forecasts using localforecast intervals for wind power output using NWP-predictedsources such as wind and solar power. Integration of this

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

292

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

E-Print Network [OSTI]

United States California Solar Initiative Coastally Trappedparticipants in the California Solar Initiative (CSI)on location. In California, solar irradiance forecasts near

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

293

Annual Energy Outlook Forecast Evaluation 2004  

Gasoline and Diesel Fuel Update (EIA)

2004 2004 * The Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) has produced annual evaluations of the accuracy of the Annual Energy Outlook (AEO) since 1996. Each year, the forecast evaluation expands on the prior year by adding the projections from the most recent AEO and replacing the historical year of data with the most recent. The forecast evaluation examines the accuracy of AEO forecasts dating back to AEO82 by calculating the average absolute percent errors for several of the major variables for AEO82 through AEO2004. (There is no report titled Annual Energy Outlook 1988 due to a change in the naming convention of the AEOs.) The average absolute percent error is the simple mean of the absolute values of the percentage difference between the Reference Case projection and the

294

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

energy data + forecasting energy data + forecasting Home FRED Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in formulating policies and energy plans based on easy to use forecasting tools, visualizations, sankey diagrams, and open data. The platform will live on OpenEI and this community was established to initiate discussion around continuous development of this tool, integrating it with new datasets, and connecting with the community of users who will want to contribute data to the tool and use the tool for planning purposes. Links: FRED beta demo energy data + forecasting Syndicate content 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2084382122

295

Wind Speed Forecasting for Power System Operation  

E-Print Network [OSTI]

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...

Zhu, Xinxin

2013-07-22T23:59:59.000Z

296

Evaluation of hierarchical forecasting for substitutable products  

Science Journals Connector (OSTI)

This paper addresses hierarchical forecasting in a production planning environment. Specifically, we examine the relative effectiveness of Top-Down (TD) and Bottom-Up (BU) strategies for forecasting the demand for a substitutable product (which belongs to a family) as well as the demand for the product family under different types of family demand processes. Through a simulation study, it is revealed that the TD strategy consistently outperforms the BU strategy for forecasting product family demand. The relative superiority of the TD strategy further improves by as much as 52% as the product demand variability increases and the degree of substitutability between the products decreases. This phenomenon, however, is not always true for forecasting the demand for the products within the family. In this case, it is found that there are a few situations wherein the BU strategy marginally outperforms the TD strategy, especially when the product demand variability is high and the degree of product substitutability is low.

S. Viswanathan; Handik Widiarta; R. Piplani

2008-01-01T23:59:59.000Z

297

Forecasting Capital Expenditure with Plan Data  

Science Journals Connector (OSTI)

The short-term forecasting of capital expenditure presents one of the most difficult problems ... reason is that year-to-year fluctuations in capital expenditure are extremely wide. Some simple methods which...

W. Gerstenberger

1977-01-01T23:59:59.000Z

298

Forecasting Agriculturally Driven Global Environmental Change  

Science Journals Connector (OSTI)

...of each variable on GDP (13, 17), combined with global GDP projections (14...population, and per capita GDP, combined with projected...measure of agricultural demand for water, is forecast...Just as demand for energy is the major cause...

David Tilman; Joseph Fargione; Brian Wolff; Carla D'Antonio; Andrew Dobson; Robert Howarth; David Schindler; William H. Schlesinger; Daniel Simberloff; Deborah Swackhamer

2001-04-13T23:59:59.000Z

299

Medium- and Long-Range Forecasting  

Science Journals Connector (OSTI)

In contrast to short and extended range forecasts, predictions for periods beyond 5 days use time-averaged, midtropospheric height fields as their primary guidance. As time ranges are increased to 3O- and 90-day outlooks, guidance increasingly ...

A. James Wagner

1989-09-01T23:59:59.000Z

300

Updated Satellite Technique to Forecast Heavy Snow  

Science Journals Connector (OSTI)

Certain satellite interpretation techniques have proven quite useful in the heavy snow forecast process. Those considered best are briefly reviewed, and another technique is introduced. This new technique was found to be most valuable in cyclonic ...

Edward C. Johnston

1995-06-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

Annual Energy Outlook Forecast Evaluation 2005  

Gasoline and Diesel Fuel Update (EIA)

Forecast Evaluation 2005 Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 Annual Energy Outlook Forecast Evaluation 2005 * Then Energy Information Administration (EIA) produces projections of energy supply and demand each year in the Annual Energy Outlook (AEO). The projections in the AEO are not statements of what will happen but of what might happen, given the assumptions and methodologies used. The projections are business-as-usual trend projections, given known technology, technological and demographic trends, and current laws and regulations. Thus, they provide a policy-neutral reference case that can be used to analyze policy initiatives. EIA does not propose or advocate future legislative and regulatory changes. All laws are assumed to remain as currently enacted; however, the impacts of emerging regulatory changes, when defined, are reflected.

302

PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico | Open  

Open Energy Info (EERE)

Weather Research and Forecasting (WRF)-Chem Modeling in Mexico Weather Research and Forecasting (WRF)-Chem Modeling in Mexico Jump to: navigation, search Name PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico Agency/Company /Organization Pacific Northwest National Laboratory Sector Energy Topics Co-benefits assessment, - Environmental and Biodiversity, - Health, Background analysis Resource Type Publications Website http://www.pnl.gov/atmospheric Country Mexico UN Region Latin America and the Caribbean References PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico[1] PNNL Publications on WRF-Chem modeling in Mexico include: Fast JD, M Shrivastava, RA Zaveri, and JC. Barnard. 2010. "Modeling particulates and direct radiative forcing from urban to synoptic scales downwind of Mexico City." Annual European Geosciences Union Assembly,

303

Forecasting energy markets using support vector machines  

Science Journals Connector (OSTI)

Abstract In this paper we investigate the efficiency of a support vector machine (SVM)-based forecasting model for the next-day directional change of electricity prices. We first adjust the best autoregressive SVM model and then we enhance it with various related variables. The system is tested on the daily Phelix index of the German and Austrian control area of the European Energy Exchange (???) wholesale electricity market. The forecast accuracy we achieved is 76.12% over a 200day period.

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

304

()!%()!%00%%$1)% (2!%%$1)% (2! (%3 % 4$ ()!%(%3 % 4$ ()!%00%%$1)% (2!5%%$1)% (2!5  

E-Print Network [OSTI]

a1 a2 a3a1 a2 a3a1 a2 an CARS PRICE FUEL SAFETY F(X1,X2,...,Xn) CAR ATTRIBUTES UTILITY FUNCTION qkdkrefls UTILITY FUNCTION PRICE FUEL SAFETY CAR 1 0 50 ? P1 + 20 ? P2 + 30 ? P3 OPTIONS UTILITY 1. 22.000 8 opi qkdkreflsheeifjek lmkd nli opi qkdkrefls OPTIONS UTILITY UTILITY FUNCTION PRICE FUEL SAFETY high

Bohanec, Marko

305

Section 2: Biomechanics 1 Section 2: Biomechanics  

E-Print Network [OSTI]

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

Kohlenbach, Ulrich

306

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

E-Print Network [OSTI]

Lead Grant Reference PI Research Organisation Project Title NE/J024678/1 Dr Christopher Davis Troposphere and the Routing of Aircraft (EXTRA)Professor Keith Shine University of Reading NE/J023760

307

< 1 2 < 1 2 1 0.3 5 1 5 5 0.1 0.3 5  

E-Print Network [OSTI]

T (573 ) + p (191 ) D=0.8Rp (C3H8) D=0.7Rp (CF4) : · 50-90% (1-4 ?) · - 10-7-10-8 · FWHM 1/CF4 · 10 /2 · 2D readout 100 PIC ( ) readout : · 30 x 30 (PCB .) · ~ 10 MSGC ­ charge division #12; () CASCADE (DETNI project) · FWHM~3.1 ( ..) () · FWHM~1 ( +1.5 . CF4) · 10

Titov, Anatoly

308

Development of short-term forecast quality for new offshore wind farms  

Science Journals Connector (OSTI)

As the rapid wind power build-out continues, a large number of new wind farms will come online but forecasters and forecasting algorithms have little experience with them. This is a problem for statistical short term forecasts, which must be trained on a long record of historical power production exactly what is missing for a new farm. Focus of the study was to analyse development of the offshore wind power forecast (WPF) quality from beginning of operation up to one year of operational experience. This paper represents a case study using data of the first German offshore wind farm "alpha ventus" and first German commercial offshore wind farm "Baltic1". The work was carried out with measured data from meteorological measurement mast FINO1, measured power from wind farms and numerical weather prediction (NWP) from the German Weather Service (DWD). This study facilitates to decide the length of needed time series and selection of forecast method to get a reliable WPF on a weekly time axis. Weekly development of WPF quality for day-ahead WPF via different models is presented. The models are physical model; physical model extended with a statistical correction (MOS) and artificial neural network (ANN) as a pure statistical model. Selforganizing map (SOM) is investigated for a better understanding of uncertainties of forecast error.

M Kurt; B Lange

2014-01-01T23:59:59.000Z

309

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

SciTech Connect (OSTI)

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

Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL

2008-01-01T23:59:59.000Z

310

(2+1)-dimensional stars  

Science Journals Connector (OSTI)

We investigate, in the framework of (2+1)-dimensional gravity, stationary rotationally symmetric gravitational sources of the perfect fluid type, embedded in a space of an arbitrary cosmological constant. We show that the matching conditions between the interior and exterior geometries imply restrictions on the physical parameters of the solutions. In particular, imposing finite sources and the absence of closed timelike curves privileges negative values of the cosmological constant, yielding exterior vacuum geometries of rotating black hole type. In the special case of static sources, we prove the complete integrability of the field equations and show that the sources masses are bounded from above and, for a vanishing cosmological constant, generally equal to 1. We also discuss and illustrate the stationary configurations by explicitly solving the field equations for constant mass-energy densities. If the pressure vanishes, we recover as interior geometries Gdel-like metrics defined on causally well behaved domains, but with unphysical values of the mass to angular momentum ratio. The introduction of pressure in the sources cures the latter problem and leads to physically more relevant models.

M. Lubo; M. Rooman; Ph. Spindel

1999-01-22T23:59:59.000Z

311

Viscosity of the mixture (1) tetrahydrothiophene-1,1-dioxide; (2) 1,2-dimethylbenzene  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2009-01-01T23:59:59.000Z

312

Neutrino_Lectures_1and2  

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

NuTeV sin 2 W Measurement Direct Neutrino Mass Measurements Neutrino Oscillation Phenomenology Solar Neutrinos (part 1) Lecture 2: Solar Neutrinos (part 2) Atmospheric and...

313

An evaluation of market penetration forecasting methodologies for new residential and commercial energy technologies  

SciTech Connect (OSTI)

Forecasting market penetration is an essential step in the development and assessment of new technologies. This report reviews several methodologies that are available for market penetration forecasting. The primary objective of this report is to help entrepreneurs understand these methodologies and aid in the selection of one or more of them for application to a particular new technology. This report also illustrates the application of these methodologies, using examples of new technologies, such as the heat pump, drawn from the residential and commercial sector. The report concludes with a brief discussion of some considerations in selecting a forecasting methodology for a particular situation. It must be emphasized that the objective of this report is not to construct a specific market penetration model for new technologies but only to provide a comparative evaluation of methodologies that would be useful to an entrepreneur who is unfamiliar with the range of techniques available. The specific methodologies considered in this report are as follows: subjective estimation methods, market surveys, historical analogy models, time series models, econometric models, diffusion models, economic cost models, and discrete choice models. In addition to these individual methodologies, which range from the very simple to the very complex, two combination approaches are also briefly discussed: (1) the economic cost model combined with the diffusion model and (2) the discrete choice model combined with the diffusion model. This discussion of combination methodologies is not meant to be exhaustive. Rather, it is intended merely to show that many methodologies often can complement each other. A combination of two or more different approaches may be better than a single methodology alone.

Raju, P.S.; Teotia, A.P.S.

1985-05-01T23:59:59.000Z

314

Quarter 1, 2012 Page 1 of 2  

E-Print Network [OSTI]

incompatible chemicals? 16. Are incompatible chemicals segregated according to SU storage scheme? 17. Is lab-level contact. (Return roster with completed Q1-12 Self-Inspection checklist to department) 22. Our lab has. Make sure that ALL rooms listed for your PI have been included as part of this quarter's self

Ford, James

315

Forecasting aggregate time series with intermittent subaggregate components: top-down versus bottom-up forecasting  

Science Journals Connector (OSTI)

......optimum value through a grid-search algorithm...method outperformed TD for estimating the aggregate data series...variable, there is no benefit of forecasting each subaggregate...forecasting strategies in estimating the `component'-level...WILLEMAIN, T. R., SMART, C. N., SHOCKOR......

S. Viswanathan; Handik Widiarta; Rajesh Piplani

2008-07-01T23:59:59.000Z

316

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

SciTech Connect (OSTI)

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.

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

2014-05-01T23:59:59.000Z

317

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

E-Print Network [OSTI]

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

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

2014-01-01T23:59:59.000Z

318

Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands  

Science Journals Connector (OSTI)

A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain planning. Demand planning, which is the very first step of supply chain planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain forecasting system is necessary for industrial planners to quickly respond to the volatile demand. Appropriate demand aggregation and statistical forecasting approaches are known to be effective in managing the demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, forecasting and disaggregating two interrelated demands. Through theoretical development and systematic analysis, guidelines are provided to select proper demand planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated demand should be employed when the aggregated demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between demands can enhance the predictability and thus result in more accurate forecasts when statistical forecasting methods are used.

Argon Chen; Jakey Blue

2010-01-01T23:59:59.000Z

319

Danielle Shorts1, RL Hale1, S Earl2, NB Grimm1, 2 Introduction  

E-Print Network [OSTI]

Danielle Shorts1, RL Hale1, S Earl2, NB Grimm1, 2 . Introduction Inputs of nitrogen (N) accumulate on a gas chromatograph. Soil moisture content and SOM data were ascertained using Loss-on-Ignition. Soil and provision of materials. I would also like to thank Rebecca Hale for all of her help in the Arizona heat

Hall, Sharon J.

320

Properties of L=1 B1 and B2* Mesons  

Science Journals Connector (OSTI)

This Letter presents the first strong evidence for the resolution of the excited B mesons B1 and B2* as two separate states in fully reconstructed decays to B+(*)?-. The mass of B1 is measured to be 5720.62.41.4??MeV/c2 and the mass difference ?M between B2* and B1 is 26.23.10.9??MeV/c2, giving the mass of the B2* as 5746.82.41.7??MeV/c2. The production rate for B1 and B2* mesons is determined to be a fraction (13.91.93.2)% of the production rate of the B+ meson.

V. M. Abazov et al. (The D0 Collaboration)

2007-10-23T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

1 Introduction 1 2 Physics of n -n Oscillation 4  

E-Print Network [OSTI]

and Others . . . . . . . . . . 16 3.2.4 Water Purification . . . . . . . . . . . . . . . . . . . . 18 3.2 Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.2.1 Water Tank.2.5 Electronics and Data Acquisition System . . . . . . . 18 3.3 Calibrations

Tokyo, University of

322

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

E-Print Network [OSTI]

58 45 51 H Content (% mass) 13.6 14.5 15.5 14.3 15.1 Heat of Combust. (MJ/kg) 43.3 43.8 44.4 43.8 441 Table S1. Fuel Properties. JP-8 Blend-1 FT-1 Blend-2 FT-2 Feedstock Petroleum Petroleum & Natural Gas Natural Gas Petroleum & Coal Coal Sulfur (ppm by mass) 1148 699 19 658 22 Alkanes (% vol.) 50

Meskhidze, Nicholas

323

()!%()!%00%%$1)% (2!%%$1)% (2! (%3 % 4$ ()!%(%3 % 4$ ()!%00%%$1)% (2!5%%$1)% (2!5  

E-Print Network [OSTI]

mnleiffjgkdfl mnle omj pqj rlelsfgmtomj pqj rlelsfgmt a1 a2 a3a1 a2 a3a1 a2 an CARS PRICE FUEL SAFETY F(X1,X2 mnle omj pqj rlelsfgmtiffjgkdfl mnle omj pqj rlelsfgmt UTILITY FUNCTION PRICE FUEL SAFETY CAR 1 0 50 FUNCTION PRICE FUEL SAFETY high high unacc unacc low low unacc unacc high low good unacc med high good

Bohanec, Marko

324

Buildings Energy Data Book: 5.2 Windows  

Buildings Energy Data Book [EERE]

1 1 Residential Prime Window Sales, by Frame Type (Million Units) (1) New Construction 1990 1995 2000 2005 2007 2009 Remodeling/Replacement 1990 1995 2000 2005 2007 2009 Total Construction 1990 1995 2000 2005 2007 2009 Note(s): Source(s): AAMA, Industry Statistical Review and Forecast 1992, 1993 for Note 2; AAMA/NWWDA, Industry Statistical Review and Forecast 1996, 1997, Table 6, p. 6 for 1990; AAMA/WDMA, 2000 AAMA/WDMA Industry Statistical Review and Forecast, Feb. 2001, p. 6 for 1995; 2003 AAMA/WDMA Industry Statistical Review and Forecast, June 2004, p. 6 for 2000 and 2003; and LBNL, Savings from Energy Efficient Windows, Apr. 1993, p. 6 for window life span; AAMA/WDMA, Study of U.S. Market For Windows, Doors, and Skylights, Apr. 2006, p. 41 for 2005; AAMA/WDMA, U.S. Industry Statistical Review and

325

Forecasting the Dark Energy Measurement with Baryon Acoustic Oscillations: Prospects for the LAMOST surveys  

E-Print Network [OSTI]

The Large Area Multi-Object Spectroscopic Telescope (LAMOST) is a dedicated spectroscopic survey telescope being built in China, with an effective aperture of 4 meters and equiped with 4000 fibers. Using the LAMOST telescope, one could make redshift survey of the large scale structure (LSS). The baryon acoustic oscillation (BAO) features in the LSS power spectrum provide standard rulers for measuring dark energy and other cosmological parameters. In this paper we investigate the meaurement precision achievable for a few possible surveys: (1) a magnitude limited survey of all galaxies, (2) a survey of color selected red luminous galaxies (LRG), and (3) a magnitude limited, high density survey of zsurvey, we use the halo model to estimate the bias of the sample, and calculate the effective volume. We then use the Fisher matrix method to forecast the error on the dark energy equation of state and other cosmological parameters for different survey parameters. In a few cases we also use the Markov Chain Monte Carlo (MCMC) method to make the same forecast as a comparison. The fiber time required for each of these surveys is also estimated. These results would be useful in designing the surveys for LAMOST.

Xin Wang; Xuelei Chen; Zheng Zheng; Fengquan Wu; Pengjie Zhang; Yongheng Zhao

2008-09-17T23:59:59.000Z

326

Table HC1.2.1. Living Space Characteristics by  

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

1. Living Space Characteristics by" 1. Living Space Characteristics by" " Total, Heated, and Cooled Floorspace, 2005" ,,,"Total Square Footage" ,"Housing Units",,"Total1",,"Heated",,"Cooled" "Living Space Characteristics","Millions","Percent","Billions","Percent","Billions","Percent","Billions","Percent" "Total",111.1,100,225.8,100,179.8,100,114.5,100 "Total Floorspace (Square Feet)1" "Fewer than 500",3.2,2.9,1.2,0.5,1.1,0.6,0.4,0.3 "500 to 999",23.8,21.4,17.5,7.7,15.9,8.8,7.3,6.4 "1,000 to 1,499",20.8,18.7,24.1,10.7,22.6,12.6,13,11.4 "1,500 to 1,999",15.4,13.9,24.5,10.9,22.2,12.4,14,12.2

327

Genomedata Documentation Release 1.3.2  

E-Print Network [OSTI]

Genomedata Documentation Release 1.3.2 Michael M. Hoffman March 26, 2012 #12;#12;CONTENTS 1 Genomedata 1.3 documentation 3 1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 Indices and tables 19 Python Module Index 21 Index 23 i #12;ii #12;Genomedata Documentation

Noble, William Stafford

328

Lagrangian with U(1)-SU(2) mixing  

E-Print Network [OSTI]

Principal axis transformation is performed for a Lagrangian with a U(1)-SU(2) mixing term, that can cause a SU(2) deconfining transition.

Bernd A. Berg

2009-11-19T23:59:59.000Z

329

cwebch1 ICON cweb_ch1.ico cwebch2 ICON cweb_ch2.ico cwebs1 ...  

E-Print Network [OSTI]

cwebch1 ICON cweb_ch1.ico cwebch2 ICON cweb_ch2.ico cwebs1 ICON cweb_s1.ico cwebs2 ICON cweb_s2.ico dvi1 ICON dvi1.ico dvi2 ICON dvi2.ico gf1...

330

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc The marketing team of a new telecommunications company is usually tasked with producing forecasts for diverse three decades of experience working with telecommunications operators around the world we seek

McBurney, Peter

331

River Forecast Application for Water Management: Oil and Water?  

Science Journals Connector (OSTI)

Managing water resources generally and managing reservoir operations specifically have been touted as opportunities for applying forecasts to improve decision making. Previous studies have shown that the application of forecasts into water ...

Kevin Werner; Kristen Averyt; Gigi Owen

2013-07-01T23:59:59.000Z

332

Data Mining in Load Forecasting of Power System  

Science Journals Connector (OSTI)

This project applies Data Mining technology to the prediction of electric power system load forecast. It proposes a mining program of electric power load forecasting data based on the similarity of time series .....

Guang Yu Zhao; Yan Yan; Chun Zhou Zhao

2013-01-01T23:59:59.000Z

333

Power System Load Forecasting Based on EEMD and ANN  

Science Journals Connector (OSTI)

In order to fully mine the characteristics of load data and improve the accuracy of power system load forecasting, a load forecasting model based on Ensemble Empirical Mode ... is proposed in this paper. Firstly,...

Wanlu Sun; Zhigang Liu; Wenfan Li

2011-01-01T23:59:59.000Z

334

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network [OSTI]

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

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

2005-01-01T23:59:59.000Z

335

Beyond "Partly Sunny": A Better Solar Forecast | Department of...  

Energy Savers [EERE]

Beyond "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar...

336

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

This paper presents the National Energy Boards long term energy demand forecasting model in its present state of ... results of recent research at the NEB. Energy demand forecasts developed with the aid of this....

R. A. Preece; L. B. Harsanyi; H. M. Webster

1980-01-01T23:59:59.000Z

337

Forecasting Energy Demand Using Fuzzy Seasonal Time Series  

Science Journals Connector (OSTI)

Demand side energy management has become an important issue for energy management. In order to support energy planning and policy decisions forecasting the future demand is very important. Thus, forecasting the f...

?Irem Ual Sar?; Basar ztaysi

2012-01-01T23:59:59.000Z

338

2.1E Supplement  

E-Print Network [OSTI]

Supplement 2.IE Update AIR SOURCE HEAT PUMP ENHANCEMENTScurve for air source electric and gas heat pumps do not useP E = PSZ HEAT-SOURCE = GAS-HEAT-PUMP A four-pipe G H P air

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

339

Synthesis, structures, and properties of novel aminodisilanes bearing bulky substituents: 1,2-bis(1,1,2-trimethylpropyl)-1,1,2,2-tetrakis(diethylamino) disilane and 1,2-di-tert-butyl-1,1,2,2-tetrakis(diethylamino) disilane  

Science Journals Connector (OSTI)

Two novel tetraaminodisilanes, 1,2-bis(1,1,2-trimethylpropyl)-1,1,2,2-tetrakis(diethylamino)disilane (1) and 1,2-di-tert-butyl-1,1,2,2-tetrakis(diethylamino)disilane (2) were synthesized and X-ray crystallography analyses of these compounds were carried out. Reflecting the steric congestion, the Si?Si bonds are very long: 2.539(2) for bis(1,1,2-trimethylpropyl)disilane, and 2.4764(9) for di-tert-butyl-disilane. UV spectra and oxidation potentials of several tetraaminodialkyldisilanes are compared and discussed. In addition, in the chlorination of 1 with HCl, 1,1,2,2-tetrachloro-1,2-bis(1,1,2-trimethylpropyl)disilane (6) was obtained with a 72% yield.

Masafumi Unno; Mina Saito; Hideyuki Matsumoto

1995-01-01T23:59:59.000Z

340

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

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

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

2010-04-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

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

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

2010-04-15T23:59:59.000Z

342

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

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

Energy, Modeling & Analysis, News, News & Events, Partnership, Photovoltaic, Renewable Energy, Solar, Systems Analysis The book, Solar Energy Forecasting and Resource...

343

Power Evaluation of Jakarta DC Railway Substation to Meet 1.2 Million Passengers Per Day  

Science Journals Connector (OSTI)

Abstract Jakarta is a metropolitan city that continues to strive to develop the capacity of its mass transport as other large cities in the world, primarily the development of the rail-based transportation. Indonesia Railway Company (PT Kereta Api (Persero)) had forecasted that there will be 1.2 million passengers per day on Jakarta in 2019. To meet these targets, it is necessary to adequate the infrastructure planning, especially the capacity of the DC substations which supply the electric power to the trains. This paper will demonstrate the strategy used to determine the point of placement of new DC substation and/or existing DC substation upgrades throughout the Jakarta area from 2013 to 2019. The current railway timetable and the future pattern of railway operation plan are used to predict the possibilities of peak load spots along the track.

Yanuarsyah Haroen; Tri Desmana Rachmildha; M. Ikhsan; M. Ivan Fikriadi

2013-01-01T23:59:59.000Z

344

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

SciTech Connect (OSTI)

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

Not Available

1994-03-01T23:59:59.000Z

345

Video Textures Arno Schodl1,2  

E-Print Network [OSTI]

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

Meenakshisundaram, Gopi

346

Structure and Dynamics of CO2 on Rutile TiO2(110)-11....  

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

CO2 on Rutile TiO2(110)-11. Structure and Dynamics of CO2 on Rutile TiO2(110)-11. Abstract: Adsorption, binding, and diffusion of CO2 molecules on rutile TiO2(110)...

347

Application of a Combination Forecasting Model in Logistics Parks' Demand  

Science Journals Connector (OSTI)

Logistics parks demand is an important basis of establishing the development policy of logistics industry and logistics infrastructure for planning. In order to improve the forecast accuracy of logistics parks demand, a combination forecasting ... Keywords: Logistics parks' demand, combine, simulated annealing algorithm, grey forecast model, exponential smoothing method

Chen Qin; Qi Ming

2010-05-01T23:59:59.000Z

348

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

349

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

· advantage: no NWP data necessary ­ very actual shortest term forecasts possible · wind power inputAccuracy of near real time updates in wind power forecasting with regard to different weather October 2007 #12;EMS/ECAM 2007 ­ Nadja Saleck Outline · Study site · Wind power forecasting - method

Heinemann, Detlev

350

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE - APRIL 2014 Anil Puri, Ph.D. -- Director, Center for Economic Analysis and Forecasting -- Dean, Mihaylo College of Business and Economics Mira Farka, Ph.D. -- Co-Director, Center for Economic Analysis and Forecasting -- Associate Professor

de Lijser, Peter

351

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data in California and for climate zones within those areas. The staff California Energy Demand 2008-2018 forecast

352

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

E-Print Network [OSTI]

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

Povinelli, Richard J.

353

2.1E Supplement  

E-Print Network [OSTI]

Tvis 33. Rfvis 34. Rbvis 35. SHGC 36. SC: 40. Layer 1D# 41.W / m (248 B t u / h - ft )] SHGC Center-of-glass solar heatG-T-C WINDOW U-SI U-IP SC SHGC Tsol Rfsol Tvis Rfvis ID WID

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

354

. . () 2012 1 / 11 () N = {1,2,...,n} ui  

E-Print Network [OSTI]

. . . () 2012 2 / 11 #12; i N ( ) t = 0,1,2...,T xi t R, : xi t+1 = xi t +f i (xi t ,ui t), (1) ui t R, xi t f i (·,·) : R?R R, xi t ui t. . . () 2012 3 / 11 #12; i N = n(¯d +1) Amax ¯n ? ¯n : ai,j max = p i,((j-1)modn)+1 j÷n p i,((j-1)modn)+1 a bi,((j-1)modn)+1

Granichin, Oleg

355

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect (OSTI)

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To generate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations, auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to simulate forecast error curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and present some experimental results by generating new error forecasts together with their statistics.

Makarov, Yuri V.; Reyes Spindola, Jorge F.; Samaan, Nader A.; Diao, Ruisheng; Hafen, Ryan P.

2010-11-02T23:59:59.000Z

356

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

E-Print Network [OSTI]

An important determinant of our energy future is the rate at which energy conservation technologies, once developed, are put into use. At Synergic Resources Corporation, we have adapted and applied a methodology to forecast the use of conservation...

Lang, K.

1982-01-01T23:59:59.000Z

357

RSE Table S1.1 and S1.2. Relative Standard Errors for Tables S1.1 and S1.2  

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

S1.1 and S1.2. Relative Standard Errors for Tables S1.1 and S1.2;" S1.1 and S1.2. Relative Standard Errors for Tables S1.1 and S1.2;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," "," ",," "," ",," "," ",," ","Shipments" "SIC"," ",,"Net","Residual","Distillate",,"LPG and",,"Coke and"," ","of Energy Sources" "Code(a)","Major Group and Industry","Total(b)","Electricity(c)","Fuel Oil","Fuel Oil(d)","Natural Gas(e)","NGL(f)","Coal","Breeze","Other(g)","Produced Onsite(h)"

358

Application of a statistical post-processing technique to a gridded, operational, air quality forecast  

Science Journals Connector (OSTI)

Abstract An automated air quality forecast bias correction scheme based on the short-term persistence of model bias with respect to recent observations is described. The scheme has been implemented in the operational Met Office five day regional air quality forecast for the UK. It has been evaluated against routine hourly pollution observations for a year-long hindcast. The results demonstrate the value of the scheme in improving performance. For the first day of the forecast the post-processing reduces the bias from 7.02 to 0.53?gm?3 for O3, from?4.70 to?0.63?gm?3 for NO2, from?4.00 to?0.13?gm?3 for PM2.5 and from?7.70 to?0.25?gm?3 for PM10. Other metrics also improve for all species. An analysis of the variation of forecast skill with lead-time is presented and demonstrates that the post-processing increases forecast skill out to five days ahead.

L.S. Neal; P. Agnew; S. Moseley; C. Ordez; N.H. Savage; M. Tilbee

2014-01-01T23:59:59.000Z

359

: ................................................................................................................................................................................................. 1 1. ......................................................  

E-Print Network [OSTI]

: ................................................................................................................................................................................................. 1 1. ............................................................................................................................................................................................................................................................ 1 2

Moon, Sue B.

360

Forecasting the Locational Dynamics of Transnational Terrorism  

E-Print Network [OSTI]

Forecasting the Locational Dynamics of Transnational Terrorism: A Network Analytic Approach Bruce A-0406 Fax: (919) 962-0432 Email: skyler@unc.edu Abstract--Efforts to combat and prevent transnational terror of terrorism. We construct the network of transnational terrorist attacks, in which source (sender) and target

Massachusetts at Amherst, University of

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Sunny outlook for space weather forecasters  

Science Journals Connector (OSTI)

... For decades, companies have tailored public weather data for private customers from farmers to airlines. On Wednesday, a group of businesses said that they ... utilities and satellite operators. But Terry Onsager, a physicist at the SWPC, says that private forecasting firms are starting to realize that they can add value to these predictions. ...

Eric Hand

2012-04-27T23:59:59.000Z

362

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

363

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

Prediction versus Projection: How weather forecasting and climate models differ. Aaron B. Wilson Context: Global http://data.giss.nasa.gov/ #12;Numerical Weather Prediction Collect Observations alters associated weather patterns. Models used to predict weather depend on the current observed state

Howat, Ian M.

364

Customized forecasting tool improves reserves estimation  

SciTech Connect (OSTI)

Unique producing characteristics of the Teapot sandstone formation, Powder River basin, Wyoming, necessitated the creation of individualized production forecasting methods for wells producing from this reservoir. The development and use of a set of production type curves and correlations for Teapot wells are described herein.

Mian, M.A.

1986-04-01T23:59:59.000Z

365

Storm-in-a-Box Forecasting  

Science Journals Connector (OSTI)

...But the WRF has no immediate...being tuned to local conditions...temperatures and winds with altitude...resulting WRF forecasts...captured the local sea-breeze winds better...spread the local operation of mesoscale...to be the WRF model now...

Richard A. Kerr

2004-05-14T23:59:59.000Z

366

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect (OSTI)

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.

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

367

TTW 2-1-10  

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

10 10 WIPP Quick Facts (As of 1-31-10) 8,188 Shipments received since opening (7,869 CH and 319 RH) 65,198 Cubic meters of waste disposed (65,043 CH and 155 RH) 127,413 Containers disposed in the underground (127,101 CH and 312 RH) WIPP operations resume following outage Employees work in WIPP's underground during the extended maintenance outage. The outage ended on December 31 and waste handling and disposal has resumed. WIPP is back in disposal mode following a six-week maintenance outage. The facility resumed disposal operations the week of January 4 with all essential projects completed. The annual outage is used to complete major maintenance and upgrade projects that cannot be done while WIPP is receiving shipments and disposing of waste underground.

368

1360 IEEE Transactions on Power Systems, Vol. 12, No. 3, August 1997 Application of Fuzzy Logic Technology for Spatial Load Forecasting  

E-Print Network [OSTI]

of historical distribution load data [2]. The increasinglypopular, accurate, and affordable Geographic Informahon Systems (GIS) technology provides an excellent data base platform for spatial load forecasting on collecting relevant geographic data. Thus spatial load forecasting becomes even more attractive than before

Chow, Mo-Yuen

369

Forecasting of preprocessed daily solar radiation time series using neural networks  

SciTech Connect (OSTI)

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

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

2010-12-15T23:59:59.000Z

370

Volume 61, Numbers 1&2 (Complete)  

E-Print Network [OSTI]

EVENTEENTH- ENTURY EWS SPRING - SUMMER 2003 Vol. 61 Nos. 1&2 budleafswbudleafse Including THE NEO-LATIN NEWS Vol. 51, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 61, Nos. 1&2 SPRING-SUMMER, 2003 SCN, an official organ of the Milton Society... 61, Nos. 1&2 SPRING-SUMMER, 2003 ESSAY Bacon?s ?Serious Satire? of the Church and the ?Golden Medioc- rity? of Induction by KENNETH ALAN HOVEY ..................................... 1 REVIEWS Jonathan F.S. Post, ed., Green Thoughts, Green Shades...

Dickson, Donald

2003-01-01T23:59:59.000Z

371

V1 1 2 3 4 5 6 First Name  

E-Print Network [OSTI]

V1 1 2 3 4 5 6 Last Name: First Name: ID: Section: Math 150A Midterm #2. March 17, 2006 Attention using the definition of the derivative. (You are not supposed to use the power rule in this problem!) a is expanding in the ocean with its area increasing with the constant rate of 100 m2 per hour. How fast

Alekseenko, Alexander

372

Forecast of U. S. Refinery Demand for NGL's (natural gas liquids) in 1978-1985  

SciTech Connect (OSTI)

A forecast of U.S. Refinery Demand for NGL's (Natural Gas Liquids) in 1978-1985 is based on a predicted 1.4%/yr decline in motor gasoline consumption from 7.4 to 6.7 million bbl/day (Mbd), including a 2.6%/yr reduction from 5.3 to 4.4 Mbd for automobiles and a 1.3%/yr growth from 2.1 to 2.3 Mbd for trucks, because of slow growth rates in the U.S. automobile fleet (1.1%/yr) and average annual miles driven (0.9%/yr), a 3.9%/yr growth in average mileage from 14.2 to 18.6 mpg, and diesel penetration to the automobile market which should increase from 0.3 to 3.3%. Leaded gasoline's share is expected to decline from 68% of the market (5.1 Mbd, including 0.8 Mbd leaded premium) to 24% (1.7 Mbd, leaded regular only), including a drop from 56 to 6% for automobiles and from approx. 100 to 60% for trucks. This will require increased production of clean-octane reformates and alkylates and reduce the need for straight-run gasolines, but because of the decline in the total gasoline demand, these changes should be minimal. Butane demand from outside-refinery production should decrease by 5-6%/yr, and natural gasoline will be consumed according to available production as an isopentane source.

Laskosky, J.

1980-01-01T23:59:59.000Z

373

Prediction and uncertainty of Hurricane Sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system  

E-Print Network [OSTI]

- eral days prior to landfall of Hurricane Sandy (2012) are assessed. The performance of the track-permitting ensemble analysis and forecast system assimilating airborne Doppler radar observations Erin B. Munsell1 University (PSU) real-time convection-permitting hurricane analysis and forecasting system (WRF

374

Certified Bitcoins Giuseppe Ateniese1,2  

E-Print Network [OSTI]

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

375

table2.1_02.xls  

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

1 Nonfuel (Feedstock) Use of Combustible Energy, 2002; 1 Nonfuel (Feedstock) Use of Combustible Energy, 2002; Level: National and Regional Data; Row: NAICS Codes; Column: Energy Sources; Unit: Physical Units or Btu. Coke Residual Distillate Natural LPG and Coal and Breeze NAICS Total Fuel Oil Fuel Oil(b) Gas(c) NGL(d) (million (million Other(e) Code(a) Subsector and Industry (trillion Btu) (million bbl) (million bbl) (billion cu ft) (million bbl) short tons) short tons) (trillion Btu) Total United States RSE Column Factors: 1.4 0.4 1.6 1.2 1.2 1.1 0.7 1.2 311 Food 8 * * 7 0 0 * * 311221 Wet Corn Milling * 0 * 0 0 0 0 * 31131 Sugar * 0 * * 0 0 * * 311421 Fruit and Vegetable Canning * * * 0 0 0 0 * 312 Beverage and Tobacco Products 1 * * * 0 0 0 1 3121 Beverages * * * 0 0 0 0 *

376

RSE Table N1.1 and N1.2. Relative Standard Errors for Tables N1.1 and N1.2  

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

1 and N1.2. Relative Standard Errors for Tables N1.1 and N1.2;" 1 and N1.2. Relative Standard Errors for Tables N1.1 and N1.2;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," "," ",," "," ",," "," ",," ","Shipments" "NAICS"," ",,"Net","Residual","Distillate",,"LPG and",,"Coke and"," ","of Energy Sources" "Code(a)","Subsector and Industry","Total(b)","Electricity(c)","Fuel Oil","Fuel Oil(d)","Natural Gas(e)","NGL(f)","Coal","Breeze","Other(g)","Produced Onsite(h)"

377

A multi-spectral spatial convolution approach of rainfall forecasting using weather satellite imagery  

Science Journals Connector (OSTI)

Flood forecasting has long been a major topic of hydrologic research. Recent events and studies indicate that the success of flood forecasting in Taiwan depends heavily on the accuracy of real-time rainfall forecasting. In this study, we demonstrate a multi-spectral spatial convolution approach for real-time rainfall forecasting using geostationary weather satellite images. The approach incorporates cloud-top temperatures of three infrared channels in a spatial convolution context. It not only characterizes the inputoutput relationship between cloud-top temperature and rainfall at the ground level, but also is more consistent with physical and remote sensing principles than single-pixel matches. Point rainfall measurements at raingauge sites are up-scaled to pixel-average-rainfall by block kriging, then related to multi-spectral cloud-top temperatures derived from Geostationary Meteorological Satellite images by spatial convolution. The kernel function of the multispectral spatial convolution equation is solved by the least squares method. Through a cross-validation procedure, we demonstrate that the proposed approach is capable of achieving high accuracy for 1- to 3-h-lead pixel-average-rainfall forecasting.

Chiang Wei; Wei-Chun Hung; Ke-Sheng Cheng

2006-01-01T23:59:59.000Z

378

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

E-Print Network [OSTI]

1 Evaluation of Advanced Wind Power Forecasting Models ­ Results of the Anemos Project I. Martí1.kariniotakis@ensmp.fr Abstract An outstanding question posed today by end-users like power system operators, wind power producers or traders is what performance can be expected by state-of-the-art wind power prediction models. This paper

Paris-Sud XI, Université de

379

Graduates 6 2 1 5 5 1 4 2 2 2 3 Percent of Graduates with  

E-Print Network [OSTI]

Placement Database As of 4/2/2013 #12;29 Number of Grads with Placement Info Art History, PhD Graduates is captured in the TGS PhD Placement Database using graduate responses from the Exit Survey and Survey of Earned Doctorates, and updated with the help of faculty and staff after each graduation. The database

Grzybowski, Bartosz A.

380

DOE M 231.1-2  

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

M 231.1-2 M 231.1-2 Approved: 08-19-03 OCCURRENCE REPORTING AND PROCESSING OF OPERATIONS INFORMATION U.S. DEPARTMENT OF ENERGY Office of Environment, Safety and Health DOE M 231.1-2 Page i (and ii) 08-19-03 SUBJECT: OCCURRENCE REPORTING AND PROCESSING OF OPERATIONS INFORMATION 1. PURPOSE. This Manual provides detailed requirements to supplement DOE O 231.1A, Environment, Safety, and Health Reporting, dated 08-19-03. This Manual is approved for use by all DOE Elements and their contractors. 2. REFERENCE. DOE O 231.1A, Environment, Safety, and Health Reporting, dated 08-19-03. 3. CANCELLATION. DOE M 232.1-1A, Occurrence Reporting and Processing of Operations Information, dated 7-21-97. Cancellation of a Manual does not, by itself, modify or otherwise

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Substituted 1,2-azaborine heterocycles  

DOE Patents [OSTI]

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

Liu, Shih-Yuan; Lamm, Ashley

2014-12-30T23:59:59.000Z

382

RSE Table S2.1 and S2.2. Relative Standard Errors for Tables S2.1 and S2.2  

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

S2.1 and S2.2. Relative Standard Errors for Tables S2.1 and S2.2;" S2.1 and S2.2. Relative Standard Errors for Tables S2.1 and S2.2;" " Unit: Percents." " "," "," ",," "," "," "," "," "," ",," " "SIC"," "," ","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Major Group and Industry","Total","Fuel Oil","Fuel Oil(b)","Natural Gas(c)","NGL(d)","Coal","and Breeze","Other(e)" ,,"Total United States" , 20,"Food and Kindred Products",5,0,8,0,0,0,0,7 21,"Tobacco Products",0,0,0,0,0,0,0,0

383

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

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

N2.1 and N2.2. Relative Standard Errors for Tables N2.1 and N2.2;" N2.1 and N2.2. Relative Standard Errors for Tables N2.1 and N2.2;" " Unit: Percents." " "," " "NAICS"," "," ","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Subsector and Industry","Total","Fuel Oil","Fuel Oil(b)","Natural Gas(c)","NGL(d)","Coal","and Breeze","Other(e)" ,,"Total United States" , 311,"Food",6,0,8,0,0,0,0,7 312,"Beverage and Tobacco Products",10,0,82,0,0,0,0,9 313,"Textile Mills",19,0,77,3,20,0,0,48 314,"Textile Product Mills",38,0,0,38,27,0,0,42

384

The orientation of 2,2?-bipyridine adsorbed at a SERS-active Au(1 1 1) electrode surface  

E-Print Network [OSTI]

The orientation of 2,2?-bipyridine adsorbed at a SERS-active Au(1 1 1) electrode surface A.G. Brolo (SERS) spectra from 2,2?-bipyridine (22BPY) adsorbed on a SERS-active Au(1 1 1) electrode at several to occur. The adsorbed 22BPY may assume several conformations, including the cis- and trans

Brolo, Alexandre G.

385

Deciphering the splicing code Yoseph Barash1,2  

E-Print Network [OSTI]

ARTICLES Deciphering the splicing code Yoseph Barash1,2 *, John A. Calarco2 *, Weijun Gao1 , Qun Pan2 , Xinchen Wang1,2 , Ofer Shai1 , Benjamin J. Blencowe2 & Brendan J. Frey1,2,3 Alternative

Frey, Brendan J.

386

Microsoft Word - UPDATE 2 - Unit 1.doc  

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

2 to: 2 to: A Dispersion Modeling Analysis of Downwash from Mirant's Potomac River Power Plant Modeling Unit 1 Emissions at Maximum and Minimum Loads ENSR Corporation December 20, 2005 Document Number 10350-002-410 (Update 2) December, 2005 1-1 1.0 INTRODUCTION This report describes AERMOD modeling results performed for Unit 1 at Mirant's Potomac River Generating Station. The purpose of these runs was to demonstrate that operation of Unit 1 for 24 hours a day at loads from 35 MW to 88 MW with the use of trona to reduce SO 2 emissions will not cause or contribute to modeled exceedances of the National Ambient Air Quality Standards (NAAQS). Mirant proposes to use trona on an as needed basis to limit SO 2 emissions to less than 0.89 lb/MMBtu

387

Short-Term World Oil Price Forecast  

Gasoline and Diesel Fuel Update (EIA)

4 4 Notes: This graph shows monthly average spot West Texas Intermediate crude oil prices. Spot WTI crude oil prices peaked last fall as anticipated boosts to world supply from OPEC and other sources did not show up in actual stocks data. So where do we see crude oil prices going from here? Crude oil prices are expected to be about $28-$30 per barrel for the rest of this year, but note the uncertainty bands on this projection. They give an indication of how difficult it is to know what these prices are going to do. Also, EIA does not forecast volatility. This relatively flat forecast could be correct on average, with wide swings around the base line. Let's explore why we think prices will likely remain high, by looking at an important market barometer - inventories - which measures the

388

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

FRED FRED http://en.openei.org/community/group/fred Description: Free Energy Database Tool on OpenEI This is an open source platform for assisting energy decision makers and policy makers in formulating policies and energy plans based on easy to use forecasting tools, visualizations, sankey diagrams, and open data. The platform will live on OpenEI and this community was established to initiate discussion around continuous development of this tool, integrating it with new datasets, and connecting with the community of users who will want to contribute data to the tool and use the tool for planning purposes. energy data + forecasting Fri, 22 Jun 2012 15:30:20 +0000 Dbrodt 34

389

FORSITE: a geothermal site development forecasting system  

SciTech Connect (OSTI)

The Geothermal Site Development Forecasting System (FORSITE) is a computer-based system being developed to assist DOE geothermal program managers in monitoring the progress of multiple geothermal electric exploration and construction projects. The system will combine conceptual development schedules with site-specific status data to predict a time-phased sequence of development likely to occur at specific geothermal sites. Forecasting includes estimation of industry costs and federal manpower requirements across sites on a year-by-year basis. The main advantage of the system, which relies on reporting of major, easily detectable industry activities, is its ability to use relatively sparse data to achieve a representation of status and future development.

Entingh, D.J.; Gerstein, R.E.; Kenkeremath, L.D.; Ko, S.M.

1981-10-01T23:59:59.000Z

390

J. E. SMITH1, 2 , L. KORSTEN1  

E-Print Network [OSTI]

230 J. E. SMITH1, 2 , L. KORSTEN1 AND T. A. S. AVELING2 " Department of Microbiology and Plant of a new anthracnose disease of cowpea (Vigna unguiculata (L.) Walp.) in South Africa (Smith & Aveling. Sunken, necrotic lesions and small black acervuli are visible on the stems (Smith & Aveling, 1997). Three

391

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

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.

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

2014-12-30T23:59:59.000Z

392

Universit Paris Descartes -UFR STAPS -Planning 2013-2014 (proposition 1B2) Arts Thrapies D 1 Ma 1 V 1 D 1 Me 1 S 1 S 1 Ma 1 J 1 D 1 Ma 1  

E-Print Network [OSTI]

Université Paris Descartes - UFR STAPS - Planning 2013-2014 (proposition 1B2) Arts Thérapies D 1 Ma 1 V 1 D 1 Me 1 S 1 S 1 Ma 1 J 1 D 1 Ma 1 L 2 Me 2 S 2 L 2 J 2 D 2 D 2 Me 2 V 2 L 2 Me 2 Ma 3 J 3 D 3 Ma 3 V 3 L 3 L 3 J 3 S 3 Ma 3 J 3 Me 4 V 4 L 4 Me 4 S 4 Ma 4 Ma 4 V 4 D 4 Me 4 V 4 J 5 S 5 Ma 5 J 5 D

Pellier, Damien

393

Refactoring MATLAB Soroush Radpour1,2  

E-Print Network [OSTI]

Refactoring MATLAB Soroush Radpour1,2 , Laurie Hendren2 , and Max Sch¨afer3 1 Google, Inc. soroush of Computer Engineering, Nanyang Technological University, Singapore schaefer@ntu.edu.sg Abstract. MATLAB and students world-wide. MATLAB programs are often developed incrementally using a mixture of MATLAB scripts

394

Exponential smoothing model selection for forecasting  

Science Journals Connector (OSTI)

Applications of exponential smoothing to forecasting time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to selecting the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected exponential smoothing: for sub-annual series it is the seasonal adaptation of trend corrected exponential smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with exponential smoothing to estimate the smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approaches provide the best basis for automated method selection, the Akaike information criteria having a slight edge over its information criteria counterparts.

Baki Billah; Maxwell L. King; Ralph D. Snyder; Anne B. Koehler

2006-01-01T23:59:59.000Z

395

Solar Wind Forecasting with Coronal Holes  

E-Print Network [OSTI]

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

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

2007-01-09T23:59:59.000Z

396

PSYCHOLOGY MAJORS --1 PSYCHOLOGY MAJORS --2  

E-Print Network [OSTI]

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

Sanders, Matthew

397

GENERAL TECHNICAL REPORT PSW-GTR-245 Forecasting Productivity in Forest Fire  

E-Print Network [OSTI]

, efficiency analysis) for economic analysis of the potential hazard posed by forest ecosystems conditionsGENERAL TECHNICAL REPORT PSW-GTR-245 50 Forecasting Productivity in Forest Fire Suppression Francisco Rodríguez y Silva2 and Armando González-Cabán3 Abstract The abandonment of land, the high energy

Standiford, Richard B.

398

European Wind Energy Conference -Brussels, Belgium, April 2008 Data mining for wind power forecasting  

E-Print Network [OSTI]

European Wind Energy Conference - Brussels, Belgium, April 2008 Data mining for wind power-term forecasting of wind energy produc- tion up to 2-3 days ahead is recognized as a major contribution the improvement of predic- tion systems performance is recognised as one of the priorities in wind energy research

Paris-Sud XI, Université de

399

Ensemble Streamflow Forecasting: Methods & Applications  

E-Print Network [OSTI]

& Architectural Engineering (CEAE), University of Colorado, Boulder, CO, USA 2 CIRES, University of Colorado)/CEAE, University of Colorado, Boulder, CO Key words: Streamflow, Climate Variability, Climate Diagnostics, Ensemble impacts on the western US hydroclimatology. The basins studied and data used are described in sections 7

Balaji, Rajagopalan

400

Slide 1  

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

Efficiency Efficiency Opportunities and Barriers Steven Nadel, Executive Director American Council for an Energy-Efficient Economy April 2010 Share of Maryland Electricity Sales That Can Be Met by Efficiency Policies - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5 2 0 1 7 2 0 1 9 2 0 2 1 2 0 2 3 2 0 2 5 Electricity Demand (GWh) CHP Building Codes RD&D Initiative Appliance Standards State and Utility Programs 15% reduction in forecasted consumption by 2015 29% reduction in forecasted consumption by 2025 Role of Efficiency in Addressing Climate Change in the U.S. Note: This graph is stylized and is not exact. Energy Efficiency Resource Standards 22 States - February 2009 Standard Voluntary Goal Pending Standard Combined RES/EERS Source: Institute for Electric Efficiency Efficiency Savings in ACES Relative

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

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

E-Print Network [OSTI]

by one blank line, and from the paper body by two blank lines. 1. INTRODUCTION Fluctuations of solarSHORT-TERM FORECASTING OF SOLAR RADIATION BASED ON SATELLITE DATA WITH STATISTICAL METHODS Annette Solar World Congress. This portion of the paper is the abstract. The abstract should not exceed 250

Heinemann, Detlev

402

EVALUATION OF PV GENERATION CAPICITY CREDIT FORECAST ON DAY-AHEAD UTILITY MARKETS  

E-Print Network [OSTI]

City, and Sacramento Municipal Utility District, in California 1. BACKGROUND The effective capacity comfortable if capacity could be ascertained operationally by knowing in advance what the output of solar of the NDFD-based solar radiation forecasts for several climatically distinct locations, the evaluation is now

Perez, Richard R.

403

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

E-Print Network [OSTI]

percent) of hurricane activity relative to climatology. (as of 11 September 2014) By Philip J. Klotzbach1 that the next two weeks will be characterized by activity at below- average levels (climatology the September 11 ­ September 24 forecast period with respect to climatology. The September 11 ­ September 24

404

U.S. Economic Outlook and Forecasts Surviving the Recovery: Shaken, and Stirred...  

E-Print Network [OSTI]

5 U.S. Economic Outlook and Forecasts Surviving the Recovery: Shaken, and Stirred... "It ain't over litany of bleak macro data and gloomy economic projec- tions. Indeed, for most sectors and most folks economic activity fell by an astounding -5.1% during the recession -- a much deeper collapse than

de Lijser, Peter

405

Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California  

Science Journals Connector (OSTI)

...given in Table 1, as well as background earthquakes...in the test region as well as forecasts that excluded...about 50 km south of the MexicoUnited States border...this is the Cerra Prieto geothermal area...earthquake in northern Mexico. This earthquake occurred...

Ya-Ting Lee; Donald L. Turcotte; James R. Holliday; Michael K. Sachs; John B. Rundle; Chien-Chih Chen; Kristy F. Tiampo

2011-01-01T23:59:59.000Z

406

Today's Forecast: Improved Wind Predictions | Department of Energy  

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

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

407

Today's Forecast: Improved Wind Predictions | Department of Energy  

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

Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions Today's Forecast: Improved Wind Predictions July 20, 2011 - 6:30pm Addthis Stan Calvert Wind Systems Integration Team Lead, Wind & Water Power Program What does this project do? It will increase the accuracy of weather forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind power production. Accurate weather forecasts are critical for making energy sources -- including wind and solar -- dependable and predictable. These forecasts also play an important role in reducing the cost of renewable energy by allowing electricity grid operators to make timely decisions on what reserve generation they need to operate their systems.

408

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

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.

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

2013-10-01T23:59:59.000Z

409

Environmental Compliance 2-1 2. Environmental Compliance  

E-Print Network [OSTI]

and DOE National Nuclear Security Administration policy to conduct its operations in compliance, and best management practices. DOE and its contractors make every effort to conduct operationsEnvironmental Compliance 2-1 2. Environmental Compliance It is DOE Oak Ridge Operations Office

Pennycook, Steve

410

Search by Voice in Mandarin Chinese Jiulong Shan1, Genqing Wu1, Zhihong Hu2, Xiliu Tang1, Martin Jansche2, Pedro J. Moreno2  

E-Print Network [OSTI]

Jansche2, Pedro J. Moreno2 1Google China Engineering Center, No. 1 Zhongguancun East Road, Beijing 100084

Tomkins, Andrew

411

TableHC2.1.xls  

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

Total.............................................................. Total.............................................................. 111.1 78.1 64.1 4.2 1.8 2.3 5.7 Census Region and Division Northeast.................................................... 20.6 13.4 10.4 1.4 1.0 0.3 0.4 New England........................................... 5.5 3.8 3.1 Q 0.3 Q Q Middle Atlantic........................................ 15.1 9.6 7.3 1.3 0.6 Q Q Midwest...................................................... 25.6 19.4 16.9 1.0 0.5 0.4 0.7 East North Central.................................. 17.7 13.6 11.7 0.7 0.5 Q 0.3 West North Central................................. 7.9 5.8 5.2 Q Q Q 0.3 South.......................................................... 40.7 28.9 23.8 0.8 Q 0.9 3.1 South Atlantic.......................................... 21.7 15.3 12.0 0.6 Q 0.9 1.5 East South Central..................................

412

Stellar Astrophysics Requirements NERSC Forecast  

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

Requirements for Requirements for m461:Stellar Explosions in Three Dimensions Tomek Plewa (Florida State University) + 3 graduate students, Artur Gawryszczak (Warsaw), Konstantinos Kifonidis (Munich), Andrzej Odrzywolek (Cracow), Ju Zhang (FIT), Andrey Zhiglo (Kharkov) 1. m461: Stellar Explosions in Three Dimensions * Summarize your projects and expected scientific objectives through 2014 * Modeling and simulations of transient phenomena in stellar astrophysics driven by either radiation or thermonuclear processes * Numerical solution of a coupled system of PDEs and ODEs * Tame nonlinearity! * Our goal is to ... * Explain observed properties of exploding stellar objects * Present focus is ... * Neutrino-driven core-collapse supernova explosions * In the next 3 years we expect to ...

413

Electric Grid - Forecasting system licensed | ornl.gov  

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

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

414

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network [OSTI]

??Electricity generated from wind power is both variable and uncertain. Wind forecasts provide valuable information for wind farm management, but they are not perfect. Chapter (more)

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

415

Forecasting supply/demand and price of ethylene feedstocks  

SciTech Connect (OSTI)

The history of the petrochemical industry over the past ten years clearly shows that forecasting in a turbulent world is like trying to predict tomorrow's headlines.

Struth, B.W.

1984-08-01T23:59:59.000Z

416

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

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

for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net Cost Recovery Adjustment Clause (SN CRAC) FY 2003 Third Quarter Review Forecast in Millions...

417

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

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

for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net Cost Recovery Adjustment Clause (SN CRAC) FY 2004 Second Quarter Review Forecast In Millions...

418

Integrating agricultural pest biocontrol into forecasts of energy biomass production  

E-Print Network [OSTI]

Analysis Integrating agricultural pest biocontrol into forecasts of energy biomass production T pollution, greenhouse gas emissions, and soil erosion (Nash, 2007; Searchinger et al., 2008). On the other

Gratton, Claudio

419

Number: SMF 9.1 Revision: 2 Effective Date: 1 Feb 13 Page 1 of 2 OSU SHIP OPERATIONS  

E-Print Network [OSTI]

Number: SMF 9.1 Revision: 2 Effective Date: 1 Feb 13 Page 1 of 2 OSU SHIP OPERATIONS OBSERVATION Effective Date: 1 Feb 13 Page 2 of 2 6 Department Head's Review and Recommendations: Department Head Signature: Date: 7 Master's Review and Recommendations: Master Signature: Date: 8 Received by Ship

Kurapov, Alexander

420

Operating Plan 573.1-2  

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

573.1-2 Title: U.S. POSTAL MAIL, SHIPPING AND RECEIVING SECURITY Owner: Cindy Mullens, ESS&H Division, Office of Institutional Operations Approving Official: Thomas Wilson, Jr.,...

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

DOE-2 supplement: Version 2.1E  

SciTech Connect (OSTI)

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

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

1993-11-01T23:59:59.000Z

422

Virtual Laboratories > 15. Renewal Processes > 1 2 3 1. Introduction  

E-Print Network [OSTI]

rise to several interrelated random processes: the sequence of interarrival times, the sequenceVirtual Laboratories > 15. Renewal Processes > 1 2 3 1. Introduction A renewal process and the process is repeated. We do not count the replacement time in our analysis; equivalently we can assume

Demeio, Lucio

423

Electric-utility DSM programs: 1990 data and forecasts to 2000  

SciTech Connect (OSTI)

In April 1992, the Energy Information Administration (EIA) released data on 1989 and 1990 electric-utility demand-site management (DMS) programs. These data represent a census of US utility DSM programs, with reports of utility expenditures, energy savings, and load reductions caused by these programs. In addition, EIA published utility estimates of the costs and effects of these programs from 1991 to 2000. These data provide the first comprehensive picture of what utilities are spending and accomplishing by utility, state, and region. This report presents, summarizes, and interprets the 1990 data and the utility forecasts of their DSM-program expenditures and impacts to the year 2000. Only utilities with annual sales greater than 120 GWh were required to report data on their DSM programs to EIA. Of the 1194 such utilities, 363 reported having a DSM program that year. These 363 electric utilities spent $1.2 billion on their DSM programs in 1990, up from $0.9 billion in 1989. Estimates of energy savings (17,100 GWh in 1990 and 14,800 GWh in 1989) and potential reductions in peak demand (24,400 MW in 1990 and about 19,400 MW in 1989) also showed substantial increases. Overall, utility DSM expenditures accounted for 0.7% of total US electric revenues, while the reductions in energy and demand accounted for 0.6% and 4.9% of their respective 1990 national totals. The investor-owned utilities accounted for 70 to 90% of the totals for DSM costs, energy savings, and demand reductions. The public utilities reported larger percentage reductions in peak demand and energy smaller percentage DSM expenditures. These averages hide tremendous variations across utilities. Utility forecasts of DSM expenditures and effects show substantial growth in both absolute and relative terms.

Hirst, E.

1992-06-01T23:59:59.000Z

424

Microsoft PowerPoint - Session2_Rogers.pptx  

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

2008 - 2015 1,400 ACTUAL FORECAST search Program 1,200 Yemen USA - Kenai Trinidad Russia ACTUAL FORECAST atural Gas Res 1,000 Qatar Peru Papua New Guinea Oman Nameplate...

425

1 Journey to Lean 2 NTU community  

E-Print Network [OSTI]

of excellence 13 Diversified culture understanding 14 Sustainable development 15 Student Guidance Administration Committee :_ #12; 1 Student Safety Division 2 Military Training Affairs 3 Traffic Safety for Student Safety 8 Campus safety meetings 9 Campus safety affairs : #12; 1 NTU Career Activity

Wu, Yih-Min

426

Microsoft Word - Documentation - Price Forecast Uncertainty.doc  

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

October 2009 October 2009 1 October 2009 Short-Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1 Summary 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 market- clearing process for risk transfer can be used to generate "price bands" around observed futures prices for crude oil, natural gas, and other commodities. These bands provide a quantitative measure of uncertainty regarding the range in which markets expect prices to

427

Stockpile Stewardship Quarterly, Volume 2, Number 1  

National Nuclear Security Administration (NNSA)

1 * May 2012 1 * May 2012 Message from the Assistant Deputy Administrator for Stockpile Stewardship, Chris Deeney Defense Programs Stockpile Stewardship in Action Volume 2, Number 1 Inside this Issue 2 LANL and ANL Complete Groundbreaking Shock Experiments at the Advanced Photon Source 3 Characterization of Activity-Size-Distribution of Nuclear Fallout 5 Modeling Mix in High-Energy-Density Plasma 6 Quality Input for Microscopic Fission Theory 8 Fiber Reinforced Composites Under Pressure: A Case Study in Non-hydrostatic Behavior in the Diamond Anvil Cell 8 Emission of Shocked Inhomogeneous Materials 9 2012 NNSA Stewardship Science Academic

428

Interplay between intramolecular and intermolecular structures of 1,1,2,2-tetrachloro-1,2-difluoroethane  

Science Journals Connector (OSTI)

We report on the interplay between the short-range order of molecules in the liquid phase of 1,1,2,2-tetrachloro-1,2-difluoroethane and the possible molecular conformations, trans and gauche. Two complementary approaches have been used to get a comprehensive picture: analysis of neutron-diffraction data by a Bayesian fit algorithm and a molecular dynamics simulation. The results of both show that the population of trans and gauche conformers in the liquid state can only correspond to the gauche conformer being more stable than the trans conformer. Distinct conformer geometries induce distinct molecular short-range orders around them, suggesting that a deep intra- and intermolecular interaction coupling is energetically favoring one of the conformers by reducing the total molecular free energy.

M. Rovira-Esteva; N. A. Murugan; L. C. Pardo; S. Busch; J. Ll. Tamarit; Sz. Pothoczki; G. J. Cuello; F. J. Bermejo

2011-08-18T23:59:59.000Z

429

Forecasting for inventory control with exponential smoothing  

Science Journals Connector (OSTI)

Exponential smoothing, often used in sales forecasting for inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that exponential smoothing remains appropriate under more general conditions, where the variance is allowed to grow or contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular, the problem of finding the predictive distribution of aggregate lead-time demand, for use in inventory control calculations, is considered using a bootstrap approach. A method for establishing order-up-to levels directly from the simulated predictive distribution is also explored.

Ralph D. Snyder; Anne B. Koehler; J.Keith Ord

2002-01-01T23:59:59.000Z

430

Probabilistic Verification of Global and Mesoscale Ensemble Forecasts of Tropical Cyclogenesis  

Science Journals Connector (OSTI)

Probabilistic forecasts of tropical cyclogenesis have been evaluated for two samples: a near-homogeneous sample of ECMWF and Weather Research and Forecasting (WRF) Modelensemble Kalman filter (EnKF) ensemble forecasts during the National Science ...

Sharanya J. Majumdar; Ryan D. Torn

2014-10-01T23:59:59.000Z

431

3:2:1 Crack Spread  

Gasoline and Diesel Fuel Update (EIA)

:2:1 Crack Spread :2:1 Crack Spread Figure 1 Source: U.S. Energy Information Administration, based on Thomson Reuters. A crack spread measures the difference between the purchase price of crude oil and the selling price of finished products, such as gasoline and distillate fuel, that a refinery produces from the crude oil. Crack spreads are an indicator of the short-term profit margin of oil refineries because they compare the cost of the crude oil inputs to the wholesale, or spot, prices of the outputs (although they do not include other variable costs or any fixed costs). The 3:2:1 crack spread approximates the product yield at a typical U.S. refinery: for every three barrels of crude oil the refinery processes, it makes two barrels of gasoline and one barrel of distillate

432

Quantization of 2+1 gravity for genus 2  

Science Journals Connector (OSTI)

In previous papers we established and discussed the algebra of observables for 2+1 gravity at both the classical and quantum levels, and gave a systematic discussion of the reduction of the expected number of independent observables to 6g-6 (g>1). In this paper the algebra of observables for the case g=2 is reduced to a very simple form. A Hilbert space of state vectors is defined and its representations are discussed using a deformation of the Euler ? function. The deformation parameter ? depends on the cosmological and Plancks constants.

J. E. Nelson and T. Regge

1994-10-15T23:59:59.000Z

433

Summary: Sections 6.1 and 6.2, Part 1 Summary: Sections 6.1 and 6.2, Part 1  

E-Print Network [OSTI]

} orthogonal basis for W y W y = c1u1 + c2u2 + · · · + cpup c1 = y · u1 u1 · u1 , c2 = y · u2 u2 · u2

Myers, Amy

434

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

SciTech Connect (OSTI)

The 1993 Pacific Northwest Loads and Resources Study establishes the Bonneville Power Administration`s (BPA) planning basis for supplying electricity to BPA customers. The Loads and Resources Study is presented in three documents: (1) this technical appendix detailing loads and resources for each major Pacific and Northwest generating utility, (2) a summary of Federal system and Pacific Northwest region loads and resources, and (3) a technical appendix detailing forecasted Pacific Northwest economic trends and loads. This analysis updates the 1992 Pacific Northwest Loads and Resources Study Technical Appendix published in December 1992. This technical appendix provides utility-specific information that BPA uses in its long-range planning. It incorporates the following for each utility (1) Electrical demand firm loads; (2) Generating resources; and (3) Contracts both inside and outside the region. This document should be used in combination with the 1993 Pacific Northwest Loads and Resources Study, published in December 1993, because much of the information in that document is not duplicated here.

United States. Bonneville Power Administration.

1993-12-01T23:59:59.000Z

435

IEEE TRANSACTIONS ON POWER SYSTEMS 1 Economic Impact of Electricity Market Price  

E-Print Network [OSTI]

IEEE TRANSACTIONS ON POWER SYSTEMS 1 Economic Impact of Electricity Market Price Forecasting Errors to forecast electricity market prices and improve forecast accuracy. However, no studies have been reported, the application of electricity market price forecasts to short-term operation scheduling of two typical

Cañizares, Claudio A.

436

Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables  

Science Journals Connector (OSTI)

The stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in previous literature, different scenarios were developed by either assigning arbitrary values or assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and inputted to the scenario set. This article focuses on the long-term forecasting of electricity demand using autoregressive, simple linear and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario's electricity demand as a case study, the annual energy, peak load and base load demand were forecasted up to the year 2025. In order to generate different scenarios, different ranges in the economic, demographic and climatic variables were used. [Received 16 October 2007; Revised 31 May 2008; Revised 25 October 2008; Accepted 1 November 2008

F. Chui; A. Elkamel; R. Surit; E. Croiset; P.L. Douglas

2009-01-01T23:59:59.000Z

437

Random switching exponential smoothing and inventory forecasting  

Science Journals Connector (OSTI)

Abstract Exponential smoothing models represent an important prediction tool both in business and in macroeconomics. This paper provides the analytical forecasting properties of the random coefficient exponential smoothing model in the multiple source of error framework. The random coefficient state-space representation allows for switching between simple exponential smoothing and local linear trend. Therefore it enables controlling, in a flexible manner, the random changing dynamic behavior of the time series. The paper establishes the algebraic mapping between the state-space parameters and the implied reduced form ARIMA parameters. In addition, it shows that the parametric mapping allows overcoming the difficulties that are likely to emerge in estimating directly the random coefficient state-space model. Finally, it presents an empirical application comparing the forecast accuracy of the suggested model vis--vis other benchmark models, both in the ARIMA and in the exponential smoothing class. Using time series relative to wholesalers inventories in the USA, the out-of-sample results show that the reduced form of the random coefficient exponential smoothing model tends to be superior to its competitors.

Giacomo Sbrana; Andrea Silvestrini

2014-01-01T23:59:59.000Z

438

A robust automatic phase-adjustment method for financial forecasting  

Science Journals Connector (OSTI)

In this work we present the robust automatic phase-adjustment (RAA) method to overcome the random walk dilemma for financial time series forecasting. It consists of a hybrid model composed of a qubit multilayer perceptron (QuMLP) with a quantum-inspired ... Keywords: Financial forecasting, Hybrid models, Quantum-inspired evolutionary algorithm, Qubit multilayer perceptron, Random walk dilemma

Ricardo de A. Arajo

2012-03-01T23:59:59.000Z

439

Short term forecasting of solar radiation based on satellite data  

E-Print Network [OSTI]

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

Heinemann, Detlev

440

Developing electricity forecast web tool for Kosovo market  

Science Journals Connector (OSTI)

In this paper is presented a web tool for electricity forecast for Kosovo market for the upcoming ten years. The input data i.e. electricity generation capacities, demand and consume are taken from the document "Kosovo Energy Strategy 2009-2018" compiled ... Keywords: .NET, database, electricity forecast, internet, simulation, web

Blerim Rexha; Arben Ahmeti; Lule Ahmedi; Vjollca Komoni

2011-02-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

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

Keller, Arturo A.

442

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network [OSTI]

production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size. On the other hand if forecasted high production events do not occur, the cost of de- optimisation Energies and Energy Systems Sophia Antipolis, France andrea.michiorri@mines-paristech.fr Abstract

Paris-Sud XI, Université de

443

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network [OSTI]

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

Washington at Seattle, University of

444

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network [OSTI]

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

Hansens, Jim

445

RSE Table 2.1 Relative Standard Errors for Table 2.1  

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

2.1 Relative Standard Errors for Table 2.1;" 2.1 Relative Standard Errors for Table 2.1;" " Unit: Percents." " "," " " "," " "NAICS"," "," ","Residual","Distillate","Natural ","LPG and",,"Coke"," " "Code(a)","Subsector and Industry","Total","Fuel Oil","Fuel Oil(b)","Gas(c)","NGL(d)","Coal","and Breeze","Other(e)" ,,"Total United States" 311,"Food",31,0,91,35,0,0,0,47 311221," Wet Corn Milling",0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0 311421," Fruit and Vegetable Canning",1,0,0,0,0,0,0,8

446

Improving baseline forecasts in a 500-industry dynamic CGE model of the USA.  

E-Print Network [OSTI]

??MONASH-style CGE models have been used to generate baseline forecasts illustrating how an economy is likely to evolve through time. One application of such forecasts (more)

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

447

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect (OSTI)

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.

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

2014-10-27T23:59:59.000Z

448

SLCA/IP Hydro Generation Estimates Month Forecast Generation  

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

5/2013 9:06 5/2013 9:06 SLCA/IP Hydro Generation Estimates Month Forecast Generation less losses (kWh) Less Proj. Use (kWh) Net Generation (kWh) SHP Deliveries (kWh) Firming Purchases (kWh) Generation above SHP Level (kWH) 2013-Oct 232,469,911 13,095,926 219,373,985 398,608,181 192,676,761 - 2013-Nov 211,770,451 2,989,074 208,781,376 408,041,232 214,204,345 - 2013-Dec 252,579,425 3,106,608 249,472,817 455,561,848 221,545,708 - 2014-Jan 337,006,077 3,105,116 333,900,962 463,462,717 139,278,887 -

449

Short-Range Direct and Diffuse Irradiance Forecasts for Solar Energy Applications Based on Aerosol Chemical Transport and Numerical Weather Modeling  

Science Journals Connector (OSTI)

This study examines 23-day solar irradiance forecasts with respect to their application in solar energy industries, such as yield prediction for the integration of the strongly fluctuating solar energy into the electricity grid. During cloud-...

Hanne Breitkreuz; Marion Schroedter-Homscheidt; Thomas Holzer-Popp; Stefan Dech

2009-09-01T23:59:59.000Z

450

1.Enefit Overview 2.Enefit280 Process  

E-Print Network [OSTI]

· Jordan 38 000 bbl/d shale oil production, 600-900 MW power production under concession · USA 50 000 bbl figures: ·oil shale consumption: 2.26 M t/y ·Oil production: 1,900 M bbl/y ·retort gas production: 75 M m3 bn tonnes of oil shale mined to date · Reserves of more than 1 bn tons · Annual production ca. 15

Utah, University of

451

Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation  

E-Print Network [OSTI]

draft). Analyzing Fuel Price Risks Under CompetitiveCouncil (NWPPC). 2002. Fuel Price Forecasts for the DraftText Box 1: A Brief Survey of Past Literature on Fuel Price

Bolinger, Mark; Wiser, Ryan; Golove, William

2003-01-01T23:59:59.000Z

452

Variational Chemical Data Assimilation1 with Approximate Adjoints2  

E-Print Network [OSTI]

conditions, improvements in boundary values29 lead to improved air quality forecasts. Considerable experience-Var assimilation has48 been used to adjust gas phase chemical tracer initial conditions (Chai et al., 2007; Zhang49

Sandu, Adrian

453

1 Introduction 1 2 Fuzzy Logic and Fuzzy Control Systems 2  

E-Print Network [OSTI]

.1.2 Simulation results : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 4.2 Transient Response : : : : : : : : : : : : : : : : : : : : : : : : : : : : 11 10 ATM network model used for transient response simulations : : : : : : : : : : : : : 12 11 Source allowed cell rate transient response of simulated ATM LAN under EPRCA congestion control

Pitsillides, Andreas

454

Increasing the Reliability of Reliability Jochen Brocker1,  

E-Print Network [OSTI]

Increasing the Reliability of Reliability Diagrams Jochen Br¨ocker1, and Leonard A. Smith1,2 1 University Oxford, UK Corresponding Author, cats@lse.ac.uk November 9, 2006 #12;Abstract The reliability; for the most part, ambiguities arise from variation in the distribution of forecast probabilities and from

Stevenson, Paul

455

Page 1 of 2 Attachment G  

E-Print Network [OSTI]

. No residences within ¼ mile; Site 2: 321 Beach Street, San Francisco, CA, Existing gasoline/diesel fueling Department Labor Market Information Data Division Cite your data sources including name of data source, date Air Emissions- Formaldehyde, Diesel Particulate Matter, Benzene, 1,3 Butadiene, Acetaldehyde PROJECT

456

World Series Baseball 2k1  

Science Journals Connector (OSTI)

From the Publisher:The great American pastime just got a shot in the arm! World Series Baseball 2K1 for the Sega Dreamcast (based on the successful arcade game World Series Baseball 99) promises to be the most immersive baseball game ever ...

Mark Cohen; David Chong; Patrick Mauro

2000-08-01T23:59:59.000Z

457

Watershed Analysis1 Alan Gallegos2  

E-Print Network [OSTI]

Watershed Analysis1 Alan Gallegos2 Abstract Watershed analyses and assessments for the Kings River delivery attributable to roads indicate concern for several stream reaches as well. The Kings River Sustainable Forest Ecosystems Project area is located in Fresno County, approximately 32 air miles northeast

Standiford, Richard B.

458

PLot P1 & P2 Visitor Parkade  

E-Print Network [OSTI]

Town Square The Cornerstone The Hub Facilities Services Discovery 2 Discovery 1 Water Tower Building Library Convocation Mall Academic QuadrangleSFU Theatre Maggie Benston Centre West Mall Centre Pool Transportation Centre NAHEENO PARK RESIDENCES ARTS SCIENCES ATHLETICS & RECREATION BURNABY MOUNTAIN CONSERVATION

Park, Edward

459

1,2,3-triazolium ionic liquids  

DOE Patents [OSTI]

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

Luebke, David; Nulwala, Hunaid; Tang, Chau

2014-12-09T23:59:59.000Z

460

1 Old Faculty Club 2 Boyd House  

E-Print Network [OSTI]

, Visitor Center 23 Carpenter Hall 24 Carson Engineering Center 25 Devon Energy Hall 26 Felgar Hall 27 Swim Center 76 Cross Center 77 OCCE Cross Center Main 78 Coats Hall, Law 79 Sam Noble Oklahoma MuseumCAMPUS MAP 1 Old Faculty Club 2 Boyd House 3 Whitehand Hall 4 Catlett Music Center 5 Fred Jones Jr

Xue, Ming

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

1. Control moisture. 2. Clean regularly.  

E-Print Network [OSTI]

run help control pollutants. When outdoor air is brought into the home, ideally it is filtered1. Control moisture. 2. Clean regularly. 3. Ventilate to improve indoor air quality. 4. Keep provides a way to remove pollutants and to control humidity. Windows that open and exhaust fans #12;that

462

MAGNETIC EXPERIMENTS1,2 INTRODUCTION  

E-Print Network [OSTI]

APPENDIX 47 APPENDIX MAGNETIC EXPERIMENTS1,2 INTRODUCTION As noted in the Handbook for Shipboard is a vertically downward magnetization, much of which is frequently an easily de- magnetized, or soft, magnetization removed by ~10-mT demagnetization. There is also sometimes a radially inward horizontal component

463

RSE Table 1.2 Relative Standard Errors for Table 1.2  

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

2 Relative Standard Errors for Table 1.2;" 2 Relative Standard Errors for Table 1.2;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," "," ",," "," ",," "," ",," ","Shipments" "NAICS"," ",,"Net","Residual","Distillate","Natural","LPG and",,"Coke and"," ","of Energy Sources" "Code(a)","Subsector and Industry","Total(b)","Electricity(c)","Fuel Oil","Fuel Oil(d)","Gas(e)","NGL(f)","Coal","Breeze","Other(g)","Produced Onsite(h)"

464

Dielectric constant of the mixture (1) tetrahydrothiophene-1,1-dioxide; (2) 2-(2-hydroxyethoxy)-ethanol  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2008-01-01T23:59:59.000Z

465

Refractive index of the mixture (1) tetrahydrothiophene-1,1-dioxide; (2) 2-(2-hydroxyethoxy)-ethanol  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2008-01-01T23:59:59.000Z

466

PIES applications to NCSX 1 D. Monticello 1 , S. Hirshman 2 , A. Reiman 1 ,  

E-Print Network [OSTI]

PIES applications to NCSX 1 D. Monticello 1 , S. Hirshman 2 , A. Reiman 1 , 1 Princeton Plasma tools, PIES[1] and VMEC[2]. We first illustrate the flux surface quality in C82 by showing PIES results­axisymmetric candidate configuration for the NCSX experiment. Next, as part of our effort to qualify the PIES code

467

Summer 2014 Rates Room Type Session 1 & 2 Session 1 Only Session 1 Extended Session 2 Only  

E-Print Network [OSTI]

conditioning and Internet 3 - Single without shared 1/2 bath is on 13th floor with air conditioning and Internet 4 - Double is on the 13th floor with air conditioning and Internet 7 - Security Deposit: A $150 Bedroom Share is a 2 bedroom apartment 2 - Single with shared 1/2 bath is on 13th floor with air

Qiu, Weigang

468

Behavior of Transaldolase (EC 2.2.1.2) and Transketolase (EC 2.2.1.1) Activities in Normal, Neoplastic, Differentiating, and Regenerating Liver  

Science Journals Connector (OSTI)

...994Lung 68 4.0 93 1.652Heart 52 2.3 53 0.826Muscle 53 0.9 21 0.7 23 100 Experimentalcondi tionsCellularity (mil Iions/g)Protein content (mg/cell x 10@)Transaldolase activity (@.tmoles/ hr/cell x 10w)Transketolase activity...

Peter C. Heinrich; Harold P. Morris; George Weber

1976-09-01T23:59:59.000Z

469

Wave Propagation Theory 2.1 The Wave Equation  

E-Print Network [OSTI]

on the right side of Eq. (2.5) can be rewritten using Eqs. (2.3) and (2.4) as 2 p = 2 c2 + 1 c c(0) ( )2 . (2

470

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts The International Energy Outlook is prepared by the Energy Information Administration (EIA). General questions concerning the contents of the report should be referred to John J. Conti (john.conti@eia.doe.gov, 202-586-2222), Director, Office of Integrated Analysis and Forecasting. Specific questions about the report should be referred to Linda E. Doman (202/586-1041) or the following analysts: World Energy and Economic Outlook Linda Doman (linda.doman@eia.doe.gov, 202-586-1041) Macroeconomic Assumptions Nasir Khilji (nasir.khilji@eia.doe.gov, 202-586-1294) Energy Consumption by End-Use Sector Residential Energy Use John Cymbalsky (john.cymbalsky@eia.doe.gov, 202-586-4815) Commercial Energy Use Erin Boedecker (erin.boedecker@eia.doe.gov, 202-586-4791)

471

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Natural Gas Natural gas is the fastest growing primary energy source in the IEO2005 forecast. Consumption of natural gas is projected to increase by nearly 70 percent between 2002 and 2025, with the most robust growth in demand expected among the emerging economies. Figure 34. World Natural Gas Consumption, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center on 202-586-8800. Figure Data Figure 35. Natural Gas Consumption by Region, 1980-2025 (Trillion Cubic Feet). Need help, contact the National Energy Information Center at 202-586-8800. Figure Data Figure 36. Increase in Natural Gas Consumption by Region and Country, 2002-2025. Need help, contact the National Energy Information Center at 202-586-8800. Figure Data

472

Annual Energy Outlook 1998 Forecasts - Preface  

Gasoline and Diesel Fuel Update (EIA)

1998 With Projections to 2020 1998 With Projections to 2020 Annual Energy Outlook 1999 Report will be Available on December 9, 1998 Preface The Annual Energy Outlook 1998 (AEO98) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA's National Energy Modeling System (NEMS). The report begins with an “Overview” summarizing the AEO98 reference case. The next section, “Legislation and Regulations,” describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. “Issues in Focus” discusses three current energy issues—electricity restructuring, renewable portfolio standards, and carbon emissions. It is followed by the analysis

473

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption by End-Use Sector Energy Consumption by End-Use Sector In the IEO2005 projections, end-use energy consumption in the residential, commercial, industrial, and transportation sectors varies widely among regions and from country to country. One way of looking at the future of world energy markets is to consider trends in energy consumption at the end-use sector level. With the exception of the transportation sector, which is almost universally dominated by petroleum products at present, the mix of energy use in the residential, commercial, and industrial sectors can vary widely from country to country, depending on a combination of regional factors, such as the availability of energy resources, the level of economic development, and political, social, and demographic factors. This chapter outlines the International Energy Outlook 2005 (IEO2005) forecast for regional energy consumption by end-use sector.

474

Volatility forecasting with smooth transition exponential smoothing  

Science Journals Connector (OSTI)

Adaptive exponential smoothing methods allow smoothing parameters to change over time, in order to adapt to changes in the characteristics of the time series. This paper presents a new adaptive method for predicting the volatility in financial returns. It enables the smoothing parameter to vary as a logistic function of user-specified variables. The approach is analogous to that used to model time-varying parameters in smooth transition generalised autoregressive conditional heteroskedastic (GARCH) models. These non-linear models allow the dynamics of the conditional variance model to be influenced by the sign and size of past shocks. These factors can also be used as transition variables in the new smooth transition exponential smoothing (STES) approach. Parameters are estimated for the method by minimising the sum of squared deviations between realised and forecast volatility. Using stock index data, the new method gave encouraging results when compared to fixed parameter exponential smoothing and a variety of GARCH models.

James W. Taylor

2004-01-01T23:59:59.000Z

475

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect (OSTI)

Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

2014-07-09T23:59:59.000Z

476

Data:D10a15c5-84ae-4ce2-bbfe-ebf0f1bae191 | Open Energy Information  

Open Energy Info (EERE)

5c5-84ae-4ce2-bbfe-ebf0f1bae191 5c5-84ae-4ce2-bbfe-ebf0f1bae191 No revision has been approved for this page. It is currently under review by our subject matter experts. Jump to: navigation, search Loading... 1. Basic Information 2. Demand 3. Energy << Previous 1 2 3 Next >> Basic Information Utility name: Northern Indiana Pub Serv Co Effective date: 2011/12/27 End date if known: Rate name: Adjustment of Charges for Regional Transmission Organization Sector: Description: CHARGES FOR REGIONAL TRANSMISSION ORGANIZATION FACTOR Energy Charges in the Rate Schedules included in this Tariff are subject to charges to reflect the recovery of net non-fuel Midwest ISO costs and revenues above and below $5,326,931 on an annual basis and 50% sharing of off-system sale margins over $7,600,638 on an annual basis. Such charges shall be increased or decreased to the nearest 0.001 mill ($.000001) per KWH in accordance with the following: RTO Factor ("RTO") = (((E x Pe) + (D x Pd)) / S1) + ((OSS x Pd) / S2) Where: "RTO" is the rate adjustment for each Rate Schedule. "E" equals the total net non-fuel Midwest ISO costs and revenues above and below the base amount which are energy allocated. "Pe" represents the Production Energy Allocation percentage for each Rate Schedule. "D" equals the total non-fuel Midwest ISO costs and revenues which are demand allocated. "Pd" represents the Production Demand Allocation percentage for each Rate Schedule. "OSS" equals the total 50% sharing of Off-System Sales Margins over the base amount "S1" is the 6-month KWH sales forecast for each Rate Schedule. "S2" is the 12-month KWH sales forecast for each Rate Schedule.

477

Coal production forecast and low carbon policies in China  

Science Journals Connector (OSTI)

With rapid economic growth and industrial expansion, China consumes more coal than any other nation. Therefore, it is particularly crucial to forecast China's coal production to help managers make strategic decisions concerning China's policies intended to reduce carbon emissions and concerning the country's future needs for domestic and imported coal. Such decisions, which must consider results from forecasts, will have important national and international effects. This article proposes three improved forecasting models based on grey systems theory: the Discrete Grey Model (DGM), the Rolling DGM (RDGM), and the p value RDGM. We use the statistical data of coal production in China from 1949 to 2005 to validate the effectiveness of these improved models to forecast the data from 2006 to 2010. The performance of the models demonstrates that the p value RDGM has the best forecasting behaviour over this historical time period. Furthermore, this paper forecasts coal production from 2011 to 2015 and suggests some policies for reducing carbon and other emissions that accompany the rise in forecasted coal production.

Jianzhou Wang; Yao Dong; Jie Wu; Ren Mu; He Jiang

2011-01-01T23:59:59.000Z

478

PN1C.EF2a  

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

C.EF2a C.EF2a (.0 U.S. DEPARTMENT OF ENERGY EERE PROJECT MANAGEMENT CENTER NEPA DETERI1 ITNATION RECIPIENT:Parker Hannifin STATE: IN PROJECI TITLE : Energy Efficient Electronics Cooling Project Funding Opportunity Announcement Number Procurement Instrument Number NEPA Control Number CID Number CDP DE-EE0000412 GFO-10-043 0 Based on my review of the information concerning the proposed action, as NEPA Compliance Officer (authorized under DOE Order 451.1A), I have made the following determination: CX, EA, EIS APPENDIX AND NUMBER: Description: B3.6 Siting, construction (or modification), operation, and decommissioning of facilities for indoor bench-scale research projects and conventional laboratory operations (for example, preparation of chemical standards and sample analysis);

479

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

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

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

2005-07-01T23:59:59.000Z

480

Thermodynamics of (2+1)-flavor QCD  

E-Print Network [OSTI]

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

C. Schmidt; T. Umeda

2006-09-21T23:59:59.000Z

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
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they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

Inverse modeling and forecasting for the exploitation of the Pauzhetsky geothermal field, Kamchatka, Russia  

SciTech Connect (OSTI)

A three-dimensional numerical model of the Pauzhetsky geothermal field has been developed based on a conceptual hydrogeological model of the system. It extends over a 13.6-km2 area and includes three layers: (1) a base layer with inflow; (2) a geothermal reservoir; and (3) an upper layer with discharge and recharge/infiltration areas. Using the computer program iTOUGH2 (Finsterle, 2004), the model is calibrated to a total of 13,675 calibration points, combining natural-state and 1960-2006 exploitation data. The principal model parameters identified and estimated by inverse modeling include the fracture permeability and fracture porosity of the geothermal reservoir, the initial natural upflow rate, the base-layer porosity, and the permeabilities of the infiltration zones. Heat and mass balances derived from the calibrated model helped identify the sources of the geothermal reserves in the field. With the addition of five makeup wells, simulation forecasts for the 2007-2032 period predict a sustainable average steam production of 29 kg/s, which is sufficient to maintain the generation of 6.8 MWe at the Pauzhetsky power plant.

Finsterle, Stefan; Kiryukhin, A.V.; Asaulova, N.P.; Finsterle, S.

2008-04-01T23:59:59.000Z

482

Incorporation of 3D Shortwave Radiative Effects within the Weather Research and Forecasting Model  

SciTech Connect (OSTI)

A principal goal of the Atmospheric Radiation Measurement (ARM) Program is to understand the 3D cloud-radiation problem from scales ranging from the local to the size of global climate model (GCM) grid squares. For climate models using typical cloud overlap schemes, 3D radiative effects are minimal for all but the most complicated cloud fields. However, with the introduction of ''superparameterization'' methods, where sub-grid cloud processes are accounted for by embedding high resolution 2D cloud system resolving models within a GCM grid cell, the impact of 3D radiative effects on the local scale becomes increasingly relevant (Randall et al. 2003). In a recent study, we examined this issue by comparing the heating rates produced from a 3D and 1D shortwave radiative transfer model for a variety of radar derived cloud fields (O'Hirok and Gautier 2005). As demonstrated in Figure 1, the heating rate differences for a large convective field can be significant where 3D effects produce areas o f intense local heating. This finding, however, does not address the more important question of whether 3D radiative effects can alter the dynamics and structure of a cloud field. To investigate that issue we have incorporated a 3D radiative transfer algorithm into the Weather Research and Forecasting (WRF) model. Here, we present very preliminary findings of a comparison between cloud fields generated from a high resolution non-hydrostatic mesoscale numerical weather model using 1D and 3D radiative transfer codes.

O'Hirok, W.; Ricchiazzi, P.; Gautier, C.

2005-03-18T23:59:59.000Z

483

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

E-Print Network [OSTI]

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

Marquez, Ricardo

2012-01-01T23:59:59.000Z

484

Figure 6.1. A 2-to-1 multiplexer. (a) Graphical symbol  

E-Print Network [OSTI]

.25. A BCD-to-7-segment display code converter. ce 1 0 1 1 1 1 1 w0 a 1 b 0 1 1 1 1 0 1 1 0 1 0 0 w1 0 1 1 0 (a) Code converter w0 a w1 b c dw2 w3 e f g a g bf d (b)

Kalla, Priyank

485

Fisica Geral IA (2010/1) Nome 1 2 3 R1 R2 R3 Media Conc  

E-Print Network [OSTI]

Fisica Geral IA (2010/1) Nome 1 2 3 R1 R2 R3 M´edia Conc 1 ANDERSON SILVEIRA SALDANHA 0.5 5.2 4.7 0.7 3.5 4.5 1.5 - - 3.57 D M´edia 2.60 4.64 5.93 5.54 6.08 8.37 4.60 #12;20 40 60 80 100 A B C D FF 23

Stariolo, Daniel Adrián

486

florida land steward A Quarterly Newsletter for Florida Landowners and Resource Professionals spring/summer 2012 volume 1, no. 2  

E-Print Network [OSTI]

enterprise, but simply providing better climate forecasts to potential users is not enough. Climate 6 Certified Forest Stewards and Tree Farmers 7 #12;2 the florida land steward ­ spring/summer 2012, designed to transfer BMP technology to forest practitioners through workshops and field demon- strations

Watson, Craig A.

487

Dielectric constant of the mixture (1) ethane-1,2-diol; (2) tetrahydrothiophene-1,1-dioxide  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2008-01-01T23:59:59.000Z

488

Refractive index of the mixture (1) ethane-1,2-diol; (2) tetrahydrothiophene-1,1-dioxide  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2008-01-01T23:59:59.000Z

489

Book2.xls?attach=1  

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

Units 1&2 Off, Units 3,4,5 on 12 hrs @ 100% load and 12 hrs at 35% load. (100% load = 107 MW). Units 1&2 Off, Units 3,4,5 on 12 hrs @ 100% load and 12 hrs at 35% load. (100% load = 107 MW). SO2 = 0.22 lb/MBtu on all three units. Run 0600 - 1800 at 100% load, and the rest at 35% load on all units. AERMOD- PRIME Monitored Background AERMOD-PRIME + Background NAAQS Distance Direction Ground Elevation Flagpole Elevation X (m) Y (m) m deg m m 3-hour 720 238.4 958 1300 322787.7 4298786.0 174.8 354 4.6 39.6 24-hour 442 53.0 495 365 322787.7 4298786.0 174.8 354 4.6 39.6 Annual 61 15.7 77 80 322787.7 4298786.0 174.8 354 4.6 39.6 3-hour 852 238.4 1,090 1300 322787.7 4298786.0 174.8 354 4.6 39.6 24-hour 417 53.0 470 365 322787.7 4298786.0 174.8 354 4.6 39.6 Annual 69 15.7 84 80 322787.7 4298786.0 174.8 354 4.6 39.6 3-hour 784 238.4 1,022 1300 322787.7 4298786.0 174.8 354 4.6 39.6 24-hour 391 53.0 444 365 322787.7 4298786.0 174.8 354 4.6 39.6

490

Viscosity of the mixture (1) tetrahydrothiophene-1,1-dioxide; (2) 1,4-dimethylbenzene  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2009-01-01T23:59:59.000Z

491

Refractive index of the mixture (1) tetrahydrothiophene-1,1-dioxide; (2) 1-methylnapthalene  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2008-01-01T23:59:59.000Z

492

Viscosity of the mixture (1) 1,3-dioxolane; (2) tetrahydrothiophene-1,1-dioxide  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2009-01-01T23:59:59.000Z

493

Viscosity of the mixture (1) tetrahydrothiophene-1,1-dioxide; (2) 1,3-dimethylbenzene  

Science Journals Connector (OSTI)

Substance name(s): tetrahydrothiophene-1,1-dioxide; tetrahydrothiophene-S,S-dioxide; tetrahydro-thiophene-1,1 ... ,1-dioxide; thiacyclopentane dioxide; tetramethylene sulfone; tetrahydrothiophene 1...

Ch. Wohlfarth

2009-01-01T23:59:59.000Z

494

(ATLAS Muon TDC version 1 & 2) User's Manual  

E-Print Network [OSTI]

1 AMT-1 & 2 (ATLAS Muon TDC version 1 & 2) User's Manual Yasuo Arai KEK, National High Energy Accelerator Research Organization 1-1 Oho, Tsukuba, Ibaraki 305, Japan yasuo.arai@kek.jp, http://atlas

van Suijlekom, Walter

495

Diplom { VP HM III/IV { Numerik 9. August 2004 Aufgabe N1: (2+2+2+2+2 Punkte)  

E-Print Network [OSTI]

daher einfach invertiert werden. (d) Keine der Aussagen (a){(c) ist korrekt. #12; e) Die 1 gegen y 0 = 1. (d) Keine der Aussagen (a){(c) ist korrekt. Aufgabe N2: (3+3+3 Punkte) Gegeben = b mit Hilfe von Aufgabenteil a). c) Betrachten Sie nun das gest orte Gleichungssystem ~ A~x = ~ b

496

2. Disks and the Buffer Cache 2-1 Part 2: Disks and Caching  

E-Print Network [OSTI]

2. Disks and the Buffer Cache 2-1 Part 2: Disks and Caching References: · Elmasri Implementierung. · Mark Gurry, Peter Corrigan: Oracle Performance Tuning, 2nd Edition (with disk). · Oracle 8i.com/] · Wikipedia (RAID systems): [http://en.wikipedia.org/wiki/Redundant Array of Independent Disks] · The PC Guide

Brass, Stefan

497

1) Start the Instrument and Software 2 1.1 Start the Instrument 2  

E-Print Network [OSTI]

adhesive film. ColorofLeftLED ColorofRightLED StatusofInstrument Orange *flashing* Orange *flashing on the computer workstation (if it is not already on). 2. Login to Windows. a. User name: operator b. Password: LC Window will appear. Click on New Ex- periment. 2. The software will open the New Experiment Window

Gruner, Daniel S.

498

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) | Open  

Open Energy Info (EERE)

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Jump to: navigation, search LEDSGP green logo.png FIND MORE DIA TOOLS This tool is part of the Development Impacts Assessment (DIA) Toolkit from the LEDS Global Partnership. Tool Summary LAUNCH TOOL Name: Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Agency/Company /Organization: Energy Sector Management Assistance Program of the World Bank Sector: Energy Focus Area: Non-renewable Energy Topics: Baseline projection, Co-benefits assessment, GHG inventory Resource Type: Software/modeling tools User Interface: Spreadsheet Complexity/Ease of Use: Simple Website: www.esmap.org/esmap/EFFECT Cost: Free Equivalent URI: www.esmap.org/esmap/EFFECT Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Screenshot

499

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory  

Gasoline and Diesel Fuel Update (EIA)

Forecasting Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels MICHAEL YE, ∗ JOHN ZYREN, ∗∗ AND JOANNE SHORE ∗∗ Abstract This paper presents a short-term monthly forecasting model of West Texas Intermedi- ate crude oil spot price using OECD petroleum inventory levels. Theoretically, petroleum inventory levels are a measure of the balance, or imbalance, between petroleum production and demand, and thus provide a good market barometer of crude oil price change. Based on an understanding of petroleum market fundamentals and observed market behavior during the post-Gulf War period, the model was developed with the objectives of being both simple and practical, with required data readily available. As a result, the model is useful to industry and government decision-makers in forecasting price and investigat- ing the impacts of changes on price, should inventories,

500

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network [OSTI]

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

Choi, Han-Lim

2009-01-01T23:59:59.000Z