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1

(1) Ensemble forecast calibration & (2) using reforecasts  

E-Print Network [OSTI]

1 (1) Ensemble forecast calibration & (2) using reforecasts Tom Hamill NOAA Earth System Research · Calibration: ; the statistical adjustment of the (ensemble) forecast ­ Rationale 1: Infer large-sample probabilities from small ensemble. ­ Rationale 2: Remove bias, increase forecast reliability while preserving

Hamill, Tom

2

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

E-Print Network [OSTI]

Ozone ensemble forecast with machine learning algorithms Vivien Mallet,1,2 Gilles Stoltz,3 forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Europe in order to forecast ozone daily peaks and ozone hourly concentrations. On the basis of past

Paris-Sud XI, Université de

3

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

E-Print Network [OSTI]

End-Use Forecasting with EPRI-REEPS 2.1. Lawrence BerkeleyEnd-Use Forecasting with EPRI-REEPS 2.1. Lawrence BerkeleyPower Research Institute. EPRI Research Project Meier, Alan

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

4

Page 1 of 2 2005-2006 MLOG Thesis Proposal 10/2/2005 Working Title Improved New Product Forecasting through Visualization of Spatial Diffusion  

E-Print Network [OSTI]

Page 1 of 2 2005-2006 MLOG Thesis Proposal 10/2/2005 Working Title Improved New Product Forecasting. Schuster, Stuart J. Allen, and David L. Brock Company Contact Several companies are interested in this area to implement an operational system for improving new product forecasting in practice. Is Innovative thinking

Brock, David

5

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

6

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network [OSTI]

1 PROBLEMS OF FORECAST1 Dmitry KUCHARAVY dmitry.kucharavy@insa-strasbourg.fr Roland DE GUIO roland for the purpose of Innovative Design. First, a brief analysis of problems for existing forecasting methods of the forecast errors. Second, using a contradiction analysis, a set of problems related to technology forecast

Paris-Sud XI, Université de

7

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

E-Print Network [OSTI]

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

Smith, Leonard A

8

Residential applliance data, assumptions and methodology for end-use forecasting with EPRI-REEPS 2.1  

SciTech Connect (OSTI)

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

Hwang, R.J,; Johnson, F.X.; Brown, R.E.; Hanford, J.W.; Kommey, J.G.

1994-05-01T23:59:59.000Z

9

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

SciTech Connect (OSTI)

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

Templeton, K.J.

1996-05-23T23:59:59.000Z

10

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

SciTech Connect (OSTI)

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

Valero, O.J.

1996-04-23T23:59:59.000Z

11

Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1  

E-Print Network [OSTI]

Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1 David Levinson 2 February, the assumed networks to the actual in-place networks and other travel behavior assumptions that went

Levinson, David M.

12

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

13

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

SciTech Connect (OSTI)

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

BARCOT, R.A.

2003-12-01T23:59:59.000Z

14

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

15

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

E-Print Network [OSTI]

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting-use forecasting of appliance energy use in the U.S. residential sector. Our analysis uses the modeling framework provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which

16

Weighted Parametric Operational Hydrology Forecasting Thomas E. Croley II1  

E-Print Network [OSTI]

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

17

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

E-Print Network [OSTI]

from photovoltaic systems is highly variable due to its dependence on meteorological conditions (model Output Statistics) by Meteomedia GmbH ECMWF* - 0.25°x 0.25° - 3 hours 4) Cener, Spain CENER Post with postprocessing 5) Ciemat, Spain HIRLAM-CI Bias correction AEMET-HIRLAMx - 0.2°x 0.2° - 1 hour 6) Meteotest

Heinemann, Detlev

18

WP1: Targeted and informative forecast system design  

E-Print Network [OSTI]

WP1: Targeted and informative forecast system design Emma Suckling, Leonard A. Smith and David Stainforth EQUIP Meeting ­ August 2011 Edinburgh #12;Targeted and informative forecast system design Develop models to support decision making (1.4) #12;Targeted and informative forecast system design KEY QUESTIONS

Stevenson, Paul

19

Using Bayesian Model Averaging to Calibrate Forecast Ensembles 1  

E-Print Network [OSTI]

Using Bayesian Model Averaging to Calibrate Forecast Ensembles 1 Adrian E. Raftery, Fadoua forecasting often exhibit a spread-skill relationship, but they tend to be underdispersive. This paper of PDFs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal

Washington at Seattle, University of

20

Residential sector end-use forecasting with EPRI-Reeps 2.1: Summary input assumptions and results  

SciTech Connect (OSTI)

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

Koomey, J.G.; Brown, R.E.; Richey, R. [and others

1995-12-01T23:59:59.000Z

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


21

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

22

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

E-Print Network [OSTI]

A H A M . 1991. Room Air Conditioner Data. Association of7. FUTURE WORK 7.1 Room Air Conditioners 7.2. Common heatingShipments of Unitary Air Conditioners and Heat Pumps. Air-

Johnson, F.X.

2010-01-01T23:59:59.000Z

23

Introducing the Canadian Crop Yield Forecaster Aston Chipanshi1  

E-Print Network [OSTI]

for crop yield forecasting and risk analysis. Using the Census Agriculture Region (CAR) as the unit Climate Decision Support and Adaptation, Agriculture and Agri-Food Canada, 1011, Innovation Blvd, Saskatoon, SK S7V 1B7, Canada The Canadian Crop Yield Forecaster (CCYF) is a statistical modelling tool

Miami, University of

24

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

E-Print Network [OSTI]

Appendix A: Fuel Price Forecast Introduction ................................................................................................................... 17 INTRODUCTION Since the millennium, the trend for fuel prices has been one of uncertainty prices, which have traditionally been relatively stable, increased by about 50 percent in 2008. Fuel

25

Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model  

E-Print Network [OSTI]

1 1 Current status of ForecastCurrent status of Forecast 2005 EPACT is in the model 2007 Federal prices are being inputted into the model 2 Sales forecast Select yearsSales forecast Select years --Draft 0.53% Irrigation 2.76% Annual Growth Rates Preliminary Electricity ForecastAnnual Growth Rates

26

APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY  

E-Print Network [OSTI]

1 APPLICATION OF PROBABILISTIC FORECASTS: DECISION MAKING WITH FORECAST UNCERTAINTY Rick Katz.isse.ucar.edu/HP_rick/dmuu.pdf #12;2 QUOTES ON USE OF PROBABILITY FORECASTS · Lao Tzu (Chinese Philosopher) "He who knows does and Value of Probability Forecasts (4) Cost-Loss Decision-Making Model (5) Simulation Example (6) Economic

Katz, Richard

27

STAFF FORECAST OF 2007 PEAK STAFFREPORT  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION STAFF FORECAST OF 2007 PEAK DEMAND STAFFREPORT June 2006 CEC-400....................................................................... .................11 Tables Table 1: Revised versus September 2005 Peak Demand Forecast ......................... 2.............................................................................................. 10 #12;Introduction and Background This document describes staff's updated 2007 peak demand forecasts

28

2009 CAPS Spring Forecast Program Plan  

E-Print Network [OSTI]

package. · Two 18 UTC update forecasts on demand basis, with the same domain and configuration, running2009 CAPS Spring Forecast Experiment Program Plan April 20, 2009 #12;2 Table of Content 1. Overview .......................................................................................................4 3. Forecast System Configuration

Droegemeier, Kelvin K.

29

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

E-Print Network [OSTI]

Administration. April. EPRI. 1982. Residential End-UseInstitute. EA-2512. July. EPRI. 1990. REEPS 2.0 HVAC ModelInstitute. October 11. EPRI, Electric Power Research

Johnson, F.X.

2010-01-01T23:59:59.000Z

30

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

E-Print Network [OSTI]

Efficiency Choice 6.3 New Home HVAC System Choice 6.4. NewJuly. EPRI. 1990. REEPS 2.0 HVAC Model Logic, prepared by1990. Review of Equipment HVAC Choice Parameters. Cambridge

Johnson, F.X.

2010-01-01T23:59:59.000Z

31

WIND POWER ENSEMBLE FORECASTING Henrik Aalborg Nielsen1  

E-Print Network [OSTI]

WIND POWER ENSEMBLE FORECASTING Henrik Aalborg Nielsen1 , Henrik Madsen1 , Torben Skov Nielsen1. In this paper we address the problems of (i) transforming the mete- orological ensembles to wind power ensembles the uncertainty which follow from historical (climatological) data. However, quite often the actual wind power

32

4, 189212, 2007 Forecast and  

E-Print Network [OSTI]

OSD 4, 189­212, 2007 Forecast and analysis assessment through skill scores M. Tonani et al. Title Science Forecast and analysis assessment through skill scores M. Tonani 1 , N. Pinardi 2 , C. Fratianni 1 Forecast and analysis assessment through skill scores M. Tonani et al. Title Page Abstract Introduction

Paris-Sud XI, Université de

33

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 2 AUGUST 15, 2013  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

Gray, William

34

CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Demand Forecast report is the product of the efforts of many current and former California Energy-2 Demand Forecast Disaggregation......................................................1-4 Statewide

35

Consensus Coal Production Forecast for  

E-Print Network [OSTI]

in the consensus forecast produced in 2006, primarily from the decreased demand as a result of the current nationalConsensus Coal Production Forecast for West Virginia 2009-2030 Prepared for the West Virginia Summary 1 Recent Developments 2 Consensus Coal Production Forecast for West Virginia 10 Risks

Mohaghegh, Shahab

36

Sustainable Energy Options for Kosovo January 19, 2012 Page: 1 of 50 http://rael.berkeley.edu  

E-Print Network [OSTI]

---------------------------------------------------------------------------------- 12 1.2 Forecast of Demand and Generation, 2010-2020 ------------------------------------------------------ 13 1.2.1 Demand Forecast ------------------------------------------------------------------------------------ 13 1.2.2 Generation Forecast

Kammen, Daniel M.

37

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

E-Print Network [OSTI]

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

Crambes, Christophe

38

Ensemble Forecast of Analyses With Uncertainty Estimation  

E-Print Network [OSTI]

Ensemble Forecast of Analyses With Uncertainty Estimation Vivien Mallet1,2, Gilles Stoltz3 2012 Mallet, Stoltz, Zhuk, Nakonechniy Ensemble Forecast of Analyses November 2012 1 / 14 hal-00947755,version1-21Feb2014 #12;Objective To produce the best forecast of a model state using a data assimilation

Boyer, Edmond

39

Session: Short-term forecasting of wind power (BT2.5) Track: Technical  

E-Print Network [OSTI]

Session: Short-term forecasting of wind power (BT2.5) Track: Technical BEST PRACTICE IN THE USE) Armines / Ecole des Mines Short-term forecasting of wind power for about 48 hours in advance is an established technique by now. Any utility getting over a few percent wind power penetration is buying a system

40

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

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

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


41

WA-RD 470.1 June 1999 Demand Forecasting for Rural Transit  

E-Print Network [OSTI]

WA-RD 470.1 June 1999 Demand Forecasting for Rural Transit This summary describes the key findings of a WSDOT project that is documented more fully in the technical report titled "Demand Forecasting for Rural to Washington for predicting demand for rural public transportation. Three Washington-based models were

42

11.1 DEVELOPMENT OF AN IMMERSED BOUNDARY METHOD TO RESOLVE COMPLEX TERRAIN IN THE WEATHER RESEARCH AND FORECASTING MODEL  

E-Print Network [OSTI]

11.1 DEVELOPMENT OF AN IMMERSED BOUNDARY METHOD TO RESOLVE COMPLEX TERRAIN IN THE WEATHER RESEARCH AND FORECASTING MODEL Katherine A. Lundquist1 , Fotini K. Chow 2 , Julie K. Lundquist 3 , and Jeffery D. Mirocha 3 in urban areas are profoundly influenced by the presence of build- ings which divert mean flow, affect

Chow, Fotini Katopodes

43

Documentation of the Hourly Time Series NCEP Climate Forecast System Reanalysis  

E-Print Network [OSTI]

- 1 - Documentation of the Hourly Time Series from the NCEP Climate Forecast System Reanalysis: ------------------------------------------------------------------------------------------------------------ Initial condition 1 Jan 1979, 0Z Record 1: f00: forecast at first time step of 3 mins Record 2: f01: forecast (either averaged over 0 to 1 hour, or instantaneous at 1 hour) Record 3: f02: forecast (either

44

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

E-Print Network [OSTI]

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

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

45

ASSESSING THE QUALITY AND ECONOMIC VALUE OF WEATHER AND CLIMATE FORECASTS  

E-Print Network [OSTI]

INFORMATION SYSTEM Forecast -- Conditional probability distribution for event Z = z indicates forecast tornado #12;(1.2) FRAMEWORK Joint Distribution of Observations & Forecasts Observed Weather = Forecast probability p (e.g., induced by Z) Reliability Diagram Observed weather: = 1 (Adverse weather occurs) = 0

Katz, Richard

46

ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL  

E-Print Network [OSTI]

5.5 ENSEMBLE RE-FORECASTING : IMPROVING MEDIUM-RANGE FORECAST SKILL USING RETROSPECTIVE FORECASTS, Colorado 1. INTRODUCTION Improving weather forecasts is a primary goal of the U.S. National Oceanic predictions has been to improve the accuracy of the numerical forecast models. Much effort has been expended

Hamill, Tom

47

FORECAST Skimming off the Malware Cream Matthias Neugschwandtner1  

E-Print Network [OSTI]

, Gregoire Jacob2 , and Christopher Kruegel2 1 Vienna University of Technology, {mneug and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post

Kruegel, Christopher

48

Forecast Correlation Coefficient Matrix of Stock Returns in Portfolio Analysis  

E-Print Network [OSTI]

Unadjusted Forecasts . . . . . . . . . . . . . . . .Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . .Unadjusted Forecasts . . . . . . . . . . . . . . . . . . .

Zhao, Feng

2013-01-01T23:59:59.000Z

49

Forecasting OctoberNovember Caribbean hurricane days Philip J. Klotzbach1  

E-Print Network [OSTI]

Forecasting October­November Caribbean hurricane days Philip J. Klotzbach1 Received 22 April 2011; revised 1 July 2011; accepted 11 July 2011; published 30 September 2011. [1] October­November Caribbean. Largescale climate parameters associated with active late seasons in the Caribbean are investigated

Gray, William

50

III.C. 2. Plastics and Competing Materials by 1985: A Delphi Forecasting Study  

E-Print Network [OSTI]

189 III.C. 2. Plastics and Competing Materials by 1985: A Delphi Forecasting Study SELWYN ENZER The application of Delphi to the identification and assessment of possible developments in plastics and competing. The ability to tailor-make plastics for various applications, enhanced by growth in understanding of organic

Bieber, Michael

51

Forecast Technical Document Forecast Types  

E-Print Network [OSTI]

Forecast Technical Document Forecast Types A document describing how different forecast types are implemented in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Forecast Types Background Different `types' of forecast are possible for a specified area

52

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

53

Online Forecast Combination for Dependent Heterogeneous Data  

E-Print Network [OSTI]

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

Sancetta, Alessio

54

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...

55

CALIFORNIA ENERGY COMMISSION0 Annual Update to the Forecasted  

E-Print Network [OSTI]

Values in TWh forthe Year2022 Formula Mid Demand Forecast Renewable Net High Demand Forecast Renewable Net Low Demand Forecast Renewable Net #12;CALIFORNIA ENERGY COMMISSION5 Demand Forecast · Retail Sales Forecast from California Energy Demand 2012 2022(CED 2011), Adopted Forecast* ­ Form 1.1c · Demand Forecast

56

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 2: Electricity Demand.Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product estimates. Margaret Sheridan provided the residential forecast. Mitch Tian prepared the peak demand

57

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST Volume 2: Electricity Demand Robert P. Oglesby Executive Director #12;i ACKNOWLEDGEMENTS The demand forecast is the combined provided estimates for demand response program impacts and contributed to the residential forecast. Mitch

58

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

SciTech Connect (OSTI)

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

BARCOT, R.A.

2005-08-17T23:59:59.000Z

59

CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST  

E-Print Network [OSTI]

1 CORPORATE GOVERNANCE AND MANAGEMENT EARNINGS FORECAST QUALITY: EVIDENCE FROM FRENCH IPOS Anis attributes, ownership retained, auditor quality, and underwriter reputation and management earnings forecast quality measured by management earnings forecast accuracy and bias. Using 117 French IPOs, we find

Paris-Sud XI, Université de

60

Forecasted Opportunities  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation ProposedUsing ZirconiaPolicyFeasibilityFieldMinds" |beamtheFor yourForForecasted

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61

ENERGY DEMAND FORECAST METHODS REPORT  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION ENERGY DEMAND FORECAST METHODS REPORT Companion Report to the California Energy Demand 2006-2016 Staff Energy Demand Forecast Report STAFFREPORT June 2005 CEC-400 .......................................................................................................................................1-1 ENERGY DEMAND FORECASTING AT THE CALIFORNIA ENERGY COMMISSION: AN OVERVIEW

62

PostScript file created: April 17, 2005 Comparison of short-term and long-term earthquake forecast models  

E-Print Network [OSTI]

forecast models for southern California Agn`es Helmstetter1,3 , Yan Y. Kagan2 and David D. Jackson2 1, Columbia University, New York Abstract We consider the problem of forecasting earthquakes on two different time scales: years, and days. We evaluate some published forecast models on these time scales

Paris-Sud XI, Université de

63

Forecasting Distributions with Experts Advice  

E-Print Network [OSTI]

) is the probability forecast based on an arbitrary vector wE in the unit simplex, experts forecasts ?E , and model {p?} . Remark 2 In most cases, we can choose c = 1/?, implying in the result below that c? = 1. Example 3 The prediction function is a mixture... 0 = 1, and #IT (k) = tk+1 ? tk. Define ek ? E. Theorem 12 Under Conditions 1 and 7, R1,...,t (pW ) ? c? K? k=0 Rt(k),...,t(k+1)?1 ( p?(e(k)) ) + c ln (#E) ?c K? k=1 ln ut(k) (ek, ek?1)? c K? k=0 t(k+1)?2? s=t(k) ln (us+1 (ek, ek)) . 9 Remark 13...

Sancetta, Alessio

2006-03-14T23:59:59.000Z

64

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

65

Rainfall-River Forecasting  

E-Print Network [OSTI]

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

US Army Corps of Engineers

66

Revised Economic andRevised Economic and Demand ForecastsDemand Forecasts  

E-Print Network [OSTI]

Revised Economic andRevised Economic and Demand ForecastsDemand Forecasts April 14, 2009 Massoud,000 MW #12;6 Demand Forecasts Price Effect (prior to conservation) - 5,000 10,000 15,000 20,000 25,000 30 Jourabchi #12;2 Changes since the Last Draft ForecastChanges since the Last Draft Forecast Improved

67

Can earnings forecasts be improved by taking into account the forecast bias?  

E-Print Network [OSTI]

Can earnings forecasts be improved by taking into account the forecast bias? François DOSSOU allow the calculation of earnings adjusted forecasts, for horizons from 1 to 24 months. We explain variables. From the forecast evaluation statistics viewpoints, the adjusted forecasts make it possible quasi

Paris-Sud XI, Université de

68

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

E-Print Network [OSTI]

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

Dacre, Helen

69

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

E-Print Network [OSTI]

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

Ebert, Beth

70

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

SciTech Connect (OSTI)

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

Not Available

1994-12-01T23:59:59.000Z

71

9A.2 FORECAST CHALLENGES AT THE NWS STORM PREDICTION CENTER RELATING TO THE FREQUENCY OF FAVORABLE SEVERE STORM ENVIRONMENTS  

E-Print Network [OSTI]

to the performance of convective watches issued by the NOAA Storm Prediction Center (SPC layer bulk wind shear, are linked to SPC convective watches and verification metrics forecast performance. 2. SPC CONVECTIVE WATCHES A convective watch is issued by the SPC

72

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 1 SEPTEMBER 14, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all are not developing any new tropical cyclones after Earl and Fiona. We expect Earl to generate large amounts of ACE This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting

Gray, William

73

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network [OSTI]

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 2: Electricity Demand by Utility ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work and expertise of numerous the residential forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid provided the projections

74

Using reforecasts for probabilistic forecast calibration  

E-Print Network [OSTI]

1 Using reforecasts for probabilistic forecast calibration Tom Hamill NOAA Earth System Research that is currently operational. #12;3 Why compute reforecasts? · For many forecast problems, such as long-lead forecasts or high-precipitation events, a few past forecasts may be insufficient for calibrating

Hamill, Tom

75

Forecast Combination With Outlier Protection Gang Chenga,  

E-Print Network [OSTI]

Forecast Combination With Outlier Protection Gang Chenga, , Yuhong Yanga,1 a313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455 Abstract Numerous forecast combination schemes with distinct on combining forecasts with minimizing the occurrence of forecast outliers in mind. An unnoticed phenomenon

Yuhong, Yang

76

Load Forecast For use in Resource Adequacy  

E-Print Network [OSTI]

-term Electricity Demand Forecasting System 1) Obtain Daily Regional Temperatures 6) Estimate Daily WeatherLoad Forecast 2019 For use in Resource Adequacy Massoud Jourabchi #12;In today's presentation d l­ Load forecast methodology ­ Drivers of the forecast f i­ Treatment of conservation

77

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 2014­2024 FINAL FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work to the residential forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid provided the projections

78

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022  

E-Print Network [OSTI]

REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022 Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast is the combined product of the hard work Sheridan provided the residential forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid

79

Sixth Northwest Conservation and Electric Power Plan Chapter 3: Electricity Demand Forecast  

E-Print Network [OSTI]

Sixth Northwest Conservation and Electric Power Plan Chapter 3: Electricity Demand Forecast Summary............................................................................................................ 2 Sixth Power Plan Demand Forecast................................................................................................ 4 Demand Forecast Range

80

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast  

E-Print Network [OSTI]

Sixth Northwest Conservation and Electric Power Plan Appendix C: Demand Forecast Energy Demand................................................................................................................................. 1 Demand Forecast Methodology.................................................................................................. 3 New Demand Forecasting Model for the Sixth Plan

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


81

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

E-Print Network [OSTI]

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

Kouroupetroglou, Georgios

82

Solar Forecasting  

Broader source: Energy.gov [DOE]

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

83

Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation  

E-Print Network [OSTI]

Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation Vivien Mallet1. [1] Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: The weights

Mallet, Vivien

84

Nambe Pueblo Water Budget and Forecasting model.  

SciTech Connect (OSTI)

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

Brainard, James Robert

2009-10-01T23:59:59.000Z

85

Forecast Technical Document Restocking in the Forecast  

E-Print Network [OSTI]

Forecast Technical Document Restocking in the Forecast A document describing how restocking of felled areas is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the forecast Background During the period of a production forecast it is assumed that, as forest sub

86

Coordinating production quantities and demand forecasts through penalty schemes  

E-Print Network [OSTI]

Coordinating production quantities and demand forecasts through penalty schemes MURUVVET CELIKBAS1 departments which enable organizations to match demand forecasts with production quantities. This research problem where demand is uncertain and the marketing de- partment provides a forecast to manufacturing

Swaminathan, Jayashankar M.

87

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

88

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (TO APPEAR, 2014) 1 Electricity Market Forecasting  

E-Print Network [OSTI]

to spatially-varying energy prices, known as locational marginal prices (LMPs) [24], [17]. Schemes inference. Day-ahead price forecast- ing is cast as a low-rank kernel learning problem. Uniquely exploiting- temporally varying prices. Through a novel nuclear norm-based regularization, kernels across pricing nodes

Giannakis, Georgios

89

24-hours ahead global irradiation forecasting using Multi-Layer Cyril Voyant1*  

E-Print Network [OSTI]

; it is shown that the most relevant is based on a multi- output MLP using endogenous and exogenous input data ahead. In the case of solar plants, the driving factor is the global solar irradiation (sum of direct and diffuse solar radiation projected on a plane (Wh/m)). This paper focuses on the 24-hours ahead forecast

Paris-Sud XI, Universit de

90

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

SciTech Connect (OSTI)

We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

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

2012-08-15T23:59:59.000Z

91

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

SciTech Connect (OSTI)

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

McNamara, Laura A.

2010-08-01T23:59:59.000Z

92

A Feasibility Study for Probabilistic Convection Initiation Forecasts Based on Explicit Numerical Guidance2  

E-Print Network [OSTI]

, Steven J. Weiss2 , Fanyou Kong4,5 , Ming Xue4,5 , Ryan A. Sobash1,4 , Andrew R. Dean2 , Israel L. Jirak2 ,6 and Christopher J. Melick2,3 8 1 NOAA/National Severe Storms Laboratory, Norman, OK10 2 NOAA/Storm Prediction Center, Norman, OK 3 Cooperative Institute for Mesoscale Meteorological Studies, Norman, OK12 4

Lakshmanan, Valliappa

93

Verification of hourly forecasts of wind turbine power output  

SciTech Connect (OSTI)

A verification of hourly average wind speed forecasts in terms of hourly average power output of a MOD-2 was performed for four sites. Site-specific probabilistic transformation models were developed to transform the forecast and observed hourly average speeds to the percent probability of exceedance of an hourly average power output. (This transformation model also appears to have value in predicting annual energy production for use in wind energy feasibility studies.) The transformed forecasts were verified in a deterministic sense (i.e., as continuous values) and in a probabilistic sense (based upon the probability of power output falling in a specified category). Since the smoothing effects of time averaging are very pronounced, the 90% probability of exceedance was built into the transformation models. Semiobjective and objective (model output statistics) forecasts were made compared for the four sites. The verification results indicate that the correct category can be forecast an average of 75% of the time over a 24-hour period. Accuracy generally decreases with projection time out to approx. 18 hours and then may increase due to the fairly regular diurnal wind patterns that occur at many sites. The ability to forecast the correct power output category increases with increasing power output because occurrences of high hourly average power output (near rated) are relatively rare and are generally not forecast. The semiobjective forecasts proved superior to model output statistics in forecasting high values of power output and in the shorter time frames (1 to 6 hours). However, model output statistics were slightly more accurate at other power output levels and times. Noticeable differences were observed between deterministic and probabilistic (categorical) forecast verification results.

Wegley, H.L.

1984-08-01T23:59:59.000Z

94

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS FORECAST IMPROVEMENTS  

E-Print Network [OSTI]

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS BRISBANE FORECAST IMPROVEMENTS The Bureau of Meteorology is progressively upgrading its forecast system to provide more detailed forecasts across Australia and Sunshine Coast. FURTHER INFORMATION : www.bom.gov.au/NexGenFWS © Commonwealth of Australia, 2013 Links

Greenslade, Diana

95

1.1.1.1.1.1 AP 1.1.1.1.2.1  

E-Print Network [OSTI]

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

Ladkin, Peter B.

96

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST Volume 2: Electricity Demand The demand forecast is the combined product of the hard work and expertise of numerous California Energy for demand response program impacts and contributed to the residential forecast. Mitch Tian prepared

97

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST Volume 1: Statewide Electricity forecast is the combined product of the hard work and expertise of numerous staff members in the Demand prepared the peak demand forecast. Ravinderpal Vaid provided the projections of commercial floor space

98

CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY DEMAND 2014­2024 REVISED FORECAST Volume 1: Statewide Electricity Demand in this report. #12;i ACKNOWLEDGEMENTS The demand forecast is the combined product of the hard work provided estimates for demand response program impacts and contributed to the residential forecast. Mitch

99

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

100

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

E-Print Network [OSTI]

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

Tanaka, Jiro

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
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
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101

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

E-Print Network [OSTI]

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

Tanaka, Jiro

102

Development and testing of improved statistical wind power forecasting methods.  

SciTech Connect (OSTI)

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

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

2011-12-06T23:59:59.000Z

103

1.1.1.1. 1) [2,3].  

E-Print Network [OSTI]

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

Joo, Su-Chong

104

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

E-Print Network [OSTI]

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

Toronto, University of

105

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

106

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

E-Print Network [OSTI]

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

Tanaka, Jiro

107

Does increasing model stratospheric resolution improve extended range forecast skill?  

E-Print Network [OSTI]

Does increasing model stratospheric resolution improve extended range forecast skill? Greg Roff,1 forecast skill at high Southern latitudes is explored. Ensemble forecasts are made for two model configurations that differ only in vertical resolution above 100 hPa. An ensemble of twelve 30day forecasts

108

Solar forecasting review  

E-Print Network [OSTI]

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

Inman, Richard Headen

2012-01-01T23:59:59.000Z

109

SolarAnywhere forecast (Perez & Hoff) This chapter describes, and presents an evaluation of, the forecast models imbedded in the  

E-Print Network [OSTI]

SolarAnywhere forecast (Perez & Hoff) ABSTRACT This chapter describes, and presents an evaluation of, the forecast models imbedded in the SolarAnywhere platform. The models include satellite derived cloud motion based forecasts for the short to medium horizon (1 5 hours) and forecasts derived from NOAA

Perez, Richard R.

110

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

E-Print Network [OSTI]

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

Tanaka, Jiro

111

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

SciTech Connect (OSTI)

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

BARCOT, R.A.

2000-08-31T23:59:59.000Z

112

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS DISTRICT FORECASTS  

E-Print Network [OSTI]

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS DISTRICT FORECASTS IMPROVEMENTS FOR QUEENSLAND across Australia From October 2013, new and improved district forecasts will be introduced in Queensland Protection times FURTHER INFORMATION : www.bom.gov.au/NexGenFWS © Commonwealth of Australia, 2013 PTO> Wind

Greenslade, Diana

113

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

E-Print Network [OSTI]

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

Tanaka, Jiro

114

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

E-Print Network [OSTI]

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

Tanaka, Jiro

115

1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1  

E-Print Network [OSTI]

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

Martinis, John M.

116

Using Wikipedia to forecast diseases  

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

Using Wikipedia to forecast diseases Using Wikipedia to forecast diseases Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of...

117

1.1. : 1.2.  

E-Print Network [OSTI]

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

Borissova, Daniela

118

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

E-Print Network [OSTI]

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

McMaster University

119

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

E-Print Network [OSTI]

Sixth Northwest Conservation and Electric Power Plan Appendix B: Economic Forecast Role of the Economic Forecast ................................................................................................. 2 Economic Drivers of Residential Demand

120

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


121

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

122

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

SciTech Connect (OSTI)

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

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

2014-04-30T23:59:59.000Z

123

Forecast Technical Document Volume Increment  

E-Print Network [OSTI]

Forecast Technical Document Volume Increment Forecasts A document describing how volume increment is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Volume increment forecasts Background A volume increment forecast is a fundamental output of the forecast

124

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

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

Boyer, Edmond

125

Simulations of Arctic Mixed-Phase Clouds in Forecasts with CAM3 and AM2 for M-PACE  

SciTech Connect (OSTI)

Simulations of mixed-phase clouds in short-range forecasts with the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) and the Geophysical Fluid Dynamics Laboratory (GFDL) climate model (AM2) for the Mixed-Phase Arctic Cloud Experiment (M-PACE) are performed under the DOE CCPP-ARM Parameterization Testbed (CAPT), which initializes the climate models with analysis data produced from numerical weather prediction (NWP) centers. It is shown that CAM3 significantly underestimates the observed boundary layer mixed-phase clouds and cannot realistically simulate the variations with temperature and cloud height of liquid water fraction in the total cloud condensate based an oversimplified cloud microphysical scheme. In contrast, AM2 reasonably reproduces the observed boundary layer clouds while its clouds contain much less cloud condensate than CAM3 and the observations. Both models underestimate the observed cloud top and base for the boundary layer clouds. The simulation of the boundary layer mixed-phase clouds and their microphysical properties is considerably improved in CAM3 when a new physically based cloud microphysical scheme is used. The new scheme also leads to an improved simulation of the surface and top of the atmosphere longwave radiative fluxes in CAM3. It is shown that the Bergeron-Findeisen process, i.e., the ice crystal growth by vapor deposition at the expense of coexisting liquid water, is important for the models to correctly simulate the characteristics of the observed microphysical properties in mixed-phase clouds. Sensitivity tests show that these results are not sensitive to the analysis data used for model initializations. Increasing model horizontal resolution helps capture the subgrid-scale features in Arctic frontal clouds but does not help improve the simulation of the single-layer boundary layer clouds. Ice crystal number density has large impact on the model simulated mixed-phase clouds and their microphysical properties and needs to be accurately represented in climate models.

Xie, Shaocheng; Boyle, James; Klein, Stephen A.; Liu, Xiaohong; Ghan, Steven J.

2008-02-29T23:59:59.000Z

126

1.1. : 1.2.  

E-Print Network [OSTI]

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

Borissova, Daniela

127

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

128

Nonparametric models for electricity load forecasting  

E-Print Network [OSTI]

Electricity consumption is constantly evolving due to changes in people habits, technological innovations1 Nonparametric models for electricity load forecasting JANUARY 23, 2015 Yannig Goude, Vincent at University Paris-Sud 11 Orsay. His research interests are electricity load forecasting, more generally time

Genève, Université de

129

2013 Midyear Economic Forecast Sponsorship Opportunity  

E-Print Network [OSTI]

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

de Lijser, Peter

130

Table of Contents Page 2National High Magnetic Field Laboratory and Its Forecasted Impact on the Florida Economy  

E-Print Network [OSTI]

Impact on the Florida Economy History and Evaluation of the Economic Impact of the Magnet Lab Forecasted Impact on the Florida Economy The National Science Foundation (NSF) awarded the National High generated by Magnet Lab activities across the broader statewide economy. Since 1990, the Magnet Lab has

Weston, Ken

131

Energy Department Announces $2.5 Million to Improve Wind Forecasting |  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGYWomentheATLANTA, GA5 & 6, 2012 MEETING OFCalifornia ConcentratingDepartment of

132

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

133

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

134

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

135

Draft for Public Comment Appendix A. Demand Forecast  

E-Print Network [OSTI]

Draft for Public Comment A-1 Appendix A. Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required component of the Council's Northwest Regional Conservation had a tradition of acknowledging the uncertainty of any forecast of electricity demand and developing

136

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

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

McBurney, Peter

137

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

SciTech Connect (OSTI)

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

Not Available

1993-12-01T23:59:59.000Z

138

RACORO Forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared at 278, 298, and 323 K.Office of Science7a.RACETRACK AT ANL S.

139

P2.3 DEVELOPMENT OF A COMPREHENSIVE SEVERE WEATHER FORECAST VERIFICATION SYSTEM AT THE STORM PREDICTION CENTER  

E-Print Network [OSTI]

: Andrew R. Dean, CIMMS, Univ. of Oklahoma, National Weather Center, Suite 2300, Norman, OK 73072-7268; e PREDICTION CENTER Andrew R. Dean*1,2 , Russell S. Schneider 2 , and Joseph T. Schaefer 2 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK 2 NOAA/NWS Storm

140

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

E-Print Network [OSTI]

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

Varela, Carlos

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


141

Forecasted Opportunities  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-SeriesFlickr Flickr Editor's note: Since theNational SupplementalFor

142

CONSULTANT REPORT DEMAND FORECAST EXPERT  

E-Print Network [OSTI]

CONSULTANT REPORT DEMAND FORECAST EXPERT PANEL INITIAL forecast, end-use demand modeling, econometric modeling, hybrid demand modeling, energyMahon, Carl Linvill 2012. Demand Forecast Expert Panel Initial Assessment. California Energy

143

MLWFA: A Multilingual Weather Forecast Text Generation Tianfang YAO Dongmo ZHANG Qian WANG  

E-Print Network [OSTI]

MLWFA: A Multilingual Weather Forecast Text Generation System1 Tianfang YAO Dongmo ZHANG Qian WANG generation; Weather forecast generation system Abstract In this demonstration, we present a system for multilingual text generation in the weather forecast domain. Multilingual Weather Forecast Assistant (MLWFA

Wu, Dekai

144

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

145

1,2 2 Daniel Eaton2 1,2 1. 2. ATR-IRC  

E-Print Network [OSTI]

2.1 * 4 1 4 1 A ID 1,BCD 2 3 4 1,E * http-M Robovie-M PC 3 Robovie-M 2.3 10m 20 Spider-IIIA #12;4 RF-CODE 2 3 WAT902-HWATEC IR96

Kanda, Takayuki

146

Continuous Model Updating and Forecasting for a Naturally Fractured Reservoir  

E-Print Network [OSTI]

result in suboptimal decisions and huge disappointments (see Sec. 1.2.2) Reservoir simulation literature indicates that an acceptable level of matching historical reservoir performance is required to establish reliable forecasts. However, this does... and field production. Such capabilities enable continuous and automatic fine-tuning of production controls to optimize project economics and/or some well or reservoir performance stated objective. Remotely activated sub-surface valves on ?smart wells...

Almohammadi, Hisham

2013-07-26T23:59:59.000Z

147

Demand Forecast INTRODUCTION AND SUMMARY  

E-Print Network [OSTI]

Demand Forecast INTRODUCTION AND SUMMARY A 20-year forecast of electricity demand is a required of any forecast of electricity demand and developing ways to reduce the risk of planning errors that could arise from this and other uncertainties in the planning process. Electricity demand is forecast

148

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

E-Print Network [OSTI]

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

Wang, Yuqing

149

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

E-Print Network [OSTI]

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

Solka, Jeff

150

Econometric model and futures markets commodity price forecasting  

E-Print Network [OSTI]

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

Just, Richard E.; Rausser, Gordon C.

1979-01-01T23:59:59.000Z

151

Probabilistic manpower forecasting  

E-Print Network [OSTI]

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

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

152

Forecasting project progress and early warning of project overruns with probabilistic methods  

E-Print Network [OSTI]

, the critical path method (CPM) and earned value management (EVM) are deterministic and fail to account for the inherent uncertainty in forecasting and project performance. The objective of this dissertation is to improve the predictive capabilities....4.1 Earned Value Management ................................................... 14 2.4.2 CPM ...................................................................................... 20 2.4.3 Monte Carlo Simulation...

Kim, Byung Cheol

2008-10-10T23:59:59.000Z

153

Multivariate Forecast Evaluation And Rationality Testing  

E-Print Network [OSTI]

10621088. MULTIVARIATE FORECASTS Chaudhuri, P. (1996): OnKingdom. MULTIVARIATE FORECASTS Kirchgssner, G. , and U. K.2005): Estimation and Testing of Forecast Rationality under

Komunjer, Ivana; OWYANG, MICHAEL

2007-01-01T23:59:59.000Z

154

1 1 1 1 1 1 2 2 Network of offshore wind farms connected by  

E-Print Network [OSTI]

2 33 3 3 1 1 1 1 1 1 2 2 Network of offshore wind farms connected by gas insulated transmission of connecting these offshore wind farms by gas in- sulated transmission lines (GIL) is investigated. Aim, Germany Corresponding author: anja.drews@forwind.de Offshore wind parks in different stages.Green- in op

Heinemann, Detlev

155

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

E-Print Network [OSTI]

,399 unique cells, and 1,028,510 to find the best lightning forecast criteria. Results show that using 30 dBZ at the -20 ?C isotherm on cells within 75 km of the radar that have been tracked for at least 2 consecutive scan produces the best forecasts... with a critical success index (CSI) of 0.71. The best VII predictor was 0.734 kg m-2 on cells within 75 km of the radar that have been tracked for at least 2 consecutive scans iv producing a CSI of 0.68. Results of this study further suggest...

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

156

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

157

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

E-Print Network [OSTI]

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

Sandborn, Peter

158

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2Beamline 2.1 Print

159

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2Beamline 2.1

160

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2Beamline 2.1Beamline

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


161

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2Beamline2.1 Print

162

3, 21452173, 2006 Probabilistic forecast  

E-Print Network [OSTI]

HESSD 3, 2145­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page for probabilistic forecasts of continuous hydrological variables F. Laio and S. Tamea DITIC ­ Department­2173, 2006 Probabilistic forecast verification F. Laio and S. Tamea Title Page Abstract Introduction

Paris-Sud XI, Université de

163

Forecast Technical Document Technical Glossary  

E-Print Network [OSTI]

Forecast Technical Document Technical Glossary A document defining some of the terms used in the 2011 Production Forecast technical documentation. Tom Jenkins Robert Matthews Ewan Mackie Lesley in the Forecast documentation. In some cases, the terms and the descriptions are "industry standard", in others

164

Forecast Technical Document Tree Species  

E-Print Network [OSTI]

Forecast Technical Document Tree Species A document listing the tree species included in the 2011 Production Forecast Tom Jenkins Justin Gilbert Ewan Mackie Robert Matthews #12;PF2011 ­ List of tree species The following is the list of species used within the Forecast System. Species are ordered alphabetically

165

TRAVEL DEMAND AND RELIABLE FORECASTS  

E-Print Network [OSTI]

TRAVEL DEMAND AND RELIABLE FORECASTS FOR TRANSIT MARK FILIPI, AICP PTP 23rd Annual Transportation transportation projects § Develop and maintain Regional Travel Demand Model § Develop forecast socio in cooperative review during all phases of travel demand forecasting 4 #12;Cooperative Review Should Include

Minnesota, University of

166

Demand Forecasting of New Products  

E-Print Network [OSTI]

Demand Forecasting of New Products Using Attribute Analysis Marina Kang A thesis submitted Abstract This thesis is a study into the demand forecasting of new products (also referred to as Stock upon currently employed new-SKU demand forecasting methods which involve the processing of large

Sun, Yu

167

Improving Inventory Control Using Forecasting  

E-Print Network [OSTI]

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

Balandran, Juan

2005-12-16T23:59:59.000Z

168

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline 12.2.2 Print3.1122.1

169

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline 12.2.2 Print3.1122.12.1

170

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

SciTech Connect (OSTI)

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

Valero, O.J.

1996-10-03T23:59:59.000Z

171

Fuel Price Forecasts INTRODUCTION  

E-Print Network [OSTI]

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

172

Solar forecasting review  

E-Print Network [OSTI]

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

Inman, Richard Headen

2012-01-01T23:59:59.000Z

173

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 16 AUGUST 29, 2013  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

174

NREL: Transmission Grid Integration - Forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and ResourcesOtherForecasting NREL researchers use solar and

175

Autoregressive Time Series Forecasting of Computational Demand  

E-Print Network [OSTI]

We study the predictive power of autoregressive moving average models when forecasting demand in two shared computational networks, PlanetLab and Tycoon. Demand in these networks is very volatile, and predictive techniques to plan usage in advance can improve the performance obtained drastically. Our key finding is that a random walk predictor performs best for one-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponential smoothing models perform better for two and three-step-ahead forecasts. A Monte Carlo bootstrap test is proposed to evaluate the continuous prediction performance of different models with arbitrary confidence and statistical significance levels. Although the prediction results differ between the Tycoon and PlanetLab networks, we observe very similar overall statistical properties, such as volatility dynamics.

Sandholm, Thomas

2007-01-01T23:59:59.000Z

176

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2 Beamline222.1 Print

177

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2 Beamline222.1

178

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2 Beamline222.1Beamline

179

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

- namic reserve quantification [8], for the optimal oper- ation of combined wind-hydro power plants [5, 1Forecasting Uncertainty Related to Ramps of Wind Power Production Arthur Bossavy, Robin Girard - The continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power

Boyer, Edmond

180

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

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
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to obtain the most current and comprehensive results.


181

Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging  

E-Print Network [OSTI]

distribution; Numerical weather prediction; Skewed distribution; Truncated data; Wind energy. 1. INTRODUCTION- native. Purely statistical methods have been applied to short-range forecasts for wind speed only a fewProbabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging J. Mc

Raftery, Adrian

182

Forecasting Building Occupancy Using Sensor Network James Howard  

E-Print Network [OSTI]

of the forecasting algorithm for the different conditions. 1. INTRODUCTION According to the U.S. Department of Energy could take advantage of times when electricity cost is lower, to chill a cold water storage tankForecasting Building Occupancy Using Sensor Network Data James Howard Colorado School of Mines

Hoff, William A.

183

Multiagent Bayesian Forecasting of Structural Time-Invariant Dynamic Systems with  

E-Print Network [OSTI]

. Alternatively, time series are represented by state-space models, also referred to as multivariate dynamic and science. We study forecasting of stochastic, dynamic systems based on observations from multivariate time to a discrete time, multivariate time series [1, 2]. The primary inference that we address is one- step

Xiang, Yang

184

Time series modeling and large scale global solar radiation forecasting from geostationary satellites data  

E-Print Network [OSTI]

1 Time series modeling and large scale global solar radiation forecasting from geostationary global solar radiation. In this paper, we use geostationary satellites data to generate 2-D time series of solar radiation for the next hour. The results presented in this paper relate to a particular territory

Paris-Sud XI, Université de

185

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

SciTech Connect (OSTI)

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

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

2007-06-01T23:59:59.000Z

186

Facebook IPO updated valuation and user forecasting  

E-Print Network [OSTI]

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

187

Solar forecasting review  

E-Print Network [OSTI]

2.1.2 European Solar Radiation Atlas (ESRA)for supplementing solar radiation network data, FinalEstimating incident solar radiation at the surface from geo-

Inman, Richard Headen

2012-01-01T23:59:59.000Z

188

Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation  

E-Print Network [OSTI]

Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation for Medium-Range Weather Forecasts, Reading, United Kingdom JEFFREY S. WHITAKER NOAA/Earth System Research As a companion to Part I, which discussed the calibration of probabilistic 2-m temperature forecasts using large

Hamill, Tom

189

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2 Beamline22

190

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2

191

Beamline 2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print EUV3.2Beamline

192

RESERVOIR RELEASE FORECAST MODEL FOR FLOOD OPERATION OF THE FOLSOM PROJECT INCLUDING PRE-RELEASES  

E-Print Network [OSTI]

1 RESERVOIR RELEASE FORECAST MODEL FOR FLOOD OPERATION OF THE FOLSOM PROJECT INCLUDING PRE-line Planning Mode, the Reservoir Release Forecast Model (RRFM) is being used to test alternatives operating River Forecast Center. The RRFM will make possible the risk-based operation of the Folsom Project

Bowles, David S.

193

THE PITTSBURGH REMI MODEL: LONG-TERM REMI MODEL FORECAST FOR  

E-Print Network [OSTI]

1 THE PITTSBURGH REMI MODEL: LONG-TERM REMI MODEL FORECAST FOR ALLEGHENY COUNTY AND THE PITTSBURGH made. REMI LONG-TERM FORECAST AND BEA PROJECTIONS This report includes UCSUR's 1998 economic and population projections for the Pittsburgh Region. The purpose of UCSUR's long-term regional forecasts

Sibille, Etienne

194

Associations Between Management Forecast Accuracy and Pricing of IPOs in Athens Stock  

E-Print Network [OSTI]

1 Associations Between Management Forecast Accuracy and Pricing of IPOs in Athens Stock Exchange Dimitrios Gounopoulos* University of Surrey, U.K. This study examines the earnings forecast accuracy earnings forecast and pricing ofIPOs. It uses a unique data set of 208 IPOs, which were floated during

Jensen, Max

195

Forecast of the electricity consumption by aggregation of specialized experts; application to Slovakian and French  

E-Print Network [OSTI]

Forecast of the electricity consumption by aggregation of specialized experts; application-term forecast of electricity consumption based on ensemble methods. That is, we use several possibly independent´erieure and CNRS. hal-00484940,version1-19May2010 #12;Forecast of the electricity consumption by aggregation

196

Structuring and integrating human knowledge in demand forecasting: a judgmental adjustment approach  

E-Print Network [OSTI]

1 Structuring and integrating human knowledge in demand forecasting: a judgmental adjustment.cheikhrouhou@epfl.ch Abstract Demand forecasting consists of using data of the past demand to obtain an approximation of the future demand. Mathematical approaches can lead to reliable forecasts in deterministic context through

Paris-Sud XI, Université de

197

EFFECT OF SHARED INFORMATION ON TRUST AND RELIANCE IN A DEMAND FORECASTING TASK  

E-Print Network [OSTI]

EFFECT OF SHARED INFORMATION ON TRUST AND RELIANCE IN A DEMAND FORECASTING TASK Ji Gao1 , John D's trust and reliance. A simulated demand forecasting task required participants to provide an initial. INTRODUCTION Demand forecasting is a task that strongly influences success in supply chains. Inappropriate

Lee, John D.

198

Development of Short-term Demand Forecasting Model Application in Analysis of Resource Adequacy  

E-Print Network [OSTI]

Development of Short-term Demand Forecasting Model And its Application in Analysis of Resource will present the methodology, testing and results from short-term forecasting model developed by Northwest and applied the short-term forecasting model to Resource Adequacy analysis. These steps are presented below. 1

199

A collaborative demand forecasting process with event-based fuzzy judgments  

E-Print Network [OSTI]

1 A collaborative demand forecasting process with event-based fuzzy judgments Naoufel Cheikhrouhoua to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However for demand planning purposes. Since forecasters have partial knowledge of the context and of future events

Boyer, Edmond

200

NCEP Products Available to Distribute to CONDUIT High-Resolution Window Forecast System (HIRESW) Full  

E-Print Network [OSTI]

NCEP Products Available to Distribute to CONDUIT Phase 1 High-Resolution Window Forecast System Ocean Forecast System (RTOFS) Full Description Product Location The RTOFS for the North Atlantic resolution nest Hurricane Weather Research and Forecast (HWRF) system Full Description Product Location (hwrf

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


201

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 15 SEPTEMBER 28, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

202

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 27 OCTOBER 10, 2013  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

203

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 2012  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

204

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 13 SEPTEMBER 26, 2013  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

205

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 18 AUGUST 31, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

206

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2012  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

207

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 17 AUGUST 30, 2012  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fourth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

208

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 4 AUGUST 17, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

209

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 29 OCTOBER 12, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

210

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

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

211

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 30 SEPTEMBER 12, 2013  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the fifth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

212

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 31 SEPTEMBER 13, 2011  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the third year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Birner, Thomas

213

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 28 SEPTEMBER 10, 2014  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the sixth year that we have issued shorter-term forecasts of tropical cyclone activity starting in early such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Collett Jr., Jeffrey L.

214

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 13 OCTOBER 26, 2010  

E-Print Network [OSTI]

with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index, which is defined to be all This is the second year that we have issued shorter-term forecasts of tropical cyclone (TC) activity starting such as named storms and hurricanes. We issue forecasts for ACE using three categories as defined in Table 1

Gray, William

215

2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA  

E-Print Network [OSTI]

2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA J models 1 INTRODUCTION Solar radiation and PV production forecasts are becoming increasingly important/) three teams of experts are benchmarking their solar radiation forecast against ground truth data

Perez, Richard R.

216

Forecasting oilfield economic performance  

SciTech Connect (OSTI)

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

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

1994-11-01T23:59:59.000Z

217

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

E-Print Network [OSTI]

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

Tanaka, Jiro

218

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

219

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

220

Forecasting sudden changes in environmental pollution patterns  

E-Print Network [OSTI]

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

Olascoaga, Maria Josefina

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


221

1 2 1 3 4 3 2 2 5 7  

E-Print Network [OSTI]

for the site identification. èéæçëíæçëíéæèæêç 2.1 Material Data from a nuclear pressure vessel steel similar such as reactor pressure vessels depends on the material resistance against brittle fracture. Cleavage which orientation (L)). It should be noted that the CT specimens were tested without side­grooves. 2.3 Specimen

Paris-Sud XI, Université de

222

Forecast Technical Document Growing Stock Volume  

E-Print Network [OSTI]

Forecast Technical Document Growing Stock Volume Forecasts A document describing how growing stock (`standing') volume is handled in the 2011 Production Forecast. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Growing stock volume forecasts Background A forecast of standing volume (or

223

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

E-Print Network [OSTI]

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

Grzybowski, Bartosz A.

224

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

E-Print Network [OSTI]

Forecast System Southwest Florida Forecast Region Maps 0 20 4010 Miles #12;Bay-S Pinellas Bay-UPR Bay 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

225

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

226

UWIG Forecasting Workshop -- Albany (Presentation)  

SciTech Connect (OSTI)

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

Lew, D.

2011-04-01T23:59:59.000Z

227

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network [OSTI]

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

228

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

E-Print Network [OSTI]

. Bromhal, National Energy Technology Laboratory Copyright 2010, Society of Petroleum Engineers This paper injection. Introduction Sustain economic growth by providing sufficient energy and controlling the CO2 emissions as byproduct of producing Energy from fossil fuels is one of the most challenging tasks

Mohaghegh, Shahab

229

Conservation The Northwest ForecastThe Northwest Forecast  

E-Print Network [OSTI]

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

230

NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS  

E-Print Network [OSTI]

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

Mohaghegh, Shahab

231

Award_1_2  

Office of Environmental Management (EM)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergy Cooperation |South42.2 (April 2012)Tie LtdAttend AMO Program682 S.Avian

232

Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets  

E-Print Network [OSTI]

2 2. Annual Energy Outlook (Administrations Annual Energy Outlook forecasted price (of Energy, Annual Energy Outlook 2004 with Projections to

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

2005-01-01T23:59:59.000Z

233

2.1 Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 . . . . . . . . . . . . . 4  

E-Print Network [OSTI]

17 #12;Web Web Web Web Navi #12;1 1 2 Web 3 2.1 Web.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2.1 Web.2.3 . . . . . . . . . . . . . . . . . . . . . 15 4.3 Web . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.3.1 Web

Tanaka, Jiro

234

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

235

Sixth Northwest Conservation and Electric Power Plan Appendix D: Wholesale Electricity Price Forecast  

E-Print Network [OSTI]

Forecast Introduction.................................................................................................................................... 6 Demand................................................................... 16 The Base Case Forecast

236

1 Introduction. 1 2 Generalities. 5  

E-Print Network [OSTI]

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

237

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

1998-01-01T23:59:59.000Z

238

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

239

Regional-seasonal weather forecasting  

SciTech Connect (OSTI)

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

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

1980-08-01T23:59:59.000Z

240

MSSM Forecast for the LHC  

E-Print Network [OSTI]

We perform a forecast of the MSSM with universal soft terms (CMSSM) for the LHC, based on an improved Bayesian analysis. We do not incorporate ad hoc measures of the fine-tuning to penalize unnatural possibilities: such penalization arises from the Bayesian analysis itself when the experimental value of $M_Z$ is considered. This allows to scan the whole parameter space, allowing arbitrarily large soft terms. Still the low-energy region is statistically favoured (even before including dark matter or g-2 constraints). Contrary to other studies, the results are almost unaffected by changing the upper limits taken for the soft terms. The results are also remarkable stable when using flat or logarithmic priors, a fact that arises from the larger statistical weight of the low-energy region in both cases. Then we incorporate all the important experimental constrains to the analysis, obtaining a map of the probability density of the MSSM parameter space, i.e. the forecast of the MSSM. Since not all the experimental information is equally robust, we perform separate analyses depending on the group of observables used. When only the most robust ones are used, the favoured region of the parameter space contains a significant portion outside the LHC reach. This effect gets reinforced if the Higgs mass is not close to its present experimental limit and persits when dark matter constraints are included. Only when the g-2 constraint (based on $e^+e^-$ data) is considered, the preferred region (for $\\mu>0$) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-$\\mu$ possibilities.

Maria Eugenia Cabrera; Alberto Casas; Roberto Ruiz de Austri

2010-12-10T23:59:59.000Z

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


241

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

242

africa conditional forecasts: Topics by E-print Network  

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

forecasts had the potential to improve resource management but instead played only a marginal role in real-world decision making. 1 A widespread perception that the quality of the...

243

QUARTERLY ECONOMIC COMMENTARY Vol 30 No 2  

E-Print Network [OSTI]

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

Mottram, Nigel

244

Atmospheric Lagrangian coherent structures considering unresolved turbulence and forecast uncertainty  

E-Print Network [OSTI]

Atmospheric Lagrangian coherent structures considering unresolved turbulence and forecast structures Stochastic trajectory Stochastic FTLE field Ensemble forecasting Uncertainty analysis a b s t r of the forecast FTLE fields is analyzed using ensemble forecasting. Unavoidable errors of the forecast velocity

Ross, Shane

245

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

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLasDelivered energy consumption by sector Transportationdiesel fuel pricediesel fuel3,diesel fuel

246

1 2 2 2 3 4 Zuttomo Zuttomo Zuttomo  

E-Print Network [OSTI]

Friends index screen. Zuttomo Zuttomo GPS 2. Zuttomo Zuttomo 2.1 Zuttomo Facebook Zuttomo Facebook Facebook Zuttomo Facebook Zuttomo 2.2 Zuttomo 2.3 Zuttomo 2.4 Zuttomo 1 2.5 ping ping ping 2 ping ping 3. Zuttomo iOS5 WebAPI 3 3.1 3.1.1 Facebook Facebook Facebook SDK SDK Facebook API Facebook Facebook #12

Tanaka, Jiro

248

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

E-Print Network [OSTI]

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

Mayberry, Marty

249

1 Introduction. 1 2 Generalities. 5  

E-Print Network [OSTI]

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

250

(1) a2 = b2 + c2 ?2bccos? (2)  

E-Print Network [OSTI]

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

charlotb

2010-07-02T23:59:59.000Z

251

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

SciTech Connect (OSTI)

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

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

2012-09-01T23:59:59.000Z

252

1 Introduction. 1 2 Generalities. 5  

E-Print Network [OSTI]

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

253

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

E-Print Network [OSTI]

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

Arumugam, Sankar

254

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

it can generate as much or more energy that it needs ? Building activities need N kWhrs per day (solar panels, heating, etc) ? Harvested from solar panels & passive solar. Amount depends on weather ? NWP models forecast DSWRF @ surface (MJ/m2... contract work Saturday Would you plan work for Saturday? Not much detail for Saturday and Sunday With more info could be easier to decide go, no go From deterministic to probabilistic ? Forecast presented as a single scenario ? One scenario presented...

Poulin, L.

2013-01-01T23:59:59.000Z

255

Forecast Technical Document Felling and Removals  

E-Print Network [OSTI]

Forecast Technical Document Felling and Removals Forecasts A document describing how volume fellings and removals are handled in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan Mackie Lesley Halsall #12;PF2011 ­ Felling and removals forecasts Background A fellings and removals

256

Assessing Forecast Accuracy Measures Department of Economics  

E-Print Network [OSTI]

Assessing Forecast Accuracy Measures Zhuo Chen Department of Economics Heady Hall 260 Iowa State forecast accuracy measures. In the theoretical direction, for comparing two forecasters, only when the errors are stochastically ordered, the ranking of the forecasts is basically independent of the form

257

A first large-scale flood inundation forecasting model  

SciTech Connect (OSTI)

At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode revealed that it is crucial to account for basin-wide hydrological response time when assessing lead time performances notwithstanding structural limitations in the hydrological model and possibly large inaccuracies in precipitation data.

Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.

2013-11-04T23:59:59.000Z

258

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

259

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

260

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network [OSTI]

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

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


261

Arnold Schwarzenegger INTEGRATED FORECAST AND  

E-Print Network [OSTI]

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

262

Value of Wind Power Forecasting  

SciTech Connect (OSTI)

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

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

2011-04-01T23:59:59.000Z

263

Comparison of Model Forecast Skill of Sea-Level Pressure Along the East and West Coasts of the United States  

E-Print Network [OSTI]

1 Comparison of Model Forecast Skill of Sea-Level Pressure Along the East and West Coasts, University of Washington, Seattle, Washington Submitted to: Weather and Forecasting May 2008 Revised recent advances in numerical weather prediction, major errors in short-range forecasts still occur

Mass, Clifford F.

264

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

265

MPX V1.2  

Energy Science and Technology Software Center (OSTI)

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

266

Tracking Severe Weather Storms in Doppler Radar Images D. Cheng 1 , R. E. Mercer 1 , J. L. Barron 1 and P. Joe 2  

E-Print Network [OSTI]

meteorologist's perception. 1 Introduction Because of the devastation caused by severe storms, the forecasting we have developed an automatic storm tracking system. Tracking past storm movement is a prerequisite to forecasting future storm movement and minimizing property damage. Physically, a storm is an area of updraft

Barron, John

267

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...

268

FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007 INTEGRATED Table of Contents General Instructions for Demand Forecast Submittals.............................................................................. 4 Protocols for Submitted Demand Forecasts

269

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

270

Analysis and forecasting of gas well performanc: a rigorous approach using decline curve analysis  

E-Print Network [OSTI]

. . . . . . . . . . . . 146 Normalized Flow Rate Profile versus Material Balance Pseudotimes for Well C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 138 139 6. 35 6, 36 6. 37 A-1 A-2 A-3 149 186 Type Curve Match...ANALYSIS AND FORECASTING OF GAS WELL PERFORMANCE A RIGOROUS APPROACH USING DECLINE CURVE ANALYSIS A Thesis by JUAN CARLOS PALACIO URAN Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment...

Palacio Uran, Juan Carlos

1993-01-01T23:59:59.000Z

271

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This final rule3of Page

272

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This final rule3of

273

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This final rule3ofF AL

274

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This final rule3ofF

275

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This final rule3ofFa

276

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This final

277

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This finalFAL 2004-01

278

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This finalFAL

279

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This finalFALFor

280

Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This finalFALForThe

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


281

Page 1 of2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page This

282

Page 1 of2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page Thisnew product, the

283

Page 1 of2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious RankCombustion | Department ofT ib l L d F SSalesOE0000652 Srivastava,Pacific1of Page Thisnew product,

284

Inventory Routing and On-line Inventory Routing File Format M. Sevaux1,2  

E-Print Network [OSTI]

demand and the precise demand for the current period. This forecast is rather accurate but not precise enough to calculate the exact demand of the next period. The forecast is represented by several figures. The problem is defined for a known a finite horizon 1 . . . H. For each time period, each node has a demand

Brest, Université de

285

Volatility Forecasts in Financial Time Series with HMM-GARCH Models  

E-Print Network [OSTI]

Volatility Forecasts in Financial Time Series with HMM-GARCH Models Xiong-Fei Zhuang and Lai {xfzhuang,lwchan}@cse.cuhk.edu.hk Abstract. Nowadays many researchers use GARCH models to generate of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH

Chen, Yiling

286

2.1E Supplement  

E-Print Network [OSTI]

F 1 2 .1E- 8 1 / 3 EXT-FUEL-BTU/HR s - PLANT-ASSIGNMENT . 28 1 / 3 ZIE- 8 1 / 3 PROCESS-CHW-BTU/HR PROCESS-CHW-POWERPROCESS-CHW-SCH PROCESS-HW-BTU/HR s - PLANT-ASSIGNMENT s -

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

287

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

E-Print Network [OSTI]

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

Bowen, James D.

288

1434 VOLUME 132M O N T H L Y W E A T H E R R E V I E W Ensemble Reforecasting: Improving Medium-Range Forecast Skill Using  

E-Print Network [OSTI]

-Range Forecast Skill Using Retrospective Forecasts THOMAS M. HAMILL University of Colorado and NOAA­CIRES Climate statistics (MOS) approach to improving 6­10-day and week 2 probabilistic forecasts of surface temperature of the NCEP medium-range forecast model with physics operational during 1998. An NCEP­NCAR reanalysis initial

Hamill, Tom

289

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

E-Print Network [OSTI]

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

Grübel, Rudolf

290

2.1E Supplement  

E-Print Network [OSTI]

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

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

291

2.1E Supplement  

E-Print Network [OSTI]

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

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

292

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline2 Print1.2 Print6.1.2

293

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

294

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

SciTech Connect (OSTI)

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

John Zack; Deborah Hanley; Dora Nakafuji

2012-07-15T23:59:59.000Z

295

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

E-Print Network [OSTI]

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

Granichin, Oleg

296

Management forecast credibility and underreaction to news  

E-Print Network [OSTI]

In this paper, we first document evidence of underreaction to management forecast news. We then hypothesize that the credibility of the forecast influences the magnitude of this underreaction. Relying on evidence that more ...

Ng, Jeffrey

297

Management Forecast Quality and Capital Investment Decisions  

E-Print Network [OSTI]

Corporate investment decisions require managers to forecast expected future cash flows from potential investments. Although these forecasts are a critical component of successful investing, they are not directly observable ...

Goodman, Theodore H.

298

Draft Fourth Northwest Conservation and Electric Power Plan, Appendix C FUEL PRICE FORECASTS  

E-Print Network [OSTI]

C-1 Draft Fourth Northwest Conservation and Electric Power Plan, Appendix C APPENDIX C FUEL PRICE FORECASTS BACKGROUND Since the Council's 1991 Power Plan, fuel prices have been following the low forecast. Figure C-1 illustrates this for world oil prices, and similar patterns apply to natural gas. The last

299

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 28 OCTOBER 11, 2011  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone (TC) activity starting in early August. We have decided to discontinue our individual for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

Gray, William

300

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2011  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

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


301

COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM SEPTEMBER 14 SEPTEMBER 27, 2012  

E-Print Network [OSTI]

that we are trying to predict with these two-week forecasts is the Accumulated Cyclone Energy (ACE) index of tropical cyclone activity starting in early August. We have decided to discontinue our individual monthly for ACE using three categories as defined in Table 1. Table 1: ACE forecast definition. Parameter

Gray, William

302

Renewable Forecast Min-Max2020.xls  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared at 278, 298,NIST31 ORV 15051SoilWind Energy Wind RenewableForecast

303

Improving week two forecasts with multi-model re-forecast ensembles  

E-Print Network [OSTI]

Improving week two forecasts with multi-model re-forecast ensembles Jeffrey S. Whitaker and Xue Wei NOAA-CIRES Climate Diagnostics Center, Boulder, CO Fr´ed´eric Vitart Seasonal Forecasting Group, ECMWF dataset of ensemble 're-forecasts' from a single model can significantly improve the skill

Whitaker, Jeffrey S.

304

Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract  

E-Print Network [OSTI]

Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract] Nicolas Gast EPFL, IC/LCA2 1015 Lausanne Switzerland nicolas.gast@epfl.ch Dan-Cristian Tomozei EPFL, IC/LCA2 1015 Lausanne Switzerland dan-cristian.tomozei@epfl.ch Jean-Yves Le Boudec EPFL, IC/LCA2 1015 Lausanne Switzerland jean

Dalang, Robert C.

305

Beamline 3.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline 12.2.21 Print1 Print2.1

306

Beamline 3.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print2.1 Print Commercial2.1 Print

307

Beamline 3.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print2.1 Print Commercial2.1

308

5, 183218, 2008 A rainfall forecast  

E-Print Network [OSTI]

HESSD 5, 183­218, 2008 A rainfall forecast model using Artificial Neural Network N. Q. Hung et al An artificial neural network model for rainfall forecasting in Bangkok, Thailand N. Q. Hung, M. S. Babel, S Geosciences Union. 183 #12;HESSD 5, 183­218, 2008 A rainfall forecast model using Artificial Neural Network N

Paris-Sud XI, Université de

309

Load forecast and treatment of conservation  

E-Print Network [OSTI]

conservation is implicitly incorporated in the short-term demand forecast? #12;3 Incorporating conservationLoad forecast and treatment of conservation July 28th 2010 Resource Adequacy Technical Committee in the short-term model Our short-term model is an econometric model which can not explicitly forecast

310

FINAL STAFF FORECAST OF 2008 PEAK DEMAND  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION FINAL STAFF FORECAST OF 2008 PEAK DEMAND STAFFREPORT June 2007 CEC-200 of the information in this paper. #12;Abstract This document describes staff's final forecast of 2008 peak demand demand forecasts for the respective territories of the state's three investor-owned utilities (IOUs

311

ELECTRICITY DEMAND FORECAST COMPARISON REPORT  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION ELECTRICITY DEMAND FORECAST COMPARISON REPORT STAFFREPORT June 2005 Gorin Principal Authors Lynn Marshall Project Manager Kae C. Lewis Acting Manager Demand Analysis Office Valerie T. Hall Deputy Director Energy Efficiency and Demand Analysis Division Scott W. Matthews Acting

312

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

313

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

314

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

SciTech Connect (OSTI)

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

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

2011-10-01T23:59:59.000Z

315

Wind Forecasting Improvement Project | Department of Energy  

Office of Environmental Management (EM)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) "of EnergyEnergyENERGYWomen Owned SmallOf TheViolations | Department of EnergyisWilliamForecasting

316

Forecast calls for better models | EMSL  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-SeriesFlickr Flickr Editor's note: Since theNational SupplementalFor theForecast

317

NREL: Resource Assessment and Forecasting - Facilities  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and Resources NREL resource assessment and forecasting

318

NREL: Resource Assessment and Forecasting - Research Staff  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the Contributions andData and Resources NREL resource assessment and forecastingResearch

319

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

E-Print Network [OSTI]

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

Genton, Marc G.

320

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

E-Print Network [OSTI]

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

Allan, Richard P.

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


321

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.21.2 Print Center for6.1.2

322

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.21.2 Print Center for6.1.21.2

323

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline2 Print1.2 Print Center

324

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline2 Print1.2 Print

325

Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines  

E-Print Network [OSTI]

1 Forecasting the Hourly Ontario Energy Price by Multivariate Adaptive Regression Splines H. In this paper, the MARS technique is applied to forecast the hourly Ontario energy price (HOEP). The MARS models values of the latest pre- dispatch price and demand information, made available by the Ontario

Cañizares, Claudio A.

326

ANN-based Short-Term Load Forecasting in Electricity Markets  

E-Print Network [OSTI]

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

Cañizares, Claudio A.

327

THE PREV AIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE; APPLICATIONS  

E-Print Network [OSTI]

THE PREV AIR SYSTEM, AN OPERATIONAL SYSTEM FOR LARGE SCALE AIR QUALITY FORECASTS OVER EUROPE Author ABSTRACT Since Summer 2003, the PREV'AIR system has been delivering through the Internet1 daily air quality forecasts over Europe. This is the visible part of a wider collaborative project

Paris-Sud XI, Université de

328

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

E-Print Network [OSTI]

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

Jamieson, Bruce

329

he long-term economic forecast calls for the continuation of the  

E-Print Network [OSTI]

T he long-term economic forecast calls for the continuation of the economic recovery in 2014 predicts a steady economic recovery for Southern Nevada from 2014 onward. The Las Vegas economy-Term Economic Forecast Figure 1: Total Employment (1990-2050) Source: Center for Business and Economic Research

Hemmers, Oliver

330

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

E-Print Network [OSTI]

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

Jacobson, Mark

331

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.2 Print2.25.4.13220.221.2

332

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.2 Print2.25.4.13220.221.21.2

333

Linear Diagnostics to Assess the Performance of an Ensemble Forecast System  

E-Print Network [OSTI]

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

Satterfield, Elizabeth A.

2011-10-21T23:59:59.000Z

334

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

335

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

336

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

337

2.1E Supplement  

E-Print Network [OSTI]

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

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

338

Beamline 3.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline 12.2.21 Print1

339

Beamline 3.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print2.1 Print Commercial

340

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.2 Print2.25.4.13220.22

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


341

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

SciTech Connect (OSTI)

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

KETUSKY, EDWARD

2005-10-31T23:59:59.000Z

342

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

343

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Office of InspectorConcentrating Solar Power Basics (TheEtelligence (SmartHome Kyoung's pictureFlintFlowerForecast

344

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

E-Print Network [OSTI]

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

Kulkarni, Siddhivinayak

2009-01-01T23:59:59.000Z

345

Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales  

E-Print Network [OSTI]

Risk Forecasting with GARCH, Skewed t Distributions, and Multiple Timescales Alec N. Kercheval describe how the histori- cal data can first be GARCH filtered and then used to calibrate parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Data and Stylized Facts . . . . . . . . . . . . . . . . . . . . . . . 16 3.3 GARCH Filter

346

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 and applications of power market simulation models around the world. Argonne's software tools are used extensively

Kemner, Ken

347

Development of a real-time quantitative hydrologic forecasting model  

E-Print Network [OSTI]

TIME ) Ftgure 2. porno(lal f loni daaaga reduction aada possible hy a gives ruspanav ties (acr rrata forecast Ivad (Iacl. The do(ted ll?es In tha figure gives wt essnple uhlclt ahoun that as tha fnrecast land tlaa Incrva. as frua 4 to l4 hours...

Bell, John Frank

1986-01-01T23:59:59.000Z

348

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.2

349

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.21.2 Print Center for X-Ray

350

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.21.2 Print Center for

351

Beamline 6.1.2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.15.3.2.21.2 Print Center

352

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

353

Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids  

E-Print Network [OSTI]

1 Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids Yogesh Simmhan, prasanna}@usc.edu I. INTRODUCTION Smart Power Grids exemplify an emerging class of Cyber Physical-on paradigm to support operational needs. Smart Grids are an outcome of instrumentation, such as Phasor

Prasanna, Viktor K.

354

Exploiting weather forecasts for sizing photovoltaic energy bids  

E-Print Network [OSTI]

1 Exploiting weather forecasts for sizing photovoltaic energy bids Antonio Giannitrapani, Simone for a photovoltaic (PV) power producer taking part into a competitive electricity market characterized by financial set from an Italian PV plant. Index Terms--Energy market, bidding strategy, photovoltaic power

Giannitrapani, Antonello

355

Scenario Generation for Price Forecasting in Restructured Wholesale Power Markets  

E-Print Network [OSTI]

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

Tesfatsion, Leigh

356

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

357

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

358

Wind Power Forecasting Data  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear SecurityTensile Strain Switched FerromagnetismWaste and MaterialsWenjun DengWISPWind Industry Soars to New1Wind

359

1 Basic Notions N = {1, 2, . . . }, Z = {0, 1, 2, . . . } Z+ = {0, 1, 2, . . . } = N {0}  

E-Print Network [OSTI]

Induction (PMI): A statement P about Z+ is true if (i) P holds for n = 0; and (ii) If P holds for all m problems than hard ones.) 1 #12;Remark: In fact, PMI and WOA are equivalent. Try to show PMI WOA. Theorem: Every n N is uniquely written as n = r i=1 pmi i , with each pi prime and mi > 0. Proof of unique

Ramakrishnan, Dinakar

360

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

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


361

Wind Power Forecasting  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsrucLasDelivered energy consumption byAbout PrintableBlenderWhatFellows - PastFarmWind

362

Geothermal wells: a forecast of drilling activity  

SciTech Connect (OSTI)

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

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

1981-07-01T23:59:59.000Z

363

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...

364

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...

365

Solid low-level waste forecasting guide  

SciTech Connect (OSTI)

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

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

1995-03-01T23:59:59.000Z

366

The Value of Wind Power Forecasting  

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

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

367

Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations  

E-Print Network [OSTI]

Wind Power Forecasting andWind Power Forecasting and Electricity Market Operations Audun Botterud://www.dis.anl.gov/projects/windpowerforecasting.html IAWind 2010 Ames, IA, April 6, 2010 #12;Outline Background Using wind power forecasts in market operations ­ Current status in U.S. markets ­ Handling uncertainties in system operations ­ Wind power

Kemner, Ken

368

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

E-Print Network [OSTI]

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

Kamat, Vineet R.

369

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

370

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline21 Print Berkeley Center

371

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May JunDatastreamsmmcrcalgovInstrumentsruc DocumentationP-Series to someone6 M. Babzien, I. Ben-Zvi, P.2.2 Beamline21 Print Berkeley

372

table1.2_02  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr May Jun Jul(Summary) " ,"ClickPipelinesProved Reserves (Billion Cubic Feet)WyomingSquare Feet 50,001 toNew1 First Use of2

373

Beamline 3.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground High2.0.1 Print

374

1 Basic Notions N = {1, 2, . . . }, Z = {0, 1, 2, . . . } # Z+ = {0, 1, 2, . . . } = N # {0}  

E-Print Network [OSTI]

Induction (PMI): A statement P about Z+ is true if (i) P holds for n = 0; and (ii) If P holds for all m often apply it to infinte sets; this is accepting the WOA. Lemma: WOA#PMI (for Z+ ). Proof: Suppose problems than hard ones.) 1 #12; Remark: In fact, PMI and WOA are equivalent. Try to show PMI# WOA. Theorem

Ramakrishnan, Dinakar

375

Olga Bilenko2 Dmitri Gavrilov1  

E-Print Network [OSTI]

Olga Bilenko2 Dmitri Gavrilov1 Boris Gorbovitski2 Vera Gorfinkel1 Michael Gouzman1 Georgy Gudkov1 Vyacheslav Khozikov1 Olga Khozikov1 Olga Kosobokova1 Nadia Lifshitz2 Serge Luryi1 Andrew Stepoukhovitch1 are important for this discussion, here and elsewhere in this paper we present as-received software

Luryi, Serge

376

, Web . Web Google[1] CLEVER[2] ,  

E-Print Network [OSTI]

Web 1 , Web , Web , Web . Web , Web , Web . , , . , , , . , , 1 . , . , 1 , . , , Web . , , . Web . Google[1] CLEVER[2] , . , 1 , [3]. , . Amitay , HTML , . , , [4]. , Web . , [5]. , HTML Amitay . , , , 1: Web . 2 , · Web . , , 200 . , . , 10 . , . , 1 . , . , . 2.1 1: Web 14 7 386 296 27.6 42.3 #12

Shirai, Kiyoaki

377

StackOverview 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  

E-Print Network [OSTI]

17 Web #12;Web Web 81% Web Web Web Web StackOverview #12;1 1 2 Web 3 2.2.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Web . . . . . . . . . . . . . . . . . . . . . . 4 . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Web 6 3.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3

Tanaka, Jiro

378

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

379

Probabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging  

E-Print Network [OSTI]

is to issue deterministic forecasts based on numerical weather prediction models. Uncertainty canProbabilistic Wind Speed Forecasting using Ensembles and Bayesian Model Averaging J. Mc discretization than is seen in other weather quantities. The prevailing paradigm in weather forecasting

Washington at Seattle, University of

380

HIERARCHY OF PRODUCTION DECISIONS Forecasts of future demand  

E-Print Network [OSTI]

HIERARCHY OF PRODUCTION DECISIONS Forecasts of future demand Aggregate plan Master production Planning and Forecast Bias · Forecast error seldom is normally distributed · There are few finite planning

Brock, David

Note: This page contains sample records for the topic "forecast 2 1" from the National Library of EnergyBeta (NLEBeta).
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to obtain the most current and comprehensive results.


381

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

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

Parsons, Simon

382

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network [OSTI]

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

383

Environmental Chemistry 2.1 INTRODUCTION  

E-Print Network [OSTI]

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

Kammen, Daniel M.

384

1 Algebra 2 2 Algebra 3  

E-Print Network [OSTI]

and Fourier Analysis 15 15 Probability 16 16 Probability 17 17 Apprentice/Undecided 18 18 Apprentice/Undecided 19 19 Apprentice/Undecided 20 1 #12;1 Algebra Mentors · Saravanan Thiyagarajan (avan

May, J. Peter

385

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

E-Print Network [OSTI]

of contractors in the HVAC market will certainly have anational version of the HVAC market share decision model,equipment 4.5. HVAC Equipment Market Shares We now define

Johnson, F.X.

2010-01-01T23:59:59.000Z

386

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

E-Print Network [OSTI]

HVAC equipment as constrained by efficiency standards and marketand HVAC equipment as a result of the market; accounts foror HVAC system (by fuel type). New home market shares data

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

387

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

E-Print Network [OSTI]

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

Johnson, F.X.

2010-01-01T23:59:59.000Z

388

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

E-Print Network [OSTI]

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

389

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

E-Print Network [OSTI]

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

Johnson, F.X.

2010-01-01T23:59:59.000Z

390

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

E-Print Network [OSTI]

residential home heating equipment, depending on product class and size. Figure E.6b: Electric Heat Pump

Johnson, F.X.

2010-01-01T23:59:59.000Z

391

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

E-Print Network [OSTI]

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

Johnson, F.X.

2010-01-01T23:59:59.000Z

392

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

E-Print Network [OSTI]

30% of electricity consumption, 70% of natural gas consumption and 90% of oil consumption in the U by the Electric Power Research Institute (McMenamin et al. 1992). This modeling framework treats space consumption in residences (EIA 1993). This report is primarily methodological in nature, taking the reader

393

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

E-Print Network [OSTI]

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

394

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

E-Print Network [OSTI]

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

395

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

E-Print Network [OSTI]

~ unitary central air and heat pumps and secondary ~ roomSystem MH SF MF Central Air Heat Pump No Central Air Source:MF SSF LSF North Central Air Heat Pump No Central Air South

Johnson, F.X.

2010-01-01T23:59:59.000Z

396

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

E-Print Network [OSTI]

non-central residential home heating equipment (GAMA1992). (AFUE for residential home heating equipment, depending onManufactured Home Room Heating Market Shares". Lawrence

Johnson, F.X.

2010-01-01T23:59:59.000Z

397

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

E-Print Network [OSTI]

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

398

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

E-Print Network [OSTI]

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

Johnson, F.X.

2010-01-01T23:59:59.000Z

399

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

E-Print Network [OSTI]

of electric or gas water heater EFFIC Average householdfreezers, clothes dryers, water heaters, clothes washers,Freezers Refrigerators Water Heaters Dishwashers Clothes

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

400

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

E-Print Network [OSTI]

45 rural demands for travel and energy.46 The U.S. Energy Information Administration (EIA 2005 2030 household energy 26 demands and GHG emissions estimates are compared under five different land use the highest rates of increase. Average energy consumption per household is estimated to fall over 30 time (by

Kockelman, Kara M.

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


401

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

E-Print Network [OSTI]

E3c: Gas Boilers Installed Price by Size and Efficiency AFUEE3b: Gas Boiler Installed Price by Efficiency Figure E-3a:E.4c: Oil Boiler Installed Price by Size and Efficiency AFUE

Johnson, F.X.

2010-01-01T23:59:59.000Z

402

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

E-Print Network [OSTI]

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

Johnson, F.X.

2010-01-01T23:59:59.000Z

403

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

E-Print Network [OSTI]

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

404

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

E-Print Network [OSTI]

US DOE. 1995a. Annual Energy Outlook 1995, with ProjectionsAdministration (ELA) 1995 Annual Energy Outlook (AEO); 1990of Energy's Annual Energy Outlook ( US DOE 1995a). A l l

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

405

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

E-Print Network [OSTI]

$/household 10e3 Site Energy Prices Electricity ElectricityAverage electricity price Average household disposableAverage price of electricity Average household disposable

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

406

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

E-Print Network [OSTI]

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

Johnson, F.X.

2010-01-01T23:59:59.000Z

407

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

E-Print Network [OSTI]

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

408

Sixth Northwest Conservation & Electric Power Plan Draft Wholesale Power Price Forecasts  

E-Print Network [OSTI]

Price Forecasts 4. Updated load-resource balance by zones\\ regions Energy Capacity 5. Impact Higher Coal Prices Medium Long-term Trend Forecasts for PNW Zones 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1 Northwest Power and Conservation Council Comparison of Annual Average Energy Draft 6th Plan vs. Interim

409

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

E-Print Network [OSTI]

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

410

Homework (sections 2.1-2.4)  

E-Print Network [OSTI]

Inputoutput tables for this function are shown below. The table on the ... stored in glucose or other organic compounds.1 In the process, oxygen is produced.

411

) " (2010: 1. KI + I2 .  

E-Print Network [OSTI]

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

Maoz, Shahar

412

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

E-Print Network [OSTI]

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

Navon, Michael

413

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

Energy Savers [EERE]

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

414

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

E-Print Network [OSTI]

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

Heinemann, Detlev

415

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

E-Print Network [OSTI]

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

Mallet, Vivien

416

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

417

Inverse Modelling to Forecast Enclosure Fire Dynamics  

E-Print Network [OSTI]

. This thesis proposes and studies a method to use measurements of the real event in order to steer and accelerate fire simulations. This technology aims at providing forecasts of the fire development with a positive lead time, i.e. the forecast of future events...

Jahn, Wolfram

418

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

419

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

420

-Assessment of current water conditions -Precipitation Forecast  

E-Print Network [OSTI]

#12;-Assessment of current water conditions - Precipitation Forecast - Recommendations for Drought of the mountains, so early demand for irrigation water should be minimal as we officially move into spring. Western, it is forecast to bring wet snow to the eastern slope of the Rockies, with less accumulations west of the divide

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


421

A NEW APPROACH FOR EVALUATING ECONOMIC FORECASTS  

E-Print Network [OSTI]

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

Vertes, Akos

422

1 , 230031 2 430074; zhigangzeng@163.com  

E-Print Network [OSTI]

> ,/|)||(|21 1 * +++ = iij n j iji cbax },,2,1{ li L (8) -+-- = kkj n j kjk cbax /|)||(|21 1 * },,1{ nlk L

Hefei Institute of Intelligent Machines

423

Scuttlebutt Volume 2, No. 1  

E-Print Network [OSTI]

, of the USS Genesis. To CPT Kieran Bock and his crew, the officers and crew of the Southern Cross send their very best wishes and congratulations, and we wish them fair seas and LOTS of stars to follow. The ScuttleButt Volume 2, Issue 1 5 USS Atlantis... will be forwarded separately to each individual member. Please forward any enquiries to the Intel Officer - ENS Ashley Walker on alwalk78@optusnet.com.au. ENS Ashley Walker Operations Intelligence Officer Welcoming New Crewmembers Savannah Clark Daniel...

2008-01-01T23:59:59.000Z

424

Large Scale Structure Forecast Constraints on Particle Production During Inflation  

E-Print Network [OSTI]

Bursts of particle production during inflation provide a well-motivated mechanism for creating bump like features in the primordial power spectrum. Current data constrains these features to be less than about 5% the size of the featureless primordial power spectrum at wavenumbers of about 0.1 h Mpc^{-1}. We forecast that the Planck cosmic microwave background experiment will be able to strengthen this constraint to the 0.5% level. We also predict that adding data from a square kilometer array (SKA) galaxy redshift survey would improve the constraint to about the 0.1% level. For features at larger wave-numbers, Planck will be limited by Silk damping and foregrounds. While, SKA will be limited by non-linear effects. We forecast for a Cosmic Inflation Probe (CIP) galaxy redshift survey, similar constraints can be achieved up to about a wavenumber of 1 h Mpc^{-1}.

Teeraparb Chantavat; Christopher Gordon; Joseph Silk

2011-04-26T23:59:59.000Z

425

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

E-Print Network [OSTI]

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

Vrugt, Jasper A.

426

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. ,irradianceforecastsforsolarenergyapplicationsbasedonusingsatellitedata. SolarEnergy67:1?3,139?150.

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

427

Large-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random Fields  

E-Print Network [OSTI]

smart grid operations [1]. Despite their ubiquity and the complexity of many forecasting methods, most predictions of upcoming values, typically to minimize a metric such as root mean squared error. However

Kolter, J. Zico

428

1. Objetivo 2 2. O que o ABNTColeo 2  

E-Print Network [OSTI]

. Visualizando os dados da norma 11 7.1 Visualização da norma 11 7.2. Referências 12 7.3. Colaboração 12 7 que há de mais moderno, com base na plataforma Microsoft.NET que oferece, entre outras vantagens período de publicação #12;11 7. Visualizando dados da norma Ao clicar no link aparecerão suas principais

Floeter, Sergio Ricardo

429

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

E-Print Network [OSTI]

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

Lin, Jiang

2008-01-01T23:59:59.000Z

430

Section 2: Biomechanics 1 Section 2: Biomechanics  

E-Print Network [OSTI]

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

Kohlenbach, Ulrich

431

1-10 nM E2 E2 30 E2  

E-Print Network [OSTI]

076 1. E2 E2 E2 E2 2. E2 E2 2 E2 1 1-10 nM E2 5), 7) E2 30 E2 7) E2 512076-0792011 Modulation of Learning and Memory slowly but also rapidly. Slow actions of estradiol (E2) occur via nuclear receptors (ER), while rapid E2

Kawato, Suguru

432

Vo l . 4 6 , N o s . 2 & 3 , 2 0 1 3 c o n t e n t s  

E-Print Network [OSTI]

potential f e a t u r e s 2 Multi-faceted forecasting 6 3D printing rises to the occasion 10 Tag-team R&D 12

Pennycook, Steve

433

Earthquake Forecast via Neutrino Tomography  

E-Print Network [OSTI]

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

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

2011-03-29T23:59:59.000Z

434

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

SciTech Connect (OSTI)

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

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

2013-06-15T23:59:59.000Z

435

3 DMC 1 -4 DMC 2 -  

E-Print Network [OSTI]

. 5. 8 1. 2. Modeling of Reactive Distillation Column 3. Simulation Results of RD system for DMC-phosgene process. [ 2-1 ] (Stoichiometric lines) [ 2-2 ] (Distillation lines) [ 2-3 ] [ 2-4 ] a + b 2c control PI control [ 8-15 ] Feed ratio control PI control [ 8-16 ] MPC #12;[ 8

Hong, Deog Ki

436

memphis.edu/law A 2 0 1 2 -2 0 1 3  

E-Print Network [OSTI]

memphis.edu/law A 2 0 1 2 - 2 0 1 3 #12;B www.memphis.edu/law The University of Memphis Cecil C are considering the Cecil C. Humphreys School of Law as you plan for a legal career. We are building upon our 3 The Cecil C. Humphreys School of Law: Preparation for Tomorrow The Cecil C. Humphreys School

Dasgupta, Dipankar

437

Isothermal vapor-liquid equilibria for 2-methyl-2-butanol + 2-methyl-1-butanol + 1-pentanol  

SciTech Connect (OSTI)

Vapor-liquid equilibria (VLE) for 2-methyl-2-butanol + 2-methyl-1-butanol and 2-methyl-2-butanol + 2-methyl-1-butanol + 1-pentanol have been measured at 373.15 K. The binary VLE results have been correlated by different liquid-phase activity coefficient models. The binary interaction parameters obtained from Wilson, NRTL, and UNIQUAC models in this and a previously study are used to predict the VLE data for the ternary system. Vapor-liquid equilibrium (VLE) data are necessary for the design of distillation processes.

Aucejo, A.; Burguet, M.C.; Monton, J.B.; Munoz, R.; Sanchotello, M.; Vazquez, M.I. (Univ. of Valencia (Spain). Dept. de Ingenieria Quimica)

1994-07-01T23:59:59.000Z

438

Rev. 1 Page 1 of 2  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security Administration the1 -the Mid-Infrared0 Resource Program September 2010 B O N N E

439

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

440

Quarter 1, 2012 Page 1 of 2  

E-Print Network [OSTI]

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

Ford, James

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


441

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

442

A New Measure of Earnings Forecast Uncertainty Xuguang Sheng  

E-Print Network [OSTI]

A New Measure of Earnings Forecast Uncertainty Xuguang Sheng American University Washington, D of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast available to analysts at the time they make their forecasts. Hence, it alleviates some of the limitations

Kim, Kiho

443

AN ANALYSIS OF FORECAST BASED REORDER POINT POLICIES : THE BENEFIT  

E-Print Network [OSTI]

AN ANALYSIS OF FORECAST BASED REORDER POINT POLICIES : THE BENEFIT OF USING FORECASTS Mohamed Zied Ch^atenay-Malabry Cedex, France Abstract: In this paper, we analyze forecast based inventory control policies for a non-stationary demand. We assume that forecasts and the associated uncertainties are given

Paris-Sud XI, Université de

444

The Complexity of Forecast Testing Lance Fortnow # Rakesh V. Vohra +  

E-Print Network [OSTI]

The Complexity of Forecast Testing Lance Fortnow # Rakesh V. Vohra + Abstract Consider a weather forecaster predicting a probability of rain for the next day. We consider tests that given a finite sequence of forecast predictions and outcomes will either pass or fail the forecaster. Sandroni shows that any test

Fortnow, Lance

445

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

446

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series  

E-Print Network [OSTI]

Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural demand time series based only on data for six years to forecast the demand for the seventh year. Both networks, Neural networks, Modeling, Forecasting, Energy demand, Time series forecasting, Power system

Abdel-Aal, Radwan E.

447

PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022  

E-Print Network [OSTI]

PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022 AUGUST 2011 CEC-200-2011-011-SD CALIFORNIA or adequacy of the information in this report. #12;i ACKNOWLEDGEMENTS The staff demand forecast forecast. Mitch Tian prepared the peak demand forecast. Ravinderpal Vaid provided the projections

448

Strategic safety stocks in supply chains with evolving forecasts  

E-Print Network [OSTI]

we have an evolving demand forecast. Under assumptions about the forecasts, the demand process their supply chain operations based on a forecast of future demand over some planning horizon. Furthermore stock inventory in a supply chain that is subject to a dynamic, evolving demand forecast. In particular

Graves, Stephen C.

449

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST Energy Demand 2008-2018 forecast supports the analysis and recommendations of the 2007 Integrated Energy Commission demand forecast models. Both the staff draft energy consumption and peak forecasts are slightly

450

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

451

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

452

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

453

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

454

STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES 2005 TO 2018 Mignon Marks Principal Author Mignon Marks Project Manager David Ashuckian Manager ELECTRICITY ANALYSIS OFFICE Sylvia Bender Acting Deputy Director ELECTRICITY SUPPLY DIVISION B.B. Blevins Executive Director

455

Essays in International Macroeconomics and Forecasting  

E-Print Network [OSTI]

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

Bejarano Rojas, Jesus Antonio

2012-10-19T23:59:59.000Z

456

1 Introduction 1 2 Physics of n -n Oscillation 4  

E-Print Network [OSTI]

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

Tokyo, University of

457

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

E-Print Network [OSTI]

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

458

Dynamic Algorithm for Space Weather Forecasting System  

E-Print Network [OSTI]

for the designation as UNDERGRADUATE RESEARCH SCHOLAR April 2010 Major: Nuclear Engineering DYNAMIC ALGORITHM FOR SPACE WEATHER FORECASTING SYSTEM A Junior Scholars Thesis by LUKE DUNCAN FISCHER Submitted to the Office of Undergraduate... 2010 Major: Nuclear Engineering iii ABSTRACT Dynamic Algorithm for Space Weather Forecasting System. (April 2010) Luke Duncan Fischer Department of Nuclear Engineering Texas A&M University Research Advisor: Dr. Stephen Guetersloh...

Fischer, Luke D.

2011-08-08T23:59:59.000Z

459

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

E-Print Network [OSTI]

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

Meskhidze, Nicholas

460

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline1 Print1

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


461

1 function fe2d_r 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  

E-Print Network [OSTI]

_ki = triangle_area*s1/(h3*h1); 100 K_ik = K_ki; 101 K_kj = triangle_area*s2/(h3*h2); 102 K_jk = K_kj; 103 K

Garvie, Marcus R

462

1 function fe2dx_d 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  

E-Print Network [OSTI]

_ki = triangle_area*s1/(h3*h1); 100 K_ik = K_ki; 101 K_kj = triangle_area*s2/(h3*h2); 102 K_jk = K_kj; 103 K

Garvie, Marcus R

463

1 function fe2d_n 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  

E-Print Network [OSTI]

_ki = triangle_area*s1/(h3*h1); 98 K_ik = K_ki; 99 K_kj = triangle_area*s2/(h3*h2); 100 K_jk = K_kj; 101 K

Garvie, Marcus R

464

1 function fe2dx_n 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  

E-Print Network [OSTI]

_ki = triangle_area*s1/(h3*h1); 98 K_ik = K_ki; 99 K_kj = triangle_area*s2/(h3*h2); 100 K_jk = K_kj; 101 K

Garvie, Marcus R

465

1 function fe2dx_r 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  

E-Print Network [OSTI]

_ki = triangle_area*s1/(h3*h1); 100 K_ik = K_ki; 101 K_kj = triangle_area*s2/(h3*h2); 102 K_jk = K_kj; 103 K

Garvie, Marcus R

466

Beispiel 19. Sei v L2([-1, 1]) eine verrauschte Funktion. Sei u eine entrauschte Abschatzung von v mit u L2([-1, 1]) und u L2([-1, 1]), die durch die Minimierung von  

E-Print Network [OSTI]

, da die folgende Gleichung gilt: J(u)(a1w1 + a2w2) = 2 +1 -1 {[a1w1(x) + a2w2(x)][u(x) - v(x)] + µ[a1w

Keeling, Stephen L.

467

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

468

Use of Data Denial Experiments to Evaluate ESA Forecast Sensitivity Patterns  

SciTech Connect (OSTI)

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

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

2011-09-13T23:59:59.000Z

469

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline1 Print

470

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline1 Print11

471

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline1 Print111

472

Beamline 8.2.1  

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

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473

2.1E Supplement  

E-Print Network [OSTI]

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

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

474

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline11 Print

475

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline11

476

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline111

477

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline1111 Print

478

Beamline 8.2.1  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth (AOD)ProductssondeadjustsondeadjustAboutScience ProgramBackground8.0.1 Print Surface and0.1 Beamline1111

479

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

SciTech Connect (OSTI)

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

Not Available

2008-06-01T23:59:59.000Z

480

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

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481

Volume 69, Numbers 1 & 2 (Complete)  

E-Print Network [OSTI]

EVENTEENTH- ENTURY EWS SPRING - SUMMER 2011 Vol. 69 Nos. 1&2 Including THE NEO-LATIN NEWS Vol. 59, Nos. 1&2 Seventeenth-Century newS VOLUME 68, Nos. 1&2 SPRING-SUMMER, 2010 SCN... University Matthew E. Davis, Texas A&M University EDITORIAL ASSISTANT Elise A. Beck, Texas A&M University contents volume 69, nos. 1&2 ................................ spring-summer, 2011 Helen Wilcox, ed. The English Poems of George Herbert. Review...

Dickson, Donald

2011-01-01T23:59:59.000Z

482

Volume 68, Numbers 1&2 (Complete)  

E-Print Network [OSTI]

EVENTEENTH- ENTURY EWS SPRING - SUMMER 2010 Vol. 68 Nos. 1&2 Including THE NEO-LATIN NEWS Vol. 58, Nos. 1&2 Seventeenth-Century newS VOLUME 68, Nos. 1&2 SPRING-SUMMER, 2010 SCN... of the Lake University E. Joe Johnson, Clayton State University EDITORIAL ASSISTANTS Jacob Schornick, Texas A&M University contents volume 68, nos. 1&2 ............................... spring-summer, 2010 John Considine, Dictionaries in Early Modern Europe...

Dickson, Donald

2010-01-01T23:59:59.000Z

483

Volume 60, Numbers 1&2 (Complete)  

E-Print Network [OSTI]

EVENTEENTH- ENTURY EWS SPRING - SUMMER 2002 Vol. 60 Nos. 1&2 budleafswbudleafse Including THE NEO-LATIN NEWS Vol. 50, Nos. 1&2 SEVENTEENTH-CENTURY NEWS VOLUME 60, Nos. 1&2 SPRING-SUMMER, 2002 SCN, an official organ of the Milton Society... Meter, Texas A&M University CONTENTS VOLUME 60, Nos. 1&2 SPRING-SUMMER, 2002 REVIEWS ?Vocation Homily, George Herbert, and the Cultural Criticism: A Review Essay? by JONATHAN NAUMAN...

Dickson, Donald

2002-01-01T23:59:59.000Z

484

Video Textures Arno Schodl1,2  

E-Print Network [OSTI]

Video Textures Arno Schodl1,2 Richard Szeliski2 David H. Salesin2,3 Irfan Essa1 1 Georgia type of medium, called a video texture, which has qualities somewhere between those of a photograph and a video. A video texture provides a continuous infinitely varying stream of images. While the individual

Meenakshisundaram, Gopi

485

2.1E Supplement  

E-Print Network [OSTI]

ECONO-LOW-LIMIT and ECONO-LOCKOUT, described below. FIXED be2. i E Update ECONOLOCKOUT accepts code-words Y E S and NL B L I M I T = 78 ECONO-LOCKOUT =YES M I N H G B R A

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

486

2.1E Supplement  

E-Print Network [OSTI]

I T Y - R A T E Command Cogeneration B L O C K - C H A R G Econsultant for the P l a n t Cogeneration a n d t h e R e fa n d chillers for cogeneration, (2) simpler f u n c t i o n

Winkelmann, F.C.

2010-01-01T23:59:59.000Z

487

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

E-Print Network [OSTI]

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

Granichin, Oleg

488

E=1/2Mv2 Pf Pb(Pf>Pb)  

E-Print Network [OSTI]

/2Mv2 , ( 3.1). 3.1 UF6 , Pf Pb(Pf>Pb) . i J2 = V1P1f V2P2f = M2 M1 1/2 x (1-x) . = y/(1-y) x/(1-x) = (M2/M1)1/2 UF6 = (M2/M1)1/2 =(238 UF6/235 UF6)1/2 = (352/349)1/2 = 1.00429 1 . 5% 900

Hong, Deog Ki

489

TTW 2-1-10  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmosphericNuclear Security AdministrationcontrollerNanocrystallineForeign ObjectOUR TECHNOLOGIES TECHNOLOGIES Signing, 20100,,

490

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

SciTech Connect (OSTI)

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

Not Available

1994-03-01T23:59:59.000Z

491

Alcohol Services Category #1 # Permit Applica2on Category #2  

E-Print Network [OSTI]

Alcohol Services Category #1 # Permit Applica2on Category #2 Category #3 If an outdoor event, a;ach UNL Police approved licensed area plan to Permit Request ADMINISTRATOR TO OBTAIN THE REQUIRED ALCOHOL SERVICES PERMIT FOR EACH EVENT

Powers, Robert

492

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

E-Print Network [OSTI]

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

Moon, Sue B.

493

Radiation in (2+1)-dimensions  

E-Print Network [OSTI]

In this paper we discuss the radiation equation of state $p=\\rho/2$ in (2+1)-dimensions. In (3+1)-dimensions the equation of state $p=\\rho/3$ may be used to describe either actual electromagnetic radiation (photons) as well as a gas of massless particles in a thermodynamic equilibrium (for example neutrinos). In this work it is shown that in the framework of (2+1)-dimensional Maxwell electrodynamics the radiation law $p=\\rho/2$ takes place only for plane waves, i.e. for $E = B$. Instead of the linear Maxwell electrodynamics, to derive the (2+1)-radiation law for more general cases with $E \

Mauricio Cataldo; Alberto A. Garca

2014-05-15T23:59:59.000Z

494

Certified Bitcoins Giuseppe Ateniese1,2  

E-Print Network [OSTI]

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

495

Do Investors Forecast Fat Firms? Evidence from the Gold Mining Industry  

E-Print Network [OSTI]

Economists Gold Price Forecasts, Australian Journal ofDo Investors Forecast Fat Firms? Evidence from the Gold

Borenstein, Severin; Farrell, Joseph

2006-01-01T23:59:59.000Z

496

Potential to Improve Forecasting Accuracy: Advances in Supply Chain Management  

E-Print Network [OSTI]

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

Datta, Shoumen

2008-07-31T23:59:59.000Z

497

Market perceptions of efficiency and news in analyst forecast errors  

E-Print Network [OSTI]

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

Chevis, Gia Marie

2004-11-15T23:59:59.000Z

498

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

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

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

499

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

E-Print Network [OSTI]

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

Grzybowski, Bartosz A.

500

Electric Grid - Forecasting system licensed | ornl.gov  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE:1 First Use of Energy for All Purposes (Fuel and Nonfuel),Feet) Year Jan Feb Mar Apr MayAtmospheric Optical Depth7-1D: Vegetation Proposed NewcatalystNeutronEnvironmentZIRKLEEFFECTS OFElaineElectric Grid - Forecasting system