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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


1

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

2

The Los Alamos dynamic radiation environment assimilation model (DREAM) for space weather specification and forecasting  

SciTech Connect (OSTI)

The Dynamic Radiation Environment Assimilation Model (DREAM) was developed at Los Alamos National Laboratory to assess, quantify, and predict the hazards from the natural space environment and the anthropogenic environment produced by high altitude nuclear explosions (HANE). DREAM was initially developed as a basic research activity to understand and predict the dynamics of the Earth's Van Allen radiation belts. It uses Kalman filter techniques to assimilate data from space environment instruments with a physics-based model of the radiation belts. DREAM can assimilate data from a variety of types of instruments and data with various levels of resolution and fidelity by assigning appropriate uncertainties to the observations. Data from any spacecraft orbit can be assimilated but DREAM was designed to function with as few as two spacecraft inputs: one from geosynchronous orbit and one from GPS orbit. With those inputs, DREAM can be used to predict the environment at any satellite in any orbit whether space environment data are available in those orbits or not. Even with very limited data input and relatively simple physics models, DREAM specifies the space environment in the radiation belts to a high level of accuracy. DREAM has been extensively tested and evaluated as we transition from research to operations. We report here on one set of test results in which we predict the environment in a highly-elliptical polar orbit. We also discuss long-duration reanalysis for spacecraft design, using DREAM for real-time operations, and prospects for 1-week forecasts of the radiation belt environment.

Reeves, Geoffrey D [Los Alamos National Laboratory; Friedel, Reiner H W [Los Alamos National Laboratory; Chen, Yue [Los Alamos National Laboratory; Koller, Josef [Los Alamos National Laboratory; Henderson, Michael G [Los Alamos National Laboratory

2008-01-01T23:59:59.000Z

3

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...

4

ARM - AMF Science Questions  

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

AzoresAMF AzoresAMF Science Questions Azores Deployment AMF Home Graciosa Island Home Data Plots and Baseline Instruments Satellite Retrievals Experiment Planning CAP-MBL Proposal Abstract and Related Campaigns Science Questions Science Plan (PDF, 4.4M) Rob Wood Website Outreach Backgrounders English Version (PDF, 363K) Portuguese Version (PDF, 327K) AMF Posters, 2009 English Version Portuguese Version Education Flyers English Version Portuguese Version News Campaign Images AMF Science Questions Which synoptic-scale features dominate the variability in subtropical low clouds on diurnal to seasonal timescales over the NEA? Do physical, optical, and cloud-forming properties of aerosols vary with these synoptic features? How well can state-of-the-art weather forecast and climate models (run in forecast mode) predict the day-to-day variability of

5

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

6

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)

7

Microsoft Word - BingQuestionThreeLogistics0830.doc  

Gasoline and Diesel Fuel Update (EIA)

Review of Transportation Issues and Comparison of Infrastructure Costs for a Renewable Fuels Standard September 2002 ii Contacts This report was prepared by the Office of Integrated Analysis and Forecasting of the Energy Information Administration. General questions concerning the report may be directed to Mary J. Hutzler (202/586- 2222, mhutzler@eia.doe.gov), Director, Office of Integrated Analysis and Forecasting, or James Kendell (202/586-9646, james.kendell@eia.doe.gov), Director, Oil and Gas Division. Specific questions about the report may be directed to the following analyst: Amar Mann 202/586-2854 amar.man@eia.doe.gov Energy Information Administration/Review of Transportation Issues and Comparison 1 Review of Transportation Issues and Comparison of

8

Questions about how plants die leads to climate change answers  

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

Questions about how plants die leads to climate change answers Understanding mechanisms of mortality will provide important input to future climate forecasts. March 12, 2012 Tree...

9

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

10

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

11

Microsoft Word - BingQuestionTwoRnwbleFuelCapacity0830.doc  

Gasoline and Diesel Fuel Update (EIA)

Renewable Motor Fuel Production Renewable Motor Fuel Production Capacity Under H.R.4 September 2002 ii Contacts This report was prepared by the Office of Integrated Analysis and Forecasting of the Energy Information Administration. General questions concerning the report may be directed to Mary J. Hutzler (202/586-2222, mhutzler@eia.doe.gov), Director, Office of Integrated Analysis and Forecasting, or James Kendell (202/586-9646, james.kendell@eia.doe.gov), Director, Oil and Gas Division. Specific questions about the report may be directed to the following analysts: Ethanol Capacity Anthony Radich 202/586-0504 anthony.radich@eia.doe.gov Price Impacts Han-Lin Lee 202/586-4247 han-lin.lee@eia.doe.gov 1 Energy Information Administration/Renewable Motor Fuel Production Capacity

12

Frequently Asked Questions  

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

Questions Questions 1. Technology Transfer 2. Patent 3. Requirements for obtaining a patent 4. Copyright 5. Public Disclosure 6. Record of Invention 7. Publishing before filing a patent 8. Policy about laboratory notebooks 9. Materials Transfer Agreement 10. Non-Disclosure Agreement 11. Open Source Software Program 12. Role of Office of Technology Commercialization and Partnerships (TCP) 13. Role of Intellectual Property Legal Group (IPLG) 14. Location of TCP/IPLG If a specific question related to commercialization or partnerships is not addressed, please contact the TCP staff members that you normally work with or Colleen Michael at 631- 344 -4919. For specific questions related to intellectual property, please contact Dorene Price at 631-

13

Specific  

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

Specific Specific energy for pulsed laser rock drilling Z. Xu, a) C. B. Reed, and G. Konercki Technology Development Division, Argonne National Laboratory, Argonne, Illinois 60540 R. A. Parker b) Parker Geoscience Consulting, LLC, Arvada, Colorado 80403 B. C. Gahan Gas Technology Institute, Des Plains, Illinois 60018 S. Batarseh c) and R. M. Graves Department of Petroleum Engineering, Colorado School of Mines, Golden, Colorado 80401 H. Figueroa Petroleos de Venezuela INTEVEP, S.A., Caracas 1070A, Venezuela N. Skinner Halliburton Energy Service, Carrollton, Texas 75006 ͑Received 20 December 2001; accepted for publication 19 August 2002͒ Application of advanced high power laser technology to oil and gas well drilling has been attracting significant research interests recently among research institutes, petroleum industries, and universities. Potential laser or laser-aided

14

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

15

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...

16

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

17

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

18

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

19

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

20

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

22

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...

23

SEED Platform Frequently Asked Questions  

Broader source: Energy.gov [DOE]

This page features answers to the most Frequently Asked Questions about the DOE Standard Energy Efficiency Data (SEED) Platform, specifically on how SEED can help manage large building portfolios' energy performance.

24

Combination of Long Term and Short Term Forecasts, with Application to Tourism  

E-Print Network [OSTI]

Combination of Long Term and Short Term Forecasts, with Application to Tourism Demand Forecasting that are combined. As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we con- sider 33 source countries, as well as the aggregate. The novel

Abu-Mostafa, Yaser S.

25

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

26

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

27

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...

28

Frequently Asked Questions  

Broader source: Energy.gov [DOE]

This section is meant to cover the most common questions regarding the EERE Postdoctoral Research Awards. The Frequently Asked Questions (FAQs) are divided into several broad categories: general...

29

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

30

Introduction An important goal in operational weather forecasting  

E-Print Network [OSTI]

sensitive areas. To answer these questions simulation experiments with state-of-the-art numerical weather prediction (NWP) models have proved great value to test future meteorological observing systems a priori102 Introduction An important goal in operational weather forecasting is to reduce the number

Haak, Hein

31

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

32

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.

33

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,

34

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

35

Frequently Asked Questions  

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

What happens to my question after I submit it to this service? What happens to my question after I submit it to this service? Questions are received and filtered by the two system operators of NEWTON. Acceptable questions are assigned an appropriate category and are forwarded to the scientists that have volunteered to consider questions from that particular category. If one or more scientists address the question, it is returned to a system operator. The question and answers are returned to the person making the request and a copy is placed into an appropriate web page and posted. When may I expect an answer to my request? Our volunteer scientists respond to acceptable questions usually within a week after it is submitted; most within two days or less. Why have I not heard "anything" about the question I submitted?

36

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.

37

Web Feedback & Questions - Hanford Site  

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

Web Feedback & Questions Web Feedback & Questions Web Feedback & Questions Web Feedback & Questions Email Email Page | Print Print Page |Text Increase Font Size Decrease Font Size...

38

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

39

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

40

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

42

Questions about how plants die leads to climate change answers  

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

Questions about how plants die leads to climate change answers Questions about how plants die leads to climate change answers Questions about how plants die leads to climate change answers Understanding mechanisms of mortality will provide important input to future climate forecasts. March 12, 2012 Tree in the desert The scientists' goal is to provide basic insights into questions such as how plants die, especially during drought. While the question of plant mortality is easy to conceptualize, it is difficult to study because of the spatial and temporal variation of processes over the plant. Get Expertise Researcher Michelle Espy Applied Modern Physics Email Researcher Sanna Sevanto Earth System Observations Email While the question of plant mortality is easy to conceptualize, it is difficult to study because of the spatial and temporal variation of

43

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

44

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

45

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...

46

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.

47

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

48

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...

49

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

50

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

51

Conceptual design of a geothermal site development forecasting system  

SciTech Connect (OSTI)

A site development forecasting system has been designed in response to the need to monitor and forecast the development of specific geothermal resource sites for electrical power generation and direct heat applications. The system is comprised of customized software, a site development status data base, and a set of complex geothermal project development schedules. The system would use site-specific development status information obtained from the Geothermal Progress Monitor and other data derived from economic and market penetration studies to produce reports on the rates of geothermal energy development, federal agency manpower requirements to ensure these developments, and capital expenditures and technical/laborer manpower required to achieve these developments.

Neham, E.A.; Entingh, D.J.

1980-03-01T23:59:59.000Z

52

Problem of Questioning  

ScienceCinema (OSTI)

Le Prof.Leprince-Ringuet, chercheur sur le plan scientifique, artistique et humain, parle de la remise en question des hommes et la remise en question scientifique fondamentale ou exemplaire- plusieurs personnes prennent la parole p.ex Jeanmairet, Adam, Gregory. Le Prof.Gregory clot la soire en remerciant le Prof.Leprince-Ringuet

None

2011-04-25T23:59:59.000Z

53

Application of a medium-range global hydrologic probabilistic forecast scheme to the Ohio River Basin  

SciTech Connect (OSTI)

A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003-2007. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium Range Weather Forecasts (ECMWF) analysis temperatures and wind, and Tropical Rainfall Monitoring Mission Multi Satellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF ensemble prediction system (EPS) 10-day ensemble forecast. A parallel set up was used where ECMWF EPS forecasts were interpolated to the spatial scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effect of initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The flood prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. Initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.

Voisin, Nathalie; Pappenberger, Florian; Lettenmaier, D. P.; Buizza, Roberto; Schaake, John

2011-08-15T23:59:59.000Z

54

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

55

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

56

ICCS questions received by March 19, 2010  

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

questions received by March 19, 2010 questions received by March 19, 2010 1. Is property tax on land or equipment directly attributable to the project eligible for cost share? 2. Is insurance, with coverage comparable to that purchased for similar projects, eligible for cost share (construction and operating)? Response: The answer to both questions is yes. The specific cost principle that the Applicants can go to for further information, as well as the regulations on cost sharing are given below. Question 1 - FAR Part 31.205-41, Taxes Question 2 - FAR Part 31.205-19, Insurance and Indemnification Cost Sharing regulations - 10 CFR 600.123; 10 CFR 600.224; or 10 CFR 600.313 Since both questions indicate that the costs to be incurred will be used as potential cost share for the project, additional details will be needed to verify and validate the

57

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":""}]}

58

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

59

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

60

Questions and Answers  

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

The following questions were submitted by DOE employees during the initial stages of the Financial Services A-76 study. Answers to these questions have been provided by subject matter experts. If you have any questions or need further clarification, please contact Paul Anderson at (803) 725-5607 or e-mail him at paul.anderson@srs.gov. This website will be updated regularly and new questions and answers posted as they are received. 1. It appears that, according to the Financial Services Plan of Action and Milestones, in any event, some of us will lose our jobs. How will it be decided which employees will lose our jobs? Answer: It is possible that the study outcome may or may not require a reduction-in-force (RIF). It would be premature at this time to address layoffs (i.e., RIF), which will

Note: This page contains sample records for the topic "forecasting specific questions" 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.


61

OLYMPIC AIR QUALITY QUESTIONABLE  

Science Journals Connector (OSTI)

OLYMPIC AIR QUALITY QUESTIONABLE ... Athletes GOING FOR GOLD worry about Beijings air ... Atmospheric chemists say the air quality during the Beijing Games literally rests on which direction the winds blow. ...

RACHEL PETKEWICH

2008-07-28T23:59:59.000Z

62

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

63

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...

64

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

65

ARM - Key Science Questions  

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

govScienceKey Science Questions govScienceKey Science Questions Science Research Themes Research Highlights Journal Articles Collaborations Atmospheric System Research (ASR) ARM Science Team Meetings User Meetings Annual Meetings of the Atmospheric System Research (ASR) Science Team and Fall Working Groups Accomplishments Read about the 20 years of accomplishments (PDF, 696KB) from the ARM Program and user facility. Performance Metrics ASR Metrics 2009 2008 2007 2006 Key Science Questions The role of clouds and water vapor in climate change is not well understood; yet water vapor is the largest greenhouse gas and directly affects cloud cover and the propagation of radiant energy. In fact, there may be positive feedback between water vapor and other greenhouse gases. Carbon dioxide and other gases from human activities slightly warm the

66

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"

67

QUESTIONS ABOUT GLOBAL WARMING  

E-Print Network [OSTI]

QUESTIONS ABOUT GLOBAL WARMING ¥IS IT REAL? ¥IS IT IMPORTANT? ¥WHAT IS IT DUE TO? ¥HOW MUCH MORE in the atmosphere, giving Earth its temperate climate. Global Atmosphere, Global Warming GLOBAL TEMPERATURE TREND?t a cure for global warming! Aerosols only last a short while in the atmosphere, they would have

68

Some Questions About Neurocognitive  

E-Print Network [OSTI]

Some Questions About Neurocognitive Networks Steven Bressler Center for Complex Systems & Brain is a Brain Network? · A brain network is a large-scale system in the brain consisting of distributed neuronal ­ Dynamic Interdependency #12;Does The Brain Need Networks? · Serial processing, as found in the PNS, is too

Bressler, Steven L.

69

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

70

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

71

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

72

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

73

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

74

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.

75

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

76

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.

77

Frequently Asked Questions  

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

Frequently Asked Questions Frequently Asked Questions About This Site What is fueleconomy.gov? Adobe Acrobat Icon Fuel Economy Estimates How are fuel economy ratings determined? EPA estimates are based on laboratory tests conducted by manufacturers according to federal regulations. EPA re-tests about 10% of vehicle models to confirm manufacturer's results. For more detailed information, visit our page on How Vehicles Are Tested. Why does my fuel economy differ from EPA estimates? No test can accurately predict fuel economy for all drivers and all driving conditions. Driver behavior, driving conditions, vehicle maintenance, fuel characteristics, weather, and other factors can all affect fuel economy significantly as explained here. What should I do if my fuel economy is excessively low?

78

Data Provider Questions  

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

Home Home Data Provider Questions At the beginning the process of archival with the ORNL DAAC, you will be asked to fill out a short online form to help us better understand your data set. These questions should only take a few minutes to answer. Information About Your Data Set Have you looked at our recommendations for the preparation of data files and documentation? Who produced this data set? What agency and program funded the project? What awards funded this project? (comma separate multiple awards) Data Set Description Provide a title for your data set. (maximum 84 characters) What type of data does your data set contain? What does the data set describe? (2-3 sentences) What parameters did you measure, derive, or generate? (comma separated, limit to ten) Have you analyzed the uncertainty in your data?

79

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

80

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

82

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

E-Print Network [OSTI]

sensitive areas. To answer these questions simulation experiments with state-of-the-art numerical weather prediction (NWP) models have proved great value to test future meteorological observing systems a prioriImproved forecasts of extreme weather events by future space borne Doppler wind lidar Gert

Marseille, Gert-Jan

83

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.

84

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

85

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

86

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

87

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

88

Doing Business Frequently Asked Questions  

Broader source: Energy.gov [DOE]

The following are frequently asked questions about working with in partnership with DOE laboratories.

89

Fundamentals, forecast combinations and nominal exchange-rate predictability  

Science Journals Connector (OSTI)

This paper investigates the out-predictability of fundamentals and forecast combinations. By adopting a panel-based specification, the paper obtains several interesting results. First, the Taylor-rule-based fundamental is the best among the four different fundamentals under consideration in out-of-sample contests. It provides strong evidence to out-predict the random walk over the PBW period. Second, relative to a single-equation prediction, panel predictions are generally able to enhance the statistical significance of beating the random walk. Third, combining forecasts from different fundamentals that have relatively strong out-predictability at a specific horizon does enhance both the statistical and economic significances of beating the random walk for the PBW period at short horizons.

Jyh-Lin Wu; Yi-Chiuan Wang

2013-01-01T23:59:59.000Z

90

H. UNREVIEWED SAFETY QUESTIONS  

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

Department of Energy Pt. 835 H. UNREVIEWED SAFETY QUESTIONS 1. The USQ process is an important tool to evaluate whether changes affect the safety basis. A contractor must use the USQ proc- ess to ensure that the safety basis for a DOE nuclear facility is not undermined by changes in the facility, the work performed, the associated hazards, or other factors that support the adequacy of the safety basis. 2. The USQ process permits a contractor to make physical and procedural changes to a nuclear facility and to conduct tests and ex- periments without prior approval, provided these changes do not cause a USQ. The USQ process provides a contractor with the flexi- bility needed to conduct day-to-day oper- ations by requiring only those changes and tests with a potential to impact the safety

91

Q# Question Answer  

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

4/2012) 4/2012) Q# Question Answer 1 Could let us know what your interests are in keeping the current staff if another contractor wins? This is a management decision of the vendor 2 Will this RFP not be automatically posted in FedBizOpps? When the RFP is issued, it will be posted on the FBO website through FedConnect. 3 Will this be a small business set-aside, and if so, what is the size standard? Yes, $7.0M 4 Who is the incumbent? Systematic Management Services Inc 5 I would appreciate it if you would provide me with that person's name and contact information. My company is potentially interested in this re-compete and we are trying to obtain sufficient data so as to enable us to make an informed decision. This information can be submitted using a freedom of Information request. The technical

92

Q# Question Answer  

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

6/2012) 6/2012) Q# Question Answer 1 Could let us know what your interests are in keeping the current staff if another contractor wins? This is a management decision of the vendor 2 Will this RFP not be automatically posted in FedBizOpps? When the RFP is issued, it will be posted on the FBO website through FedConnect. 3 Will this be a small business set-aside, and if so, what is the size standard? Yes, $7.0M 4 Who is the incumbent? Systematic Management Services Inc 5 I would appreciate it if you would provide me with that person's name and contact information. My company is potentially interested in this re-compete and we are trying to obtain sufficient data so as to enable us to make an informed decision. This information can be submitted using a freedom of Information request. The technical

93

H. UNREVIEWED SAFETY QUESTIONS  

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

3 3 Department of Energy Pt. 835 H. UNREVIEWED SAFETY QUESTIONS 1. The USQ process is an important tool to evaluate whether changes affect the safety basis. A contractor must use the USQ proc- ess to ensure that the safety basis for a DOE nuclear facility is not undermined by changes in the facility, the work performed, the associated hazards, or other factors that support the adequacy of the safety basis. 2. The USQ process permits a contractor to make physical and procedural changes to a nuclear facility and to conduct tests and ex- periments without prior approval, provided these changes do not cause a USQ. The USQ process provides a contractor with the flexi- bility needed to conduct day-to-day oper- ations by requiring only those changes and tests with a potential to impact the safety

94

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

95

SCE Responses to Customer Data Questions  

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

SCE Responses to Customer Data Questions SCE Responses to Customer Data Questions 1. Who owns energy consumption data? SCE Response: Customer-specific data gathered or developed by a utility in the course of providing utility services is owned by the utility. Such data is subject to confidentiality and privacy requirements. In California, customers have the right to access their customer- specific information and can authorize third-party access to their information. 2. Who should be entitled to privacy protections relating to energy information? SCE Response: All customers receiving electric service from a utility should be entitled to privacy protections relating to their customer-specific energy information. Furthermore, utilities should not be required to enforce the compliance of customer-authorized third

96

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

97

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

98

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

99

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.

100

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...

Note: This page contains sample records for the topic "forecasting specific questions" 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

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...

102

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

103

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

104

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

105

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

106

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

107

From Question Answering to Visual Exploration  

SciTech Connect (OSTI)

Research in Question Answering has focused on the quality of information retrieval or extraction using the metrics of precision and recall to judge success; these metrics drive toward finding the specific best answer(s) and are best supportive of a lookup type of search. These do not address the opportunity that users? natural language questions present for exploratory interactions. In this paper, we present an integrated Question Answering environment that combines a visual analytics tool for unstructured text and a state-of-the-art query expansion tool designed to compliment the cognitive processes associated with an information analysts work flow. Analysts are seldom looking for factoid answers to simple questions; their information needs are much more complex in that they may be interested in patterns of answers over time, conflicting information, and even related non-answer data may be critical to learning about a problem or reaching prudent conclusions. In our visual analytics tool, questions result in a comprehensive answer space that allows users to explore the variety within the answers and spot related information in the rest of the data. The exploratory nature of the dialog between the user and this system requires tailored evaluation methods that better address the evolving user goals and counter cognitive biases inherent to exploratory search tasks.

McColgin, Dave W.; Gregory, Michelle L.; Hetzler, Elizabeth G.; Turner, Alan E.

2006-08-11T23:59:59.000Z

108

DOE Federal Register Questions  

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

COMMENTS-PAGE 1 COMMENTS-PAGE 1 Comments of the Bonneville Power Administration On May 11, the Department of Energy (DOE) issued a Request for Information (RFI) (NBP RFI: Communications Requirements) seeking comments and information from interested parties to assist DOE in understanding the communications requirement of utilities, including, but not limited to, the requirements of the Smart Grid. The RFI also seeks to collect information about electricity infrastructure's current and projected communications requirements, as well as the types of networks and communications services that may be used for grid modernization, including specifically information on what types of communications capabilities utilities think that they will need and what type of communications capabilities the communications carriers think that they can

109

Energy Information Administration (EIA)- Frequently Asked Questions (FAQ)  

Gasoline and Diesel Fuel Update (EIA)

Frequently Asked Questions about the Commercial Buildings Energy Frequently Asked Questions about the Commercial Buildings Energy Consumption Survey (CBECS) For the 2007 CBECS, why is there information only for large hospitals and not the rest of the commercial building population? What do you mean by "commercial"? Can I get this information by State (or county, or city, etc.)? What does the figure "4,859,000 total commercial buildings" represent? Are the consumption and expenditures estimates annual data? Are historical CBECS data available? Do you have any forecasts for growth in the commercial sector? Are the data available by NAICS or SIC code? Why are the CBECS figures different from the commercial data in the Annual Energy Review or the Monthly Energy Review? Is it possible to obtain a list of all the buildings that

110

Frequently Asked Global Change Questions  

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

Asked Global Change Questions Asked Global Change Questions This page lists global change questions that have been received at CDIAC and the answers that were provided to a diverse audience. If you have a question relating to carbon dioxide and global change and cannot find the answer you need here, you can "Ask Us a Question", and we will be glad to try to help you. Questions Should we grow trees to remove carbon in the atmosphere? What are the present tropospheric concentrations, global warming potentials (100 year time horizon), and atmospheric lifetimes of CO2, CH4, N2O, CFC-11, CFC-12, CFC-113, CCl4, methyl chloroform, HCFC-22, sulphur hexafluoride, trifluoromethyl sulphur pentafluoride, perfluoroethane, and surface ozone? Where can I find information on the naming of halocarbons?

111

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

112

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

113

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

114

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

115

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

116

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

117

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

118

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

119

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...

120

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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...

122

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...

123

QuestionQuestion How does nitrogen deposition affect roadside  

E-Print Network [OSTI]

al. 2004. Concentrations of ammonia and nitrogen dioxide at roadside verges, and their contributionQuestionQuestion How does nitrogen deposition affect roadside plant community composition? 1. Is there a gradient of nitrogen deposition to freeway verges from traffic exhaust? 2. Are there other sources of N

Hall, Sharon J.

124

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

125

Jens Hjorth the Urbino Questions  

E-Print Network [OSTI]

reduce the effects of air pollution to human health and the environment in Europe by 2020. The strategy-DRIVEN SYNTHESIS Answers by the atmospheric chemistry and air pollution research community to questions posed Strategy on Air Pollution 5.06 #12;editors Frank Raes Jens Hjorth Answers to the Urbino Questions ACCENTs

126

LED Frequently Asked Questions | Department of Energy  

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

Frequently Asked Questions LED Frequently Asked Questions This document provides answers to LED frequently asked questions for plant-wide improvements, such as "Are LEDs ready for...

127

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

128

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

129

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

130

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

131

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

132

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

133

EECBG Rebate Frequently Asked Questions  

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

EECBG Rebate Frequently Asked Questions When do I need to use funds I receive from rebates associated with EECBG funds? Rebates consist of incentives received by a grant recipient...

134

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

135

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

136

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

137

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

138

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

139

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

140

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

142

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

143

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

144

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

145

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

146

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

147

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.

148

Questions  

E-Print Network [OSTI]

(a) Ian = Ui'=1 Ar, prove that B" = ULI 3,, for n =1, 2, 3, . (b) HR = U'iil A prove ... Let Kc R1 consist of 0 and the numbers 1/11, for n = 1, 2, 3, ... . Prove that K lS.

CamScanner

149

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

150

Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability?A Review  

Science Journals Connector (OSTI)

Load forecasting is very essential to the operation of Electricity companies. It enhances the energy efficient and reliable operation of power system. Forecasting of load demand data forms an important component in planning generation schedules in a power system. The purpose of this paper is to identify issues and better method for load foecasting. In this paper we focus on fuzzy logic system based short term load forecasting. It serves as overview of the state of the art in the intelligent techniques employed for load forecasting in power system planning and reliability. Literature review has been conducted and fuzzy logic method has been summarized to highlight advantages and disadvantages of this technique. The proposed technique for implementing fuzzy logic based forecasting is by Identification of the specific day and by using maximum and minimum temperature for that day and finally listing the maximum temperature and peak load for that day. The results show that Load forecasting where there are considerable changes in temperature parameter is better dealt with Fuzzy Logic system method as compared to other short term forecasting techniques.

R. M. Holmukhe; Mrs. Sunita Dhumale; Mr. P. S. Chaudhari; Mr. P. P. Kulkarni

2010-01-01T23:59:59.000Z

151

Natural Calamities and Their Forecasting  

Science Journals Connector (OSTI)

Man and the Environment (habitat) comprise an integrated system consisting of numerous inter-related elements, possessing specific traits, and streamlined within certain boundaries. The interaction between Man...

Valery A. Menshikov; Anatoly N. Perminov

2012-01-01T23:59:59.000Z

152

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

153

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

154

Microsoft Word - BingQuestionOne1004.doc  

Gasoline and Diesel Fuel Update (EIA)

Supply Impacts of an MTBE Ban Supply Impacts of an MTBE Ban September 2002 ii Energy Information Administration/Supply Impacts of An MTBE Ban Contacts This report was prepared by the Office of Oil and Gas of the Energy Information Administration. General questions concerning the report may be directed to Mary J. Hutzler (202/586-2222, mhutzler@eia.doe.gov), Director, Office of Integrated Analysis and Forecasting, or Joanne Shore (202/586-4677, joanne.shore@eia.doe.gov), Team Leader, Petroleum Division. 1 Energy Information Administration/Supply Impacts of An MTBE Ban Supply Impacts of an MTBE Ban On June 17, 2002, Senator Jeff Bingaman, Chairman of the Senate Committee on Energy and Natural Resources, requested (Appendix A) that the Energy Information Administration (EIA) provide analysis of eight factors related to the Senate-passed fuels

155

Microsoft Word - BingQuestionEightBoutiques.doc  

Gasoline and Diesel Fuel Update (EIA)

Gasoline Type Proliferation and Price Volatility September 2002 ii Energy Information Administration/Gasoline Type Proliferation and Price Volatility Contacts This report was prepared by the Office of Oil and Gas of the Energy Information Administration. General questions concerning the report may be directed to Mary J. Hutzler (202/586-2222, mhutzler@eia.doe.gov), Director, Office of Integrated Analysis and Forecasting, or Joanne Shore (202/586-4677, joanne.shore@eia.doe.gov), Team Leader, Petroleum Division 1 Energy Information Administration/Gasoline Type Proliferation and Price Volatility Gasoline Type Proliferation & Price Volatility On June 17, 2002, Senator Jeff Bingaman, Chairman of the Senate Committee on Energy and Natural Resources, requested that the Energy Information Administration (EIA)

156

Microsoft Word - BingQuestionFive0923.doc  

Gasoline and Diesel Fuel Update (EIA)

Timing of Startups of the Low-Sulfur Timing of Startups of the Low-Sulfur and RFS Programs September 2002 ii Energy Information Administration/Time of Startups Contacts This report was prepared by the Office of Oil and Gas of the Energy Information Administration. General questions concerning the report may be directed to Mary J. Hutzler (202/586-2222, mhutzler@eia.doe.gov), Director, Office of Integrated Analysis and Forecasting, or Joanne Shore (202/586-4677, joanne.shore@eia.doe.gov), Team Leader, Petroleum Division 1 Energy Information Administration/Time of Startups Timing of Startups of the Low-Sulfur and RFS Programs On June 17, 2002, Senator Jeff Bingaman, Chairman of the Senate Committee on Energy and Natural Resources, requested (Appendix A) that the Energy Information

157

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

158

QUESTIONS & ANSWERS ABOUT LUNG CANCER  

E-Print Network [OSTI]

QUESTIONS & ANSWERS ABOUT LUNG CANCER Q: What are the early signs of lung cancer? How would I know I have it? A: Some of the early warning signs of lung cancer are: · A cough that doesn't go away what may be causing these symptoms. Q: How is lung cancer diagnosed? A: Your doctor may do one or more

159

Open-domain question: answering  

Science Journals Connector (OSTI)

The top-performing Question-Answering (QA) systems have been of two types: consistent, solid, well-established and multi-faceted systems that do well year after year, and ones that come out of nowhere employing totally innovative approaches and which ...

John Prager

2006-01-01T23:59:59.000Z

160

Pre-Solicitation Questions with Answers Q# Question Answer  

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

Q# Question Answer Q# Question Answer map), but I cannot find the date and time for the site visit. Please tell me the date and time of the site visit and if it is mandatory or not. the tab "Industry Day Information." Attending the Industry Day is not mandatory. 4 Good afternoon, I am interested in attending the pre proposal conference for the Emergency Operations Training and Support (EOTA) contract. Please send me further information on the where about of this conference. I have the time and date from GovWin, which is 1/6/12 at 9am. I look forward to speaking with you shortly. All information is contained in the RFP and in the pre Solicitation Documents in FedConnect and Federal Business Opportunities. This information can also be found in the Reading

Note: This page contains sample records for the topic "forecasting specific questions" 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

Open-domain textual question answering techniques  

Science Journals Connector (OSTI)

Textual question answering is a technique of extracting a sentence or text snippet from a document or document collection that responds directly to a query. Open-domain textual question answering presupposes that questions are natural and unrestricted ...

Sanda M. Harabagiu; Steven J. Maiorano; Marius A. Pa?ca

2003-09-01T23:59:59.000Z

162

10 CFR 707 Frequently Asked Questions  

Broader source: Energy.gov [DOE]

NOTE: The Questions on this site were compiled from questions asked during the four DOE complex wide tele-videos, as well as, questions submitted by e-mail and telephone. The answers provided are...

163

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

164

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

165

Building Energy Asset Score Frequently Asked Questions  

Broader source: Energy.gov [DOE]

This page features answers to the most frequently asked questions about the Building Energy Asset Score. Choose from the list of questions below to learn more:

166

Career Pathways Frequently Asked Questions (FAQs) | Department...  

Energy Savers [EERE]

Questions (FAQs) The following frequently asked questions were developed by OPM's Student Programs Office. They will clarify the use of the authority and assist managers,...

167

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:

168

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

169

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.

170

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

171

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

172

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

173

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

174

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

175

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...

176

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

177

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

178

Gas Prices: Frequently Asked Questions  

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

Prices: Frequently Asked Questions Prices: Frequently Asked Questions What determines the price of gasoline? Energy Information Administration What's going on with gasoline prices? Factors Affecting Gasoline Prices This Week in Petroleum (updated weekly) Gasoline Price Pass-through Oil Market Basics Primer on Gasoline Sources and Markets What's up (and down) with gasoline prices? Illustration showing component costs of gasoline What are the average national and regional gasoline prices? Energy Information Administration Gasoline and Diesel Fuel Update (updated weekly) This Week in Petroleum (updated weekly) California Energy Commission California Gasoline & Gasoline Prices What is the outlook for gasoline prices? Energy Information Administration Short-Term Energy Outlook Why are gasoline prices so different from one state (or region) to another?

179

Question 2: Gas procurement strategy  

SciTech Connect (OSTI)

This article is a collection of responses from natural gas distribution company representatives to questions on how the start-up of the natural gas futures market has changed gas procurement strategies, identification of procurement problems related to pipeline capacity, deliverability, or pregranted abandonment of firm transportation, the competition of separate utility subsidiaries with brokers, marketers, and other gas suppliers who sell gas to large-volume industrial or other 'noncore' customers.

Carrigg, J.A.; Crespo, J.R.; Davis, E.B. Jr.; Farman, R.D.; Green, R.C. Jr.; Hale, R.W.; Howard, J.J.; McCormick, W.T. Jr.; Page, T.A.; Ryan, W.F.; Schrader, T.F.; Schuchart, J.A.; Smith, J.F.; Stys, R.D.; Thorpe, J.A.

1990-10-25T23:59:59.000Z

180

Sector 1 Frequently Asked Questions  

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

Sector 1 - General Information Sector 1 - General Information Sector 1 Safety Plan (pdf) Useful X-Ray Related Numbers Si a0 = 5.4308 Angstrom CeO2 a0=5.411 Angstrom Cd-109 gamma = 88.036 keV X-ray energy/wavelength conversion constant = 12.39842 Angstrom/keV Useful 1-ID Operations Information Always set the undulator by closing from large to small gap. Always scan the Kohzu monochromator from high to low energy. A Cd-109 source that can be used to calibrate detectors can be obtained by contacting Ali. It has Ag flourescent lines and a 88.036 keV gamma line. Tim Mooney's telephone number is 2-5417. Frequently Asked Questions The following questions come up often when using the Sector 1 beamlines. If you have a question (and maybe answer) that would be of general interest to Sector 1 users, please give it to Jon or Greg for inclusion in this list.

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

182

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

183

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...

184

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

185

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

186

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

187

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

188

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

189

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

190

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

191

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.

192

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

193

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

194

Potpourri of deposition and resuspension questions  

SciTech Connect (OSTI)

Twenty questions and answers are listed dealing with particulate deposition, resuspension, and precipitation scavenging.

Slinn, W.G.N.

1983-01-01T23:59:59.000Z

195

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

196

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

197

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

198

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

199

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.

200

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

202

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

203

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

204

Cool Roofs: Your Questions Answered | Department of Energy  

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

Roofs: Your Questions Answered Roofs: Your Questions Answered Cool Roofs: Your Questions Answered January 6, 2011 - 2:58pm Addthis John Schueler John Schueler Former New Media Specialist, Office of Public Affairs Last month Secretary Chu announced that the Department of Energy had installed a "cool roof" atop the west building of our Washington, DC headquarters. The announcement elicited a fair number of questions from his Facebook fans, so we decided to reach out to the people behind the project for their insight on the specific benefits of switching to a cool roof, and the process that went into making that choice. Jim Bullis (Facebook): So what is the percentage saving of energy bills for this building? Answer: The West Building cool roof is estimated to save about $2,000 per

205

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

206

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

207

Pre-Solicitation Questions with Answers Q# Question Answer  

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

it if you would provide me with that person's name and it if you would provide me with that person's name and contact information. My company is potentially interested in this re- compete and we are trying to obtain sufficient data so as to enable us to make an informed decision. This information can be submitted using a freedom of Information request. The technical point of contact cannot disclose any information regarding the future contract source selection. 6 Is this new work? This is a re-compete of a previous contract 7 Can you please tell me who is the incumbent and what is the name of the contract they are providing support under? See Question 4 above. Emergency Operations Training Academy (EOTA) Support Services. 8 Will this reading room be a physical location, or will this be a forum online? Online web address (Virtual Library)

208

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.

209

A-76 QUESTIONS AND ANSWERS  

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

SOURCING/A-76 SOURCING/A-76 QUESTIONS AND ANSWERS COMPETITIVE SOURCING What is the President's Competitive Sourcing Initiative? The President stated, as part of his Management Agenda, that "Government should be market- based - we should not be afraid of competition, innovation, and choice. I will open government to the discipline of competition." He charged each agency with conducting a review of at least fifty percent of its federal workforce performing commercial activities to determine if any of the activities being performed could be provided by the private sector at less cost to the government. Agencies are being asked to study fifteen percent of their commercial activity inventories by the end of FY 2003. President Bush's mandate follows the enactment of the Federal Activities Inventory Reform Act

210

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

211

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

212

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

213

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

214

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

215

On the interpretation of concealed questions  

E-Print Network [OSTI]

Determiner phrases have the ability to act as "concealed questions" (CQs), embedded questions in sentences like John knows the time (i.e., John knows what time it is). The fact that know and wonder differ in their ability ...

Nathan, Lance Edward

2006-01-01T23:59:59.000Z

216

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

Hello, I am so happy to visited your site It was full of answers to my questions So, I have a question about thermal Energy...We have some kind of energy in the world. electric...

217

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

have a question, but first, thank you for the terrific new web site. You did a fantastic job. Question: Where does present theory say the energy of a red shifted photon goes? The...

218

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

219

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

220

A Question of Prosperity Poverty in Saskatchewan  

E-Print Network [OSTI]

JUNE 2008 A Question of Prosperity Poverty in Saskatchewan Garson Hunter and Fiona Douglas with Sarah Pedersen #12;A Question of Prosperity: Poverty in Saskatchewan June 2008 Hunter, G. F. Douglas & S. Pedersen. "A Question of Prosperity: Poverty in Saskatchewan." Poverty Profiles 1, 2008. Regina

Argerami, Martin

Note: This page contains sample records for the topic "forecasting specific questions" 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

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.

222

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

223

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.

224

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

225

Frequently Asked Questions | National Nuclear Security Administration  

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

Frequently Asked Questions | National Nuclear Security Administration Frequently Asked Questions | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Frequently Asked Questions Home > About Us > Our Operations > Management and Budget > Human Resources > Pay-banding > Frequently Asked Questions Frequently Asked Questions General What is a demonstration project?

226

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...

227

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

228

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

229

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...

230

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...

231

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

232

Georgia Newspaper Coverage Discovering Conventional Practices of the 'Cherokee Question': Prelude to the Removal, 1828-1832.  

E-Print Network [OSTI]

??This thesis analyzes the specific journalistic conventional practices of newspapers in Georgia as they focused on the Cherokee Question in 1828-1832, the critical period during (more)

Hobgood, Jr., James Hollister

2008-01-01T23:59:59.000Z

233

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

234

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

235

Evaluation of artificial neural networks as a model for forecasting consumption of wood products  

Science Journals Connector (OSTI)

In specific sciences, such as forest policy, the need for anticipation becomes more urgent because it has to manage valuable natural resources whose protection and sustainable management is rendered essential. In this paper, a modern method has been used, known as artificial neural networks (ANNs). In order to forecast the necessary future volumes of timber in Greece, a neural network has been developed and trained, using a variety of time series derived from the database of the Food and Agriculture Organisation of the United Nations (FAO) (concerning Greece) as external values and as internal value the Consumer Price Index has been used. Comparing the results of this project with linear and non-linear econometric forecasting models, it has been found that neural networks correspond, as confirmed by the econometric indicators MAPE (average absolute percentage error) and RMSE (the square root of the percentage by the average sum of squares differences).

Giorgos Tigas; Panagiotis Lefakis; Konstantinos Ioannou; Athanasios Hasekioglou

2013-01-01T23:59:59.000Z

236

Attn Technology Transfer Questions.txt - Notepad  

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

Attn Technology Transfer Questions.txt Attn Technology Transfer Questions.txt From: eschaput [esandc@prodigy.net] Sent: Monday, January 26, 2009 10:31 PM To: GC-62 Subject: Attn: Technology Transfer Questions We have reviewed the DOE "Questions Concerning Technology Transfer Practices at DOE Laboratories" (Federal Register notice of November 26, 2008), with the following comments and suggestions for your consideration. DOE asked five questions and the following thoughts be provided for your consideration: Question #1 - Are existing arrangements adequate? Answer #1 - The existing types of arrangement are generally adequate, but their application should be broadened and their implementation streamlined. a.. The application of "User Agreements" should be broadened to soften the effect

237

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

Question About Splitting Molecules "Can you use particle accelerators to break up molecules into their elements?" The short answer is yes. The long answer is more complicated. You...

238

Program Evaluation Topics and Questions Library  

Broader source: Energy.gov [DOE]

Menu of initial questions for a program administrator to use in developing a real-time evaluation survey to collect qualitative data from program contractors.

239

CRAD, Facility Safety - Unreviewed Safety Question Requirements...  

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

a recommended approach and the types of information to gather to assess elements of a DOE contractor's programs. CRAD, Facility Safety - Unreviewed Safety Question Requirements...

240

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

242

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...........................................................................................

243

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

244

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

245

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

246

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.

247

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

248

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

249

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

250

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

251

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

252

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...

253

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

254

Application of Ensemble Sensitivity Analysis to Observation Targeting for Short-term Wind Speed Forecasting  

SciTech Connect (OSTI)

The operators of electrical grids, sometimes referred to as Balancing Authorities (BA), typically make critical decisions on how to most reliably and economically balance electrical load and generation in time frames ranging from a few minutes to six hours ahead. At higher levels of wind power generation, there is an increasing need to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts on this time scale have typically been strongly dependent on short-term trends indicated by the time series of power production and meteorological data from a wind farm. Additional input information is often available from the output of Numerical Weather Prediction (NWP) models and occasionally from off-site meteorological towers in the region surrounding the wind generation facility. A widely proposed approach to improve short-term forecasts is the deployment of off-site meteorological towers at locations upstream from the wind generation facility in order to sense approaching wind perturbations. While conceptually appealing, it turns out that, in practice, it is often very difficult to derive significant benefit in forecast performance from this approach. The difficulty is rooted in the fact that the type, scale, and amplitude of the processes controlling wind variability at a site change from day to day if not from hour to hour. Thus, a location that provides some useful forecast information for one time may not be a useful predictor a few hours later. Indeed, some processes that cause significant changes in wind power production operate predominantly in the vertical direction and thus cannot be monitored by employing a network of sensors at off-site locations. Hence, it is very challenging to determine the type of sensors and deployment locations to get the most benefit for a specific short-term forecast application. Two tools recently developed in the meteorological research community have the potential to help determine the locations and parameters to measure in order to get the maximum positive impact on forecast performance for a particular site and short-term look-ahead period. Both tools rely on the use of NWP models to assess the sensitivity of a forecast for a particular location to measurements made at a prior time (i.e. the look-ahead period) at points surrounding the target location. The fundamental hypothesis is that points and variables with high sensitivity are good candidates for measurements since information at those points are likely to have the most impact on the forecast for the desired parameter, location and look-ahead period. One approach is called the adjoint method (Errico and Vukicevic, 1992; Errico, 1997) and the other newer approach is known as Ensemble Sensitivity Analysis (ESA; Ancell and Hakim 2007; Torn and Hakim 2008). Both approaches have been tested on large-scale atmospheric prediction problems (e.g. forecasting pressure or precipitation over a relatively large region 24 hours ahead) but neither has been applied to mesoscale space-time scales of winds or any other variables near the surface of the earth. A number of factors suggest that ESA is better suited for short-term wind forecasting applications. One of the most significant advantages of this approach is that it is not necessary to linearize the mathematical representation of the processes in the underlying atmospheric model as required by the adjoint approach. Such a linearization may be especially problematic for the application of short-term forecasting of boundary layer winds in complex terrain since non-linear shifts in the structure of boundary layer due to atmospheric stability changes are a critical part of the wind power production forecast problem. The specific objective of work described in this paper is to test the ESA as a tool to identify measurement locations and variables that have the greatest positive impact on the accuracy of wind forecasts in the 0- to 6-hour look-ahead periods for the wind generation area of California's Tehachapi Pass during the warm (high generation) season. The paper is organized

Zack, J; Natenberg, E; Young, S; Manobianco, J; Kamath, C

2010-02-21T23:59:59.000Z

255

Questions and Answers March 9, 2012  

E-Print Network [OSTI]

-602 Alternative Fuels Infrastructure: Electric, Natural Gas, Propane, E85 & Diesel Substitutes Terminals 5Questions and Answers March 9, 2012 for PON-11-602 Alternative Fuels Infrastructure: Electric, Natural Gas, Propane, E85 & Diesel Substitutes Terminals General Definitions/Clarification 1. QUESTION

256

BYU HONORS PROGRAM The Great Questions Requirement  

E-Print Network [OSTI]

, often provoked by new discoveries and developments in technology (e.g., questions of medical ethics, use developing the knowledge, skills, and disposition you will need to write the essay. The essay, therefore of natural resources, including very powerful ones like nuclear energy, etc.). These kinds of questions lie

Olsen Jr., Dan R.

257

TECHNOLOGY TRANSFER QUESTIONS..txt - Notepad  

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

TECHNOLOGY TRANSFER QUESTIONS..txt TECHNOLOGY TRANSFER QUESTIONS..txt From: Bob Fien [rfien@campbellap.com] Sent: Monday, January 26, 2009 5:34 PM To: GC-62 Subject: TECHNOLOGY TRANSFER QUESTIONS. Sensitivity: Confidential To Whom It May Concern, Campbell Applied Physics, Inc has been working with several DOE labs (e.g., Oak Ridge, Pacific Northwest, Laurence Livermore, Sandia) on various commercialization projects and, has been asked to submit answers to the questions presented in 72036 Federal Register / Vol. 73, No. 229 concerning our experiences. Please accept the following as our response to that request. 1. Existing and Other Agreements (4sub questions): The DOE labs currently offer CRADAs, WFO Agreements, and User Agreements, all briefly referenced below. The DOE Orders and model agreements for CRADAs, WFO and

258

Frequently Asked Questions | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

Programs > Nuclear Security > Nuclear Materials Programs > Nuclear Security > Nuclear Materials Management & Safeguards System > NMMSS Information, Reports & Forms > Frequently Asked Questions Frequently Asked Questions U.S. Department of Energy / U.S. Nuclear Regulatory Commission Nuclear Materials Management & Safeguards System Frequently Asked Questions (FAQs) User Frequently Asked Questions What is the History of NMMSS? What Are the Other Uses of NMMSS? NMMSS is sponsored by the National Nuclear Security Administration's (NNSA) Office of Materials Integration within the U.S. Department of Energy and the U.S. Nuclear Regulatory Commission Printer-friendly version Printer-friendly version Facebook Twitter Youtube Flickr Learn More Users Frequently Asked Questions What Are the Other Uses of NMMSS?

259

Questions and Answers for the Smart Grid Investment Grant Program...  

Energy Savers [EERE]

Questions and Answers for the Smart Grid Investment Grant Program: Frequently Asked Questions Questions and Answers for the Smart Grid Investment Grant Program: Frequently Asked...

260

EECBG Rebate Frequently Asked Questions | Department of Energy  

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

Rebate Frequently Asked Questions EECBG Rebate Frequently Asked Questions This document contains Frequently Asked Questions about EECBG Rebates. finalrebatefaqs120104.pdf More...

Note: This page contains sample records for the topic "forecasting specific questions" 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

Natural Gas from Shale: Questions and Answers | Department of...  

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

Shale: Questions and Answers Natural Gas from Shale: Questions and Answers Natural Gas from Shale: Questions and Answers More Documents & Publications Shale Gas Development...

262

Fermilab Legal Office - Ethics Program - Submit a Question  

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

Submit a Question Ask the Ethicist Ask the Ethicist Questions & Answers Submit a Question Annual Reminders Reporting Fraud Holiday Parties Complete the form below to submit a...

263

Questions and Answers for the Smart Grid Investment Grant Program...  

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

Questions and Answers for the Smart Grid Investment Grant Program: Buy American Questions and Answers for the Smart Grid Investment Grant Program: Buy American Additional questions...

264

Questioning the questions that have been asked about the infant brain using near-infrared spectroscopy  

E-Print Network [OSTI]

Questioning the questions that have been asked about the infant brain using near-infrared, University of Rochester, Rochester, NY, USA Near-infrared spectroscopy (NIRS) is a noninvasive diffuse; Near-infrared spectroscopy. "Sheddinglight"onascientificquestiontookonnew meaning when

Aslin, Richard N.

265

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

266

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

267

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

268

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.

269

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

270

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

271

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

272

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

273

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

274

Questions and Answers | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

Questions and Answers | National Nuclear Security Administration Questions and Answers | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog The National Nuclear Security Administration Questions and Answers Home > About Us > Our Operations > Acquisition and Project Management > Major Contract Solicitations > Environmental Program Services Contract >

275

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

276

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

277

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

278

Last Call for Questions | Department of Energy  

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

Last Call for Questions Last Call for Questions Last Call for Questions October 7, 2010 - 5:06pm Addthis Sec. Chu observes as workers demonstrate energy efficient water heaters. | Department of Energy Photo | Courtesy of National Renewable Energy Laboratory | Sec. Chu observes as workers demonstrate energy efficient water heaters. | Department of Energy Photo | Courtesy of National Renewable Energy Laboratory | John Schueler John Schueler Former New Media Specialist, Office of Public Affairs Just a quick reminder that the deadline for submitting your home energy efficiency questions directly to Secretary Chu is midnight tonight (10/07). We've already received some great queries and personal experiences but want to make sure the Secretary has a chance to respond to as many of you

279

Frequently Asked Questions | Department of Energy  

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

Frequently Asked Questions Frequently Asked Questions Frequently Asked Questions November 1, 2013 - 11:40am Addthis Frequently asked questions (FAQs) and their corresponding answers regarding industrial distributed energy (DE) and combined heat and power (CHP) are provided below. What is CHP? Why is there so much interest in CHP? Is CHP the same as cogeneration? What is the difference between CHP, CCHP, BCHP, DER, IES? Why can't I use my backup generator for on-site power production? Backup generators have been around for decades, what is new about on-site power generation? What types of power generators can I buy? How are generators classified, what is a kW? What is a recuperator and why is it important? What is an HRSG? What is a desiccant dehumidifier? How do desiccant dehumidifiers use waste heat in a CHP system?

280

Response to Weatherization Questions | Department of Energy  

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

Response to Weatherization Questions Response to Weatherization Questions Response to Weatherization Questions August 30, 2010 - 4:53pm Addthis Andy Oare Andy Oare Former New Media Strategist, Office of Public Affairs Last week as part of Vice President Biden's announcement of 200,000 homes weatherized under the Recovery act, we asked you to send us your questions and comments about the weatherization process. Today, we're following up with answers experts from the Department's Weatherization and Intergovernmental Program: 1) From edmooney via Twitter: @Energy Besides caulking, what are the best values in weatherization for the Northeast region. #weatherization Nationwide, the energy-efficient retrofits that consistently provide the best return on investment involve sealing gaps in the building envelope

Note: This page contains sample records for the topic "forecasting specific questions" 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

The Big Questions For Biodiversity Informatics  

E-Print Network [OSTI]

of biodiversity information. This emerging field of biodiversity informatics has been growing quickly, but without overarching scientific questions to guide its development; the result has been developments that have no connection to genuine insight and forward...

Peterson, A. Townsend; Knapp, Sandra; Guralnick, Robert P.; Soberó n, Jorge; Holder, Mark T.

2010-01-01T23:59:59.000Z

282

TECHNOLOGY TRANSFER QUESTIONS..txt - Notepad  

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

rfien@campbellap.com Sent: Monday, January 26, 2009 5:34 PM To: GC-62 Subject: TECHNOLOGY TRANSFER QUESTIONS. Sensitivity: Confidential To Whom It May Concern, Campbell Applied...

283

Solar Instructor Training Network Frequently Asked Questions  

Broader source: Energy.gov [DOE]

These frequently asked questions (FAQs) relate to the solar instructor training network. This project was launched by the U.S. Department of Energy (DOE) Solar Energy Technologies Program (SETP or...

284

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

Pure Antineutron Beams Hello, I am a physics student in Germany. I haven't had particle physics yet, so I'd be glad if you answered me one question: How do you create more or less...

285

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

Question: How many studies have been done to figure out what escapes from the accelerators into the environment and how much of it escapes? I heard from a tour guide that...

286

Questions about Groundwater Conservation Districts in Texas  

E-Print Network [OSTI]

Groundwater conservation districts (GCDs) are being created in many parts of Texas to allow local citizens to manage and protect their groundwater. This publication answers frequently asked questions about groundwater and GCDs....

Lesikar, Bruce J.; Silvy, Valeen

2008-09-22T23:59:59.000Z

287

BPA Transmission land acquisition frequently asked questions...  

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

Q's and A's Questions and answers 1. What is the PEP form I'm receiving in the mail? BPA sends the Permission to Enter Property (PEP) form to all property owners along a...

288

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

Color of Atoms Mr. Pordes- I have a question for science. As you probably know, we have been studying all about particles and the particle model of matter and John Dalton and...

289

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

How many neutrons? Dear Mrs. Pordes, Hello. My name is Andrew Schmidt. I am writing to you concerning a question I have. I am in your daughters science class and it would be...

290

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

be quite right. Thank you for the opportunity to ask this question. Regards, Bob Dowe Hello Bob, Let us start from the beginning. First I have to tell you that there are usually...

291

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

appreciate it if you could send it to me. That would be awesome. Thanks Luke Luke - Hello. I am a scientist here at Fermilab and your question got forwarded to me. In some...

292

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

Why can radio waves pass through a wall but light cannot? Hello, My name is Mike P. and this is my question. If radio & light waves are both properties of the electromagnetic...

293

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

proton and an anti electron (a positron, i do not know the anti particle of proton). Hello Killian, thanks for your very interesting question. When you talk about particles, the...

294

UESC Frequently Asked Questions Panel Discussion  

Broader source: Energy.gov [DOE]

Presentationgiven at the April 2012 Federal Utility Partnership Working Group (FUPWG) meetingcovers frequently asked questions (FAQs) about utility energy service contracts (UESCs), including term lengths, bonding, and a UESC within a utility's service territory.

295

Response to Weatherization Questions | Department of Energy  

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

Response to Weatherization Questions Response to Weatherization Questions Response to Weatherization Questions August 30, 2010 - 4:53pm Addthis Andy Oare Andy Oare Former New Media Strategist, Office of Public Affairs Last week as part of Vice President Biden's announcement of 200,000 homes weatherized under the Recovery act, we asked you to send us your questions and comments about the weatherization process. Today, we're following up with answers experts from the Department's Weatherization and Intergovernmental Program: 1) From edmooney via Twitter: @Energy Besides caulking, what are the best values in weatherization for the Northeast region. #weatherization Nationwide, the energy-efficient retrofits that consistently provide the best return on investment involve sealing gaps in the building envelope

296

AGREE-DISAGREE QUESTIONS: PROBLEMS AND SOME  

E-Print Network [OSTI]

AGREE-DISAGREE QUESTIONS: PROBLEMS AND SOME SOLUTIONS Allyson L. Holbrook Associate Professor that established a woman's right to an abortion?" #12;EXAMPLE SCALES: HANDBOOK OF MARKETING SCALES (2010) Ten

Illinois at Chicago, University of

297

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

about learning, I'd like to cordially invite you to Physics Forums - Black Holes to the Speed of Light . Dear Stephen: Your question refers to the fact that "vacuum is not...

298

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

between my friends and me. Theorhetical Question: If a vehicle was traveling at the speed of light and a light source was emitted from the vehicle would there be a visible...

299

Technology data characterizing refrigeration in commercial buildings: Application to end-use forecasting with COMMEND 4.0  

SciTech Connect (OSTI)

In the United States, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology. The disaggregation of the refrigeration end use in terms of specific technologies, however, is complicated by several factors. First, the number of configurations of refrigeration cases and systems is quite large. Also, energy use is a complex function of the refrigeration-case properties and the refrigeration-system properties. The Electric Power Research Institute`s (EPRI`s) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework. Expanding end-use forecasting models so that they address individual technology options requires characterization of the present floorstock in terms of service requirements, energy technologies used, and cost-efficiency attributes of the energy technologies that consumers may choose for new buildings and retrofits. This report describes the process by which we collected refrigeration technology data. The data were generated for COMMEND 4.0 but are also generally applicable to other end-use forecasting frameworks for the commercial sector.

Sezgen, O.; Koomey, J.G.

1995-12-01T23:59:59.000Z

300

Using question classification to model user intentions of different levels  

Science Journals Connector (OSTI)

User information need detection is a fundamental issue in automatic question answering systems. Based on real questions collected from on-line question answering communities, this paper proposes a three-level question type taxonomy to model user information ... Keywords: multi-label classification, pragmatics, question answering, three-level question type taxonomy, user intention modeling

Yaoyun Zhang; Xuan Wang; Xiaolong Wang; Shixi Fan; Daoxu Zhang

2009-10-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasting specific questions" 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

Functional Specifications  

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

Functional Specifications Functional Specifications Services Overview ECS Audio/Video Conferencing Fasterdata IPv6 Network Network Performance Tools (perfSONAR) ESnet OID Registry PGP Key Service Virtual Circuits (OSCARS) OSCARS Case Study Documentation User Manual FAQ Design Specifications Functional Specifications Notifications Publications Authorization Policy Default Attributes Message Security Clients For Developers Interfaces Links Hardware Requirements DOE Grids Service Transition Contact Us Technical Assistance: 1 800-33-ESnet (Inside the US) 1 800-333-7638 (Inside the US) 1 510-486-7600 (Globally) 1 510-486-7607 (Globally) Report Network Problems: trouble@es.net Provide Web Site Feedback: info@es.net Functional Specifications OSCARS Reservation Manager - Functional Specifications Year 3 Update (DRAFT)

302

Comparison of AEO 2007 Natural Gas Price Forecast to NYMEX FuturesPrices  

SciTech Connect (OSTI)

On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we once again find that the AEO 2007 reference case gas price forecast falls well below where NYMEX natural gas futures contracts were trading at the time the EIA finalized its gas price forecast. Specifically, the NYMEX-AEO 2007 premium is $0.73/MMBtu levelized over five years. In other words, on average, one would have had to pay $0.73/MMBtu more than the AEO 2007 reference case natural gas price forecast in order to lock in natural gas prices over the coming five years and thereby replicate the price stability provided intrinsically by fixed-price renewable generation (or other forms of generation whose costs are not tied to the price of natural gas). Fixed-price generation (like certain forms of renewable generation) obviously need not bear this added cost, and moreover can provide price stability for terms well in excess of five years.

Bolinger, Mark; Wiser, Ryan

2006-12-06T23:59:59.000Z

303

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

304

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,

305

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

306

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network [OSTI]

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting Seasonal Prediction · POAMA · Issues for future Outline #12;Pacific Adaptation Strategy Assistance Program Major source Adaptation Strategy Assistance Program El Nino Mean State · Easterlies westward surface current upwelling

Lim, Eun-pa

307

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network [OSTI]

Forecasting volatility has held the attention of academics and practitioners all over the world. The objective for this master's thesis is to predict the volatility in stock market by using generalized autoregressive ...

Yang, Xiaorong

2008-01-01T23:59:59.000Z

308

Initial conditions estimation for improving forecast accuracy in exponential smoothing  

Science Journals Connector (OSTI)

In this paper we analyze the importance of initial conditions in exponential smoothing models on forecast errors and prediction intervals. We work with certain exponential smoothing models, namely Holts additive...

E. Vercher; A. Corbern-Vallet; J. V. Segura; J. D. Bermdez

2012-07-01T23:59:59.000Z

309

A Bayesian approach to forecast intermittent demand for seasonal products  

Science Journals Connector (OSTI)

This paper investigates the forecasting of a large fluctuating seasonal demand prior to peak sale season using a practical time series, collected from the US Census Bureau. Due to the extreme natural events (e.g. excessive snow fall and calamities), sales may not occur, inventory may not replenish and demand may set off unrecorded during the peak sale season. This characterises a seasonal time series to an intermittent category. A seasonal autoregressive integrated moving average (SARIMA), a multiplicative exponential smoothing (M-ES) and an effective modelling approach using Bayesian computational process are analysed in the context of seasonal and intermittent forecast. Several forecast error indicators and a cost factor are used to compare the models. In cost factor analysis, cost is measured optimally using dynamic programming model under periodic review policy. Experimental results demonstrate that Bayesian model performance is much superior to SARIMA and M-ES models, and efficient to forecast seasonal and intermittent demand.

Mohammad Anwar Rahman; Bhaba R. Sarker

2012-01-01T23:59:59.000Z

310

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...

311

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones  

Science Journals Connector (OSTI)

The authors develop a statistical guidance product, the tropical cyclone tornado parameter (TCTP), for forecasting the probability of one or more tornadoes during a 6-h period that are associated with landfalling tropical cyclones affecting the ...

Matthew J. Onderlinde; Henry E. Fuelberg

2014-10-01T23:59:59.000Z

312

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

313

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network [OSTI]

storms 17 4. Genesis Forecasts 17 5. Summary and Concluding Remarks 18 a. Atlantic Summary 18 statistical models, provided the best intensity guidance at each time period. The 2007 season marked the first

314

Recently released EIA report presents international forecasting data  

SciTech Connect (OSTI)

This report presents information from the Energy Information Administration (EIA). Articles are included on international energy forecasting data, data on the use of home appliances, gasoline prices, household energy use, and EIA information products and dissemination avenues.

NONE

1995-05-01T23:59:59.000Z

315

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network [OSTI]

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

316

Information-Based Skill Scores for Probabilistic Forecasts  

Science Journals Connector (OSTI)

The information content, that is, the predictive capability, of a forecast system is often quantified with skill scores. This paper introduces two ranked mutual information skill (RMIS) scores, RMISO and RMISY, for the evaluation of probabilistic ...

Bodo Ahrens; Andr Walser

2008-01-01T23:59:59.000Z

317

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

318

Evolutionary Optimization of an Ice Accretion Forecasting System  

Science Journals Connector (OSTI)

The ability to model and forecast accretion of ice on structures is very important for many industrial sectors. For example, studies conducted by the power transmission industry indicate that the majority of failures are caused by icing on ...

Pawel Pytlak; Petr Musilek; Edward Lozowski; Dan Arnold

2010-07-01T23:59:59.000Z

319

Diagnosing the Origin of Extended-Range Forecast Errors  

Science Journals Connector (OSTI)

Experiments with the ECMWF model are carried out to study the influence that a correct representation of the lower boundary conditions, the tropical atmosphere, and the Northern Hemisphere stratosphere would have on extended-range forecast skill ...

T. Jung; M. J. Miller; T. N. Palmer

2010-06-01T23:59:59.000Z

320

Application of an Improved SVM Algorithm for Wind Speed Forecasting  

Science Journals Connector (OSTI)

An improved Support Vector Machine (SVM) algorithm is used to forecast wind in Doubly Fed Induction Generator (DFIG) wind power system without aerodromometer. The ... Validation (CV) method. Finally, 3.6MW DFIG w...

Huaqiang Zhang; Xinsheng Wang; Yinxiao Wu

2011-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasting specific questions" 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

Research on Development Trends of Power Load Forecasting Methods  

Science Journals Connector (OSTI)

In practical problem, number of samples is often limited, for complex issues such as power load forecasting, generally available historical data and information of impact factor are very ... support vector mechan...

Litong Dong; Jun Xu; Haibo Liu; Ying Guo

2014-01-01T23:59:59.000Z

322

Representing Forecast Error in a Convection-Permitting Ensemble System  

Science Journals Connector (OSTI)

Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have ...

Glen S. Romine; Craig S. Schwartz; Judith Berner; Kathryn R. Fossell; Chris Snyder; Jeff L. Anderson; Morris L. Weisman

2014-12-01T23:59:59.000Z

323

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

324

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

325

Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach  

Science Journals Connector (OSTI)

The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good ...

Juan J. Flores; Roberto Loaeza; Hctor Rodrguez; Erasmo Cadenas

2009-11-01T23:59:59.000Z

326

A model for short term electric load forecasting  

E-Print Network [OSTI]

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

Tigue, John Robert

1975-01-01T23:59:59.000Z

327

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

328

Design Specifications  

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

Design Design Specifications Services Overview ECS Audio/Video Conferencing Fasterdata IPv6 Network Network Performance Tools (perfSONAR) ESnet OID Registry PGP Key Service Virtual Circuits (OSCARS) OSCARS Case Study Documentation User Manual FAQ Design Specifications Functional Specifications Notifications Publications Authorization Policy Default Attributes Message Security Clients For Developers Interfaces Links Hardware Requirements DOE Grids Service Transition Contact Us Technical Assistance: 1 800-33-ESnet (Inside the US) 1 800-333-7638 (Inside the US) 1 510-486-7600 (Globally) 1 510-486-7607 (Globally) Report Network Problems: trouble@es.net Provide Web Site Feedback: info@es.net Design Specifications OSCARS Reservation Manager - Design Specifications Year 3 Update (DRAFT) David Robertson, Chin Guok

329

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

330

Weather-based forecasts of California crop yields  

SciTech Connect (OSTI)

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

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

2005-09-26T23:59:59.000Z

331

Wave height forecasting in Dayyer, the Persian Gulf  

Science Journals Connector (OSTI)

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.

B. Kamranzad; A. Etemad-Shahidi; M.H. Kazeminezhad

2011-01-01T23:59:59.000Z

332

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

E-Print Network [OSTI]

of the Department of Energy's Office of Industrial Technologies, EIA extracted energy use infonnation from the Annual Energy Outlook (AEO) - 2000 (8) for each of the seven # The Pacific Northwest National Laboratory is operated by Battelle Memorial Institute...-6, 2000 NEMS The NEMS industrial module is the official forecasting model for EIA and thus the Department of Energy. For this reason, the energy prices and output forecasts used to drive the ITEMS model were taken from EIA's AEO 2000. Understanding...

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

333

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

334

Questions and Answers - Which jobs use electromagnets?  

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

does adding coils to anelectromagnet make it stronger? does adding coils to an<br>electromagnet make it stronger? Previous Question (Why does adding coils to an electromagnet make it stronger?) Questions and Answers Main Index Next Question (Why is a non-permanent, but long lasting, magnet called a permanent magnet?) Why is a non-permanent, but long lasting,magnet called a permanent magnet? Which jobs use electromagnets? In today's world almost all jobs other than a goat herder use some type of electromagnet. They are everywhere. Electric motors are a type of electromagnet. Cars have dozens of electromagnets that move things or generate electricity. There are all sorts of interesting applications for larger electromagnets. The most obvious and biggest example is electricity. There are some interesting applications like dumping shredded garbage

335

Questions and Answers - Who discovered the elements?  

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

Will scientists everfind smaller elements? Will scientists ever<br>find smaller elements? Previous Question (Will scientists ever find smaller elements?) Questions and Answers Main Index Next Question (What are boiling and melting points?) What are boiling and melting points? Who discovered the element gold, silver, copper, neon, etc...? Below is a list of all of the known elements, who they were discovered by and the year they were discovered. Some elements, such as gold, silver and iron, have been known since ancient times, so it is impossible to credit a single person for their discovery. Other elements were discovered around the same time by two or more scientists who were working independently of each other. In these cases, each scientist is listed along with the year they made their discovery. Other elements were discovered by teams of

336

Landowners' Frequently Asked Questions about Wind Development  

Wind Powering America (EERE)

Landowners' Frequently Asked Questions Landowners' Frequently Asked Questions about Wind Development 1 Landowners' Frequently Asked Questions about Wind Development Jay Haley, P.E. 1. How much money can I make? Based on wind projects in southern Minnesota and northern Iowa, landowners can expect to receive annual land-lease payments ranging from $2,000 to more than $4,000 per turbine. The amount depends on the size of the wind turbine and how much electricity it produces as well as the selling price of the electricity. The same turbine will produce more in one location than another depending on the annual average wind speed at the site. The payments typically represent from 2% to 4% of the annual gross revenue of the turbine. 2. How many turbines can be placed on a section of

337

Questions and Answers - What is a meniscus?  

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

What is a material with a freezingpoint above 0 degrees Celsius? What is a material with a freezing<br>point above 0 degrees Celsius? Previous Question (What is a material with a freezing point above 0 degrees Celsius?) Questions and Answers Main Index Next Question (How can I explain the Quantum/Wave theory to my class?) How can I explain the Quantum/Wavetheory to my class? What is a meniscus? A meniscus is what happens when you put a liquid into a container. When you put water in a beaker or test tube, you see a curved surface. With most liquids, the attractive force between the liquid and the container is greater than the attraction between the individual liquid molecules. So the liquid "sticks" to the side of the container. Your teacher might have told you that you have to read the test tube to the

338

Frequently Asked Questions | Department of Energy  

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

Frequently Asked Questions Frequently Asked Questions Frequently Asked Questions Q. Can contractors participate in the CFC? A. Generally contractors are not permitted to participate in CFC events. Contractor personnel, credit union employees and other persons present on Federal premises, as well as retired Federal employees, may make single contributions to the CFC through checks, money orders, or cash, or by electronic means, including credit cards. These non-Federal personnel may not be solicited, but donations offered by such persons must be accepted by the local campaigns. Q. I am online attempting to give through paperless payroll deduction; however I am only given the options of credit card, debit card or electronic check. Is paperless payroll deduction still a viable giving

339

Questions and Answers - Is krypton dangerous?  

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

does krypton look like? does krypton look like? Previous Question (What does krypton look like?) Questions and Answers Main Index Next Question (Why aren't Cl-35 and Cl-37 two different elements?) Why aren't Cl-35 and Cl-37two different elements? Is krypton dangerous? Krypton is a gas that is found in small amounts in the earth's atmosphere. Krypton's atomic structure is very stable. It has just enough electrons to fill its outer 'shell', so it doesn't tend to combine with other elements. Since it doesn't combine with other elements, it doesn't take part in chemical reactions, so it will not burn, cause corrosion, or do other chemical-like things. It is, however, possible for krypton to hurt you. If, for example, a room were to be filled with krypton, anyone entering the room would most likely

340

A Question of Savings | Department of Energy  

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

A Question of Savings A Question of Savings A Question of Savings March 15, 2011 - 5:39pm Addthis Andy Oare Andy Oare Former New Media Strategist, Office of Public Affairs The New York Times recently published a piece which gave criticism to wide-scale energy efficiency measures. Yesterday, Henry Kelly, the Acting Assistant Secretary for Energy Efficiency and Renewable Energy, published a letter to the editor in response. In addition, Shannon Baker-Branstetter, a policy advisor for Consumers Union, chimed in to clarify some of the Consumer Reports data that was cited. Text of Henry Kelly's letter. Published 3/14/11: To the Editor: John Tierney's column "When Energy Efficiency Sullies the Environment" (Findings, March 8) misses the well-established point that energy efficiency represents the most cost-effective way to reduce energy

Note: This page contains sample records for the topic "forecasting specific questions" 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

Specific Viscosity  

Science Journals Connector (OSTI)

n Equal to the relative viscosity of the same solution minus one. It represents the increase in viscosity that may be contributed by the polymeric solute. The specific viscosity, ?sp is defined by th...

Jan W. Gooch

2011-01-01T23:59:59.000Z

342

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

Oil Markets Oil Markets IEO2005 projects that world crude oil prices in real 2003 dollars will decline from their current level by 2010, then rise gradually through 2025. In the International Energy Outlook 2005 (IEO2005) reference case, world demand for crude oil grows from 78 million barrels per day in 2002 to 103 million barrels per day in 2015 and to just over 119 million barrels per day in 2025. Much of the growth in oil consumption is projected for the emerging Asian nations, where strong economic growth results in a robust increase in oil demand. Emerging Asia (including China and India) accounts for 45 percent of the total world increase in oil use over the forecast period in the IEO2005 reference case. The projected increase in world oil demand would require an increment to world production capability of more than 42 million barrels per day relative to the 2002 crude oil production capacity of 80.0 million barrels per day. Producers in the Organization of Petroleum Exporting Countries (OPEC) are expected to be the major source of production increases. In addition, non-OPEC supply is expected to remain highly competitive, with major increments to supply coming from offshore resources, especially in the Caspian Basin, Latin America, and deepwater West Africa. The estimates of incremental production are based on current proved reserves and a country-by-country assessment of ultimately recoverable petroleum. In the IEO2005 oil price cases, the substantial investment capital required to produce the incremental volumes is assumed to exist, and the investors are expected to receive at least a 10-percent return on investment.

343

Common Questions Why should I soil test?  

E-Print Network [OSTI]

Common Questions Why should I soil test? Soil testing is an important diagnostic tool to evaluate nutrient imbalances and understand plant growth. The most important reason to soil test is to have a basis for intelligent application of fertilizer and lime. Testing also allows for growers and homeowners to maintain

Isaacs, Rufus

344

Frequently Asked Questions 1. Technology Transfer  

E-Print Network [OSTI]

Frequently Asked Questions 1. Technology Transfer 2. Patent 3. Requirements for obtaining a patent is not addressed, please contact Colleen Michael at 631-344 -4919. #12;What is Technology Transfer? Technology Transfer is the process of developing practical applications for the results of scientific research

345

Extracting Simplified Statements for Factual Question Generation  

E-Print Network [OSTI]

Minister Vladimir V. Putin, the country's paramount leader, cut short a trip to Siberia, returning to Moscow to oversee the federal response. Mr. Putin built his reputation in part on his success set of questions:3 (2) Prime Minister Vladimir V. Putin is the country's paramount leader. (3) Prime

Eskenazi, Maxine

346

BOMB THREAT CHECKLIST QUESTIONS TO ASK  

E-Print Network [OSTI]

BOMB THREAT CHECKLIST QUESTIONS TO ASK: 1. When is the bomb going to explode? 2. Where is it right ___Clearing Throat ___Laughter ___Deep Breathing __Crying ___Cracking voice __Normal ___Disguised THREAT __Foul ___Taped __Message read by threat maker __Irrational If voice is familiar, who did it sound like

Cui, Yan

347

Student Learning Commons Questions & Answers for Faculty  

E-Print Network [OSTI]

Student Learning Commons Questions & Answers for Faculty What is the SFU Student Learning Commons? The Student Learning Commons (SLC), is an academic learning centre which provides peer-based assistance with library reference, computer assistance, and other student academic support services. SLC programs

348

Preprocessing Documents to Answer Dutch Questions  

E-Print Network [OSTI]

Preprocessing Documents to Answer Dutch Questions Valentin Jijkoun Gilad Mishne Maarten de Rijke extraction of certain types of informa- tion from a document collection, and discuss its usage for answering track shows that our strategy yields a significant improvement in the performance of our overall system

Jijkoun, Valentin

349

Physics 321 Exam 1 Sample Questions  

E-Print Network [OSTI]

of motion. Briefly explain the meaning of each law. 4. Describe one case where Newton's third law does .T At what frequency is the response a maximum? What does FWHM mean? What is the expression we usePhysics 321 Exam 1 Sample Questions 1. Write the second order differential equation as two first

Hart, Gus

350

Rangeland ecology: Key global research issues & questions  

E-Print Network [OSTI]

1 Rangeland ecology: Key global research issues & questions Robin Reid and Maria Fernandez-Gimenez This paper discusses developments in our understanding about rangeland ecology and rangeland dynamics in the last 20 years. Before the late 1980's, the mainstream view in range ecology was that livestock

351

Question and Answers Alternative Fuel Readiness Plans  

E-Print Network [OSTI]

Question and Answers Alternative Fuel Readiness Plans PON-13-603 September 3, 2013 Eligibility Q1 to readiness plans? A1 This solicitation is limited to readiness planning only for alternative fuels. Q2 In regards to PON-13-603 - Alternative Fuel Readiness Plans, is electricity used for transportation

352

Background Material Important Questions about Magnetism  

E-Print Network [OSTI]

Background Material Important Questions about Magnetism: 1) What is Magnetism?Magnetism is a force or repulsion due to charge is called the electric force. But what about magnetism, is there a fundamental property of some matter that makes things magnetic? The answer is: "sort of." Electric current

Mojzsis, Stephen J.

353

Fish and Wildlife Management Questions and RM&E Strategies Key Management Questions  

E-Print Network [OSTI]

1 Fish and Wildlife Management Questions and RM&E Strategies Key Management Questions 1. Are we meeting biological and programmatic performance objectives established within the Columbia Basin Fish implemented and accomplished as proposed? Strategic Category: Fish Population Status Monitoring The following

354

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

SciTech Connect (OSTI)

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

Porter, K.; Rogers, J.

2012-04-01T23:59:59.000Z

355

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

356

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

357

An assessment of electrical load forecasting using artificial neural network  

Science Journals Connector (OSTI)

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using artificial neural networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. For annual forecasts, there should be 10 to 12 years of historical monthly data available for each electrical system or electrical buss. A case study is performed by using the proposed method of peak load data of a state electricity board of India which maintain high quality, reliable, historical data providing the best possible results. Model's quality is directly dependent upon data integrity.

V. Shrivastava; R.B. Misra; R.C. Bansal

2012-01-01T23:59:59.000Z

358

Face Recognition Deficits in Autism Spectrum Disorders Are Both Domain Specific and Process Specific  

E-Print Network [OSTI]

Although many studies have reported face identity recognition deficits in autism spectrum disorders (ASD), two fundamental question remains: 1) Is this deficit process specific for face memory in particular, or does it ...

Weigelt, Sarah

359

Numerical Simulation of 2010 Pakistan Flood in the Kabul River Basin by Using Lagged Ensemble Rainfall Forecasting  

Science Journals Connector (OSTI)

Lagged ensemble forecasting of rainfall and rainfallrunoffinundation (RRI) forecasting were applied to the devastating flood in the Kabul River basin, the first strike of the 2010 Pakistan flood. The forecasts were performed using the Global ...

Tomoki Ushiyama; Takahiro Sayama; Yuya Tatebe; Susumu Fujioka; Kazuhiko Fukami

2014-02-01T23:59:59.000Z

360

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 "forecasting specific questions" 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

Hanford Speakers Bureau

Frequently Asked Questions - Hanford...
 

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

Speakers Bureau > Hanford Speakers Bureau Frequently Asked Questions Hanford Speakers Bureau Hanford Speakers Bureau Request Form Hanford Speakers Bureau Frequently Asked Questions...

362

Electric Vehicle Deployment: Policy Questions and Impacts to...  

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

Vehicle Deployment: Policy Questions and Impacts to the U.S. Electric Grid - EAC Recommendations (November 2011) Electric Vehicle Deployment: Policy Questions and Impacts to the...

363

Frequently Asked Questions on Energy Efficiency and Conservation...  

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

Frequently Asked Questions on Energy Efficiency and Conservation Block Grant Financing Program Compliance and Reporting Frequently Asked Questions on Energy Efficiency and...

364

Questions and Issues on Hydrogen Pipelines: Pipeline Transmission...  

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

Questions and Issues on Hydrogen Pipelines: Pipeline Transmission of Hydrogen Questions and Issues on Hydrogen Pipelines: Pipeline Transmission of Hydrogen Pipping of GH2 Pipeline....

365

DOE response to questions from AHAM on the supplemental proposed...  

Energy Savers [EERE]

response to questions from AHAM on the supplemental proposed test procedure for residential clothes washers DOE response to questions from AHAM on the supplemental proposed test...

366

48C Qualifying Advanced Energy Project Credit Questions | Department...  

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

Credit Questions More Documents & Publications 48C Qualifying Advanced Energy Project Credit Questions FACT SHEET: 48C MANUFACTURING TAX CREDITS Microsoft Word -...

367

Home Energy Score: Frequently Asked Questions for Partners |...  

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

Partners Home Energy Score: Frequently Asked Questions for Partners Below you will find answers to frequently asked questions for homeowners and Partners about the Home Energy...

368

Expert Panel: Forecast Future Demand for Medical Isotopes | Department of  

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

Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes Expert Panel: Forecast Future Demand for Medical Isotopes The Expert Panel has concluded that the Department of Energy and National Institutes of Health must develop the capability to produce a diverse supply of radioisotopes for medical use in quantities sufficient to support research and clinical activities. Such a capability would prevent shortages of isotopes, reduce American dependence on foreign radionuclide sources and stimulate biomedical research. The expert panel recommends that the U.S. government build this capability around either a reactor, an accelerator or a combination of both technologies as long as isotopes for clinical and research applications can be supplied reliably, with diversity in adequate

369

Forecasting correlated time series with exponential smoothing models  

Science Journals Connector (OSTI)

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection criterion is introduced into the forecasting scheme for selecting the most adequate multivariate model for describing the behaviour of the time series under study. The forecasting performance of this procedure is tested using some real examples.

Ana Corbern-Vallet; Jos D. Bermdez; Enriqueta Vercher

2011-01-01T23:59:59.000Z

370

Application of GIS on forecasting water disaster in coal mines  

SciTech Connect (OSTI)

In many coal mines of China, water disasters occur very frequently. It is the most important problem that water gets inrush into drifts and coal faces, locally known as water gush, during extraction and excavation. Its occurrence is controlled by many factors such as geological, hydrogeological and mining technical conditions, and very difficult to be predicted and prevented by traditional methods. By making use of overlay analysis of Geographic Information System, a multi-factor model can be built to forecast the potential of water gush. This paper introduced the method of establishment of the water disaster forecasting system and forecasting model and two practical successful cases of application in Jiaozuo and Yinzhuang coal mines. The GIS proved helpful for ensuring the safety of coal mines.

Sun Yajun; Jiang Dong; Ji Jingxian [China Univ. of Mining and Technology, Jiangshy (China)] [and others

1996-08-01T23:59:59.000Z

371

Frequently Asked Questions | Superconducting Magnet Division  

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

Frequently Asked Questions Frequently Asked Questions What is the official EMS Policy at BNL? Refer to the Environmental, Safety, Security, and Health policy. What do you know about EMS? Refer to the information on the Process Evaluations web page. What are the environmental aspects related to your work? An environmental aspect is a waste or an operating/work activity that, if done improperly or if a component fails, could pollute the environment. Some environmental aspects are: Hazardous waste, radioactive waste, atmospheric discharge, soil activation, storage/use of chemicals, etc. For a listing of the SMD Significant Aspects and Processes see the Aspects Matrix (pdf) and the Summary of Significant Aspects. You can also find more information see the Pollution Prevention web page.

372

EMSL: About EMSL: Frequently Asked Questions  

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

EMSL Frequently Asked Questions EMSL Frequently Asked Questions What is EMSL's Mission? The Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) national scientific user facility located at Pacific Northwest National Laboratory (PNNL) in Richland, Washington, provides integrated experimental and computational resources for discovery and technological innovation in the environmental molecular sciences to support the needs of DOE and the nation. What is a DOE Scientific User Facility? The Office of Science oversees the construction and operation of some of the Nation's most advanced scientific research and development user facilities, located at national laboratories and universities. These state-of-the-art facilities are made available to the science community

373

Frequently Asked Questions | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

Operations > Management and Budget > Human Resources Operations > Management and Budget > Human Resources > Pay-banding > Frequently Asked Questions Frequently Asked Questions General What is a demonstration project? What employees are affected by the pay-banding and pay-for-performance demonstration project? When was pay-banding implemented? What is the duration of the demonstration project? What happens after 5 years? What is the process for converting employees from the pay-banding system back to the General Schedule system (in the event of a transfer to another federal agency)? What is a share? What occupational series will be covered in each career path? Conversions What is the process for converting employees to pay-banding? How does conversion to a pay band affect an employee's next step increase?

374

QUESTIONS BY AREA OF INTEREST : AOI #4  

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

QUESTIONS BY AREA OF INTEREST : AOI #4 QUESTIONS BY AREA OF INTEREST : AOI #4 Q4. How does the application process work? We are getting conflicting information with the Federal Stimulus money that we need to go through the Clean Cities Coalitions. Do they need to be the applicants? Or can they just be partners in our project? Same with the Transit Authorities..originally we did not need them to be a partner; however, new information states we need to have both the Clean Cities Coalitions and local Transit Authority as participants/applicants? A. For Areas of Interest #1-3, there are no restrictions to eligibility for apply for funds. While it is not mandatory that the applicant be a Clean Cities coalition (designated or non-designated), it is strongly encouraged that teams include one or more Clean Cities

375

Fermilab | Tune IT Up | Questions & Answers  

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

Questions & Answers Questions & Answers Posted: November 18, 2009 (Updated on November 24, 2009) Q. I am located at CERN and have no computers at FNAL. My personal laptop is a CERN laptop. Do I still have to complete the Tune IT Up assessment? A. If it is not an FNAL-owned computer at CERN, you do not need to complete the assessment. Posted: September 15, 2009 Q. For which computers do I need to fill out the Tune IT Up assessment? I have a personal desktop and laptop. But I also manage and/or am the custodian for various other servers at Fermilab. A. You should complete the assessment only for the desktop, laptop or smart phone you personally use to do your work. There is no need to do it for servers. Posted: September 15, 2009 Q. If the person who uses a computer is not the person who manages it, who

376

Technology data characterizing space conditioning in commercial buildings: Application to end-use forecasting with COMMEND 4.0  

SciTech Connect (OSTI)

In the US, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology. The disaggregation of space conditioning end uses in terms of specific technologies is complicated by several factors. First, the number of configurations of heating, ventilating, and air conditioning (HVAC) systems and heating and cooling plants is very large. Second, the properties of the building envelope are an integral part of a building`s HVAC energy consumption characteristics. Third, the characteristics of commercial buildings vary greatly by building type. The Electric Power Research Institute`s (EPRI`s) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework. This report describes the process by which the authors collected space-conditioning technology data and then mapped it into the COMMEND 4.0 input format. The data are also generally applicable to other end-use forecasting frameworks for the commercial sector.

Sezgen, O.; Franconi, E.M.; Koomey, J.G.; Greenberg, S.E.; Afzal, A.; Shown, L.

1995-12-01T23:59:59.000Z

377

Discordances ontologiques et questions d'interoprabilit  

E-Print Network [OSTI]

liens, et en quoi elles sont pertinentes pour les sciences sociales. Mots clés : Epistémologie, ontologie, interopérabilité, intégration de schémas, homologie structurale Summary : Beyond the search terrain d'étude ethnologique. Après avoir quelque peu précisé ces deux questions, ainsi que les liens qu

Boyer, Edmond

378

Questions and Answers - What is plasma?  

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

Why do protons and neutronshave the same mass? Why do protons and neutrons<br>have the same mass? Previous Question (Why do protons and neutrons have the same mass?) Questions and Answers Main Index Next Question (How do you know plasma is real if you can't see it?) How do you know plasmais real if you can't see it? What is plasma? Plasma is the fourth state of matter. Many places teach that there are three states of matter; solid, liquid and gas, but there are actually four. The fourth is plasma. To put it very simply, a plasma is an ionized gas, a gas into which sufficient energy is provided to free electrons from atoms or molecules and to allow both species, ions and electrons, to coexist. The funny thing about that is, that as far as we know, plasmas are the most common state of matter in the universe. They are even common here on earth.

379

NREL: Energy Analysis - Energy Forecasting and Modeling Staff  

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

Energy Forecasting and Modeling Energy Forecasting and Modeling The following includes summary bios of staff expertise and interests in analysis relating to energy economics, energy system planning, risk and uncertainty modeling, and energy infrastructure planning. Team Lead: Nate Blair Administrative Support: Geraly Amador Clayton Barrows Greg Brinkman Brian W Bush Stuart Cohen Carolyn Davidson Paul Denholm Victor Diakov Aron Dobos Easan Drury Kelly Eurek Janine Freeman Marissa Hummon Jennie Jorganson Jordan Macknick Trieu Mai David Mulcahy David Palchak Ben Sigrin Daniel Steinberg Patrick Sullivan Aaron Townsend Laura Vimmerstedt Andrew Weekley Owen Zinaman Photo of Clayton Barrows. Clayton Barrows Postdoctoral Researcher Areas of expertise Power system modeling Primary research interests Power and energy systems

380

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

Forecast of contracting and subcontracting opportunities. Fiscal year 1996  

SciTech Connect (OSTI)

This forecast of prime and subcontracting opportunities with the U.S. Department of Energy and its MAO contractors and environmental restoration and waste management contractors, is the Department`s best estimate of small, small disadvantaged and women-owned small business procurement opportunities for fiscal year 1996. The information contained in the forecast is published in accordance with Public Law 100-656. It is not an invitation for bids, a request for proposals, or a commitment by DOE to purchase products or services. Each procurement opportunity is based on the best information available at the time of publication and may be revised or cancelled.

NONE

1996-02-01T23:59:59.000Z

382

Sales forecasting strategies for small businesses: an empirical investigation of statistical and judgemental methods  

Science Journals Connector (OSTI)

This study evolved from the mixed results shown in the reviewed forecasting literature and from the lack of sufficient forecasting research dealing with micro data. The main purpose of this study is to investigate and compare the accuracy of different quantitative and qualitative forecasting techniques, and to recommend a forecasting strategy for small businesses. Emphasis is placed on the testing of combining as a tool to improve forecasting accuracy. Of particular interest is whether combining time series and judgemental forecasts provides more accurate results than individual methods. A case study of a small business was used for this purpose to assess the accuracy and applicability of combining forecasts. The evidence indicates that combining qualitative and quantitative methods results in better and improved forecasts.

Imad J. Zbib

2006-01-01T23:59:59.000Z

383

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

E-Print Network [OSTI]

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

Bush, Sarah, 1973-

2003-01-01T23:59:59.000Z

384

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

E-Print Network [OSTI]

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

Ganguly, Auroop Ratan

2002-01-01T23:59:59.000Z

385

A Comparison of Measures-Oriented and Distributions-Oriented Approaches to Forecast Verification  

Science Journals Connector (OSTI)

The authors have carried out verification of 590 1224-h high-temperature forecasts from numerical guidance products and human forecasters for Oklahoma City, Oklahoma, using both a measures-oriented verification scheme and a distributions-...

Harold E. Brooks; Charles A. Doswell III

1996-09-01T23:59:59.000Z

386

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

387

Improving Seasonal Forecast Skill of North American Surface Air Temperature in Fall Using a Postprocessing Method  

Science Journals Connector (OSTI)

A statistical postprocessing approach is applied to seasonal forecasts of surface air temperatures (SAT) over North America in fall, when the original uncalibrated predictions have little skill. The data used are ensemble-mean seasonal forecasts ...

XiaoJing Jia; Hai Lin; Jacques Derome

2010-05-01T23:59:59.000Z

388

Computing electricity spot price prediction intervals using quantile regression and forecast averaging  

Science Journals Connector (OSTI)

We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity

Jakub Nowotarski; Rafa? Weron

2014-08-01T23:59:59.000Z

389

Medium-term forecasting of demand prices on example of electricity prices for industry  

Science Journals Connector (OSTI)

In the paper, a method of forecasting demand prices for electric energy for the industry has been suggested. An algorithm of the forecast for 20062010 based on the data for 19972005 has been presented.

V. V. Kossov

2014-09-01T23:59:59.000Z

390

Price Forecasting and Optimal Operation of Wholesale Customers in a Competitive Electricity Market.  

E-Print Network [OSTI]

??This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning (more)

Zareipour, Hamidreza

2006-01-01T23:59:59.000Z

391

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

SciTech Connect (OSTI)

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

Piwko, R.; Jordan, G.

2011-11-01T23:59:59.000Z

392

Combining Multi Wavelet and Multi NN for Power Systems Load Forecasting  

Science Journals Connector (OSTI)

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series ... introduced. In addition, multi neural network for load forecast including...

Zhigang Liu; Qi Wang; Yajun Zhang

2008-01-01T23:59:59.000Z

393

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

E-Print Network [OSTI]

Production forecasting in shale (ultra-low permeability) gas reservoirs is of great interest due to the advent of multi-stage fracturing and horizontal drilling. The well renowned production forecasting model, Arps? Hyperbolic Decline Model...

Statton, James Cody

2012-07-16T23:59:59.000Z

394

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...

395

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

E-Print Network [OSTI]

andvalidation. SolarEnergy. 73:5,307? Perez,R. ,irradianceforecastsforsolarenergyapplicationsbasedonforecastdatabase. SolarEnergy. 81:6,809?812.

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

396

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height  

Science Journals Connector (OSTI)

The Weather Research and Forecasting Model (WRF) with 10-km horizontal grid spacing was used to explore improvements in wind speed forecasts at a typical wind turbine hub height (80 m). An ensemble consisting of WRF model simulations with ...

Adam J. Deppe; William A. Gallus Jr.; Eugene S. Takle

2013-02-01T23:59:59.000Z

397

Fermilab | Science | Inquiring Minds | Questions About Physics  

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

are used at Fermilab? I understand that they are used in "detectors" and "particle accelerators", but I would like more specific information. Student of Physics, Ami Dear Ami:...

398

Improving the forecasting function for a Credit Hire operator in the UK  

Science Journals Connector (OSTI)

This study aims to test on the predictability of Credit Hire services for the automobile and insurance industry. A relatively sophisticated time series forecasting procedure, which conducts a competition among exponential smoothing models, is employed to forecast demand for a leading UK Credit Hire operator (CHO). The generated forecasts are compared against the Naive method, resulting that demand for CHO services is indeed extremely hard to forecast, as the underlying variable is the number of road accidents a truly stochastic variable.

Nicolas D. Savio; K. Nikolopoulos; Konstantinos Bozos

2009-01-01T23:59:59.000Z

399

Central Wind Power Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities  

SciTech Connect (OSTI)

The report addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America.

Porter, K.; Rogers, J.

2009-12-01T23:59:59.000Z

400

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

E-Print Network [OSTI]

there is significant uncertainty in its future intensity, the current forecast is for a slowly strengthening TC which, 3) forecast output from global models, 4) the current and projected state of the Madden with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all

Gray, William

402

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.

403

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

404

Lessons from Deploying NLG Technology for Marine Weather Forecast Text Generation  

E-Print Network [OSTI]

model along with other sources of weather data such as satellite pictures and their own forecastingLessons from Deploying NLG Technology for Marine Weather Forecast Text Generation Somayajulu G Language Generation (NLG) system that produces textual weather forecasts for offshore oilrigs from

Sripada, Yaji

405

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

406

2013 Better Buildings Federal Award Frequently Asked Questions  

Broader source: Energy.gov [DOE]

Document answers frequently asked questions for the Federal Energy Management Program's 2013 Better Buildings Federal Award.

407

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

408

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

409

Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance  

Science Journals Connector (OSTI)

Modeling the lead-time demand for the multiple slow-moving inventory items in the case when the available requests history is very short is a challenge for inventory management. The classical forecasting technique, which is based on the aggregation of the stock keeping units to overcome the mentioned historical data peculiarity, is known to lead to very poor performance in many cases important for industrial applications. An alternative approach to the demand forecasting for the considered problem is based on the Bayesian paradigm, when the initially developed population-averaged demand probability distribution is modified for each item using its specific requests history. This paper follows this approach and presents a new model, which relies on the beta distribution as a prior for the request probability, and allows to account for disparity in variance of demand between different stock keeping units. To estimate the model parameters, a special computationally effective technique based on the generalized method of moments is developed. Simulation results indicate the superiority of the proposed model over the known ones, while the computational burden does not increase.

Alexandre Dolgui; Maksim Pashkevich

2008-01-01T23:59:59.000Z

410

Building Technologies Office: Frequently Asked Questions About the  

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

Frequently Asked Frequently Asked Questions About the Buildings Performance Database to someone by E-mail Share Building Technologies Office: Frequently Asked Questions About the Buildings Performance Database on Facebook Tweet about Building Technologies Office: Frequently Asked Questions About the Buildings Performance Database on Twitter Bookmark Building Technologies Office: Frequently Asked Questions About the Buildings Performance Database on Google Bookmark Building Technologies Office: Frequently Asked Questions About the Buildings Performance Database on Delicious Rank Building Technologies Office: Frequently Asked Questions About the Buildings Performance Database on Digg Find More places to share Building Technologies Office: Frequently Asked Questions About the Buildings Performance Database on AddThis.com...

411

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

SciTech Connect (OSTI)

Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e.g., futures, swaps, and fixed-price physical supply contracts) to contemporaneous forecasts of spot natural gas prices, with the purpose of identifying any systematic differences between the two. Although our data set is quite limited, we find that over the past three years, forward gas prices for durations of 2-10 years have been considerably higher than most natural gas spot price forecasts, including the reference case forecasts developed by the Energy Information Administration (EIA). This difference is striking, and implies that resource planning and modeling exercises based on these forecasts over the past three years have yielded results that are biased in favor of gas-fired generation (again, presuming that long-term stability is desirable). As discussed later, these findings have important ramifications for resource planners, energy modelers, and policy-makers.

Bolinger, Mark; Wiser, Ryan; Golove, William

2003-08-13T23:59:59.000Z

412

Questions and Answers - In the chemical equation for methane gas why is  

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

carbon found in all organicand inorganic matter? carbon found in all organic<br>and inorganic matter? Previous Question (Is carbon found in all organic and inorganic matter?) Questions and Answers Main Index Next Question (How do you separate tungsten from its ore?) How do you separatetungsten from its ore? In the chemical equation CH4 for methane gas why is there more hydrogen than carbon? This is a very good question, and the answer is at the heart of modern atomic physics. The nucleus is at the center of the atom, like the sun is at the center of the solar system. Electrons move around in orbits around the nucleus, like the planets around the sun. But there is an important difference: electrons can only have very special energies, which correspond to specific orbits. The orbits in the atoms are called shells, and each shell can only hold so

413

10 Questions for a Signature Scientist: Nathan Baker | Department of Energy  

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

10 Questions for a Signature Scientist: Nathan Baker 10 Questions for a Signature Scientist: Nathan Baker 10 Questions for a Signature Scientist: Nathan Baker June 2, 2011 - 6:28pm Addthis Nathan Baker | Photo Courtesy of PNNL Nathan Baker | Photo Courtesy of PNNL Niketa Kumar Niketa Kumar Public Affairs Specialist, Office of Public Affairs "A signature is something that pops up in a lot of fields, but very few fields specifically define it in an abstract way." Nathan Baker, Signature Scientist Meet Pacific Northwest National Laboratory's Chief Scientist for Signature Science, Nathan Baker. At PNNL, he's working to advance the innovative application of data analytics and algorithms to real-world challenges, ranging from smart grids and bioforensics to nuclear non-proliferation and medical treatments. Check out our latest 10 Questions

414

DOE response to questions from AHAM on the supplemental proposed test  

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

response to questions from AHAM on the supplemental proposed response to questions from AHAM on the supplemental proposed test procedure for residential clothes washers DOE response to questions from AHAM on the supplemental proposed test procedure for residential clothes washers The intent of Part B is to use the methodology that AHAM had suggested specifically for extra-hot cycles, but to make it more broadly applicable to any temperature combination that is locked out of the Normal cycle. DOE_response_to_questions_from_AHAM.pdf More Documents & Publications Additional Guidance Regarding Application of Current Procedures for Testing Energy Consumption of Clothes Washers with Warm Rinse Cycles, Issued: June 30, 2010 Microsoft Word - CCE_Final_Rule_02-07-11 _Final Word_.docx Federal Register Vol. 76 No. 44, 12422-12505 - Energy Conservation Program:

415

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts  

E-Print Network [OSTI]

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts Antonio that the inherent variability in wind power generation and the related difficulty in predicting future generation profiles, raise major challenges to wind power integration into the electricity grid. In this work we study

Giannitrapani, Antonello

416

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

417

Detecting and Forecasting Economic Regimes in Automated Exchanges  

E-Print Network [OSTI]

, such as over- supply or scarcity, from historical data using computational methods to construct price density. The agent can use this information to make both tactical decisions such as pricing and strategic decisions historical data and identified from observable data. We outline how to identify regimes and forecast regime

Ketter, Wolfgang

418

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

Forecasting Market Demand for New Telecommunications Services: An Introduction Peter Mc, 2000 Abstract The marketing team of a new telecommunications company is usually tasked with producing involved in doing so. Based on our three decades of experience working with telecommunications operators

Parsons, Simon

419

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS  

E-Print Network [OSTI]

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS Detlev Heinemann Oldenburg.girodo@uni-oldenburg.de ABSTRACT Solar energy is expected to contribute major shares of the future global energy supply. Due to its and solar energy conversion processes has to account for this behaviour in respective operating strategies

Heinemann, Detlev

420

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks  

E-Print Network [OSTI]

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

Cerpa, Alberto E.

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

E-Print Network [OSTI]

is familiar with solar energy issues, we hope that you will take a few moments to answer this short survey on your needs for information on solar energy resources and forecasting. This survey is conducted with the California Solar Energy Collaborative (CSEC) and the California Solar Initiative (CSI) our objective

Islam, M. Saif

422

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

423

Forecasting Building Occupancy Using Sensor Network James Howard  

E-Print Network [OSTI]

) into the future. Our approach is to train a set of standard forecasting models to our time series data. Each model conditioning (HVAC) systems. In particular, if occupancy can be accurately pre- dicted, HVAC systems can potentially be controlled to op- erate more efficiently. For example, an HVAC system can pre-heat or pre

Hoff, William A.

424

Forecasting Hospital Bed Availability Using Simulation and Neural Networks  

E-Print Network [OSTI]

Forecasting Hospital Bed Availability Using Simulation and Neural Networks Matthew J. Daniels is a critical factor for decision-making in hospitals. Bed availability (or alternatively the bed occupancy in emergency departments, and many other important hospital decisions. To better enable a hospital to make

Kuhl, Michael E.

425

Predicting Solar Generation from Weather Forecasts Using Machine Learning  

E-Print Network [OSTI]

Predicting Solar Generation from Weather Forecasts Using Machine Learning Navin Sharma, Pranshu Sharma, David Irwin, and Prashant Shenoy Department of Computer Science University of Massachusetts Amherst Amherst, Massachusetts 01003 {nksharma,pranshus,irwin,shenoy}@cs.umass.edu Abstract--A key goal

Shenoy, Prashant

426

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect (OSTI)

Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

2011-03-28T23:59:59.000Z

427

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

428

Power load forecasting using data mining and knowledge discovery technology  

Science Journals Connector (OSTI)

Considering the importance of the peak load to the dispatching and management of the electric system, the error of peak load is proposed in this paper as criteria to evaluate the effect of the forecasting model. This paper proposes a systemic framework that attempts to use data mining and knowledge discovery (DMKD) to pretreat the data. And a new model is proposed which combines artificial neural networks with data mining and knowledge discovery for electric load forecasting. With DMKD technology, the system not only could mine the historical daily loading which had the same meteorological category as the forecasting day to compose data sequence with highly similar meteorological features, but also could eliminate the redundant influential factors. Then an artificial neural network is constructed to predict according to its characteristics. Using this new model, it could eliminate the redundant information, accelerate the training speed of neural network and improve the stability of the convergence. Compared with single BP neural network, this new method can achieve greater forecasting accuracy.

Yongli Wang; Dongxiao Niu; Ling Ji

2011-01-01T23:59:59.000Z

429

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

430

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

431

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

432

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

433

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

434

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

435

Leveraging Weather Forecasts in Renewable Energy Navin Sharmaa,  

E-Print Network [OSTI]

Leveraging Weather Forecasts in Renewable Energy Systems Navin Sharmaa, , Jeremy Gummesonb , David, Binghamton, NY 13902 Abstract Systems that harvest environmental energy must carefully regulate their us- age to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic, since

Shenoy, Prashant

436

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems  

E-Print Network [OSTI]

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems Navin Sharma,gummeson,irwin,shenoy}@cs.umass.edu Abstract--To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands

Shenoy, Prashant

437

Weather forecast-based optimization of integrated energy systems.  

SciTech Connect (OSTI)

In this work, we establish an on-line optimization framework to exploit detailed weather forecast information in the operation of integrated energy systems, such as buildings and photovoltaic/wind hybrid systems. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can translate in high operating costs. To overcome this problem, we propose the use of a supervisory dynamic optimization strategy that can lead to more proactive and cost-effective operations. The strategy is based on the solution of a receding-horizon stochastic dynamic optimization problem. This permits the direct incorporation of economic objectives, statistical forecast information, and operational constraints. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. The statistical ambient information is obtained from a set of realizations generated by the weather model executed in an operational setting. We present proof-of-concept simulation studies to demonstrate that the proposed framework can lead to significant savings (more than 18% reduction) in operating costs.

Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

2009-03-01T23:59:59.000Z

438

Journey data based arrival forecasting for bicycle hire schemes  

E-Print Network [OSTI]

Journey data based arrival forecasting for bicycle hire schemes Marcel C. Guenther and Jeremy T. The global emergence of city bicycle hire schemes has re- cently received a lot of attention of future bicycle migration trends, as these assist service providers to ensure availability of bicycles

Imperial College, London

439

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

440

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

Note: This page contains sample records for the topic "forecasting specific questions" 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

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

442

Seasonal Forecasting of Extreme Wind and Precipitation Frequencies in Europe  

E-Print Network [OSTI]

Seasonal Forecasting of Extreme Wind and Precipitation Frequencies in Europe Matthew J. Swann;Abstract Flood and wind damage to property and livelihoods resulting from extreme precipitation events variability of these extreme events can be closely related to the large-scale atmospheric circulation

Feigon, Brooke

443

Use of wind power forecasting in operational decisions.  

SciTech Connect (OSTI)

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

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

2011-11-29T23:59:59.000Z

444

Environmental Radiation Effects: A Need to Question Old Paradigms  

SciTech Connect (OSTI)

A historical perspective is given of the current paradigm that does not explicitly protect the environment from radiation, but instead, relies on the concept that if dose limits are set to protect humans then the environment is automatically protected as well. We summarize recent international questioning of this paradigm and briefly present three different frameworks for protecting biota that are being considered by the U.S. DOE, the Canadian government and the International Commission on Radiological Protection. We emphasize that an enhanced collaboration is required between what has traditionally been separated disciplines of radiation biology and radiation ecology if we are going to properly address the current environmental radiation problems. We then summarize results generated from an EMSP grant that allowed us to develop a Low Dose Irradiation Facility that specifically addresses effects of low-level, chronic irradiation on multiple levels of biological organization.

Hinton, T.G.; Bedford, J.; Ulsh, B.; Whicker, F. Ward

2003-03-27T23:59:59.000Z

445

VEHICLE SPECIFICATIONS  

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

VEHICLE SPECIFICATIONS 1 Vehicle Features Base Vehicle: 2011 Chevrolet Volt VIN: 1G1RD6E48BUI00815 Class: Compact Seatbelt Positions: 4 Type 2 : Multi-Mode PHEV (EV, Series, and Power-split) Motor Type: 12-pole permanent magnet AC synchronous Max. Power/Torque: 111 kW/370 Nm Max. Motor Speed: 9500 rpm Cooling: Active - Liquid cooled Generator Type: 16-pole permanent magnet AC synchronous Max. Power/Torque: 55 kW/200 Nm Max. Generator Speed: 6000 rpm Cooling: Active - Liquid cooled Battery Manufacturer: LG Chem Type: Lithium-ion Cathode/Anode Material: LiMn 2 O 4 /Hard Carbon Number of Cells: 288 Cell Config.: 3 parallel, 96 series Nominal Cell Voltage: 3.7 V Nominal System Voltage: 355.2 V Rated Pack Capacity: 45 Ah Rated Pack Energy: 16 kWh Weight of Pack: 435 lb

446

VEHICLE SPECIFICATIONS  

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

Page 1 of 5 Page 1 of 5 VEHICLE SPECIFICATIONS 1 Vehicle Features Base Vehicle: 2011 Nissan Leaf VIN: JN1AZ0CP5BT000356 Class: Mid-size Seatbelt Positions: 5 Type: EV Motor Type: Three-Phase, Four-Pole Permanent Magnet AC Synchronous Max. Power/Torque: 80 kW/280 Nm Max. Motor Speed: 10,390 rpm Cooling: Active - Liquid cooled Battery Manufacturer: Automotive Energy Supply Corporation Type: Lithium-ion - Laminate type Cathode/Anode Material: LiMn 2 O 4 with LiNiO 2 /Graphite Pack Location: Under center of vehicle Number of Cells: 192 Cell Configuration: 2 parallel, 96 series Nominal Cell Voltage: 3.8 V Nominal System Voltage: 364.8 V Rated Pack Capacity: 66.2 Ah Rated Pack Energy: 24 kWh Max. Cell Charge Voltage 2 : 4.2 V Min. Cell Discharge Voltage 2 : 2.5 V

447

Questions and Answers - Why is a non-permanent, but long lasting, magnet  

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

Which jobs use electromagnets? Which jobs use electromagnets? Previous Question (Which jobs use electromagnets?) Questions and Answers Main Index Next Question (Can you turn something into a magnet by banging on it in a specific way?) Can you turn something into a magnetby banging on it in a specific way? Why is a non-permanent, but long lasting, magnet called a permanent magnet? Permanent magnets are magnets that you don't have to use energy to make them magnetic. Some types of permanent magnets, relative to the length of lives of humans, are pretty close to permanent. They decay slowly, but they do decay. When most of the magnetic domains in a material align in one direction you can call that a magnet. It helps if you can imagine a magnet as being made up of a bunch of little magnets. Each one has tiny North and

448

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

449

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

450

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

451

Detecting API Usage Obstacles: A Study of iOS and Android Developer Questions  

E-Print Network [OSTI]

Detecting API Usage Obstacles: A Study of iOS and Android Developer Questions Wei Wang and Michael that can be later customized for a specific task. When developers invoke API methods in a framework, they often encounter obstacles in finding the correct usage of the API, let alone to employ best practices

Godfrey, Michael W.

452

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

E-Print Network [OSTI]

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

Fievet, Lucas; Cauwels, Peter; Sornette, Didier

2014-01-01T23:59:59.000Z

453

Frequently Asked Questions … Certification Grace Period  

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

AUGUST 17, 2010 AUGUST 17, 2010 Manufacturers and/or private labelers who have not submitted a certification report and compliance statement for the products identified in Question 1 are in violation of Federal regulations and are subject to enforcement, including civil penalties and injunctive action to prohibit distribution of covered products in commerce in the United States. You should submit the required documents immediately in order to limit potential civil penalties. The information provided below is intended to help companies understand their responsibilities under the energy efficiency/water conservation regulations, with respect to the requirement to certify compliance with the regulations. It is not intended to create or remove any rights or duties, nor is it intended to affect any other aspect of EPCA or

454

Career Opportunities - Frequently Asked Questions | Argonne National  

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

Apply for a Job Apply for a Job Connect with Argonne LinkedIn Facebook Twitter YouTube Google+ More Social Media » Career Opportunities - Frequently Asked Questions How do I apply for a job at Argonne? Using Argonne's online application system, create a profile. Note that if you have your resume handy (in Microsoft Word or PDF format), you can upload it, and basic information will be extracted and populate the profile fields. Once you complete your profile, you can apply to any job openings that match your skills and qualifications by logging into the system. Where do I find the online application system? You can find links that will allow you to search full- and part-time openings, postdoctoral openings, or student openings by clicking the appropriate link on the APPLY FOR A JOB page.

455

Frequently Asked Questions … Certification Grace Period  

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

MARCH 16, 2010 MARCH 16, 2010 Manufacturers and/or private labelers who have not submitted a certification report and compliance statement for the products identified in Question are in violation of Federal regulations and are subject to enforcement, including civil penalties and injunctive action to prohibit distribution of covered products in commerce in the United States. You should submit the required documents immediately in order to limit potential civil penalties. 1 The information provided below is intended to help companies understand their responsibilities under the energy efficiency/water conservation regulations, with respect to the requirement to certify compliance with the regulations. It is not intended to create or remove any rights or duties, nor is it intended to affect any other aspect of EPCA or

456

Solid-State Lighting: Frequently Asked Questions About the Technology  

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

Frequently Asked Questions Frequently Asked Questions About the Technology Demonstration GATEWAY Program to someone by E-mail Share Solid-State Lighting: Frequently Asked Questions About the Technology Demonstration GATEWAY Program on Facebook Tweet about Solid-State Lighting: Frequently Asked Questions About the Technology Demonstration GATEWAY Program on Twitter Bookmark Solid-State Lighting: Frequently Asked Questions About the Technology Demonstration GATEWAY Program on Google Bookmark Solid-State Lighting: Frequently Asked Questions About the Technology Demonstration GATEWAY Program on Delicious Rank Solid-State Lighting: Frequently Asked Questions About the Technology Demonstration GATEWAY Program on Digg Find More places to share Solid-State Lighting: Frequently Asked Questions About the Technology Demonstration GATEWAY Program on

457

#AskEnergySaver: Answering Your Home Heating Questions | Department...  

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

Us Now Addthis Now's your chance to ask Energy Department experts your questions about saving energy at home. This month, we're answering your questions about home heating. |...

458

Questions remain on funding for cleanup of Oak Ridge Reservation...  

Office of Environmental Management (EM)

Questions remain on funding for cleanup of Oak Ridge Reservation Questions remain on funding for cleanup of Oak Ridge Reservation March 18, 2013 - 12:00pm Addthis There is great...

459

Nano Lect 1 Questions and Keypoints Key Points  

E-Print Network [OSTI]

Nano Lect 1 ­ Questions and Keypoints Key Points 1. What is nano technology: a. Very small technology with device in the 1nm to 100nm lots of useful properties Questions 1. Define nanotechnology. Is an nano

Smy, Tom

460

Forecast Calls for Better Models: Examining the Core  

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

Forecast Calls for Better Models: Examining the Core Forecast Calls for Better Models: Examining the Core Components of Arctic Clouds to Clear Their Influence on Climate For original submission and image(s), see ARM Research Highlights http://www.arm.gov/science/highlights/ Research Highlight Predicting how atmospheric aerosols influence cloud formation and the resulting feedback to climate is a challenge that limits the accuracy of atmospheric models. This is especially true in the Arctic, where mixed-phase (both ice- and liquid-based) clouds are frequently observed, but the processes that determine their composition are poorly understood. To obtain a closer look at what makes up Arctic clouds, scientists characterized cloud droplets and ice crystals collected at the North Slope of Alaska as part of the Indirect and Semi-Direct Aerosol Campaign (ISDAC) field study

Note: This page contains sample records for the topic "forecasting specific questions" 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

2015 Federal Energy and Water Management Awards: Frequently Asked Questions  

Broader source: Energy.gov [DOE]

Document answers frequently asked questions about the 2015 Federal Energy and Water Management Awards' guidelines and criteria for nominations.

462

Questions and Answers from Storage Pre-Solicitation Meeting  

Broader source: Energy.gov [DOE]

Questions and Answers from "Grand Challenge" Hydrogen Storage Pre-Solicitation Meeting held June 19, 2003 in Washington, DC

463

Frequently Asked Questions About the Building Performance Database  

Broader source: Energy.gov [DOE]

On this page you will find answers to frequently asked questions pertaining to the DOE Buildings Performance Database (BPD).

464

Weatherization Assistance Program (WAP) Closeout Frequently Asked Questions  

Broader source: Energy.gov [DOE]

This document provides a list of frequently asked questions in regards to the Weatherization Assistance Program (WAP) Closeout procedures.

465

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

Broader source: Energy.gov [DOE]

The Wind Forecast Improvement Project (WFIP) is a U. S. Department of Energy (DOE) sponsored research project whose overarching goals are to improve the accuracy of short-term wind energy forecasts, and to demonstrate the economic value of these improvements.

466

Continuous Model Updating and Forecasting for a Naturally Fractured Reservoir  

E-Print Network [OSTI]

CONTINUOUS MODEL UPDATING AND FORECASTING FOR A NATURALLY FRACTURED RESERVOIR A Thesis by HISHAM HASSAN S. ALMOHAMMADI Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... guidance and support throughout my time here in Texas A&M University. I also would like to thank my committee members, Dr. Eduardo Gildin and Dr. Michael Sherman, for providing valued insight and help during the course of this research. I am indebted...

Almohammadi, Hisham

2013-07-26T23:59:59.000Z

467

NOAA National Weather Service I'm a weather forecaster.  

E-Print Network [OSTI]

.S.D EPARTMENT OF COM M ERCE How Do You Make a Weather Satellite? How Do You Make a Weather Satellite? #12;Well you put a truck in orbit? So it can carry all the things needed to make a working weather satelliteNOAA National Weather Service I'm a weather forecaster. I need to see clouds and storms from way up

Waliser, Duane E.

468

Improved Answer Ranking in Social Question-Answering Portals  

E-Print Network [OSTI]

@cl.uni-heidelberg.de ABSTRACT Community QA portals provide an important resource for non-factoid question; Community question answering; Query expansion 1. INTRODUCTION Community Question-Answering (QA) portals can more recent work deploys Yahoo! Answers data for complete QA [24, 25], we focus on the aspect of answer

Riezler, Stefan

469

Questions about? Needing help wards? Worried about? Who can  

E-Print Network [OSTI]

Questions about? Needing help wards? Worried about? Who can bout? Needing help with? Headi can help? Questio g towards? Worrie elp? Questions about? Needing he bout? Needing help with? Headi ed about? Who can help? Quest ng help with? Heading towards? W an help? Questions about? Needin ng towards? Worried

Netoff, Theoden

470

Terminological variants for document selection and question/answer matching  

E-Print Network [OSTI]

retrieved documents. The QALC system, presented in this paper, participated to the Question Answering track documents. The QALC system, presented in this paper, participated to the Question Answering track in the documents. 1 Introduction The Question Answering (QA) track at TREC8 and TREC9 is due to the recent need

Illouz, Gabriel

471

Annual Energy Outlook 2006 with Projections to 2030 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

Forecast Comparisons Forecast Comparisons Annual Energy Outlook 2006 with Projections to 2030 Only GII produces a comprehensive energy projection with a time horizon similar to that of AEO2006. Other organizations address one or more aspects of the energy markets. The most recent projection from GII, as well as others that concentrate on economic growth, international oil prices, energy consumption, electricity, natural gas, petroleum, and coal, are compared here with the AEO2006 projections. Economic Growth In the AEO2006 reference case, the projected growth in real GDP, based on 2000 chain-weighted dollars, is 3.0 percent per year from 2004 to 2030 (Table 19). For the period from 2004 to 2025, real GDP growth in the AEO2006 reference case is similar to the average annual growth projected in AEO2005. The AEO2006 projections of economic growth are based on the August short-term forecast of GII, extended by EIA through 2030 and modified to reflect EIA’s view on energy prices, demand, and production.

472

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

473

Questions and Answers for the Smart Grid Investment Grant Program:  

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

Meter Installations Meter Installations Questions and Answers for the Smart Grid Investment Grant Program: Reporting of Meter Installations Questions and answers related to the reporting of meter installations in the SmartGrid Integrated Project Reporting Information System (SIPRIS), both for the intial report and subsequent reports. Projects reporting jobs are part of the Smart Grid Investment Grant Project under the American Recovery and Reinvestment Act. Questions and Answers for the Smart Grid Investment Grant Program: Reporting of Meter Installations More Documents & Publications Questions and Answers for the Smart Grid Investment Grant Program: Reporting Webinar Questions and Answers for the Smart Grid Investment Grant Program: Reporting of Jobs Created/Retained in SIPRIS

474

Users Frequently Asked Questions | National Nuclear Security Administration  

National Nuclear Security Administration (NNSA)

Users Frequently Asked Questions | National Nuclear Security Administration Users Frequently Asked Questions | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Users Frequently Asked Questions Home > About Us > Our Programs > Nuclear Security > Nuclear Materials Management & Safeguards System > NMMSS Information, Reports & Forms > Frequently Asked Questions > Users Frequently Asked Questions

475

Frequently Asked Questions about the Office of Energy Efficiency and  

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

Frequently Asked Questions about the Office of Energy Efficiency Frequently Asked Questions about the Office of Energy Efficiency and Renewable Energy Frequently Asked Questions about the Office of Energy Efficiency and Renewable Energy Below are some of the questions we are regularly asked at EERE. These frequently asked questions cover topics such as EERE, tax credits and financial assistance, home energy, vehicles, industry, and energy education and training. If you don't find the answer to your question, please contact us with your question. General Information What does EERE stand for? What does the EERE do? Tax Credits and Financial Assistance Are there federal tax credits available for energy improvements to my home or business? Can I get a grant or other financial assistance to help me develop my energy-related innovation?

476

Building Technologies Office: Home Energy Score Frequently Asked Questions  

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

Home Energy Score Home Energy Score Frequently Asked Questions to someone by E-mail Share Building Technologies Office: Home Energy Score Frequently Asked Questions on Facebook Tweet about Building Technologies Office: Home Energy Score Frequently Asked Questions on Twitter Bookmark Building Technologies Office: Home Energy Score Frequently Asked Questions on Google Bookmark Building Technologies Office: Home Energy Score Frequently Asked Questions on Delicious Rank Building Technologies Office: Home Energy Score Frequently Asked Questions on Digg Find More places to share Building Technologies Office: Home Energy Score Frequently Asked Questions on AddThis.com... About Take Action to Save Energy Partner With DOE Activities Solar Decathlon Building America Home Energy Score Get Involved

477

Questions and Answers for the Smart Grid Investment Grant Program:  

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

Questions and Answers for the Smart Grid Investment Grant Program: Questions and Answers for the Smart Grid Investment Grant Program: Miscellaneous Questions and Answers for the Smart Grid Investment Grant Program: Miscellaneous Additional questions and answers from recipients of awards under the Smart Grid Investment Grant Program on the Buy American provision of the American Recovery and Reinvestment Act. Questions and Answers for the Smart Grid Investment Grant Program: Miscellaneous More Documents & Publications Questions and Answers for the Smart Grid Investment Grant Program: Confidentiality (Revised Answer) Q&A for the Smart Grid Investment Program: National Environmental Policy Act (NEPA), January 29, 2010 Questions and Answers for the Smart Grid Investment Grant Program: Davis-Bacon / Buy American Relationship,

478

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

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

Beyond "Partly Sunny": A Better Solar Forecast 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 forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. Minh Le Minh Le Program Manager, Solar Program What Do These Projects Do? The Energy Department is investing $8 million in two cutting-edge projects to increase the accuracy of solar forecasting at sub-hourly,

479

Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach  

Science Journals Connector (OSTI)

Abstract A hybrid mid-term electricity market clearing price (MCP) forecasting model combining both least squares support vector machine (LSSVM) and auto-regressive moving average with external input (ARMAX) modules is presented in this paper. Mid-term electricity MCP forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. PJM interconnection data have been utilized to illustrate the proposed model with numerical examples. The proposed hybrid model showed improved forecasting accuracy compared to a forecasting model using a single LSSVM.

Xing Yan; Nurul A. Chowdhury

2013-01-01T23:59:59.000Z

480

Log-normal distribution based EMOS models for probabilistic wind speed forecasting  

E-Print Network [OSTI]

Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods an...

Baran, Sndor

2014-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasting specific questions" 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.


481

Critical Question #4: What are the Best Off-the-Shelf HVAC Solutions for  

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

Critical Question #4: What are the Best Off-the-Shelf HVAC Critical Question #4: What are the Best Off-the-Shelf HVAC Solutions for Low-Load, High-Performance Homes and Apartments? Critical Question #4: What are the Best Off-the-Shelf HVAC Solutions for Low-Load, High-Performance Homes and Apartments? What is currently in the market? What are the limitations of these systems? What are the desired specifications for these systems? What are the realistic space conditioning loads of these high-performance homes and apartments? cq4_forced_air_systems_walker.pdf cq4_simplified_space_cond_prahl.pdf cq4_ground_heat_exchanger_im.pdf More Documents & Publications Track C - Market-Driven Research Solutions Track B - Critical Guidance for Peak Performance Homes Energy Storage & Power Electronics 2008 Peer Review - Power Electronics

482

Step 10. Get Assistance on Energy Code and Compliance Questions | Building  

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

10. Get Assistance on Energy Code and Compliance Questions 10. Get Assistance on Energy Code and Compliance Questions Direct assistance on building energy code compliance questions is available from several sources. In addition, there are many training courses available to learn more about specific code requirements. Resources Contact the local jurisdiction having authority BECP Helpdesk ICC Technical Opinions and Interpretations ASHRAE Standards Interpretations ASHRAE Standard 90.1-2007 ASHRAE Standard 90.1-2010 BECP Training Courses Residential Requirements of the 2009 IECC Residential Requirements of the 2012 IECC Commercial Building Envelope Requirements of the 2009 IECC Commercial Lighting Requirements of the 2009 IECC Commercial Mechanical Requirements of the 2009 IECC Requirements of ASHRAE Standard 90.1-2007

483

Frequently Asked Questions on FederalReporting.gov | Department of Energy  

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

Frequently Asked Questions on FederalReporting.gov Frequently Asked Questions on FederalReporting.gov Frequently Asked Questions on FederalReporting.gov Categories: Applicability / Exemptions Communications Compensation Contractors Data Fields Delegation General Jobs Loans and Loan Guarantees Other Reporting Registration Systems Timing Vendors I. APPLICABILITY / EXEMPTIONS Q: Do 1512 reporting requirements only apply to non-Federal recipients who receive funding provided through discretionary appropriations? Yes. The reporting requirements do not apply to funding received through entitlement or other mandatory programs, except as specifically required by Office of Management and Budget. Q: Do Prime Contractors awarded less than $25,000 need to report? Yes, to date, there is no government-wide floor for prime reporting.

484

ENERGY-SPECIFIC SOLAR RADIATION DATA FROM MSG: CURRENT STATUS OF THE HELIOSAT-3 PROJECT  

E-Print Network [OSTI]

ENERGY-SPECIFIC SOLAR RADIATION DATA FROM MSG: CURRENT STATUS OF THE HELIOSAT-3 PROJECT Marion Solar energy technologies such as photovoltaics, solar thermal power plants, passive solar heating and operating of solar energy systems and as basis data set for electricity load forecasting. Both long term

Heinemann, Detlev

485

Central Wind Forecasting Programs in North America by Regional Transmission Organizations and Electric Utilities: Revised Edition  

SciTech Connect (OSTI)

The report and accompanying table addresses the implementation of central wind power forecasting by electric utilities and regional transmission organizations in North America. The first part of the table focuses on electric utilities and regional transmission organizations that have central wind power forecasting in place; the second part focuses on electric utilities and regional transmission organizations that plan to adopt central wind power forecasting in 2010. This is an update of the December 2009 report, NREL/SR-550-46763.

Rogers, J.; Porter, K.

2011-03-01T23:59:59.000Z

486

Comparison of Airbus, Boeing, Rolls-Royce and AVITAS market forecasts  

Science Journals Connector (OSTI)

Forecasts of future world demand for commercial aircraft are published fairly regularly by Airbus and Boeing. Other players in the aviation business, Rolls Royce and AVITAS, have also published forecasts in the past year. This article analyses and compares the methods used and assumptions made by the several forecasters. It concludes that there are wide areas of similarity in the approaches used and highlights the most significant area of divergence.

Ralph Anker

2000-01-01T23:59:59.000Z

487

Forecasting the monthly volume of orders for southern pine lumber - an econometric model  

E-Print Network [OSTI]

to measure various aspects of the California redwood lumber industry. The first sought to explain the economic struc- ture of the short-run market for redwood lumber by preparing short-range forecasts of price, new orders, shipments, produc- tion, stocks... regression coefficients (20) . The second study was directed at developing a short-run forecast of new orders for redwood lumber (21) . Several forecasting techniques were developed, but econometrics, i. e. , multiple regression analysis, provided...

Jackson, Ben Douglas

2012-06-07T23:59:59.000Z

488

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....

489

Intra-hour wind power variability assessment using the conditional range metric : quantification, forecasting and applications.  

E-Print Network [OSTI]

??The research presented herein concentrates on the quantification, assessment and forecasting of intra-hour wind power variability. Wind power is intrinsically variable and, due to the (more)

Boutsika, Thekla

2013-01-01T23:59:59.000Z

490

Crude oil prices and petroleum inventories : remedies for a broken oil price forecasting model.  

E-Print Network [OSTI]

??The empirical relationship between crude oil prices and petroleum inventories has been exploited in a number of short-term oil price forecasting models. Some of the (more)

Grimstad, Dan

2007-01-01T23:59:59.000Z

491

Study and implementation of mesoscale weather forecasting models in the wind industry.  

E-Print Network [OSTI]

?? As the wind industry is developing, it is asking for more reliable short-term wind forecasts to better manage the wind farms operations and electricity (more)

Jourdier, Bndicte

2012-01-01T23:59:59.000Z

492

Value of Improved Wind Power Forecasting in the Western Interconnection (Poster)  

SciTech Connect (OSTI)

Wind power forecasting is a necessary and important technology for incorporating wind power into the unit commitment and dispatch process. It is expected to become increasingly important with higher renewable energy penetration rates and progress toward the smart grid. There is consensus that wind power forecasting can help utility operations with increasing wind power penetration; however, there is far from a consensus about the economic value of improved forecasts. This work explores the value of improved wind power forecasting in the Western Interconnection of the United States.

Hodge, B.

2013-12-01T23:59:59.000Z

493

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

E-Print Network [OSTI]

iscriticalforcoastalCaliforniasolarforecasting. affectingsolarirradianceinsouthernCalifornia. solar photovoltaicgeneration(thesouthernCalifornia

Mathiesen, Patrick; Collier, Craig; Kleissl, Jan

2013-01-01T23:59:59.000Z

494

E-Print Network 3.0 - analytical energy forecasting Sample Search...  

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

of PV energy production using... Short term forecasting of solar radiation based on satellite data Elke Lorenz, Annette Hammer... , Detlev Heinemann Energy and Semiconductor...

495

Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain  

Broader source: Energy.gov [DOE]

The DOE Wind Program has issued a Notice of Intent for a funding opportunity, tentatively titled Wind Forecasting Improvement Project in Complex Terrain.

496

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

497

Questions Received February 17-18  

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

Received February 17-18 Received February 17-18 1. I am formally requesting information on the length of the entire CCS Program. a. Is there a minimum time requirement on the Demonstration portion of the project? There is no specific minimum duration for the Demonstration Phase. However, projects will be evaluated on their ability to meet DOE's target of capture and sequestration of one million tons of CO 2 per year. Another objective of the program is to demonstrate geologic sequestration options in a variety of geologic settings in order to evaluate costs, operational processes, and technical performance. Excessively short Demonstration Phase durations will not assist DOE in meetings its objectives, and will be taken into consideration during the evaluation.

498

Forecast of Contracting and Subcontracting Opportunities, Fiscal year 1995  

SciTech Connect (OSTI)

Welcome to the US Department of Energy`s Forecast of Contracting and Subcontracting Opportunities. This forecast, which is published pursuant to Public Low 100--656, ``Business Opportunity Development Reform Act of 1988,`` is intended to inform small business concerns, including those owned and controlled by socially and economically disadvantaged individuals, and women-owned small business concerns, of the anticipated fiscal year 1995 contracting and subcontracting opportunities with the Department of Energy and its management and operating contractors and environmental restoration and waste management contractors. This document will provide the small business contractor with advance notice of the Department`s procurement plans as they pertain to small, small disadvantaged and women-owned small business concerns.Opportunities contained in the forecast support the mission of the Department, to serve as advocate for the notion`s energy production, regulation, demonstration, conservation, reserve maintenance, nuclear weapons and defense research, development and testing, when it is a national priority. The Department`s responsibilities include long-term, high-risk research and development of energy technology, the marketing of Federal power, and maintenance of a central energy data collection and analysis program. A key mission for the Department is to identify and reduce risks, as well as manage waste at more than 100 sites in 34 states and territories, where nuclear energy or weapons research and production resulted in radioactive, hazardous, and mixed waste contamination. Each fiscal year, the Department establishes contracting goals to increase contracts to small business concerns and meet our mission objectives.

Not Available

1995-02-01T23:59:59.000Z

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Got a Question? We Have an Answer! | Department of Energy  

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

Got a Question? We Have an Answer! Got a Question? We Have an Answer! Got a Question? We Have an Answer! January 28, 2011 - 10:52am Addthis Elizabeth Spencer Communicator, National Renewable Energy Laboratory Editor's Note: This entry has been cross-posted from energysavers.gov. Have you ever had a question-maybe about energy efficiency, renewable energy, the Department of Energy or the like-and not had any idea where to find the answer? Have you ever gone through the Office of Energy Efficiency and Renewable Energy's website and not known where to find what you're looking for? The reason I am asking all these questions is because I want to bring your attention to a well-established resource that you might not know about: the EERE Information Center! You can call us, e-mail your question, or contact us through a new "live

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Appliance Rebates: Frequently Asked Questions | Department of Energy  

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

Appliance Rebates: Frequently Asked Questions Appliance Rebates: Frequently Asked Questions Appliance Rebates: Frequently Asked Questions April 26, 2010 - 4:55pm Addthis Andrea Spikes Communicator at DOE's National Renewable Energy Laboratory The appliance rebate program has been wildly successful in many states. So successful, in fact, that people have plenty of questions and aren't always finding the answers they need. Whether you're just learning about the appliance rebate program or have some nagging questions, I hope this information is helpful to you. For the most up-to-date information on rebates in your state, click on your state on Approved Energy Efficient Appliance Rebate Programs. Below is a list of the most common questions, taken from the Energy Savers Appliance Rebates page. (Note: The FAQs below are current as of this