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


1

Category:Seattle, WA | Open Energy Information  

Open Energy Info (EERE)

Seattle, WA Seattle, WA Jump to: navigation, search Go Back to PV Economics By Location Media in category "Seattle, WA" The following 16 files are in this category, out of 16 total. SVFullServiceRestaurant Seattle WA Puget Sound Energy Inc.png SVFullServiceRestauran... 60 KB SVHospital Seattle WA Puget Sound Energy Inc.png SVHospital Seattle WA ... 58 KB SVLargeHotel Seattle WA Puget Sound Energy Inc.png SVLargeHotel Seattle W... 57 KB SVLargeOffice Seattle WA Puget Sound Energy Inc.png SVLargeOffice Seattle ... 57 KB SVMediumOffice Seattle WA Puget Sound Energy Inc.png SVMediumOffice Seattle... 61 KB SVMidriseApartment Seattle WA Puget Sound Energy Inc.png SVMidriseApartment Sea... 58 KB SVOutPatient Seattle WA Puget Sound Energy Inc.png SVOutPatient Seattle W... 63 KB

2

RACORO Forecasting  

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

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

3

WA_1993_040_REGENTS_OF_THE_UNIVERSITY_OF_CALIFORNIA_Waiver_o...  

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

WA1993040REGENTSOFTHEUNIVERSITYOFCALIFORNIAWaivero.pdf WA1993040REGENTSOFTHEUNIVERSITYOFCALIFORNIAWaivero.pdf WA1993040REGENTSOFTHEUNIVERSITYOFCALIFORNI...

4

WA_00_030_ASE_AMERICAS_Request_to_Assign_Title_to_Waiver-Inv...  

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

WA1995019DONNELLYCORPORATIONWaiverofDomesticandFore.pdf WA1995018OPTICALCOATINGLABORATORYINCWaiverofDomesti.pdf WA03032RWESCHOTTSOLARINCWaiverof...

5

Category:Yakima, WA | Open Energy Information  

Open Energy Info (EERE)

Yakima, WA Yakima, WA Jump to: navigation, search Go Back to PV Economics By Location Media in category "Yakima, WA" The following 16 files are in this category, out of 16 total. SVFullServiceRestaurant Yakima WA Puget Sound Energy Inc.png SVFullServiceRestauran... 61 KB SVHospital Yakima WA Puget Sound Energy Inc.png SVHospital Yakima WA P... 58 KB SVLargeHotel Yakima WA Puget Sound Energy Inc.png SVLargeHotel Yakima WA... 58 KB SVLargeOffice Yakima WA Puget Sound Energy Inc.png SVLargeOffice Yakima W... 58 KB SVMediumOffice Yakima WA Puget Sound Energy Inc.png SVMediumOffice Yakima ... 57 KB SVMidriseApartment Yakima WA Puget Sound Energy Inc.png SVMidriseApartment Yak... 59 KB SVOutPatient Yakima WA Puget Sound Energy Inc.png SVOutPatient Yakima WA... 63 KB SVPrimarySchool Yakima WA Puget Sound Energy Inc.png

6

Advance Patent Waiver W(A)2013-013 | Department of Energy  

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

& Publications WA98023McDERMOTTTECHNOLOGYINCWaiverofDomesticandFo.pdf WA96016AIRPRODUCTSANDCHEMICALSINCWaiverofDomestic.pdf WA96004GECORPORATERESEARCHand...

7

WA_1995_018_OPTICAL_COATING_LABORATORY_INC_Waiver_of_Domesti...  

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

Publications WA1995019DONNELLYCORPORATIONWaiverofDomesticandFore.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995009AIRPRODUCTSANDCHEMICAL...

8

WA_04_057_CHEMICAL_RESEARCH_AND_LICENSING_CO_Waiver_of_Paten...  

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

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

9

WA_98_023_McDERMOTT_TECHNOLOGY_INC_Waiver_of_Domestic_and_Fo...  

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

WA06013McDERMOTTTECHNOLOGYINCWaiverofPatentRightst.pdf WA00018PRAXAIRWaiveofDomesticandForeignInventionRi.pdf WA00007COMBUSTIONENGINEERINGINCW...

10

WA_02_021_H2GEN_INNOVATIONS_Waiver_of_Domestic_and_Foreign_P...  

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

WA02046QUESTAAIRTECHNOLOGIESWaiverofDomesticandFor.pdf WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA04034NUVERAFUELCELLSINCWaiver...

11

WA_04_009_ROCKWELL_SCIENTIFIC_CO_Wailve_of_Domestic_And_Fore...  

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

WA1995019DONNELLYCORPORATIONWaiverofDomesticandFore.pdf WA1995018OPTICALCOATINGLABORATORYINCWaiverofDomesti.pdf WA00030ASEAMERICASRequesttoAssign...

12

WA_1995_019_DONNELLY_CORPORATION_Waiver_of_Domestic_and_Fore...  

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

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

13

RAPID/Roadmap/11-WA-a | Open Energy Information  

Open Energy Info (EERE)

Toolkit About Bulk Transmission Geothermal Solar Tools Contribute Contact Us 11-WA-a State Cultural Considerations Overview 11-WA-a - State Cultural Considerations Overview.pdf...

14

WA_1995_009_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...  

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

9AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995009AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995009AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti...

15

WA_96_016_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domestic_...  

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

16AIRPRODUCTSANDCHEMICALSINCWaiverofDomestic.pdf WA96016AIRPRODUCTSANDCHEMICALSINCWaiverofDomestic.pdf WA96016AIRPRODUCTSANDCHEMICALSINCWaiverofDomest...

16

WA_1995_014_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...  

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

14AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995014AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1995014AIRPRODUCTSANDCHEMICALSINCWaiverofDomest...

17

WA_1994_034_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Domesti...  

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

4034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDomesti.pdf WA1994034AIRPRODUCTSANDCHEMICALSINCWaiverofDom...

18

WA_99_017_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Domestic_and_...  

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

9017AIRPRODUCTSANDCHEMICALSWaiverofDomesticand.pdf WA99017AIRPRODUCTSANDCHEMICALSWaiverofDomesticand.pdf WA99017AIRPRODUCTSANDCHEMICALSWaiverofDomesti...

19

WA_04_028_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_patent_Rights...  

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

8AIRPRODUCTSANDCHEMICALSWaiverofpatentRights.pdf WA04028AIRPRODUCTSANDCHEMICALSWaiverofpatentRights.pdf WA04028AIRPRODUCTSANDCHEMICALSWaiverofpatentRigh...

20

WA_1993_028_ALLIANCE_ELECTRIC_COMPANY_Waiver_of_Domestic_and...  

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

3028ALLIANCEELECTRICCOMPANYWaiverofDomesticand.pdf WA1993028ALLIANCEELECTRICCOMPANYWaiverofDomesticand.pdf WA1993028ALLIANCEELECTRICCOMPANYWaiverofDomestic...

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


21

WA_98_006_WESTINGHOUSE_POWER_GENERATION_A_FORMER_DIVISION_OF...  

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

6WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98006WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98006WESTINGHOUSEPOWERGENERATIONAFORMERDIVISION...

22

WA_98_005_WESTINGHOUSE_POWER_GENERATION_A_FORMER_DIVISION_OF...  

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

5WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98005WESTINGHOUSEPOWERGENERATIONAFORMERDIVISIONOF.pdf WA98005WESTINGHOUSEPOWERGENERATIONAFORMERDIVISION...

23

WA-TRIBE-STILLAGUAMISH TRIBE OF INDIANS  

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

WA-TRIBE-STILLAGUAMISH TRIBE OF INDIANS WA-TRIBE-STILLAGUAMISH TRIBE OF INDIANS Energy Efficiency and Conservation Block Grant Program Location: Tribe WA-TRIBE- STILLAGUAMISH TRIBE OF INDIANS WA American Recovery and Reinvestment Act: Proposed Action or Project Description The Stillaguamish Tribe proposes to expand its Stillaguamish Tribe Transit Services (STTS). For the past three years, the STTS has employed 14-passenger buses to transport clients to and from the tribal medical, dental, behavioral health and massage clinics. Often the demand-response requests that come to STTS are for one to three passengers at a time; therefore, funds are being requested to purchase a hybrid sedan to transport clients. Conditions: None Categorical Exclusion(s) Applied: A1, B1.32, B5.1 *-For the complete DOE National Environmental Policy Act regulations regarding categorical exclusions, see Subpart D of 10 CFR10 21

24

Forecast Prices  

Gasoline and Diesel Fuel Update (EIA)

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

25

WA_98_016_ABB_POWER_T_AND_D_COMPANY_Waiver_of_Domestic_and_F...  

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

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

26

Advance Patent Waiver W(A)2009-030 | Department of Energy  

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

public. Advance Patent Waiver W(A)2009-030 More Documents & Publications WA97030AIRPRODUCTSWaiverofDomesticandForeignPaten.pdf WA98001REYNOLDSMETALSCOMPANYW...

27

WA_04_085_THE_BOEING_COMPANY_Waiver_of_domestic_and_Foreign_...  

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

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

28

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...

29

WA_00_035_ALCOA_INC_Waiver_of_Domestic_and_Foreign_Rights_in...  

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

& Publications WA03054HEILTRAILERINTERNATIONALWaiverofDomesticand.pdf WA00011HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf Advance Patent Waiver W(A)2010-051...

30

WA_03_054_HEIL_TRAILER_INTERNATIONAL_Waiver_of_Domestic_and_...  

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

& Publications WA00035ALCOAINCWaiverofDomesticandForeignRightsin.pdf WA00011HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf Advance Patent Waiver W(A)2010-051...

31

RAPID/Roadmap/4-WA-a | Open Energy Information  

Open Energy Info (EERE)

4-WA-a State Exploration Process 4-WA-a State Exploration Process.pdf Click to View Fullscreen Permit Overview Developers desiring to conduct geothermal exploration activities on...

32

RAPID/Roadmap/3-WA-b | Open Energy Information  

Open Energy Info (EERE)

needs access to state lands for exploratory purposes, then they should begin the State Exploration Process. Green arrow.PNG 4-WA-a: State Exploration Process 3-WA-b.3 to...

33

RAPID/Roadmap/19-WA-f | Open Energy Information  

Open Energy Info (EERE)

9-WA-f Water Well NOI for Replacement or Additional Wells 19-WA-f - Water Well NOI for Replacement or Additional Wells.pdf Click to View Fullscreen Permit Overview A developer...

34

RECIPIENT:WA Dept. of Commerce STATE: WA PROJECT SEP ARRA SIRTI -  

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

WA Dept. of Commerce STATE: WA WA Dept. of Commerce STATE: WA PROJECT SEP ARRA SIRTI - Demand Energy - Energy Storage System Tied to Solar on Commercial Facility TITLE: Funding Opportunity Announcement Number Procurement Instrument Number NEPA Control Number cm Number DE-FOA-0000052 DE-EEOOO0139 GFO-o000139-031 Based on my review ofthe information concerning the proposed action, as NEPA Compliance Officer (authorized under DOE Order 4S1.1A), I have made the following determination: CX, EA, EIS APPENDIX AND NUMBER: Description: A9 Information gathering (including, but not limited to, literature surveys, inventories, audits), data analysis (including computer modeling), document preparation (such as conceptual design or feasibility studies, analytical energy supply and demand studies), and dissemination (including, but not limited to, document mailings, publication, and distribution;

35

Advance Patent Waiver W(A)2011-011 | Department of Energy  

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

W(A)2011-011 More Documents & Publications Advance Patent Waiver W(A)2008-011 Advance Patent Waiver W(A)2008-045 WA07038POETPROJECTLIBERTYLLCWaiverofDomesticandFo...

36

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...

37

Advance Patent Waiver W(A)2002-023 | Department of Energy  

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

2-023 Advance Patent Waiver W(A)2002-023 Advance Patent Waiver W(A)2002-023 More Documents & Publications Advance Patent Waiver W(A)2006-028 WA05056IBMWATSONRESEARCHCENTERWa...

38

WA_-01_001_PHILLIPS_PETROLEUM_Waiver_of_Domestic_and_Foreign...  

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

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

39

WA_07_038_POET_PROJECT_LIBERTY_LLC_Waiver_of_Domestic_and_Fo...  

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

7038POETPROJECTLIBERTYLLCWaiverofDomesticandFo.pdf WA07038POETPROJECTLIBERTYLLCWaiverofDomesticandFo.pdf WA07038POETPROJECTLIBERTYLLCWaiverofDomestic...

40

WA_1993_041_ROCKETDYNE_AND_LLNL_Waiver_of_the_Governments_U.pdf...  

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

1ROCKETDYNEANDLLNLWaiveroftheGovernmentsU.pdf WA1993041ROCKETDYNEANDLLNLWaiveroftheGovernmentsU.pdf WA1993041ROCKETDYNEANDLLNLWaiveroftheGovernmentsU.pd...

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


41

WA_1993_042_UNITED_TECHNOLOGIES_CORPORATION_Waiver_of_the_Go...  

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

TIONWaiveroftheGo.pdf More Documents & Publications WA1993040REGENTSOFTHEUNIVERSITYOFCALIFORNIAWaivero.pdf WA1993041ROCKETDYNEANDLLNLWaiveroftheGovernmentsU...

42

WA_99_015_FORD_MOTOR_COMPANY_Waiver_of_Domestic_and_Foreign_...  

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

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

43

WA_99_022_AIR_PRODUCTS_AND_CHEMICAL_Waiver_of_Domestic_and_F...  

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

9022AIRPRODUCTSANDCHEMICALWaiverofDomesticandF.pdf WA99022AIRPRODUCTSANDCHEMICALWaiverofDomesticandF.pdf WA99022AIRPRODUCTSANDCHEMICALWaiverofDomestic...

44

WA_02_015_AIR_PRODUCTS_AND_CHEMICALS_INC_Waiver_of_Patent_Ri...  

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

15AIRPRODUCTSANDCHEMICALSINCWaiverofPatentRi.pdf WA02015AIRPRODUCTSANDCHEMICALSINCWaiverofPatentRi.pdf WA02015AIRPRODUCTSANDCHEMICALSINCWaiverofPatent...

45

WA_04_063_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Patent_Rights...  

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

63AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04063AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04063AIRPRODUCTSANDCHEMICALSWaiverofPatentRig...

46

WA_04_083_AIR_PRODUCTS_AND_CHEMICALS_Waiver_of_Patent_Rights...  

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

83AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04083AIRPRODUCTSANDCHEMICALSWaiverofPatentRights.pdf WA04083AIRPRODUCTSANDCHEMICALSWaiverofPatentRig...

47

WA_01_005__PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_patent...  

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

1005PRAXAIRINCWaiverofDomesticandForeignpatent.pdf WA01005PRAXAIRINCWaiverofDomesticandForeignpatent.pdf WA01005PRAXAIRINCWaiverofDomesticandForeign...

48

WA_01_022_PRAXAIR_INC_AND_BP_AMOCO_Waiver_of_Domestic_and_Fo...  

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

1022PRAXAIRINCANDBPAMOCOWaiverofDomesticandFo.pdf WA01022PRAXAIRINCANDBPAMOCOWaiverofDomesticandFo.pdf WA01022PRAXAIRINCANDBPAMOCOWaiverofDomestic...

49

WA_03_018_HONEYWELL_INTERNATIONAL_Waiver_of_Domestic_and_For...  

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

3018HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf WA03018HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf WA03018HONEYWELLINTERNATIONALWaiverofDomestica...

50

WA_02_028_TRANE_CO__Waiver_of_Domestic_and_Foreign_Rights_in...  

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

5YORKINTERNATIONALCORPORATIONWaiverofDomesti.pdf WA01034INGERSOLL-RANDENERGYSYSTEMSWaiverofDomestica.pdf WA01011HONEYWELLLABORATORIESWaiverofDomesticandFore...

51

WA_04_040_HONEYWELL_INTERNATIONAL_INC_Waiver_of_Patent_Right...  

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

40HONEYWELLINTERNATIONALINCWaiverofPatentRight.pdf WA04040HONEYWELLINTERNATIONALINCWaiverofPatentRight.pdf WA04040HONEYWELLINTERNATIONALINCWaiverofPatentRi...

52

WA_03_041_HONEYWELL_INTERNATIONAL_Waiver_of_Domestic_and_For...  

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

3041HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf WA03041HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf WA03041HONEYWELLINTERNATIONALWaiverofDomestica...

53

WA_01_011_HONEYWELL_LABORATORIES_Waiver_of_Domestic_and_Fore...  

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

11HONEYWELLLABORATORIESWaiverofDomesticandFore.pdf WA01011HONEYWELLLABORATORIESWaiverofDomesticandFore.pdf WA01011HONEYWELLLABORATORIESWaiverofDomesticandF...

54

WA_00_011_HONEYWELL_INTERNATIONAL_Waiver_of_Domestic_and_For...  

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

0011HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf WA00011HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf WA00011HONEYWELLINTERNATIONALWaiverofDomestica...

55

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...

56

WA_04_034_NUVERA_FUEL_CELLS_INC_Waiver_of_Domestic_and_Forei...  

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

34NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04034NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04034NUVERAFUELCELLSINCWaiverofDomesticandFo...

57

WA_04_041_NUVERA_FUEL_CELLS_INC_Waiver_of_Domestic_and_Forei...  

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

41NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04041NUVERAFUELCELLSINCWaiverofDomesticandForei.pdf WA04041NUVERAFUELCELLSINCWaiverofDomesticandFo...

58

BayWa Group | Open Energy Information  

Open Energy Info (EERE)

BayWa Group BayWa Group Jump to: navigation, search Name BayWa Group Place Munich, Germany Zip 81925 Sector Services, Solar Product Germany-based company with international operations specialised in wholesale and retail and in providing services. The company is also active in the biofuel and solar sectors. Coordinates 48.136415°, 11.577531° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":48.136415,"lon":11.577531,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

59

BayWa Sunways JV | Open Energy Information  

Open Energy Info (EERE)

Sunways JV Jump to: navigation, search Name: BayWa & Sunways JV Place: Germany Sector: Solar Product: Germany-based JV that specialises in developing, planning and realizing...

60

RAPID/Roadmap/14-WA-b | Open Energy Information  

Open Energy Info (EERE)

RAPID Regulatory and Permitting Information Desktop Toolkit BETA RAPID Toolkit About Bulk Transmission Geothermal Solar Tools Contribute Contact Us 14-WA-b NPDES Permit...

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


61

RAPID/Roadmap/9-WA-a | Open Energy Information  

Open Energy Info (EERE)

(EIS) is necessary. Contact Information Agency Washington State Department of Ecology Position State Environmental Issues Contact Name Fran Sant Email fran.sant@ecy.wa.gov...

62

Fourth Annual SECA Meeting - Seattle, WA  

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

Fourth Annual SECA Meeting - Seattle, WA Fourth Annual SECA Meeting - Seattle, WA April 15-16, 2003 Table of Contents Disclaimer Papers and Presentations Expanded Applications of SECA Fuel Cells SECA Industrial Team Reports Military Applications of Fuel Cells Technology Highlights Environmental Considerations Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government or any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

63

Measurement of Fukushima Aerosol Debris in Sequim and Richland, WA and Ketchikan, AK  

SciTech Connect (OSTI)

Aerosol collections were initiated at several locations by PNNL shortly after the Great East Japan Earthquake of May 2011. Aerosol samples were transferred to laboratory high-resolution gamma spectrometers for analysis. Similar to treaty monitoring stations operating across the Northern hemisphere, iodine and other isotopes which could be volatilized at high temperature were detected. Though these locations are not far apart, they have significant variations with respect to water, mountain-range placement, and local topography. Variation in computed source terms will be shown to bound the variability of this approach to source estimation.

Miley, Harry S.; Bowyer, Ted W.; Engelmann, Mark D.; Eslinger, Paul W.; Friese, Judah I.; Greenwood, Lawrence R.; Haas, Derek A.; Hayes, James C.; Keillor, Martin E.; Kiddy, Robert A.; Kirkham, Randy R.; Landen, Jonathan W.; Lepel, Elwood A.; Lidey, Lance S.; Litke, Kevin E.; Morris, Scott J.; Olsen, Khris B.; Thompson, Robert C.; Valenzuela, Blandina R.; Woods, Vincent T.; Biegalski, Steven R.

2013-05-01T23:59:59.000Z

64

waTer economics. environmenTand Policy  

E-Print Network [OSTI]

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

Botea, Adi

65

RECIPIENT:WA Department of Commerce STATE: WA PROJECT Van Dyk Dairy Anaerobic Digester  

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

of5 of5 RECIPIENT:WA Department of Commerce STATE: WA PROJECT Van Dyk Dairy Anaerobic Digester TITLE: Funding Opportunity Announcement Number Procurement Instrument Number NEPA Control Number cm Number DE-EE0000139 GF0-10-604 Based on my review oftbe information concerning the proposed action, as NEPA CompUance Officer (authorized under DOE Order 451.1A), I have made the foUowing determination: cx, EA, EIS APPENDIX AND NUMBER: Description: A9 Information gathering (including, but not limited to, literature surveys, inventories, audits), data analysis (including computer modeling), document preparation (such as conceptual design or feasibility studies, analytical energy supply and demand studies), and dissemination (including, but not limited to, document mailings, publication, and distribution;

66

Hanford, WA Selected as Plutonium Production Facility | National Nuclear  

National Nuclear Security Administration (NNSA)

Hanford, WA Selected as Plutonium Production Facility | National Nuclear Hanford, WA Selected as Plutonium Production Facility | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Home > About Us > Our History > NNSA Timeline > Hanford, WA Selected as Plutonium Production Facility Hanford, WA Selected as Plutonium Production Facility January 16, 1943 Hanford, WA

67

Advance Patent Waiver W(A)2008-035 | Department of Energy  

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

public. Advance Patent Waiver W(A)2008-035 More Documents & Publications WA07038POETPROJECTLIBERTYLLCWaiverofDomesticandFo.pdf Advance Patent Waiver W(A)2008-022...

68

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

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

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

69

WA_00_008_PLUG_POWER_Waiver_of_Patent_Rights_in_Performance_...  

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

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

70

WA_99_012_AIR_PRODUCTS_Waiver_of_Patent_Rights_Under_AN_NVO_...  

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

2AIRPRODUCTSWaiverofPatentRightsUnderANNVO.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNVO.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNV...

71

WA_1994_027_FORD_MOTOR_COMPANY_Waiver_of_Domestic_and_Foreig...  

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

2FORDMOTORCOMPANYWaiverofDomesticandForeig.pdf WA97038FORDMOTORCOMPANYWaiverofDomesticandForeign.pdf WA99012AIRPRODUCTSWaiverofPatentRightsUnderANNVO...

72

WA_00_018_PRAXAIR_Waive_of_Domestic_and_Foreign_Invention_Ri...  

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

18PRAXAIRWaiveofDomesticandForeignInventionRi.pdf WA00018PRAXAIRWaiveofDomesticandForeignInventionRi.pdf WA00018PRAXAIRWaiveofDomesticandForeignInvention...

73

WA_02_046_QUESTA_AIR_TECHNOLOGIES_Waiver_of_Domestic_and_For...  

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

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

74

WA_03_024_PRAXAIR_Waiver_of_Domestic_and_Foreign_Invention_R...  

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

24PRAXAIRWaiverofDomesticandForeignInventionR.pdf WA03024PRAXAIRWaiverofDomesticandForeignInventionR.pdf WA03024PRAXAIRWaiverofDomesticandForeignInventio...

75

WA_01_039_PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_Patent_...  

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

1039PRAXAIRINCWaiverofDomesticandForeignPatent.pdf WA01039PRAXAIRINCWaiverofDomesticandForeignPatent.pdf WA01039PRAXAIRINCWaiverofDomesticandForeignP...

76

WA_02_055_PRAXAIR_Waiver_of_Domestic_and_Foreign_Patent_Righ...  

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

2055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA02055PRAXAIRWaiverofDomesticandForeignPatentRigh.pdf WA02055PRAXAIRWaiverofDomesticandForeignPaten...

77

WA_00_001_PRAXAIR_INC_Waiver_of_Domestic_and_Foreign_Inventi...  

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

01PRAXAIRINCWaiverofDomesticandForeignInventi.pdf WA00001PRAXAIRINCWaiverofDomesticandForeignInventi.pdf WA00001PRAXAIRINCWaiverofDomesticandForeignInve...

78

WA_04_079_PRAXAIR_INC_Waiver_of_Patent_Rights_Under_a_Subcon...  

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

04079PRAXAIRINCWaiverofPatentRightsUnderaSubcon.pdf WA04079PRAXAIRINCWaiverofPatentRightsUnderaSubcon.pdf WA04079PRAXAIRINCWaiverofPatentRightsUndera...

79

WA_02_022_HONEYWELL_INC_Waiver_of_Domestic_and_Foreign_Inven...  

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

22HONEYWELLINCWaiverofDomesticandForeignInven.pdf WA02022HONEYWELLINCWaiverofDomesticandForeignInven.pdf WA02022HONEYWELLINCWaiverofDomesticandForeignIn...

80

WA_02_045_KENNAMETAL_INC_Waiver_of_Domestic_and_Foreign_Righ...  

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

LINCWaiverofDomesticandForeignRigh.pdf More Documents & Publications WA00011HONEYWELLINTERNATIONALWaiverofDomesticandFor.pdf WA01034INGERSOLL-RANDENERGYSYSTEMS...

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


81

WA_04_039_HONEYWELL_INTERNATIONAL_Waiver_of_Patent_Rights_Un...  

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

9HONEYWELLINTERNATIONALWaiverofPatentRightsUn.pdf WA04039HONEYWELLINTERNATIONALWaiverofPatentRightsUn.pdf WA04039HONEYWELLINTERNATIONALWaiverofPatentRights...

82

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...

83

WA_03_011_ROCKWELL_AUTOMATION_Waiver_of_Patent_Rights_Under_...  

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

3011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA03011ROCKWELLAUTOMATIONWaiverofPatentRightsUnder.pdf WA03011ROCKWELLAUTOMATIONWaiverofPatentRights...

84

Forecasting wireless communication technologies  

Science Journals Connector (OSTI)

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

Sabrina Patino; Jisun Kim; Tugrul U. Daim

2010-01-01T23:59:59.000Z

85

Wind Power Forecasting  

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

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

86

Solar forecasting review  

E-Print Network [OSTI]

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

Inman, Richard Headen

2012-01-01T23:59:59.000Z

87

Wind Power Forecasting  

Science Journals Connector (OSTI)

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

Sue Ellen Haupt; William P. Mahoney; Keith Parks

2014-01-01T23:59:59.000Z

88

Energy Demand Forecasting  

Science Journals Connector (OSTI)

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

S. C. Bhattacharyya

2011-01-01T23:59:59.000Z

89

Isotopic Studies of Contaminant Transport at the Hanford Site, WA  

E-Print Network [OSTI]

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

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

2008-01-01T23:59:59.000Z

90

RAPID/Roadmap/18-WA-b | Open Energy Information  

Open Energy Info (EERE)

period in which the WSDE has to respond to the Demonstration of Compliance. 18-WA-b.17 - Conduct Public Meeting, if Requested If the public requests a public meeting on the...

91

Improving Inventory Control Using Forecasting  

E-Print Network [OSTI]

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

Balandran, Juan

2005-12-16T23:59:59.000Z

92

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

93

CAPP 2010 Forecast.indd  

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

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

94

Valuing Climate Forecast Information  

Science Journals Connector (OSTI)

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

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

1987-09-01T23:59:59.000Z

95

Comparing Forecast Skill  

Science Journals Connector (OSTI)

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

Timothy DelSole; Michael K. Tippett

2014-12-01T23:59:59.000Z

96

Gaseous Detonation-Driven Fracture of Tubes Tong Wa Chao  

E-Print Network [OSTI]

Gaseous Detonation-Driven Fracture of Tubes Thesis by Tong Wa Chao In Partial Fulfillment An experimental investigation of fracture response of aluminum 6061-T6 tubes under internal gaseous detonation on the detonation velocity, strain history, blast pressure from the crack opening, and crack speeds. The curved

97

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.

98

DOE - Office of Legacy Management -- University of Washington - WA 0-01  

Office of Legacy Management (LM)

Washington - WA 0-01 Washington - WA 0-01 FUSRAP Considered Sites Site: UNIVERSITY OF WASHINGTON (WA.0-01) Eliminated from further consideration under FUSRAP Designated Name: Not Designated Alternate Name: None Location: Seattle , Washington WA.0-01-1 Evaluation Year: 1987 WA.0-01-1 Site Operations: Research activities involving small quantities of radioactive materials in a controlled environment. WA.0-01-1 Site Disposition: Eliminated - Potential for residual radioactive contamination considered remote - Operating under active NRC license WA.0-01-1 Radioactive Materials Handled: Yes Primary Radioactive Materials Handled: None Indicated WA.0-01-1 Radiological Survey(s): None Indicated Site Status: Eliminated from further consideration under FUSRAP Also see

99

Kenai, AK Liquefied Natural Gas Exports to Russia (Dollars per...  

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

Kenai, AK Liquefied Natural Gas Exports to Russia (Dollars per Thousand Cubic Feet) Kenai, AK Liquefied Natural Gas Exports to Russia (Dollars per Thousand Cubic Feet) Decade...

100

Sandia National Laboratories: solar forecasting  

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

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

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


101

Consensus Coal Production Forecast for  

E-Print Network [OSTI]

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

Mohaghegh, Shahab

102

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

103

On Sequential Probability Forecasting  

E-Print Network [OSTI]

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

McCarl, Bruce A.

104

File:INL-geothermal-wa.pdf | Open Energy Information  

Open Energy Info (EERE)

wa.pdf wa.pdf Jump to: navigation, search File File history File usage Washington Geothermal Resources Size of this preview: 699 × 600 pixels. Full resolution ‎(4,835 × 4,147 pixels, file size: 3.28 MB, MIME type: application/pdf) Description Washington Geothermal Resources Sources Idaho National Laboratory Authors Patrick Laney; Julie Brizzee Related Technologies Geothermal Creation Date 2003-11-01 Extent State Countries United States UN Region Northern America States Washington File history Click on a date/time to view the file as it appeared at that time. Date/Time Thumbnail Dimensions User Comment current 12:45, 16 December 2010 Thumbnail for version as of 12:45, 16 December 2010 4,835 × 4,147 (3.28 MB) MapBot (Talk | contribs) Automated upload from NREL's "mapsearch" data

105

GRR/Section 15-WA-a - Air Quality Notice of Construction Permit | Open  

Open Energy Info (EERE)

5-WA-a - Air Quality Notice of Construction Permit 5-WA-a - Air Quality Notice of Construction Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 15-WA-a - Air Quality Notice of Construction Permit 15-WA-a - Air Quality Notice of Construction Permit.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies WAC 173-400-110 WAC 173-400-111 WAC 173-400-171 Triggers None specified This flowchart illustrates the process for obtaining an Air Quality Notice of Construction Permit. The Washington State Department of Ecology (WSDE) oversees the permitting process under WAC 173-400. 15-WA-a - Air Quality Notice of Construction Permit.pdf 15-WA-a - Air Quality Notice of Construction Permit.pdf 15-WA-a - Air Quality Notice of Construction Permit.pdf

106

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

107

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

108

Price forecasting for notebook computers.  

E-Print Network [OSTI]

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

Rutherford, Derek Paul

2012-01-01T23:59:59.000Z

109

Ensemble Forecasts and their Verification  

E-Print Network [OSTI]

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

Maryland at College Park, University of

110

GRR/Section 9-WA-b - State Environmental Review | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 9-WA-b - State Environmental Review GRR/Section 9-WA-b - State Environmental Review < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 9-WA-b - State Environmental Review 9-WA-b - State Environmental Review.pdf Click to View Fullscreen Triggers None specified Once the lead agency is determined they are responsible for continuing forward with environmental review. In Washington, environmental review is effectuated through the developer completing an Environmental Checklist which assists the lead agency in determining whether the proposal will likely result in negative impacts on the environment. 9-WA-b - State Environmental Review.pdf 9-WA-b - State Environmental Review.pdf Error creating thumbnail: Page number not in range.

111

Probabilistic manpower forecasting  

E-Print Network [OSTI]

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

Koonce, James Fitzhugh

1966-01-01T23:59:59.000Z

112

Diagnosing Forecast Errors in Tropical Cyclone Motion  

Science Journals Connector (OSTI)

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

Thomas J. Galarneau Jr.; Christopher A. Davis

2013-02-01T23:59:59.000Z

113

Project Profile: Forecasting and Influencing Technological Progress...  

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

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

114

Forecasting with adaptive extended exponential smoothing  

Science Journals Connector (OSTI)

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

John T. Mentzer Ph.D.

115

Electricity price forecasting in a grid environment.  

E-Print Network [OSTI]

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

Li, Guang, 1974-

2007-01-01T23:59:59.000Z

116

Energy Department Forecasts Geothermal Achievements in 2015 ...  

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

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

117

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

118

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

119

AK-TRIBE-NATIVE VILLAGE OF NAPAKIAK  

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

AK-TRIBE-NATIVE VILLAGE OF NAPAKIAK AK-TRIBE-NATIVE VILLAGE OF NAPAKIAK Energy Efficiency and Conservation Block Grant Program Location: Tribe AK-TRIBE-NATIVE VILLAGE OF NAPAKIAK AK American Recovery and Reinvestment Act: Proposed Action or Project Description The Native Village of Napakiak proposes to renovate/retrofit two buildings (Health Clinic and Community Center [former Transportation Building]) to become more energy efficient. Energy efficiency retrofits would include improvements to lighting systems, supplemental loads, air distribution systems, and/or heating and cooling systems, insulation, and windows/doors. Conditions: None Categorical Exclusion(s) Applied: B2.5, B5.1 *-For the complete DOE National Environmental Policy Act regulations regarding categorical exclusions, see Subpart D of 10 CFR10 21

120

W(A)94-022 STATEMENT OF CONSIDERATIONS  

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

4-022 4-022 STATEMENT OF CONSIDERATIONS Request by Cummins Power Generation, Inc., for an Advance Waiver of Domestic and Foreign Patent Rights to Inventions made under a contract entitled "Utility Scale Joint Venture Project," between Cummins Power Generation, Inc. and Sandia National Laboratories (Contract No. AB- 8717B) under Management and Operations Contract DE-AL04-84AL85000, DOE Docket No. W(A)94-022. The petitioner, Cummins Power Generation, Inc., (CPG) has requested a waiver of all domestic and foreign patent rights to inventions which it may conceive or first actually reduce to practice in the course of work under the Utility Scale Joint Venture Project between Petitioner and Sandia National Laboratories (Sandia) under contract No. AB- 8717B. Sandia is operated by Sandia Corporation for the U.S. Department of Energy (DOE).

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


121

W(A)93-013 STATEMENT OF CONSIDERATIONS  

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

13 13 STATEMENT OF CONSIDERATIONS Request by AlliedSignal, Inc. for Waiver of Domestic and Foreign Patent Rights to inventions that may arise under Contract No. DE-FC04-93AL94462 between the United States Department of Energy (DOE) and AlliedSignal, Inc. DOE Docket: W(A)93-013 The Petitioner, AlliedSignal, Inc. (AlliedSignal), has requested a waiver of all domestic and foreign patent rights to inventions which it may conceive or reduce to practice in the course of work under Contract No. DE-FC04-93AL94462, a Cooperative Agreement with DOE. The project period is May 14, 1993 through May 13, 1996. The Cooperative Agreement covers work in designing a biological/chemical production process for caprolactam using microbial bioprocesses that convert cyclohexane to

122

W(A)93-039 STATEMENT OF CONSIDERATIONS  

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

39 39 STATEMENT OF CONSIDERATIONS Request by Air Products and Chemicals, Inc. for Waiver of Domestic and Foreign Patent Rights to inventions that may arise under Contract No. DE-FC04-93AL94461 between the United States Department of Energy (DOE) and Air Products and Chemicals, Inc. DOE Docket: W(A)93-039. The Petitioner, Air Products and Chemicals, Inc., (Air Products) has requested a waiver of all domestic and foreign patent rights to inventions which it may conceive or reduce to practice in the course of work under Contract No. DE-FC04-93AL94461 a Cooperative Agreement with DOE. The contract covers a four phase development program for a recently patented technology developed at Air Products entitled "Novel Selective Surface Flow (SSF T ) Membranes for the

123

GRR/Section 12-WA-a - Live Wildlife Taking Permit | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 12-WA-a - Live Wildlife Taking Permit GRR/Section 12-WA-a - Live Wildlife Taking Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 12-WA-a - Live Wildlife Taking Permit 12-WA-a - Live Wildlife Taking Permit.pdf Click to View Fullscreen Contact Agencies Washington State Department of Fish and Wildlife Regulations & Policies WAC 232-12-064 Triggers None specified In Washington, it is unlawful to take wildlife from the wild without permission from the Washington State Department of Fish and Wildlife (WDFW). The WDFW issues Live Wildlife Taking Permits under WAC 232-12-064. 12-WA-a - Live Wildlife Taking Permit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range.

124

EIS-0473: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project  

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

73: W.A. Parish Post-Combustion CO2 Capture and Sequestration 73: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project (PCCS), Fort Bend County, TX EIS-0473: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project (PCCS), Fort Bend County, TX SUMMARY This EIS evaluates the environmental impacts of a proposal to provide financial assistance for a project proposed by NRG Energy, Inc (NRG). DOE selected NRG's 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

125

Fisher & Paykel Appliances: ENERGY STAR Referral (WA42T26GW1) | Department  

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

Fisher & Paykel Appliances: ENERGY STAR Referral (WA42T26GW1) Fisher & Paykel Appliances: ENERGY STAR Referral (WA42T26GW1) Fisher & Paykel Appliances: ENERGY STAR Referral (WA42T26GW1) June 12, 2013 DOE referred the matter of Fisher & Paykel Appliances residential clothes washer, model WA42T26GW1, to the U.S. Environmental Protection Agency, brand manager for the ENERGY STAR Program, for appropriate action after DOE testing showed that the model does not meet the ENERGY STAR specification. Fisher & Paykel Appliances: ENERGY STAR Referral (WA42T26GW1) More Documents & Publications Regulatory Burden RFI DOE response to questions from AHAM on the supplemental proposed test procedure for residential clothes washers Scoping Study to Evaluate Feasibility of National Databases for EM&V Documents and Measure Savings: Appendices

126

EIS-0473: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project  

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

EIS-0473: W.A. Parish Post-Combustion CO2 Capture and Sequestration EIS-0473: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project (PCCS), Fort Bend County, TX EIS-0473: W.A. Parish Post-Combustion CO2 Capture and Sequestration Project (PCCS), Fort Bend County, TX SUMMARY This EIS evaluates the environmental impacts of a proposal to provide financial assistance for a project proposed by NRG Energy, Inc (NRG). DOE selected NRG's 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

127

GRR/Section 3-WA-b - Land Access Overview | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 3-WA-b - Land Access Overview GRR/Section 3-WA-b - Land Access Overview < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-WA-b - Land Access Overview 3-WA-b - Land Access Overview.pdf Click to View Fullscreen Contact Agencies Washington State Department of Natural Resources Triggers None specified Any developer that needs access to or through state lands must obtain the appropriate permit or lease. The developer will obtain such permit or lease through the Washington State Department of Natural Resources. 3-WA-b - Land Access Overview.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative

128

Correcting and combining time series forecasters  

Science Journals Connector (OSTI)

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

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

2014-02-01T23:59:59.000Z

129

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

E-Print Network [OSTI]

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

130

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

E-Print Network [OSTI]

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

Chakraborty, Romy

2010-01-01T23:59:59.000Z

131

Forecast Energy | Open Energy Information  

Open Energy Info (EERE)

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

132

Price forecasting for notebook computers  

E-Print Network [OSTI]

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

Rutherford, Derek Paul

2012-06-07T23:59:59.000Z

133

Forecasting phenology under global warming  

Science Journals Connector (OSTI)

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

2010-01-01T23:59:59.000Z

134

Demand Forecasting of New Products  

E-Print Network [OSTI]

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

Sun, Yu

135

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

E-Print Network [OSTI]

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

Goto, Susumu

2007-01-01T23:59:59.000Z

136

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

Office of Environmental Management (EM)

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

137

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

E-Print Network [OSTI]

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

Sakauchi, Tsuginosuke

2011-01-01T23:59:59.000Z

138

Solar Energy Market Forecast | Open Energy Information  

Open Energy Info (EERE)

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

139

EIS-0467: Hanford Site Natural Gas Pipeline, Richland, WA | Department of  

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

7: Hanford Site Natural Gas Pipeline, Richland, WA 7: Hanford Site Natural Gas Pipeline, Richland, WA EIS-0467: Hanford Site Natural Gas Pipeline, Richland, WA Summary This EIS will evaluate the environmental impacts of a proposal to enter into a contract with a licensed natural gas supplier in Washington State to construct, operate, and maintain a natural gas pipeline. The pipeline would deliver natural gas to support the Waste Treatment Plant and the 242-A Evaporator operations in the 200 East Area of the Hanford Site. Public Comment Opportunities None available at this time. For more information, contact: Mr. Douglas Chapin, NEPA Document Manager U.S. Department of Energy Richland Operations Office P.O. Box 550, MSIN A5-11 Richland, WA 99352 Documents Available for Download January 23, 2012 EIS-0467: Notice of Intent to Prepare an Environmental Impact Statement and

140

GRR/Section 9-WA-c - State Environmental Impact Statement | Open Energy  

Open Energy Info (EERE)

GRR/Section 9-WA-c - State Environmental Impact Statement GRR/Section 9-WA-c - State Environmental Impact Statement < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 9-WA-c - State Environmental Impact Statement 9-WA-c - State Environmental Impact Statement.pdf Click to View Fullscreen Triggers None specified The primary purpose of an Environmental Impact Statement (EIS) is to ensure that the Washington State Environmental Policy Act (SEPA) policies are an integral part of the ongoing programs and actions of state and local government. An EIS must provide impartial discussion of significant environmental impacts and must inform decision makers and the public of reasonable alternatives, including mitigation measures that would avoid or minimize adverse impacts or enhance environmental quality. WAC 197-11-400.

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While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

GRR/Section 19-WA-f - Water Well NOI for Replacement or Additional Wells |  

Open Energy Info (EERE)

GRR/Section 19-WA-f - Water Well NOI for Replacement or Additional Wells GRR/Section 19-WA-f - Water Well NOI for Replacement or Additional Wells < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-WA-f - Water Well NOI for Replacement or Additional Wells 19-WA-f - Water Well NOI for Replacement or Additional Wells.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Revised Code of Washington 90.44.100 Revised Code of Washington 18.104.048 Washington Administrative Code 173-160-151 Triggers None specified A developer seeking to use ground water for an activity may need to drill a new well in a different location than a previous well, drill an additional well at an existing location, or drill a replacement well at the same

142

GRR/Section 11-WA-a - State Cultural Considerations Overview | Open Energy  

Open Energy Info (EERE)

GRR/Section 11-WA-a - State Cultural Considerations Overview GRR/Section 11-WA-a - State Cultural Considerations Overview < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 11-WA-a - State Cultural Considerations Overview 11-WA-a - State Cultural Considerations Overview.pdf Click to View Fullscreen Triggers None specified The developer will be required to comply with Washington state law when human remains or other cultural resources are discovered on a project site. Cultural resources include both historic and archaeological resources and sites. The discovery of cultural resources may require obtaining a permit and providing public notice and notice to Indian Tribes. Once the necessary procedures have been followed, the developer may continue with the project.

143

GRR/Section 14-WA-c - Underground Injection Control Permit | Open Energy  

Open Energy Info (EERE)

GRR/Section 14-WA-c - Underground Injection Control Permit GRR/Section 14-WA-c - Underground Injection Control Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 14-WA-c - Underground Injection Control Permit 14-WA-c - Underground Injection Control Permit.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Chapter 173-218 WAC Non-endangerment Standard Triggers None specified The Safe Drinking Water Act requires Washington to implement technical criteria and standards to protect underground sources of drinking water from contamination. Under Chapter 173-218 WAC, the Washington State Department of Ecology (WSDE) regulates and permits underground injection control (UIC) wells in Washington. The Environmental Protection Agency

144

GRR/Section 18-WA-a - Underground Storage Tank Process | Open Energy  

Open Energy Info (EERE)

GRR/Section 18-WA-a - Underground Storage Tank Process GRR/Section 18-WA-a - Underground Storage Tank Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 18-WA-a - Underground Storage Tank Process 18-WA-a - Underground Storage Tank Process.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Revised Code of Washington Chapter 90.76 Washington Administrative Code Chapter 173-360 Triggers None specified Washington has a federally-approved state Underground Storage Tank (UST) program regulated by the Washington State Department of Ecology (WSDE) under Revised Code of Washington Chapter 90.76 and Washington Administrative Code Chapter 173-360. Washington defines an "Underground

145

GRR/Section 5-WA-a - Drilling and Well Development | Open Energy  

Open Energy Info (EERE)

GRR/Section 5-WA-a - Drilling and Well Development GRR/Section 5-WA-a - Drilling and Well Development < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 5-WA-a - Drilling and Well Development 5-WA-a.pdf Click to View Fullscreen Contact Agencies Washington State Department of Natural Resources Regulations & Policies Geothermal Act 78.60 RCW Geothermal Rules 332-17 WAC Triggers None specified In Washington geothermal drilling and well development are regulated by the Washington State Department of Natural Resources (WSDNR). Geothermal production wells and core holes deeper than 750ft require the developer go through the whole WSDNR permitting process (which requires a public hearing) and require that the developer complete the State Environmental

146

GRR/Section 3-WA-e - State Right of Way Process | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 3-WA-e - State Right of Way Process GRR/Section 3-WA-e - State Right of Way Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-WA-e - State Right of Way Process 3-WA-e - State Right of Way Process.pdf Click to View Fullscreen Contact Agencies Washington State Department of Natural Resources Regulations & Policies RCW 79-36-350 RCW 79-36-520 RCW 79-36-530 Triggers None specified This flowchart illustrates the process for obtaining a right of way over state lands in Washington. The right of way process is overseen by the Washington State Department of Natural Resources (WSDNR). The right of way process is regulated under Revised Code of Washington (RCW) 79-36-350. The developer may apply for an easement, permit or license for a right of

147

GRR/Section 14-WA-d - Section 401 Water Quality Certification | Open Energy  

Open Energy Info (EERE)

GRR/Section 14-WA-d - Section 401 Water Quality Certification GRR/Section 14-WA-d - Section 401 Water Quality Certification < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 14-WA-d - Section 401 Water Quality Certification 14-WA-d - 401 Water Quality Certification.pdf Click to View Fullscreen Contact Agencies U S Army Corps of Engineers Washington State Department of Ecology Regulations & Policies Revised Statute of Washington Chapter 90.48 Washington Administrative Code Chapter 173-201A Washington Administrative Code 173-225-030 Triggers None specified Developers requiring a Section 404 Dredge and Fill Permit from the U S Army Corps of Engineers (Corps) are required to obtain a Section 401 Water Quality Certification from the state of Washington. The Washington State

148

GRR/Section 19-WA-d - Water Conservancy Board Transfer or Change of Water  

Open Energy Info (EERE)

19-WA-d - Water Conservancy Board Transfer or Change of Water 19-WA-d - Water Conservancy Board Transfer or Change of Water Right < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-WA-d - Water Conservancy Board Transfer or Change of Water Right 19-WA-d - Water Conservancy Board Transfer or Change of Water Right.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Revised Code of Washington Chapter 90.80 RCW 90.03.380 90.03.390 RCW 90.44.100 Triggers None specified In 1997, the Washington Legislature authorized the creation of water conservancy boards through the enactment of Revised Code of Washington Chapter 90.80 to expedite the administrative process for voluntary water right transfers within individual counties. In counties where a water

149

GRR/Section 19-WA-e - Water Well Notice of Intent for New Well | Open  

Open Energy Info (EERE)

GRR/Section 19-WA-e - Water Well Notice of Intent for New Well GRR/Section 19-WA-e - Water Well Notice of Intent for New Well < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-WA-e - Water Well Notice of Intent for New Well 19-WA-e - Water Well Notice of Intent for New Well.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Revised Code of Washington 18.104.048 Washington Administrative Code 173-160-151 Triggers None specified A developer seeking to use ground water for an activity may need to drill a new well to access the ground water. When a developer needs to drill a new well, the developer must complete the Notice of Intent (NOI) to Drill a Well form and submit the form to the Washington State Department of Ecology

150

GRR/Section 14-WA-b - State NPDES Permitting Process | Open Energy  

Open Energy Info (EERE)

GRR/Section 14-WA-b - State NPDES Permitting Process GRR/Section 14-WA-b - State NPDES Permitting Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 14-WA-b - State NPDES Permitting Process 14-WA-b - State NPDES Permitting Process.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology United States Environmental Protection Agency Regulations & Policies Clean Water Act Chapter 90.48 RCW Chapter 173-216 WAC Triggers None specified Section 402 of the Clean Water Act (CWA) required the Environmental Protection Agency (EPA) to establish the National Pollutant Discharge Elimination System (NPDES) to regulate discharge of pollutants from point sources. In Washington, the EPA has delegated responsibility of NPDES to

151

GRR/Section 4-WA-a - State Exploration Process | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 4-WA-a - State Exploration Process GRR/Section 4-WA-a - State Exploration Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 4-WA-a - State Exploration Process 4-WA-a State Exploration Process.pdf Click to View Fullscreen Contact Agencies Washington State Department of Natural Resources Regulations & Policies Geothermal Act 78.60 RCW Geothermal Rules 332-17 WAC Triggers None specified Geothermal exploration in Washington requires a Geothermal Exploration Permit from the Washington State Department of Natural Resources (WSDNR) for invasive exploration or drilling. Operations that require an exploration or drilling permit will also require the developer to initiate the State Environmental Policy Act (SEPA). In Washington geothermal resources are regulated under Chapter 78.60 RCW

152

GRR/Section 3-WA-d - State Land Lease | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 3-WA-d - State Land Lease GRR/Section 3-WA-d - State Land Lease < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-WA-d - State Land Lease 3-WA-d - State Land Lease.pdf Click to View Fullscreen Contact Agencies Washington State Department of Natural Resources Regulations & Policies RCW 79-13-020 RCW 79-13-140 RCW 79-13-150 WAC 332-22-030 WAC 332-22-105 WAC 332-22-110 Triggers None specified This flowchart illustrates the process used to lease state lands in Washington. The Washington State Department of Natural Resources (WSDNR) oversees the land leasing process through the Commissioner of Public Lands ("commissioner"). The WSDNR may lease state lands for purposes it deems advisable, including commercial, industrial, residential, agricultural, and

153

GRR/Section 3-WA-c - Utility Franchise or Permit Process | Open Energy  

Open Energy Info (EERE)

GRR/Section 3-WA-c - Utility Franchise or Permit Process GRR/Section 3-WA-c - Utility Franchise or Permit Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-WA-c - Utility Franchise or Permit Process 3-WA-c - Utility Franchise or Permit Process (1).pdf Click to View Fullscreen Contact Agencies Washington State Department of Transportation Regulations & Policies WAC 468-34-060 WAC 468-34-080 WAC 468-34-110 WAC 468-34-160 WAC 468-34-170 Triggers None specified This flowchart illustrates the process of obtaining a franchise or permit through a state highway right of way in Washington State. A utility permit or franchise is required for occupancy of a highway right of way by utility facilities, including private lines. WAC 468-34-160. The process is

154

GRR/Section 19-WA-b - New Water Right Permit Process | Open Energy  

Open Energy Info (EERE)

GRR/Section 19-WA-b - New Water Right Permit Process GRR/Section 19-WA-b - New Water Right Permit Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-WA-b - New Water Right Permit Process 19-WA-b - New Water Right Permit Process.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Revised Code of Washington Chapter 90.03 Revised Code of Washington Chapter 90.44 Triggers None specified Washington uses a prior appropriation system for the distribution of both surface water and ground water rights in which water users receive the right to use water on a "first in time, first in right" basis. Under Washington law, the waters of Washington belong collectively to the public

155

GRR/Section 19-WA-c - Transfer or Change of Water Right | Open Energy  

Open Energy Info (EERE)

9-WA-c - Transfer or Change of Water Right 9-WA-c - Transfer or Change of Water Right < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-WA-c - Transfer or Change of Water Right 19-WA-c - Transfer or Change of Water Right.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Revised Code of Washington 90.03.380 Revised Code of Washington 90.44.100 Revised Code of Washington Chapter 90.80 Triggers None specified Much of Washington's public waters have been accounted for through water right claims, permits, or certificates. As a result, many individuals seeking water rights try to acquire existing water rights already in use or change the use of a current water right they already hold. Certain elements

156

NEURAL NETWORKS FOR DISCRETE TOMOGRAPHY K.J. Batenburg a W.A. Kosters b  

E-Print Network [OSTI]

NEURAL NETWORKS FOR DISCRETE TOMOGRAPHY K.J. Batenburg a W.A. Kosters b a Mathematical Institute of crystalline solids at atomic resolution from electron microscopic images can be considered the ``holy grail

Kosters, Walter

157

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.

158

GRR/Section 3-WA-a - State Geothermal Lease | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 3-WA-a - State Geothermal Lease GRR/Section 3-WA-a - State Geothermal Lease < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-WA-a - State Geothermal Lease 3-WA-a State Geothermal Lease.pdf Click to View Fullscreen Contact Agencies Washington State Department of Natural Resources Regulations & Policies Chapter 79.14 RCW Chapter 344-12 WAC Triggers None specified The State of Washington is still in the process of developing and finalizing the rules and regulations related to geothermal leases on state lands; however, the Washington State Department of Natural Resources (WSDNR) expects the process to be similar to the process for leasing state lands for oil and natural gas development. The rules and regulations for

159

GRR/Section 11-WA-c - Archaeological Resource Discovery Process | Open  

Open Energy Info (EERE)

GRR/Section 11-WA-c - Archaeological Resource Discovery Process GRR/Section 11-WA-c - Archaeological Resource Discovery Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 11-WA-c - Archaeological Resource Discovery Process 11-WA-c - Archaeological Resource Discovery Process.pdf Click to View Fullscreen Triggers None specified In the state of Washington, cultural resource concerns are integrated as early as possible into the planning for capital projects and are protected if discovered during construction. Washington defines "Cultural resources" as archeological and historical sites and artifacts, and traditional areas or items of religious, ceremonial and social uses to affected tribes. Washington defines an "Archaeological resource" as any

160

GRR/Section 19-WA-a - Water Access and Water Rights Overview | Open Energy  

Open Energy Info (EERE)

9-WA-a - Water Access and Water Rights Overview 9-WA-a - Water Access and Water Rights Overview < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-WA-a - Water Access and Water Rights Overview 19-WA-a - Water Access and Water Rights Overview.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies Revised Code of Washington Chapter 90.03 Revised Code of Washington Chapter 90.44 RCW 90.44.050 Triggers None specified Similar to many western states, only a small amount of water is available for appropriation in Washington. As a result, Washington has developed a comprehensive regulatory scheme for the distribution of water rights and use of water in the state. Washington employs a prior appropriation or

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


161

Summary Verification Measures and Their Interpretation for Ensemble Forecasts  

Science Journals Connector (OSTI)

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

A. Allen Bradley; Stuart S. Schwartz

2011-09-01T23:59:59.000Z

162

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

163

Aggregate vehicle travel forecasting model  

SciTech Connect (OSTI)

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

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

1995-05-01T23:59:59.000Z

164

Communication of uncertainty in temperature forecasts  

Science Journals Connector (OSTI)

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

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

165

FORECASTING THE ROLE OF RENEWABLES IN HAWAII  

E-Print Network [OSTI]

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

Sathaye, Jayant

2013-01-01T23:59:59.000Z

166

Massachusetts state airport system plan forecasts.  

E-Print Network [OSTI]

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

Mathaisel, Dennis F. X.

167

Antarctic Satellite Meteorology: Applications for Weather Forecasting  

Science Journals Connector (OSTI)

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

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

2003-02-01T23:59:59.000Z

168

Forecasting Water Use in Texas Cities  

E-Print Network [OSTI]

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

Shaw, Douglas T.; Maidment, David R.

169

Energy demand forecasting: industry practices and challenges  

Science Journals Connector (OSTI)

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

Mathieu Sinn

2014-06-01T23:59:59.000Z

170

Consensus Coal Production And Price Forecast For  

E-Print Network [OSTI]

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

Mohaghegh, Shahab

171

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

172

Annual Energy Outlook Forecast Evaluation  

Gasoline and Diesel Fuel Update (EIA)

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

173

Load Forecasting of Supermarket Refrigeration  

E-Print Network [OSTI]

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

174

Essays on macroeconomics and forecasting  

E-Print Network [OSTI]

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

Liu, Dandan

2006-10-30T23:59:59.000Z

175

Forecasting-based SKU classification  

Science Journals Connector (OSTI)

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

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

2013-01-01T23:59:59.000Z

176

RAPID/Roadmap/6-AK-a | Open Energy Information  

Open Energy Info (EERE)

About Bulk Transmission Geothermal Solar Tools Contribute Contact Us 6-AK-a Transportation Permit 06AKATransportationOversizeOverweight.pdf Click to View Fullscreen Permit...

177

RAPID/Roadmap/14-AK-a | Open Energy Information  

Open Energy Info (EERE)

RAPID Regulatory and Permitting Information Desktop Toolkit BETA RAPID Toolkit About Bulk Transmission Geothermal Solar Resources Contribute Contact Us 14-AK-a Nonpoint Source...

178

Forecasting wind speed financial return  

E-Print Network [OSTI]

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

D'Amico, Guglielmo; Prattico, Flavio

2013-01-01T23:59:59.000Z

179

GRR/Section 15-WA-b - Air Operating Permit | Open Energy Information  

Open Energy Info (EERE)

Page Page Edit with form History Facebook icon Twitter icon » GRR/Section 15-WA-b - Air Operating Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 15-WA-b - Air Operating Permit 15-WA-b - Air Operating Permit.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies WAC 173-401-500 WAC 173-401-800 WAC 173-401-810 WAC 173-401-735 WAC 173-401-610 Triggers None specified This flowchart illustrates the process for obtaining an Air Operating Permit in Washington State. The Washington State Department of Ecology (WSDE) issues Air Operating Permit under WAC 173-401. An Air Operating Permit is required if a facility has the potential to emit

180

GRR/Section 9-WA-a - State Environmental Overview | Open Energy Information  

Open Energy Info (EERE)

Page Page Edit with form History Facebook icon Twitter icon » GRR/Section 9-WA-a - State Environmental Overview < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 9-WA-a - State Environmental Overview 9-WA-a - State Environmental Overview.pdf Click to View Fullscreen Triggers None specified The Washington State Environmental Policy Act (SEPA), chapter 43.21 RCW, requires all governmental agencies to consider the environmental impacts of a proposal before making decisions. Washington uses an Environmental Checklist and Environmental Review (ER) to provide information to help government agencies identify impacts from their proposals and determine whether an Environmental Impact Statement (EIS) is necessary.

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


181

MEMORANDUM : APPROVAL TO MODIFY ADVANCE WAIVER OF PATENT RIGHTS W(A)  

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

: APPROVAL TO MODIFY ADVANCE WAIVER OF PATENT RIGHTS W(A) : APPROVAL TO MODIFY ADVANCE WAIVER OF PATENT RIGHTS W(A) 2009-047 GRANTED FOR US SOLAR HOLDINGS LLC UNDER AGREEMENT NO. DE-FC36-08G018 155 US Solar Holdings LLC ("US Solar") has requested that the Department of Energy ("DOE") modify or clarify the cost share requirements set forth in the statement of considerations for the granted advance patent waiver W(A) 2009-047. Specifically, the statement of considerations, as originally granted, states the following: The total cost of the award is approximately $4 million with the Petitioner providing about 50% cost sharing. This waiver is contingent upon the Petitioner maintaining, in aggregate, the above cost sharing percentage over the course of the agreement. Rather than just provide an aggregate cost share requirement of 50% for the agreement, US Solar

182

GRR/Section 7-WA-a - Energy Facility Siting Process | Open Energy  

Open Energy Info (EERE)

form form View source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Page Edit with form History Facebook icon Twitter icon » GRR/Section 7-WA-a - Energy Facility Siting Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 7-WA-a - Energy Facility Siting Process 7-WA-a - Energy Facility Siting Process (1).pdf Click to View Fullscreen Contact Agencies Washington State Energy Facility Site Evaluation Council Regulations & Policies RCW 80.50.60(1) WAC 463-60 RCW 80.50.090(2) WAC 463-30-270 WAC 463-30-320 Triggers None specified Under RCW 80.50.60(1) a developer may not begin construction of a new energy facility site until they obtain Energy Facility Siting certification

183

GRR/Section 11-WA-b - Human Remains Process | Open Energy Information  

Open Energy Info (EERE)

Page Page Edit with form History Facebook icon Twitter icon » GRR/Section 11-WA-b - Human Remains Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 11-WA-b - Human Remains Process 11-WA-b - Human Remains Process (1).pdf Click to View Fullscreen Triggers None specified This flowchart illustrates the necessary procedure when a developer discovers human remains on a project site. In Washington, every person has the duty to notify the coroner upon the discovery of any human remains in the most expeditious manner possible. The Washington Department of Archaeology and Historic Preservation (DAHP) handles the disposition of non-forensic remains, while the county coroner handles the disposition of

184

EA-1949: Admiralty Inlet Pilot Tidal Project, Puget Sound, WA | Department  

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

49: Admiralty Inlet Pilot Tidal Project, Puget Sound, WA 49: Admiralty Inlet Pilot Tidal Project, Puget Sound, WA EA-1949: Admiralty Inlet Pilot Tidal Project, Puget Sound, WA SUMMARY This EA analyzes the potential environmental effects of a proposal by the Public Utility District No. 1 of Snowhomish County, Washington to construct and operate the Admiralty Inlet Tidal Project. The proposed 680-kilowatt project would be located on the east side of Admiralty Inlet in Puget Sound, Washington, about 1 kilometer west of Whidbey Island, entirely within Island County, Washington. The Federal Energy Regulatory Commission (FERC) is the lead agency. DOE is a cooperating agency. PUBLIC COMMENT OPPORTUNITIES None available at this time. DOCUMENTS AVAILABLE FOR DOWNLOAD August 9, 2013 EA-1949: FERC Notice of Availability Errata Sheet

185

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

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

Poulin, L.

2013-01-01T23:59:59.000Z

186

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

Science Journals Connector (OSTI)

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

Jianguo Liu; Zhenghui Xie

2014-04-01T23:59:59.000Z

187

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

Science Journals Connector (OSTI)

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

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

2010-01-01T23:59:59.000Z

188

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

189

Funding Opportunity Announcement for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

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

190

Upcoming Funding Opportunity for Wind Forecasting Improvement...  

Office of Environmental Management (EM)

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

191

Huge market forecast for linear LDPE  

Science Journals Connector (OSTI)

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

1980-08-25T23:59:59.000Z

192

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

E-Print Network [OSTI]

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

193

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

Gasoline and Diesel Fuel Update (EIA)

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

194

GRR/Section 18-WA-b - Dangerous Waste Permit | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 18-WA-b - Dangerous Waste Permit GRR/Section 18-WA-b - Dangerous Waste Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 18-WA-b - Dangerous Waste Permit 18-WA-b - Dangerous Waste Permit.pdf Click to View Fullscreen Contact Agencies Washington State Department of Ecology Regulations & Policies WAC 173-303-020 WAC 173-303-060 WAC 173-303-070 WAC 173-303-071 WAC 173-303-072 WAC 173-303-081 WAC 173-303-082 WAC 173-303-090 WAC 173-303-100 WAC 173-303-110 WAC 173-303-140 WAC 173-303-220 WAC 173-303-281 WAC 173-303-282 WAC 173-303-803 WAC 173-303-845 Triggers None specified The Washington State Department of Ecology (WSDE) oversees the permitting process for dangerous and solid waste. In Washington, a developer must obtain a permit if they handle dangerous waste and solid waste and are

195

DOE Zero Energy Ready Home Case Study: Clifton View Homes, Whidbey Island, WA  

Broader source: Energy.gov [DOE]

Case study of a DOE Zero Energy Ready home on Whidbey Island, WA, that scores HERS 37 without PV or HERS -13 with 10 kW PV, enough to power the home and an electric car. The two-story custom home...

196

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

197

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

198

Ensemble typhoon quantitative precipitation forecasts model in Taiwan  

Science Journals Connector (OSTI)

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

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

199

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

E-Print Network [OSTI]

the Hanford Site, WA, USA. Woods, Katharine N. ; Singleton,reside at DOE sites across the USA. Nitrate concentrations >

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

2003-01-01T23:59:59.000Z

200

Forecast of geothermal drilling activity  

SciTech Connect (OSTI)

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

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

1981-10-01T23:59:59.000Z

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


201

New Concepts in Wind Power Forecasting Models  

E-Print Network [OSTI]

New Concepts in Wind Power Forecasting Models Vladimiro Miranda, Ricardo Bessa, João Gama, Guenter to the training of mappers such as neural networks to perform wind power prediction as a function of wind for more accurate short term wind power forecasting models has led to solid and impressive development

Kemner, Ken

202

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

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

Malmberg, Anders

203

QUIKSCAT MEASUREMENTS AND ECMWF WIND FORECASTS  

E-Print Network [OSTI]

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

Malmberg, Anders

204

PROBLEMS OF FORECAST1 Dmitry KUCHARAVY  

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

205

UHERO FORECAST PROJECT DECEMBER 5, 2014  

E-Print Network [OSTI]

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

206

Amending Numerical Weather Prediction forecasts using GPS  

E-Print Network [OSTI]

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

Stoffelen, Ad

207

A Forecasting Support System Based on Exponential Smoothing  

Science Journals Connector (OSTI)

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

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

2010-01-01T23:59:59.000Z

208

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

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

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

209

Improved Prediction of Runway Usage for Noise Forecast :.  

E-Print Network [OSTI]

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

Dhanasekaran, D.

2014-01-01T23:59:59.000Z

210

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

Energy Savers [EERE]

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

211

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

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

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

212

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

SciTech Connect (OSTI)

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

United States. Bonneville Power Administration.

1994-02-01T23:59:59.000Z

213

Ak-Chin Electric Utility Authority | Open Energy Information  

Open Energy Info (EERE)

Ak-Chin Electric Utility Authority Ak-Chin Electric Utility Authority Jump to: navigation, search Name Ak-Chin Electric Utility Authority Place Arizona Utility Id 25866 Utility Location Yes Ownership S NERC Location WECC NERC WECC Yes Activity Buying Transmission Yes Activity Distribution Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] Energy Information Administration Form 826[2] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png No rate schedules available. Average Rates Residential: $0.1010/kWh Commercial: $0.0815/kWh Industrial: $0.0550/kWh The following table contains monthly sales and revenue data for Ak-Chin Electric Utility Authority (Arizona).

214

Building Energy Software Tools Directory: AkWarm  

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

AkWarm AkWarm AkWarm logo. Innovative, user-friendly, Windows-based software for home energy modeling. AkWarm is designed for weatherization assessment and the EPA Energy Star Home energy rating program. Features include: Graphical display of energy use by building component, improvement options analysis, design heat load, calculates CO2 emissions, and shows code compliance. Utility, weather data, and other libraries are maintained in a database library for easy updating. A separate database is available to archive all input and output data for detailed analysis of housing types, trends, amd energy use. Keywords home energy rating systems, home energy, residential modeling, weatherization Validation/Testing N/A Expertise Required Basic understanding of building construction, with a minimal level of

215

RAPID/Roadmap/1-AK-a | Open Energy Information  

Open Energy Info (EERE)

Under AS 38.04.060, the DNR is required to prepare and maintain current statewide inventory of all state land and water for resources and other values. 1-AK-a.4 - Prepare...

216

RAPID/Roadmap/20-AK-a | Open Energy Information  

Open Energy Info (EERE)

operations will commence so that a representative of the commission can witness the operations. (20 AAC 25.112(h)). 20-AK-a.4 - Conduct Plugging or Abandonment Operation...

217

RAPID/Roadmap/19-AK-b | Open Energy Information  

Open Energy Info (EERE)

9-AK-b Temporary Use of Water Permit 19AKBTemporaryUseOfWaterPermit.pdf Click to View Fullscreen Permit Overview In Alaska, water is declared a public resource belonging to the...

218

AK-TRIBE-CENTRAL COUNCIL OF TLINGIT AND HAIDA INDIANS  

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

AK-TRIBE-CENTRAL COUNCIL OF TLINGIT AND HAIDA INDIANS AK-TRIBE-CENTRAL COUNCIL OF TLINGIT AND HAIDA INDIANS Location: Tribe AK-TRIBE- CENTRAL COUNCIL OF TLINGIT AND HAIDA INDIANS AK American Recovery and Reinvestment Act: Proposed Action or Project Description The Central Council of the Tlingit and Haida Indian Tribes of Alaska propose to conduct energy audits of tribally owned facilities. Specific retrofit activities will be determined based on the results of the audits, and these retrofit activities will be submitted for appropriate NEPA review. Conditions: None Categorical Exclusion(s) Applied: A9, B5.1 *-For the complete DOE National Environmental Policy Act regulations regarding categorical exclusions, see Subpart D of 10 CFR10 21 This action would not: threaten a violation of applicable statutory, regulatory, or permit requirements for environment, safety, and health,

219

Port Nikiski, AK Liquefied Natural Gas Exports to Japan (Dollars...  

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

Dollars per Thousand Cubic Feet) Port Nikiski, AK Liquefied Natural Gas Exports to Japan (Dollars per Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

220

Port Nikiski, AK Liquefied Natural Gas Exports to Japan (Million...  

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

Million Cubic Feet) Port Nikiski, AK Liquefied Natural Gas Exports to Japan (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's...

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


221

Anemometer Data (Wind Speed, Direction) for Quinault #3, WA (2004 - 2005) |  

Open Energy Info (EERE)

Quinault #3, WA (2004 - 2005) Quinault #3, WA (2004 - 2005) Dataset Summary Description Wind data collected from Quinault Indian Reservation in Washington from an anemometer as part of the Native American anemometer loan program. Monthly mean wind speed is available for 2004 through 2005, as is wind direction and turbulence data. Data is reported from a height of 20 m. The data was originally made available by Wind Powering America, a DOE Office of Energy Efficiency & Renewable Energy (EERE) program. A dynamic map displaying all available data from DOE anemometer loan programs is available http://www.windpoweringamerica.gov/anemometerloans/projects.asp. Source EERE Date Released December 02nd, 2010 (4 years ago) Date Updated December 02nd, 2010 (4 years ago) Keywords wind

222

EA-1855: Creston-Bell Rebuild Project, Spokane and Lincoln Counties, WA |  

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

855: Creston-Bell Rebuild Project, Spokane and Lincoln 855: Creston-Bell Rebuild Project, Spokane and Lincoln Counties, WA EA-1855: Creston-Bell Rebuild Project, Spokane and Lincoln Counties, WA Summary This EA (also known as DOE/EA-4406 or DOE/BP-4406) evaluates the potential environmental impacts from rebuilding the Creston-Bell No. 1 115-kV transmission line, including the replacement of wood poles and associated structural components and conductor and access road improvements. The 54-mile long, wood pole line extends from the Bonneville Power Administration (BPA) Creston substation to the BPA Bell substation near Spokane in Lincoln and Spokane Counties, Washington. Additional information about this project is available on the BPA website. Public Comment Opportunities None available at this time. Documents Available for Download

223

1993 Solid Waste Reference Forecast Summary  

SciTech Connect (OSTI)

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

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

1993-08-01T23:59:59.000Z

224

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

SciTech Connect (OSTI)

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

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

2013-10-01T23:59:59.000Z

225

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

226

Forecasting Uncertainty Related to Ramps of Wind Power Production  

E-Print Network [OSTI]

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

Boyer, Edmond

227

The effect of multinationality on management earnings forecasts  

E-Print Network [OSTI]

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

Runyan, Bruce Wayne

2005-08-29T23:59:59.000Z

228

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

SciTech Connect (OSTI)

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

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

2011-10-01T23:59:59.000Z

229

DOE Zero Energy Ready Home Case Study, Clifton View Homes, Coupeville, WA, Systems Home  

Broader source: Energy.gov [DOE]

Case-study of a DOE Zero Energy Ready Home on Whidbey Island, WA, that scored HERS 45 without PV. This 2,908 ft2 custom/system home has a SIP roof and walls, R-20 rigid foam under slab, triple-pane windows, ground source heat pump for radiant floor heat, and a unique balanced ventilation system using separate exhaust fans to bring air into and out of home.

230

DOE Zero Energy Ready Home Case Study TC Legend, Seattle, WA, Custom Home  

Broader source: Energy.gov [DOE]

Case study of a DOE Zero Energy Ready Home in Seattle, WA, that scored HERS 37 without PV, HERS -1 with PV. This 1,915 ft2 custom home has SIP walls and roof, R-20 XPS under the slab, triple-pane windows, an air to water heat pump for radiant heat, and balanced ventilation with timer controlled fans to bring in and exhaust air.

231

DOE Zero Energy Ready Home Case Study, Dwell Development, Seattle, WA, Systems Home  

Broader source: Energy.gov [DOE]

Case study of a DOE Zero Energy Ready Home in Seattle, WA, that scored HERS 34 without PV. This 2,000 ft2 system home has R-45 double-stud walls, an unvented flat roof with 2 inches of spray foam plus 18 inches blown cellulose, R-42 XPS under slab, triple-pane windows, and a ductless mini-split heat pump.

232

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

E-Print Network [OSTI]

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

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

233

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

E-Print Network [OSTI]

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

Mathiesen, Patrick James

2013-01-01T23:59:59.000Z

234

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

235

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

236

Annual Energy Outlook Forecast Evaluation 2004  

Gasoline and Diesel Fuel Update (EIA)

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

237

Annual Energy Outlook 2001 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

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

238

energy data + forecasting | OpenEI Community  

Open Energy Info (EERE)

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

239

Wind Speed Forecasting for Power System Operation  

E-Print Network [OSTI]

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

Zhu, Xinxin

2013-07-22T23:59:59.000Z

240

Evaluation of hierarchical forecasting for substitutable products  

Science Journals Connector (OSTI)

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

S. Viswanathan; Handik Widiarta; R. Piplani

2008-01-01T23:59:59.000Z

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


241

Testing Competing High-Resolution Precipitation Forecasts  

E-Print Network [OSTI]

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

Gilleland, Eric

242

Forecasting Capital Expenditure with Plan Data  

Science Journals Connector (OSTI)

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

W. Gerstenberger

1977-01-01T23:59:59.000Z

243

Forecasting Agriculturally Driven Global Environmental Change  

Science Journals Connector (OSTI)

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

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

2001-04-13T23:59:59.000Z

244

Medium- and Long-Range Forecasting  

Science Journals Connector (OSTI)

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

A. James Wagner

1989-09-01T23:59:59.000Z

245

Updated Satellite Technique to Forecast Heavy Snow  

Science Journals Connector (OSTI)

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

Edward C. Johnston

1995-06-01T23:59:59.000Z

246

Annual Energy Outlook Forecast Evaluation 2005  

Gasoline and Diesel Fuel Update (EIA)

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

247

Forecasting energy markets using support vector machines  

Science Journals Connector (OSTI)

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

Theophilos Papadimitriou; Periklis Gogas; Efthimios Stathakis

2014-01-01T23:59:59.000Z

248

NJ WY AK AL CA AR CO CT DE FL GA HI ID KS IL IN IA IA KY LA  

Gasoline and Diesel Fuel Update (EIA)

0.00-1.99 0.00-1.99 2.00-2.99 3.00-3.99 4.00-4.99 5.00-5.99 6.00-6.99 7.00+ NJ WY AK AL CA AR CO CT DE FL GA HI ID KS IL IN IA IA KY LA ME MI MA MD MN MS MT MO NE ND OH NV NM NY NH NC OK OR PA RI SC SD TN TX UT VT WA WV WI AZ VA DC 0.00-1.99 2.00-2.99 3.00-3.99 4.00-4.99 5.00-5.99 6.00-6.99 7.00+ 18. Average Price of Natural Gas Delivered to U.S. Onsystem Industrial Consumers, 1996 (Dollars per Thousand Cubic Feet) Figure 19. Average Price of Natural Gas Delivered to U.S. Electric Utilities, 1996 (Dollars per Thousand Cubic Feet) Figure Sources: Federal Energy Regulatory Commission (FERC), Form FERC-423, "Monthly Report of Cost and Quality of Fuels for Electric Plants," and Energy Information Administration (EIA), Form EIA-176, "Annual Report of Natural and Supplemental Gas Supply and Disposition." Note: In 1996, consumption of natural gas for agricultural use

249

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

Science Journals Connector (OSTI)

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

S. Viswanathan; Handik Widiarta; Rajesh Piplani

2008-07-01T23:59:59.000Z

250

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

SciTech Connect (OSTI)

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

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

2014-05-01T23:59:59.000Z

251

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

E-Print Network [OSTI]

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

Mosier, Richard Matthew

2011-02-22T23:59:59.000Z

252

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

253

AK-TRIBE-ASSOCIATION OF VILLAGE COUNCIL PRESIDENTS, INC  

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

U.S. Department of Energy U.S. Department of Energy Categorical Exclusion Determination Form Program or Field Office: Energy Efficiency and Conservation Block Grant Program Project Title AK-TRIBE-ASSOCIATION OF VILLAGE COUNCIL PRESIDENTS, INC Location: Tribe AK-TRIBE- ASSOCIATION OF VILLAGE COUNCIL PRESIDENTS, INC AK American Recovery and Reinvestment Act: Proposed Action or Project Description: The Association of Village Council Presidents, Inc., (AVCP) proposes to renovate a steel-constructed building, built circa 1990 (First Avenue Building, US Survey 1002 Parcel 1, Lot 1), located in Bethel, Alaska, to an office building. Proposed building retrofits would include installation of an (EPA certified) wood-fired central boiler, a conventional (household size) energy efficient oil-fired boiler, a heat distribution

254

12-32021E2_Forecast  

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

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

255

Building Energy Software Tools Directory: Degree Day Forecasts  

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

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

256

Building Energy Software Tools Directory: Energy Usage Forecasts  

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

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

257

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

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

McBurney, Peter

258

River Forecast Application for Water Management: Oil and Water?  

Science Journals Connector (OSTI)

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

Kevin Werner; Kristen Averyt; Gigi Owen

2013-07-01T23:59:59.000Z

259

Data Mining in Load Forecasting of Power System  

Science Journals Connector (OSTI)

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

Guang Yu Zhao; Yan Yan; Chun Zhou Zhao

2013-01-01T23:59:59.000Z

260

Operational Rainfall and Flow Forecasting for the Panama Canal Watershed  

Science Journals Connector (OSTI)

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

Konstantine P. Georgakakos; Jason A. Sperfslage

2005-01-01T23:59:59.000Z

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


261

Power System Load Forecasting Based on EEMD and ANN  

Science Journals Connector (OSTI)

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

Wanlu Sun; Zhigang Liu; Wenfan Li

2011-01-01T23:59:59.000Z

262

U.S. Regional Demand Forecasts Using NEMS and GIS  

E-Print Network [OSTI]

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

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

2005-01-01T23:59:59.000Z

263

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

Energy Savers [EERE]

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

264

The Energy Demand Forecasting System of the National Energy Board  

Science Journals Connector (OSTI)

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

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

1980-01-01T23:59:59.000Z

265

Forecasting Energy Demand Using Fuzzy Seasonal Time Series  

Science Journals Connector (OSTI)

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

?Irem Ual Sar?; Basar ztaysi

2012-01-01T23:59:59.000Z

266

File:06-WA-b - Washington Construction Storm Water Permit.pdf | Open Energy  

Open Energy Info (EERE)

File File Edit History Facebook icon Twitter icon » File:06-WA-b - Washington Construction Storm Water Permit.pdf Jump to: navigation, search File File history File usage Metadata File:06-WA-b - Washington Construction Storm Water Permit.pdf Size of this preview: 463 × 599 pixels. Other resolution: 464 × 600 pixels. Go to page 1 2 Go! next page → next page → Full resolution ‎(1,275 × 1,650 pixels, file size: 60 KB, MIME type: application/pdf, 2 pages) File history Click on a date/time to view the file as it appeared at that time. Date/Time Thumbnail Dimensions User Comment current 15:28, 6 December 2013 Thumbnail for version as of 15:28, 6 December 2013 1,275 × 1,650, 2 pages (60 KB) Alevine (Talk | contribs) 15:25, 6 December 2013 Thumbnail for version as of 15:25, 6 December 2013 1,275 × 1,650, 2 pages (60 KB) Alevine (Talk | contribs)

267

File:EIA-Eastern-OR-WA-BOE.pdf | Open Energy Information  

Open Energy Info (EERE)

Eastern-OR-WA-BOE.pdf Eastern-OR-WA-BOE.pdf Jump to: navigation, search File File history File usage Eastern Oregon and Washington By 2001 BOE Reserve Class Size of this preview: 776 × 600 pixels. Full resolution ‎(1,650 × 1,275 pixels, file size: 460 KB, MIME type: application/pdf) Description Eastern Oregon and Washington By 2001 BOE Reserve Class Sources Energy Information Administration Authors Samuel H. Limerick; Lucy Luo; Gary Long; David F. Morehouse; Jack Perrin; Robert F. King Related Technologies Oil, Natural Gas Creation Date 2005-09-01 Extent Regional Countries United States UN Region Northern America States Oregon, Washington File history Click on a date/time to view the file as it appeared at that time. Date/Time Thumbnail Dimensions User Comment current 18:00, 20 December 2010 Thumbnail for version as of 18:00, 20 December 2010 1,650 × 1,275 (460 KB) MapBot (Talk | contribs) Automated bot upload

268

Wind power forecasting in U.S. electricity markets.  

SciTech Connect (OSTI)

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

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

2010-04-01T23:59:59.000Z

269

Wind power forecasting in U.S. Electricity markets  

SciTech Connect (OSTI)

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

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

2010-04-15T23:59:59.000Z

270

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

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

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

271

Application of a Combination Forecasting Model in Logistics Parks' Demand  

Science Journals Connector (OSTI)

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

Chen Qin; Qi Ming

2010-05-01T23:59:59.000Z

272

A BAYESIAN MODEL COMMITTEE APPROACH TO FORECASTING GLOBAL SOLAR RADIATION  

E-Print Network [OSTI]

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

Boyer, Edmond

273

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

274

Accuracy of near real time updates in wind power forecasting  

E-Print Network [OSTI]

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

Heinemann, Detlev

275

CSUF ECONOMIC OUTLOOK AND FORECASTS MIDYEAR UPDATE -APRIL 2014  

E-Print Network [OSTI]

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

de Lijser, Peter

276

Forecasting wave height probabilities with numerical weather prediction models  

E-Print Network [OSTI]

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

Stevenson, Paul

277

CALIFORNIA ENERGY DEMAND 2008-2018 STAFF REVISED FORECAST  

E-Print Network [OSTI]

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

278

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

E-Print Network [OSTI]

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

Povinelli, Richard J.

279

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect (OSTI)

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

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

2010-11-02T23:59:59.000Z

280

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

E-Print Network [OSTI]

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

Lang, K.

1982-01-01T23:59:59.000Z

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


281

Forecasting the Locational Dynamics of Transnational Terrorism  

E-Print Network [OSTI]

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

Massachusetts at Amherst, University of

282

Do quantitative decadal forecasts from GCMs provide  

E-Print Network [OSTI]

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

Stevenson, Paul

283

Sunny outlook for space weather forecasters  

Science Journals Connector (OSTI)

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

Eric Hand

2012-04-27T23:59:59.000Z

284

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

285

Prediction versus Projection: How weather forecasting and  

E-Print Network [OSTI]

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

Howat, Ian M.

286

Customized forecasting tool improves reserves estimation  

SciTech Connect (OSTI)

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

Mian, M.A.

1986-04-01T23:59:59.000Z

287

Storm-in-a-Box Forecasting  

Science Journals Connector (OSTI)

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

Richard A. Kerr

2004-05-14T23:59:59.000Z

288

FORECAST OF VACANCIES Until end of 2016  

E-Print Network [OSTI]

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

289

Online short-term solar power forecasting  

SciTech Connect (OSTI)

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

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

2009-10-15T23:59:59.000Z

290

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect (OSTI)

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

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18T23:59:59.000Z

291

UNCERTAINTY IN THE GLOBAL FORECAST SYSTEM  

SciTech Connect (OSTI)

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

Werth, D.; Garrett, A.

2009-04-15T23:59:59.000Z

292

Forecastability as a Design Criterion in Wind Resource Assessment: Preprint  

SciTech Connect (OSTI)

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

Zhang, J.; Hodge, B. M.

2014-04-01T23:59:59.000Z

293

KRNFYSIK AK FKF011 Nuclear Physics, Basic Course  

E-Print Network [OSTI]

K?RNFYSIK AK FKF011 Nuclear Physics, Basic Course Poäng: 3.0 Betygskala: TH Obligatorisk för: F3 Valfri för: E4 Kursansvarig: Docent Per Kristiansson, per.kristiansson@nuclear.lu.se Förkunskapskrav

294

KRNFYSIK AK FKF 011 Nuclear Physics, Basic Course  

E-Print Network [OSTI]

K?RNFYSIK AK FKF 011 Nuclear Physics, Basic Course Antal poäng: 3.0. Obligatorisk för: F3. Valfri för: E4. Kursansvarig: Docent Per Kristiansson, per.kristiansson@nuclear.lu.se Förkunskapskrav

295

Ak-Chin Indian Community Biomass Feasiiblity Study  

SciTech Connect (OSTI)

Study of the conversion of chicken litter to biogas for the production of energy. There was an additional requirement that after extracting the energy from the chicken litter the nutrient value of the raw chicken litter had to be returned to the Ak-Chin Farms for use as fertilizer in a form and delivery method acceptable to the Farm.

Mark A. Moser, RCM Digesters, Inc.; Mark Randall, Daystar Consulting, LLC; Leonard S. Gold, Ak-Chin Energy Services & Utility Strategies Consulting Group

2005-12-31T23:59:59.000Z

296

Kenai, AK Liquefied Natural Gas Exports to Japan (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Million Cubic Feet) Kenai, AK Liquefied Natural Gas Exports to Japan (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2011 1,856 1,908 1,915 1,913 1,915...

297

ANL Wind Power Forecasting and Electricity Markets | Open Energy  

Open Energy Info (EERE)

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

298

DOE Challenge Home Case Study TC Legend, Seattle, WA, Custom Home  

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

TC Legend TC Legend Homes Seattle, WA BUILDING TECHNOLOGIES OFFICE DOE Challenge Home builders are in the top 1% of builders in the country meeting the extraordinary levels of excellence and quality specifi ed by the U.S. Department of Energy. Every DOE Challenge Home starts with ENERGY STAR for Homes Version 3 for an energy-effi cient home built on a solid foundation of building science research. Then, even more advanced technologies are designed in for a home that goes above and beyond current code to give you the superior quality construction, HVAC, appliances, indoor air quality, safety, durability, comfort, and solar-ready components along with ultra-low or no utility bills. This provides homeowners with a quality home that will last for generations to come.

299

Recipient: County of Kitsap, WA ENERGY EFFICIENCY AND CONSERVATION BLOCK GRANTS NEPA COMPLIANCE FORM  

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

it: EE 000 0853 it: EE 000 0853 Recipient: County of Kitsap, WA ENERGY EFFICIENCY AND CONSERVATION BLOCK GRANTS NEPA COMPLIANCE FORM Activities Determination/ Categorical Exclusion Reviewer's Specific Instructions and Rationale (Restrictions and Allowable Activity) Kitsap Built Green Projects B5.1 Waste Stream, Engineering, and Historic Preservation clauses. Kitsap County Building Retrofits and Energy Efficiency Upgrades (Green Jobs Initiative) B5.1 except geothermal Waste Stream, Engineering, and Historic Preservation clauses. Prohibited: Any implementation of geothermal projects/construction activities without NEPA approval from DOE. Geothermal projects are to be provided to DOE for analysis. Energy Efficiency Implementation and Strategy A9, All, B5.1 None Energy Services Corps A9, All, B5.1

300

DOE Challenge Home Case Study, Dwell Development, Seattle, WA, Systems Home  

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

Dwell Dwell Development Seattle, WA BUILDING TECHNOLOGIES OFFICE DOE Challenge Home builders are in the top 1% of builders in the country meeting the extraordinary levels of excellence and quality specifi ed by the U.S. Department of Energy. Every DOE Challenge Home starts with ENERGY STAR for Homes Version 3 for an energy-effi cient home built on a solid foundation of building science research. Then, even more advanced technologies are designed in for a home that goes above and beyond current code to give you the superior quality construction, HVAC, appliances, indoor air quality, safety, durability, comfort, and solar-ready components along with ultra-low or no utility bills. This provides homeowners with a quality home that will last for generations to come.

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


301

Microsoft Word - CX-AccessRoads-KingCoWA-FY13_WEB.doc  

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

22, 2013 22, 2013 REPLY TO ATTN OF: KEPR-Covington SUBJECT: Environmental Clearance Memorandum Rick Ross Engineer - TELF-TPP-3 Proposed Action: Covington District Culvert Replacements Categorical Exclusion Applied (from Subpart D, 10 C.F.R. Part 1021): Appendix B1.3, Routine Maintenance Location: King County, WA Proposed by: Bonneville Power Administration (BPA) Description of the Proposed Action: BPA is proposing to replace existing culverts at 12 access road stream crossings that present barriers to fish passage. These improvements will be made on BPA easement access roads within DNR owned and managed lands. BPA will make these improvements by installing new fish friendly culverts and/or bridges at each stream crossing. The current stream crossings do not meet DNR fish passage standards that will be in

302

DOE Challenge Home Case Study, Clifton View Homes, Coupeville, WA, Systems Home  

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

Clifton View Clifton View Homes Coupeville, WA BUILDING TECHNOLOGIES OFFICE DOE Challenge Home builders are in the top 1% of builders in the country meeting the extraordinary levels of excellence and quality specifi ed by the U.S. Department of Energy. Every DOE Challenge Home starts with ENERGY STAR for Homes Version 3 for an energy-effi cient home built on a solid foundation of building science research. Then, even more advanced technologies are designed in for a home that goes above and beyond current code to give you the superior quality construction, HVAC, appliances, indoor air quality, safety, durability, comfort, and solar-ready components along with ultra-low or no utility bills. This provides homeowners with a quality home that will last for generations to come.

303

Nr. 077 / 2014 // 6. Mai 2014 Prof. Ngugi wa Thiong'o whrend seiner Rede zur Annahme der Ehrendoktorwrde  

E-Print Network [OSTI]

Nr. 077 / 2014 // 6. Mai 2014 1/5 Prof. Ngugi wa Thiong'o während seiner Rede zur Annahme der der afrikanischen Partneruniversitäten im Netzwerk der BIGSAS würden den Geehrten begleiten. Die Rede

Ullmann, G. Matthias

304

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

E-Print Network [OSTI]

October 14 WA Division Newsletter Page 4 Tool durability and steel microstructure in friction stir welding of mild steel A. De1 , H. K. D. H. Bhadeshia2 and T. DebRoy3 1 Indian Institute of Technology- ium alloys has been applied to the FSW of steel. The calculations were extended to predict

Cambridge, University of

305

Short-Term World Oil Price Forecast  

Gasoline and Diesel Fuel Update (EIA)

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

306

OpenEI Community - energy data + forecasting  

Open Energy Info (EERE)

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

307

Voluntary Green Power Market Forecast through 2015  

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

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

308

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

309

FORSITE: a geothermal site development forecasting system  

SciTech Connect (OSTI)

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

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

1981-10-01T23:59:59.000Z

310

Forecasting hotspots using predictive visual analytics approach  

SciTech Connect (OSTI)

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

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

2014-12-30T23:59:59.000Z

311

Exponential smoothing model selection for forecasting  

Science Journals Connector (OSTI)

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

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

2006-01-01T23:59:59.000Z

312

Solar Wind Forecasting with Coronal Holes  

E-Print Network [OSTI]

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

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

2007-01-09T23:59:59.000Z

313

CORE DATA PROCESSING SOFTWARE PLAN REVIEW | SEATTLE, WA | SEPTEMBER 19-20, 2013Name of Mee)ng Loca)on Date -Change in Slide Master CDP FINAL DESIGN REVIEW  

E-Print Network [OSTI]

CORE DATA PROCESSING SOFTWARE PLAN REVIEW | SEATTLE, WA | SEPTEMBER 19-20, 2013Name of Mee DATA PROCESSING SOFTWARE PLAN REVIEW | SEATTLE, WA | SEPTEMBER 19-20, 2013 Outline Single PLAN REVIEW | SEATTLE, WA | SEPTEMBER 19-20, 2013 Background Estimation ­ One of the unsolved issues

Masci, Frank

314

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

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

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

315

Annual Energy Outlook with Projections to 2025-Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

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

316

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

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

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

317

Metrics for Evaluating the Accuracy of Solar Power Forecasting: Preprint  

SciTech Connect (OSTI)

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

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

2013-10-01T23:59:59.000Z

318

File:INL-geothermal-ak.pdf | Open Energy Information  

Open Energy Info (EERE)

ak.pdf ak.pdf Jump to: navigation, search File File history File usage Alaska Geothermal Resources Size of this preview: 697 × 599 pixels. Other resolution: 698 × 600 pixels. Full resolution ‎(5,418 × 4,660 pixels, file size: 2.26 MB, MIME type: application/pdf) Description Alaska Geothermal Resources Sources Idaho National Laboratory Authors Patrick Laney; Julie Brizzee Related Technologies Geothermal Creation Date 2003-11-01 Extent State Countries United States UN Region Northern America States Alaska File history Click on a date/time to view the file as it appeared at that time. Date/Time Thumbnail Dimensions User Comment current 12:21, 16 December 2010 Thumbnail for version as of 12:21, 16 December 2010 5,418 × 4,660 (2.26 MB) MapBot (Talk | contribs) Automated upload from NREL's "mapsearch" data

319

Recovery Act: Waste Energy Project at AK Steel Corporation Middletown  

SciTech Connect (OSTI)

In 2008, Air Products and Chemicals, Inc. (Air Products) began development of a project to beneficially utilize waste blast furnace topgas generated in the course of the iron-making process at AK Steel Corporations Middletown, Ohio works. In early 2010, Air Products was awarded DOE Assistance Agreement DE-EE002736 to further develop and build the combined-cycle power generation facility. In June 2012, Air Products and AK Steel Corporation terminated work when it was determined that the project would not be economically viable at that time nor in the foreseeable future. The project would have achieved the FOA-0000044 Statement of Project Objectives by demonstrating, at a commercial scale, the technology to capture, treat, and convert blast furnace topgas into electric power and thermal energy.

Joyce, Jeffrey

2012-06-30T23:59:59.000Z

320

Electric Grid - Forecasting system licensed | ornl.gov  

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

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

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


321

Managing Wind Power Forecast Uncertainty in Electric Grids.  

E-Print Network [OSTI]

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

Mauch, Brandon Keith

2012-01-01T23:59:59.000Z

322

Forecasting supply/demand and price of ethylene feedstocks  

SciTech Connect (OSTI)

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

Struth, B.W.

1984-08-01T23:59:59.000Z

323

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

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

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

324

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

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

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

325

Integrating agricultural pest biocontrol into forecasts of energy biomass production  

E-Print Network [OSTI]

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

Gratton, Claudio

326

Forecasting for inventory control with exponential smoothing  

Science Journals Connector (OSTI)

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

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

2002-01-01T23:59:59.000Z

327

Probabilistic Verification of Global and Mesoscale Ensemble Forecasts of Tropical Cyclogenesis  

Science Journals Connector (OSTI)

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

Sharanya J. Majumdar; Ryan D. Torn

2014-10-01T23:59:59.000Z

328

GRR/Section 6-AK-a - Transportation | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 6-AK-a - Transportation GRR/Section 6-AK-a - Transportation < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 6-AK-a - Transportation 06AKATransportationOversizeOverweight.pdf Click to View Fullscreen Contact Agencies Alaska Department of Transportation and Public Facilities Regulations & Policies 17 AAC 25: Operations, Wheeled Vehicles Triggers None specified Click "Edit With Form" above to add content 06AKATransportationOversizeOverweight.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative _ 6-AK-a.1 to 6-AK-a.2 - Does the Load Exceed the Size or Weight Regulations for State Highway Transportation Established by 17 AAC 25?

329

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

E-Print Network [OSTI]

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

Collins, Gary S.

330

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

E-Print Network [OSTI]

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

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

2003-01-01T23:59:59.000Z

331

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

E-Print Network [OSTI]

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

Parry, Graham

2006-01-01T23:59:59.000Z

332

Random switching exponential smoothing and inventory forecasting  

Science Journals Connector (OSTI)

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

Giacomo Sbrana; Andrea Silvestrini

2014-01-01T23:59:59.000Z

333

Voluntary Green Power Market Forecast through 2015  

SciTech Connect (OSTI)

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

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

2010-05-01T23:59:59.000Z

334

Expert Panel: Forecast Future Demand for Medical Isotopes  

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

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

335

A robust automatic phase-adjustment method for financial forecasting  

Science Journals Connector (OSTI)

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

Ricardo de A. Arajo

2012-03-01T23:59:59.000Z

336

Short term forecasting of solar radiation based on satellite data  

E-Print Network [OSTI]

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

Heinemann, Detlev

337

Developing electricity forecast web tool for Kosovo market  

Science Journals Connector (OSTI)

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

Blerim Rexha; Arben Ahmeti; Lule Ahmedi; Vjollca Komoni

2011-02-01T23:59:59.000Z

338

FORECASTING WATER DEMAND USING CLUSTER AND REGRESSION ANALYSIS  

E-Print Network [OSTI]

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

Keller, Arturo A.

339

Impact of PV forecasts uncertainty in batteries management in microgrids  

E-Print Network [OSTI]

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

Paris-Sud XI, Université de

340

Revised 1997 Retail Electricity Price Forecast Principal Author: Ben Arikawa  

E-Print Network [OSTI]

Revised 1997 Retail Electricity Price Forecast March 1998 Principal Author: Ben Arikawa Electricity 1997 FORE08.DOC Page 1 CALIFORNIA ENERGY COMMISSION ELECTRICITY ANALYSIS OFFICE REVISED 1997 RETAIL ELECTRICITY PRICE FORECAST Introduction The Electricity Analysis Office of the California Energy Commission

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


341

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center  

E-Print Network [OSTI]

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

Washington at Seattle, University of

342

A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size  

E-Print Network [OSTI]

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

Hansens, Jim

343

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

E-Print Network [OSTI]

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

Mavromatis, Peter George

2013-01-01T23:59:59.000Z

344

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

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

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

345

Uncertainty Reduction in Power Generation Forecast Using Coupled Wavelet-ARIMA  

SciTech Connect (OSTI)

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

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

2014-10-27T23:59:59.000Z

346

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

347

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

348

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

349

Annual Energy Outlook 1998 Forecasts - Preface  

Gasoline and Diesel Fuel Update (EIA)

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

350

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

351

Volatility forecasting with smooth transition exponential smoothing  

Science Journals Connector (OSTI)

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

James W. Taylor

2004-01-01T23:59:59.000Z

352

Incorporating Forecast Uncertainty in Utility Control Center  

SciTech Connect (OSTI)

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

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

2014-07-09T23:59:59.000Z

353

Annual Energy Outlook Forecast Evaluation - Tables  

Gasoline and Diesel Fuel Update (EIA)

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

354

Coal production forecast and low carbon policies in China  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

355

U.S. Regional Demand Forecasts Using NEMS and GIS  

SciTech Connect (OSTI)

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

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

2005-07-01T23:59:59.000Z

356

Measuring the forecasting accuracy of models: evidence from industrialised countries  

Science Journals Connector (OSTI)

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

Athanasios Koulakiotis; Apostolos Dasilas

2009-01-01T23:59:59.000Z

357

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

E-Print Network [OSTI]

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

Marquez, Ricardo

2012-01-01T23:59:59.000Z

358

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

E-Print Network [OSTI]

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

Greenslade, Diana

359

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

SciTech Connect (OSTI)

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

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

1985-10-01T23:59:59.000Z

360

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) | Open  

Open Energy Info (EERE)

Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Jump to: navigation, search LEDSGP green logo.png FIND MORE DIA TOOLS This tool is part of the Development Impacts Assessment (DIA) Toolkit from the LEDS Global Partnership. Tool Summary LAUNCH TOOL Name: Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Agency/Company /Organization: Energy Sector Management Assistance Program of the World Bank Sector: Energy Focus Area: Non-renewable Energy Topics: Baseline projection, Co-benefits assessment, GHG inventory Resource Type: Software/modeling tools User Interface: Spreadsheet Complexity/Ease of Use: Simple Website: www.esmap.org/esmap/EFFECT Cost: Free Equivalent URI: www.esmap.org/esmap/EFFECT Energy Forecasting Framework and Emissions Consensus Tool (EFFECT) Screenshot

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


361

Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory  

Gasoline and Diesel Fuel Update (EIA)

Forecasting Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels MICHAEL YE, ∗ JOHN ZYREN, ∗∗ AND JOANNE SHORE ∗∗ Abstract This paper presents a short-term monthly forecasting model of West Texas Intermedi- ate crude oil spot price using OECD petroleum inventory levels. Theoretically, petroleum inventory levels are a measure of the balance, or imbalance, between petroleum production and demand, and thus provide a good market barometer of crude oil price change. Based on an understanding of petroleum market fundamentals and observed market behavior during the post-Gulf War period, the model was developed with the objectives of being both simple and practical, with required data readily available. As a result, the model is useful to industry and government decision-makers in forecasting price and investigat- ing the impacts of changes on price, should inventories,

362

Adaptive sampling and forecasting with mobile sensor networks  

E-Print Network [OSTI]

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

Choi, Han-Lim

2009-01-01T23:59:59.000Z

363

Pacific Adaptation Strategy Assistance Program Dynamical Seasonal Forecasting  

E-Print Network [OSTI]

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

Lim, Eun-pa

364

Forecasting Volatility in Stock Market Using GARCH Models  

E-Print Network [OSTI]

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

Yang, Xiaorong

2008-01-01T23:59:59.000Z

365

Exponential smoothing with covariates applied to electricity demand forecast  

Science Journals Connector (OSTI)

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

José D. Bermúdez

2013-01-01T23:59:59.000Z

366

Initial conditions estimation for improving forecast accuracy in exponential smoothing  

Science Journals Connector (OSTI)

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

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

2012-07-01T23:59:59.000Z

367

A Bayesian approach to forecast intermittent demand for seasonal products  

Science Journals Connector (OSTI)

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

Mohammad Anwar Rahman; Bhaba R. Sarker

2012-01-01T23:59:59.000Z

368

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

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

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

369

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones  

Science Journals Connector (OSTI)

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

Matthew J. Onderlinde; Henry E. Fuelberg

2014-10-01T23:59:59.000Z

370

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

E-Print Network [OSTI]

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

Kemner, Ken

371

2007 National Hurricane Center Forecast Verification Report James L. Franklin  

E-Print Network [OSTI]

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

372

Recently released EIA report presents international forecasting data  

SciTech Connect (OSTI)

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

NONE

1995-05-01T23:59:59.000Z

373

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

374

Information-Based Skill Scores for Probabilistic Forecasts  

Science Journals Connector (OSTI)

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

Bodo Ahrens; Andr Walser

2008-01-01T23:59:59.000Z

375

A methodology for forecasting carbon dioxide flooding performance  

E-Print Network [OSTI]

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

Marroquin Cabrera, Juan Carlos

2012-06-07T23:59:59.000Z

376

Evolutionary Optimization of an Ice Accretion Forecasting System  

Science Journals Connector (OSTI)

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

Pawel Pytlak; Petr Musilek; Edward Lozowski; Dan Arnold

2010-07-01T23:59:59.000Z

377

Diagnosing the Origin of Extended-Range Forecast Errors  

Science Journals Connector (OSTI)

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

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

2010-06-01T23:59:59.000Z

378

Application of an Improved SVM Algorithm for Wind Speed Forecasting  

Science Journals Connector (OSTI)

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

Huaqiang Zhang; Xinsheng Wang; Yinxiao Wu

2011-01-01T23:59:59.000Z

379

Research on Development Trends of Power Load Forecasting Methods  

Science Journals Connector (OSTI)

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

Litong Dong; Jun Xu; Haibo Liu; Ying Guo

2014-01-01T23:59:59.000Z

380

Representing Forecast Error in a Convection-Permitting Ensemble System  

Science Journals Connector (OSTI)

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

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

2014-12-01T23:59:59.000Z

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


381

Weather Research and Forecasting Model 2.2 Documentation  

E-Print Network [OSTI]

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

Sadjadi, S. Masoud

382

Network Bandwidth Utilization Forecast Model on High Bandwidth Network  

SciTech Connect (OSTI)

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

Yoo, Wucherl; Sim, Alex

2014-07-07T23:59:59.000Z

383

Wind Speed Forecasting Using a Hybrid Neural-Evolutive Approach  

Science Journals Connector (OSTI)

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

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

2009-11-01T23:59:59.000Z

384

GRR/Section 3-AK-c - Encroachment Permit | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 3-AK-c - Encroachment Permit GRR/Section 3-AK-c - Encroachment Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-AK-c - Encroachment Permit 03AKCEncroachmentOverview.pdf Click to View Fullscreen Contact Agencies Alaska Department of Transportation and Public Facilities Regulations & Policies 17 AAC 10.011: Encroachments Authorized 17 AAC 10.012: Approval Requirements 17 AAC 15.011: Utility Permits Triggers None specified Click "Edit With Form" above to add content 03AKCEncroachmentOverview.pdf 03AKCEncroachmentOverview.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative 3-AK-c.1 - Will the Developer Construct a Utility Within ADOT ROW or

385

A model for short term electric load forecasting  

E-Print Network [OSTI]

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

Tigue, John Robert

1975-01-01T23:59:59.000Z

386

Radiation fog forecasting using a 1-dimensional model  

E-Print Network [OSTI]

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

Peyraud, Lionel

2012-06-07T23:59:59.000Z

387

Annual Energy Outlook with Projections to 2025 - Forecast Comparisons  

Gasoline and Diesel Fuel Update (EIA)

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

388

Weather-based forecasts of California crop yields  

SciTech Connect (OSTI)

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

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

2005-09-26T23:59:59.000Z

389

Wave height forecasting in Dayyer, the Persian Gulf  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

390

Results from ORNL characterization of ZrO2-500-AK2 - surrogate TRISO material  

SciTech Connect (OSTI)

This document is a compilation of the characterization data for the TRISO-coated surrogate particles designated ZrO2-500-AK2 that was produced at Oak Ridge National Laboratory (ORNL) as part of the Advanced Gas Reactor Fuel Development and Qualification (AGR) program. The ZrO2-500-AK2 material contains nominally 500 {micro}m kernels of yttria-stabilized zirconia (YSZ) coated with all TRISO layers (buffer, inner pyrocarbon, silicon carbide, and outer pyrocarbon). The ZrO2-500-AK2 material was created for: (1) irradiation testing in the High Flux Isotope Reactor (HFIR) and (2) limited dissemination to laboratories as deemed appropriate to the AGR program. This material was created midway into a TRISO fuel development program to accommodate a sudden opportunity to perform irradiation testing on surrogate material. While the layer deposition processes were chosen based on the best technical understanding at the time, technical progress at ORNL has led to an evolution in the perceived optimal deposition conditions since the creation of ZrO2-500-AK2. Thus, ZrO2-500-AK2 contains a reasonable TRISO microstructure, but does differ significantly from currently produced TRISO surrogates and fuel at ORNL. In this document, characterization data of the ZrO2-500-AK2 surrogate includes: size, shape, coating thickness, and density.

Kercher, Andrew K [ORNL; Hunn, John D [ORNL

2005-06-01T23:59:59.000Z

391

Results from ORNL Characterization of Zr02-500-AK2 - Surrogate TRISO Material  

SciTech Connect (OSTI)

This document is a compilation of the characterization data for the TRISO-coated surrogate particle batch designated ZrO2-500-AK2 that was produced at Oak Ridge National Laboratory (ORNL) as part of the Advanced Gas Reactor Fuel Development and Qualification (AGR) program. The ZrO2-500-AK2 material contains nominally 500 {micro}m kernels of yttria-stabilized zirconia (YSZ) coated with all TRISO layers (buffer, inner pyrocarbon, silicon carbide, and outer pyrocarbon). The ZrO2-500-AK2 material was created for: (1) irradiation testing in the High Flux Isotope Reactor (HFIR) and (2) limited dissemination to laboratories as deemed appropriate to the AGR program. This material was created midway into a TRISO fuel development program to accommodate a sudden opportunity to perform irradiation testing on surrogate material. While the layer deposition processes were chosen based on the best technical understanding at the time, technical progress at ORNL has led to an evolution in the perceived optimal deposition conditions since the createion of ZrO2-500-AK2. Thus, ZrO2-500-AK2 contains a reasonable TRISO microstructure, but does differ significanly from currently produced TRISO surrogates and fuel at ORNL. In this document, characterization data of the ZrO2-500-AK2 surrogate includes: size, shape, coating thickness, and density.

Hunn, John D [ORNL; Kercher, Andrew K [ORNL

2005-06-01T23:59:59.000Z

392

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

E-Print Network [OSTI]

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

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

393

W.A. Parish Post-Combustion CO2 Capture and Sequestration Project, Final Environmental Impact Statement (DOE/EIS-0473)  

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

W.A. W.A. Parish Post-Combustion CO 2 Capture and Sequestration Project Final Environmental Impact Statement Summary February 2013 DOE/EIS-0473 Office of Fossil Energy National Energy Technology Laboratory INTENTIONALLY LEFT BLANK COVER SHEET Responsible Federal Agency: U.S. Department of Energy (DOE) Title: W.A. Parish Post-Combustion CO 2 Capture and Sequestration Project, Final Environmental Impact Statement (DOE/EIS-0473) Location: Southeastern Texas, including Fort Bend, Wharton, and Jackson counties Contacts: For further information about this Environmental Impact Statement, contact: For general information on the DOE process for implementing the National Environmental Policy Act, contact: Mark W. Lusk U.S. Department of Energy National Energy Technology Laboratory 3610 Collins Ferry Road Morgantown, WV 26507-0880 (304) 285-4145 or Mark.Lusk@netl.doe.gov

394

W.A. Parish Post-Combustion CO2 Capture and Sequestration Project, Final Environmental Impact Statement (DOE/EIS-0473)  

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

NRG W.A. PARISH PCCS PROJECT NRG W.A. PARISH PCCS PROJECT FINAL ENVIRONMENTAL IMPACT STATEMENT APPENDIX H. BEG MODELING REPORT APPENDIX H BEG MODELING REPORT DOE/EIS-0473 NRG W.A. PARISH PCCS PROJECT FINAL ENVIRONMENTAL IMPACT STATEMENT APPENDIX H. BEG MODELING REPORT INTENTIONALLY LEFT BLANK 1 Reservoir modeling and simulation for estimating migration extents of injectate-CO 2 in support of West Ranch oilfield NEPA/EIS Gulf Coast Carbon Center, Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin May 4, 2012 Summary It is anticipated that anthropogenic carbon dioxide (CO2-A) will be injected into the deep (5,000-6,000 ft below sea level) subsurface for enhanced oil recovery (EOR) at the West Ranch oilfield beginning in early 2015. The purpose of this report is to present reservoir modeling and simulation

395

Anemometer Data (Wind Speed, Direction) for Ugashik, AK (2001 - 2002) |  

Open Energy Info (EERE)

0 0 Varnish cache server Browse Upload data GDR 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2142278290 Varnish cache server Anemometer Data (Wind Speed, Direction) for Ugashik, AK (2001 - 2002) Dataset Summary Description Wind data collected from Ugashik Traditional Village in Alaska from an anemometer as part of the Native American anemometer loan program. Monthly mean wind speed is available for 2001 through 2002, as is wind direction and turbulence data. Data is reported from a height of 20 m. The data was originally made available by Wind Powering America, a DOE Office of Energy Efficiency & Renewable Energy (EERE) program. A dynamic map displaying all available data from DOE anemometer loan programs is available http://www.windpoweringamerica.gov/anemometerloans/projects.asp.

396

Anemometer Data (Wind Speed, Direction) for Tanana, AK (2001 - 2002) |  

Open Energy Info (EERE)

40 40 Varnish cache server Anemometer Data (Wind Speed, Direction) for Tanana, AK (2001 - 2002) Dataset Summary Description Wind data collected from Tanana Village in Alaska from an anemometer as part of the Native American anemometer loan program. Monthly mean wind speed is available for 2001 through 2002, as is wind direction and turbulence data. Data is reported from a height of 20 m. The data was originally made available by Wind Powering America, a DOE Office of Energy Efficiency & Renewable Energy (EERE) program. A dynamic map displaying all available data from DOE anemometer loan programs is available http://www.windpoweringamerica.gov/anemometerloans/projects.asp. Source EERE Date Released November 09th, 2010 (4 years ago) Date Updated November 09th, 2010 (4 years ago)

397

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

Science Journals Connector (OSTI)

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

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

2015-01-01T23:59:59.000Z

398

EIA - Forecasts and Analysis of Energy Data  

Gasoline and Diesel Fuel Update (EIA)

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

399

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

SciTech Connect (OSTI)

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

Porter, K.; Rogers, J.

2012-04-01T23:59:59.000Z

400

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

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


401

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

SciTech Connect (OSTI)

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

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

1992-02-01T23:59:59.000Z

402

An assessment of electrical load forecasting using artificial neural network  

Science Journals Connector (OSTI)

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

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

2012-01-01T23:59:59.000Z

403

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

Science Journals Connector (OSTI)

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

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

2014-02-01T23:59:59.000Z

404

Corinna Cisneros, S.M. Golam Mortuza, and Soumik Banerjee School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99163  

E-Print Network [OSTI]

Engineering, Washington State University, Pullman, WA 99163 Organic Photovoltaic Solar Cells: A Molecular of fossil fuel resources has lead to global energy crisis with an increasing effort in the scientific community to develop renewable energy technologies. Solar cell technology has generated significant

Collins, Gary S.

405

Proc. of the ACM Int'l. Symp on Softw. Testing and Analysis, Seattle, WA, August 1994, pages 169184. Selecting Tests and Identifying Test Coverage Requirements for  

E-Print Network [OSTI]

of structural coverage crite­ ria. Our technique partitions an existing test suite into two subsets: testsProc. of the ACM Int'l. Symp on Softw. Testing and Analysis, Seattle, WA, August 1994, pages 169­184. Selecting Tests and Identifying Test Coverage Requirements for Modified Software* Gregg Rothermel and Mary

Rothermel, Gregg

406

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

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

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

407

Forecasting correlated time series with exponential smoothing models  

Science Journals Connector (OSTI)

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

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

2011-01-01T23:59:59.000Z

408

Application of GIS on forecasting water disaster in coal mines  

SciTech Connect (OSTI)

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

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

1996-08-01T23:59:59.000Z

409

NREL: Energy Analysis - Energy Forecasting and Modeling Staff  

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

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

410

Conceptual design of a geothermal site development forecasting system  

SciTech Connect (OSTI)

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

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

1980-03-01T23:59:59.000Z

411

CCPP-ARM Parameterization Testbed Model Forecast Data  

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

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

Klein, Stephen

412

Forecast of contracting and subcontracting opportunities. Fiscal year 1996  

SciTech Connect (OSTI)

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

NONE

1996-02-01T23:59:59.000Z

413

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

Science Journals Connector (OSTI)

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

Imad J. Zbib

2006-01-01T23:59:59.000Z

414

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

E-Print Network [OSTI]

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

Bush, Sarah, 1973-

2003-01-01T23:59:59.000Z

415

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

E-Print Network [OSTI]

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

Ganguly, Auroop Ratan

2002-01-01T23:59:59.000Z

416

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

Science Journals Connector (OSTI)

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

Harold E. Brooks; Charles A. Doswell III

1996-09-01T23:59:59.000Z

417

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

Science Journals Connector (OSTI)

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

Anthony G. Barnston

1992-12-01T23:59:59.000Z

418

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

Science Journals Connector (OSTI)

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

XiaoJing Jia; Hai Lin; Jacques Derome

2010-05-01T23:59:59.000Z

419

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

Science Journals Connector (OSTI)

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

Jakub Nowotarski; Rafa? Weron

2014-08-01T23:59:59.000Z

420

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

Science Journals Connector (OSTI)

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

V. V. Kossov

2014-09-01T23:59:59.000Z

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


421

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

E-Print Network [OSTI]

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

Zareipour, Hamidreza

2006-01-01T23:59:59.000Z

422

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

SciTech Connect (OSTI)

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

Piwko, R.; Jordan, G.

2011-11-01T23:59:59.000Z

423

Combining Multi Wavelet and Multi NN for Power Systems Load Forecasting  

Science Journals Connector (OSTI)

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

Zhigang Liu; Qi Wang; Yajun Zhang

2008-01-01T23:59:59.000Z

424

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

E-Print Network [OSTI]

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

Statton, James Cody

2012-07-16T23:59:59.000Z

425

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

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

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

426

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

E-Print Network [OSTI]

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

Mathiesen, Patrick; Kleissl, Jan

2011-01-01T23:59:59.000Z

427

A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height  

Science Journals Connector (OSTI)

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

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

2013-02-01T23:59:59.000Z

428

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

Science Journals Connector (OSTI)

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

Nicolas D. Savio; K. Nikolopoulos; Konstantinos Bozos

2009-01-01T23:59:59.000Z

429

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

SciTech Connect (OSTI)

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

Porter, K.; Rogers, J.

2009-12-01T23:59:59.000Z

430

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

E-Print Network [OSTI]

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

Boyer, Edmond

431

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

E-Print Network [OSTI]

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

Abu-Mostafa, Yaser S.

432

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

E-Print Network [OSTI]

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

Gray, William

433

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

E-Print Network [OSTI]

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

Perez, Richard R.

434

Products and Service of Center for Weather Forecast and Climate Studies  

E-Print Network [OSTI]

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

435

Lessons from Deploying NLG Technology for Marine Weather Forecast Text Generation  

E-Print Network [OSTI]

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

Sripada, Yaji

436

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

E-Print Network [OSTI]

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

Boyer, Edmond

437

GRR/Section 9-AK-a - Alaska Environmental Process | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 9-AK-a - Alaska Environmental Process GRR/Section 9-AK-a - Alaska Environmental Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 9-AK-a - Alaska Environmental Process 09AKAStateEnvironmentalProcess (1).pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Regulations & Policies AS 38.05.035: Powers & Duties of ADNR Director AS 38.05.082: Leases for Shore Fisheries AS 38.05.115: Conditions of Sale AS 38.05.850: Permits AS 38.05.945: Notice AS 38.05.946: Hearings Triggers None specified Click "Edit With Form" above to add content 09AKAStateEnvironmentalProcess (1).pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range.

438

GRR/Section 14-AK-c - Alaska UIC Permit | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 14-AK-c - Alaska UIC Permit GRR/Section 14-AK-c - Alaska UIC Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 14-AK-c - Alaska UIC Permit 14AKCAlaskaUICPermit.pdf Click to View Fullscreen Triggers None specified Click "Edit With Form" above to add content 14AKCAlaskaUICPermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative The Alaska Underground Injection Control Permit is regulated by the Environmental Protection Agency. The EPA regulates Class V injection wells on Federal lands, many tribal lands, and in some states like Alaska. Injection wells are overseen by either a state or Tribal Agency or one of

439

GRR/Section 8-AK-a - Transmission | Open Energy Information  

Open Energy Info (EERE)

8-AK-a - Transmission 8-AK-a - Transmission < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 8-AK-a - Transmission 08AKATransmission.pdf Click to View Fullscreen Triggers None specified Click "Edit With Form" above to add content 08AKATransmission.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative Under the Alaska Public Utilities Regulatory Act, transmission is included in Alaska's regulation of public utilities. According to AS 42.05.990(5), "public utility" or "utility" includes every corporation whether public, cooperative, or otherwise, company, individual, or association of

440

GRR/Section 4-AK-c - Geothermal Exploration Permit | Open Energy  

Open Energy Info (EERE)

4-AK-c - Geothermal Exploration Permit 4-AK-c - Geothermal Exploration Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 4-AK-c - Geothermal Exploration Permit 04AKCGeothermalExplorationPermit.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Oil and Gas Regulations & Policies Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 04AKCGeothermalExplorationPermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative The Alaska Department of Natural Resources requires filing an application

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


441

GRR/Section 14-AK-a - Nonpoint Source Pollution | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 14-AK-a - Nonpoint Source Pollution GRR/Section 14-AK-a - Nonpoint Source Pollution < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 14-AK-a - Nonpoint Source Pollution 14AKANonpointSourcePollution.pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation Regulations & Policies Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 14AKANonpointSourcePollution.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative Alaska's Nonpoint Source Water Pollution Control Strategy is a statewide plan for protecting Alaska's natural resources from polluted runoff also

442

GRR/Section 19-AK-a - Water Access and Water Rights Issues | Open Energy  

Open Energy Info (EERE)

GRR/Section 19-AK-a - Water Access and Water Rights Issues GRR/Section 19-AK-a - Water Access and Water Rights Issues < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-AK-a - Water Access and Water Rights Issues 19AKAWaterAccessWaterRights.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Mining Land and Water Regulations & Policies Alaska Water Use Act Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 19AKAWaterAccessWaterRights.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative In Alaska, water is declared a public resource belonging to the people of

443

GRR/Section 3-AK-b - Right of Ways (ROWs) | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 3-AK-b - Right of Ways (ROWs) GRR/Section 3-AK-b - Right of Ways (ROWs) < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-AK-b - Right of Ways (ROWs) 03AKBRightOfWaysROWs.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Mining Land and Water Regulations & Policies Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 03AKBRightOfWaysROWs.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative The Alaska Division of Mining Land and Water (ML&W) oversees land use within the state and issues right of ways, easements or permit to use state

444

GRR/Section 3-AK-e - Land Use Permit | Open Energy Information  

Open Energy Info (EERE)

3-AK-e - Land Use Permit 3-AK-e - Land Use Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-AK-e - Land Use Permit 03AKELandUsePermit.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Mining Land and Water Regulations & Policies Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 03AKELandUsePermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative A land use permit in Alaska covers a number of uses of state land that are less invasive and do not require a full property interest such as a lease

445

DOE - Office of Legacy Management -- Amchitka Island Test Center - AK 01  

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

Amchitka Island Test Center - AK 01 Amchitka Island Test Center - AK 01 FUSRAP Considered Sites Site: Amchitka Island Test Center (AK.01) Designated Name: Alternate Name: Location: Evaluation Year: Site Operations: Site Disposition: Radioactive Materials Handled: Primary Radioactive Materials Handled: Radiological Survey(s): Site Status: Also see Amchitka Island Test Center Documents Related to Amchitka Island Test Center Draft Long-Term Surveillance Plan for the Amchitka Island, Alaska, Project Site (September 2013) An Assessment of the Reported Leakage of Anthropogenic Radionuclides From the Underground Nuclear Test Sites at Amchitka Island, Alaska, USA to the Surface Environment. Conceptual Site Models as a Tool in Evaluation Ecological health; The Case of the Department of Energys Amchitka Island Nuclear Test Site.

446

GRR/Section 11-AK-a - State Cultural Considerations | Open Energy  

Open Energy Info (EERE)

1-AK-a - State Cultural Considerations 1-AK-a - State Cultural Considerations < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 11-AK-a - State Cultural Considerations 11AKAStateCulturalConsiderations (2).pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Regulations & Policies AS 41.35.060: Power to Acquire AS 41.35.070: Preservation of Historic Resources AS 41.35.090: Notice AS 41.35.100: Excavation Triggers None specified Click "Edit With Form" above to add content 11AKAStateCulturalConsiderations (2).pdf 11AKAStateCulturalConsiderations (2).pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative It is the policy of the State of Alaska to preserve and protect the

447

GRR/Section 3-AK-a - State Competitive Mineral Leasing Process | Open  

Open Energy Info (EERE)

GRR/Section 3-AK-a - State Competitive Mineral Leasing Process GRR/Section 3-AK-a - State Competitive Mineral Leasing Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-AK-a - State Competitive Mineral Leasing Process 03AKAStateCompetitiveMineralLeasingProcess.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Oil and Gas Regulations & Policies Alaska Land Act: AS 38.05 Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 03AKAStateCompetitiveMineralLeasingProcess.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range.

448

GRR/Section 5-AK-a - Drilling and Well Development | Open Energy  

Open Energy Info (EERE)

GRR/Section 5-AK-a - Drilling and Well Development GRR/Section 5-AK-a - Drilling and Well Development < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 5-AK-a - Drilling and Well Development 05AKADrillingWellDevelopment.pdf Click to View Fullscreen Contact Agencies Alaska Oil and Gas Conservation Commission Alaska Department of Natural Resources Regulations & Policies Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 05AKADrillingWellDevelopment.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative All wells drilled in search or in support of the recovery of geothermal

449

GRR/Section 14-AK-d - Section 401 Water Quality Certification | Open Energy  

Open Energy Info (EERE)

GRR/Section 14-AK-d - Section 401 Water Quality Certification GRR/Section 14-AK-d - Section 401 Water Quality Certification < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 14-AK-d - Section 401 Water Quality Certification 14AKDSection401WaterQualityCertification.pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation United States Environmental Protection Agency U S Army Corps of Engineers Regulations & Policies Alaska Water Quality Standards Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 14AKDSection401WaterQualityCertification.pdf 14AKDSection401WaterQualityCertification.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range.

450

GRR/Section 18-AK-c - Waste Disposal Permit Process | Open Energy  

Open Energy Info (EERE)

AK-c - Waste Disposal Permit Process AK-c - Waste Disposal Permit Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 18-AK-c - Waste Disposal Permit Process 18AKC - WasteDisposalPermitProcess (1).pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation Regulations & Policies AS 46.03.110 Waste Disposal Permit Regulations 18 AAC 60.200 et seq Triggers None specified Click "Edit With Form" above to add content 18AKC - WasteDisposalPermitProcess (1).pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative The Alaska Department of Environmental Conservation (DEC) is responsible

451

GRR/Section 15-AK-a - Air Quality Assessment Process | Open Energy  

Open Energy Info (EERE)

GRR/Section 15-AK-a - Air Quality Assessment Process GRR/Section 15-AK-a - Air Quality Assessment Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 15-AK-a - Air Quality Assessment Process 15AKAAirQualityAssessmentProcess.pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation Regulations & Policies Alaska Statutes Alaska Statute Title 46 Alaska Administrative Code 18 AAC 50 Air Quality Regulations 40 CFR 71 Operating Permits Triggers None specified Click "Edit With Form" above to add content 15AKAAirQualityAssessmentProcess.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range.

452

GRR/Section 7-AK-c - Certificate of Public Convenience and Necessity | Open  

Open Energy Info (EERE)

GRR/Section 7-AK-c - Certificate of Public Convenience and Necessity GRR/Section 7-AK-c - Certificate of Public Convenience and Necessity < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 7-AK-c - Certificate of Public Convenience and Necessity 07AKCCertificateOfPublicConvenienceAndNecessity.pdf Click to View Fullscreen Contact Agencies Regulatory Commission of Alaska Regulations & Policies AS 42.05.175: Timeline for Final Orders AS 42.05.221: Certificates Required AS 42.05.711: Exemptions 3 AAC 48.645: Application 3 AAC 48.648: Complete Applications 3 AAC 48.650: Incomplete Applications AAC Title 3 2012 Supplement Triggers None specified Click "Edit With Form" above to add content 07AKCCertificateOfPublicConvenienceAndNecessity.pdf Error creating thumbnail: Page number not in range.

453

GRR/Section 20-AK-a - Well Abandonment Process | Open Energy Information  

Open Energy Info (EERE)

20-AK-a - Well Abandonment Process 20-AK-a - Well Abandonment Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 20-AK-a - Well Abandonment Process 20AKAWellAbandonmentProcess.pdf Click to View Fullscreen Contact Agencies Alaska Oil and Gas Conservation Commission Regulations & Policies 20 AAC 25.105 20 AAC 25.112 Triggers None specified Click "Edit With Form" above to add content 20AKAWellAbandonmentProcess.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative This flowchart illustrates the process for abandoning wells in the state of Alaska. The Alaska Oil and Gas Conservation Commission ("commission")

454

GRR/Section 6-AK-b - Construction Storm Water Permitting | Open Energy  

Open Energy Info (EERE)

GRR/Section 6-AK-b - Construction Storm Water Permitting GRR/Section 6-AK-b - Construction Storm Water Permitting < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 6-AK-b - Construction Storm Water Permitting 06AKBConstructionStormWaterPermitting (1).pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation Regulations & Policies 18 AAC 72: Wastewater Treatment and Disposal Triggers None specified Click "Edit With Form" above to add content 06AKBConstructionStormWaterPermitting (1).pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative From DEC Website: The goal of the Storm Water Program is to reduce or eliminate pollutants in

455

GRR/Section 3-AK-d - State Noncompetitive Mineral Leasing Process | Open  

Open Energy Info (EERE)

GRR/Section 3-AK-d - State Noncompetitive Mineral Leasing Process GRR/Section 3-AK-d - State Noncompetitive Mineral Leasing Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 3-AK-d - State Noncompetitive Mineral Leasing Process 03AKDStateNoncompetitiveMineralLeasingProcess.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Oil and Gas Regulations & Policies Alaska Land Act: AS 38.05 Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 03AKDStateNoncompetitiveMineralLeasingProcess.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range.

456

GRR/Section 18-AK-b - Hazardous Waste Permit Process | Open Energy  

Open Energy Info (EERE)

8-AK-b - Hazardous Waste Permit Process 8-AK-b - Hazardous Waste Permit Process < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 18-AK-b - Hazardous Waste Permit Process 18AKB - HazardousWastePermitProcess (1).pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation United States Environmental Protection Agency Regulations & Policies AS 46.03.302 18 AAC 60.020 Triggers None specified Click "Edit With Form" above to add content 18AKB - HazardousWastePermitProcess (1).pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative The Alaska Department of Environmental Conservation defers to the federal

457

GRR/Section 15-AK-c - Title V Operating Permit | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 15-AK-c - Title V Operating Permit GRR/Section 15-AK-c - Title V Operating Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 15-AK-c - Title V Operating Permit 15AKCTitleVOperatingPermit.pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation United States Environmental Protection Agency Regulations & Policies Alaska Statutes Alaska Administrative Code 18 AAC 50 Air Quality Control Triggers None specified Click "Edit With Form" above to add content 15AKCTitleVOperatingPermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative One of the major initiatives Congress added to the Clean Air Act in 1990 is

458

GRR/Section 6-AK-c - Drinking Water Permit | Open Energy Information  

Open Energy Info (EERE)

6-AK-c - Drinking Water Permit 6-AK-c - Drinking Water Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 6-AK-c - Drinking Water Permit 06AKCDrinkingWaterPermit.pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation Regulations & Policies 18 AAC 80 Drinking Water 40 CFR 141 40 CFR 142 40 CFR 143 Triggers None specified Click "Edit With Form" above to add content 06AKCDrinkingWaterPermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative Alaska's drinking water program is monitored under the Alaska Department of Environmental Conservation. The type of permit required depends on the

459

GRR/Section 15-AK-b - Air Quality Minor Permit | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 15-AK-b - Air Quality Minor Permit GRR/Section 15-AK-b - Air Quality Minor Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 15-AK-b - Air Quality Minor Permit 15AKBAirQualityMinorPermit.pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation Regulations & Policies Alaska Statutes Alaska Administrative Code 18 AAC 50 Air Quality Control Regulations 40 CFR Chapter I, Subchapter C - Air Programs Triggers None specified Click "Edit With Form" above to add content 15AKBAirQualityMinorPermit.pdf 15AKBAirQualityMinorPermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative The mission of the Air Permit Program is to protect the Alaskan environment

460

GRR/Section 18-AK-a - Storage Tank Registration | Open Energy Information  

Open Energy Info (EERE)

GRR/Section 18-AK-a - Storage Tank Registration GRR/Section 18-AK-a - Storage Tank Registration < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 18-AK-a - Storage Tank Registration 18AKA - StorageTankRegistration (1).pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation Regulations & Policies AS 46.03.380 As 46.03.385 18 AAC 78 Underground Storage Tanks Triggers None specified Click "Edit With Form" above to add content 18AKA - StorageTankRegistration (1).pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative Any project that requires installation or operation of a storage tank must

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


461

GRR/Section 14-AK-b - Alaska Pollutant Discharge Elimination System Permit  

Open Energy Info (EERE)

GRR/Section 14-AK-b - Alaska Pollutant Discharge Elimination System Permit GRR/Section 14-AK-b - Alaska Pollutant Discharge Elimination System Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 14-AK-b - Alaska Pollutant Discharge Elimination System Permit 14AKBAlaskaPollutantDischargeEliminationSystemPermit (1).pdf Click to View Fullscreen Contact Agencies Alaska Department of Environmental Conservation United States Environmental Protection Agency Regulations & Policies Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 14AKBAlaskaPollutantDischargeEliminationSystemPermit (1).pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range.

462

GRR/Section 4-AK-b - Geophysical Exploration Permit | Open Energy  

Open Energy Info (EERE)

4-AK-b - Geophysical Exploration Permit 4-AK-b - Geophysical Exploration Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 4-AK-b - Geophysical Exploration Permit 04AKBGeophysicalExplorationPermit.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Oil and Gas Regulations & Policies Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 04AKBGeophysicalExplorationPermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative A Geophysical Exploration Permit is necessary for conducting seismic

463

GRR/Section 19-AK-b - Temporary Use of Water Permit | Open Energy  

Open Energy Info (EERE)

9-AK-b - Temporary Use of Water Permit 9-AK-b - Temporary Use of Water Permit < GRR Jump to: navigation, search GRR-logo.png GEOTHERMAL REGULATORY ROADMAP Roadmap Home Roadmap Help List of Sections Section 19-AK-b - Temporary Use of Water Permit 19AKBTemporaryUseOfWaterPermit.pdf Click to View Fullscreen Contact Agencies Alaska Department of Natural Resources Alaska Division of Mining Land and Water Regulations & Policies Alaska Water Use Act Alaska Statutes Alaska Administrative Code Triggers None specified Click "Edit With Form" above to add content 19AKBTemporaryUseOfWaterPermit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number not in range. Flowchart Narrative In Alaska, water is declared a public resource belonging to the people of

464

Optimal Bidding Strategies for Wind Power Producers with Meteorological Forecasts  

E-Print Network [OSTI]

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

Giannitrapani, Antonello

465

Does Money Matter in Inflation Forecasting? JM Binner 1  

E-Print Network [OSTI]

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

Tino, Peter

466

Detecting and Forecasting Economic Regimes in Automated Exchanges  

E-Print Network [OSTI]

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

Ketter, Wolfgang

467

Forecasting Market Demand for New Telecommunications Services: An Introduction  

E-Print Network [OSTI]

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

Parsons, Simon

468

SOLAR IRRADIANCE FORECASTING FOR THE MANAGEMENT OF SOLAR ENERGY SYSTEMS  

E-Print Network [OSTI]

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

Heinemann, Detlev

469

Short-Term Solar Energy Forecasting Using Wireless Sensor Networks  

E-Print Network [OSTI]

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

Cerpa, Alberto E.

470

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

E-Print Network [OSTI]

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

Islam, M. Saif

471

A FORECAST MODEL OF AGRICULTURAL AND LIVESTOCK PRODUCTS PRICE  

E-Print Network [OSTI]

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

Boyer, Edmond

472

Forecasting Building Occupancy Using Sensor Network James Howard  

E-Print Network [OSTI]

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

Hoff, William A.

473

Forecasting Hospital Bed Availability Using Simulation and Neural Networks  

E-Print Network [OSTI]

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

Kuhl, Michael E.

474

Predicting Solar Generation from Weather Forecasts Using Machine Learning  

E-Print Network [OSTI]

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

Shenoy, Prashant

475

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect (OSTI)

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

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

2011-03-28T23:59:59.000Z

476

Development and Deployment of an Advanced Wind Forecasting Technique  

E-Print Network [OSTI]

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

Kemner, Ken

477

Power load forecasting using data mining and knowledge discovery technology  

Science Journals Connector (OSTI)

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

Yongli Wang; Dongxiao Niu; Ling Ji

2011-01-01T23:59:59.000Z

478

What constrains spread growth in forecasts ini2alized from  

E-Print Network [OSTI]

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

Hamill, Tom

479

Probabilistic Forecasts of Wind Speed: Ensemble Model Output Statistics  

E-Print Network [OSTI]

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

Washington at Seattle, University of

480

Introduction An important goal in operational weather forecasting  

E-Print Network [OSTI]

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

Haak, Hein

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


481

Operational Forecasts of Cloud Cover and Water Vapour  

E-Print Network [OSTI]

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

482

Increasing NOAA's computational capacity to improve global forecast modeling  

E-Print Network [OSTI]

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

Hamill, Tom

483

Measuring forecast skill: is it real skill or  

E-Print Network [OSTI]

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

Hamill, Tom

484

URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA  

E-Print Network [OSTI]

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

Boyer, Edmond

485

Leveraging Weather Forecasts in Renewable Energy Navin Sharmaa,  

E-Print Network [OSTI]

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

Shenoy, Prashant

486

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems  

E-Print Network [OSTI]

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

Shenoy, Prashant

487

Weather forecast-based optimization of integrated energy systems.  

SciTech Connect (OSTI)

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

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

2009-03-01T23:59:59.000Z

488

Journey data based arrival forecasting for bicycle hire schemes  

E-Print Network [OSTI]

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

Imperial College, London

489

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network [OSTI]

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

Katz, Richard

490

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network [OSTI]

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

Katz, Richard

491

FORECAST VERIFICATION OF EXTREMES: USE OF EXTREME VALUE THEORY  

E-Print Network [OSTI]

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

Katz, Richard

492

Seasonal Forecasting of Extreme Wind and Precipitation Frequencies in Europe  

E-Print Network [OSTI]

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

Feigon, Brooke

493

Use of wind power forecasting in operational decisions.  

SciTech Connect (OSTI)

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

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

2011-11-29T23:59:59.000Z

494

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

SciTech Connect (OSTI)

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

Das, S.

1991-12-01T23:59:59.000Z

495

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

E-Print Network [OSTI]

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

Boyer, Edmond

496

Phase-space explorations in time-dependent density functional theory A.K. Rajam a  

E-Print Network [OSTI]

Phase-space explorations in time-dependent density functional theory A.K. Rajam a , Paul Hessler b online xxxx Keywords: Time-dependent density functional theory Phase-space Momentum-distributions Density to phase-space densities, discuss some formal aspects of such a ``phase-space density functional theory

497

Jasmine R. Scott1, Nathan Tarlyn2, Amit Dhingra2 and Kate Evans2 1Fort Valley State University, GA and 2Department of Horticulture, Washington State University, WA  

E-Print Network [OSTI]

and 2Department of Horticulture, Washington State University, WA Time travel with apples: Can you see on selection media in the dark. After a week, cultures were moved to the light to induce shoot development

Collins, Gary S.

498

Forecast Calls for Better Models: Examining the Core  

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

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

499

Fundamentals, forecast combinations and nominal exchange-rate predictability  

Science Journals Connector (OSTI)

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

Jyh-Lin Wu; Yi-Chiuan Wang

2013-01-01T23:59:59.000Z

500

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

Broader source: Energy.gov [DOE]

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