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Note: This page contains sample records for the topic "forecasting wa id" 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.


1

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

2

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

3

WA_97_032_CHEMICAL_INDUSTRY_ENVIROMENTAL_TECHNOLOGY_PROJECTS...  

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

2CHEMICALINDUSTRYENVIROMENTALTECHNOLOGYPROJECTS.pdf WA97032CHEMICALINDUSTRYENVIROMENTALTECHNOLOGYPROJECTS.pdf WA97032CHEMICALINDUSTRYENVIROMENTALTECHNOLOGYPROJEC...

4

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

5

WA_00_030_ASE_AMERICAS_Request_to_Assign_Title_to_Waiver-Inv...  

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

WA1995019DONNELLYCORPORATIONWaiverofDomesticandFore.pdf WA1995018OPTICALCOATINGLABORATORYINCWaiverofDomesti.pdf WA03032RWESCHOTTSOLARINCWaiverof...

6

RAPID/Roadmap/12-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a State12-ID-a12-WA-a Live Wildlife

7

RAPID/Roadmap/12-WA-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a State12-ID-a12-WA-a Live

8

RAPID/Roadmap/13-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a13-ID-a State Land UseWA-a <

9

RAPID/Roadmap/14-WA-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a13-ID-a4-NV-c14-OR-dd4-WA-c

10

RAPID/Roadmap/14-WA-d | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a13-ID-a4-NV-c14-OR-dd4-WA-cd <

11

RAPID/Roadmap/14-WA-e | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a13-ID-a4-NV-c14-OR-dd4-WA-cd <e

12

WA_1995_018_OPTICAL_COATING_LABORATORY_INC_Waiver_of_Domesti...  

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

Publications WA1995019DONNELLYCORPORATIONWaiverofDomesticandFore.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995009AIRPRODUCTSANDCHEMICAL...

13

WA_04_057_CHEMICAL_RESEARCH_AND_LICENSING_CO_Waiver_of_Paten...  

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

& Publications WA04064VELOCYSINCWaiverofPatentRgithsUnderaDOECo.pdf WA04063AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04028AIRPRODUCTSANDCHEMICAL...

14

WA_02_021_H2GEN_INNOVATIONS_Waiver_of_Domestic_and_Foreign_P...  

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

WA02046QUESTAAIRTECHNOLOGIESWaiverofDomesticandFor.pdf WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA04034NUVERAFUELCELLSINCWaiver...

15

WA_1994010__SCHWITZER_U.S._INC_Waiver_of_Domestic_and_Foreig...  

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

Publications WA1994007KYOCERAINDUSTRIALCERAMICSCORPORATIONWaivero.pdf WA1994011EATONCORPORATIONWaiverofDomesticandForeign.pdf WA02028TRANECOWaiverofDomesti...

16

WA_04_009_ROCKWELL_SCIENTIFIC_CO_Wailve_of_Domestic_And_Fore...  

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

WA1995019DONNELLYCORPORATIONWaiverofDomesticandFore.pdf WA1995018OPTICALCOATINGLABORATORYINCWaiverofDomesti.pdf WA00030ASEAMERICASRequesttoAssign...

17

WA_1995_019_DONNELLY_CORPORATION_Waiver_of_Domestic_and_Fore...  

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

WA00030ASEAMERICASRequesttoAssignTitletoWaiver-Inv.pdf WA1995018OPTICALCOATINGLABORATORYINCWaiverofDomesti.pdf WA04009ROCKWELLSCIENTIFICCOWailve...

18

WA_1995_009_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...  

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

9AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995009AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995009AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti...

19

WA_1995_014_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...  

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

14AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995014AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995014AIRPRODUCTSANDCHEMICALSINCWaiverofDomest...

20

WA_1994_034_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...  

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

4034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDom...

Note: This page contains sample records for the topic "forecasting wa id" 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

WA_99_017_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Domestic_and_...  

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

9017AIRPRODUCTSANDCHEMICALSWaiverofDomesticand.pdf WA99017AIRPRODUCTSANDCHEMICALSWaiverofDomesticand.pdf WA99017AIRPRODUCTSANDCHEMICALSWaiverofDomesti...

22

WA_04_028_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_patent_Rights...  

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

8AIRPRODUCTSANDCHEMICALSWaiverofpatentRights.pdf WA04028AIRPRODUCTSANDCHEMICALSWaiverofpatentRights.pdf WA04028AIRPRODUCTSANDCHEMICALSWaiverofpatentRigh...

23

WA_00_007_COMBUSTION_ENGINEERING_INC_Waiver_of_Domestic_and_...  

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

07COMBUSTIONENGINEERINGINCWaiverofDomesticand.pdf WA00007COMBUSTIONENGINEERINGINCWaiverofDomesticand.pdf WA00007COMBUSTIONENGINEERINGINCWaiverofDomestica...

24

WA_98_005_WESTINGHOUSE_POWER_GENERATION_A_FORMER_DIVISION_OF...  

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

5WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98005WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98005WESTINGHOUSEPOWERGENERATIONAFORMERDIVISION...

25

WA_98_006_WESTINGHOUSE_POWER_GENERATION_A_FORMER_DIVISION_OF...  

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

6WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98006WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98006WESTINGHOUSEPOWERGENERATIONAFORMERDIVISION...

26

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....

27

Map ID  

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: VegetationEquipment Surfaces and InterfacesAdministrationManufacturingvitality throughSimulation of theID

28

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

29

WA_98_016_ABB_POWER_T_AND_D_COMPANY_Waiver_of_Domestic_and_F...  

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

More Documents & Publications Advance Patent Waiver W(A)2011-046 Advance Patent Waiver W(A)2009-016 WA96016AIRPRODUCTSANDCHEMICALSINCWaiverofDomestic...

30

WA_04_085_THE_BOEING_COMPANY_Waiver_of_domestic_and_Foreign_...  

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

More Documents & Publications Advance Patent Waiver W(A)2010-018 Advance Patent Waiver W(A)2007-012 WA99017AIRPRODUCTSANDCHEMICALSWaiverofDomesticand...

31

WA_00_025_PRAXAIR_INC_Waiver_Request.pdf | Department of Energy  

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

25PRAXAIRINCWaiverRequest.pdf WA00025PRAXAIRINCWaiverRequest.pdf WA00025PRAXAIRINCWaiverRequest.pdf More Documents & Publications WA00001PRAXAIRINCWaiverofDo...

32

11. CONTRACT ID CODE IPAGE1 OFI AMENDMENT OF SOLICITATION/MODIFICATION OF CONTRACT 2  

E-Print Network [OSTI]

11. CONTRACT ID CODE IPAGE1 OFI PAGES AMENDMENT OF SOLICITATION/MODIFICATION OF CONTRACT 2 2 County, WA 99352 10A. MODIFICATION OF CONTRACT/ ORDER NO. DUNS# 032987476 DE-AC05-76RL01830 ~ 10B. DATED AND APPROPRIATION DATA (Ifrequired} CHECK ONE D D D 13. THIS ITEM APPLIES ONLY TO MODIFICATIONS OF CONTRACTS

33

Advance Patent Waiver W(A)2005-006 | Department of Energy  

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

W(A)2005-006 More Documents & Publications Advance Patent Waiver W(A)2008-022 WA04079PRAXAIRINCWaiverofPatentRightsUnderaSubcon.pdf Advance Patent Waiver W(A)2011-063...

34

> 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

35

PO Box 2349 White Salmon, WA 98672  

E-Print Network [OSTI]

PO Box 2349 White Salmon, WA 98672 509.493.4468 www.newbuildings.org COMMERCIAL ROOFTOP HVAC ENERGY from utility-sponsored field service measures on small (typically 3-10 tons) commercial rooftop unitary utility-funded RTU service programs. New Buildings Institute (NBI) staff has been managing the research

36

WA_00_010_ROCKWELL_SCIENCE_CENTER_A_Subcontractor_of_SILICON...  

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

NTERASubcontractorofSILICON.pdf More Documents & Publications WA03011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA01034INGERSOLL-RANDENERGYSYSTEMSWaiverof...

37

WA_99_022_AIR_PRODUCTS_AND_CHEMICAL_Waiver_of_Domestic_and_F...  

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

9022AIRPRODUCTSANDCHEMICALWaiverofDomesticandF.pdf WA99022AIRPRODUCTSANDCHEMICALWaiverofDomesticandF.pdf WA99022AIRPRODUCTSANDCHEMICALWaiverofDomestic...

38

WA_02_015_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Patent_Ri...  

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

15AIRPRODUCTSANDCHEMICALSINCWaiverofPatentRi.pdf WA02015AIRPRODUCTSANDCHEMICALSINCWaiverofPatentRi.pdf WA02015AIRPRODUCTSANDCHEMICALSINCWaiverofPatent...

39

WA_04_063_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Patent_Rights...  

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

63AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04063AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04063AIRPRODUCTSANDCHEMICALSWaiverofPatentRig...

40

WA_04_083_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Patent_Rights...  

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

83AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04083AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04083AIRPRODUCTSANDCHEMICALSWaiverofPatentRig...

Note: This page contains sample records for the topic "forecasting wa id" 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_01_005__PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_patent...  

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

1005PRAXAIRINCWaiverofDomesticandForeignpatent.pdf WA01005PRAXAIRINCWaiverofDomesticandForeignpatent.pdf WA01005PRAXAIRINCWaiverofDomesticandForeign...

42

WA_01_022_PRAXAIR_INC_AND_BP_AMOCO_Waiver_of_Domestic_and_Fo...  

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

1022PRAXAIRINCANDBPAMOCOWaiverofDomesticandFo.pdf WA01022PRAXAIRINCANDBPAMOCOWaiverofDomesticandFo.pdf WA01022PRAXAIRINCANDBPAMOCOWaiverofDomestic...

43

WA_99_015_FORD_MOTOR_COMPANY_Waiver_of_Domestic_and_Foreign_...  

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

COMPANYWaiverofDomesticandForeign.pdf More Documents & Publications WA97038FORDMOTORCOMPANYWaiverofDomesticandForeign.pdf WA98008GENERALELECTRICCOMPANYWaive...

44

WA_03_021_DELPHI_AUTOMOTIVE_SYSTEMS_Waiver_of_Patent_Rights_...  

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

1DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA03021DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA03021DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRight...

45

WA_04_082_DELPHI_AUTOMOTIVE_SYSTEMS_Waiver_of_Patent_Rights_...  

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

82DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA04082DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA04082DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRigh...

46

WA_04_025_AIR_LIQUIDE_AMERICA_Waiver_of_Patent_Rights_under_...  

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

25AIRLIQUIDEAMERICAWaiverofPatentRightsunder.pdf WA04025AIRLIQUIDEAMERICAWaiverofPatentRightsunder.pdf WA04025AIRLIQUIDEAMERICAWaiverofPatentRightsund...

47

WA_1993_003_EATON_CORPORATION_Waiver_of_Domestic_and_Foreign...  

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

3003EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1993003EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1993003EATONCORPORATIONWaiverofDomesticandFor...

48

WA_1994_011_EATON_CORPORATION_Waiver_of_Domestic_and_Foreign...  

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

1EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1994011EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1994011EATONCORPORATIONWaiverofDomesticandForeign...

49

WA_04_034_NUVERA_FUEL_CELLS_INC_Waiver_of_Domestic_and_Forei...  

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

34NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04034NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04034NUVERAFUELCELLSINCWaiverofDomesticandFo...

50

WA_04_041_NUVERA_FUEL_CELLS_INC_Waiver_of_Domestic_and_Forei...  

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

41NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04041NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04041NUVERAFUELCELLSINCWaiverofDomesticandFo...

51

WA_-01_001_PHILLIPS_PETROLEUM_Waiver_of_Domestic_and_Foreign...  

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

-01001PHILLIPSPETROLEUMWaiverofDomesticandForeign.pdf WA-01001PHILLIPSPETROLEUMWaiverofDomesticandForeign.pdf WA-01001PHILLIPSPETROLEUMWaiverofDomesticand...

52

WA_04_080_HYBRID_POWER_GENERATION_SYSTEMS_Waiver_of_Patent_R...  

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

80HYBRIDPOWERGENERATIONSYSTEMSWaiverofPatentR.pdf WA04080HYBRIDPOWERGENERATIONSYSTEMSWaiverofPatentR.pdf WA04080HYBRIDPOWERGENERATIONSYSTEMSWaiverofPaten...

53

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

54

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

55

> 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

56

,"Sumas, WA Natural Gas Pipeline Imports From Canada (MMcf)"  

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

Imports From Canada (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Sumas, WA...

57

Recipient: County of Kitsap, WA ENERGY EFFICIENCY AND CONSERVATION...  

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

it: EE 000 0853 Recipient: County of Kitsap, WA ENERGY EFFICIENCY AND CONSERVATION BLOCK GRANTS NEPA COMPLIANCE FORM Activities Determination Categorical Exclusion Reviewer's...

58

FITCH RATES ENERGY NORTHWEST (WA) ELECTRIC REV REF BONDS 'AA...  

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

FITCH RATES ENERGY NORTHWEST (WA) ELECTRIC REV REF BONDS 'AA'; OUTLOOK STABLE Fitch Ratings-Austin-08 April 2015: Fitch Ratings assigns 'AA' ratings to the following Energy...

59

Category:Seattle, WA | 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 SolarElectricEnergyCTBarreis aCallahanWind FarmAddSRML Map Files Jump to:WA Jump to:

60

Vol. 0 18 (??? 2011) VM Shadow: IDS  

E-Print Network [OSTI]

Shadow proc FUSE8) Shadow proc FUSE Shadow proc Transcall U ID stat status U ID U U task struct 5 init task task ID stat status task struct thread group Transcall cmdline 5 task struct Transcall task structVol. 0 1­8 (??? 2011) VM Shadow: IDS 1 1,2 IDS IDS IDS IDS IDS VM Shadow VM Shadow IDS OS proc IDS

Kourai, Kenichi

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


61

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...

62

RAPID/Roadmap/6-ID-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a State6-CO-b Construction Stormb6-ID-b

63

RAPID/Roadmap/7-ID-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e BLM/CEC Joint7-HI-bID-c <

64

RAPID/Roadmap/8-ID-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e8-HI-a Transmission Siting8-ID-c

65

Advance Patent Waiver W(A)2010-042 | Department of Energy  

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

Waiver W(A)2010-042 More Documents & Publications Advance Patent Waiver W(A)2005-023 WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf ClassWaiverWC-2003-001.pdf...

66

Advance Patent Waiver W(A)2012-003 | Department of Energy  

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

Waiver W(A)2012-003 More Documents & Publications Advance Patent Waiver W(A)2013-019 Class Patent Waiver W(C)2012-003 WA02048EATONCORPORATIONWaviverofPatentRightsUnderA...

67

waTer economics. environmenTand Policy  

E-Print Network [OSTI]

41 cenTre for waTer economics. environmenTand Policy "Men and nature must work hand in hand and public policy insights for the supply, demand, management, and governance of water CWEEP pronounced `sweep' as in to survey so as to obtain a whole and continuous view of the world #12;42 waTer is a cri

Botea, Adi

68

WA_03_011_ROCKWELL_AUTOMATION_Waiver_of_Patent_Rights_Under_...  

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

3011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA03011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA03011ROCKWELLAUTOMATIONWaiverofPatentRights...

69

WA_04_007_OSHKOSH_TRUCK_CORP_Waiver_of_Patent_Rights_Under_N...  

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

WaiverofPatentRightsUnderN.pdf More Documents & Publications WA03011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA04008GENERALMOTORSCORPWaiverofPatentRi...

70

WA_00_008_PLUG_POWER_Waiver_of_Patent_Rights_in_Performance_...  

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

POWERWaiverofPatentRightsinPerformance.pdf More Documents & Publications WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNVO.pdf WA99022AIRPRODUCTSANDCHEMICAL...

71

WA_99_012_AIR_PRODUCTS_Waiver_of_Patent_Rights_Under_AN_NVO_...  

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

2AIRPRODUCTSWaiverofPatentRightsUnderANNVO.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNVO.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNV...

72

WA_1994_027_FORD_MOTOR_COMPANY_Waiver_of_Domestic_and_Foreig...  

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

2FORDMOTORCOMPANYWaiverofDomesticandForeig.pdf WA97038FORDMOTORCOMPANYWaiverofDomesticandForeign.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNVO...

73

WA_00_018_PRAXAIR_Waive_of_Domestic_and_Foreign_Invention_Ri...  

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

18PRAXAIRWaiveofDomesticandForeignInventionRi.pdf WA00018PRAXAIRWaiveofDomesticandForeignInventionRi.pdf WA00018PRAXAIRWaiveofDomesticandForeignInvention...

74

WA_02_046_QUESTA_AIR_TECHNOLOGIES_Waiver_of_Domestic_and_For...  

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

IRTECHNOLOGIESWaiverofDomesticandFor.pdf More Documents & Publications WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA02021H2GENINNOVATIONSWaiverof...

75

WA_03_024_PRAXAIR_Waiver_of_Domestic_and_Foreign_Invention_R...  

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

24PRAXAIRWaiverofDomesticandForeignInventionR.pdf WA03024PRAXAIRWaiverofDomesticandForeignInventionR.pdf WA03024PRAXAIRWaiverofDomesticandForeignInventio...

76

WA_01_039_PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_Patent_...  

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

1039PRAXAIRINCWaiverofDomesticandForeignPatent.pdf WA01039PRAXAIRINCWaiverofDomesticandForeignPatent.pdf WA01039PRAXAIRINCWaiverofDomesticandForeignP...

77

WA_02_055_PRAXAIR_Waiver_of_Domestic_and_Foreign_Patent_Righ...  

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

2055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA02055PRAXAIRWaiverofDomesticandForeignPaten...

78

WA_00_001_PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_Inventi...  

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

01PRAXAIRINCWaiverofDomesticandForeignInventi.pdf WA00001PRAXAIRINCWaiverofDomesticandForeignInventi.pdf WA00001PRAXAIRINCWaiverofDomesticandForeignInve...

79

WA_04_079_PRAXAIR_INC_Waiver_of_Patent_Rights_Under_a_Subcon...  

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

04079PRAXAIRINCWaiverofPatentRightsUnderaSubcon.pdf WA04079PRAXAIRINCWaiverofPatentRightsUnderaSubcon.pdf WA04079PRAXAIRINCWaiverofPatentRightsUndera...

80

WA_04_074_EATON_CORPORATION_Waiver_of_Domestic_and_Foreign_I...  

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

74EATONCORPORATIONWaiverofDomesticandForeignI.pdf WA04074EATONCORPORATIONWaiverofDomesticandForeignI.pdf WA04074EATONCORPORATIONWaiverofDomesticandForeig...

Note: This page contains sample records for the topic "forecasting wa id" 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

WA_02_048_EATON_CORPORATION_Waviver_of_Patent_Rights_Under_A...  

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

48EATONCORPORATIONWaviverofPatentRightsUnderA.pdf WA02048EATONCORPORATIONWaviverofPatentRightsUnderA.pdf WA02048EATONCORPORATIONWaviverofPatentRightsUnde...

82

WA_1994_017_GOLDEN_TECHNOLOGIES_COMPANY_Waiver_of_Domestic_a...  

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

for An Advance Waiver of Domestic and Foreign Rights. January 10, 1995 WA1994011EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1994014GOLDENTECHNOLOGIESCOMPA...

83

WA_04_059_EATON_CORPORATION_Waiver_of_Patent_Rights_Under_a_...  

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

59EATONCORPORATIONWaiverofPatentRightsUndera.pdf WA04059EATONCORPORATIONWaiverofPatentRightsUndera.pdf WA04059EATONCORPORATIONWaiverofPatentRightsUnder...

84

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

85

Isotopic Studies of Contaminant Transport at the Hanford Site, WA  

E-Print Network [OSTI]

MR-0132. Westinghouse Hanford Company, Richland WA. Bretz,in recharge at the Hanford Site. Northwest Science. 66:237-M.J. , ed. 2000. Hanford Site groundwater Monitoring

Christensen, J.N.; Conrad, M.E.; DePaolo, D.J.; Dresel, P.E.

2008-01-01T23:59:59.000Z

86

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

87

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

88

Department ID Conversion Chart -Boise State University -Revised 7/01 NEW Dept. ID OLD Dept. ID Description  

E-Print Network [OSTI]

Department ID Conversion Chart - Boise State University - Revised 7/01 NEW Dept. ID OLD Dept. ID;Department ID Conversion Chart - Boise State University - Revised 7/01 NEW Dept. ID OLD Dept. ID Description - Cheatgrss 006G106041 00066041 Stat Analysis Grn Strip Mod #9 006G106044 00066044 Threats To Collared Lizards

Barrash, Warren

89

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

90

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

91

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

92

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

93

RAPID/Roadmap/19-WA-e | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎gWA-c Transfer or Change9-WA-e

94

RAPID/Roadmap/3-WA-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a <3-FD-d3-WA-b Land Access Overview 3-WA-b

95

RAPID/Roadmap/3-WA-e | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a <3-FD-d3-WA-b Land AccessWA-e Access

96

RAPID/Roadmap/4-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a State Exploration Process 4-WA-a State

97

RAPID/Roadmap/6-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a State6-CO-bc <6-WA-a

98

RAPID/Roadmap/6-WA-d | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a State6-CO-bcRAPID/Roadmap/6-WA-d <

99

RAPID/Roadmap/9-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e8-HI-a8-NV-cc <9-FD-aa9-WA-a

100

RAPID/Roadmap/9-WA-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e8-HI-a8-NV-cc9-WA-c State

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


101

Advance Patent Waiver W(A)2009-039 | Department of Energy  

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

Advance Patent Waiver W(A)2010-007 Advance Patent Waiver W(A)2012-034 Stabilized Lithium Metal Powder, Enabling Material and Revolutionary Technology for High Energy Li-ion...

102

WA_00_013_GENECOR_INTERNATIONAL_Waiver_of_US_Competitiveness...  

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

WaiverofUSCompetitiveness.pdf More Documents & Publications U.S. Biofuels Industry: Mind the Gap Advance Patent Waiver W(A)2008-045 WA01008NOVOZYMEBIOTECHWaiverofDomesti...

103

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

104

carleton universityottaWa, canaDa international  

E-Print Network [OSTI]

carleton universityottaWa, canaDa international aDmissions 2014 #12;Carleton University provides high-quality education to students from Canada and around the world. We offer a wide range of programs and be a part of this extraordinary university! Wonderful country The United Nations consistently ranks Canada

Dawson, Jeff W.

105

Computer Science & Engineering Box 352350 Seattle, WA 98195-2350  

E-Print Network [OSTI]

Computer Science & Engineering #12;Box 352350 Seattle, WA 98195-2350 Nonprofit Org US Postage PAID in the Computer Science Department. He is a superb researcher in the design of interactive, visual data, Carnegie Mellon University's Finmeccanica Associate Professor in the School of Computer Science, is widely

Borenstein, Elhanan

106

EIS-0397: Lyle Falls Fish Passage Project, WA  

Broader source: Energy.gov [DOE]

This EIS analyzes BPA's decision to modify funding to the existing Lyle Falls Fishway on the lower Klickitat River in Klickitat County, WA. The proposed project would help BPA meet its off-site mitigation responsibilities for anadromous fish affected by the development of the Federal Columbia River Power System and increase overall fish production in the Columbia Basin.

107

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

108

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

109

STUDENT ID Page 2 m  

E-Print Network [OSTI]

Write your name, student ID number, recitation instructor's name and recitation time in the space provided above. Also write your name at the top of pages 2, 3,...

110

Data ID Service  

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 UserProduct: CrudeOffice ofINL is aID Service First DOI for a DOE

111

APS Beamline 6-ID-D  

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

MM-Group Home MMG Advisory Committees 6-ID-D Home Recent Publications Beamline Info Optics Instrumentation Software User Info Beamline 6-ID-D Beamline 6-ID-D is operated by the...

112

Advance Patent Waiver W(A)2005-025  

Broader source: Energy.gov [DOE]

This is a request by G.E. NUCLEAR ENERGY for a DOE waiver of domestic and foreign patent rights under agreement DE-FC07-05ID14635

113

Advance Patent Waiver W(A)2005-026  

Broader source: Energy.gov [DOE]

This is a request by G.E. NUCLEAR ENERGY for a DOE waiver of domestic and foreign patent rights under agreement DE-FC07-05ID14036.

114

Advances in Geosciences, 7, 327331, 2006 SRef-ID: 1680-7359/adgeo/2006-7-327  

E-Print Network [OSTI]

Advances in Geosciences, 7, 327­331, 2006 SRef-ID: 1680-7359/adgeo/2006-7-327 European Geosciences Cyclogenesis in the lee of the Atlas Mountains: a factor separation numerical study K. Horvath1, L. Fita2, R of Atlas Mountains is in- vestigated by a series of numerical experiments using the MM5 forecast model

Romero, Romu

115

ID-69 Sodium drain experiments  

SciTech Connect (OSTI)

This paper describes experiments to determine the sodium retention and drainage from the two key areas of an ID-69. This information is then used as the initiation point for guidelines of how to proceed with washing an ID-69 in the IEM Cell Sodium Removal System.

Johnston, D.C.

1996-09-19T23:59:59.000Z

116

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

117

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

118

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

119

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

120

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

Note: This page contains sample records for the topic "forecasting wa id" 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

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

122

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

123

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

124

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

125

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

126

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

127

Integrated Datasets (IDs) Wood/Bretherton proposal  

E-Print Network [OSTI]

Integrated Datasets (IDs) Wood/Bretherton proposal ID Rationale Space/Time scale; Location; Platforms Parameters Combined Drizzle Dataset (CD ID) Collocated precipitation, aerosol and cloud micro, precip. rate, cloud Cross- Section Dataset (XS-ID) Data on E-W cross- section along 20°S from coast

Wood, Robert

128

Review Vendor Payments by Dept ID NUFinancials  

E-Print Network [OSTI]

Review Vendor Payments by Dept ID NUFinancials Purchasing Job Aid Review PaymentsVendorsDeptID Last up payments to vendors by DeptID in NUFinancials. Note: You must have security access authorization search criteria as shown below: #12;Review Vendor Payments by Dept ID NUFinancials Purchasing Job Aid

Shull, Kenneth R.

129

Cognitive Science Minor Approval Form Name __________________________________________ ID # _________________________  

E-Print Network [OSTI]

Cognitive Science Minor Approval Form Name __________________________________________ ID: ___________________________________________________ _______________ Cognitive Science Minor Committee Date

Gering, Jon C.

130

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

131

WA_1993_022_NORTON_COMPANY_Waiver_of_Domestic_and_Foreign_Ri...  

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

Golden Technologies Company, Inc. Request for An Advance Waiver of Domestic and Foreign Rights. January 10, 1995 WA1994011EATONCORPORATIONWaiverofDomesticandForeign...

132

Proceedings of the Western Protective Relay Conference, Spokane, WA, 2006 New wide-area algorithms for  

E-Print Network [OSTI]

- 1 - Proceedings of the Western Protective Relay Conference, Spokane, WA, 2006 New wide the critical areas automatically by using the synchrophasors, and proceed to mitigate the instability

133

Bull Test ID 1140 2013 Florida Bull Test  

E-Print Network [OSTI]

Bull Test ID 1140 2013 Florida Bull Test #12;Bull Test ID 1141 2013 Florida Bull Test #12;Bull Test ID 1142 2013 Florida Bull Test #12;Bull Test ID 1143 2013 Florida Bull Test #12;Bull Test ID 1144 2013 Florida Bull Test #12;Bull Test ID 1145 2013 Florida Bull Test #12;Bull Test ID 1146 2013 Florida

Jawitz, James W.

134

Bull Test ID 1098 2013 Florida Bull Test  

E-Print Network [OSTI]

Bull Test ID 1098 2013 Florida Bull Test #12;Bull Test ID 1099 2013 Florida Bull Test #12;Bull Test ID 1100 2013 Florida Bull Test #12;Bull Test ID 1101 2013 Florida Bull Test #12;Bull Test ID 1102 2013 Florida Bull Test #12;Bull Test ID 1103 2013 Florida Bull Test #12;Bull Test ID 1104 2013 Florida

Jawitz, James W.

135

Bull Test ID 1181 2013 Florida Bull Test  

E-Print Network [OSTI]

Bull Test ID 1181 2013 Florida Bull Test #12;Bull Test ID 1182 2013 Florida Bull Test #12;Bull Test ID 1183 2013 Florida Bull Test #12;Bull Test ID 1184 2013 Florida Bull Test #12;Bull Test ID 1185 2013 Florida Bull Test #12;Bull Test ID 1186 2013 Florida Bull Test #12;Bull Test ID 1187 2013 Florida

Jawitz, James W.

136

Bull Test ID 1160 2013 Florida Bull Test  

E-Print Network [OSTI]

Bull Test ID 1160 2013 Florida Bull Test #12;Bull Test ID 1161 2013 Florida Bull Test #12;Bull Test ID 1162 2013 Florida Bull Test #12;Bull Test ID 1163 2013 Florida Bull Test #12;Bull Test ID 1164 2013 Florida Bull Test #12;Bull Test ID 1165 2013 Florida Bull Test #12;Bull Test ID 1166 2013 Florida

Jawitz, James W.

137

Bull Test ID 1118 2013 Florida Bull Test  

E-Print Network [OSTI]

Bull Test ID 1118 2013 Florida Bull Test #12;Bull Test ID 1119 2013 Florida Bull Test #12;Bull Test ID 1120 2013 Florida Bull Test #12;Bull Test ID 1121 2013 Florida Bull Test #12;Bull Test ID 1122 2013 Florida Bull Test #12;Bull Test ID 1123 2013 Florida Bull Test #12;Bull Test ID 1124 2013 Florida

Jawitz, James W.

138

BayWa Sunways JV | 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 Inspector GeneralDepartmentAUDIT REPORT Americium/CuriumSunways JV Jump to: navigation, search Name: BayWa

139

RAPID/Roadmap/18-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎ |18-MT-b8-WA-a Underground

140

RAPID/Roadmap/18-WA-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎ |18-MT-b8-WA-a

Note: This page contains sample records for the topic "forecasting wa id" 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

RAPID/Roadmap/19-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎g <RAPID/Roadmap/19-WA-a

142

RAPID/Roadmap/19-WA-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎gWA-c Transfer or Change of

143

RAPID/Roadmap/19-WA-d | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎gWA-c Transfer or Change

144

RAPID/Roadmap/19-WA-f | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎gWA-c Transfer or

145

RAPID/Roadmap/3-WA-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a <3-FD-d3-WA-b Land Access Overview

146

RAPID/Roadmap/3-WA-d | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a <3-FD-d3-WA-b Land Access

147

RAPID/Roadmap/6-WA-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a State6-CO-bc

148

RAPID/Roadmap/7-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e BLM/CEC7-OR-d

149

RAPID/Roadmap/8-WA-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e8-HI-a8-NV-cc < RAPID‎

150

RAPID/Roadmap/9-WA-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e8-HI-a8-NV-cc

151

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

152

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

153

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

154

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

155

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

156

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

157

VA VT CT RI MT WY CO ID UT OR NV CA AZ NM WA TN WV NC AR OK  

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 The 2012Nuclear Guide Remote Access08:Energy 94:Service2 1

158

VA VT CT RI MT WY CO ID UT OR NV CA AZ NM WA TN WV NC AR OK  

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 The 2012Nuclear Guide Remote Access08:Energy 94:Service2 1 2 1

159

VA VT CT RI MT WY CO ID UT OR NV CA AZ NM WA TN WV NC AR OK  

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 The 2012Nuclear Guide Remote Access08:Energy 94:Service2 1 2 1

160

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

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


161

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

162

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

163

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

164

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

165

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

166

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.

167

Microbial community changes during sustained Cr(VI) reduction at the 100H site in Hanford, WA  

E-Print Network [OSTI]

at the 100H site in Hanford, WA Romy Chakraborty 1 , Eoin Lcontaminated aquifer at the Hanford (WA) 100H site in 2004.Cr(VI) reduction at Hanford, and a comparison of the

Chakraborty, Romy

2010-01-01T23:59:59.000Z

168

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

169

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

170

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

171

Bull Test ID 1077 2013 Florida Bull Test  

E-Print Network [OSTI]

14th Annual Florida Bull Test #12;Bull Test ID 1077 2013 Florida Bull Test #12;Bull Test ID 1078 2013 Florida Bull Test #12;Bull Test ID 1079 2013 Florida Bull Test #12;Bull Test ID 1080 2013 Florida Bull Test #12;Bull Test ID 1081 2013 Florida Bull Test #12;Bull Test ID 1082 2013 Florida Bull Test #12

Jawitz, James W.

172

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

173

DOWNSTREAM MOVEMENT OF SALMON IDS  

E-Print Network [OSTI]

DOWNSTREAM MOVEMENT OF SALMON IDS AT BONNEVILLE DAM Marine Biological Laboratory APR 1 7 1958 WOODS Washington, D. C January 1958 #12;ABSTRACT At Bonneville Deun most downstream-migrant salmonlds were ca TABLES 1. Hourly catches of downstream-migrant seLLmonids in 1952. Each hour represents the suomation

174

Name: SU ID: Phone: Email  

E-Print Network [OSTI]

Name: SU ID: Phone: Email: Today's Date: Month/YrB.S. expected: Mathematics and Science Requirement for Computer Scientists (see note 3) 5 STAT One of: Stat 141, 203, 205, 215, 225 Mathematics Unit Total (23 (I.e., 22 units min. for track and elective courses). Students who complete STATS 116, MS&E 120

Pratt, Vaughan

175

Name: SU ID: Phone: Email  

E-Print Network [OSTI]

Name: SU ID: Phone: Email: Today's Date: Month/YrB.S. expected: Mathematics and Science Requirement for Computer Scientists(see note 3) 5 STAT One of: Stat 141, 203, 205, 215, 225 3 to 5 Mathematics Unit Total complete STATS 116, MS&E 120, or CME 106 in Winter 2008-09 or earlier may count that course as satisfying

Pratt, Vaughan

176

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

177

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

178

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

179

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

180

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

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


181

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

182

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

183

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

184

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

185

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

186

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

187

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

188

RAPID/Roadmap/5-WA-a | 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 Home5b9fcbce19 No revisionEnvReviewNonInvasiveExplorationUT-g Grant of Access Permit5-ID-a Drilling and Well5-OR-a

189

RAPID/Roadmap/14-WA-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a13-ID-a4-NV-c14-OR-dd

190

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

191

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

192

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

193

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

194

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

195

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

196

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...

197

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

198

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

199

27-ID and 35-ID Construction Schedule | Advanced Photon Source  

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‰PNGExperience hands-onASTROPHYSICS H.CarbonMarch Value ofRPT-55983,7 Long27-ID

200

REQUEST FOR TRAVEL AUTHORIZATION Document ID #  

E-Print Network [OSTI]

REQUEST FOR TRAVEL AUTHORIZATION Document ID # Name: UTEID: Travel Dates: Begin: End: Destination," please allow one month for processssing. Helpful Information: Navigant (Travel Management) (512

Texas at Austin, University of

Note: This page contains sample records for the topic "forecasting wa id" 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

4-ID-D Instrumentation  

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‰PNGExperience hands-onASTROPHYSICS H.CarbonMarch Value4 3.P D ATFOR 4-ID-D

202

4-ID-D optics  

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‰PNGExperience hands-onASTROPHYSICS H.CarbonMarch Value4 3.P D ATFOR 4-ID-D

203

Microsoft Word - DOE-ID-INL-12-015.doc  

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

(AST) Site ID 7230Facility ID 6-120614Tank ID 05MFC00035 and replace the two tanks with a 10,000 gallon aboveground storage tank (AST) split tank (5,000 gallons for...

204

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

205

2/21/2014 terragreen.teriin.org/popup.php?section_id=1905&category_id=10 http://terragreen.teriin.org/popup.php?section_id=1905&category_id=10 1/2  

E-Print Network [OSTI]

2/21/2014 terragreen.teriin.org/popup.php?section_id=1905&category_id=10 http://terragreen.teriin.org/popup.php in the material because of the durable nickel alloy and smart aerodynamic design. #12;2/21/2014 terragreen.teriin.org/popup.php?section_id=1905&category_id=10 http://terragreen.teriin.org/popup.php?section_id=1905&category_id=10 2/2 "The

Chiao, Jung-Chih

206

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

207

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.

208

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

209

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.

210

College of Design ID Interior Design  

E-Print Network [OSTI]

College of Design ID Interior Design KEY: # = new course * = course changed = course dropped University of Kentucky 2013-2014 Undergraduate Bulletin 1 ID 101 INTRODUCTION TO INTERIOR DESIGN. (1) An introduction to the profession of Interior Design: historical perspective, career specializations, and career

MacAdam, Keith

211

Reporting Tools Course ID: FMS121  

E-Print Network [OSTI]

Reporting Tools Course ID: FMS121 PS Query 03/31/2009 © 2009 Northwestern University FMS121 0 Introduction to Query For Query Developers Query is an ad-hoc reporting tool that allows you to retrieve data will have access to both query viewer and query manager pages. #12;Reporting Tools Course ID: FMS121 PS

Shull, Kenneth R.

212

Reporting Tools Course ID: FMS121  

E-Print Network [OSTI]

Reporting Tools Course ID: FMS121 PS Query 03/31/2009 © 2009 Northwestern University FMS121 0 Introduction to Query For Query Viewers Query is an ad-hoc reporting tool that allows you to retrieve data will have access to both query viewer and query manager pages. #12;Reporting Tools Course ID: FMS121 PS

Shull, Kenneth R.

213

Kentucky WRI Pilot Test Universal ID  

E-Print Network [OSTI]

Kentucky WRI Pilot Test ­ Universal ID Commercial Motor Vehicle Roadside Technology Corridor Safety Universal ID Pilot TestKentucky Pilot Test #12;Kentucky Pilot Test (Not to Scale) Sorter WIM USDOT Reader >>>> Park/Proceed Signs Mainline >>>> #12;Kentucky Pilot Test · Information is captured from the commercial

214

Durability of Diesel Engine Particulate Filters (Agreement ID...  

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

Durability of Diesel Engine Particulate Filters (Agreement ID:10461) Durability of Diesel Engine Particulate Filters (Agreement ID:10461) 2013 DOE Hydrogen and Fuel Cells Program...

215

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

216

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

217

(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

218

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

219

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

220

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

Note: This page contains sample records for the topic "forecasting wa id" 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

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

222

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

223

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

224

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

225

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

226

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

227

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

228

Id-1 and Id-2 genes and products as markers of epithelial cancer  

DOE Patents [OSTI]

A method for detection and prognosis of breast cancer and other types of cancer. The method comprises detecting expression, if any, for both an Id-1 and an Id-2 genes, or the ratio thereof, of gene products in samples of breast tissue obtained from a patient. When expressed, Id-1 gene is a prognostic indicator that breast cancer cells are invasive and metastatic, whereas Id-2 gene is a prognostic indicator that breast cancer cells are localized and noninvasive in the breast tissue.

Desprez, Pierre-Yves (El Cerrito, CA); Campisi, Judith (Berkeley, CA)

2011-10-04T23:59:59.000Z

229

Id-1 and Id-2 genes and products as markers of epithelial cancer  

DOE Patents [OSTI]

A method for detection and prognosis of breast cancer and other types of cancer. The method comprises detecting expression, if any, for both an Id-1 and an Id-2 genes, or the ratio thereof, of gene products in samples of breast tissue obtained from a patient. When expressed, Id-1 gene is a prognostic indicator that breast cancer cells are invasive and metastatic, whereas Id-2 gene is a prognostic indicator that breast cancer cells are localized and noninvasive in the breast tissue.

Desprez, Pierre-Yves (El Cerrito, CA); Campisi, Judith (Berkeley, CA)

2008-09-30T23:59:59.000Z

230

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

231

Electrical impedance tomography and Calderon's Department of Mathematics, University of Washington, Seattle, WA 98195, USA  

E-Print Network [OSTI]

Electrical impedance tomography and Calder´on's problem G Uhlmann Department of Mathematics, University of Washington, Seattle, WA 98195, USA E-mail: gunther@math.washington.edu Abstract. We survey mathematical developments in the inverse method of Electrical Impedance Tomography which consists

Uhlmann, Gunther

232

An International Pellet Ablation Database L.R. Baylor, A. Geraud*, W.A. Houlberg,  

E-Print Network [OSTI]

An International Pellet Ablation Database L.R. Baylor, A. Geraud*, W.A. Houlberg, D. Frigione+, M of an international pellet ablation database (IPADBASE) that has been assembled to enable studies of pellet ablation theories that are used to describe the physics of an ablating fuel pellet in a tokamak plasma. The database

233

7900 SE 28th Street, Suite 200 Mercer, Island, WA 98040-2970  

E-Print Network [OSTI]

7900 SE 28th Street, Suite 200 Mercer, Island, WA 98040-2970 v 206.236.7200 f 206.236.3019 www-standing rivalries over the distribution of the Northwest's premiere asset. It will allow the customers to apply to acquire new generation assets. This conclusion, also reached by the Comprehensive Review of the Northwest

234

U.S. NUclear WaSte techNical revieW Board  

E-Print Network [OSTI]

U.S. NUclear WaSte techNical revieW Board Report to The U.S. Congress and The Secretary STATES NUCLEAR WASTE TECHNICAL REVIEW BOARD 2300 Clarendon Boulevard, Suite 1300 Arlington, VA 22201 June Speaker Hastert, Senator Stevens, and Secretary Bodman: The U.S. Nuclear Waste Technical Review Board

235

Natural Data Mining Techniques J. N. Kok and W.A. Kosters  

E-Print Network [OSTI]

, enrichment of data (for example using external data bases), coding, data mining and reporting. In data support for their operations. A usual problem in the #12;eld of data mining is that the combinationNatural Data Mining Techniques J. N. Kok and W.A. Kosters Leiden Institute of Advanced Computer

Kosters, Walter

236

____________________Rowan ID# K. Bryant 3/2013  

E-Print Network [OSTI]

____________________Rowan ID# K. Bryant 3/2013 Private/Alternative Education Loan Understanding receipt) the form to: Cooper Medical School of Rowan University, Office of Financial Aid Kyhna Bryant

Rusu, Adrian

237

Document ID: POLUMITPUR01702 Information Technology  

E-Print Network [OSTI]

Document ID: POLUMITPUR01702 Information Technology Supersedes: POLUMITPUR01701 Effective Date: 02 Sep 2014 Page 1 of 5 Document Title: Purchasing Computerized Systems/Software Applications Miletic Manager Quality Assurance Research Compliance and Quality Assurance Made revisions based

Shyu, Mei-Ling

238

Leverhulme research network: ID20060104 EUROBRISA Page 1 of 6  

E-Print Network [OSTI]

% in Argentina, Chile, Ecuador, Peru and Suriname. Improved seasonal rainfall forecasts would help South American

239

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

240

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

Note: This page contains sample records for the topic "forecasting wa id" 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

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...

242

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...

243

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

244

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...

245

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

246

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.

247

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

248

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

249

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

250

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

251

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.

252

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

253

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

254

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

255

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

256

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

257

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...

258

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

259

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

260

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

Note: This page contains sample records for the topic "forecasting wa id" 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

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

262

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

263

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

264

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

265

-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

266

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

267

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

268

Testing Buda-Lund hydro model on particle correlations and spectra in NA44, WA93 and WA98 heavy ion experiments  

E-Print Network [OSTI]

Analytic and numerical approximations to a hydrodynamical model describing longitudinally expanding, cylindrically symmetric, finite systems are fitted to preliminary NA44 data measured in 200 AGeV central $S + Pb$ reactions. The model describes the measured spectra and HBT radii of pions, kaons and protons, simultaneously. The source is characterized by a central freeze-out temperature of T_0 = 154 +/- 8 +/- 11 MeV, a "surface" temperature of T_r = 107 +/- 28 +/- 18 MeV and by a well-developed transverse flow, = 0.53 +/- 0.17 +/- 0.11. The transverse geometrical radius and the mean freeze-out time are found to be R_G = 5.4 +/- 0.9 +/- 0.7 fm and tau_0 = 5.1 +/- 0.3 +/- 0.3 fm/c, respectively. Fits to preliminary WA93 200 AGeV S + Au and WA98 158 AGeV Pb + Pb data dominated by pions indicate similar model parameters. The absolute normalization of the measured particle spectra together with the experimental determination of both the statistical and the systematic errors were needed to obtain successful fits.

A. Ster; T. Csorgo; B. Lorstad

1998-09-28T23:59:59.000Z

269

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

270

ID Nom Prnom Groupe 11206695 ABDOU CHAFIN B  

E-Print Network [OSTI]

ID Nom Prénom Groupe 11206695 ABDOU CHAFIN B 11207912 ABDOU-RAZACK AIDIDE D 11207680 ACOLATSE REGIS

Mironescu, Petru

271

ID3, SEQUENTIAL BAYES, NAIVE BAYES AND BAYESIAN NEURAL NETWORKS  

E-Print Network [OSTI]

to ID3. ID3 learning algorithm (Quinlan 1979) and its successors ACLS (Paterson & Niblett 1982), C4#cient in many learning tasks. It is shown how Sequential Bayes can be transformed into ID3 by replacing of network's execution (Kononenko 1989) enables the us­ age of a neural network as an expert system shell

Kononenko, Igor

272

Optimal IDS Sensor Placement And Alert Prioritization Using Attack Graphs  

E-Print Network [OSTI]

1 Optimal IDS Sensor Placement And Alert Prioritization Using Attack Graphs Steven Noel and Sushil optimally place intrusion detection system (IDS) sensors and prioritize IDS alerts using attack graph. The set of all such paths through the network constitutes an attack graph, which we aggregate according

Noel, Steven

273

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

274

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

275

11. CONTRACT ID CODE IPAG~ OF PAGES  

E-Print Network [OSTI]

11. CONTRACT ID CODE IPAG~ OF PAGES AMENDMENT OF SOLICITATION/MODIFICATION OF CONTRACT I 2 2. MODIFICATION OF CONTRACT/ ORDER NO. DUNS # 032987476 DE-AC05-76RL01830l8l 10B. DATED (SEE ITEM 13) CODE ONLY TO MODIFICATIONS OF CONTRACTS/ORDERS. IT MODIFIES THE CONTRACT/ORDER NO. AS SET FORTH IN ITEM 14

276

Master Project Assessment Form Student: ID number  

E-Print Network [OSTI]

Master Project Assessment Form Student: ID number: Master Program: Graduation supervisor Graduation presentation Defense Execution of the project Grade Signature of supervisor Date * Hand in at the student administration (MF 3.068) together with an official result form (uitslagbon) #12;"Master Project

Franssen, Michael

277

Name: SU ID: Email: Local Phone  

E-Print Network [OSTI]

Name: SU ID: Email: Local Phone: Date: Date B.S. expected: Mathematics and Science Requirement (see note 3) 5 STAT One of: Stat 141, 203, 205, 215, 225 3 to 5 Mathematics Unit Total (23 units office. Students who complete STATS 116, MS&E 120, or CME 106 in Winter 2008-09 or earlier may count

Pratt, Vaughan

278

Name: SU ID: Email: Local Phone  

E-Print Network [OSTI]

Name: SU ID: Email: Local Phone: Date: Date B.S. expected: Mathematics and Science Requirement (see note 3) 5 STAT One of: Stat 141, 203, 205, 215, 225 3 to 5 Mathematics Unit Total (23 units by the Computer Science undergraduate program office. Students who complete STATS 116, MS&E 120, or CME 106

Pratt, Vaughan

279

Name: SU ID: Email: Local Phone  

E-Print Network [OSTI]

Name: SU ID: Email: Local Phone: Date: Date B.S. expected: Mathematics and Science Requirement Introduction to Probability for Computer Scientists (see note 3) 5 STAT One of: Stat 141, 203, 205, 215, 225 3. Students who complete STATS 116, MS&E 120, or CME 106 in Winter 2008-09 or earlier may count that course

Pratt, Vaughan

280

Mercury Chamber NF-IDS Meeting  

E-Print Network [OSTI]

-Battelle for the U.S. Department of Energy Mercury Chamber Update Oct 2011 Starting Point: Coil and Shielding Concept IDS120H #12;3 Managed by UT-Battelle for the U.S. Department of Energy Mercury Chamber Update Oct 2011 · Penetrations (ports) into chamber ­ Nozzle ­ Hg drains (overflow and maintenance) ­ Vents (in and out) ­ Beam

McDonald, Kirk

Note: This page contains sample records for the topic "forecasting wa id" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Article ID #eqr106 REPLACEMENT STRATEGIES  

E-Print Network [OSTI]

Article ID #eqr106 REPLACEMENT STRATEGIES Elmira Popova Associate Professor, Department)-296-5795 e-mail: popovai@seattleu.edu Corresponding Contributor: Elmira Popova Keywords: Replacement Policies define what is a replacement policy for a system that fails randomly in time and its main characteristics

Popova, Elmira

282

1993 Solid Waste Reference Forecast Summary  

SciTech Connect (OSTI)

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

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

1993-08-01T23:59:59.000Z

283

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

284

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

285

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

286

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

287

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

288

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

289

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.

290

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

291

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.

292

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

293

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

294

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.

295

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

296

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

297

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

298

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

299

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

300

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

Note: This page contains sample records for the topic "forecasting wa id" 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

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

302

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

303

Advance Patent Waiver W(A)2011-034 | Department of Energy  

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

AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE:Year in Review: Top Five EERE Blog Posts1-034 Advance Patent Waiver W(A)2011-034 This document

304

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

305

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

306

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

307

,"Eastport, ID Natural Gas Pipeline Imports From Canada (MMcf...  

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

Imports From Canada (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Eastport, ID...

308

Microsoft Word - DOE-ID-INL-12-021.docx  

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

DOE-ID-INL-12-021 SECTION A. Project Title: High Frequency Sounder - Permanent Installation at Water Reactor Research Test Facility (WRRTF) SECTION B. Project Description: The...

309

Field Experience/Internship Proposal Student's Name:_____________________________________ ID#:_____________________  

E-Print Network [OSTI]

Field Experience/Internship Proposal Student's Name:_____________________________________ ID:________________________ Email:______________________________________________ Internship Site Supervisor's Name and Title:___________________________________________________________ Course Information (Internship/Field Experience/Independent Study) (Where applicable) Course name

New Hampshire, University of

310

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

311

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

312

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

313

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

314

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

315

Id-1 and Id-2 genes and products as therapeutic targets for treatment of breast cancer and other types of carcinoma  

DOE Patents [OSTI]

A method for treatment and amelioration of breast, cervical, ovarian, endometrial, squamous cells, prostate cancer and melanoma in a patient comprising targeting Id-1 or Id-2 gene expression with a delivery vehicle comprising a product which modulates Id-1 or Id-2 expression.

Desprez, Pierre-Yves; Campisi, Judith

2014-09-30T23:59:59.000Z

316

4-ID-D User FAQs  

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‰PNGExperience hands-onASTROPHYSICS H.CarbonMarch Value4 3.P D ATFOR 4-ID-D

317

4-ID-D User's Manual  

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‰PNGExperience hands-onASTROPHYSICS H.CarbonMarch Value4 3.P D ATFOR 4-ID-D

318

7-ID Home Page | Advanced Photon Source  

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‰PNGExperience hands-onASTROPHYSICSHe β- Decay Evaluated7-ID Home Page

319

Data ID Service | DOE Data Explorer  

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 UserProduct: CrudeOffice ofINL is aID Service First DOI for a DOEOSTI's

320

APS Beamline 6-ID-B,C  

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‰PNGExperience4AJ01) (See95TI07)Operations2AP-XPS Measures MIEC5Diagnostics6-ID-B,C

Note: This page contains sample records for the topic "forecasting wa id" 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

Property:DSIRE/Id | 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 Home5b9fcbce19 No revision hasInformationInyoCoolingTowerWaterUseSummerConsumed Jump to:DOEInvolve Jump to:DtAdd Jump to:Id Jump

322

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

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

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

2010-04-01T23:59:59.000Z

323

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

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

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

2010-04-15T23:59:59.000Z

324

Wind Power Forecasting Error Distributions over Multiple Timescales (Presentation)  

SciTech Connect (OSTI)

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

Hodge, B. M.; Milligan, M.

2011-07-01T23:59:59.000Z

325

A role for transcriptional regulator Id2 in natural killer T cells  

E-Print Network [OSTI]

proteins (Id) 14-16 . Id proteins lack the DNA bindingto analyze protein expression directly. Due to the lack of aprotein-2 (Id2) fail to develop natural killer cells, CD8? + dendritic cells, ?? IELs, Langerhans cells, and lack

Monticelli, Laurel Anne

2008-01-01T23:59:59.000Z

326

0 20 4010 Miles NOAA Harmful Algal Bloom Operational Forecast System  

E-Print Network [OSTI]

0 20 4010 Miles NOAA Harmful Algal Bloom Operational Forecast System Texas Forecast Region Maps to Sargent BCH NOAA Harmful Algal Bloom Operational Forecast System Texas Forecast Region Maps 0 5 102 Bloom Operational Forecast System Texas Forecast Region Maps 0 5 102.5 Miles West Bay #12;Aransas Bay

327

October 14 WA Division Newsletter Page 4 Tool durability and steel microstructure in friction stir welding of mild steel  

E-Print Network [OSTI]

scheme to assess tool durability and tool life in the friction stir welding (FSW) of difficult alumin referencing is freely avail- able at: http://tinyurl.com/mst-fsw. Tools for friction stir welding (FSWOctober 14 WA Division Newsletter Page 4 Tool durability and steel microstructure in friction stir

Cambridge, University of

328

PSO (FU 2101) Ensemble-forecasts for wind power  

E-Print Network [OSTI]

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

329

TRANSPORTATION ENERGY FORECASTS FOR THE 2007 INTEGRATED ENERGY  

E-Print Network [OSTI]

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

330

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

331

Forecast Combinations of Computational Intelligence and Linear Models for the  

E-Print Network [OSTI]

Forecast Combinations of Computational Intelligence and Linear Models for the NN5 Time Series Forecasting competition Robert R. Andrawis Dept Computer Engineering Cairo University, Giza, Egypt robertrezk@eg.ibm.com November 6, 2010 Abstract In this work we introduce a forecasting model with which we participated

Atiya, Amir

332

GET your forecast at the click of a button.  

E-Print Network [OSTI]

GET your forecast at the click of a button. EXPLORE your local weather in detail. PLAN your days favourite locations; · Pan and zoom to any area in Australia; · Combine the latest weather and forecast current temperatures across Australia. MetEyeTM computer screen image displaying the weather forecast

Greenslade, Diana

333

Compatibility of Stand Basal Area Predictions Based on Forecast Combination  

E-Print Network [OSTI]

Compatibility of Stand Basal Area Predictions Based on Forecast Combination Xiongqing Zhang Carr.) in Beijing, forecast combination was used to adjust predicted stand basal areas from these three types of models. The forecast combination method combines information and disperses errors from

Cao, Quang V.

334

MOUNTAIN WEATHER PREDICTION: PHENOMENOLOGICAL CHALLENGES AND FORECAST METHODOLOGY  

E-Print Network [OSTI]

MOUNTAIN WEATHER PREDICTION: PHENOMENOLOGICAL CHALLENGES AND FORECAST METHODOLOGY Michael P. Meyers of the American Meteorological Society Mountain Weather and Forecasting Monograph Draft from Friday, May 21, 2010 of weather analysis and forecasting in complex terrain with special emphasis placed on the role of humans

Steenburgh, Jim

335

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

336

Earnings forecast bias -a statistical analysis Franois Dossou  

E-Print Network [OSTI]

Earnings forecast bias - a statistical analysis François Dossou Sandrine Lardic** Karine Michalon' earnings forecasts is an important aspect of research for different reasons: Many empirical studies employ analysts' consensus forecasts as a proxy for the market's expectations of future earnings in order

Paris-Sud XI, Université de

337

Using Large Datasets to Forecast Sectoral Employment Rangan Gupta*  

E-Print Network [OSTI]

Using Large Datasets to Forecast Sectoral Employment Rangan Gupta* Department of Economics Bayesian and classical methods to forecast employment for eight sectors of the US economy. In addition-sample period and January 1990 to March 2009 as the out-of- sample horizon, we compare the forecast performance

Ahmad, Sajjad

338

Power load forecasting Organization: Huizhou Electric Power, P. R. China  

E-Print Network [OSTI]

Power load forecasting Organization: Huizhou Electric Power, P. R. China Presenter: Zhifeng Hao can be divided into load forecasting and electrical consumption predicting according to forecasting in generators macroeconomic control, power exchange plan and so on. And the prediction is from one day to seven

339

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

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

Heinemann, Detlev

340

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

Note: This page contains sample records for the topic "forecasting wa id" 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

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

342

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

E-Print Network [OSTI]

AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand of the energy demand forecasting are achieved by integrating nonlinear transformations within the models

Povinelli, Richard J.

343

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST forecast is the combined product of the hard work and expertise of numerous staff members in the Demand, and utilities. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption

344

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST forecast is the combined product of the hard work and expertise of numerous staff in the Demand Analysis. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption data

345

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

346

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

347

Using Belief Functions to Forecast Demand for Mobile Satellite Services  

E-Print Network [OSTI]

Using Belief Functions to Forecast Demand for Mobile Satellite Services Peter McBurney and Simon.j.mcburney,s.d.parsonsg@elec.qmw.ac.uk Abstract. This paper outlines an application of belief functions to forecasting the demand for a new service in a new category, based on new technology. Forecasting demand for a new product or service

McBurney, Peter

348

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

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

de Lijser, Peter

349

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

350

FORECASTING SOLAR RADIATION PRELIMINARY EVALUATION OF AN APPROACH  

E-Print Network [OSTI]

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

Perez, Richard R.

351

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

E-Print Network [OSTI]

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

Lang, K.

1982-01-01T23:59:59.000Z

352

Issues in midterm analysis and forecasting, 1996  

SciTech Connect (OSTI)

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

NONE

1996-08-01T23:59:59.000Z

353

Forecasting Turbulent Modes with Nonparametric Diffusion Models  

E-Print Network [OSTI]

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

Tyrus Berry; John Harlim

2015-01-27T23:59:59.000Z

354

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

355

Issues in midterm analysis and forecasting 1998  

SciTech Connect (OSTI)

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

NONE

1998-07-01T23:59:59.000Z

356

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS MARINE SERVICE  

E-Print Network [OSTI]

> BUREAU HOME > AUSTRALIA > QUEENSLAND > FORECASTS MARINE SERVICE IMPROVEMENTS FOR QUEENSLAND across Australia. FURTHER INFORMATION: www.bom.gov.au/NexGenFWS © Commonwealth of Australia, 2013 From © Copyright Commonwealth of Australia 2013, Bureau of Meteorology Queensland Australia Coastal Waters Zones

Greenslade, Diana

357

Modeling of Uncertainty in Wind Energy Forecast  

E-Print Network [OSTI]

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

358

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

359

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

to validate the amounts of humidity in Numerical Weather Prediction (NWP) model forecasts. This paper presents. Satellite images and Numerical Weather Prediction (NWP) models are used together with the synoptic surface. In this paper, a case is presented for which the operational Numerical Weather Prediction Model (NWP) HIRLAM

Stoffelen, Ad

360

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

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

Howat, Ian M.

Note: This page contains sample records for the topic "forecasting wa id" 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

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

362

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

363

Segmenting Time Series for Weather Forecasting  

E-Print Network [OSTI]

) models is summarised as weather forecast texts. In the domain of gas turbines, sensor data from an operational gas turbine is summarised for the maintenance engineers. More details on SUMTIME have been to develop a generic model for summarisation of time series data. Initially, we have applied standard

Sripada, Yaji

364

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

365

University of Connecticut New Vendor Form Taxpayer ID # SSN  

E-Print Network [OSTI]

University of Connecticut New Vendor Form Taxpayer ID # SSN FEIN UCONN Student PeopleSoft ID SSN/FEIN (last four digits only) Purchase Order Vendor Business/Individual Legal Name Business Name , Trade Name not attach documents to vendor e-doc that contain sensitive information e.g. social security number Fax

Lozano-Robledo, Alvaro

366

Efficient DHT attack mitigation through peers' ID distribution  

E-Print Network [OSTI]

Efficient DHT attack mitigation through peers' ID distribution Thibault Cholez, Isabelle Chrisment.festor}@loria.fr Abstract--We present a new solution to protect the widely deployed KAD DHT against localized attacks which DHT attacks by comparing real peers' ID distributions to the theoretical one thanks to the Kullback

Paris-Sud XI, Université de

367

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

E-Print Network [OSTI]

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

368

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect (OSTI)

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

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

369

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

370

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

SciTech Connect (OSTI)

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

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

1983-07-01T23:59:59.000Z

371

Property:RAPID/Contact/ID3/Name | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/Website PropertyID2/WebsiteID3/Name

372

Property:RAPID/Contact/ID5/Email | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/WebsiteID3/Phone PropertyID5/Email

373

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

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

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

2014-12-30T23:59:59.000Z

374

Solar Wind Forecasting with Coronal Holes  

E-Print Network [OSTI]

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

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

2007-01-09T23:59:59.000Z

375

A survey on wind power ramp forecasting.  

SciTech Connect (OSTI)

The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

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

2011-02-23T23:59:59.000Z

376

EIS-0473: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project (PCCS), Fort Bend County, TX  

Broader source: Energy.gov [DOE]

This EIS evaluates the environmental impacts of a proposal to provide financial assistance for a project proposed by NRG Energy, Inc (NRG). DOE selected NRGs proposed W.A. Parish Post-Combustion CO2 Capture and Sequestration Project for a financial assistance award through a competitive process under the Clean Coal Power Initiative Program. NRG would design, construct and operate a commercial-scale carbon dioxide (CO2) capture facility at its existing W.A. Parish Generating Station in Fort Bend County, Texas; deliver the CO2 via a new pipeline to the existing West Ranch oil field in Jackson County, Texas, for use in enhanced oil recovery operations; and demonstrate monitoring techniques to verify the permanence of geologic CO2 storage.

377

A review of "Defining the Jacobean Church: the Politics of Religious Controversy, 1603-1625." by Charles W.A. Prior  

E-Print Network [OSTI]

REVIEWS 151 Charles W.A. Prior. Defining the Jacobean Church: the Politics of Religious Controversy, 1603-1625. Cambridge: Cambridge University Press, 2005. xiv + 294 pp. $85.00. Review by GRAHAM PARRY, UNIVERSITY OF YORK. Defining the Jacobean...

Parry, Graham

2006-01-01T23:59:59.000Z

378

A WASHINGTON STATE UNIVERSITY POSTDOCTORAL POSITION FOR WORK AT LIGO HANFORD, WA Applications are invited for a postdoctoral position in the Gravity Group at the Department of Physics  

E-Print Network [OSTI]

A WASHINGTON STATE UNIVERSITY POSTDOCTORAL POSITION FOR WORK AT LIGO HANFORD, WA Applications characterization for the Advanced Laser Interferometer Gravitational wave Observatory (LIGO) at the Hanford site characterization at the LIGO Hanford observatory. Familiarity with data analysis pipelines for searching

Collins, Gary S.

379

Application of a modified denitrifying bacteria method for analyzing groundwater and vadose zone pore water nitrate at the Hanford Site, WA, USA.  

E-Print Network [OSTI]

zone pore water nitrate at the Hanford Site, WA, USA. Woods,and Conrad, Mark The Hanford Site in southern WashingtonL have been reported for Hanford groundwaters, where nitrate

Woods, Katharine N.; Singleton, Michael J.; Conrad, Mark

2003-01-01T23:59:59.000Z

380

Draft Fourth Northwest Conservation and Electric Power Plan, Appendix D ECONOMIC AND DEMAND FORECASTS  

E-Print Network [OSTI]

AND DEMAND FORECASTS INTRODUCTION AND SUMMARY Role of the Demand Forecast A demand forecast of at least 20 years is one of the explicit requirements of the Northwest Power Act. A demand forecast is, of course analysis. Because the future is inherently uncertain, the Council forecasts a range of future demand levels

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


381

Microsoft Word - DOE-ID-INL-13-025.doc  

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

4 EC Document No.: DOE-ID-INL-13-025 SECTION A. Project Title: Willow Creek Building Pedestrian Bridge Replacement SECTION B. Project Description: The purpose and need for the...

382

Microsoft Word - DOE-ID-INL-12-016.doc  

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

No.: DOE-ID-INL-12-016 SECTION A. Project Title: Reverse Osmosis System Removal SECTION B. Project Description: The project will remove a reverse osmosis water treatment system...

383

Microsoft Word - DOE-ID-INL-13-026.docx  

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

1 EC Document No.: DOE-ID-INL-13-026 SECTION A. Project Title: Relief Valve Test Stand Relocation SECTION B. Project Description: The scope of this modification is to relocate the...

384

Microsoft Word - DOE-ID-INL-13-013.docx  

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

NEPA CX DETERMINATION Idaho National Laboratory Page 2 of 2 CX Posting No.: DOE-ID-INL-13-013 References: 10 CFR 1021, Appendix B to Subpart D item B2.2 "Building and...

385

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

Forecasting solar energy generation is a challenging task due to the variety of solar power systems and weather regimes encountered. Forecast inaccuracies can result in substantial economic losses and power system reliability issues. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, applications, etc.). In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design of experiments methodology, in conjunction with response surface and sensitivity analysis methods. The results show that the developed metrics can efficiently evaluate the quality of solar forecasts, and assess the economic and reliability impact of improved solar forecasting.

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

2013-10-01T23:59:59.000Z

386

Property:RAPID/Contact/ID3/Email | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/Website PropertyID2/Website

387

Property:RAPID/Contact/ID3/Website | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/WebsiteID3/Phone Property

388

Property:RAPID/Contact/ID5/Name | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/WebsiteID3/Phone

389

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network [OSTI]

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

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

390

Western Area Power Administration Starting Forecast Month: Sierra...  

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

CVP Generation Project Use First Preference Purchases and Exchanges Base Resource February 2014 Twelve-Month Forecast of CVP Generation and Base Resource February 2014 January...

391

Western Area Power Administration Starting Forecast Month: Sierra...  

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

Use First Preference Purchases and Exchanges Base Resource April 2014 Twelve-Month Forecast of CVP Generation and Base Resource April 2014 March 2015 Exceedence Level: 90% (Dry)...

392

Western Area Power Administration Starting Forecast Month: Sierra...  

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

Preference Month CVP Generation Project Use First Preference Purchases and Exchanges Base Resource May 2014 Twelve-Month Forecast of CVP Generation and Base Resource May 2014 April...

393

Western Area Power Administration Starting Forecast Month: Sierra...  

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

based on Green Book ("Above Normal") values. Base Resource March 2014 Twelve-Month Forecast of CVP Generation and Base Resource March 2014 February 2015 Exceedence Level: 90%...

394

analytical energy forecasting: Topics by E-print Network  

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

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

395

Wind Power Forecasting Error Distributions: An International Comparison; Preprint  

SciTech Connect (OSTI)

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

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

2012-09-01T23:59:59.000Z

396

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

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

forecast models for predicting substantial changes in winds at heights important for wind energy up to six hours in advance, allowing grid operators to predict expected wind...

397

Forecasting the underlying potential governing climatic time series  

E-Print Network [OSTI]

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

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

2012-01-01T23:59:59.000Z

398

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

399

Using Customers' Reported Forecasts to Predict Future Sales  

E-Print Network [OSTI]

Using Customers' Reported Forecasts to Predict Future Sales Nihat Altintas , Alan Montgomery , Michael Trick Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213. nihat

Gordon, Geoffrey J.

400

State-of-the art of freight forecast modeling: lessons learned and the road ahead  

E-Print Network [OSTI]

of-the art of freight forecast modeling: lessons learned andof goods as well as to forecast the expected future truckused for the short-term forecasts of freight volumes on

Chow, Joseph Y.; Yang, Choon Heon; Regan, Amelia C.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "forecasting wa id" 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

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

E-Print Network [OSTI]

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

Bolinger, Mark; Wiser, Ryan

2004-01-01T23:59:59.000Z

402

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

E-Print Network [OSTI]

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

Bolinger, Mark A.

2010-01-01T23:59:59.000Z

403

Electricity Demand Forecasting using Gaussian Processes Manuel Blum and Martin Riedmiller  

E-Print Network [OSTI]

Electricity Demand Forecasting using Gaussian Processes Manuel Blum and Martin Riedmiller Abstract We present an electricity demand forecasting algorithm based on Gaussian processes. By introducing. Introduction Electricity demand forecasting is an important aspect of the control and scheduling of power

Teschner, Matthias

404

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

E-Print Network [OSTI]

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

Smith, Emily (Emily C.)

2010-01-01T23:59:59.000Z

405

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

E-Print Network [OSTI]

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

Wickham, Richard Robert

1995-01-01T23:59:59.000Z

406

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

407

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

408

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

409

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

410

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

411

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

E-Print Network [OSTI]

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

Marquez, Ricardo

2012-01-01T23:59:59.000Z

412

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

413

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

E-Print Network [OSTI]

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

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

414

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect (OSTI)

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

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

2014-10-27T23:59:59.000Z

415

Distribution Based Data Filtering for Financial Time Series Forecasting  

E-Print Network [OSTI]

recent past. In this paper, we address the challenge of forecasting the behavior of time series using@unimelb.edu.au Abstract. Changes in the distribution of financial time series, particularly stock market prices, can of stock prices, which aims to forecast the future values of the price of a stock, in order to obtain

Bailey, James

416

On the reliability of seasonal forecasts Antje Weisheimer  

E-Print Network [OSTI]

On the reliability of seasonal forecasts Antje Weisheimer Weisheimer to achieving a "5"? à Use reliability of non-climatological forecastsDon: · if (X) C(X) à climatological (reliable) informaDon · if (X) C(X) à

Stevenson, Paul

417

SATELLITE BASED SHORT-TERM FORECASTING OF SOLAR IRRADANCE  

E-Print Network [OSTI]

SATELLITE BASED SHORT-TERM FORECASTING OF SOLAR IRRADANCE - COMPARISON OF METHODS AND ERROR Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources method was used to derive motion vector fields from two consecutive images. The future image

Heinemann, Detlev

418

Predicting Solar Generation from Weather Forecasts Using Machine Learning  

E-Print Network [OSTI]

of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables existing forecast-based models. I. INTRODUCTION A key goal of smart grid efforts is to substantially-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than

Shenoy, Prashant

419

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

420

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS by Bruce Bishop Professor of Civil resources resulting in water stress. Effective water management ­ a solution Supply side management Demand side management #12;Developing a regression equation based on cluster analysis for forecasting water

Keller, Arturo A.

Note: This page contains sample records for the topic "forecasting wa id" 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

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

422

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network [OSTI]

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

Washington at Seattle, University of

423

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network [OSTI]

-- Photovoltaic systems, Batteries, Forecasting I. INTRODUCTION This paper presents first results of a study Energies and Energy Systems Sophia Antipolis, France andrea.michiorri@mines-paristech.fr Abstract production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size

Paris-Sud XI, Université de

424

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

425

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

426

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network [OSTI]

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size Andrew. R.Lawrence@ecmwf.int #12;Abstract An ensemble-based data assimilation approach is used to transform old en- semble. The impact of the transformations are propagated for- ward in time over the ensemble's forecast period

Hansens, Jim

427

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.

428

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

429

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

SciTech Connect (OSTI)

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

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

2012-07-01T23:59:59.000Z

430

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

431

NatioNal aNd Global Forecasts West VirGiNia ProFiles aNd Forecasts  

E-Print Network [OSTI]

· NatioNal aNd Global Forecasts · West VirGiNia ProFiles aNd Forecasts · eNerGy · Healt Global Insight, paid for by the West Virginia Department of Revenue. 2013 WEST VIRGINIA ECONOMIC OUTLOOKWest Virginia Economic Outlook 2013 is published by: Bureau of Business & Economic Research West

Mohaghegh, Shahab

432

Decommissioning samples from the Ft. Lewis, WA, solvent refined coal pilot plant: chemical analysis and biological testing  

SciTech Connect (OSTI)

This report presents the results from chemical analyses and limited biological assays of three sets of samples from the Ft. Lewis, WA solvent refined coal (SRC) pilot plant. The samples were collected during the process of decommissioning this facility. Chemical composition was determined for chemical class fractions of the samples by using high-resolution gas chromatography (GC), high-resolution GC/mass spectrometry (MS) and high-resolution MS. Biological activity was measuring using both the histidine reversion microbial mutagenicity assay with Salmonella typhimurium, TA98 and an initiation/promotion mouse-skin tumorigenicity assay. 19 refs., 7 figs., 27 tabs.

Weimer, W.C.; Wright, C.W.

1985-10-01T23:59:59.000Z

433

RAPID/Roadmap/14-ID-f | 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 Home5b9fcbce19 No revisionEnvReviewNonInvasiveExploration JumpSanyalTempWellheadWahkiakum CountyPzero-FD-b34-HI-b NPDES4-ID-c4-ID-f

434

Property:RAPID/Contact/ID2/Phone | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/Website Property TypeID2/Phone

435

Property:RAPID/Contact/ID2/Website | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/Website PropertyID2/Website Property

436

Property:RAPID/Contact/ID3/Phone | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/WebsiteID3/Phone Property Type String

437

Property:RAPID/Contact/ID3/Position | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/WebsiteID3/Phone Property Type

438

Property:RAPID/Contact/ID5/Organization | 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 CenterFranconia, Virginia: EnergyPotentialUrbanUtilityScalePVCapacity Jump to: navigation, searchID1/WebsiteID3/PhoneOrganization"

439

RAPID/Roadmap/14-ID-b | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado < RAPID‎ |1-TX-a13-ID-a State14-FD-c EPA4-ID-b

440

RAPID/Roadmap/18-ID-d | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎ | Roadmap8-FD-cc8-ID-c8-ID-d

Note: This page contains sample records for the topic "forecasting wa id" 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

SEARLE SCHOLARS PROGRAM (http://www.searlescholars.net/go.php?id=23)  

E-Print Network [OSTI]

SEARLE SCHOLARS PROGRAM (http://www.searlescholars.net/go.php?id=23) The University of Pittsburgh at http://www.searlescholars.net/go.php?id=49. The University is invited to submit one nomination

Sibille, Etienne

442

A Cosmology Forecast Toolkit -- CosmoLib  

E-Print Network [OSTI]

The package CosmoLib is a combination of a cosmological Boltzmann code and a simulation toolkit to forecast the constraints on cosmological parameters from future observations. In this paper we describe the released linear-order part of the package. We discuss the stability and performance of the Boltzmann code. This is written in Newtonian gauge and including dark energy perturbations. In CosmoLib the integrator that computes the CMB angular power spectrum is optimized for a $\\ell$-by-$\\ell$ brute-force integration, which is useful for studying inflationary models predicting sharp features in the primordial power spectrum of metric fluctuations. The numerical code and its documentation are available at http://www.cita.utoronto.ca/~zqhuang/CosmoLib.

Zhiqi Huang

2012-06-11T23:59:59.000Z

443

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect (OSTI)

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

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

2014-07-09T23:59:59.000Z

444

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

445

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

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

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

2005-07-01T23:59:59.000Z

446

RAPID/Roadmap/20-ID-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a < RAPID‎gWA-c Transfer

447

RAPID/Roadmap/4-ID-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a <3-FD-d3-WA-b Land4-FD-b4-HI-a

448

RAPID/Roadmap/6-ID-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a State6-CO-b Construction Stormb

449

RAPID/Roadmap/6-ID-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a State6-CO-b Construction

450

RAPID/Roadmap/7-ID-c | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e BLM/CEC Joint7-HI-b

451

RAPID/Roadmap/9-ID-a | 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 CenterFranconia, Virginia:FAQ < RAPID Jump to: navigation, search RAPIDColorado <17-HI-a4-WA-a7-CA-e8-HI-a8-NV-cc <9-FD-aa <

452

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

E-Print Network [OSTI]

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

Marquez, Ricardo

2012-01-01T23:59:59.000Z

453

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

454

Solving the problem of inadequate scoring rules for assessing probabilistic football forecast models  

E-Print Network [OSTI]

Solving the problem of inadequate scoring rules for assessing probabilistic football forecast forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three their forecast accuracy. Moreover, the various scoring rules used for validation in previous studies

Fenton, Norman

455

Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System  

E-Print Network [OSTI]

Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observa- tions and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature

456

Distributed Forcing of Forecast and Assimilation Error Systems BRIAN F. FARRELL  

E-Print Network [OSTI]

Distributed Forcing of Forecast and Assimilation Error Systems BRIAN F. FARRELL Division forecast system gov- erning forecast error growth and the tangent linear observer system governing deterministic and stochastic forcings of the forecast and observer systems over a chosen time interval

Farrell, Brian F.

457

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

458

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

459

November 14, 2000 A Quarterly Forecast of U.S. Trade  

E-Print Network [OSTI]

November 14, 2000 A Quarterly Forecast of U.S. Trade in Services and the Current Account, 2000 of Forecast*** We forecast that the services trade surplus, which declined from 1997 to 1998 and edged upward. That is, from a level of $80.6 billion in 1999, we forecast that the services trade surplus will be $80

Shyy, Wei

460

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

E-Print Network [OSTI]

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

Note: This page contains sample records for the topic "forecasting wa id" 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

Wind Energy Technology Trends: Comparing and Contrasting Recent Cost and Performance Forecasts (Poster)  

SciTech Connect (OSTI)

Poster depicts wind energy technology trends, comparing and contrasting recent cost and performance forecasts.

Lantz, E.; Hand, M.

2010-05-01T23:59:59.000Z

462

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

463

A comparison of univariate methods for forecasting electricity demand up to a day ahead  

E-Print Network [OSTI]

A comparison of univariate methods for forecasting electricity demand up to a day ahead James W methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short of Forecasters. Published by Elsevier B.V. All rights reserved. Keywords: Electricity demand forecasting

McSharry, Patrick E.

464

Resource Adequacy Load Forecast A Report to the Resource Adequacy Advisory Committee  

E-Print Network [OSTI]

one hour peak demand and monthly energy assuming normal weather. The Council forecast includes loadsResource Adequacy Load Forecast A Report to the Resource Adequacy Advisory Committee Tomás of the assessment is the load forecast. The Council staff has recently developed a load forecast to be used

465

A collaborative demand forecasting process with event-based fuzzy judgements Naoufel Cheikhrouhou a,  

E-Print Network [OSTI]

A collaborative demand forecasting process with event-based fuzzy judgements Naoufel Cheikhrouhou a July 2011 Keywords: Collaborative forecasting Demand planning Judgement Time series Fuzzy logic a b s t r a c t Mathematical forecasting approaches can lead to reliable demand forecast in some

466

Demand forecast accuracy and performance of inventory policies under multi-level rolling  

E-Print Network [OSTI]

Demand forecast accuracy and performance of inventory policies under multi-level rolling schedule is to study the behaviour of lot-sizing rules in a multi- level context when forecast demand is subject Interchange to ameliorate demand forecast. Although the presence or absence of forecast errors matters more

Paris-Sud XI, Université de

467

UGA ID Number Last Name First MI Academic Term THE UNIVERSITY OF GEORGIA  

E-Print Network [OSTI]

UGA ID Number Last Name First MI Academic Term THE UNIVERSITY OF GEORGIA APPLICATION TO MAKE LATE's Office UGA ID Number Last Name First MI Academic Term THE UNIVERSITY OF GEORGIA APPLICATION TO MAKE LATE's Office UGA ID Number Last Name First MI Academic Term THE UNIVERSITY OF GEORGIA APPLICATION TO MAKE LATE

Arnold, Jonathan

468

ATLAS ID Upgrade R&D Plan: Development of a Short-Strip Silicon Detector Module  

E-Print Network [OSTI]

ATLAS ID Upgrade R&D Plan: Development of a Short-Strip Silicon Detector Module and a Frontend of the optimum technology and layout of the tracking detectors for the upgraded ATLAS ID. The goal for the intermediate tracking region in the upgraded ATLAS ID. We anticipate that much of the work would then also

California at Santa Cruz, University of

469

TidFP: Mining Frequent Patterns in Different Databases with Transaction ID  

E-Print Network [OSTI]

techniques as well as sequential mining. Keywords: Data mining, Transaction id, Frequent PatternsTidFP: Mining Frequent Patterns in Different Databases with Transaction ID C.I. Ezeife and Dan) are unique and would not usually be frequent, mining frequent patterns with transaction ids, show- ing

Ezeife, Christie

470

ORNL 2010-G00967/jcn UT-B ID 200802057  

E-Print Network [OSTI]

-site, so that water does not need to be transported to a treatment facility. It also permits fuel ·· Permits on-site treatment ·· Allows fuel processing waters to be safely discharged or reused ·· ReducesORNL 2010-G00967/jcn UT-B ID 200802057 Treatment of Fuel Process Wastewater Using Fuel Cells

471

, 1. CONTRACT ID CODE IPAG~ O F PAGES  

E-Print Network [OSTI]

·' , 1. CONTRACT ID CODE IPAG~ O F PAGES AMENDMENT OF SOLICITATION/MODIFICATION OF CONTRACT I 2 2. MODIFICATION OF CONTRACT/ ORDER NO. DUNS# 032987476 l8l DE-AC05-76RL01830 10B. DATED (SEE ITEM 13) CODE ONLY TO MODIFICATIONS OF CONTRACTS/ORDERS, IT MODIFIES THE CONTRACT/ORDER NO. AS SET FORTH IN ITEM 14

472

UW China Hong Kong Entrance Scholarship University of Waterloo ID#  

E-Print Network [OSTI]

UW ­ China Hong Kong Entrance Scholarship Name: University of Waterloo ID#: Program Applied of Waterloo who currently lives in or who previously lived in Hong Kong or mainland China. Candidates must also intend to return to Hong Kong or China after graduation. Selection will be based on academic

Le Roy, Robert J.

473

ORNL 2010-G00627/jcn UT-B ID 200601740  

E-Print Network [OSTI]

ORNL 2010-G00627/jcn UT-B ID 200601740 Power Charging and Supply System for Electric Vehicles Technology Summary A versatile new power electronics system for electric and hybrid electric vehicles (EVs charger and enables these vehicles to function as mobile electrical power generators for emergency

474

UCRL-ID-119170 LAWRENCE LIVERMORE NATIONAL LABORATORY  

E-Print Network [OSTI]

June 1995 UCRL-ID-119170 LAWRENCE LIVERMORE NATIONAL LABORATORY University of California · Livermore, California · 94550 Science on High-Energy Lasers: From Today to the NIF Richard W. Lee, Richard. WorkperformedundertheauspicesoftheU.S.DepartmentofEnergybyLawrenceLivermoreNationalLaboratoryunder Contract W-7405-Eng-48. #12

475

ORNL 2010-G0613-jcn UT-B ID 200902238  

E-Print Network [OSTI]

, and longer burn-up times. This translates to more megawatts per nuclear power plant and less spent fuel the fuel to burn longer ·· Fuel will be cooler, experiencing significantly less damage and allowing higherORNL 2010-G0613-jcn UT-B ID 200902238 Composite Nuclear Fuel Pellet Technology Summary To improve

476

https://doyouliveunited.org 1. Enter you user ID  

E-Print Network [OSTI]

Search' button. 7. Enter you search terms for the agency of your choice and click on `Search'. #12;httpshttps://doyouliveunited.org 1. Enter you user ID: your email address Enter your password: welcome be different then the options listed here. 5. For a payroll pledge, enter the amount per pay or the total

477

Student ID Number Date of birth Cell Phone  

E-Print Network [OSTI]

Name Student ID Number Date of birth Cell Phone New Housing Student Current Housing Student All's phone Crime 2 Information about charges or crime convicted of Date of conviction Court convicted in Sentence received Probation dates Probation officer's name Probation officer's phone Consent: I authorize

Pantaleone, Jim

478

ORNL 2010-G01074/jcn UT-B ID 200301298  

E-Print Network [OSTI]

. The device permits the heat pump to extract and store heat from ground and air via the dynamic exchangeORNL 2010-G01074/jcn UT-B ID 200301298 Super Energy Saver Heat Pump Technology Summary ORNL heat pumps, inventing a super energy saver heat pump. This invention significantly improves heating

479

Contribution ID : 133 The TAG Collector -A Tool for Atlas  

E-Print Network [OSTI]

CHEP04 Contribution ID : 133 The TAG Collector - A Tool for Atlas Code Release Management Thursday 30 Sep 2004 at 10:00 (00h00') The Tag Collector is a web interfaced database application for release distributed geographically. The Tag Collector was designed and implemented during the summer of 2001

Paris-Sud XI, Universit de

480

Introduction to Health and Social Care (ID:250)  

E-Print Network [OSTI]

Introduction to Health and Social Care (ID:250) Outline This is a day event which will be designed will be given short talks from different staff about the various health and social care courses on offer details Learning outcomes: · The different health and social care courses offered at Swansea University

Harman, Neal.A.

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


481

Aquatic Chemistry Course Id: CHEM 605 (3 cr.)  

E-Print Network [OSTI]

Aquatic Chemistry Fall 2010 Course Id: CHEM 605 (3 cr.) Lecture: TR 3:40-5:20pm, REIC 165 of this course is to introduce students to the concepts and models used in aquatic chemistry while providing-base chemistry, complexation, precipitation-dissolution and reduction-oxidation reactions. Student Learning

Wagner, Diane

482

2012 -2013 BIRTH DATE VERIFICATION STUDENT NAME: SPU ID  

E-Print Network [OSTI]

an official copy of your Birth Certificate, Passport, or Driver's License with this form and we will update2012 - 2013 BIRTH DATE VERIFICATION STUDENT NAME: SPU ID: The date of birth reported on your Free Application for Federal Student Aid (FAFSA) does not match the date of birth reported in one of the following

Nelson, Tim

483

2013 -2014 BIRTH DATE VERIFICATION STUDENT NAME: SPU ID  

E-Print Network [OSTI]

an official copy of your Birth Certificate, Passport, or Driver's License with this form and we will update2013 - 2014 BIRTH DATE VERIFICATION STUDENT NAME: SPU ID: The date of birth reported on your Free Application for Federal Student Aid (FAFSA) does not match the date of birth reported in one of the following

Nelson, Tim

484

ORNL 2010-G00654/jcn UT-B ID 200701899  

E-Print Network [OSTI]

ORNL 2010-G00654/jcn UT-B ID 200701899 Photoactive Wound Dressing Technology Summary Improved this product. This technology can be used to treat wounds, burns, and other types of skin trauma in both of the nanoparticles causes the release of antimicrobial oxygen radicals, enhancing the therapeutic effect

485

ORNL 2010-G00976/jcn UT-B ID 200802116  

E-Print Network [OSTI]

ORNL 2010-G00976/jcn UT-B ID 200802116 Detection of Latent Prints by Raman Imaging Technology, allowing the evidence to be visualized. The technology is capable of imaging latent prints on porous and non-porous surfaces, and is especially useful for prints that are low in oil (or "clean") and children

486

ORNL 2010-G01078/jcn UT-B ID 201002389  

E-Print Network [OSTI]

ORNL 2010-G01078/jcn UT-B ID 201002389 Energy Saving Absorption Heat Pump Water Heater Technology Summary ORNL's new absorption heat pump and water heater technology offers substantial energy savings and can reduce the use of fossil fuels by buildings. While conventional heat pump water heater designs

Pennycook, Steve

487

Article ID: Query Translation on the Fly in Deep Web  

E-Print Network [OSTI]

Article ID: Query Translation on the Fly in Deep Web Integration Jiang Fangjiao, Jia Linlin, Meng users to access the desired information, many researches have dedicated to the Deep Web (i.e. Web databases) integration. We focus on query translation which is an important part of the Deep Web integration

488

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network [OSTI]

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

Yang, Xiaorong

2008-01-01T23:59:59.000Z

489

Forecasting Returns and Volatilities in GARCH Processes Using the Bootstrap  

E-Print Network [OSTI]

Forecasting Returns and Volatilities in GARCH Processes Using the Bootstrap Lorenzo Pascual, Juan generated by GARCH processes. The main advantage over other bootstrap methods previously proposed for GARCH by having conditional heteroscedasticity. Generalized Autoregressive Conditionally Heteroscedastic (GARCH

Romo, Juan

490

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

491

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network [OSTI]

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

492

Recently released EIA report presents international forecasting data  

SciTech Connect (OSTI)

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

NONE

1995-05-01T23:59:59.000Z

493

Optimally controlling hybrid electric vehicles using path forecasting  

E-Print Network [OSTI]

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

Katsargyri, Georgia-Evangelina

2008-01-01T23:59:59.000Z

494

Variable Selection and Inference for Multi-period Forecasting Problems  

E-Print Network [OSTI]

Variable Selection and Inference for Multi-period Forecasting Problems? M. Hashem Pesaran Cambridge University and USC Andreas Pick De Nederlandsche Bank and Cambridge University, CIMF Allan Timmermann UC San Diego and CREATES January 26, 2009...

Pesaran, M Hashem; Pick, Andreas; Timmermann, Allan

495

Optimally Controlling Hybrid Electric Vehicles using Path Forecasting  

E-Print Network [OSTI]

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

Kolmanovsky, Ilya V.

496

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.

497

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...

498

An econometric analysis and forecasting of Seoul office market  

E-Print Network [OSTI]

This study examines and forecasts the Seoul office market, which is going to face a big supply in the next few years. After reviewing several previous studies on the Dynamic model and the Seoul Office market, this thesis ...

Kim, Kyungmin

2011-01-01T23:59:59.000Z

499

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

500

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