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  1. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-baa <baWA-b <

  2. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-baa <baWA-b <d

  3. WA_1995_001_US_AUTO_MATERIALS_PARTNERSHIPS_Waiver_of_Patent_...

    Office of Environmental Management (EM)

    WA1995001USAUTOMATERIALSPARTNERSHIPSWaiverofPatent.pdf WA1995001USAUTOMATERIALSPARTNERSHIPSWaiverofPatent.pdf WA1995001USAUTOMATERIALSPARTNERSHIPSWaiver...

  4. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ | Roadmap Jump to: navigation, searcheWA-a < RAPID‎ |

  5. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b <ai <bb <

  6. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b <ai <bb

  7. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b <ai <bbd

  8. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b <ai <bbde

  9. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-ba < RAPID‎a

  10. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-baa <ba

  11. WA_1994010__SCHWITZER_U.S._INC_Waiver_of_Domestic_and_Foreig...

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

    Publications WA1994007KYOCERAINDUSTRIALCERAMICSCORPORATIONWaivero.pdf WA1994011EATONCORPORATIONWaiverofDomesticandForeign.pdf WA02028TRANECOWaiverofDomesti...

  12. WA_02_021_H2GEN_INNOVATIONS_Waiver_of_Domestic_and_Foreign_P...

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

    WA02046QUESTAAIRTECHNOLOGIESWaiverofDomesticandFor.pdf WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA04034NUVERAFUELCELLSINCWaiver...

  13. WA_98_005_WESTINGHOUSE_POWER_GENERATION_A_FORMER_DIVISION_OF...

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

    5WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98005WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98005WESTINGHOUSEPOWERGENERATIONAFORMERDIVISION...

  14. WA_98_006_WESTINGHOUSE_POWER_GENERATION_A_FORMER_DIVISION_OF...

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

    6WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98006WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98006WESTINGHOUSEPOWERGENERATIONAFORMERDIVISION...

  15. WA_00_007_COMBUSTION_ENGINEERING_INC_Waiver_of_Domestic_and_...

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

    07COMBUSTIONENGINEERINGINCWaiverofDomesticand.pdf WA00007COMBUSTIONENGINEERINGINCWaiverofDomesticand.pdf WA00007COMBUSTIONENGINEERINGINCWaiverofDomestica...

  16. WA_1994_034_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...

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

    4034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDom...

  17. WA_99_017_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Domestic_and_...

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

    9017AIRPRODUCTSANDCHEMICALSWaiverofDomesticand.pdf WA99017AIRPRODUCTSANDCHEMICALSWaiverofDomesticand.pdf WA99017AIRPRODUCTSANDCHEMICALSWaiverofDomesti...

  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_96_016_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domestic_...

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

    16AIRPRODUCTSANDCHEMICALSINCWaiverofDomestic.pdf WA96016AIRPRODUCTSANDCHEMICALSINCWaiverofDomestic.pdf WA96016AIRPRODUCTSANDCHEMICALSINCWaiverofDomest...

  20. WA_1995_014_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...

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

    14AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995014AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995014AIRPRODUCTSANDCHEMICALSINCWaiverofDomest...

  1. WA_04_028_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_patent_Rights...

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

    8AIRPRODUCTSANDCHEMICALSWaiverofpatentRights.pdf WA04028AIRPRODUCTSANDCHEMICALSWaiverofpatentRights.pdf WA04028AIRPRODUCTSANDCHEMICALSWaiverofpatentRigh...

  2. 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...

  3. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOEThe Bonneville PowerCherries 82981-1cnHigh SchoolIn12electronEnergy Manufacturing Energy andYou areID 90 Map

  4. 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...

  5. 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

  6. WA_04_080_HYBRID_POWER_GENERATION_SYSTEMS_Waiver_of_Patent_R...

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

    80HYBRIDPOWERGENERATIONSYSTEMSWaiverofPatentR.pdf WA04080HYBRIDPOWERGENERATIONSYSTEMSWaiverofPatentR.pdf WA04080HYBRIDPOWERGENERATIONSYSTEMSWaiverofPaten...

  7. 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...

  8. WA_96_004_GE_CORPORATE_RESEARCH_and_DEVELOPMENT_Waiver_of_Dome...

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

    RATERESEARCHandDEVELOPMENTWaiverofDome.pdf More Documents & Publications WA1993012GENERALELECTRICCOMPANY--CORPORATERESEARCHAND.pdf WA1994013GENERALELECTRICCOMPANY...

  9. WA_03_021_DELPHI_AUTOMOTIVE_SYSTEMS_Waiver_of_Patent_Rights_...

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

    1DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA03021DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA03021DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRight...

  10. WA_04_082_DELPHI_AUTOMOTIVE_SYSTEMS_Waiver_of_Patent_Rights_...

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

    82DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA04082DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRights.pdf WA04082DELPHIAUTOMOTIVESYSTEMSWaiverofPatentRigh...

  11. WA_04_033_CARGILL_Waiver_of_Patent_Rights_to_CARGILL_DOWN_L.pdf...

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

    tentRightstoCARGILLDOWNL.pdf More Documents & Publications WA00022CARGILLDOWPOLYMERSLLCWaiverofDomesticandFo.pdf WA05022DOWCHEMICALCOMPANYWaiverofdomestica...

  12. WA_1993_003_EATON_CORPORATION_Waiver_of_Domestic_and_Foreign...

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

    3003EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1993003EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1993003EATONCORPORATIONWaiverofDomesticandFor...

  13. WA_1994_011_EATON_CORPORATION_Waiver_of_Domestic_and_Foreign...

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

    1EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1994011EATONCORPORATIONWaiverofDomesticandForeign.pdf WA1994011EATONCORPORATIONWaiverofDomesticandForeign...

  14. WA_04_083_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Patent_Rights...

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

    83AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04083AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04083AIRPRODUCTSANDCHEMICALSWaiverofPatentRig...

  15. WA_04_025_AIR_LIQUIDE_AMERICA_Waiver_of_Patent_Rights_under_...

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

    25AIRLIQUIDEAMERICAWaiverofPatentRightsunder.pdf WA04025AIRLIQUIDEAMERICAWaiverofPatentRightsunder.pdf WA04025AIRLIQUIDEAMERICAWaiverofPatentRightsund...

  16. WA_02_046_QUESTA_AIR_TECHNOLOGIES_Waiver_of_Domestic_and_For...

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

    6QUESTAAIRTECHNOLOGIESWaiverofDomesticandFor.pdf WA02046QUESTAAIRTECHNOLOGIESWaiverofDomesticandFor.pdf WA02046QUESTAAIRTECHNOLOGIESWaiverofDomesticandF...

  17. WA_99_022_AIR_PRODUCTS_AND_CHEMICAL_Waiver_of_Domestic_and_F...

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

    9022AIRPRODUCTSANDCHEMICALWaiverofDomesticandF.pdf WA99022AIRPRODUCTSANDCHEMICALWaiverofDomesticandF.pdf WA99022AIRPRODUCTSANDCHEMICALWaiverofDomestic...

  18. WA_02_015_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Patent_Ri...

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

    15AIRPRODUCTSANDCHEMICALSINCWaiverofPatentRi.pdf WA02015AIRPRODUCTSANDCHEMICALSINCWaiverofPatentRi.pdf WA02015AIRPRODUCTSANDCHEMICALSINCWaiverofPatent...

  19. WA_04_063_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Patent_Rights...

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

    63AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04063AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04063AIRPRODUCTSANDCHEMICALSWaiverofPatentRig...

  20. WA_01_005__PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_patent...

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

    1005PRAXAIRINCWaiverofDomesticandForeignpatent.pdf WA01005PRAXAIRINCWaiverofDomesticandForeignpatent.pdf WA01005PRAXAIRINCWaiverofDomesticandForeign...

  1. WA_01_022_PRAXAIR_INC_AND_BP_AMOCO_Waiver_of_Domestic_and_Fo...

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

    1022PRAXAIRINCANDBPAMOCOWaiverofDomesticandFo.pdf WA01022PRAXAIRINCANDBPAMOCOWaiverofDomesticandFo.pdf WA01022PRAXAIRINCANDBPAMOCOWaiverofDomestic...

  2. 27-ID and 35-ID Construction Schedule | Advanced Photon Source

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

    27-ID and 35-ID Past 27-ID and 35-ID Installation schedule for the sector 27 Control room. Receive materials on Friday March 10, 2014 Installation starts on Monday March 10, 2014...

  3. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

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

  4. Beamline 29-ID

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

    (29-ID): The Intermediate Energy X-Ray (IEX) beamline 29-ID is currently under commissioning and construction. The general user program is expected to start in 2015. This...

  5. ,"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...

  6. BayWa Group | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:EzfeedflagBiomass Conversions IncBay County, Florida: Energy ResourcesBayWa Group Jump to:

  7. Advance Patent Waiver W(A)2010-028 | Department of Energy

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

    W(A)2010-028 More Documents & Publications Advance Patent Waiver W(A)2009-028 Novel Materials for High Efficiency Direct Methanol Fuel Cells Advance Patent Waiver W(A)2008-019...

  8. 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...

  9. waTer economics. environmenTand Policy

    E-Print Network [OSTI]

    Botea, Adi

    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

  10. WA_03_011_ROCKWELL_AUTOMATION_Waiver_of_Patent_Rights_Under_...

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

    3011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA03011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA03011ROCKWELLAUTOMATIONWaiverofPatentRights...

  11. 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...

  12. WA_04_074_EATON_CORPORATION_Waiver_of_Domestic_and_Foreign_I...

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

    74EATONCORPORATIONWaiverofDomesticandForeignI.pdf WA04074EATONCORPORATIONWaiverofDomesticandForeignI.pdf WA04074EATONCORPORATIONWaiverofDomesticandForeig...

  13. WA_02_048_EATON_CORPORATION_Waviver_of_Patent_Rights_Under_A...

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

    48EATONCORPORATIONWaviverofPatentRightsUnderA.pdf WA02048EATONCORPORATIONWaviverofPatentRightsUnderA.pdf WA02048EATONCORPORATIONWaviverofPatentRightsUnde...

  14. 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...

  15. WA_04_059_EATON_CORPORATION_Waiver_of_Patent_Rights_Under_a_...

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

    59EATONCORPORATIONWaiverofPatentRightsUndera.pdf WA04059EATONCORPORATIONWaiverofPatentRightsUndera.pdf WA04059EATONCORPORATIONWaiverofPatentRightsUnder...

  16. WA_99_012_AIR_PRODUCTS_Waiver_of_Patent_Rights_Under_AN_NVO_...

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

    2AIRPRODUCTSWaiverofPatentRightsUnderANNVO.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNVO.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNV...

  17. WA_00_001_PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_Inventi...

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

    01PRAXAIRINCWaiverofDomesticandForeignInventi.pdf WA00001PRAXAIRINCWaiverofDomesticandForeignInventi.pdf WA00001PRAXAIRINCWaiverofDomesticandForeignInve...

  18. WA_04_079_PRAXAIR_INC_Waiver_of_Patent_Rights_Under_a_Subcon...

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

    04079PRAXAIRINCWaiverofPatentRightsUnderaSubcon.pdf WA04079PRAXAIRINCWaiverofPatentRightsUnderaSubcon.pdf WA04079PRAXAIRINCWaiverofPatentRightsUndera...

  19. WA_02_055_PRAXAIR_Waiver_of_Domestic_and_Foreign_Patent_Righ...

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

    2055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA02055PRAXAIRWaiverofDomesticandForeignPaten...

  20. WA_03_024_PRAXAIR_Waiver_of_Domestic_and_Foreign_Invention_R...

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

    24PRAXAIRWaiverofDomesticandForeignInventionR.pdf WA03024PRAXAIRWaiverofDomesticandForeignInventionR.pdf WA03024PRAXAIRWaiverofDomesticandForeignInventio...

  1. WA_00_018_PRAXAIR_Waive_of_Domestic_and_Foreign_Invention_Ri...

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

    18PRAXAIRWaiveofDomesticandForeignInventionRi.pdf WA00018PRAXAIRWaiveofDomesticandForeignInventionRi.pdf WA00018PRAXAIRWaiveofDomesticandForeignInvention...

  2. WA_01_039_PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_Patent_...

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

    1039PRAXAIRINCWaiverofDomesticandForeignPatent.pdf WA01039PRAXAIRINCWaiverofDomesticandForeignPatent.pdf WA01039PRAXAIRINCWaiverofDomesticandForeignP...

  3. RAPID/Roadmap/7-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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ | Roadmap Jump to: navigation, searcheWA-aHI-a <a <

  4. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-e <20 <-ID-a <

  5. RAPID/Roadmap/3-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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b < RAPID‎ |

  6. RAPID/Roadmap/3-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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b < RAPID‎

  7. RAPID/Roadmap/3-ID-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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b <

  8. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-b <aibHI-a

  9. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-baa <b <a <

  10. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-baa <b <a

  11. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-eID-baa <b <ac

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

    E-Print Network [OSTI]

    Borenstein, Elhanan

    Seattle, WA Permit #62Jeff Heer will join us from Stanford University, where he is a faculty member, a Presidential Early Career Award for Scientists and Engineers, the IJCAI Computers and Thought Award

  13. DOE/ID-Number

    Energy Savers [EERE]

    Report UCRL-ID-133846. Walker, J.S. 2009. The Road to Yucca Mountain. Berkeley, CA: University of California Press. Warner, D.L. 1972. Survey of Industrial Waste Injection...

  14. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

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

  15. 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

  16. Rainfall-River Forecasting

    E-Print Network [OSTI]

    US Army Corps of Engineers

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

  17. Advance Patent Waiver W(A)2005-027

    Broader source: Energy.gov [DOE]

    This is a request by WESTINGHOUSE ELECTRIC CORPORATION for a DOE waiver of domestic and foreign patent rights under agreement DE-FC07-05ID14636.

  18. 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...

  19. 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...

  20. 4-ID-D optics

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

    4-ID-D Beamline Optics A schetch of the major optical components for beam line 4-ID-D are shown above. All these components located in the B-station upstream from the D...

  1. Beamline 4-ID-D

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

    D Home Recent Publications Beamline Info Optics Instrumentation Software User Info FAQs Beamline 4-ID-D Beamline 4-ID-D is operated by the Magnetic Materials Group in the X-ray...

  2. 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.

  3. NAME: Eelgrass Restoration in Puget Sound LOCATION: Puget Sound, WA

    E-Print Network [OSTI]

    US Army Corps of Engineers

    NAME: Eelgrass Restoration in Puget Sound LOCATION: Puget Sound, WA ACRES: 3,700 acres of subtidal restoration efforts and to contribute to the Puget Sound Partnership's Action Agenda recovery goal of 20% more within the Puget Sound region of the Salish Sea: the Nisqually, Elwha, and Skokomish Rivers. These major

  4. carleton universityottaWa, canaDa international

    E-Print Network [OSTI]

    Dawson, Jeff W.

    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

  5. Probabilistic manpower forecasting

    E-Print Network [OSTI]

    Koonce, James Fitzhugh

    1966-01-01T23:59:59.000Z

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

  6. UPF Forecast | Y-12 National Security Complex

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

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

  7. Long Term Forecast ofLong Term Forecast of TsunamisTsunamis

    E-Print Network [OSTI]

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

  8. 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

  9. Steam System Forecasting and Management

    E-Print Network [OSTI]

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

    1982-01-01T23:59:59.000Z

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

  10. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOfficeOctoberDaniel WoodID Service First DOI

  11. T ID CODE I

    National Nuclear Security Administration (NNSA)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn AprilA Approved:AdministrationAnalysisDarby Dietrich5 | NUMBER 1 | MARCHT ID CODE I

  12. Consensus Coal Production Forecast for

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    Consensus Coal Production Forecast for West Virginia 2009-2030 Prepared for the West Virginia Summary 1 Recent Developments 2 Consensus Coal Production Forecast for West Virginia 10 Risks References 27 #12;W.Va. Consensus Coal Forecast Update 2009 iii List of Tables 1. W.Va. Coal Production

  13. Improving Inventory Control Using Forecasting

    E-Print Network [OSTI]

    Balandran, Juan

    2005-12-16T23:59:59.000Z

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

  14. timber quality Modelling and forecasting

    E-Print Network [OSTI]

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

  15. Demand Forecast INTRODUCTION AND SUMMARY

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Romero, Romu

    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

  17. 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...

  18. METEOROLOGICAL Weather and Forecasting

    E-Print Network [OSTI]

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

  19. 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

  20. Solar forecasting review

    E-Print Network [OSTI]

    Inman, Richard Headen

    2012-01-01T23:59:59.000Z

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

  1. ID-69 Sodium drain experiments

    SciTech Connect (OSTI)

    Johnston, D.C.

    1996-09-19T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Mathiesen, Patrick James

    2013-01-01T23:59:59.000Z

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

  3. 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...

  4. Climate Action Champions: Seattle, WA | Department of Energy

    Energy Savers [EERE]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directed offOCHCO OverviewAttachments4 Chairs Meeting - AprilEvents CleanSeattle, WA Climate Action

  5. RAPID/Roadmap/1-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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformation TexasTexas) Redirect pageNV-a <TX-aWA-a

  6. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |g <RAPID/Roadmap/19-WA-c

  7. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-e < RAPID‎ |

  8. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-e < RAPID‎

  9. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione <RAPID/Roadmap/7-FD-k <TX-c <WA-a

  10. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformation TexasTexas)ID-a < RAPID‎ID-a

  11. Forecasting oilfield economic performance

    SciTech Connect (OSTI)

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

    1994-11-01T23:59:59.000Z

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

  12. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect (OSTI)

    Lew, D.

    2011-04-01T23:59:59.000Z

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

  13. ELECTRICITY DEMAND FORECAST COMPARISON REPORT

    E-Print Network [OSTI]

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

  14. 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

  15. Bull Test ID 1118 2013 Florida Bull Test

    E-Print Network [OSTI]

    Jawitz, James W.

    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

  16. Bull Test ID 1181 2013 Florida Bull Test

    E-Print Network [OSTI]

    Jawitz, James W.

    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

  17. Bull Test ID 1098 2013 Florida Bull Test

    E-Print Network [OSTI]

    Jawitz, James W.

    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

  18. Bull Test ID 1160 2013 Florida Bull Test

    E-Print Network [OSTI]

    Jawitz, James W.

    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

  19. Bull Test ID 1140 2013 Florida Bull Test

    E-Print Network [OSTI]

    Jawitz, James W.

    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

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

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

    2 1 Locations of Smart Grid Demonstration and Large-Scale Energy Storage Projects NH 32 Awards Support Projects in 24 States 6 11 MA...

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn AprilA group current C3EDepartment ofPrivilegesUnauthorized Access | Department2 1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn AprilA group current C3EDepartment ofPrivilegesUnauthorized Access | Department2 1 2 1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOn AprilA group current C3EDepartment ofPrivilegesUnauthorized Access | Department2 1 2 1 7

  4. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

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

  5. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

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

  6. REVISED CALIFORNIA ENERGY DEMAND FORECAST 20122022

    E-Print Network [OSTI]

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

  7. NATIONAL AND GLOBAL FORECASTS WEST VIRGINIA PROFILES AND FORECASTS

    E-Print Network [OSTI]

    Mohaghegh, Shahab

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

  8. 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

  9. ENERGY DEMAND FORECAST METHODS REPORT

    E-Print Network [OSTI]

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

  10. Mathematical Forecasting Donald I. Good

    E-Print Network [OSTI]

    Boyer, Robert Stephen

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

  11. Regional-seasonal weather forecasting

    SciTech Connect (OSTI)

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

    1980-08-01T23:59:59.000Z

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

  12. APS Beamline 6-ID-B,C

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

    x-ray scattering studies of materials. The beamline has 2 end-stations: 6-ID-B: Psi -Diffractomter & In-Field Studies 6-ID-C: UHV in-situ growth Recent Research Highlights...

  13. APS Beamline 6-ID-D

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

    D Home Recent Publications Beamline Info Optics Instrumentation Software User Info Beamline 6-ID-D Beamline 6-ID-D is operated by the Magnetic Materials Group in the X-ray Science...

  14. RAPID/Roadmap/15-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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ | Roadmap Jump to: navigation, search RAPIDaUT-ab <

  15. RAPID/Roadmap/3-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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ | Roadmap Jump to: navigation, searche <c <caca <

  16. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformation TexasTexas)ID-a < RAPID‎ |TX-a

  17. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformation TexasTexas)ID-a < RAPID‎ |TX-ab <

  18. REQUEST FOR ADVANCE Employee Name: SU ID #

    E-Print Network [OSTI]

    Carter, John

    (RINA 219) Fax: 206-398-4402 Email: bixlers@seattleu.edu FOR OFFICIAL USE ONLY SU ID #: Previous Request

  19. DOE Zero Energy Ready Home Case Study: TC Legend Homes, Bellingham, WA

    Broader source: Energy.gov [DOE]

    Case study of a DOE Zero Energy Ready home in Bellingham, WA, that achieves HERS 43 without PV or HERS 13 with 3.2 kW of PV.

  20. Advance Patent Waiver W(A)2010-007 | Department of Energy

    Office of Environmental Management (EM)

    0-007 Advance Patent Waiver W(A)2010-007 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice...

  1. Bull Test ID 1077 2013 Florida Bull Test

    E-Print Network [OSTI]

    Jawitz, James W.

    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

  2. ID SYSTEM DEBIT ACCOUNT Payroll Deduction Form

    E-Print Network [OSTI]

    Karsai, Istvan

    ID SYSTEM DEBIT ACCOUNT Payroll Deduction Form This is my authorization for the ETSU Payroll Department to make a monthly deduction from my paycheck to be deposited to my ETSU ID System Debit Card 37614-0611 PHONE: 423/439-8316 http://www.etsu.edu/students/univcent/id.htm e-mail IDBUCS@etsu.edu #12;

  3. 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

  4. 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

  5. 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

  6. Value of Wind Power Forecasting

    SciTech Connect (OSTI)

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

    2011-04-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Goto, Susumu

    2007-01-01T23:59:59.000Z

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

  8. 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

  9. 16-12-12Web Archiv e (naf wa.org)-Hosted By Hurricane Electric -Resurrection Of Ex... 1/3naf wa.org/.../11052-resurrection-of -extinct-enzy mes-rev eals-ev olutionary -strategy -f or-the-inv enti...

    E-Print Network [OSTI]

    16-12-12Web Archiv e (naf wa.org)-Hosted By Hurricane Electric - Resurrection Of Ex... 1/3naf wa Archiv e (naf wa.org)-Hosted By Hurricane Electric - Resurrection Of Ex... naf wa.org/.../11052

  10. Forecasting consumer products using prediction markets

    E-Print Network [OSTI]

    Trepte, Kai

    2009-01-01T23:59:59.000Z

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

  11. Massachusetts state airport system plan forecasts.

    E-Print Network [OSTI]

    Mathaisel, Dennis F. X.

    This report is a first step toward updating the forecasts contained in the 1973 Massachusetts State System Plan. It begins with a presentation of the forecasting techniques currently available; it surveys and appraises the ...

  12. Management Forecast Quality and Capital Investment Decisions

    E-Print Network [OSTI]

    Goodman, Theodore H.

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

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

    E-Print Network [OSTI]

    Kemner, Ken

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

  14. 1995 shipment review & five year forecast

    SciTech Connect (OSTI)

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

    1996-01-01T23:59:59.000Z

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

  15. REQUEST FOR TRAVEL AUTHORIZATION Document ID #

    E-Print Network [OSTI]

    Texas at Austin, University of

    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

  16. Consensus Coal Production And Price Forecast For

    E-Print Network [OSTI]

    Mohaghegh, Shahab

    Consensus Coal Production And Price Forecast For West Virginia: 2011 Update Prepared for the West December 2011 Copyright 2011 WVU Research Corporation #12;#12;W.Va. Consensus Coal Forecast Update 2011 i Table of Contents Executive Summary 1 Recent Developments 3 Consensus Coal Production And Price Forecast

  17. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

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

  18. ONION (Allium cepa, 47 cultivars) B.K. Schroeder, Washington State University (WSU), Pullman, Enterobacter bulb decay; Enterobacter cloacae WA 99164; T.D. Waters, WSU Franklin Co. Extension, Pasco WA

    E-Print Network [OSTI]

    Schroeder, Brenda K.

    , Enterobacter bulb decay; Enterobacter cloacae WA 99164; T.D. Waters, WSU Franklin Co. Extension, Pasco WA 99301 in storage in Washington State, 2008-2009. An onion bulb storage trial was completed to survey 47 storage to Enterobacter cloacae, causal agent of Enterobacter bulb decay. Seeds of each cultivar were planted near Pasco

  19. ONION (Allium cepa, 55 cultivars) B.K. Schroeder, Washington State University (WSU), Pullman, Enterobacter bulb decay; Enterobacter cloacae WA 99164; T. Waters, WSU Franklin Co. Extension, Pasco WA

    E-Print Network [OSTI]

    Schroeder, Brenda K.

    , Enterobacter bulb decay; Enterobacter cloacae WA 99164; T. Waters, WSU Franklin Co. Extension, Pasco WA 99301 for resistance to Enterobacter cloacae in storage, 2007-2008. An onion bulb storage trial was completed to survey for resistance to Enterobacter cloacae, causal agent of Enterobacter bulb decay. Seeds of each cultivar were

  20. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID Operations

  1. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID

  2. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID26, 2013

  3. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID26,

  4. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID26,5, 2013

  5. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID26,5,

  6. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID26,5,6,

  7. DOE-ID Operations Summary

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan24, 2013 DOE-ID26,5,6,27,

  8. LOAD FORECASTING Eugene A. Feinberg

    E-Print Network [OSTI]

    Feinberg, Eugene A.

    , regression, artificial intelligence. 1. Introduction Accurate models for electric power load forecasting to make important decisions including decisions on pur- chasing and generating electric power, load for different operations within a utility company. The natures 269 #12;270 APPLIED MATHEMATICS FOR POWER SYSTEMS

  9. Calculator simplifies field production forecasting

    SciTech Connect (OSTI)

    Bixler, B.

    1982-05-01T23:59:59.000Z

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

  10. Reporting Tools Course ID: FMS121

    E-Print Network [OSTI]

    Shull, Kenneth R.

    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

  11. Reporting Tools Course ID: FMS121

    E-Print Network [OSTI]

    Shull, Kenneth R.

    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

  12. 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...

  13. ID BUC$ EQUIPMENT REQUEST FORM CAMPUS EVENT PAYMENT OPTION

    E-Print Network [OSTI]

    Karsai, Istvan

    ID BUC$ EQUIPMENT REQUEST FORM CAMPUS EVENT PAYMENT OPTION FOR ETSU ORGANIZATIONS Name ID BUC$. ETSU account transfer or a check requested? o ETSU Account

  14. Modeling transport of disposed dredged material from placement sites in Grays Harbor, WA

    E-Print Network [OSTI]

    US Army Corps of Engineers

    Modeling transport of disposed dredged material from placement sites in Grays Harbor, WA E- to mid- term dredge material management strategies for the Federal Navigation Project at Grays Harbor dredging quantities. However, the most heavily used dredged material placement sites lie in proximity

  15. U.S. NUclear WaSte techNical revieW Board

    E-Print Network [OSTI]

    technical context as important decisions are made on managing the nation's spent nuclear fuel and high, packaging, and transporting spent nuclear fuel and high-level radioactive waste is presented. The technicalU.S. NUclear WaSte techNical revieW Board Report to The U.S. Congress and The Secretary

  16. 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

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

    E-Print Network [OSTI]

    Uhlmann, Gunther

    Electrical impedance tomography and Calderon'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

  18. 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 (for N-1 contingency) or with the help of Special Protection Schemes (SPS) or Remedial Action Schemes of the relay actions that may have resulted in the angle stability phenomenon. The concept of a real

  19. General Disposal Authority for State Government Information The State Records Office of WA

    E-Print Network [OSTI]

    Tobar, Michael

    ) consolidates and amends the GDAs for Administrative Records, Human Resource Management Records, and Financial and Record Categories covered 4 of 170 Reference Activity / Record Category Page 16 CHEQUE MANAGEMENT 37 17General Disposal Authority for State Government Information The State Records Office of WA

  20. 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 Administration. The Joint Proposal is a comprehensive settlement that will bring to an end the long costs, when the responsibility for reaching agreements with IPPs, is dispersed directly to the load

  1. Comment on the future of the Bonneville Power Administration Jim G. Likes, Thurston County, WA

    E-Print Network [OSTI]

    Comment on the future of the Bonneville Power Administration Jim G. Likes, Thurston County, WA Bonneville is a regional agency that markets federal hydropower and augments its power supply with market, everyday citizens, to pay illegally inflated power costs. Because of this, Bonneville should have the legal

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

    E-Print Network [OSTI]

    Kosters, Walter

    , 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

  3. NREL: Transmission Grid Integration - Forecasting

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

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

  4. 4D-Polytopes and Their Dual Polytopes of the Coxeter Group $W(A_{4})$ Represented by Quaternions

    E-Print Network [OSTI]

    Mehmet Koca; Nazife Ozdes Koca; Mudhahir Al-Ajmi

    2011-02-06T23:59:59.000Z

    4-dimensional $A_{4}$ polytopes and their dual polytopes have been constructed as the orbits of the Coxeter-Weyl group $W(A_{4})$ where the group elements and the vertices of the polytopes are represented by quaternions. Projection of an arbitrary $W(A_{4})$ orbit into three dimensions is made using the subgroup $W(A_{3})$. A generalization of the Catalan solids for 3D polyhedra has been developed and dual polytopes of the uniform $A_{4}$ polytopes have been constructed.

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

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

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

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

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

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

  7. Solid low-level waste forecasting guide

    SciTech Connect (OSTI)

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

    1995-03-01T23:59:59.000Z

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

  8. Geothermal wells: a forecast of drilling activity

    SciTech Connect (OSTI)

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

    1981-07-01T23:59:59.000Z

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

  9. Online Forecast Combination for Dependent Heterogeneous Data

    E-Print Network [OSTI]

    Sancetta, Alessio

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

  10. 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...

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

    E-Print Network [OSTI]

    Kamat, Vineet R.

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

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

    SciTech Connect (OSTI)

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

    2011-10-04T23:59:59.000Z

    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.

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

    DOE Patents [OSTI]

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

    2008-09-30T23:59:59.000Z

    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.

  14. UT-B ID 201102665 Technology Summary

    E-Print Network [OSTI]

    Pennycook, Steve

    also enable users to evaluate future energy technologies, including renewable energies. Advantages users to evaluate future energy technologies including renewables Potential Applications UtilityUT-B ID 201102665 06.2012 Technology Summary Promoting energy efficiency is a primary focus

  15. ____________________Rowan ID# K. Bryant 3/2013

    E-Print Network [OSTI]

    Rusu, Adrian

    ____________________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

  16. Document ID: POLUMITPUR01702 Information Technology

    E-Print Network [OSTI]

    Shyu, Mei-Ling

    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

  17. Kentucky WRI Pilot Test Universal ID

    E-Print Network [OSTI]

    screening deployment experience Significant cost savings to FMCSA Enabling technology already deployedKentucky WRI Pilot Test Universal ID Commercial Motor Vehicle Roadside Technology Corridor Safety Technology Showcase October 14, 2010 #12;Utilizes existing automated screening system Uses assorted

  18. JOB DESCRIPTION Requisition ID 4206BR

    E-Print Network [OSTI]

    general office and administrative policies. May supervise lower level staff members. Schedules in accordance with established procedures. Performs research and/or statistical analyses and assistsJOB DESCRIPTION Requisition ID 4206BR ASU Job Title Administrative Secretary Job Title

  19. 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

  20. Dynamic Filtering and Mining Triggers in Mesoscale Meteorology Forecasting

    E-Print Network [OSTI]

    Plale, Beth

    Dynamic Filtering and Mining Triggers in Mesoscale Meteorology Forecasting Nithya N. Vijayakumar {rramachandran, xli}@itsc.uah.edu Abstract-- Mesoscale meteorology forecasting as a data driven application Triggers, Data Mining, Stream Processing, Meteorology Forecasting I. INTRODUCTION Mesoscale meteorologists

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

    E-Print Network [OSTI]

    A. Ster; T. Csorgo; B. Lorstad

    1998-09-28T23:59:59.000Z

    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.

  2. 1992 five year battery forecast

    SciTech Connect (OSTI)

    Amistadi, D.

    1992-12-01T23:59:59.000Z

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

  3. Forecast Energy | Open Energy Information

    Open Energy Info (EERE)

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

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

    Office of Environmental Management (EM)

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

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

    E-Print Network [OSTI]

    Heinemann, Detlev

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

  6. Alternative methods for forecasting GDP Dominique Gugan

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

  7. A NEW APPROACH FOR EVALUATING ECONOMIC FORECASTS

    E-Print Network [OSTI]

    Vertes, Akos

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

  8. 2013 Midyear Economic Forecast Sponsorship Opportunity

    E-Print Network [OSTI]

    de Lijser, Peter

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

  9. Dynamic Algorithm for Space Weather Forecasting System

    E-Print Network [OSTI]

    Fischer, Luke D.

    2011-08-08T23:59:59.000Z

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

  10. Nonparametric models for electricity load forecasting

    E-Print Network [OSTI]

    Genève, Université de

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

  11. CALIFORNIA ENERGY DEMAND 20142024 REVISED FORECAST

    E-Print Network [OSTI]

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

  12. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect (OSTI)

    United States. Bonneville Power Administration.

    1994-02-01T23:59:59.000Z

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

  13. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF DRAFT FORECAST

    E-Print Network [OSTI]

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

  14. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

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

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

    SciTech Connect (OSTI)

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

    2011-10-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2013-10-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Islam, M. Saif

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

  18. 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

  19. 1993 Solid Waste Reference Forecast Summary

    SciTech Connect (OSTI)

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

    1993-08-01T23:59:59.000Z

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

  20. ID Nom Prnom Groupe 11206695 ABDOU CHAFIN B

    E-Print Network [OSTI]

    Mironescu, Petru

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

  1. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

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

  2. Wind Speed Forecasting for Power System Operation

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

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

  3. 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

  4. Wind Speed Forecasting for Power System Operation

    E-Print Network [OSTI]

    Zhu, Xinxin

    2013-07-22T23:59:59.000Z

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

  5. Potential Economic Value of Seasonal Hurricane Forecasts

    E-Print Network [OSTI]

    Emanuel, Kerry Andrew

    This paper explores the potential utility of seasonal Atlantic hurricane forecasts to a hypothetical property insurance firm whose insured properties are broadly distributed along the U.S. Gulf and East Coasts. Using a ...

  6. Text-Alternative Version LED Lighting Forecast

    Broader source: Energy.gov [DOE]

    The DOE report Energy Savings Forecast of Solid-State Lighting in General Illumination Applications estimates the energy savings of LED white-light sources over the analysis period of 2013 to 2030....

  7. Essays in International Macroeconomics and Forecasting

    E-Print Network [OSTI]

    Bejarano Rojas, Jesus Antonio

    2012-10-19T23:59:59.000Z

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

  8. Optimal IDS Sensor Placement And Alert Prioritization Using Attack Graphs

    E-Print Network [OSTI]

    Noel, Steven

    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

  9. Article ID #eqr106 REPLACEMENT STRATEGIES

    E-Print Network [OSTI]

    Popova, Elmira

    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

  10. Master Project Assessment Form Student: ID number

    E-Print Network [OSTI]

    Franssen, Michael

    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

  11. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect (OSTI)

    Brainard, James Robert

    2009-10-01T23:59:59.000Z

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

  12. LAYNE, HOSPEDALES, GONG: RE-ID: HUNTING ATTRIBUTES IN THE WILD 1 Re-id: Hunting Attributes in the Wild

    E-Print Network [OSTI]

    Gong, Shaogang

    LAYNE, HOSPEDALES, GONG: RE-ID: HUNTING ATTRIBUTES IN THE WILD 1 Re-id: Hunting Attributes in the Wild Ryan Layne r.d.c.layne@qmul.ac.uk Timothy M. Hospedales t.hospedales@qmul.ac.uk Shaogang Gong s.gong, HOSPEDALES, GONG: RE-ID: HUNTING ATTRIBUTES IN THE WILD Much re-identification research breaks down into two

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

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

    2014-05-01T23:59:59.000Z

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

  20. 1994 Solid waste forecast container volume summary

    SciTech Connect (OSTI)

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

    1994-09-01T23:59:59.000Z

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

  1. Inside-Outside ID: 00050001778e 0

    E-Print Network [OSTI]

    Shirai, Kiyoaki

    EDR 1 EDR EDR 1 Inside-Outside [2, 3] [1] EDR [5, 6] 2 2 EDR · ( ) · ( ) 1 EDR ID: 00050001778e 0 2222 1 @@@@@ 2 3 ¨¨ rr hhhhh 4 $$$$ 5 ¨¨ 6 44 1: EDR 1 1 1 · " " " " [ ] #12;1: 6 5 4 5 6 3 2 3 1 2 4 0 1 · " " : EDR " " " " [ ] · " " : " " " " · " " : " " [ ] · " " : EDR

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

    E-Print Network [OSTI]

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

  3. To appear in: Proc. CVPR'94, Seattle, WA Global Surface Reconstruction by Purposive Control of Observer Motion

    E-Print Network [OSTI]

    Jepson, Allan D.

    To appear in: Proc. CVPR'94, Seattle, WA Global Surface Reconstruction by Purposive Control Department University of Wisconsin Madison, Wisconsin 53706 Abstract What real-time, qualitative viewpoint-control markings, building a global model of an arbitrary object, or recognizing an object? In this paper we

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

    E-Print Network [OSTI]

    Singhal, Gaurav

    2012-10-19T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Datta, Shoumen

    2008-07-31T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Washington at Seattle, University of

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

  7. The effect of multinationality on management earnings forecasts

    E-Print Network [OSTI]

    Runyan, Bruce Wayne

    2005-08-29T23:59:59.000Z

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

  8. Measurements of Turbulence at Two Tidal Energy Sites in Puget Sound, WA

    SciTech Connect (OSTI)

    Thomson, Jim; Polagye, Brian; Durgesh, Vibhav; Richmond, Marshall C.

    2012-06-05T23:59:59.000Z

    Field measurements of turbulence are pre- sented from two sites in Puget Sound, WA (USA) that are proposed for electrical power generation using tidal current turbines. Rapidly sampled data from multiple acoustic Doppler instruments are analyzed to obtain statistical mea- sures of fluctuations in both the magnitude and direction of the tidal currents. The resulting turbulence intensities (i.e., the turbulent velocity fluctuations normalized by the harmonic tidal currents) are typically 10% at the hub- heights (i.e., the relevant depth bin) of the proposed turbines. Length and time scales of the turbulence are also analyzed. Large-scale, anisotropic eddies dominate the energy spectra, which may be the result of proximity to headlands at each site. At small scales, an isotropic turbulent cascade is observed and used to estimate the dissipation rate of turbulent kinetic energy. Data quality and sampling parameters are discussed, with an emphasis on the removal of Doppler noise from turbulence statistics.

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

    SciTech Connect (OSTI)

    Hodge, B. M.; Milligan, M.

    2011-07-01T23:59:59.000Z

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

  10. 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

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

    E-Print Network [OSTI]

    Boyer, Edmond

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

  12. FORECASTING SOLAR RADIATION PRELIMINARY EVALUATION OF AN APPROACH

    E-Print Network [OSTI]

    Perez, Richard R.

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

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

    E-Print Network [OSTI]

    Povinelli, Richard J.

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

  14. Univariate Modeling and Forecasting of Monthly Energy Demand Time Series

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

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

  15. 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

  16. 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

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

    E-Print Network [OSTI]

    Lang, K.

    1982-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    New Hampshire, University of

    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

  19. ,"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...

  20. CALIFORNIA ENERGY DEMAND 20122022 FINAL FORECAST

    E-Print Network [OSTI]

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

  1. 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

  2. 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

  3. Issues in midterm analysis and forecasting 1998

    SciTech Connect (OSTI)

    NONE

    1998-07-01T23:59:59.000Z

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

  4. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Sripada, Yaji

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

  5. Forecasting sudden changes in environmental pollution patterns

    E-Print Network [OSTI]

    Olascoaga, Maria Josefina

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

  6. New Concepts in Wind Power Forecasting Models

    E-Print Network [OSTI]

    Kemner, Ken

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

  7. CALIFORNIA ENERGY DEMAND 20142024 FINAL FORECAST

    E-Print Network [OSTI]

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

  8. SIMULATION AND FORECASTING IN INTERMODAL CONTAINER TERMINAL

    E-Print Network [OSTI]

    Gambardella, Luca Maria

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

  9. Issues in midterm analysis and forecasting, 1996

    SciTech Connect (OSTI)

    NONE

    1996-08-01T23:59:59.000Z

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

  10. Forecast Technical Document Felling and Removals

    E-Print Network [OSTI]

    of local investment and business planning. Timber volume production will be estimated at sub. Planning of operations. Control of the growing stock. Wider reporting (under UKWAS). The calculation fellings and removals are handled in the 2011 Production Forecast system. Tom Jenkins Robert Matthews Ewan

  11. Forecasting Turbulent Modes with Nonparametric Diffusion Models

    E-Print Network [OSTI]

    Tyrus Berry; John Harlim

    2015-01-27T23:59:59.000Z

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

  12. 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

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

    SciTech Connect (OSTI)

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.

    2014-04-01T23:59:59.000Z

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

  15. Copyright 2004 Auto-ID Labs, All Rights Reserved The Auto-ID Labs

    E-Print Network [OSTI]

    Brock, David

    Reserved Several Types of Webs The Web of Information HTML and the World Wide Web The Web of Things-ID Labs, All Rights Reserved A Special Word of Thanks to my Colleagues Stuart J. Allen - Professor Reserved A Special Word of Thanks to my Colleagues (continued) Nhat-So Lam Family Retail Business

  16. DOE-ID Mission and Vision

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOffice ofHale Plan by(formerlyand5,ReadingID

  17. 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: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisitingContract Management Fermi SitePARTOfficeOctoberDaniel WoodID Service First

  18. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,PillarPublicationType JumpDOEInvolve Jump to: navigation, search PropertyDtAddId

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

    SciTech Connect (OSTI)

    Desprez, Pierre-Yves; Campisi, Judith

    2014-09-30T23:59:59.000Z

    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.

  20. A role for transcriptional regulator Id2 in natural killer T cells

    E-Print Network [OSTI]

    Monticelli, Laurel Anne

    2008-01-01T23:59:59.000Z

    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

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

    SciTech Connect (OSTI)

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

    1983-07-01T23:59:59.000Z

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

  2. Efficient DHT attack mitigation through peers' ID distribution

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    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

  3. 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.

  4. Forecasting hotspots using predictive visual analytics approach

    DOE Patents [OSTI]

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

    2014-12-30T23:59:59.000Z

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

  5. A survey on wind power ramp forecasting.

    SciTech Connect (OSTI)

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

    2011-02-23T23:59:59.000Z

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

  6. Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint

    SciTech Connect (OSTI)

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

    2013-10-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Poulin, L.

    2013-01-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Not Available

    1984-03-01T23:59:59.000Z

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

  9. Wind Power Forecasting Error Distributions: An International Comparison; Preprint

    SciTech Connect (OSTI)

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

    2012-09-01T23:59:59.000Z

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

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

    Office of Environmental Management (EM)

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

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

    E-Print Network [OSTI]

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

    2011-01-01T23:59:59.000Z

    Forecasting Dangerous Inmate Misconduct: An Applications ofof Term Length more dangerous than other inmates servingIV beds or moving less dangerous Level IV inmates to Level

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    Forecasting Dangerous Inmate Misconduct: An Applications ofof Term Length more dangerous than other inmates servingIV beds or moving less dangerous Level IV inmates to Level

  13. Forecasting the underlying potential governing climatic time series

    E-Print Network [OSTI]

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

    2012-01-01T23:59:59.000Z

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

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

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

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

  15. 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...

  16. Econometric model and futures markets commodity price forecasting

    E-Print Network [OSTI]

    Just, Richard E.; Rausser, Gordon C.

    1979-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    M. Fisher

    2007-02-14T23:59:59.000Z

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

  18. Weather-based yield forecasts developed for 12 California crops

    E-Print Network [OSTI]

    Lobell, David; Cahill, Kimberly Nicholas; Field, Christopher

    2006-01-01T23:59:59.000Z

    RESEARCH ARTICLE Weather-based yield forecasts developed fordepend largely on the weather, measurements from existingpredictions. We developed weather-based models of statewide

  19. Using Customers' Reported Forecasts to Predict Future Sales

    E-Print Network [OSTI]

    Gordon, Geoffrey J.

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

  20. Property:RAPID/Contact/ID8/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 onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag Jump to:ID8/Organization RAPID/Contact/ID8/Position RAPID/Contact/ID8/Name

  1. Property:RAPID/Contact/ID8/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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethod Jump to: navigation,ID2/PhoneID7/Phone"ID8/Name"

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

    E-Print Network [OSTI]

    Smith, Emily (Emily C.)

    2010-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

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

    2005-01-01T23:59:59.000Z

    of two methods to forecast natural gas prices: using theof two methods to forecast natural gas prices is performed:accurate average forecast of natural gas prices than the

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01T23:59:59.000Z

    Gas Price Forecast With natural gas prices significantlyto the EIAs natural gas price forecasts in AEO 2004 and AEOon the AEO 2005 natural gas price forecasts will likely once

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

    E-Print Network [OSTI]

    Wickham, Richard Robert

    1995-01-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2004-01-01T23:59:59.000Z

    revisions to the 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

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

    E-Print Network [OSTI]

    Bolinger, Mark A.

    2010-01-01T23:59:59.000Z

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

  8. Solar Energy Market Forecast | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ |RippeyInformationSodaAtlas (PACA RegionEnergyMarket

  9. H id lb U i it G Heidelberg University, Germany

    E-Print Network [OSTI]

    Fischer, Wolfgang

    H id lb U i it G Topics: Heidelberg University, Germany Talks on 15th of July 2011 Neue Universitt-Ming University and Heidelberg University 14. 15. July 2011 Heidelberg University, Germany #12;NYMU - HD 2011 2

  10. Dissertation Checklist Coversheet Created June 2014 Student Name: Student ID

    E-Print Network [OSTI]

    Northern British Columbia, University of

    Dissertation Checklist Coversheet Created June 2014 Student Name: Student ID: Program: Supervisor's Name: Dissertation Defence Checklist Coversheet Office of Graduate Programs (OGP) University supervisory committee member has read the dissertation and agreed that it is examinable. Completed GR364

  11. Dissertation Checklist Coversheet Revised Nov 2014 Student Name: Student ID

    E-Print Network [OSTI]

    Northern British Columbia, University of

    Dissertation Checklist Coversheet Revised Nov 2014 Student Name: Student ID: Program: Supervisor's Name: Dissertation Defence Checklist Coversheet Office of Graduate Programs (OGP) University supervisory committee member has read the dissertation and agreed that it is examinable. Completed GR364

  12. Renewable Forecast Min-Max2020.xls

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What's PossibleRadiation Protection Technical s o Freiberge s 3 c/)RenewableRenewable EnergyForecast of

  13. Forecast and Funding Arrangements - Hanford Site

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC) Environmental Assessments (EA)Budget(DANCE) Target 1Annual Waste Forecast and Funding

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01T23:59:59.000Z

    to predictdailysolarradiation. AgricultureandForestandChuo,S. 2008. SolarradiationforecastingusingShort?termforecastingofsolarradiation: Astatistical

  15. Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA

    SciTech Connect (OSTI)

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

    2014-10-27T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Heinemann, Detlev

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

  17. Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center

    E-Print Network [OSTI]

    Washington at Seattle, University of

    Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts acknowledges the support of PPM Energy, Inc. The data used in this work were obtained from Oregon State

  18. 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

  19. A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

    E-Print Network [OSTI]

    Hansens, Jim

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

  20. RESERVOIR INFLOW FORECASTING USING NEURAL NETWORKS CHANDRASHEKAR SUBRAMANIAN

    E-Print Network [OSTI]

    Manry, Michael

    a mixture of hydroelectric and non- hydroelectric power, the economics of the hydroelectric plants depend, and to economically allocate the load between various non-hydroelectric plants. Neural networks provide an attractive technology for inflow forecasting, because of (1) their success in power load forecasting 1- 6 , and (2

  1. Introducing the Canadian Crop Yield Forecaster Aston Chipanshi1

    E-Print Network [OSTI]

    Miami, University of

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

  2. Wind-Wave Probabilistic Forecasting based on Ensemble

    E-Print Network [OSTI]

    have to be jointly taken into account in some decision-making problems, e.g. offshore wind farmWind-Wave Probabilistic Forecasting based on Ensemble Predictions Maxime FORTIN Kongens Lyngby 2012.imm.dtu.dk IMM-PhD-2012-86 #12;Summary Wind and wave forecasts are of a crucial importance for a number

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

    E-Print Network [OSTI]

    Kemner, Ken

    Wind Power Forecasting: State-of-the-Art 2009 ANL/DIS-10-1 Decision and Information Sciences about Argonne and its pioneering science and technology programs, see www.anl.gov. #12;Wind Power................................................ 14 2.2.3 Critical Processes for Wind Forecast

  4. PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022

    E-Print Network [OSTI]

    PRELIMINARY CALIFORNIA ENERGY DEMAND FORECAST 2012-2022 AUGUST 2011 CEC-200-2011-011-SD CALIFORNIA for electric vehicles. #12;ii #12;iii ABSTRACT The Preliminary California Energy Demand Forecast 2012 includes three full scenarios: a high energy demand case, a low energy demand case, and a mid energy demand

  5. CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST, and utilities. Mitch Tian prepared the peak demand forecast. Ted Dang prepared the historic energy consumption STAFFFINALREPORT NOVEMBER 2007 CEC-200-2007-015-SF2 Arnold Schwarzenegger, Governor #12;CALIFORNIA ENERGY

  6. CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST

    E-Print Network [OSTI]

    CALIFORNIA ENERGY COMMISSION CALIFORNIA ENERGY DEMAND 2006-2016 STAFF ENERGY DEMAND FORECAST Manager Kae Lewis Acting Manager Demand Analysis Office Valerie T. Hall Deputy Director Energy Efficiency Demand Forecast report is the product of the efforts of many current and former California Energy

  7. Distribution Based Data Filtering for Financial Time Series Forecasting

    E-Print Network [OSTI]

    Bailey, James

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

  8. Managing Wind Power Forecast Uncertainty in Electric Brandon Keith Mauch

    E-Print Network [OSTI]

    i Managing Wind Power Forecast Uncertainty in Electric Grids Brandon Keith Mauch Co for the modeled wind- CAES system would not cover annualized capital costs. We also estimate market prices-ahead market is roughly $100, with large variability due to electric power prices. Wind power forecast errors

  9. Draft for Public Comment Appendix A. Demand Forecast

    E-Print Network [OSTI]

    in the planning process. Electricity demand is forecast to grow from 20,080 average megawatts in 2000 to 25 forecast of electricity demand is a required component of the Council's Northwest Regional Conservation and Electric Power Plan.1 Understanding growth in electricity demand is, of course, crucial to determining

  10. FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS

    E-Print Network [OSTI]

    Keller, Arturo A.

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

  11. Forecasting Uncertainty Related to Ramps of Wind Power Production

    E-Print Network [OSTI]

    Boyer, Edmond

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

  12. Impact of PV forecasts uncertainty in batteries management in microgrids

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

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

  13. Forecasting Building Occupancy Using Sensor Network James Howard

    E-Print Network [OSTI]

    Hoff, William A.

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

  14. Arrival time and magnitude of airborne fission products from the Fukushima, Japan, reactor incident as measured in Seattle, WA, USA

    E-Print Network [OSTI]

    Leon, J Diaz; Knecht, A; Miller, M L; Robertson, R G H; Schubert, A G

    2011-01-01T23:59:59.000Z

    We report results of air monitoring started due to the recent natural catastrophe on March 11, 2011 in Japan and the severe ensuing damage to the Fukushima nuclear reactor complex. On March 17-18, 2011 we detected the first arrival of the airborne fission products 131-I, 132-I, 132-Te, 134-Cs, and 137-Cs in Seattle, WA, USA, by identifying their characteristic gamma rays using a germanium detector. The highest detected activity to date is <~32 mBq/m^3 of 131-I.

  15. Voluntary Green Power Market Forecast through 2015

    SciTech Connect (OSTI)

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

    2010-05-01T23:59:59.000Z

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

  16. Comparison of Wind Power and Load Forecasting Error Distributions: Preprint

    SciTech Connect (OSTI)

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

    2012-07-01T23:59:59.000Z

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

  17. Verification of hourly forecasts of wind turbine power output

    SciTech Connect (OSTI)

    Wegley, H.L.

    1984-08-01T23:59:59.000Z

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

  18. NatioNal aNd Global Forecasts West VirGiNia ProFiles aNd Forecasts

    E-Print Network [OSTI]

    Mohaghegh, Shahab

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

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

    SciTech Connect (OSTI)

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

    2011-12-06T23:59:59.000Z

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

  20. Branching of the W(H4) Polytopes and Their Dual Polytopes under the Coxeter Groups W(A4) and W(H3) Represented by Quaternions

    E-Print Network [OSTI]

    Mehmet Koca; Nazife Ozdes Koca; Mudhahir Al-Ajmi

    2011-06-15T23:59:59.000Z

    4-dimensional H4 polytopes and their dual polytopes have been constructed as the orbits of the Coxeter-Weyl group W(H4) where the group elements and the vertices of the polytopes are represented by quaternions. Projection of an arbitrary W(H4) orbit into three dimensions is made preserving the icosahedral subgroup W(H3) and the tetrahedral subgroup W(A3), the latter follows a branching under the Coxeter group W(A4) . The dual polytopes of the semi-regular and quasi-regular H4 polytopes have been constructed.

  1. Incorporating Forecast Uncertainty in Utility Control Center

    SciTech Connect (OSTI)

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

    2014-07-09T23:59:59.000Z

    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)

  2. U.S. Regional Demand Forecasts Using NEMS and GIS

    SciTech Connect (OSTI)

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

    2005-07-01T23:59:59.000Z

    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.

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

    E-Print Network [OSTI]

    Shyy, Wei

    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

  4. Smard Grid Software Applications for Distribution Network Load Forecasting Eugene A. Feinberg, Jun Fei

    E-Print Network [OSTI]

    Feinberg, Eugene A.

    of the distribution network. Keywords: load forecasting, feeder, transformer, load pocket, SmartGrid I. INTRODUCTION

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

    E-Print Network [OSTI]

    Marquez, Ricardo

    2012-01-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Lantz, E.; Hand, M.

    2010-05-01T23:59:59.000Z

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

  7. 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

  8. Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

    Broader source: Energy.gov [DOE]

    Development and Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices

  9. ASSESSING THE QUALITY AND ECONOMIC VALUE OF WEATHER AND CLIMATE FORECASTS

    E-Print Network [OSTI]

    Katz, Richard

    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

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

    E-Print Network [OSTI]

    Poulin, L.

    2013-01-01T23:59:59.000Z

    GEPS 21 members ? Provides probabilistic forecasts ? Can give useful outlooks for longer term weather forecasts ? Scribe matrix from GDPS ? includes UMOS post processed model data ? Variables like Temperature, humidity post processed by UMOS ? See...://collaboration.cmc.ec.gc.ca/cmc/cmoi/cmc-prob-products/ ? Link to experimental 3-day outlook of REPS quilts ? http://collaboration.cmc.ec.gc.ca/cmc/cmoi/cmc-prob-products.reps Users can also make their own products from ensemble forecast data? Sample ascii matrix of 2m temperature could be fed...

  11. Natural Priors, CMSSM Fits and LHC Weather Forecasts

    E-Print Network [OSTI]

    Allanach, B C; Cranmer, Kyle; Lester, Christopher G; Weber, Arne M

    2007-08-07T23:59:59.000Z

    ar X iv :0 70 5. 04 87 v3 [ he p- ph ] 5 J ul 20 07 Preprint typeset in JHEP style - HYPER VERSION DAMTP-2007-18 Cavendish-HEP-2007-03 MPP-2007-36 Natural Priors, CMSSM Fits and LHC Weather Forecasts Benjamin C Allanach1, Kyle Cranmer2... s likely discoveries. There are big differences between nature of the questions answered by a forecast, and the ques- tions that will be answered by the experiments themselves when they have acquired compelling data. A weather forecast predicting severe...

  12. RAPID/Roadmap/3-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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformatione < RAPID‎ |gWA-e

  13. 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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethod Jump to: navigation,ID2/Phone Jump to:EmailID3/Organization

  14. Property:RAPID/Contact/ID7/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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethod Jump to: navigation,ID2/PhoneID7/Phone" Showing 2

  15. Property:RAPID/Contact/ID7/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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethod Jump to: navigation,ID2/PhoneID7/Phone" Showing

  16. Property:RAPID/Contact/ID8/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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethod Jump to: navigation,ID2/PhoneID7/Phone"

  17. RAPID/Roadmap/13-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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformation TexasTexas)ID-a < RAPID‎ID-a <

  18. RAPID/Roadmap/14-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 onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformation TexasTexas)ID-aRAPID/Roadmap/14-ID-d <

  19. MA 261 EXAM II Fall 2001 Page 1/6 NAME STUDENT ID ...

    E-Print Network [OSTI]

    1910-20-20T23:59:59.000Z

    I.D.# is your 9 digit ID (probably your social security number). Also write your name at the top of ... information about the nature of f(1, -1). D. fxx(1, -1)fyy(1, -1) < 0.

  20. Optimally controlling hybrid electric vehicles using path forecasting

    E-Print Network [OSTI]

    Katsargyri, Georgia-Evangelina

    2008-01-01T23:59:59.000Z

    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. ...

  1. Recently released EIA report presents international forecasting data

    SciTech Connect (OSTI)

    NONE

    1995-05-01T23:59:59.000Z

    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.

  2. Grid-scale Fluctuations and Forecast Error in Wind Power

    E-Print Network [OSTI]

    G. Bel; C. P. Connaughton; M. Toots; M. M. Bandi

    2015-03-29T23:59:59.000Z

    The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_{\\tau}$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_{\\zeta}$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_{\\tau}$) and the scaling error ($e_{\\zeta}$).

  3. OCTOBER-NOVEMBER FORECAST FOR 2014 CARIBBEAN BASIN HURRICANE ACTIVITY

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

    and hurricanes, but instead predicts both hurricane days and Accumulated Cyclone Energy (ACE). Typically, while) tropical cyclone (TC) activity. We have decided to issue this forecast, because Klotzbach (2011) has

  4. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07T23:59:59.000Z

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

  5. A methodology for forecasting carbon dioxide flooding performance

    E-Print Network [OSTI]

    Marroquin Cabrera, Juan Carlos

    1998-01-01T23:59:59.000Z

    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...

  6. The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss

    E-Print Network [OSTI]

    Auffhammer, Maximilian

    2005-01-01T23:59:59.000Z

    Agency: 1982-2005a, Annual Energy Outlook, EIA, Washington,Agency: 2004, Annual Energy Outlook Forecast Evaluation,Agency: 2005b, Annual Energy Outlook, EIA, Washington, D.C.

  7. The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss

    E-Print Network [OSTI]

    Auffhammer, Maximilian

    2005-01-01T23:59:59.000Z

    2005a, Annual Energy Outlook, EIA, Washington, D.C. Energy2005b, Annual Energy Outlook, EIA, Washington, D.C. Granger,Paper ???? The Rationality of EIA Forecasts under Symmetric

  8. Forecasting and strategic inventory placement for gas turbine aftermarket spares

    E-Print Network [OSTI]

    Simmons, Joshua T. (Joshua Thomas)

    2007-01-01T23:59:59.000Z

    This thesis addresses the problem of forecasting demand for Life Limited Parts (LLPs) in the gas turbine engine aftermarket industry. It is based on work performed at Pratt & Whitney, a major producer of turbine engines. ...

  9. Optimally Controlling Hybrid Electric Vehicles using Path Forecasting

    E-Print Network [OSTI]

    Kolmanovsky, Ilya V.

    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 ...

  10. Post-Construction Evaluation of Forecast Accuracy Pavithra Parthasarathi1

    E-Print Network [OSTI]

    Levinson, David M.

    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

  11. 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...

  12. accident risk forecasting: 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...

  13. Forecasting Volatility in Stock Market Using GARCH Models

    E-Print Network [OSTI]

    Yang, Xiaorong

    2008-01-01T23:59:59.000Z

    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...

  14. Forecasting Returns and Volatilities in GARCH Processes Using the Bootstrap

    E-Print Network [OSTI]

    Romo, Juan

    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

  15. Adaptive sampling and forecasting with mobile sensor networks

    E-Print Network [OSTI]

    Choi, Han-Lim

    2009-01-01T23:59:59.000Z

    This thesis addresses planning of mobile sensor networks to extract the best information possible out of the environment to improve the (ensemble) forecast at some verification region in the future. To define the information ...

  16. Dispersion in analysts' forecasts: does it make a difference?

    E-Print Network [OSTI]

    Adut, Davit

    2004-09-30T23:59:59.000Z

    Financial analysts are an important group of information intermediaries in the capital markets. Their reports, including both earnings forecasts and stock recommendations, are widely transmitted and have a significant impact on stock prices (Womack...

  17. FINAL DEMAND FORECAST FORMS AND INSTRUCTIONS FOR THE 2007

    E-Print Network [OSTI]

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

  18. An econometric analysis and forecasting of Seoul office market

    E-Print Network [OSTI]

    Kim, Kyungmin

    2011-01-01T23:59:59.000Z

    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 ...

  19. Improving the Accuracy of Solar Forecasting Funding Opportunity

    Broader source: Energy.gov [DOE]

    Through the Improving the Accuracy ofSolar Forecasting Funding Opportunity,DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and other...

  20. Variable Selection and Inference for Multi-period Forecasting Problems

    E-Print Network [OSTI]

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

    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...

  1. Grid-scale Fluctuations and Forecast Error in Wind Power

    E-Print Network [OSTI]

    Bel, G; Toots, M; Bandi, M M

    2015-01-01T23:59:59.000Z

    The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_{\\tau}$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_{\\zeta}$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_{\\tau}$) and the scaling error ($e_{\\zeta}$).

  2. Dispersion in analysts' forecasts: does it make a difference?

    E-Print Network [OSTI]

    Adut, Davit

    2004-09-30T23:59:59.000Z

    Financial analysts are an important group of information intermediaries in the capital markets. Their reports, including both earnings forecasts and stock recommendations, are widely transmitted and have a significant impact on stock prices (Womack...

  3. Mesoscale predictability and background error convariance estimation through ensemble forecasting

    E-Print Network [OSTI]

    Ham, Joy L

    2002-01-01T23:59:59.000Z

    Over the past decade, ensemble forecasting has emerged as a powerful tool for numerical weather prediction. Not only does it produce the best estimate of the state of the atmosphere, it also could quantify the uncertainties associated with the best...

  4. Subhourly wind forecasting techniques for wind turbine operations

    SciTech Connect (OSTI)

    Wegley, H.L.; Kosorok, M.R.; Formica, W.J.

    1984-08-01T23:59:59.000Z

    Three models for making automated forecasts of subhourly wind and wind power fluctuations were examined to determine the models' appropriateness, accuracy, and reliability in wind forecasting for wind turbine operation. Such automated forecasts appear to have value not only in wind turbine control and operating strategies, but also in improving individual wind turbine control and operating strategies, but also in improving individual wind turbine operating strategies (such as determining when to attempt startup). A simple persistence model, an autoregressive model, and a generalized equivalent Markhov (GEM) model were developed and tested using spring season data from the WKY television tower located near Oklahoma City, Oklahoma. The three models represent a pure measurement approach, a pure statistical method and a statistical-dynamical model, respectively. Forecasting models of wind speed means and measures of deviations about the mean were developed and tested for all three forecasting techniques for the 45-meter level and for the 10-, 30- and 60-minute time intervals. The results of this exploratory study indicate that a persistence-based approach, using onsite measurements, will probably be superior in the 10-minute time frame. The GEM model appears to have the most potential in 30-minute and longer time frames, particularly when forecasting wind speed fluctuations. However, several improvements to the GEM model are suggested. In comparison to the other models, the autoregressive model performed poorly at all time frames; but, it is recommended that this model be upgraded to an autoregressive moving average (ARMA or ARIMA) model. The primary constraint in adapting the forecasting models to the production of wind turbine cluster power output forecasts is the lack of either actual data, or suitable models, for simulating wind turbine cluster performance.

  5. Streamflow forecasting for large-scale hydrologic systems

    E-Print Network [OSTI]

    Awwad, Haitham Munir

    1991-01-01T23:59:59.000Z

    STREAMFLOW FORECASTING FOR LARGE-SCALE HYDROLOGIC SYSTEMS A Thesis by HAITHAM MUNIR AWWAD Submitted to the Office of Graduate Studies of Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May... 1991 Major Subject: Civil Engineering STREAMFLOW FORECASTING FOR LARGE-SCALE HYDROLOGIC SYSTEMS A Thesis by HAITHAM MUNIR AWWAD Approved as to style and content by: uan B. Valdes (Chair of Committee) alph A. Wurbs (Member) Marshall J. Mc...

  6. A model for short term electric load forecasting

    E-Print Network [OSTI]

    Tigue, John Robert

    1975-01-01T23:59:59.000Z

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

  7. International Conference on Advanced Robotics ICAR 2005 July 2005, Seattle WA Abstract--Integrating human and robot into a single system

    E-Print Network [OSTI]

    Rosen, Jacob

    The 12th International Conference on Advanced Robotics ICAR 2005 July 2005, Seattle WA Abstract to the fine manipulation joints (the wrist). An inverted phenomenon was observed during fine manipulation) and functions as a human- amplifier. Its joints and links correspond to those of the human body, and its

  8. Arrival time and magnitude of airborne fission products from the Fukushima, Japan, reactor incident as measured in Seattle, WA, USA

    E-Print Network [OSTI]

    J. Diaz Leon; D. A. Jaffe; J. Kaspar; A. Knecht; M. L. Miller; R. G. H. Robertson; A. G. Schubert

    2011-08-23T23:59:59.000Z

    We report results of air monitoring started due to the recent natural catastrophe on 11 March 2011 in Japan and the severe ensuing damage to the Fukushima Dai-ichi nuclear reactor complex. On 17-18 March 2011, we registered the first arrival of the airborne fission products 131-I, 132-I, 132-Te, 134-Cs, and 137-Cs in Seattle, WA, USA, by identifying their characteristic gamma rays using a germanium detector. We measured the evolution of the activities over a period of 23 days at the end of which the activities had mostly fallen below our detection limit. The highest detected activity amounted to 4.4 +/- 1.3 mBq/m^3 of 131-I on 19-20 March.

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

    E-Print Network [OSTI]

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

    Comparisons are made of energy forecasts using results from the Industrial module of the National Energy Modeling System (NEMS) and an industrial economic-engineering model called the Industrial Technology and Energy Modeling System (ITEMS), a model...

  10. TidFP: Mining Frequent Patterns in Different Databases with Transaction ID

    E-Print Network [OSTI]

    Ezeife, Christie

    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

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

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

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-01-01T23:59:59.000Z

    vs. AEO 2001 Price Forecast Natural Gas Price (nominal $/if forwards forecasts) or natural gas-fired generation (ifs reference case forecast of natural gas prices delivered to

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

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    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

  14. 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

  15. ORNL 2010-G01074/jcn UT-B ID 200301298

    E-Print Network [OSTI]

    ORNL 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 of the hybrid phase change material in the heat pump cycle. The material combines Group I and II halides

  16. Bachelor of Science, Geophysics, 2013-2014 Name ID# Date

    E-Print Network [OSTI]

    Barrash, Warren

    Bachelor of Science, Geophysics, 2013-2014 Name ID# Date General Degree Requirements Residency with Lab 4 COMPSCI 115 Introduction to C 2 GEOPH 201 Seeing the Unseen: an Introduction to Geophysics 4 GEOPH 300 Physics of the Earth 3 GEOPH 305 Applied Geophysics 3 GEOPH 420 Geophysical Applications

  17. Introduction to Health and Social Care (ID:250)

    E-Print Network [OSTI]

    Harman, Neal.A.

    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

  18. Hindawi Publishing Corporation Volume 2012, Article ID 507894, 8 pages

    E-Print Network [OSTI]

    Barbas III, Carlos F.

    is properly cited. Sickle cell disease (SCD) and -thalassemia patients are phenotypically normal if they carry]. Sickle cell disease (SCD) and -thalassemia patients are phenotypically normal if they carry compensatoryHindawi Publishing Corporation Anemia Volume 2012, Article ID 507894, 8 pages doi:10

  19. ORNL 2012-G00212/tcc UT-B ID 200902214

    E-Print Network [OSTI]

    Pennycook, Steve

    Technology Summary Glass used in building materials (curtain walls), windshields, goggles, glasses, opticalORNL 2012-G00212/tcc UT-B ID 200902214 08.2012 Superhydrophobic Transparent Glass Thin Films researchers have invented a method to produce durable, superhydrophobic, antireflective glass thin films

  20. Exam 1 Phys 105 Section______Fall 2002 Name__________________________________ ID

    E-Print Network [OSTI]

    Gary, Dale E.

    Exam 1 Phys 105 Section______Fall 2002 Name__________________________________ ID: Closed book exam each. Work out problems are 4 points each. Passing of the exam requires at least 50% of the maximum an expression, a t2 /2 where a is acceleration and t is time. The dimension of this expression in the SI system

  1. 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

  2. Bachelor of Applied Science, 2014-2015 Name ID# Date

    E-Print Network [OSTI]

    Barrash, Warren

    Writing and Research 3 CID BAS 300 Communication in the Applied Sciences 3 UF 100 Intellectual FoundationsBachelor of Applied Science, 2014-2015 Name ID# Date General Degree Requirements Residency: Total 3 UF 200 Civic and Ethical Foundations 3 FF BAS 400 Capstone in Applied Sciences 3 DLM Mathematics 3

  3. Bachelor of Applied Science, 2012-2013 Name ID# Date

    E-Print Network [OSTI]

    Barrash, Warren

    Writing and Research 3 CID BAS 300 Communication in the Applied Sciences 3 UF 100 Intellectual FoundationsBachelor of Applied Science, 2012-2013 Name ID# Date General Degree Requirements Residency: Total 3 UF 200 Civic and Ethical Foundations 3 FF BAS 400 Capstone in Applied Sciences 3 DLM Mathematics 3

  4. Bachelor of Applied Science, 2013-2014 Name ID# Date

    E-Print Network [OSTI]

    Barrash, Warren

    Writing and Research 3 CID BAS 300 Communication in the Applied Sciences 3 UF 100 Intellectual FoundationsBachelor of Applied Science, 2013-2014 Name ID# Date General Degree Requirements Residency: Total 3 UF 200 Civic and Ethical Foundations 3 FF BAS 400 Capstone in Applied Sciences 3 DLM Mathematics 3

  5. UW China Hong Kong Entrance Scholarship University of Waterloo ID#

    E-Print Network [OSTI]

    Le Roy, Robert J.

    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

  6. ORNL 2010-G01078/jcn UT-B ID 201002389

    E-Print Network [OSTI]

    Pennycook, Steve

    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

  7. Wet-Nanotechnology: fl id t NIUnanofluids at NIU

    E-Print Network [OSTI]

    Kostic, Milivoje M.

    .kostic.niu.edu 4 Mechanical Engineering NORTHERN ILLINOIS UNIVERSITY #12;One Step Nanofluid Production ImprovementOne-Step Nanofluid Production Improvement Insulated and vertically-adjustable boat- heater evaporator NIU with i fl id heater evaporatorLaboratoryLaboratory S.S. ChoiChoi J. Hull,J. Hull, and othersand others

  8. Leverhulme research network: ID20060104 EUROBRISA Page 1 of 6

    E-Print Network [OSTI]

    periods). For example, seasonal forecasts of rainfall are used for decision-making in hydropower electricity production in South America. Hydropower accounts for the major source of electricity production management, hydropower production, and agriculture). B Description of the institutions involved The proposed

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2012-04-01T23:59:59.000Z

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

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

    SciTech Connect (OSTI)

    Elkins, R.D.

    1988-06-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Sixth Northwest Conservation and Electric Power Plan Appendix D: Wholesale Electricity Price.................................................................................................................................. 27 INTRODUCTION The Council prepares and periodically updates a 20-year forecast of wholesale to forecast wholesale power prices. AURORAxmp® provides the ability to inco

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

    E-Print Network [OSTI]

    Mathiesen, Patrick; Kleissl, Jan

    2011-01-01T23:59:59.000Z

    andvalidation. SolarEnergy. 73:5,307? Perez,R. ,forecastdatabase. SolarEnergy. 81:6,809?812. forecastsintheUS. SolarEnergy. 84:12,2161?2172.

  13. Integration of Behind-the-Meter PV Fleet Forecasts into Utility...

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

    Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Clean...

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

    E-Print Network [OSTI]

    Statton, James Cody

    2012-07-16T23:59:59.000Z

    . This study suggests a type curve is most useful when 24 months or less is available to forecast. The SEPD model generally provides more conservative forecasts and EUR estimates than Arps' model with a minimum decline rate of 5%....

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

    E-Print Network [OSTI]

    Heinemann, Detlev

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

  16. Sixth Northwest Conservation and Electric Power Plan Appendix A: Fuel Price Forecast

    E-Print Network [OSTI]

    ............................................................................................................................... 12 Oil Price Forecast Range. The price of crude oil was $25 a barrel in January of 2000. In July 2008 it averaged $127, even approachingSixth Northwest Conservation and Electric Power Plan Appendix A: Fuel Price Forecast Introduction

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

    SciTech Connect (OSTI)

    Piwko, R.; Jordan, G.

    2011-11-01T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    Homes, Christopher C.

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

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2008-01-01T23:59:59.000Z

    late January 2008, extend its natural gas futures strip anComparison of AEO 2008 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts from

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2006-01-01T23:59:59.000Z

    Comparison of AEO 2007 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

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

    E-Print Network [OSTI]

    Bolinger, Mark; Wiser, Ryan

    2005-01-01T23:59:59.000Z

    Comparison of AEO 2006 Natural Gas Price Forecast to NYMEXs reference case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

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

    E-Print Network [OSTI]

    Bolinger, Mark

    2009-01-01T23:59:59.000Z

    Comparison of AEO 2009 Natural Gas Price Forecast to NYMEXs reference-case long-term natural gas price forecasts fromAEO series to contemporaneous natural gas prices that can be

  3. Status of Centralized Wind Power Forecasting in North America: May 2009-May 2010

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2010-04-01T23:59:59.000Z

    Report surveys grid wind power forecasts for all wind generators, which are administered by utilities or regional transmission organizations (RTOs), typically with the assistance of one or more wind power forecasting companies.

  4. Improving Groundwater Predictions Utilizing Seasonal Precipitation Forecasts from General Circulation Models

    E-Print Network [OSTI]

    Arumugam, Sankar

    Improving Groundwater Predictions Utilizing Seasonal Precipitation Forecasts from General. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater

  5. Earnings Management Pressure on Audit Clients: Auditor Response to Analyst Forecast Signals

    E-Print Network [OSTI]

    Newton, Nathan J.

    2013-06-26T23:59:59.000Z

    This study investigates whether auditors respond to earnings management pressure created by analyst forecasts. Analyst forecasts create an important earnings target for management, and professional standards direct auditors to consider how...

  6. Forecasting the demand for electric vehicles: accounting for attitudes and perceptions

    E-Print Network [OSTI]

    Bierlaire, Michel

    prediction, transportation, attitudes and perceptions, hybrid choice models, fractional factorial design: survey design, model estimation and forecasting. We develop a stated preferences (SP) survey with issues related to the application of models designed to forecast demand for new alternatives, most

  7. Price forecasting for U.S. cattle feeders: which technique to apply?

    E-Print Network [OSTI]

    Hicks, Geoff Cody

    1997-01-01T23:59:59.000Z

    both feeder cattle costs and corn costs, and maximizing fed cattle prices. This research strives to evaluate the accuracy of six distinct price forecasting techniques over an eleven year period. The forecast techniques selected for this analysisare...

  8. Streamflow Forecasting Based on Statistical Applications and Measurements Made with Rain Gage and Weather Radar

    E-Print Network [OSTI]

    Hudlow, M.D.

    Techniques for streamflow forecasting are developed and tested for the Little Washita River in Oklahoma. The basic input for streamflow forecasts is rainfall. the rainfall amounts may be obtained from several sources; however, this study...

  9. Web: http://dust.ess.uci.edu/prp/prp ids/prp ids.pdf NASA International Polar Year (IPY) Proposal Submitted: April 17, 2006

    E-Print Network [OSTI]

    Zender, Charles

    Web: http://dust.ess.uci.edu/prp/prp ids/prp ids.pdf NASA International Polar Year (IPY) Proposal Researchers and Postdocs on CRY- OLIST and on ESS Website. 6. 20070723: Registered for SPAC Workshop for potential collaborators/contributors: 1. Use CVS to obtain source to this proposal: cvs -d :ext:esmf.ess

  10. Forecast of contracting and subcontracting opportunities. Fiscal year 1996

    SciTech Connect (OSTI)

    NONE

    1996-02-01T23:59:59.000Z

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

  11. CCPP-ARM Parameterization Testbed Model Forecast Data

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

    Klein, Stephen

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

  12. Applying Fuzzy ID3 Decision Tree for Software Effort Estimation

    E-Print Network [OSTI]

    Elyassami, Sanaa

    2011-01-01T23:59:59.000Z

    Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and control, and project improvement investment analysis. In this paper, we investigate the use of Fuzzy ID3 decision tree for software cost estimation; it is designed by integrating the principles of ID3 decision tree and the fuzzy set-theoretic concepts, enabling the model to handle uncertain and imprecise data when describing the software projects, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used as measures of prediction accuracy for this study. A series of experiments is reported using two different software projects datasets namely, Tukutuku and COCOMO'81 datasets. The results are compared with those produced by the crisp version of the ID3 decision tree.

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

    SciTech Connect (OSTI)

    Porter, K.; Rogers, J.

    2009-12-01T23:59:59.000Z

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

  14. J.C. Hillesheim, F.I. Parra,M. Barnes, N.A. Crocker, H. Meyer, W.A. Peebles, R. Scannell, A. Thornton, and the MAST Team

    E-Print Network [OSTI]

    J.C. Hillesheim, F.I. Parra,M. Barnes, N.A. Crocker, H. Meyer, W.A. Peebles, R. Scannell, A, 4 H. Meyer, 1 W.A. Peebles, 4 R. Scannell, 1 A. Thornton, 1 and the MAST Team 1 1 CCFE, Culham rotation on collisionality in MAST J. C. Hillesheim,1, F.I. Parra,2, 1 M. Barnes,3 N.A. Crocker,4 H. Meyer

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Gray, William

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

  19. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 12 OCTOBER 25, 2012

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Gray, William

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

  3. COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 11 OCTOBER 24, 2013

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

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

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

    E-Print Network [OSTI]

    Birner, Thomas

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

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

    E-Print Network [OSTI]

    Collett Jr., Jeffrey L.

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

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

    E-Print Network [OSTI]

    Gray, William

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

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

    E-Print Network [OSTI]

    Kolter, J. Zico

    -Gaussian case using the copula transform. On a wind power forecasting task, we show that this probabilisticLarge-scale Probabilistic Forecasting in Energy Systems using Sparse Gaussian Conditional Random high-dimensional conditional Gaussian distributions to forecasting wind power and extend it to the non

  15. EUROBRISA: A EURO-BRazilian Initiative for improving South American seasonal forecasts

    E-Print Network [OSTI]

    EUROBRISA: A EURO-BRazilian Initiative for improving South American seasonal forecasts by Caio A. S. van Oldenborgh, 2006: Towards an integrated seasonal forecasting system for South America. J. Climate and promote exchange of expertise and information between European and South American seasonal forecasters

  16. Hourly Temperature Forecasting Using Abductive Networks R. E. Abdel-Aal

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    ANNGSF) and for forecasting the one-hour-ahead heat load for a district heat load network (Seppl et al and network analysis functions in power utilities. Since high-low temperature forecasts are usually provided-Rohani & Maratukulam, 1998). In other agricultural and environmental applications, even high-low temperature forecasts

  17. Development, testing, and applications of site-specific tsunami inundation models for real-time forecasting

    E-Print Network [OSTI]

    can the forecasts completely cover the evolution of earthquake-generated tsunami waves: generationDevelopment, testing, and applications of site-specific tsunami inundation models for real and applications of site-specific tsunami inundation models (forecast models) for use in NOAA's tsunami forecast

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

    E-Print Network [OSTI]

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

  19. 2008 European PV Conference, Valencia, Spain COMPARISON OF SOLAR RADIATION FORECASTS FOR THE USA

    E-Print Network [OSTI]

    Perez, Richard R.

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

  20. Robust Pareto Optimum Routing of Ships Deterministic and Ensemble Weather Forecasts

    E-Print Network [OSTI]

    Berlin,Technische Universitt

    Robust Pareto Optimum Routing of Ships utilizing Deterministic and Ensemble Weather Forecasts the SEAROUTES project, who provided me with exquisite weather forecasts, and who inspired me to apply ensemble ship operation. The more reliable weather forecasts and performance simulation of ships in a seaway

  1. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint

    SciTech Connect (OSTI)

    Zhang, J.; Hodge, B. M.; Gomez-Lazaro, E.; Lovholm, A. L.; Berge, E.; Miettinen, J.; Holttinen, H.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Dobschinski, J.

    2013-10-01T23:59:59.000Z

    One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year; (ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted to characterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.

  2. Forecasting potential project risks through leading indicators to project outcome

    E-Print Network [OSTI]

    Choi, Ji Won

    2007-09-17T23:59:59.000Z

    , the Construction Industry Institute (CII) formed a research team to develop a new tool that can forecast the potential risk of not meeting specific project outcomes based on assessing leading indicators. Thus, the leading indicators were identified and then the new...

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

    E-Print Network [OSTI]

    Tesfatsion, Leigh

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

  4. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect (OSTI)

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

    2011-03-28T23:59:59.000Z

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

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

    E-Print Network [OSTI]

    de Freitas, Nando

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

  6. Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract

    E-Print Network [OSTI]

    Dalang, Robert C.

    Optimal Storage Policies with Wind Forecast Uncertainties [Extended Abstract] Nicolas Gast EPFL, IC generation. The use of energy storage compensates to some extent these negative effects; it plays a buffer role between demand and production. We revisit a model of real storage proposed by Bejan et al.[1]. We

  7. 1994 battery shipment review and five-year forecast report

    SciTech Connect (OSTI)

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

    1995-12-31T23:59:59.000Z

    This paper presents a 1994 battery shipment review and five year forecast report. Data is presented on replacement battery shipments, battery shipments, car and truck production, truck sales, original equipment, shipments for passenger cars and light commercial vehicles, and ten year battery service life trend.

  8. The Galactic Center Weather Forecast M. Moscibrodzka1

    E-Print Network [OSTI]

    Gammie, Charles F.

    The Galactic Center Weather Forecast M. Moscibrodzka1 , H. Shiokawa2 , C. F. Gammie2,3 , J*. The > 3M cloud will #12; 2 interact strongly with gas near nominal pericenter at rp 300AU 8000GM/c2 transient phase while the flow circularizes-- accompanied by transient emission--it is natural to think

  9. GenForecast(26yr)(avg).PDF

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

    SLCAIP Historical & Forecast Generation at Plant Total Range of Hydrology 0 2,000,000,000 4,000,000,000 6,000,000,000 8,000,000,000 10,000,000,000 12,000,000,000 1 9 7 0 1 9 7 2 1...

  10. WIND POWER ENSEMBLE FORECASTING Henrik Aalborg Nielsen1

    E-Print Network [OSTI]

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

  11. Weather forecast-based optimization of integrated energy systems.

    SciTech Connect (OSTI)

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

    2009-03-01T23:59:59.000Z

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

  12. URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA

    E-Print Network [OSTI]

    Boyer, Edmond

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

  13. Navy Mobility Fuels Forecasting System. Phase I report

    SciTech Connect (OSTI)

    Davis, R.M.; Hadder, G.R.; Singh, S.P.N.; Whittle, C.

    1985-07-01T23:59:59.000Z

    The Department of the Navy (DON) requires an improved capability to forecast mobility fuel availability and quality. The changing patterns in fuel availability and quality are important in planning the Navy's Mobility Fuels R and D Program. These changes come about primarily because of the decline in the quality of crude oil entering world markets as well as the shifts in refinery capabilities domestically and worldwide. The DON requested ORNL's assistance in assembling and testing a methodology for forecasting mobility fuel trends. ORNL reviewed and analyzed domestic and world oil reserve estimates, production and price trends, and recent refinery trends. Three publicly available models developed by the Department of Energy were selected as the basis of the Navy Mobility Fuels Forecasting System. The system was used to analyze the availability and quality of jet fuel (JP-5) that could be produced on the West Coast of the United States under an illustrative business-as-usual and a world oil disruption scenario in 1990. Various strategies were investigated for replacing the lost JP-5 production. This exercise, which was strictly a test case for the forecasting system, suggested that full recovery of lost fuel production could be achieved by relaxing the smoke point specifications or by increasing the refiners' gate price for the jet fuel. A more complete analysis of military mobility fuel trends is currently under way.

  14. Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging

    E-Print Network [OSTI]

    Raftery, Adrian

    the chance of winds high enough to pose dangers for boats or aircraft. In situations calling for a cost/loss analysis, the probabilities of different outcomes need to be known. For wind speed, this issue often arisesProbabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging J. Mc

  15. Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

    E-Print Network [OSTI]

    Shenoy, Prashant

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

  16. Leveraging Weather Forecasts in Renewable Energy Navin Sharmaa,

    E-Print Network [OSTI]

    Shenoy, Prashant

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

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

    E-Print Network [OSTI]

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

  18. Forecasting Hospital Bed Availability Using Simulation and Neural Networks

    E-Print Network [OSTI]

    Kuhl, Michael E.

    Forecasting Hospital Bed Availability Using Simulation and Neural Networks Matthew J. Daniels, NY 14623 Elisabeth Hager Hager Consulting Pittsford, NY 14534 Abstract The availability of beds is a critical factor for decision-making in hospitals. Bed availability (or alternatively the bed occupancy

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

    E-Print Network [OSTI]

    Cerpa, Alberto E.

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

  20. SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS

    E-Print Network [OSTI]

    Heinemann, Detlev

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

  1. Development and Deployment of an Advanced Wind Forecasting Technique

    E-Print Network [OSTI]

    Kemner, Ken

    findings. Part 2 addresses how operators of wind power plants and power systems can incorporate advanced the output of advanced wind energy forecasts into decision support models for wind power plant and power and applications of power market simulation models around the world. Argonne's software tools are used extensively

  2. Integrating agricultural pest biocontrol into forecasts of energy biomass production

    E-Print Network [OSTI]

    Gratton, Claudio

    Analysis Integrating agricultural pest biocontrol into forecasts of energy biomass production T), University of Lome, 114 Rue Agbalepedogan, BP: 20679, Lome, Togo e Center for Agricultural & Energy Policy model of potential biomass supply that incorporates the effect of biological control on crop choice

  3. Radiation fog forecasting using a 1-dimensional model

    E-Print Network [OSTI]

    Peyraud, Lionel

    2001-01-01T23:59:59.000Z

    The importance of fog forecasting to the aviation community, to road transportation and to the public at large is irrefutable. The deadliest aviation accident in history was in fact partly a result of fog back on 27 March 1977. This has, along...

  4. Classification and forecasting of load curves Nolwen Huet

    E-Print Network [OSTI]

    Cuesta, Juan Antonio

    Classification and forecasting of load curves Nolwen Huet Abstract The load curve, which gives of electricity customer uses. This load curve is only available for customers with automated meter reading. For the others, EDF must estimate this curve. Usually a clustering of the load curves is performed, followed

  5. What constrains spread growth in forecasts ini2alized from

    E-Print Network [OSTI]

    Hamill, Tom

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

  6. CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014

    E-Print Network [OSTI]

    de Lijser, Peter

    CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE - APRIL 2014 Anil Puri, Ph.D. -- Director-year increase in the debt ceiling -- both of which proceeded without the usual drama. Second, the private sector, corporate coffers are flush with cash, and low US energy prices have dramatically improved the global

  7. Exploiting weather forecasts for sizing photovoltaic energy bids

    E-Print Network [OSTI]

    Giannitrapani, Antonello

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

  8. Adaptive Energy Forecasting and Information Diffusion for Smart Power Grids

    E-Print Network [OSTI]

    Prasanna, Viktor K.

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

  9. TRANSPORTATION ENERGY FORECASTS AND ANALYSES FOR THE 2009

    E-Print Network [OSTI]

    Page Manager FOSSIL FUELS OFFICE Mike Smith Deputy Director FUELS AND TRANSPORTATION DIVISION Melissa, Weights and Measurements/Gary Castro, Allan Morrison, John Mough, Ed Williams Clean Energy FuelsCALIFORNIA ENERGY COMMISSION TRANSPORTATION ENERGY FORECASTS AND ANALYSES FOR THE 2009 INTEGRATED

  10. Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts

    E-Print Network [OSTI]

    Giannitrapani, Antonello

    bid is computed by exploiting the forecast energy price for the day ahead market, the historical wind renewable energy resources, such as wind and photovoltaic, has grown rapidly. It is well known the problem of optimizing energy bids for an independent Wind Power Producer (WPP) taking part

  11. Detecting and Forecasting Economic Regimes in Automated Exchanges

    E-Print Network [OSTI]

    Ketter, Wolfgang

    Detecting and Forecasting Economic Regimes in Automated Exchanges Wolfgang Ketter , John Collins. of Mgmt., Erasmus University Dept. of Computer Science and Engineering, University of Minnesota Dept,gini,schrater}@cs.umn.edu, agupta@csom.umn.edu Abstract We present basic building blocks of an agent that can use observable market

  12. Detecting and Forecasting Economic Regimes in Automated Exchanges

    E-Print Network [OSTI]

    Ketter, Wolfgang

    Detecting and Forecasting Economic Regimes in Automated Exchanges Wolfgang Ketter # , John Collins, Rotterdam Sch. of Mgmt., Erasmus University + Dept. of Computer Science and Engineering, University wketter@rsm.nl, {jcollins,gini,schrater}@cs.umn.edu, agupta@csom.umn.edu Abstract We present basic

  13. THE DESIRE TO ACQUIRE: FORECASTING THE EVOLUTION OF HOUSEHOLD

    E-Print Network [OSTI]

    energy-using devices in the average U.S. household that used over 4,700 kWh of electricity, natural gas-using devices to energy price, household income, and the cost of these devices. This analysis findsTHE DESIRE TO ACQUIRE: FORECASTING THE EVOLUTION OF HOUSEHOLD ENERGY SERVICES by Steven Groves BASc

  14. Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging

    E-Print Network [OSTI]

    Washington at Seattle, University of

    February 24, 2006 1J. McLean Sloughter is Graduate Research Assistant, Adrian E. Raftery is BlumsteinProbabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging J. McLean Sloughter, Adrian E. Raftery and Tilmann Gneiting 1 Department of Statistics, University of Washington

  15. Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging

    E-Print Network [OSTI]

    Raftery, Adrian

    : J. McLean Sloughter, Department of Mathematics, Seattle University, 901 12th Ave., P.O. Box 222000Probabilistic Wind Vector Forecasting Using Ensembles and Bayesian Model Averaging J. MCLEAN SLOUGHTER Seattle University, Seattle, Washington TILMANN GNEITING Heidelberg University, Heidelberg

  16. air pollution forecast: Topics by E-print Network

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

    air pollution forecast First Page Previous Page 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 ENVIRONMENTAL INFORMATION SYSTEM...

  17. Use of wind power forecasting in operational decisions.

    SciTech Connect (OSTI)

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

    2011-11-29T23:59:59.000Z

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

  18. Idaho National Laboratory Supervisory Control and Data Acquisition Intrusion Detection System (SCADA IDS)

    SciTech Connect (OSTI)

    Jared Verba; Michael Milvich

    2008-05-01T23:59:59.000Z

    Current Intrusion Detection System (IDS) technology is not suited to be widely deployed inside a Supervisory, Control and Data Acquisition (SCADA) environment. Anomaly- and signature-based IDS technologies have developed methods to cover information technology-based networks activity and protocols effectively. However, these IDS technologies do not include the fine protocol granularity required to ensure network security inside an environment with weak protocols lacking authentication and encryption. By implementing a more specific and more intelligent packet inspection mechanism, tailored traffic flow analysis, and unique packet tampering detection, IDS technology developed specifically for SCADA environments can be deployed with confidence in detecting malicious activity.

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

    E-Print Network [OSTI]

    Boyer, Edmond

    the fluctuating output from wind farms into power plant dispatching and energy trading, wind power predictionsEWEC 2006, Athens, The Anemos Wind Power Forecasting Platform Technology 1 The Anemos Wind Power a professional, flexible platform for operating wind power prediction models, laying the main focus on state

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

    SciTech Connect (OSTI)

    Das, S.

    1991-12-01T23:59:59.000Z

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