National Library of Energy BETA

Sample records for forecasting ak wa

  1. BayWa Group | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BayWa Group Jump to: navigation, search Name: BayWa Group Place: Munich, Germany Zip: 81925 Sector: Services, Solar Product: Germany-based company with international operations...

  2. Category:Seattle, WA | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    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....

  3. WA_1994_003_GOLDEN_PHOTOCON_INC_Waiver_of_Domestic_and_Forei...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    WA1994003GOLDENPHOTOCONINCWaiverofDomesticandForei.pdf WA1994003GOLDENPHOTOCONINCWaiverofDomesticandForei.pdf PDF icon WA1994003GOLDENPHOTOCONINCWaiverof...

  4. Sumas, WA Liquefied Natural Gas Imports (Million Cubic Feet)

    Gasoline and Diesel Fuel Update

    Sumas, WA Liquefied Natural Gas Imports (Million Cubic Feet) Year Jan Feb Mar Apr May Jun ... Referring Pages: U.S. Liquefied Natural Gas Imports by Point of Entry Sumas, WA LNG ...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    2-023 Advance Patent Waiver W(A)2002-023 Advance Patent Waiver W(A)2002-023 (1.52 MB) More Documents & Publications Advance Patent Waiver W(A)2006-028 WA05056IBMWATSONRESEARCH...

  6. WA_04_069__EATON_CORPORATION_Waiver_of_Domestic_and_Foreign_...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    More Documents & Publications WA04059EATONCORPORATIONWaiverofPatentRightsUndera.pdf WA02048EATONCORPORATIONWaviverofPatentRightsUnderA.pdf ...

  7. Hanford, WA Selected as Plutonium Production Facility | National Nuclear

    National Nuclear Security Administration (NNSA)

    Security Administration | (NNSA) Hanford, WA Selected as Plutonium Production Facility Hanford, WA Selected as Plutonium Production Facility Hanford, WA Groves selects Hanford, Washington, as site for full-scale plutonium production and separation facilities. Three reactors--B, D, and F--are built

  8. Solar Forecasting

    Energy.gov [DOE]

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

  9. Recent results from CERN-WA98

    SciTech Connect

    Stankus, P.; WA98 Collaboration

    1997-02-01

    The CERN experiment WA98 is a general-survey, open-spectrometer experiment designed to examine 160 A GeV/c Pb+A collisions at the CERN-SPS. The experiment has a broad physics agenda, as suggested by its many different subsystems. A diagram of the experiment as it stood in 1995 is shown in the report. Detectors whose results are presented here are described briefly.

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

    SciTech Connect

    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.

    2013-05-01

    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.

  11. Ak Chin Indian Community- 2004 Project

    Energy.gov [DOE]

    The Ak-Chin Indian Community will study the feasibility of siting a biopower installation on community lands.

  12. RAPID/Roadmap/19-WA-e | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Water Well Notice of Intent for New Well (19-WA-e) A...

  13. RAPID/Roadmap/11-WA-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Human Remains Process (11-WA-b) This flowchart...

  14. RAPID/Roadmap/19-WA-d | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Water Conservancy Board Transfer or Change of Water Right (19-WA-d) In 1997, the Washington...

  15. RAPID/Roadmap/19-WA-c | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Geothermal Hydropower Solar Tools Contribute Contact Us Transfer or Change of Water Right (19-WA-c) Much of Washington's public waters have been accounted for through...

  16. RAPID/Roadmap/19-WA-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us New Water Right Permit Process (19-WA-b) Washington uses a prior appropriation system for the...

  17. RAPID/Roadmap/5-WA-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Geothermal Hydropower Solar Tools Contribute Contact Us Drilling and Well Development (5-WA-a) In Washington geothermal drilling and well development are regulated by the...

  18. RAPID/Roadmap/15-WA-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Air Quality Permit - Notice of Construction Permit (15-WA-a) This flowchart illustrates...

  19. RAPID/Roadmap/15-WA-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Air Quality Permit - Operating Permit (15-WA-b) This flowchart illustrates the process for...

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

    OpenEI (Open Energy Information) [EERE & EIA]

    RAPIDRoadmap14-WA-b < RAPID | Roadmap Jump to: navigation, search RAPID Regulatory and Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal...

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

    OpenEI (Open Energy Information) [EERE & EIA]

    Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Dangerous Solid Waste Permit (18-WA-b) The Washington State Department of Ecology (WSDE) oversees the...

  2. RAPID/Roadmap/14-WA-c | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Geothermal Hydropower Solar Tools Contribute Contact Us Underground Injection Control Permit (14-WA-c) The Safe Drinking Water Act requires Washington to implement...

  3. RAPID/Roadmap/12-WA-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Geothermal Hydropower Solar Tools Contribute Contact Us State Trust Lands Habitat Conservation Plan Compliance (12-WA-b) The State of Washington has a Habitat Conservation Plan...

  4. Microsoft Word - WA Parish_MAP_Final.docx

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    and compression system at Unit 8 of the existing W.A. ... subject of a mitigation commitment. (b) In certain ... systems to produce electricity at greater efficiencies, ...

  5. Forecast Change

    Annual Energy Outlook

    Forecast Change 2011 2012 2013 2014 2015 2016 from 2015 United States Usage (kWh) 3,444 3,354 3,129 3,037 3,151 3,302 4.8% Price (centskWh) 12.06 12.09 12.58 13.04 12.95 12.84 ...

  6. DOE Zero Energy Ready Home Case Study: TC Legend, Seattle, WA...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Legend, Seattle, WA, Custom Home DOE Zero Energy Ready Home Case Study: TC Legend, Seattle, WA, Custom Home Case study of a DOE Zero Energy Ready Home in Seattle, WA, that scored ...

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

    Energy Information Administration (EIA) (indexed site)

    Port Nikiski, AK Liquefied Natural Gas Exports to Japan (Dollars per Thousand Cubic Feet) Port Nikiski, AK Liquefied Natural Gas Exports to Japan (Dollars per Thousand Cubic Feet)...

  8. Petra Nova - W.A. Parish Project | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Petra Nova - W.A. Parish Project Petra Nova - W.A. Parish Project The project will demonstrate Mitsubishi Heavy Industries' (MHI) CO2 capture technology at an existing coal-fired power plant. The project will demonstrate Mitsubishi Heavy Industries' (MHI) CO2 capture technology at an existing coal-fired power plant. PETRA NOVA CCS PROJECT On June 18, 2010, the U.S. Department of Energy (DOE) announced the signing of a Cooperative Agreement with NRG Energy Inc. (NRG) for the W.A. Parish

  9. BayWa Sunways JV | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    JV that specialises in developing, planning and realizing medium-sized to large photovoltaic systems and solar plants. References: BayWa & Sunways JV1 This article is a stub....

  10. RAPID/Roadmap/19-WA-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Water Access and Water Rights Overview (19-WA-a) Pursuant to RCW 78.60.060, developers that...

  11. Price of Sumas, WA Liquefied Natural Gas Imports from Canada...

    Annual Energy Outlook

    from Canada (Dollars per Thousand Cubic Feet) Price of Sumas, WA Liquefied Natural Gas Imports from Canada (Dollars per Thousand Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug...

  12. Price of Sumas, WA Liquefied Natural Gas Imports (Dollars per...

    Gasoline and Diesel Fuel Update

    (Dollars per Thousand Cubic Feet) Price of Sumas, WA Liquefied Natural Gas Imports (Dollars per Thousand Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 8.42...

  13. RAPID/Roadmap/6-WA-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    in the Washington Administrative Code. 6-WA-a - Oversize-Overweight Load Permit.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number...

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

    OpenEI (Open Energy Information) [EERE & EIA]

    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...

  15. Sumas, WA Liquefied Natural Gas Imports from Canada (Million...

    Energy Information Administration (EIA) (indexed site)

    from Canada (Million Cubic Feet) Sumas, WA Liquefied Natural Gas Imports from Canada (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 5 2015 4 4 2 1 ...

  16. Microsoft Word - WA Parish_MAP_Final.docx

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    W.A. Parish Post-Combustion CO 2 Capture and Sequestration Project U. S. Department of Energy National Energy Technology Laboratory June 2013 INTRODUCTION: The United States (U.S.) Department of Energy (DOE) issued a final environmental impact statement (EIS; DOE/EIS-0473) for the W.A. Parish Post-Combustion CO 2 Capture and Sequestration Project (Parish PCCS Project) in March 2013. DOE announced its decision to provide $167 million in cost-shared funding to NRG Energy, Inc. (NRG) for the

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Seattle, WA Climate Action Champions: Seattle, WA The City of Seattle has long been at the leading edge of environmental innovation. Seattle has been recycling for over 25 years and today has one of the highest recycling and composting rates nationwide. In 2005, Seattle City Light became the first electric utility in the nation to be carbon neutral. Recently, Seattle was recognized as the “most sustainable city in the nation” by STAR communities with a 5-STAR rating and the highest

  18. Advance Patent Waiver W(A)2013-011 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    1 Advance Patent Waiver W(A)2013-011 This document waives certain patent rights the ... Advance Patent Waiver W(A)2013-011 (996.94 KB) More Documents & Publications Advance ...

  19. Advance Patent Waiver W(A)2012-034 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    34 Advance Patent Waiver W(A)2012-034 This document waives certain patent rights the ... Advance Patent Waiver W(A)2012-034 (1.07 MB) More Documents & Publications Advance Patent ...

  20. Advance Patent Waiver W(A)2009-055 | Department of Energy

    Energy.gov [DOE] (indexed site)

    serves the interests of the United States and the general public. Advance Patent Waiver W(A)2009-055 (232.19 KB) More Documents & Publications WA03026EIDUPONTDENEMOURSWaiver...

  1. Mechanism of somatic hypermutation at the WA motif by human DNA...

    Office of Scientific and Technical Information (OSTI)

    at the WA motif by human DNA polymerase eta Citation Details In-Document Search Title: Mechanism of somatic hypermutation at the WA motif by human DNA polymerase eta Authors: ...

  2. File:06-WA-b - Washington Construction Storm Water Permit.pdf...

    OpenEI (Open Energy Information) [EERE & EIA]

    6-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...

  3. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - April 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  4. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - November 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  5. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - May 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  6. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - February 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  7. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - June 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  8. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - February 2009 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  9. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - January 2009 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  10. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - March 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  11. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - October 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  12. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - January 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  13. WA_00_022_CARGILL_DOW_POLYMERS_LLC_Waiver_of_Domestic_and_Fo.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 22_CARGILL_DOW_POLYMERS_LLC_Waiver_of_Domestic_and_Fo.pdf WA_00_022_CARGILL_DOW_POLYMERS_LLC_Waiver_of_Domestic_and_Fo.pdf (1.38 MB) More Documents & Publications WA_04_033_CARGILL_Waiver_of_Patent_Rights_to_CARGILL_DOWN_L.pdf WA_03_029_CARGILL_DOW_LLC_Waiver_of_Domestic_and_Foreign_Pat.pdf WA_02_052_CARGILL_DOW_Waiver_of_Domestic_and_Foreign_Patent_

  14. WA_01_018_IBM_Waiver_of_Governement_US_and_Foreign_Patent_Ri.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 1_018_IBM_Waiver_of_Governement_US_and_Foreign_Patent_Ri.pdf WA_01_018_IBM_Waiver_of_Governement_US_and_Foreign_Patent_Ri.pdf (18.1 MB) More Documents & Publications WA_04_053_IBM_CORP_Waiver_of_the_Government_U.S._and_Foreign.pdf WA_00_015_COMPAQ_FEDERAL_LLC_Waiver_Domestic_and_Foreign_Pat.pdf Advance Patent Waiver W(A)2002-023

  15. WA_03_010_SHELL_SOLAR_INDUSTRIES_Waiver_of_Domestic_and_Fore.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 10_SHELL_SOLAR_INDUSTRIES_Waiver_of_Domestic_and_Fore.pdf WA_03_010_SHELL_SOLAR_INDUSTRIES_Waiver_of_Domestic_and_Fore.pdf (1.41 MB) More Documents & Publications WA_02_039_SHELL_SOLAR_SYSTEMS_Waiver_of_Patent_Rights_Under_.pdf WA_05_059_SHELL_SOLAR_INDUSTRIES_LP_Waiver_of_Domestic_and_F.pdf Advance Patent Waiver W(A)2005-060

  16. WA_03_040_UNITED_TECHNOLOGIES_RESEARCH_CENTER_Waiver_of_Dome.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 40_UNITED_TECHNOLOGIES_RESEARCH_CENTER_Waiver_of_Dome.pdf WA_03_040_UNITED_TECHNOLOGIES_RESEARCH_CENTER_Waiver_of_Dome.pdf (705.58 KB) More Documents & Publications WA_02_054_ADVANCED_TECHNLOGY_MATERIALS_Waiver_of_Domestic_an.pdf WA_02_038_UNITED_TECHNOLOGIES_CORP_Waiver_of_Domestic_and_Fo.pdf Advance Patent Waiver W(A)2006-021

  17. WA_04_047_CATERPILLAR_INC_Waiver_of_Patent_Rights_to_Inventi.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 47_CATERPILLAR_INC_Waiver_of_Patent_Rights_to_Inventi.pdf WA_04_047_CATERPILLAR_INC_Waiver_of_Patent_Rights_to_Inventi.pdf (607.19 KB) More Documents & Publications WA_04_046_CATERPILLAR_INC_Waiver_of_Patent_Rights_to_Inventi.pdf WA_04_071_CATERPILLAR_INC_Waiver_of_Patent_Rights_to_Inventi.pdf Advance Patent Waiver W(A)2005-052

  18. WA_06_016_BP_SOLAR_INTERNATIONAL_Waiver_of_Patent_Rights_Und.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 6_016_BP_SOLAR_INTERNATIONAL_Waiver_of_Patent_Rights_Und.pdf WA_06_016_BP_SOLAR_INTERNATIONAL_Waiver_of_Patent_Rights_Und.pdf (1.22 MB) More Documents & Publications WA_07_016_OSRAM_SYLVANIA_Waiver_of_Patent_Rights_Under_a_DOE.pdf Advance Patent Waiver W(A)2005-060 WA_98_010_SOLAREX_Waiver_of_of_Patent_Rights_Under_an_NREL_S.pdf

  19. WA_97_019_INTEROTEX_LTD_Waiver_of_Domestic_and_Foreign_Right.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 19_INTEROTEX_LTD_Waiver_of_Domestic_and_Foreign_Right.pdf WA_97_019_INTEROTEX_LTD_Waiver_of_Domestic_and_Foreign_Right.pdf (3.96 MB) More Documents & Publications WA_97_020_LENNOX_INDUSTRIES_INC_Waiver_of_Domestic_and_Forei.pdf WA_01_026_UNITED_TECHNOLOGIES_Waiver_of_Domestic_and_Foreign.pdf Advance Patent Waiver W(A)2005-03

  20. Project Reports for Ak Chin Indian Community- 2004 Project

    Office of Energy Efficiency and Renewable Energy (EERE)

    The Ak-Chin Indian Community will study the feasibility of siting a biopower installation on community lands.

  1. EIS-0397: Lyle Falls Fish Passage Project, WA

    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.

  2. probabilistic energy production forecasts

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    energy production forecasts - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary ...

  3. Wind Power Forecasting Data

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Operations Call 2012 Retrospective Reports 2012 Retrospective Reports 2011 Smart Grid Wind Integration Wind Integration Initiatives Wind Power Forecasting Wind Projects Email...

  4. Solar Forecasting Technical Workshop

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Forecasting Technical Workshop August 3, 2016 901 D St SW, Suite #930, Washington, DC Agenda 8:00-8:30 Check-in 8:30-8:45 Welcome & Opening remarks Guohui Yuan, DOE 8:45-9:15 Overview of Motivation and Techniques for Solar Forecasting Jan Kleissl, UCSD 9:15-9:45 Collaborative Research on Solar Power Forecasting: Challenges, Methods, and Assessment Tara Jensen, NCAR 9:45-10:00 Break 10:00-10:30 Machine-learning Based Enhancements for Renewable Energy Forecasting: From Research to Applications

  5. Wind Power Forecasting

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    data Presentations BPA Super Forecast Methodology Related Links Near Real-time Wind Animation Meteorological Data Customer Supplied Generation Imbalance Dynamic Transfer Limits...

  6. ERSUG Meeting: January 12 - 13, 1995 (Richland, WA)

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ERSUG Meeting: January 12 - 13, 1995 (Richland, WA) Dates January 12th & 13th, 1995 Location Pacific Northwest Laboratory Richland, Washington Presentations Summary of ERSUG Meeting January 12 - 13, 1995, Richland, Washington The latest Energy Research Supercomputer Users Group (ERSUG) meeting was held at Pacific Northwest Laboratory in Richland, Washington, on January 12 - 13, 1995. Some of the talks are summarized below. The View from Washington (Tom Kitchens) Tom Kitchens presented the

  7. 1990,"AK","Total Electric Power Industry","All Sources",4208809...

    Energy Information Administration (EIA) (indexed site)

    1990,"AK","Electric Utility","Coal",646430,832,2881 1990,"AK","Electric Utility","Natural Gas",1886585,9,4364 1990,"AK","Electric Utility","Petroleum",281115,1562,592 ...

  8. NREL: Transmission Grid Integration - Forecasting

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Forecasting NREL researchers use solar and wind resource assessment and forecasting techniques to develop models that better characterize the potential benefits and impacts of ...

  9. WA_00_015_COMPAQ_FEDERAL_LLC_Waiver_Domestic_and_Foreign_Pat.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 15_COMPAQ_FEDERAL_LLC_Waiver_Domestic_and_Foreign_Pat.pdf WA_00_015_COMPAQ_FEDERAL_LLC_Waiver_Domestic_and_Foreign_Pat.pdf (1.8 MB) More Documents & Publications WA_01_018_IBM_Waiver_of_Governement_US_and_Foreign_Patent_Ri.pdf Advance Patent Waiver W(A)2002-023 WC_1997_004_CLASS_ADVANCE_WAIVER_Under_Domestic_First_and_Se

  10. WA_02_034_BP_SOLAR_INTERNATIONAL_LLC_Waiver_of_Domestic_and_.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 4_BP_SOLAR_INTERNATIONAL_LLC_Waiver_of_Domestic_and_.pdf WA_02_034_BP_SOLAR_INTERNATIONAL_LLC_Waiver_of_Domestic_and_.pdf (734.86 KB) More Documents & Publications WA_02_035_BP_SOLAR_INTERNATIONAL_Waiver_of_Domestic_and_Fore.pdf WA_06_016_BP_SOLAR_INTERNATIONAL_Waiver_of_Patent_Rights_Und

  11. WA_02_035_BP_SOLAR_INTERNATIONAL_Waiver_of_Domestic_and_Fore.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 5_BP_SOLAR_INTERNATIONAL_Waiver_of_Domestic_and_Fore.pdf WA_02_035_BP_SOLAR_INTERNATIONAL_Waiver_of_Domestic_and_Fore.pdf (1.18 MB) More Documents & Publications WA_06_016_BP_SOLAR_INTERNATIONAL_Waiver_of_Patent_Rights_Und.pdf WA_02_034_BP_SOLAR_INTERNATIONAL_LLC_Waiver_of_Domestic_and_

  12. WA_1993_033_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 3_033_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign.pdf WA_1993_033_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign.pdf (1.15 MB) More Documents & Publications WA_1995_030_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign.pdf WA_1994_003_GOLDEN_PHOTOCON_INC_Waiver_of_Domestic_and_Forei

  13. WA_1995_030_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 5_030_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign.pdf WA_1995_030_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign.pdf (8.82 MB) More Documents & Publications WA_1994_003_GOLDEN_PHOTOCON_INC_Waiver_of_Domestic_and_Forei.pdf WA_1993_033_GOLDEN_PHOTON_INC_Waiver_of_Domestic_and_Foreign

  14. WA_1995_033_BECHTEL_NEVADA_CORPORATION_OR_FCI_ENVIRONMENTAL_.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 3_BECHTEL_NEVADA_CORPORATION_OR_FCI_ENVIRONMENTAL_.pdf WA_1995_033_BECHTEL_NEVADA_CORPORATION_OR_FCI_ENVIRONMENTAL_.pdf (3.94 MB) More Documents & Publications WA_1993_015_XSIRIUS_INC_Waiver_of_the_Governments_US_and_.pdf Advance Patent Waiver W(A)2012-030 Identified Patent Waiver W(I)2008-008

  15. WA_98_001_REYNOLDS_METALS_COMPANY_Waiver_of_Domestic_and_For.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 8_001_REYNOLDS_METALS_COMPANY_Waiver_of_Domestic_and_For.pdf WA_98_001_REYNOLDS_METALS_COMPANY_Waiver_of_Domestic_and_For.pdf (1.1 MB) More Documents & Publications Advance Patent Waiver W(A)1998-014 WA_00_023_ALCOA_INC_Waiver_of_Domestic_and_Foreign_Patent_Ri.pdf U.S. Energy Requirements for Aluminum Production

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Bellingham, WA DOE Zero Energy Ready Home Case Study: TC Legend Homes, Bellingham, WA DOE Zero Energy Ready Home Case Study: TC Legend Homes, Bellingham, WA 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. The 1,055-ft2 two-story production home has 6-in. SIP walls, a 10-in. SIP roof, and ICF foundation walls with R-20 high-density rigid EPS foam under the slab. A single ductless heat pump heats

  17. WA_02_036_DE_NORA_NORTH_AMERICA_Waiver_of_Domestic_and_Foreg.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 6_DE_NORA_NORTH_AMERICA_Waiver_of_Domestic_and_Foreg.pdf WA_02_036_DE_NORA_NORTH_AMERICA_Waiver_of_Domestic_and_Foreg.pdf (778.5 KB) More Documents & Publications WA_02_037_EI__DUPONT_deNEMOURS_and_CO_Waiver_of_Domestic_and.pdf WA_04_034_NUVERA_FUEL_CELLS_INC_Waiver_of_Domestic_and_Forei.pdf WA_04_015_EI_DUPONT_DE_NEMOURS_Waiver_of_Domestic_and_Foreig

  18. WA_04_033_CARGILL_Waiver_of_Patent_Rights_to_CARGILL_DOWN_L.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 33_CARGILL_Waiver_of_Patent_Rights_to_CARGILL_DOWN_L.pdf WA_04_033_CARGILL_Waiver_of_Patent_Rights_to_CARGILL_DOWN_L.pdf (1.05 MB) More Documents & Publications WA_00_022_CARGILL_DOW_POLYMERS_LLC_Waiver_of_Domestic_and_Fo.pdf WA_05_022_DOW_CHEMICAL_COMPANY_Waiver_of_domestic_and_Foreig.pdf WA_03_029_CARGILL_DOW_LLC_Waiver_of_Domestic_and_Foreign_Pat.pdf

  19. RAPID/Roadmap/6-AK-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Construction Storm Water Permit (6-AK-b) From DEC...

  20. RAPID/Roadmap/14-AK-d | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us 401 Water Quality Certification (14-AK-d) In accordance...

  1. Ak-Chin Indian Community - Biomass Feasibiltiy Study

    Energy Saver

    ... AK-CHIN INDIAN COMMUNITY BIOMASS FEASIBILITY STUDY Chicken Litter Test * Digestion - Dry BTU Content - Wet Digestibility - Nutrient Value * Gasification - Energy Values - ...

  2. RAPID/Roadmap/15-AK-c | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Air Quality Permit - Title V Operating Permit (15-AK-c) One of the major initiatives...

  3. RAPID/Roadmap/14-AK-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    RAPIDRoadmap14-AK-b < RAPID | Roadmap Jump to: navigation, search RAPID Regulatory and Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal...

  4. RAPID/Roadmap/3-AK-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    RAPIDRoadmap3-AK-a < RAPID | Roadmap Jump to: navigation, search RAPID Regulatory and Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal Hydropower...

  5. RAPID/Roadmap/17-AK-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    RAPIDRoadmap17-AK-a < RAPID | Roadmap Jump to: navigation, search RAPID Regulatory and Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal...

  6. RAPID/Roadmap/8-AK-c | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    8-AK-c < RAPID | Roadmap Jump to: navigation, search RAPID Regulatory and Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal Hydropower Solar Tools...

  7. RAPID/Roadmap/3-AK-c | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    AK-c < RAPID | Roadmap Jump to: navigation, search RAPID Regulatory and Permitting Information Desktop Toolkit BETA About Bulk Transmission Geothermal Hydropower Solar Tools...

  8. RAPID/Roadmap/18-AK-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Hazardous Waste Permit (18-AK-b) The Alaska Department of Environmental Conservation defers to the...

  9. RAPID/Roadmap/14-AK-c | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Geothermal Hydropower Solar Tools Contribute Contact Us Underground Injection Control Permit (14-AK-c) 14AKCAlaskaUICPermit.pdf Error creating thumbnail: Page number not...

  10. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    August 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric Utility Authority for August 2008. Monthly Electric Utility Sales...

  11. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    December 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric Utility Authority for December 2008. Monthly Electric Utility...

  12. RAPID/Roadmap/18-AK-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Underground Storage Tank Permit (18-AK-a) 18AKA - StorageTankRegistration (1).pdf Error creating...

  13. Microsoft Word - CCP-AK-LANL-006-Revision 13

    Office of Environmental Management (EM)

    P2010-3583 CCP-AK-LANL-006 Central Characterization Program Acceptable Knowledge Summary Report For LOS ALAMOS NATIONAL LABORATORY TA-55 MIXED TRANSURANIC WASTE WASTE STREAMS: ...

  14. 2016 SSL Forecast Report

    Energy.gov [DOE]

    The DOE report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, is a biannual report which models the adoption of LEDs in the U.S. general-lighting market,...

  15. SSL Forecast Report

    Energy.gov [DOE]

    The DOE report, Energy Savings Forecast of Solid-State Lighting in General Illumination Applications, is the latest edition of a biannual report which models the adoption of LEDs in the U.S....

  16. Acquisition Forecast | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Acquisition Forecast Acquisition Forecast Acquisition Forecast It is the policy of the U.S. Department of Energy (DOE) to provide timely information to the public regarding DOE's forecast of future prime contracting opportunities and subcontracting opportunities which are available via the Department's major site and facilities management contractors. This forecast has been expanded to also provide timely status information for ongoing prime contracting actions that are valued in excess of the

  17. (The 1990 run of the WA80 experiment)

    SciTech Connect

    Young, G.R.

    1990-09-18

    The traveler spent six weeks at CERN participating in the 1990 run of the WA80 experiment. The traveler concentrated on trigger electronics for the first two weeks and on operation of the experiment for much of the next four. New electronics designed at ORNL for reading out the new BGO spectrometer were tested with the BGO in beam. Improvements were made, in collaboration with the ORNL engineers who designed the electronics. Plans were made for constructing the electronics in large quantities. Conversations were had with other members of WA80 about the analysis of results from this year's run and our plans for the 1991/1992 runs proposed for CERN. Lengthy conversations were had about the draft of a first paper concerning limits on direct photon production. Finally, the traveler attended an all-day session of the dilepton working group chartered to consider dilepton and photon experiments using heavy-ion beams in CERN's to-be-proposed Large Hadron Collider (LHC). At this meeting the traveler presented recent results from the group working on such a proposal for RHIC and updated his earlier presentation of June 1990 in this working group.

  18. WA_07_040_GRAFTECH_INTERNATIONAL_LTD_Waiver_of_Patent_Rights.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 7_040_GRAFTECH_INTERNATIONAL_LTD_Waiver_of_Patent_Rights.pdf WA_07_040_GRAFTECH_INTERNATIONAL_LTD_Waiver_of_Patent_Rights.pdf (904.04 KB) More Documents & Publications Advance Patent Waiver W(A)2008-004 Next Generation Bipolar Plates for Automotive PEM Fuel Cells Specialty Vehicles and Material Handling Equipment

  19. Advance Patent Waiver W(A)2013-010 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    0 Advance Patent Waiver W(A)2013-010 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ALSTOM POWER, INC under agreement DE-EE0005494, as the DOE has determined that granting such a waiver best serves the interests of the United States and the general public. Advance Patent Waiver W(A)2013-010 (1.05 MB) More Documents & Publications Advance Patent Waiver W(A)2008-044 Advance Patent Waiver

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

    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.

  1. Best Practices Case Study: Devoted Builders, LLC, Mediterrtanean Villas, Pasco,WA

    SciTech Connect

    2010-12-01

    Devoted Builders of Kennewick, WA worked with Building America's BIRA team to achieve the 50% Federal tax credit level energy savings on 81 homes at its Mediterranean Villas community in eastern Washington.

  2. Advance Patent Waiver W(A)2011-039 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2011-039 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by 3M ...

  3. Advance Patent Waiver W(A)2006-028 | Department of Energy

    Office of Environmental Management (EM)

    Advance Patent Waiver W(A)2006-028 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  4. Advance Patent Waiver W(A)2010-017 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2010-017 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  5. Advance Patent Waiver W(A)2011-006 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2011-006 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  6. Advance Patent Waiver W(A)2010-041 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2010-041 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2010-028 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2009-030 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  9. Advance Patent Waiver W(A)2010-037 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2010-037 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  10. Advance Patent Waiver W(A)2013-006 | Department of Energy

    Office of Environmental Management (EM)

    Advance Patent Waiver W(A)2013-006 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  11. Advance Patent Waiver W(A)2008-006 | Department of Energy

    Office of Environmental Management (EM)

    Advance Patent Waiver W(A)2008-006 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  12. Advance Patent Waiver W(A)2012-021 | Department of Energy

    Office of Environmental Management (EM)

    Advance Patent Waiver W(A)2012-021 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2011-034 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  14. Advance Patent Waiver W(A)2010-038 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2010-038 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  15. Advance Patent Waiver W(A)2010-046 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2010-046 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  16. Advance Patent Waiver W(A)2009-058 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Advance Patent Waiver W(A)2009-058 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by ...

  17. Fisher & Paykel Appliances: ENERGY STAR Referral (WA42T26GW1)

    Energy.gov [DOE]

    DOE referred the matter of Fisher & Paykel Appliances residential clothes washer, model WA42T26GW1, to the EPA for appropriate action after DOE testing showed that the model does not meet the ENERGY STAR specification.

  18. WA_97_027_GENERAL_ATOMICS__CORPORATION_Waiver_of_Domestic_an.pdf |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 27_GENERAL_ATOMICS__CORPORATION_Waiver_of_Domestic_an.pdf WA_97_027_GENERAL_ATOMICS__CORPORATION_Waiver_of_Domestic_an.pdf (8.04 MB) More Documents & Publications WA_99_014_UNITED_SOLAR_SYSTEMS_CORP_Waiver_of_Domestic_and_F.pdf Inspection Report: INS-O-00-02 Class Patent Waiver W(C)2004-00

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

    Annual Energy Outlook

    Liquefied Natural Gas Exports to Taiwan (Dollars per Thousand Cubic Feet) Kenai, AK Liquefied Natural Gas Exports to Taiwan (Dollars per Thousand Cubic Feet) Year Jan Feb Mar Apr...

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

    Energy Information Administration (EIA) (indexed site)

    Exports to Taiwan Liquefied Natural Gas (Million Cubic Feet) Kenai, AK Exports to Taiwan Liquefied Natural Gas (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov ...

  1. RAPID/Roadmap/7-AK-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Us Power Plant Siting Process (7-AK-a) Add text. 07AKAPowerPlantSitingConstruction.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number...

  2. RAPID/Roadmap/3-AK-h | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Settlement Lands Leasing (3-AK-h) 03AKHAlaskaNativeClaimsSettlementLandsLeasing.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number...

  3. RAPID/Roadmap/13-AK-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Contribute Contact Us State Land Use Assessment (13-AK-a) 13AKALandUseAssessment.pdf Error creating thumbnail: Page number not in range. Error creating thumbnail: Page number...

  4. RAPID/Roadmap/15-AK-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Air Quality Assessment Process (15-AK-a) The Clean Air Act is the law that defines the...

  5. RAPID/Roadmap/15-AK-b | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    BETA About Bulk Transmission Geothermal Hydropower Solar Tools Contribute Contact Us Air Quality Permit - Minor Permit (15-AK-b) The mission of the Air Permit Program is to...

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

    OpenEI (Open Energy Information) [EERE & EIA]

    of a load upon a highway. Examples of such vehicles are self-propelled cranes, pump trucks, off-road construction equipment or other road maintenance equipment. 6-AK-a.3 -...

  7. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    March 2009 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric Utility Authority for March 2009. Monthly Electric Utility Sales and...

  8. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    OpenEI (Open Energy Information) [EERE & EIA]

    July 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric Utility Authority for July 2008. Monthly Electric Utility Sales and...

  9. RAPID/Roadmap/12-AK-a | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    12-AK-a.1 - Will the Project Affect Streams or Other Bodies of Water? The Anadromous Fish Act (AS 16.05.871-.901) requires that an individual or government agency provide prior...

  10. AK-CHIN INDIAN COMMUNITY BIOMASS FEASIBILITY STUDY

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    4 Program Review Meeting Feasibility Study for Renewable Energy Development on Community Lands Solicitation # DE-PS36-02GO92006 October 20, 2004 © 2004 L. S. Gold & Associates, Inc. Page 2 October 20, 2004 AK-CHIN INDIAN COMMUNITY BIOMASS FEASIBILITY STUDY Topics * Ak-Chin Indian Community * Project Background and Objectives * Project Description and Diagram * Project Team * Proposed Project Schedule * Items To Be Reviewed * Resource Assessment * Digester Technology * Decision Factors *

  11. Baseline and Target Values for PV Forecasts: Toward Improved...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting ... Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting Jie ...

  12. Revamping AK-Ashland gas cleaning system

    SciTech Connect

    Brandes, H.; Koerbel, R.; Haberkamp, K.; Keeton, S.

    1995-07-01

    AK Steel`s (formerly Armco) BOF shop was using a static precipitator for the primary collection. The system was designed for full combustion in the gas collecting hoods. No secondary dust collection was in place. A detailed study on alternative solutions led to a completely different system in 1990, and an order was awarded to Mannesmann Demag Corp. (MDC) in Dec. 1990. The new gas collection system is using suppressed combustion with the capability to collect Co at a later stage. The gas cleaning uses the Mannesmann Demag Baumco scrubber with a venturi throat for gas flow control. All auxiliary components, water treatment plant, electric substations and sludge handling were designed and supplied by MDC. The secondary dust collection covers the hot metal and scrap charging into the BOF`s, reladling, desulfurization and deslagging by a pulse jet baghouse. All emission limits set by the EPA and guaranteed by MDC have been met by the systems installed.

  13. Using Wikipedia to forecast diseases

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Using Wikipedia to forecast diseases Using Wikipedia to forecast diseases Scientists can now monitor and forecast diseases around the globe more effectively by analyzing views of Wikipedia articles. November 13, 2014 Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Del Valle and her team observe findings from their research on disease patterns from analyzing Wikipedia articles. Contact Nancy Ambrosiano Communications Office (505)

  14. UWIG Forecasting Workshop -- Albany (Presentation)

    SciTech Connect

    Lew, D.

    2011-04-01

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

  15. Forecast Energy | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Zip: 94965 Region: Bay Area Sector: Services Product: Intelligent Monitoring and Forecasting Services Year Founded: 2010 Website: www.forecastenergy.net Coordinates:...

  16. The forecast calls for flu

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Science on the Hill: The forecast calls for flu Using mathematics, computer programs, ... We're getting close. Using mathematics, computer programs, statistics and information ...

  17. Solar Energy Market Forecast | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Market Forecast Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Solar Energy Market Forecast AgencyCompany Organization: United States Department of Energy Sector:...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am ...

  19. Intermediate future forecasting system

    SciTech Connect

    Gass, S.I.; Murphy, F.H.; Shaw, S.H.

    1983-12-01

    The purposes of the Symposium on the Department of Energy's Intermediate Future Forecasting System (IFFS) were: (1) to present to the energy community details of DOE's new energy market model IFFS; and (2) to have an open forum in which IFFS and its major elements could be reviewed and critiqued by external experts. DOE speakers discussed the total system, its software design, and the modeling aspects of oil and gas supply, refineries, electric utilities, coal, and the energy economy. Invited experts critiqued each of these topics and offered suggestions for modifications and improvement. This volume documents the proceedings (papers and discussion) of the Symposium. Separate abstracts have been prepared for each presentation for inclusion in the Energy Data Base.

  20. Advance Patent Waiver W(A)2009-001 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    01 Advance Patent Waiver W(A)2009-001 This document waives certain patent rights the Department of Energy (DOE) has to inventions conceived or first actually reduced to practice by GE GLOBAL RESEARCHH CENTER under agreement DE-FC36-GO18085, as the DOE has determined that granting such a waiver best serves the interests of the United States and the general public. Advance Patent Waiver W(A)2009-001 (166.78 KB) More Documents & Publications Identified Patent Waiver W(I)2009-001 Advance Patent

  1. W.A. Parish Post-Combustion CO{sub 2} Capture and Sequestration Project

    Office of Scientific and Technical Information (OSTI)

    Phase 1 Definition (Other) | SciTech Connect Other: W.A. Parish Post-Combustion CO{sub 2} Capture and Sequestration Project Phase 1 Definition Citation Details In-Document Search Title: W.A. Parish Post-Combustion CO{sub 2} Capture and Sequestration Project Phase 1 Definition For a secure and sustainable energy future, the United States (U.S.) must reduce its dependence on imported oil and reduce its emissions of carbon dioxide (CO{sub 2}) and other greenhouse gases (GHGs). To meet these

  2. Science on Tap - Forecasting illness

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Science on Tap - Forecasting illness Science on Tap - Forecasting illness WHEN: Mar 17, 2016 5:30 PM - 7:00 PM WHERE: UnQuarked Wine Room 145 Central Park Square, Los Alamos, New Mexico 87544 USA CONTACT: Linda Anderman (505) 665-9196 CATEGORY: Bradbury INTERNAL: Calendar Login Event Description Mark your calendars for this event held every third Thursday from 5:30 to 7 p.m. A short presentation is followed by a lively discussion on a different subject each month. Forecasting the flu (and other

  3. Acquisition Forecast Download | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Acquisition Forecast Download Acquisition Forecast Download Click on the link to download a copy of the DOE HQ Acquisition Forecast. Acquisition-Forecast-2016-11-10.xlsx (70.03 KB) More Documents & Publications National Nuclear Security Administration - Juliana Heynes Small Business Program Manager Directory EA-1900: Notice of Availability of a Draft Environmental Assessment

  4. Value of Wind Power Forecasting

    SciTech Connect

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

    2011-04-01

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

  5. 2016 Solar Forecasting Workshop | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Solar Forecasting Workshop 2016 Solar Forecasting Workshop On August 3, 2016, the SunShot Initiative's systems integration subprogram hosted the Solar Forecasting Workshop to convene experts in the areas of bulk power system operations, distribution system operations, weather and solar irradiance forecasting, and photovoltaic system operation and modeling. The goal was to identify the technical challenges and opportunities in solar forecasting as a capability that can significantly reduce the

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

    Office of Energy Efficiency and Renewable Energy (EERE)

    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...

  7. Ak-Chin Indian Community Biomass Feasiiblity Study

    SciTech Connect

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

    2005-12-31

    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.

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

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

  9. Picture of the Week: Forecasting Flu

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    3 Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? March 6, 2016 flu epidemics modellled using social media Watch the video on YouTube. Forecasting Flu What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus or Zika were going to spread? Using real-time data from Wikipedia and social media, Sara del

  10. EIA lowers forecast for summer gasoline prices

    Energy Information Administration (EIA) (indexed site)

    EIA lowers forecast for summer gasoline prices U.S. gasoline prices are expected to be ... according to the new monthly forecast from the U.S. Energy Information Administration. ...

  11. Wind Forecasting Improvement Project | Department of Energy

    Energy Saver

    Forecasting Improvement Project Wind Forecasting Improvement Project October 3, 2011 - 12:12pm Addthis This is an excerpt from the Third Quarter 2011 edition of the Wind Program ...

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

    Energy.gov [DOE]

    Case study of a DOE Zero Energy Ready Home in Seattle, WA, that scored HERS 34 without PV. This 2,000-square-foot 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.

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

    Office of Energy Efficiency and Renewable Energy (EERE)

    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-square-foot 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.

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

    Office of Energy Efficiency and Renewable Energy (EERE)

    Case-study of a DOE Zero Energy Ready Home on Whidbey Island, WA, that scored HERS 45 without PV. This 2,908-square-foot 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.

  15. HIA 2015 DOE Zero Energy Ready Home Case Study: Dwell Development, Reclaimed Modern, Seattle, WA

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Dwell Development Reclaimed Modern Seattle, WA DOE ZERO ENERGY READY HOME(tm) The U.S. Department of Energy invites home builders across the country to meet the extraordinary levels of excellence and quality specified in DOE's Zero Energy Ready Home program (formerly known as Challenge Home). Every DOE Zero Energy Ready Home starts with ENERGY STAR Certified Homes Version 3.0 for an energy-efficient home built on a solid foundation of building science research. Advanced technologies are designed

  16. July 2016 Systems Integration Solar Forecasting:

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    2016 Systems Integration Solar Forecasting: Maximizing its value for grid integration Introduction The forecasting of power generated by variable energy resources such as wind and solar has been the focus of academic and industrial research and development for as long as significant amounts of these renewable energy resources have been connected to the electric grid. The progress of forecasting capabilities has largely followed the penetration of the respective resources, with wind forecasting

  17. Forecasting Water Quality & Biodiversity

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Forecasting Water Quality & Biodiversity March 25, 2015 Cross-cutting Sustainability Platform Review Principle Investigator: Dr. Henriette I. Jager Organization: Oak Ridge National Laboratory This presentation does not contain any proprietary, confidential, or otherwise restricted information 2015 DOE Bioenergy Technologies Office (BETO) Project Peer Review Goal Statement Addresses the following MYPP BETO goals:  Advance scientific methods and models for measuring and understanding

  18. Recovery Act: Waste Energy Project at AK Steel Corporation Middletown

    SciTech Connect

    Joyce, Jeffrey

    2012-06-30

    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.

  19. Uncertainty Reduction in Power Generation Forecast Using Coupled...

    Office of Scientific and Technical Information (OSTI)

    Research Org: Pacific Northwest National Laboratory (PNNL), Richland, WA (US) Sponsoring Org: USDOE Country of Publication: United States Language: English Subject: Power system ...

  20. ARM Climate Modeling Best Estimate Barrow, AK (ARMBE-ATM NSAC1...

    Office of Scientific and Technical Information (OSTI)

    Barrow, AK (ARMBE-ATM NSAC1 V4) Title: ARM Climate Modeling Best Estimate Barrow, AK (ARMBE-ATM NSAC1 V4) The ARM CMBE-ATM Xie, McCoy, Klein et al. data file contains a best ...

  1. Price of Sumas, WA Liquefied Natural Gas Imports from Canada (Dollars per

    Energy Information Administration (EIA) (indexed site)

    Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's -- 8.42 6.22 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: U.S. Price of Liquefied Natural Gas Imports by Point of Entry Sumas, WA Natural Gas Imports by Pipeline

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

    Energy Information Administration (EIA) (indexed site)

    Imports From Canada (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Sumas, WA Natural Gas Pipeline Imports From Canada (MMcf)",1,"Annual",2015 ,"Release Date:","10/31/2016" ,"Next Release Date:","11/30/2016" ,"Excel File

  3. Kenai, AK Liquefied Natural Gas Exports (Million Cubic Feet)

    Energy Information Administration (EIA) (indexed site)

    (Million Cubic Feet) Kenai, AK Liquefied Natural Gas Exports (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2014 1,886 2,809 2,846 2,886 2,884 2015 2,748 2,754 2,753 2,753 2,755 2,755 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: U.S.

  4. The Value of Wind Power Forecasting

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

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

  5. July 2016 Systems Integration Solar Forecasting:

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    ... Those costs comprise fuel costs from expensive generators ... an improved-accuracy forecast of the solar power generation. ... analog ensemble for short-term probabilistic solar power ...

  6. NREL: Resource Assessment and Forecasting Home Page

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    are used to plan and develop renewable energy technologies and support climate change research. Learn more about NREL's resource assessment and forecasting research:...

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

    Energy.gov [DOE] (indexed site)

    There is no cost to participate and all applicants are encouraged to attend. To join the ... Related Articles Upcoming Funding Opportunity for Wind Forecasting Improvement Project in ...

  8. Module 6 - Metrics, Performance Measurements and Forecasting...

    Energy.gov [DOE] (indexed site)

    This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices. In addition, this module will outline forecasting tools such as ...

  9. Development and Demonstration of Advanced Forecasting, Power...

    Energy.gov [DOE] (indexed site)

    Demonstration of Advanced Forecasting, Power and Environmental Planning and Management Tools and Best Practices 63wateruseoptimizationprojectanlgasper.ppt (7.72 MB) More ...

  10. NREL: Resource Assessment and Forecasting - Webmaster

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    email address: Your message: Send Message Printable Version Resource Assessment & Forecasting Home Capabilities Facilities Working with Us Research Staff Data & Resources Did...

  11. Forecast and Funding Arrangements - Hanford Site

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Annual Waste Forecast and Funding Arrangements About Us Hanford Site Solid Waste Acceptance Program What's New Acceptance Criteria Acceptance Process Becoming a new Hanford...

  12. The Alaska Arctic Vegetation Archive (AVA-AK)

    DOE PAGES [OSTI]

    Walker, Donald; Breen, Amy; Druckenmiller, Lisa; Wirth, Lisa W.; Fisher, Will; Raynolds, Martha K.; Sibik, Jozef; Walker, Marilyn D.; Hennekens, Stephan; Boggs, Keith; et al

    2016-05-17

    The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and providesmore » access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. Here, we present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis.« less

  13. Sensing, Measurement, and Forecasting | Grid Modernization | NREL

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Sensing, Measurement, and Forecasting NREL measures weather resources and power systems, forecasts renewable resources and grid conditions, and converts measurements into operational intelligence to support a modern grid. Photo of solar resource monitoring equipment Modernizing the grid involves assessing its health in real time, predicting its behavior and potential disruptions, and quickly responding to events-which requires understanding vital parameters throughout the electric

  14. File:NREL-ak-50m.pdf | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    Help Apps Datasets Community Login | Sign Up Search File Edit with form History File:NREL-ak-50m.pdf Jump to: navigation, search File File history File usage Alaska Mainland...

  15. File:NREL-ak2-50m.pdf | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    File Edit with form History File:NREL-ak2-50m.pdf Jump to: navigation, search File File history File usage Alaska Panhandle Annual Average Wind Speed at 50 Meters (PDF) Size of...

  16. Study forecasts disappearance of conifers due to climate change

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in ...

  17. Data Collection and Comparison with Forecasted Unit Sales of...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types Data Collection and Comparison with Forecasted Unit Sales of Five Lamp Types PDF icon Data Collection ...

  18. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen MJ ... Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory M Jensen, ...

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

    SciTech Connect

    Hunn, John D; Kercher, Andrew K

    2005-06-01

    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.

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

    SciTech Connect

    Kercher, Andrew K; Hunn, John D

    2005-06-01

    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.

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

    SciTech Connect

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

    2012-06-05

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

  2. 1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.

    SciTech Connect

    United States. Bonneville Power Administration.

    1994-02-01

    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.

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

    SciTech Connect

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

    2013-10-01

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

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

    SciTech Connect

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

    2011-10-01

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

  5. Offshore Lubricants Market Forecast | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

    Offshore Lubricants Market Forecast Home There are currently no posts in this category. Syndicate...

  6. Coal Fired Power Generation Market Forecast | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

    Coal Fired Power Generation Market Forecast Home There are currently no posts in this category. Syndicate...

  7. Flood Forecasting in River System Using ANFIS

    SciTech Connect

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  8. ARM - CARES - Tracer Forecast for CARES

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    News & Press Backgrounder (PDF, 1.45MB) G-1 Aircraft Fact Sheet (PDF, 1.3MB) Contacts Rahul Zaveri, Lead Scientist Tracer Forecasts for CARES This webpage contains 72-hr...

  9. Text-Alternative Version LED Lighting Forecast

    Energy.gov [DOE]

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

  10. energy data + forecasting | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

    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...

  11. Nambe Pueblo Water Budget and Forecasting model.

    SciTech Connect

    Brainard, James Robert

    2009-10-01

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

  12. Funding Opportunity Announcement: Solar Forecasting 2 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Energy Solar Forecasting 2 Funding Opportunity Announcement: Solar Forecasting 2 Subprogram: Systems Integration Funding Number: DE-FOA-0001649 Funding Amount: $10 million Description The Solar Forecasting 2 funding program will support projects that enable grid operators to better forecast how much solar energy will be added to the grid and accelerate the integration of these forecasts into energy management systems used by grid operators and utility companies. These tools will enable grid

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

    Energy.gov [DOE]

    This EIS evaluates the environmental impacts of a proposal to provide financial assistance for a project proposed by NRG Energy, Inc (NRG). DOE selected 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 County, Texas, for use in enhanced oil recovery operations; and demonstrate monitoring techniques to verify the permanence of geologic CO2 storage.

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

    SciTech Connect

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

    2014-05-01

    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.

  15. 1994 Solid waste forecast container volume summary

    SciTech Connect

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

    1994-09-01

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

  16. ARM Climate Modeling Best Estimate Barrow, AK (ARMBE-ATM NSAC1 V4)

    Office of Scientific and Technical Information (OSTI)

    (Dataset) | Data Explorer Barrow, AK (ARMBE-ATM NSAC1 V4) Title: ARM Climate Modeling Best Estimate Barrow, AK (ARMBE-ATM NSAC1 V4) The ARM CMBE-ATM [Xie, McCoy, Klein et al.] data file contains a best estimate of several selected atmospheric quantities from ACRF observations and NWP analysis data. Authors: McCoy, Renata ; Xie, Shaocheng Publication Date: 2013-12-26 OSTI Identifier: 1039932 DOE Contract Number: DE-AC05-00OR22725 Resource Type: Dataset Data Type: Numeric Data Research Org:

  17. ARM Climate Modeling Best Estimate Barrow, AK (ARMBE-ATM NSAC1 V4)

    Office of Scientific and Technical Information (OSTI)

    (Dataset) | Data Explorer Barrow, AK (ARMBE-ATM NSAC1 V4) Title: ARM Climate Modeling Best Estimate Barrow, AK (ARMBE-ATM NSAC1 V4) The ARM CMBE-ATM [Xie, McCoy, Klein et al.] data file contains a best estimate of several selected atmospheric quantities from ACRF observations and NWP analysis data. Authors: McCoy, Renata ; Xie, Shaocheng Publication Date: 2013-12-26 OSTI Identifier: 1111572 DOE Contract Number: DE-AC05-00OR22725 Resource Type: Dataset Data Type: Numeric Data Research Org:

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

    Energy Information Administration (EIA) (indexed site)

    Cubic Feet) 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 Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2000's -- 12.12 -- -- 2010's -- - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: U.S. Price

  19. Kenai, AK Liquefied Natural Gas Exports to Taiwan (Dollars per Thousand

    Energy Information Administration (EIA) (indexed site)

    Cubic Feet) Taiwan (Dollars per Thousand Cubic Feet) Kenai, AK Liquefied Natural Gas Exports to Taiwan (Dollars per Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 2010's -- 7.49 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: U.S. Price of Liquefied Natural Gas Exports by Point of Exit Kenai, AK

  20. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    SciTech Connect

    Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.

  1. Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting

    DOE PAGES [OSTI]

    Zhang, Jie; Hodge, Bri -Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat; Black, Jon; Tedesco, John

    2015-11-10

    Accurate solar photovoltaic (PV) power forecasting allows utilities to reliably utilize solar resources on their systems. However, to truly measure the improvements that any new solar forecasting methods provide, it is important to develop a methodology for determining baseline and target values for the accuracy of solar forecasting at different spatial and temporal scales. This paper aims at developing a framework to derive baseline and target values for a suite of generally applicable, value-based, and custom-designed solar forecasting metrics. The work was informed by close collaboration with utility and independent system operator partners. The baseline values are established based onmore » state-of-the-art numerical weather prediction models and persistence models in combination with a radiative transfer model. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of PV power output. The proposed reserve-based methodology is a reasonable and practical approach that can be used to assess the economic benefits gained from improvements in accuracy of solar forecasting. Lastly, the financial baseline and targets can be translated back to forecasting accuracy metrics and requirements, which will guide research on solar forecasting improvements toward the areas that are most beneficial to power systems operations.« less

  2. The Value of Improved Short-Term Wind Power Forecasting

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... up-ramp reserves c down cost in MWh of down-ramp reserves R down MW range for ... power forecasting and the increased gas usage that comes with less-accurate forecasting. ...

  3. DOE Taking Wind Forecasting to New Heights | Department of Energy

    Energy Saver

    Taking Wind Forecasting to New Heights DOE Taking Wind Forecasting to New Heights May 18, 2015 - 3:24pm Addthis A 2013 study conducted for the U.S. Department of Energy (DOE) by ...

  4. Solar Forecasting Gets a Boost from Watson, Accuracy Improved...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM ...

  5. PBL FY 2003 Second Quarter Review Forecast of Generation Accumulated...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    the rate period (i.e., FY 2002-2006), a forecast of that end-of-year Accumulated Net Revenue (ANR) will be completed. If the ANR at the end of the forecast year falls below the...

  6. Wind power forecasting in U.S. electricity markets.

    SciTech Connect

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

    2010-04-01

    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.

  7. Wind power forecasting in U.S. Electricity markets

    SciTech Connect

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

    2010-04-15

    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)

  8. Improving the Accuracy of Solar Forecasting Funding Opportunity |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Improving the Accuracy of Solar Forecasting Funding Opportunity Improving the Accuracy of Solar Forecasting Funding Opportunity Through the Improving the Accuracy of Solar Forecasting Funding Opportunity, DOE is funding solar projects that are helping utilities, grid operators, solar power plant owners, and other stakeholders better forecast when, where, and how much solar power will be produced at the desired locations in the United States. More accurate solar

  9. Combined Heat And Power Installation Market Forecast | OpenEI...

    OpenEI (Open Energy Information) [EERE & EIA]

    Combined Heat And Power Installation Market Forecast Home There are currently no posts in this category. Syndicate...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    ANL Software Improves Wind Power Forecasting ANL Software Improves Wind Power Forecasting May 1, 2012 - 3:19pm Addthis This is an excerpt from the Second Quarter 2012 edition of the Wind Program R&D Newsletter. Since 2008, Argonne National Laboratory and INESC TEC (formerly INESC Porto) have conducted a research project to improve wind power forecasting and better use of forecasting in electricity markets. One of the main results from the project is ARGUS PRIMA (PRediction Intelligent

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

    SciTech Connect

    Hodge, B. M.; Milligan, M.

    2011-07-01

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

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    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

  13. Issues in midterm analysis and forecasting, 1996

    SciTech Connect

    1996-08-01

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

  14. Issues in midterm analysis and forecasting 1998

    SciTech Connect

    1998-07-01

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

  15. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting

    SciTech Connect

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-10-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  16. The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

    SciTech Connect

    Wilczak, James M.; Finley, Cathy; Freedman, Jeff; Cline, Joel; Bianco, L.; Olson, J.; Djalaova, I.; Sheridan, L.; Ahlstrom, M.; Manobianco, J.; Zack, J.; Carley, J.; Benjamin, S.; Coulter, R. L.; Berg, Larry K.; Mirocha, Jeff D.; Clawson, K.; Natenberg, E.; Marquis, M.

    2015-10-30

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

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

    SciTech Connect

    Lundquist, J; Glascoe, L; Obrecht, J

    2010-03-18

    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.

  18. 2013,1,"AK",3522,"Chugach Electric Assn Inc",0,,,,0,0,,,,0,0...

    Energy Information Administration (EIA) (indexed site)

    ...034,15248,226,1,0,15475 2013,1,"VT",7601,"Green Mountain Power Corp",39.65,10.83,0,0,50.48... 2013,1,"WA",15500,"Puget Sound Energy Inc",334.185,64.655,0,0,398.84,26734.802,...

  19. HIA 2015 DOE Zero Energy Ready Home Case Study: TC Legend Homes, Bellingham Power House, Bellingham, WA

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Bellingham Power House Bellingham, WA DOE ZERO ENERGY READY HOME(tm) The U.S. Department of Energy invites home builders across the country to meet the extraordinary levels of excellence and quality specified in DOE's Zero Energy Ready Home program (formerly known as Challenge Home). Every DOE Zero Energy Ready Home starts with ENERGY STAR Certified Homes Version 3.0 for an energy-efficient home built on a solid foundation of building science research. Advanced technologies are designed in to

  20. Analyses of soils at commercial radioactive-waste-disposal sites. [Barnwell, SC; Richland, WA

    SciTech Connect

    Piciulo, P.L.; Shea, C.E.; Barletta, R.E.

    1982-01-01

    Brookhaven National Laboratory, in order to provide technical assistance to the NRC, has measured a number of physical and chemical characteristics of soils from two currently operating commercial radioactive waste disposal sites; one at Barnwell, SC, and the other near Richland, WA. Soil samples believed to be representative of the soil that will contact the buried waste were collected and analyzed. Earth resistivities (field measurements), from both sites, supply information to identify variations in subsurface material. Barnwell soil resistivities (laboratory measurements) range from 3.6 x 10/sup 5/ ohm-cm to 8.9 x 10/sup 4/ ohm-cm. Soil resistivities of the Hanford sample vary from 3.0 x 10/sup 5/ ohm-cm to 6.6 x 10/sup 3/ ohm-cm. The Barnwell and Hanford soil pH ranges from 4.8 to 5.4 and from 4.0 to 7.2 respectively. The pH of a 1:2 mixture of soil to 0.01 M CaCl/sub 2/ resulted in a pH for the Barnwell samples of 3.9 +- 0.1 and for the Hanford samples of 7.4 +- 0.2. These values are comparable to the pH measurements of the water extract of the soils used for the analyses of soluble ion content of the soils. The exchange acidity of the soils was found to be approximately 7 mg-eq per 100 g of dry soil for clay material from Barnwell, whereas the Hanford soils showed an alkaline reaction. Aqueous extracts of saturated pastes were used to determine the concentrations of the following ions: Ca/sup 2 +/, Mg/sup 2 +/, K/sup +/, Na/sup +/, HCO/sub 3//sup -/, SO/sub 4//sup =/, and Cl/sup -/. The sulfide content of each of the soils was measured in a 1:2.5 mixture of soil to an antioxidant buffer solution. The concentrations of soluble ions found in the soils from both sites are consistent with the high resistivities.

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

    SciTech Connect

    Zhang, J.; Hodge, B. M.

    2014-04-01

    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.

  2. Forecasting hotspots using predictive visual analytics approach

    SciTech Connect

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

    2014-12-30

    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.

  3. Global disease monitoring and forecasting with Wikipedia

    SciTech Connect

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

  4. A survey on wind power ramp forecasting.

    SciTech Connect

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J.

    2011-02-23

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

  5. Global disease monitoring and forecasting with Wikipedia

    DOE PAGES [OSTI]

    Generous, Nicholas; Fairchild, Geoffrey; Deshpande, Alina; Del Valle, Sara Y.; Priedhorsky, Reid; Salathé, Marcel

    2014-11-13

    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: accessmore » logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.« less

  6. Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

    SciTech Connect

    Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

    2015-08-05

    Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

  7. HIA 2015 DOE Zero Energy Ready Home Case Study: Clifton View Homes, Marine Drive and Port Hadlcok, Coupeville and Port Hadlock WA

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Homes Marine Drive and Port Hadlock Coupeville, WA Port Hadlock, WA DOE ZERO ENERGY READY HOME(tm) The U.S. Department of Energy invites home builders across the country to meet the extraordinary levels of excellence and quality specified in DOE's Zero Energy Ready Home program (formerly known as Challenge Home). Every DOE Zero Energy Ready Home starts with ENERGY STAR Certified Homes Version 3.0 for an energy-efficient home built on a solid foundation of building science research. Advanced

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

    SciTech Connect

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

    2013-10-01

    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.

  9. Wind speed forecasting in the central California wind resource area

    SciTech Connect

    McCarthy, E.F.

    1997-12-31

    A wind speed forecasting program was implemented in the summer seasons of 1985 - 87 in the Central California Wind Resource Area (WRA). The forecasting program is designed to use either meteorological observations from the WRA and local upper air observations or upper air observations alone to predict the daily average windspeed at two locations. Forecasts are made each morning at 6 AM and are valid for a 24 hour period. Ease of use is a hallmark of the program as the daily forecast can be made using data entered into a programmable HP calculator. The forecasting program was the first step in a process to examine whether the electrical energy output of an entire wind power generation facility or defined subsections of the same facility could be predicted up to 24 hours in advance. Analysis of the results of the summer season program using standard forecast verification techniques show the program has skill over persistence and climatology.

  10. Kenai, AK Liquefied Natural Gas Exports to China (Million Cubic Feet)

    Energy Information Administration (EIA) (indexed site)

    to China (Million Cubic Feet) Kenai, AK Liquefied Natural Gas Exports to China (Million Cubic Feet) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2011 1,127 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: U.S.

  11. Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Office of Scientific and Technical Information (OSTI)

    (BNL) Field Campaign Report (Technical Report) | SciTech Connect Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report Citation Details In-Document Search Title: Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf] campaign was scheduled to take place from 15 July

  12. Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory

    Office of Scientific and Technical Information (OSTI)

    (BNL) Field Campaign Report (Technical Report) | SciTech Connect Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report Citation Details In-Document Search Title: Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) Field Campaign Report The Radar Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory (BNL) [http://www.arm.gov/campaigns/osc2013rwpcf] campaign was scheduled to take place from 15 July

  13. Funding Opportunity Announcement for Wind Forecasting Improvement Project

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    in Complex Terrain | Department of Energy Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain Funding Opportunity Announcement for Wind Forecasting Improvement Project in Complex Terrain April 4, 2014 - 9:47am Addthis On April 4, 2014 the U.S. Department of Energy announced a $2.5 million funding opportunity entitled "Wind Forecasting Improvement Project in Complex Terrain." By researching the physical processes that take place in complex

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report Wind Forecast Improvement Project Southern Study Area Final Report.pdf (15.76 MB) More Documents & Publications QER - Comment of Edison Electric Institute (EEI) 1 QER - Comment of Canadian Hydropower Association Team roster: Dan Paikowsky, Management; Christian Bain, Entrepreneurship; Noah Meunier, Mechanical Engineering &

  15. Module 6 - Metrics, Performance Measurements and Forecasting | Department

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    of Energy 6 - Metrics, Performance Measurements and Forecasting Module 6 - Metrics, Performance Measurements and Forecasting This module focuses on the metrics and performance measurement tools used in Earned Value. This module reviews metrics such as cost and schedule variance along with cost and schedule performance indices. In addition, this module will outline forecasting tools such as estimate to complete (ETC) and estimate at completion (EAC). Begin Module >> (471

  16. DOE Benefits Forecasts: Report of the External Peer Review Panel |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Benefits Forecasts: Report of the External Peer Review Panel DOE Benefits Forecasts: Report of the External Peer Review Panel A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts. Report of the External Peer Review Panel (777.84 KB) More Documents & Publications Industrial Technologies Funding Profile by Subprogram Survey of Emissions Models for Distributed Combined Heat and Power

  17. Upcoming Funding Opportunity for Wind Forecasting Improvement Project in

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Complex Terrain | Department of Energy Wind Forecasting Improvement Project in Complex Terrain Upcoming Funding Opportunity for Wind Forecasting Improvement Project in Complex Terrain February 12, 2014 - 10:47am Addthis On February 11, 2014 the Wind Program announced a Notice of Intent to issue a funding opportunity entitled "Wind Forecasting Improvement Project in Complex Terrain." By researching the physical processes that take place in complex terrain, this funding would improve

  18. Validation of Global Weather Forecast and Climate Models Over...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Validation of Global Weather Forecast and Climate Models Over the North Slope of Alaska Xie, Shaocheng Lawrence Livermore National Laboratory Klein, Stephen Lawrence Livermore ...

  19. DOE Publishes New Forecast of Energy Savings from LED Lighting...

    Office of Environmental Management (EM)

    Addthis Related Articles DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting DOE Publishes Pricing and Efficacy Trend Analysis for Utility Program ...

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

    SciTech Connect

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

    2012-09-01

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

  1. NREL: Resource Assessment and Forecasting - Data and Resources

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Data and Resources National Solar Radiation Database NREL resource assessment and forecasting research information is available from the following sources. Renewable Resource Data ...

  2. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis: Preprint

    SciTech Connect

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-12-08

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  3. Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis

    SciTech Connect

    Cheung, WanYin; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Sun, Qian; Lehman, Brad

    2015-10-02

    Uncertainties associated with solar forecasts present challenges to maintain grid reliability, especially at high solar penetrations. This study aims to quantify the errors associated with the day-ahead solar forecast parameters and the theoretical solar power output for a 51-kW solar power plant in a utility area in the state of Vermont, U.S. Forecasts were generated by three numerical weather prediction (NWP) models, including the Rapid Refresh, the High Resolution Rapid Refresh, and the North American Model, and a machine-learning ensemble model. A photovoltaic (PV) performance model was adopted to calculate theoretical solar power generation using the forecast parameters (e.g., irradiance, cell temperature, and wind speed). Errors of the power outputs were quantified using statistical moments and a suite of metrics, such as the normalized root mean squared error (NRMSE). In addition, the PV model's sensitivity to different forecast parameters was quantified and analyzed. Results showed that the ensemble model yielded forecasts in all parameters with the smallest NRMSE. The NRMSE of solar irradiance forecasts of the ensemble NWP model was reduced by 28.10% compared to the best of the three NWP models. Further, the sensitivity analysis indicated that the errors of the forecasted cell temperature attributed only approximately 0.12% to the NRMSE of the power output as opposed to 7.44% from the forecasted solar irradiance.

  4. DOE Announces Webinars on Solar Forecasting Metrics, the DOE...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    DOE Announces Webinars on Solar Forecasting Metrics, the DOE ... from adopting the latest energy efficiency and renewable ... to liquids technology, advantages of using natural gas, ...

  5. Radar Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    forecasts for solar-energy applications and 2) to provide vertical profiling capabilities for the study of dynamics (i.e., vertical velocity) and hydrometeors in winter storms. ...

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

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Bonneville Power Administration Power Business Line Generation (PBL) Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

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

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    2003 Bonneville Power Administration Power Business Line Generation Accumulated Net Revenue Forecast for Financial-Based Cost Recovery Adjustment Clause (FB CRAC) and Safety-Net...

  8. Improving the Accuracy of Solar Forecasting Funding Opportunity...

    Energy Saver

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

  9. Evaluation of contaminant flux rates from sediments of Sinclair Inlet, WA, using a benthic flux sampling device. Final report

    SciTech Connect

    Chadwick, D.B.; Lieberman, S.H.; Reimers, C.E.; Young, D.

    1993-02-01

    A Benthic Flux Sampling Device (BFSD) was demonstrated on site to determine the mobility of contaminants in sediments off the Puget Sound Naval Shipyard (PSNS) in Sinclair Inlet, WA. Quantification of toxicant flux from the sediments will support ongoing assessment studies and facilitate the design of appropriate remediation strategies, if required. In general, where release of contaminants was found, the measured rates do not represent a significant source relative to other major inputs such as sewer discharges, nonpoint source runoff, and marinas. They may, however, represent an exposure pathway for benthic biota with a subsequent potential for toxicological effects and/or bioaccumulation. Environmental assessment, CIVAPP:Toxicity, CIVAPP:Marine chemistry, Hazardous waste.

  10. Selected papers on fuel forecasting and analysis

    SciTech Connect

    Gordon, R.L.; Prast, W.G.

    1983-05-01

    Of the 19 presentations at this seminar, covering coal, uranium, oil, and gas issues as well as related EPRI research projects, eleven papers are published in this volume. Nine of the papers primarily address coal-market analysis, coal transportation, and uranium supply. Two additional papers provide an evaluation and perspective on the art and use of coal-supply forecasting models and on the relationship between coal and oil prices. The authors are energy analysts and EPRI research contractors from academia, the consulting profession, and the coal industry. A separate abstract was prepared for each of the 11 papers.

  11. Voluntary Green Power Market Forecast through 2015

    SciTech Connect

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

    2010-05-01

    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.

  12. Technical analysis in short-term uranium price forecasting

    SciTech Connect

    Schramm, D.S.

    1990-03-01

    As market participants anticipate the end of the current uranium price decline and its subsequent reversal, increased attention will be focused upon forecasting future price movements. Although uranium is economically similar to other mineral commodities, it is questionable whether methodologies used to forecast price movements of such commodities may be successfully applied to uranium.

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

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

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

    SciTech Connect

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

    2012-07-01

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

  15. 3TIER Environmental Forecast Group Inc 3TIER | Open Energy Information

    OpenEI (Open Energy Information) [EERE & EIA]

    TIER Environmental Forecast Group Inc 3TIER Jump to: navigation, search Name: 3TIER Environmental Forecast Group Inc (3TIER) Place: Seattle, Washington Zip: 98121 Sector: Renewable...

  16. Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

    Reports and Publications

    2010-01-01

    The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

  17. Incorporating Forecast Uncertainty in Utility Control Center

    SciTech Connect

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

    2014-07-09

    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)

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

    SciTech Connect

    Mendes, J.; Bessa, R.J.; Keko, H.; Sumaili, J.; Miranda, V.; Ferreira, C.; Gama, J.; Botterud, A.; Zhou, Z.; Wang, J.

    2011-12-06

    Wind power forecasting (WPF) provides important inputs to power system operators and electricity market participants. It is therefore not surprising that WPF has attracted increasing interest within the electric power industry. In this report, we document our research on improving statistical WPF algorithms for point, uncertainty, and ramp forecasting. Below, we provide a brief introduction to the research presented in the following chapters. For a detailed overview of the state-of-the-art in wind power forecasting, we refer to [1]. Our related work on the application of WPF in operational decisions is documented in [2]. Point forecasts of wind power are highly dependent on the training criteria used in the statistical algorithms that are used to convert weather forecasts and observational data to a power forecast. In Chapter 2, we explore the application of information theoretic learning (ITL) as opposed to the classical minimum square error (MSE) criterion for point forecasting. In contrast to the MSE criterion, ITL criteria do not assume a Gaussian distribution of the forecasting errors. We investigate to what extent ITL criteria yield better results. In addition, we analyze time-adaptive training algorithms and how they enable WPF algorithms to cope with non-stationary data and, thus, to adapt to new situations without requiring additional offline training of the model. We test the new point forecasting algorithms on two wind farms located in the U.S. Midwest. Although there have been advancements in deterministic WPF, a single-valued forecast cannot provide information on the dispersion of observations around the predicted value. We argue that it is essential to generate, together with (or as an alternative to) point forecasts, a representation of the wind power uncertainty. Wind power uncertainty representation can take the form of probabilistic forecasts (e.g., probability density function, quantiles), risk indices (e.g., prediction risk index) or scenarios

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

    SciTech Connect

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

    2005-07-01

    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.

  20. Science and Engineering of an Operational Tsunami Forecasting System

    ScienceCinema

    Gonzalez, Frank

    2016-07-12

    After a review of tsunami statistics and the destruction caused by tsunamis, a means of forecasting tsunamis is discussed as part of an overall program of reducing fatalities through hazard assessment, education, training, mitigation, and a tsunami warning system. The forecast is accomplished via a concept called Deep Ocean Assessment and Reporting of Tsunamis (DART). Small changes of pressure at the sea floor are measured and relayed to warning centers. Under development is an international modeling network to transfer, maintain, and improve tsunami forecast models.

  1. Wa s h i n g t o n U n i v e r s i t y i n S t . L o...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Wa s h i n g t o n U n i v e r s i t y i n S t . L o u i s - - P A R C ' s H o s t & A d mi n s t r a t i v e H o me - B o b B l a n k e n s h i p , P A R C D i r e c t o r - D e ...

  2. A novel long noncoding RNA AK001796 acts as an oncogene and is involved in cell growth inhibition by resveratrol in lung cancer

    SciTech Connect

    Yang, Qiaoyuan; Xu, Enwu; Dai, Jiabin; Liu, Binbin; Han, Zhiyuan; Wu, Jianjun; Zhang, Shaozhu; Peng, Baoying; Zhang, Yajie; Jiang, Yiguo

    2015-06-01

    Lung cancer is the most common form of cancer throughout the world. The specific targeting of long noncoding RNAs (lncRNAs) by resveratrol opened a new avenue for cancer chemoprevention. In this study, we found that 21 lncRNAs were upregulated and 19 lncRNAs were downregulated in lung cancer A549 cells with 25 μmol/L resveratrol treatment determined by microarray analysis. AK001796, the lncRNA with the most clearly altered expression, was overexpressed in lung cancer tissues and cell lines, but its expression was downregulated in resveratrol-treated lung cancer cells. By monitoring cell proliferation and growth in vitro and tumor growth in vivo, we observed a significant reduction in cell viability in lung cancer cells and a slow growth in the tumorigenesis following AK001796 knockdown. We also found that AK001796 knockdown caused a cell-cycle arrest, with significant increases in the percentage of cells in G{sub 0}/G{sub 1} in lung cancer cells. By using cell cycle pathway-specific PCR arrays, we detected changes in a number of cell cycle-related genes related to lncRNA AK001796 knockdown. We further investigated whether AK001796 participated in the anticancer effect of resveratrol and the results showed that reduced lncRNA AK001796 level potentially impaired the inhibitory effect of resveratrol on cell proliferation. To our knowledge, this is the first study to report the changes in an lncRNA expression profile induced by resveratrol in lung cancer. - Highlights: • LncRNA AK001796 played an oncogenic role in lung carcinogenesis. • LncRNA AK001796 was downregulated in resveratrol-treated lung cancer cells. • LncRNA AK001796 was involved in the inhibition of cell growth by resveratrol.

  3. 1990,"AK","Combined Heat and Power, Commercial Power","All Sources",,4,85.9,80.09

    Energy Information Administration (EIA) (indexed site)

    State Code","Producer Type","Fuel Source","Generators","Facilities","Nameplate Capacity (Megawatts)","Summer Capacity (Megawatts)" 1990,"AK","Combined Heat and Power, Commercial Power","All Sources",,4,85.9,80.09 1990,"AK","Combined Heat and Power, Commercial Power","Coal",,3,65.5,61.1 1990,"AK","Combined Heat and Power, Commercial

  4. 2015,"AK","Total Electric Power Industry","All Sources",18,8,232.7,225.8

    Energy Information Administration (EIA) (indexed site)

    "Planned Year","State Code","Producer Type","Fuel Source","Generators","Facilities","Nameplate Capacity (Megawatts)","Summer Capacity (Megawatts)" 2015,"AK","Total Electric Power Industry","All Sources",18,8,232.7,225.8 2015,"AK","Total Electric Power Industry","Coal",1,1,50,50 2015,"AK","Total Electric Power

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

    SciTech Connect

    Lantz, E.; Hand, M.

    2010-05-01

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

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

    Energy.gov [DOE]

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

  7. Solar Trackers Market Forecast | OpenEI Community

    OpenEI (Open Energy Information) [EERE & EIA]

    Solar Trackers Market Forecast Home John55364's picture Submitted by John55364(100) Contributor 12 May, 2015 - 03:54 Solar Trackers Market - Global Industry Analysis, Size, Share,...

  8. Energy Forecasting Framework and Emissions Consensus Tool (EFFECT...

    OpenEI (Open Energy Information) [EERE & EIA]

    Tool (EFFECT) EFFECT is an open, Excel-based modeling tool used to forecast greenhouse gas emissions from a range of development scenarios at the regional and national levels....

  9. Value of Improved Short-Term Wind Power Forecasting

    SciTech Connect

    Hodge, B. M.; Florita, A.; Sharp, J.; Margulis, M.; Mcreavy, D.

    2015-02-01

    This report summarizes an assessment of improved short-term wind power forecasting in the California Independent System Operator (CAISO) market and provides a quantification of its potential value.

  10. Recently released EIA report presents international forecasting data

    SciTech Connect

    1995-05-01

    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.

  11. DOE Releases Latest Report on Energy Savings Forecast of Solid...

    Energy.gov [DOE] (indexed site)

    The sixth iteration of the Energy Savings Forecast of Solid-State Lighting in General Illumination Applications compares the annual lighting energy consumption in the U.S. with and ...

  12. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    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.

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

    Energy.gov [DOE] (indexed site)

    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 ...

  14. 915 MHz Wind Profiler for Cloud Forecasting at Brookhaven National...

    Office of Scientific and Technical Information (OSTI)

    U.S. DEPARTMENT OF HP IENERGY Office of Science DOESC-ARM-15-024 915-MHz Wind Profiler ... M Jensen et al., March 2016, DOESC-ARM-15-024 915-MHz Wind Profiler for Cloud Forecasting ...

  15. PBL FY 2002 Second Quarter Review Forecast of Generation Accumulated...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Slice true-ups, and actual expense levels. Any variation of these can change the net revenue situation. FY 2002 Forecasted Second Quarter Results 170 (418) FY 2002 Unaudited...

  16. Forecasting Crude Oil Spot Price Using OECD Petroleum Inventory Levels

    Reports and Publications

    2003-01-01

    This paper presents a short-term monthly forecasting model of West Texas Intermediate crude oil spot price using Organization for Economic Cooperation and Development (OECD) petroleum inventory levels.

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Energy 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

  18. Energy Department Forecasts Geothermal Achievements in 2015 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Energy Forecasts Geothermal Achievements in 2015 Energy Department Forecasts Geothermal Achievements in 2015 The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector, including Jay Nathwani, Acting Director of the Energy Department's Geothermal Technologies Office. Nathwani shared achievements and challenges in the program's technical portfolio. The 40th annual Stanford Geothermal Workshop in January featured speakers in the geothermal sector,

  19. Study forecasts disappearance of conifers due to climate change

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Study forecasts disappearance of conifers due to climate change Study forecasts disappearance of conifers due to climate change New results, reported in a paper released today in the journal Nature Climate Change, suggest that global models may underestimate predictions of forest death. December 21, 2015 Los Alamos scientist Nate McDowell discusses how climate change is killing trees with PBS NewsHour reporter Miles O'Brien. Los Alamos scientist Nate McDowell discusses how climate change is

  20. 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National

    Office of Scientific and Technical Information (OSTI)

    Laboratory (Technical Report) | SciTech Connect 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory Citation Details In-Document Search Title: 915-MHz Wind Profiler for Cloud Forecasting at Brookhaven National Laboratory When considering the amount of shortwave radiation incident on a photovoltaic solar array and, therefore, the amount and stability of the energy output from the system, clouds represent the greatest source of short-term (i.e., scale of minutes to

  1. Project Profile: Forecasting and Influencing Technological Progress in

    Energy Saver

    Solar Energy | Department of Energy Soft Costs » Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Project Profile: Forecasting and Influencing Technological Progress in Solar Energy Logos of the University of North Carolina at Charlotte, Arizona State University, and the University of Oxford. -- This project is inactive -- The University of North Carolina at Charlotte, along with their partners at Arizona State University and the University of Oxford,

  2. Weather-based forecasts of California crop yields

    SciTech Connect

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

    2005-09-26

    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.

  3. Notice of Intent to Issue Solar Forecasting 2 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Solar Forecasting 2 Notice of Intent to Issue Solar Forecasting 2 Subprogram: Systems Integration Funding Number: DE-FOA-0001658 Funding Amount: $10,000,000 The SunShot Initiative intends to release a funding opportunity announcement (FOA) to support advancements in solar forecasting to enable higher penetration of solar power in the electric grid. The Solar Forecasting 2 FOA will focus on improving solar forecasting skills, especially during challenging conditions, such as partly cloudy weather

  4. Interpolating Low Time-Resolution Forecast Data

    Energy Science and Technology Software Center

    2015-11-03

    Methodology that interpolates low time-resolution data (e.g., hourly) to high time-resolution (e.g., minutely) with variability patterns extracted from historical records. Magnitude of the variability inserted into the low timeresolution data can be adjusted according to the installed capacity represented by the low time-resolution data compared to that by historical records. This approach enables detailed analysis of the impacts from wind and solar on power system intra-hour operations and balancing reserve requirements even with only hourlymore » data. It also allows convenient creation of high resolution wind or solar generation data with various degree of variability to investigate their operational impacts. The methodology comprises of the following steps: 1. Smooth the historical data (set A) with an appropriate window length l to get its trend (set B); l can be a fraction of an hour (e.g., 15 minutes) or longer than an hour, of which the length of the variability patterns will be; 2. Extract the variable component (set C) of historical data by subtracting the smooth trend from it, i.e. set C = set A – set B 3. For each window length l of the variable component data set, find the average value x (will call it base component) of the corresponding window of the historical data set; 4. Define a series of segments (set D) that the values of data will be grouped into, e.g. (0, 0.1), (0.1, 0.2), …, (0.9, 1.0) after normalization; Link each variability pattern to a data segment based on its corresponding base component x; after this step, each data segment should be linked to multiple variability patterns after this step; 5. Use spline function to interpolate the low time-resolution forecast data (set E) to become a high time-resolution smooth curve (set F); 6. Based on the window length l , calculate the average value y in each window length of set F; find the data segment that y belongs to; then randomly select one of the variability patterns linked to this

  5. AVLIS: a technical and economic forecast

    SciTech Connect

    Davis, J.I.; Spaeth, M.L.

    1986-01-01

    The AVLIS process has intrinsically large isotopic selectivity and hence high separative capacity per module. The critical components essential to achieving the high production rates represent a small fraction (approx.10%) of the total capital cost of a production facility, and the reference production designs are based on frequent replacement of these components. The specifications for replacement frequencies in a plant are conservative with respect to our expectations; it is reasonable to expect that, as the plant is operated, the specifications will be exceeded and production costs will continue to fall. Major improvements in separator production rates and laser system efficiencies (approx.power) are expected to occur as a natural evolution in component improvements. With respect to the reference design, such improvements have only marginal economic value, but given the exigencies of moving from engineering demonstration to production operations, we continue to pursue these improvements in order to offset any unforeseen cost increases. Thus, our technical and economic forecasts for the AVLIS process remain very positive. The near-term challenge is to obtain stable funding and a commitment to bring the process to full production conditions within the next five years. If the funding and commitment are not maintained, the team will disperse and the know-how will be lost before it can be translated into production operations. The motivation to preserve the option for low-cost AVLIS SWU production is integrally tied to the motivation to maintain a competitive nuclear option. The US industry can certainly survive without AVLIS, but our tradition as technology leader in the industry will certainly be lost.

  6. Forecasting the 2013–2014 influenza season using Wikipedia

    DOE PAGES [OSTI]

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

  7. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

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

    Energy Information Administration (EIA) (indexed site)

    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 1,911 1,916 1,936 2012 2,794 2,386 2,443 1,719 2014 1,886 2,809 2,846 2,886 2,884 2015 2,753 2,753 2,755 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 10/31/2016 Next Release Date: 11/30/2016 Referring Pages: U.S.

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

    SciTech Connect

    Porter, K.; Rogers, J.

    2012-04-01

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

  10. Wa s h i n g t o n Ma r r i o t t e n Me t r o C e n t e r

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Wa s h i n g t o n Ma r r i o t t e n Me t r o C e n t e r C o n f e r e n c i a y P r o g r a ma d e C a p a c i t a c i ó n d e J u s t i c i a A mb i e n t a l N a c i o n a l 2 0 1 7

  11. A Scenario Generation Method for Wind Power Ramp Events Forecasting

    SciTech Connect

    Cui, Ming-Jian; Ke, De-Ping; Sun, Yuan-Zhang; Gan, Di; Zhang, Jie; Hodge, Bri-Mathias

    2015-07-03

    Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

  12. 1980 annual report to Congress: Volume three, Forecasts: Summary

    SciTech Connect

    Not Available

    1981-05-27

    This report presents an overview of forecasts of domestic energy consumption, production, and prices for the year 1990. These results are selected from more detailed projections prepared and published in Volume 3 of the Energy Information Administration 1980 Annual Report to Congress. This report focuses specifically upon the 1980's and concentrates upon similarities and differences in the domestic energy system, as forecast, compared to the national experience in the years immediately following the 1973--1974 oil embargo. Interest in the 1980's stems not only from its immediacy in time, but also from its importance as a time in which certain adjustments to higher energy prices are expected to take place. The forecasts presented do not attempt to account for all of this wide range of potentially important forces that could conceivably alter the energy situation. Instead, the projections are based on a particular set of assumptions that seems reasonable in light of what is currently known. 9 figs., 25 tabs.

  13. Franklin PUD, Pasco WA

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    have been replaced by Hans Berg (State of Washington, Wid Ritchie (Idaho Falls) and Shawn Collins (The Energy Project). A few participants reported new LIEE activities, due at...

  14. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer

    Klein, Stephen

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

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

    SciTech Connect

    1996-02-01

    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.

  16. Making Wind Energy Predictable: New Profilers Provide Hourly Forecasts |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Wind Energy Predictable: New Profilers Provide Hourly Forecasts Making Wind Energy Predictable: New Profilers Provide Hourly Forecasts May 11, 2016 - 6:48pm Addthis Balancing the power grid is an art-or at least a scientific study in chaos-and the Energy Department is hoping wind energy can take a greater role in the act. Yet, the intermittency of wind-sometimes it's blowing, sometimes it's not-makes adding it smoothly to the nation's electrical grid a challenge. If wind

  17. CCPP-ARM Parameterization Testbed Model Forecast Data

    DOE Data Explorer

    Klein, Stephen

    2008-01-15

    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).

  18. Are there Gains from Pooling Real-Time Oil Price Forecasts?

    Energy Information Administration (EIA) (indexed site)

    ... Gamma and that of Q is inverse Wishart. 5 Our forecasts take into account that the model parameters continue to drift over the forecast horizon according to their law of motion. ...

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

    SciTech Connect

    Piwko, R.; Jordan, G.

    2011-11-01

    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.

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

    SciTech Connect

    Porter, K.; Rogers, J.

    2010-04-01

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

  1. Final Report - Integration of Behind-the-Meter PV Fleet Forecasts...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

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

  2. DOE Releases Latest Report on Energy Savings Forecast of Solid-State Lighting

    Energy.gov [DOE]

    DOE has published a new report forecasting the energy savings of LED white-light sources compared with conventional white-light sources. The sixth iteration of the Energy Savings Forecast of Solid...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    "Partly Sunny": A Better Solar Forecast Beyond "Partly Sunny": A Better Solar Forecast December 7, 2012 - 10:00am Addthis The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods of cloud coverage. | Photo by Dennis Schroeder/NREL. The Energy Department is investing in better solar forecasting techniques to improve the reliability and stability of solar power plants during periods

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

    SciTech Connect

    Porter, K.; Rogers, J.

    2009-12-01

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

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

    SciTech Connect

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

    2011-03-28

    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.

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

    SciTech Connect

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

    2009-03-01

    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.

  7. Weather Research and Forecasting Model with the Immersed Boundary Method

    Energy Science and Technology Software Center

    2012-05-01

    The Weather Research and Forecasting (WRF) Model with the immersed boundary method is an extension of the open-source WRF Model available for wwww.wrf-model.org. The new code modifies the gridding procedure and boundary conditions in the WRF model to improve WRF's ability to simutate the atmosphere in environments with steep terrain and additionally at high-resolutions.

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

    SciTech Connect

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

    2013-10-01

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

  9. Use of wind power forecasting in operational decisions.

    SciTech Connect

    Botterud, A.; Zhi, Z.; Wang, J.; Bessa, R.J.; Keko, H.; Mendes, J.; Sumaili, J.; Miranda, V.

    2011-11-29

    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

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

    SciTech Connect

    Das, S.

    1991-12-01

    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.

  11. Photometric analysis of overcontact binaries AK Her, HI Dra, V1128 Tau, and V2612 Oph

    SciTech Connect

    Çalışkan, Ş.; Özavcı, İ.; Baştürk, Ö.; Şenavcı, H. V.; Kılıçoğlu, T.; Yılmaz, M.; Selam, S. O.; Latković, O.; Djurašević, G.; Cséki, A. E-mail: ozavci@science.ankara.edu.tr E-mail: hvsenavci@ankara.edu.tr E-mail: mesutyilmaz@ankara.edu.tr E-mail: olivia@aob.rs E-mail: attila@aob.rs

    2014-12-01

    We analyze new, high quality multicolor light curves of four overcontact binaries: AK Her, HI Dra, V1128 Tau, and V2612 Oph, and determine their orbital and physical parameters using the modeling program of G. Djurasevic and recently published results of radial velocity studies. The achieved precision in absolute masses is between 10% and 20%, and the precision in absolute radii is between 5% and 10%. All four systems are W UMa-type binaries with bright or dark spots indicative of mass and energy transfer or surface activity. We estimate the distances and the ages of the systems using the luminosities computed through our analysis, and perform an O – C study for V1128 Tau, which reveals a complex period variation that can be interpreted in terms of mass loss/exchange and either the presence of the third body, or the magnetic activity on one of the components. We conclude that further observations of these systems are needed to deepen our understanding of their nature and variability.

  12. Forecasting photovoltaic array power production subject to mismatch losses

    SciTech Connect

    Picault, D.; Raison, B.; Bacha, S.; de la Casa, J.; Aguilera, J.

    2010-07-15

    The development of photovoltaic (PV) energy throughout the world this last decade has brought to light the presence of module mismatch losses in most PV applications. Such power losses, mainly occasioned by partial shading of arrays and differences in PV modules, can be reduced by changing module interconnections of a solar array. This paper presents a novel method to forecast existing PV array production in diverse environmental conditions. In this approach, field measurement data is used to identify module parameters once and for all. The proposed method simulates PV arrays with adaptable module interconnection schemes in order to reduce mismatch losses. The model has been validated by experimental results taken on a 2.2 kW{sub p} plant, with three different interconnection schemes, which show reliable power production forecast precision in both partially shaded and normal operating conditions. Field measurements show interest in using alternative plant configurations in PV systems for decreasing module mismatch losses. (author)

  13. NREL: Energy Analysis - Energy Forecasting and Modeling Staff

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    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: Elizabeth Torres Clayton Barrows Dave Bielen Aaron Bloom Greg Brinkman Brian W Bush Stuart Cohen Wesley Cole Paul Denholm Nicholas DiOrio Aron Dobos Kelly Eurek Janine Freeman Bethany Frew Pieter Gagnon Elaine Hale

  14. Forecast of transportation energy demand through the year 2010

    SciTech Connect

    Mintz, M.M.; Vyas, A.D.

    1991-04-01

    Since 1979, the Center for Transportation Research (CTR) at Argonne National Laboratory (ANL) has produced baseline projections of US transportation activity and energy demand. These projections and the methodologies used to compute them are documented in a series of reports and research papers. As the lastest in this series of projections, this report documents the assumptions, methodologies, and results of the most recent projection -- termed ANL-90N -- and compares those results with other forecasts from the current literature, as well as with the selection of earlier Argonne forecasts. This current forecast may be used as a baseline against which to analyze trends and evaluate existing and proposed energy conservation programs and as an illustration of how the Transportation Energy and Emission Modeling System (TEEMS) works. (TEEMS links disaggregate models to produce an aggregate forecast of transportation activity, energy use, and emissions). This report and the projections it contains were developed for the US Department of Energy's Office of Transportation Technologies (OTT). The projections are not completely comprehensive. Time and modeling effort have been focused on the major energy consumers -- automobiles, trucks, commercial aircraft, rail and waterborne freight carriers, and pipelines. Because buses, rail passengers services, and general aviation consume relatively little energy, they are projected in the aggregate, as other'' modes, and used primarily as scaling factors. These projections are also limited to direct energy consumption. Projections of indirect energy consumption, such as energy consumed in vehicle and equipment manufacturing, infrastructure, fuel refining, etc., were judged outside the scope of this effort. The document is organized into two complementary sections -- one discussing passenger transportation modes, and the other discussing freight transportation modes. 99 refs., 10 figs., 43 tabs.

  15. Toward a science of tumor forecasting for clinical oncology

    DOE PAGES [OSTI]

    Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; Rericha, Erin C.

    2015-03-15

    We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapiesmore » is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.« less

  16. Toward a science of tumor forecasting for clinical oncology

    SciTech Connect

    Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; Rericha, Erin C.

    2015-03-15

    We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.

  17. Assessment of the possibility of forecasting future natural gas curtailments

    SciTech Connect

    Lemont, S.

    1980-01-01

    This study provides a preliminary assessment of the potential for determining probabilities of future natural-gas-supply interruptions by combining long-range weather forecasts and natural-gas supply/demand projections. An illustrative example which measures the probability of occurrence of heating-season natural-gas curtailments for industrial users in the southeastern US is analyzed. Based on the information on existing long-range weather forecasting techniques and natural gas supply/demand projections enumerated above, especially the high uncertainties involved in weather forecasting and the unavailability of adequate, reliable natural-gas projections that take account of seasonal weather variations and uncertainties in the nation's energy-economic system, it must be concluded that there is little possibility, at the present time, of combining the two to yield useful, believable probabilities of heating-season gas curtailments in a form useful for corporate and government decision makers and planners. Possible remedial actions are suggested that might render such data more useful for the desired purpose in the future. The task may simply require the adequate incorporation of uncertainty and seasonal weather trends into modeling systems and the courage to report projected data, so that realistic natural gas supply/demand scenarios and the probabilities of their occurrence will be available to decision makers during a time when such information is greatly needed.

  18. Towards a Science of Tumor Forecast for Clinical Oncology

    DOE PAGES [OSTI]

    Yankeelov, Tom; Quaranta, Vito; Evans, Katherine J; Rericha, Erin

    2015-01-01

    We propose that the quantitative cancer biology community make a concerted effort to apply the methods of weather forecasting to develop an analogous theory for predicting tumor growth and treatment response. Currently, the time course of response is not predicted, but rather assessed post hoc by physical exam or imaging methods. This fundamental limitation of clinical oncology makes it extraordinarily difficult to select an optimal treatment regimen for a particular tumor of an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoplymore » of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful theory of tumor forecasting, it should be possible to integrate large tumor specific datasets of varied types, and effectively defeat cancer one patient at a time.« less

  19. Towards a Science of Tumor Forecast for Clinical Oncology

    SciTech Connect

    Yankeelov, Tom; Quaranta, Vito; Evans, Katherine J; Rericha, Erin

    2015-01-01

    We propose that the quantitative cancer biology community make a concerted effort to apply the methods of weather forecasting to develop an analogous theory for predicting tumor growth and treatment response. Currently, the time course of response is not predicted, but rather assessed post hoc by physical exam or imaging methods. This fundamental limitation of clinical oncology makes it extraordinarily difficult to select an optimal treatment regimen for a particular tumor of an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful theory of tumor forecasting, it should be possible to integrate large tumor specific datasets of varied types, and effectively defeat cancer one patient at a time.

  20. ORISE "AK RlDGE lNSTlT"TE FOR SCIENCE AND EDUCATION

    Office of Legacy Management (LM)

    t\i,;;; il.,. (' . d ORISE "AK RlDGE lNSTlT"TE FOR SCIENCE AND EDUCATION August 1,200l Robert Atkin U.S. Department of Energy Oak Ridge Operations Office P.O. Box 2001 Oak Ridge, TN 3783 1 SUBJECT: CONTRACT NO. DE-AC05000R22750 FINAL REPORT-VERIFICATION SURVEY OF THE NEW BRUNSWICK LABORATORY SITE, NEW BRUNSWICK, NEW JERSEY Dear Mr. Atkin: The Environmental Survey and Site Assessment Program (ESSAP) of the Oak Ridge Institute for Science and Education (ORISE) conducted verification

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

    SciTech Connect

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

    2012-08-15

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

  2. Economic Evaluation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results; Preprint

    SciTech Connect

    Orwig, K.; Hodge, B. M.; Brinkman, G.; Ela, E.; Milligan, M.; Banunarayanan, V.; Nasir, S.; Freedman, J.

    2012-09-01

    Historically, a number of wind energy integration studies have investigated the value of using day-ahead wind power forecasts for grid operational decisions. These studies have shown that there could be large cost savings gained by grid operators implementing the forecasts in their system operations. To date, none of these studies have investigated the value of shorter-term (0 to 6-hour-ahead) wind power forecasts. In 2010, the Department of Energy and National Oceanic and Atmospheric Administration partnered to fund improvements in short-term wind forecasts and to determine the economic value of these improvements to grid operators, hereafter referred to as the Wind Forecasting Improvement Project (WFIP). In this work, we discuss the preliminary results of the economic benefit analysis portion of the WFIP for the Electric Reliability Council of Texas. The improvements seen in the wind forecasts are examined, then the economic results of a production cost model simulation are analyzed.

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Operations | Department of Energy Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Clean Power Research logo.jpg This project will address the need for a more accurate approach to forecasting net utility load by taking into consideration the contribution of customer-sited PV energy generation. Tasks within the project are designed to integrate novel PV power

  4. Final Report - Integration of Behind-the-Meter PV Fleet Forecasts into

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Utility Grid System Operations | Department of Energy Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Final Report - Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations Awardee: Clean Power Research Location: Napa, CA Subprogram: Systems Integration Funding Program: Solar Utility Networks: Replicable Innovations in Solar Energy (SUNRISE) Project: Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid

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

    SciTech Connect

    Rogers, J.; Porter, K.

    2011-03-01

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

  6. ARM - PI Product - CCPP-ARM Parameterization Testbed Model Forecast Data

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ProductsCCPP-ARM Parameterization Testbed Model Forecast Data ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send PI Product : CCPP-ARM Parameterization Testbed Model Forecast Data 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

  7. Report of the external expert peer review panel: DOE benefits forecasts

    SciTech Connect

    None, None

    2006-12-20

    A report for the FY 2007 GPRA methodology review, highlighting the views of an external expert peer review panel on DOE benefits forecasts.

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

    SciTech Connect

    Hodge, B.

    2013-12-01

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

  9. Energy Savings Forecast of Solid-State Lighting in General Illuminatio...

    Energy Saver

    (1.54 MB) More Documents & Publications Energy Savings Potential of Solid-State Lighting in General Illumination Applications - Report 2016 SSL Forecast Report LED ADOPTION REPORT

  10. The Value of Improved Wind Power Forecasting in the Western Interconne...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    of this research will facilitate a better functional understanding of wind forecasting accuracy and power system operations at various spatial and temporal scales.* Of particular ...

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

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Forecasting behind-the-meter distributed PV generation power production within a region ... This project is expected to reduce the costs of integrating higher penetrations of PV into ...

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

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Metrics for Evaluating the Accuracy of Solar Power Forecasting Preprint J. Zhang, B.-M. Hodge, and A. Florita National Renewable Energy Laboratory S. Lu and H. F. Hamann IBM TJ Watson Research Center V. Banunarayanan U.S. Department of Energy To be presented at 3rd International Workshop on Integration of Solar Power into Power Systems London, England October 21 - 22, 2013 Conference Paper NREL/CP-5500-60142 October 2013 NOTICE The submitted manuscript has been offered by an employee of the

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

    SciTech Connect

    Finley, Cathy

    2014-04-30

    This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements in wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the

  14. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the systems ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  15. Recent Trends in Variable Generation Forecasting and Its Value to the Power System

    DOE PAGES [OSTI]

    Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; et al

    2014-12-23

    We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value ofmore » adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.« less

  16. Recent Trends in Variable Generation Forecasting and Its Value to the Power System

    SciTech Connect

    Orwig, Kirsten D.; Ahlstrom, Mark L.; Banunarayanan, Venkat; Sharp, Justin; Wilczak, James M.; Freedman, Jeffrey; Haupt, Sue Ellen; Cline, Joel; Bartholomy, Obadiah; Hamann, Hendrik F.; Hodge, Bri-Mathias; Finley, Catherine; Nakafuji, Dora; Peterson, Jack L.; Maggio, David; Marquis, Melinda

    2014-12-23

    We report that the rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value of adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.

  17. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison (Presentation)

    SciTech Connect

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

    2013-10-01

    This presentation summarizes the work to investigate the uncertainty in wind forecasting at different times of year and compare wind forecast errors in different power systems using large-scale wind power prediction data from six countries: the United States, Finland, Spain, Denmark, Norway, and Germany.

  18. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    SciTech Connect

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together into larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.

  19. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE PAGES [OSTI]

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-06-23

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

  20. Thirty-Year Solid Waste Generation Maximum and Minimum Forecast for SRS

    SciTech Connect

    Thomas, L.C.

    1994-10-01

    This report is the third phase (Phase III) of the Thirty-Year Solid Waste Generation Forecast for Facilities at the Savannah River Site (SRS). Phase I of the forecast, Thirty-Year Solid Waste Generation Forecast for Facilities at SRS, forecasts the yearly quantities of low-level waste (LLW), hazardous waste, mixed waste, and transuranic (TRU) wastes generated over the next 30 years by operations, decontamination and decommissioning and environmental restoration (ER) activities at the Savannah River Site. The Phase II report, Thirty-Year Solid Waste Generation Forecast by Treatability Group (U), provides a 30-year forecast by waste treatability group for operations, decontamination and decommissioning, and ER activities. In addition, a 30-year forecast by waste stream has been provided for operations in Appendix A of the Phase II report. The solid wastes stored or generated at SRS must be treated and disposed of in accordance with federal, state, and local laws and regulations. To evaluate, select, and justify the use of promising treatment technologies and to evaluate the potential impact to the environment, the generic waste categories described in the Phase I report were divided into smaller classifications with similar physical, chemical, and radiological characteristics. These smaller classifications, defined within the Phase II report as treatability groups, can then be used in the Waste Management Environmental Impact Statement process to evaluate treatment options. The waste generation forecasts in the Phase II report includes existing waste inventories. Existing waste inventories, which include waste streams from continuing operations and stored wastes from discontinued operations, were not included in the Phase I report. Maximum and minimum forecasts serve as upper and lower boundaries for waste generation. This report provides the maximum and minimum forecast by waste treatability group for operation, decontamination and decommissioning, and ER activities.

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

    SciTech Connect

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

    2005-02-09

    This paper evaluates the accuracy of two methods to forecast natural gas prices: using the Energy Information Administration's ''Annual Energy Outlook'' forecasted price (AEO) and the ''Henry Hub'' compared to U.S. Wellhead futures price. A statistical analysis is performed to determine the relative accuracy of the two measures in the recent past. A statistical analysis suggests that the Henry Hub futures price provides a more accurate average forecast of natural gas prices than the AEO. For example, the Henry Hub futures price underestimated the natural gas price by 35 cents per thousand cubic feet (11.5 percent) between 1996 and 2003 and the AEO underestimated by 71 cents per thousand cubic feet (23.4 percent). Upon closer inspection, a liner regression analysis reveals that two distinct time periods exist, the period between 1996 to 1999 and the period between 2000 to 2003. For the time period between 1996 to 1999, AEO showed a weak negative correlation (R-square = 0.19) between forecast price by actual U.S. Wellhead natural gas price versus the Henry Hub with a weak positive correlation (R-square = 0.20) between forecasted price and U.S. Wellhead natural gas price. During the time period between 2000 to 2003, AEO shows a moderate positive correlation (R-square = 0.37) between forecasted natural gas price and U.S. Wellhead natural gas price versus the Henry Hub that show a moderate positive correlation (R-square = 0.36) between forecast price and U.S. Wellhead natural gas price. These results suggest that agencies forecasting natural gas prices should consider incorporating the Henry Hub natural gas futures price into their forecasting models along with the AEO forecast. Our analysis is very preliminary and is based on a very small data set. Naturally the results of the analysis may change, as more data is made available.

  2. Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

    Energy.gov [DOE]

    Energy Conservation Program: Data Collection and Comparison with Forecasted Unit Sales for Five Lamp Types, Notice of Data Availability

  3. Weather Research and Forecasting Model with Vertical Nesting Capability

    Energy Science and Technology Software Center

    2014-08-01

    The Weather Research and Forecasting (WRF) model with vertical nesting capability is an extension of the WRF model, which is available in the public domain, from www.wrf-model.org. The new code modifies the nesting procedure, which passes lateral boundary conditions between computational domains in the WRF model. Previously, the same vertical grid was required on all domains, while the new code allows different vertical grids to be used on concurrently run domains. This new functionality improvesmore » WRF's ability to produce high-resolution simulations of the atmosphere by allowing a wider range of scales to be efficiently resolved and more accurate lateral boundary conditions to be provided through the nesting procedure.« less

  4. An Optimized Autoregressive Forecast Error Generator for Wind and Load Uncertainty Study

    SciTech Connect

    De Mello, Phillip; Lu, Ning; Makarov, Yuri V.

    2011-01-17

    This paper presents a first-order autoregressive algorithm to generate real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast errors. The methodology aims at producing random wind and load forecast time series reflecting the autocorrelation and cross-correlation of historical forecast data sets. Five statistical characteristics are considered: the means, standard deviations, autocorrelations, and cross-correlations. A stochastic optimization routine is developed to minimize the differences between the statistical characteristics of the generated time series and the targeted ones. An optimal set of parameters are obtained and used to produce the RT, HA, and DA forecasts in due order of succession. This method, although implemented as the first-order regressive random forecast error generator, can be extended to higher-order. Results show that the methodology produces random series with desired statistics derived from real data sets provided by the California Independent System Operator (CAISO). The wind and load forecast error generator is currently used in wind integration studies to generate wind and load inputs for stochastic planning processes. Our future studies will focus on reflecting the diurnal and seasonal differences of the wind and load statistics and implementing them in the random forecast generator.

  5. U.S. oil production forecast revised up for 2016 and 2017

    Energy Information Administration (EIA) (indexed site)

    oil production forecast revised up for 2016 and 2017 U.S. crude oil production is expected to be higher this year and in 2017 than previously forecast, because of a slower decline in onshore production. In its new monthly forecast, the U.S. Energy Information Administration revised up its estimate for domestic oil production by about 110,000 barrels per day for 2016 and by 150,000 barrels per day next year. EIA said increased drilling activity in the Permian Basin area located in West Texas and

  6. World oil inventories forecast to grow significantly in 2016 and 2017

    Energy Information Administration (EIA) (indexed site)

    World oil inventories forecast to grow significantly in 2016 and 2017 Global oil inventories are expected to continue strong growth over the next two years which should keep oil prices low. In its new monthly forecast, the U.S. Energy Information Administration said world oil stocks are likely to increase by 1.6 million barrels per day this year and by 600,000 barrels per day next year. The higher forecast for inventory builds are the result of both higher global oil production and less oil

  7. Energy Department Announces $2.5 Million to Improve Wind Forecasting |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Improve Wind Forecasting Energy Department Announces $2.5 Million to Improve Wind Forecasting January 8, 2015 - 12:00pm Addthis The Energy Department today announced $2.5 million for a new project to research the atmospheric processes that generate wind in mountain-valley regions. This in-depth research, conducted by Vaisala of Louisville, Colorado, will be used to improve the wind industry's weather models for short-term wind forecasts, especially for those issued less

  8. Review of Variable Generation Forecasting in the West: July 2013 - March 2014

    SciTech Connect

    Widiss, R.; Porter, K.

    2014-03-01

    This report interviews 13 operating entities (OEs) in the Western Interconnection about their implementation of wind and solar forecasting. The report updates and expands upon one issued by NREL in 2012. As in the 2012 report, the OEs interviewed vary in size and character; the group includes independent system operators, balancing authorities, utilities, and other entities. Respondents' advice for other utilities includes starting sooner rather than later as it can take time to plan, prepare, and train a forecast; setting realistic expectations; using multiple forecasts; and incorporating several performance metrics.

  9. U.S. diesel fuel price forecast to be 1 penny lower this summer...

    Energy Information Administration (EIA) (indexed site)

    That's down 12 percent from last summer's record exports. Biodiesel production, which averaged 68,000 barrels a day last summer, is forecast to jump to 82,000 barrels a day this ...

  10. Energy Savings Forecast of Solid-State Lighting in General Illumination Applications

    Office of Energy Efficiency and Renewable Energy (EERE)

    Report forecasting the U.S. energy savings of LED white-light sources compared to conventional white-light sources (i.e., incandescent, halogen, fluorescent, and high-intensity discharge) over the...

  11. Short-Term Load Forecasting Error Distributions and Implications for Renewable Integration Studies: Preprint

    SciTech Connect

    Hodge, B. M.; Lew, D.; Milligan, M.

    2013-01-01

    Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of the day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.

  12. Ramping Effect on Forecast Use: Integrated Ramping as a Mitigation Strategy; NREL (National Renewable Energy Laboratory)

    SciTech Connect

    Diakov, Victor; Barrows, Clayton; Brinkman, Gregory; Bloom, Aaron; Denholm, Paul

    2015-06-23

    Power generation ramping between forecasted (net) load set-points shift the generation (MWh) from its scheduled values. The Integrated Ramping is described as a method that mitigates this problem.

  13. Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics: Preprint

    SciTech Connect

    Hodge, B. M.; Orwig, K.; Milligan, M.

    2012-06-01

    In this paper, we examine the parameters associated with the calculation of the Renyi entropy in order to further the understanding of its application to assessing wind power forecasting errors.

  14. Analysis and Synthesis of Load Forecasting Data for Renewable Integration Studies: Preprint

    SciTech Connect

    Steckler, N.; Florita, A.; Zhang, J.; Hodge, B. M.

    2013-11-01

    As renewable energy constitutes greater portions of the generation fleet, the importance of modeling uncertainty as part of integration studies also increases. In pursuit of optimal system operations, it is important to capture not only the definitive behavior of power plants, but also the risks associated with systemwide interactions. This research examines the dependence of load forecast errors on external predictor variables such as temperature, day type, and time of day. The analysis was utilized to create statistically relevant instances of sequential load forecasts with only a time series of historic, measured load available. The creation of such load forecasts relies on Bayesian techniques for informing and updating the model, thus providing a basis for networked and adaptive load forecast models in future operational applications.

  15. U.S. Crude Oil Production Forecast-Analysis of Crude Types

    Energy Information Administration (EIA) (indexed site)

    of Energy Washington, DC 20585 U.S. Energy Information Administration | U.S. Crude Oil Production Forecast-Analysis of Crude Types i This report was prepared by the U.S....

  16. Resource Information and Forecasting Group; Electricity, Resources, & Building Systems Integration (ERBSI) (Fact Sheet)

    SciTech Connect

    Not Available

    2009-11-01

    Researchers in the Resource Information and Forecasting group at NREL provide scientific, engineering, and analytical expertise to help characterize renewable energy resources and facilitate the integration of these clean energy sources into the electricity grid.

  17. Gasoline price forecast to stay below 3 dollar a gallon in 2015

    Energy Information Administration (EIA) (indexed site)

    Gasoline price forecast to stay below 3 a gallon in 2015 The national average pump price of gasoline is expected to stay below 3 per gallon during 2015. In its new monthly ...

  18. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect

    Not Available

    1993-12-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  19. Solar energy conversion: Technological forecasting. (Latest citations from the Aerospace database). Published Search

    SciTech Connect

    1995-01-01

    The bibliography contains citations concerning current forecasting of Earth surface-bound solar energy conversion technology. Topics consider research, development and utilization of this technology in relation to electric power generation, heat pumps, bioconversion, process heat and the production of renewable gaseous, liquid, and solid fuels for industrial, commercial, and domestic applications. Some citations concern forecasts which compare solar technology with other energy technologies. (Contains 250 citations and includes a subject term index and title list.)

  20. Forecasting Wind and Solar Generation: Improving System Operations, Greening the Grid

    SciTech Connect

    Tian; Tian; Chernyakhovskiy, Ilya

    2016-01-01

    This document discusses improving system operations with forecasting and solar generation. By integrating variable renewable energy (VRE) forecasts into system operations, power system operators can anticipate up- and down-ramps in VRE generation in order to cost-effectively balance load and generation in intra-day and day-ahead scheduling. This leads to reduced fuel costs, improved system reliability, and maximum use of renewable resources.

  1. Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

    SciTech Connect

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

    2013-05-01

    Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

  2. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE (Presentation)

    SciTech Connect

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B.M.

    2014-11-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

  3. Impact of Improved Solar Forecasts on Bulk Power System Operations in ISO-NE: Preprint

    SciTech Connect

    Brancucci Martinez-Anido, C.; Florita, A.; Hodge, B. M.

    2014-09-01

    The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

  4. U.S. Department of Energy Workshop Report: Solar Resources and Forecasting

    SciTech Connect

    Stoffel, T.

    2012-06-01

    This report summarizes the technical presentations, outlines the core research recommendations, and augments the information of the Solar Resources and Forecasting Workshop held June 20-22, 2011, in Golden, Colorado. The workshop brought together notable specialists in atmospheric science, solar resource assessment, solar energy conversion, and various stakeholders from industry and academia to review recent developments and provide input for planning future research in solar resource characterization, including measurement, modeling, and forecasting.

  5. Wind Power Forecasting Error Frequency Analyses for Operational Power System Studies: Preprint

    SciTech Connect

    Florita, A.; Hodge, B. M.; Milligan, M.

    2012-08-01

    The examination of wind power forecasting errors is crucial for optimal unit commitment and economic dispatch of power systems with significant wind power penetrations. This scheduling process includes both renewable and nonrenewable generators, and the incorporation of wind power forecasts will become increasingly important as wind fleets constitute a larger portion of generation portfolios. This research considers the Western Wind and Solar Integration Study database of wind power forecasts and numerical actualizations. This database comprises more than 30,000 locations spread over the western United States, with a total wind power capacity of 960 GW. Error analyses for individual sites and for specific balancing areas are performed using the database, quantifying the fit to theoretical distributions through goodness-of-fit metrics. Insights into wind-power forecasting error distributions are established for various levels of temporal and spatial resolution, contrasts made among the frequency distribution alternatives, and recommendations put forth for harnessing the results. Empirical data are used to produce more realistic site-level forecasts than previously employed, such that higher resolution operational studies are possible. This research feeds into a larger work of renewable integration through the links wind power forecasting has with various operational issues, such as stochastic unit commitment and flexible reserve level determination.

  6. Climate Leadership Conference (Seattle, WA)

    Energy.gov [DOE]

    Sustainability leaders from the private, public, academic, and non-profit communities meet to explore market transformation, carbon management, and building climate resilience on an annual basis.

  7. Wind power forecasting : state-of-the-art 2009.

    SciTech Connect

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

    2009-11-20

    Many countries and regions are introducing policies aimed at reducing the environmental footprint from the energy sector and increasing the use of renewable energy. In the United States, a number of initiatives have been taken at the state level, from renewable portfolio standards (RPSs) and renewable energy certificates (RECs), to regional greenhouse gas emission control schemes. Within the U.S. Federal government, new energy and environmental policies and goals are also being crafted, and these are likely to increase the use of renewable energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20 targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990), increase the amount of renewable energy to 20% of the energy supply, and reduce the overall energy consumption by 20% through energy efficiency. With the current focus on energy and the environment, efficient integration of renewable energy into the electric power system is becoming increasingly important. In a recent report, the U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy provides 20% of the U.S. electricity demand in 2030. The report discusses a set of technical and economic challenges that have to be overcome for this scenario to unfold. In Europe, several countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid growth in installed wind power capacity is expected to continue in the United States as well as in Europe. A large-scale introduction of wind power causes a number of challenges for electricity market and power system operators who will have to deal with the variability and uncertainty in wind power generation when making their scheduling and dispatch decisions. Wind power forecasting (WPF) is frequently identified as an important tool to address the variability and

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

    SciTech Connect

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

    2014-04-30

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

  9. Final Report on California Regional Wind Energy Forecasting Project:Application of NARAC Wind Prediction System

    SciTech Connect

    Chin, H S

    2005-07-26

    Wind power is the fastest growing renewable energy technology and electric power source (AWEA, 2004a). This renewable energy has demonstrated its readiness to become a more significant contributor to the electricity supply in the western U.S. and help ease the power shortage (AWEA, 2000). The practical exercise of this alternative energy supply also showed its function in stabilizing electricity prices and reducing the emissions of pollution and greenhouse gases from other natural gas-fired power plants. According to the U.S. Department of Energy (DOE), the world's winds could theoretically supply the equivalent of 5800 quadrillion BTUs of energy each year, which is 15 times current world energy demand (AWEA, 2004b). Archer and Jacobson (2005) also reported an estimation of the global wind energy potential with the magnitude near half of DOE's quote. Wind energy has been widely used in Europe; it currently supplies 20% and 6% of Denmark's and Germany's electric power, respectively, while less than 1% of U.S. electricity is generated from wind (AWEA, 2004a). The production of wind energy in California ({approx}1.2% of total power) is slightly higher than the national average (CEC & EPRI, 2003). With the recently enacted Renewable Portfolio Standards calling for 20% of renewables in California's power generation mix by 2010, the growth of wind energy would become an important resource on the electricity network. Based on recent wind energy research (Roulston et al., 2003), accurate weather forecasting has been recognized as an important factor to further improve the wind energy forecast for effective power management. To this end, UC-Davis (UCD) and LLNL proposed a joint effort through the use of UCD's wind tunnel facility and LLNL's real-time weather forecasting capability to develop an improved regional wind energy forecasting system. The current effort of UC-Davis is aimed at developing a database of wind turbine power curves as a function of wind speed and

  10. Demand forecasting for automotive sector in Malaysia by system dynamics approach

    SciTech Connect

    Zulkepli, Jafri Abidin, Norhaslinda Zainal; Fong, Chan Hwa

    2015-12-11

    In general, Proton as an automotive company needs to forecast future demand of the car to assist in decision making related to capacity expansion planning. One of the forecasting approaches that based on judgemental or subjective factors is normally used to forecast the demand. As a result, demand could be overstock that eventually will increase the operation cost; or the company will face understock, which resulted losing their customers. Due to automotive industry is very challenging process because of high level of complexity and uncertainty involved in the system, an accurate tool to forecast the future of automotive demand from the modelling perspective is required. Hence, the main objective of this paper is to forecast the demand of automotive Proton car industry in Malaysia using system dynamics approach. Two types of intervention namely optimistic and pessimistic experiments scenarios have been tested to determine the capacity expansion that can prevent the company from overstocking. Finding from this study highlighted that the management needs to expand their production for optimistic scenario, whilst pessimistic give results that would otherwise. Finally, this study could help Proton Edar Sdn. Bhd (PESB) to manage the long-term capacity planning in order to meet the future demand of the Proton cars.

  11. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

  12. Baseline data for the residential sector and development of a residential forecasting database

    SciTech Connect

    Hanford, J.W.; Koomey, J.G.; Stewart, L.E.; Lecar, M.E.; Brown, R.E.; Johnson, F.X.; Hwang, R.J.; Price, L.K.

    1994-05-01

    This report describes the Lawrence Berkeley Laboratory (LBL) residential forecasting database. It provides a description of the methodology used to develop the database and describes the data used for heating and cooling end-uses as well as for typical household appliances. This report provides information on end-use unit energy consumption (UEC) values of appliances and equipment historical and current appliance and equipment market shares, appliance and equipment efficiency and sales trends, cost vs efficiency data for appliances and equipment, product lifetime estimates, thermal shell characteristics of buildings, heating and cooling loads, shell measure cost data for new and retrofit buildings, baseline housing stocks, forecasts of housing starts, and forecasts of energy prices and other economic drivers. Model inputs and outputs, as well as all other information in the database, are fully documented with the source and an explanation of how they were derived.

  13. Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy

    SciTech Connect

    Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.

    2014-08-15

    Purpose: To create models that forecast longitudinal trends in changing tumor morphology and to evaluate and compare their predictive potential throughout the course of radiation therapy. Methods: Two morphology feature vectors were used to describe 35 gross tumor volumes (GTVs) throughout the course of intensity-modulated radiation therapy for oropharyngeal tumors. The feature vectors comprised the coordinates of the GTV centroids and a description of GTV shape using either interlandmark distances or a spherical harmonic decomposition of these distances. The change in the morphology feature vector observed at 33 time points throughout the course of treatment was described using static, linear, and mean models. Models were adjusted at 0, 1, 2, 3, or 5 different time points (adjustment points) to improve prediction accuracy. The potential of these models to forecast GTV morphology was evaluated using leave-one-out cross-validation, and the accuracy of the models was compared using Wilcoxon signed-rank tests. Results: Adding a single adjustment point to the static model without any adjustment points decreased the median error in forecasting the position of GTV surface landmarks by the largest amount (1.2 mm). Additional adjustment points further decreased the forecast error by about 0.4 mm each. Selection of the linear model decreased the forecast error for both the distance-based and spherical harmonic morphology descriptors (0.2 mm), while the mean model decreased the forecast error for the distance-based descriptor only (0.2 mm). The magnitude and statistical significance of these improvements decreased with each additional adjustment point, and the effect from model selection was not as large as that from adding the initial points. Conclusions: The authors present models that anticipate longitudinal changes in tumor morphology using various models and model adjustment schemes. The accuracy of these models depended on their form, and the utility of these models

  14. Regional four-dimensional variational data assimilation in a quasi-operational forecasting environment

    SciTech Connect

    Zupanski, M. )

    1993-08-01

    Four-dimensional variational data assimilation is applied to a regional forecast model as part of the development of a new data assimilation system at the National Meteorological Center (NMC). The assimilation employs an operational version of the NMC's new regional forecast model defined in eta vertical coordinates, and data used are operationally produced optimal interpolation (OI) analyses (using the first guess from the NMC's global spectral model), available every 3 h. Humidity and parameterized processes are not included in the adjoint model integration. The calculation of gradients by the adjoint model is approximate since the forecast model is used in its full-physics operational form. All experiments are over a 12-h assimilation period with subsequent 48-h forecast. Three different types of assimilation experiments are performed: (a) adjustment of initial conditions only (standard [open quotes]adjoint[close quotes] approach), (b) adjustment of a correction to the model equations only (variational continuous assimilation), and (c) simultaneous or sequential adjustment of both initial conditions and the correction term. Results indicate significantly better results when the correction term is included in the assimilation. It is shown, for a single case, that the new technique [experiment (c)] is able to produce a forecast better than the current conventional OI assimilation. It is very important to note that these results are obtained with an approximate gradient, calculated from a simplified adjoint model. Thus, it may be possible to perform an operational four-dimensional variational data assimilation of realistic forecast models, even before more complex adjoint models are developed. Also, the results suggest that it may be possible to reduce the large computational cost of assimilation by using only a few iterations of the minimization algorithm. This fast convergence is encouraging from the prospective of operational use. 37 refs., 10 figs., 1 tab.

  15. Summer gasoline price forecast slightly higher, but drivers still pay less than last year

    Energy Information Administration (EIA) (indexed site)

    Summer gasoline price forecast slightly higher, but drivers still pay less than last year Rising crude oil prices are likely to be passed on to consumers at the pump, but U.S. drivers are still expected to pay the lowest summer gasoline prices since 2004, and for all of 2016 the average household will spend $900 less on gasoline than it did two years ago." In its new monthly forecast, the U.S. Energy Information Administration said the retail price for regular grade gasoline will average

  16. ARM - Field Campaign - 915 MHz Wind Profiler for Cloud Forecasting at BNL

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    govCampaigns915 MHz Wind Profiler for Cloud Forecasting at BNL Campaign Links Field Campaign Report ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : 915 MHz Wind Profiler for Cloud Forecasting at BNL 2011.05.31 - 2012.05.31 Lead Scientist : Michael Jensen For data sets, see below. Abstract In support of the installation of a 37 MW solar array on the grounds of Brookhaven National Laboratory (BNL), a study

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

    SciTech Connect

    Bolinger, Mark A.; Wiser, Ryan H.

    2010-01-04

    On December 14, 2009, the reference-case projections from Annual Energy Outlook 2010 were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in itigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings.

  18. EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Improved by 30% | Department of Energy Forecasting Gets a Boost from Watson, Accuracy Improved by 30% EERE Success Story-Solar Forecasting Gets a Boost from Watson, Accuracy Improved by 30% October 27, 2015 - 11:48am Addthis IBM Youtube Video | Courtesy of IBM Remember when IBM's super computer Watson defeated Jeopardy! champions Ken Jennings and Brad Rutter? With funding from the U.S. Department of Energy SunShot Initiative, IBM researchers are using Watson-like technology to improve solar

  19. ARM - Field Campaign - Radar Wind Profiler for Cloud Forecasting at BNL

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    govCampaignsRadar Wind Profiler for Cloud Forecasting at BNL Campaign Links Field Campaign Report ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Campaign : Radar Wind Profiler for Cloud Forecasting at BNL 2013.07.15 - 2015.08.06 Lead Scientist : Michael Jensen For data sets, see below. Abstract In support of recent activities funded by the DOE Energy Efficiency and Renewable Energy (EERE) to produce short-term

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

    SciTech Connect

    BARCOT, R.A.

    2003-12-01

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

  1. Industrial end-use forecasting that incorporates DSM and air quality

    SciTech Connect

    Tutt, T.; Flory, J.

    1995-05-01

    The California Energy Commission (CEC) and major enregy utilities in California have generally depended on simple aggregate intensity or economic models to forecast energy use in the process industry sector (which covers large industries employing basic processes to transform raw materials, such as paper mills, glass plants, and cement plants). Two recent trends suggests that the time has come to develop a more disaggregate process industry forecasting model. First, recent efforts to improve air quality, especially by the South Coast Air Quality Management District (SCAQMD), could significantly affect energy use by the process industry by altering the technologies and processes employed in order to reduce emissions. Second, there is a renewed interest in Demand-Side Management (DSM), not only for utility least-cost planning, but also for improving the economic competitiveness and environmental compliance of the pro{minus}cess industries. A disaggregate forecasting model is critical to help the CEC and utilities evaluate both the air quality and DSM impacts on energy use. A crucial obstacle to the development and use of these detailed process industry forecasting models is the lack of good data about disaggregate energy use in the sector. The CEC is nearing completion of a project to begin to overcome this lack of data. The project is testing methds of developing detailed energy use data, collecting an initial database for a large portion of southern California, and providing recommendations and direction for further data collection efforts.

  2. Expectations models of electric utilities' forecasts: a case study of econometric estimation with influential data points

    SciTech Connect

    Vellutini, R. de A.S.; Mount, T.D.

    1983-01-01

    This study develops an econometric model for explaining how electric utilities revise their forecasts of future electricity demand each year. The model specification is developed from the adaptive expectations hypothesis and it relates forecasted growth rates to actual lagged growth rates of electricity demand. Unlike other studies of the expectation phenomenon, expectations of future demand levels constitute an observable variable and thus can be incorporated explicitly into the model. The data used for the analysis were derived from the published forecasts of the nine National Electric Reliability Councils in the US for the years 1974 to 1980. Three alternative statistical methods are used for estimation purposes: ordinary least-squares, robust regression and a diagnostic analysis to identify influential observations. The results obtained with the first two methods are very similar, but are both inconsistent with the underlying economic logic of the model. The estimated model obtained from the diagnostics approach after deleting two aberrant observations is consistent with economic logic, and supports the hypothesis that the low growth demand experienced immediately following the oil embargo in 1973 were disregarded by the industry for forecasting purposes. The model includes transitory effects associated with the oil embargo that gradually disappear over time, the estimated coefficients for the lagged values of actual growth approach a structure with declining positive weights. The general shape of this asymptotic structure is similar to the findings in many economic applications using distributed lag models.

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

    SciTech Connect

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

    2012-09-01

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

  4. Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

    SciTech Connect

    Brown, C. W.; Hood, Raleigh R.; Long, Wen; Jacobs, John M.; Ramers, D. L.; Wazniak, C.; Wiggert, J. D.; Wood, R.; Xu, J.

    2013-09-01

    The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanisticempirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.

  5. Radioactive Waste Characterization Strategies; Comparisons Between AK/PK, Dose to Curie Modeling, Gamma Spectroscopy, and Laboratory Analysis Methods- 12194

    SciTech Connect

    Singledecker, Steven J.; Jones, Scotty W.; Dorries, Alison M.; Henckel, George; Gruetzmacher, Kathleen M. [Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)

    2012-07-01

    In the coming fiscal years of potentially declining budgets, Department of Energy facilities such as the Los Alamos National Laboratory (LANL) will be looking to reduce the cost of radioactive waste characterization, management, and disposal processes. At the core of this cost reduction process will be choosing the most cost effective, efficient, and accurate methods of radioactive waste characterization. Central to every radioactive waste management program is an effective and accurate waste characterization program. Choosing between methods can determine what is classified as low level radioactive waste (LLRW), transuranic waste (TRU), waste that can be disposed of under an Authorized Release Limit (ARL), industrial waste, and waste that can be disposed of in municipal landfills. The cost benefits of an accurate radioactive waste characterization program cannot be overstated. In addition, inaccurate radioactive waste characterization of radioactive waste can result in the incorrect classification of radioactive waste leading to higher disposal costs, Department of Transportation (DOT) violations, Notice of Violations (NOVs) from Federal and State regulatory agencies, waste rejection from disposal facilities, loss of operational capabilities, and loss of disposal options. Any one of these events could result in the program that mischaracterized the waste losing its ability to perform it primary operational mission. Generators that produce radioactive waste have four characterization strategies at their disposal: - Acceptable Knowledge/Process Knowledge (AK/PK); - Indirect characterization using a software application or other dose to curie methodologies; - Non-Destructive Analysis (NDA) tools such as gamma spectroscopy; - Direct sampling (e.g. grab samples or Surface Contaminated Object smears) and laboratory analytical; Each method has specific advantages and disadvantages. This paper will evaluate each method detailing those advantages and disadvantages

  6. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  7. XMM-NEWTON MONITORING OF THE CLOSE PRE-MAIN-SEQUENCE BINARY AK SCO. EVIDENCE OF TIDE-DRIVEN FILLING OF THE INNER GAP IN THE CIRCUMBINARY DISK

    SciTech Connect

    Gomez de Castro, Ana Ines; Lopez-Santiago, Javier; Talavera, Antonio; Sytov, A. Yu.; Bisikalo, D.

    2013-03-20

    AK Sco stands out among pre-main-sequence binaries because of its prominent ultraviolet excess, the high eccentricity of its orbit, and the strong tides driven by it. AK Sco consists of two F5-type stars that get as close as 11 R{sub *} at periastron passage. The presence of a dense (n{sub e} {approx} 10{sup 11} cm{sup -3}) extended envelope has been unveiled recently. In this article, we report the results from an XMM-Newton-based monitoring of the system. We show that at periastron, X-ray and UV fluxes are enhanced by a factor of {approx}3 with respect to the apastron values. The X-ray radiation is produced in an optically thin plasma with T {approx} 6.4 Multiplication-Sign 10{sup 6} K and it is found that the N{sub H} column density rises from 0.35 Multiplication-Sign 10{sup 21} cm{sup -2} at periastron to 1.11 Multiplication-Sign 10{sup 21} cm{sup -2} at apastron, in good agreement with previous polarimetric observations. The UV emission detected in the Optical Monitor band seems to be caused by the reprocessing of the high-energy magnetospheric radiation on the circumstellar material. Further evidence of the strong magnetospheric disturbances is provided by the detection of line broadening of 278.7 km s{sup -1} in the N V line with Hubble Space Telescope/Space Telescope Imaging Spectrograph. Numerical simulations of the mass flow from the circumbinary disk to the components have been carried out. They provide a consistent scenario with which to interpret AK Sco observations. We show that the eccentric orbit acts like a gravitational piston. At apastron, matter is dragged efficiently from the inner disk border, filling the inner gap and producing accretion streams that end as ring-like structures around each component of the system. At periastron, the ring-like structures come into contact, leading to angular momentum loss, and thus producing an accretion outburst.

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

    SciTech Connect

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

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are

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

    SciTech Connect

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

    2010-02-21

    The operators of electrical grids, sometimes referred to as Balancing Authorities (BA), typically make critical decisions on how to most reliably and economically balance electrical load and generation in time frames ranging from a few minutes to six hours ahead. At higher levels of wind power generation, there is an increasing need to improve the accuracy of 0- to 6-hour ahead wind power forecasts. Forecasts on this time scale have typically been strongly dependent on short-term trends indicated by the time series of power production and meteorological data from a wind farm. Additional input information is often available from the output of Numerical Weather Prediction (NWP) models and occasionally from off-site meteorological towers in the region surrounding the wind generation facility. A widely proposed approach to improve short-term forecasts is the deployment of off-site meteorological towers at locations upstream from the wind generation facility in order to sense approaching wind perturbations. While conceptually appealing, it turns out that, in practice, it is often very difficult to derive significant benefit in forecast performance from this approach. The difficulty is rooted in the fact that the type, scale, and amplitude of the processes controlling wind variability at a site change from day to day if not from hour to hour. Thus, a location that provides some useful forecast information for one time may not be a useful predictor a few hours later. Indeed, some processes that cause significant changes in wind power production operate predominantly in the vertical direction and thus cannot be monitored by employing a network of sensors at off-site locations. Hence, it is very challenging to determine the type of sensors and deployment locations to get the most benefit for a specific short-term forecast application. Two tools recently developed in the meteorological research community have the potential to help determine the locations and parameters to

  10. EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day

    Energy Information Administration (EIA) (indexed site)

    EIA revises up forecast for U.S. 2013 crude oil production by 70,000 barrels per day The forecast for U.S. crude oil production keeps going higher. The U.S. Energy Information Administration revised upward its projection for crude oil output in 2013 by 70,000 barrels per day and for next year by 190,000 barrels per day. U.S. oil production is now on track to average 7.5 million barrels per day this year and rise to 8.4 million barrels per day in 2014, according to EIA's latest monthly forecast.

  11. Oxygenate Supply/Demand Balances in the Short-Term Integrated Forecasting Model (Released in the STEO March 1998)

    Reports and Publications

    1998-01-01

    The blending of oxygenates, such as fuel ethanol and methyl tertiary butyl ether (MTBE), into motor gasoline has increased dramatically in the last few years because of the oxygenated and reformulated gasoline programs. Because of the significant role oxygenates now have in petroleum product markets, the Short-Term Integrated Forecasting System (STIFS) was revised to include supply and demand balances for fuel ethanol and MTBE. The STIFS model is used for producing forecasts in the Short-Term Energy Outlook. A review of the historical data sources and forecasting methodology for oxygenate production, imports, inventories, and demand is presented in this report.

  12. A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    System A Processor to get UV-A and UV-B Radiation Products from the ECMWF Forecast System Morcrette, Jean-Jacques European Centre for Medium-Range Weather Forecasts Category: Radiation A new processor for evaluating the UV-B and UV-A radiation at the surface, based on modifications to the current shortwave radiation scheme of the ECMWF forecast system is described. Sensitivity studies of the UV surface irradiance and Erythemal Dose Rate to spectral resolution, representation and atmospheric

  13. Integration of Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Chakrabarti, Bhujanga B.; Subbarao, Krishnappa; Loutan, Clyde; Guttromson, Ross T.

    2010-04-20

    In this paper, a new approach to evaluate the uncertainty ranges for the required generation performance envelope, including the balancing capacity, ramping capability and ramp duration is presented. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (CAISO) real life data have shown the effectiveness and efficiency of the proposed approach.

  14. Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

    SciTech Connect

    Constantinescu, E. M.; Zavala, V. M.; Rocklin, M.; Lee, S.; Anitescu, M.

    2009-10-09

    We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.

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

    SciTech Connect

    Bolinger, Mark; Wiser, Ryan

    2004-12-13

    On December 9, the reference case projections from ''Annual Energy Outlook 2005 (AEO 2005)'' were posted on the Energy Information Administration's (EIA) web site. As some of you may be aware, we at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk. As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past four years, forward natural gas contracts (e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past four years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation (presuming that long-term price stability is valued). In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2005. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or, more recently (and briefly), http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past four AEO releases (AEO 2001-AE0 2004), we once again find that the AEO 2005 reference case gas price forecast falls well below

  16. The impact of forecasted energy price increases on low-income consumers

    SciTech Connect

    Eisenberg, Joel F.

    2005-10-31

    The Department of Energy’s Energy Information Administration (EIA) recently released its short term forecast for residential energy prices for the winter of 2005-2006. The forecast indicates significant increases in fuel costs, particularly for natural gas, propane, and home heating oil, for the year ahead. In the following analysis, the Oak Ridge National Laboratory has integrated the EIA price projections with the Residential Energy Consumption Survey (RECS) for 2001 in order to project the impact of these price increases on the nation’s low-income households by primary heating fuel type, nationally and by Census Region. The statistics are intended for the use of policymakers in the Department of Energy’s Weatherization Assistance Program and elsewhere who are trying to gauge the nature and severity of the problems that will be faced by eligible low-income households during the 2006 fiscal year.

  17. Comparison of AEO 2006 Natural Gas Price Forecast to NYMEX FuturesPrices

    SciTech Connect

    Bolinger, Mark; Wiser, Ryan

    2005-12-19

    On December 12, 2005, the reference case projections from ''Annual Energy Outlook 2006'' (AEO 2006) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have in the past compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past five years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past five years at least, levelized cost comparisons of fixed-price renewable generation with variable price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are ''biased'' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2006. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past five AEO releases (AEO 2001-AEO

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

    SciTech Connect

    Bolinger, Mark; Wiser, Ryan

    2006-12-06

    On December 5, 2006, the reference case projections from 'Annual Energy Outlook 2007' (AEO 2007) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables play in mitigating such risk (see, for example, http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf). As such, we were curious to see how the latest AEO gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. As a refresher, our past work in this area has found that over the past six years, forward natural gas contracts (with prices that can be locked in--e.g., gas futures, swaps, and physical supply) have traded at a premium relative to contemporaneous long-term reference case gas price forecasts from the EIA. As such, we have concluded that, over the past six years at least, levelized cost comparisons of fixed-price renewable generation with variable-price gas-fired generation that have been based on AEO natural gas price forecasts (rather than forward prices) have yielded results that are 'biased' in favor of gas-fired generation, presuming that long-term price stability is valued. In this memo we simply update our past analysis to include the latest long-term gas price forecast from the EIA, as contained in AEO 2007. For the sake of brevity, we do not rehash information (on methodology, potential explanations for the premiums, etc.) contained in our earlier reports on this topic; readers interested in such information are encouraged to download that work from http://eetd.lbl.gov/ea/EMS/reports/53587.pdf or http://eetd.lbl.gov/ea/ems/reports/54751.pdf. As was the case in the past six AEO releases (AEO 2001-AEO 2006), we

  19. 2007 Wholesale Power Rate Case Final Proposal : Market Price Forecast Study.

    SciTech Connect

    United States. Bonneville Power Administration.

    2006-07-01

    This study presents BPA's market price forecasts for the Final Proposal, which are based on AURORA modeling. AURORA calculates the variable cost of the marginal resource in a competitively priced energy market. In competitive market pricing, the marginal cost of production is equivalent to the market-clearing price. Market-clearing prices are important factors for informing BPA's power rates. AURORA was used as the primary tool for (a) estimating the forward price for the IOU REP Settlement benefits calculation for fiscal years (FY) 2008 and 2009, (b) estimating the uncertainty surrounding DSI payments and IOU REP Settlements benefits, (c) informing the secondary revenue forecast and (d) providing a price input used for the risk analysis. For information about the calculation of the secondary revenues, uncertainty regarding the IOU REP Settlement benefits and DSI payment uncertainty, and the risk run, see Risk Analysis Study WP-07-FS-BPA-04.

  20. A Chronological Reliability Model Incorporating Wind Forecasts to Assess Wind Plant Reserve Allocation: Preprint

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    * NREL/CP-500-32210 A Chronological Reliability Model Incorporating Wind Forecasts to Assess Wind Plant Reserve Allocation Preprint Michael Milligan To be presented at the American Wind Energy Association WindPower 2002 Conference Portland, Oregon June 3 - June 5, 2002 National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401-3393 NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute * Battelle * Bechtel Contract No. DE-AC36-99-GO10337

  1. Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

    SciTech Connect

    Intharathirat, Rotchana; Abdul Salam, P.; Kumar, S.; Untong, Akarapong

    2015-05-15

    Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.

  2. New Forecasting Tools Enhance Wind Energy Integration In Idaho and Oregon

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    New Forecasting Tools Enhance Wind Energy Integration in Idaho and Oregon Page 1 Under the American Recovery and Reinvestment Act of 2009, the U.S. Department of Energy and the electricity industry have jointly invested over $7.9 billion in 99 cost-shared Smart Grid Investment Grant projects to modernize the electric grid, strengthen cybersecurity, improve interoperability, and collect an unprecedented level of data on smart grid and customer operations. 1. Summary Idaho Power Company (IPC)

  3. Modeling and forecasting the distribution of Vibrio vulnificus in Chesapeake Bay

    SciTech Connect

    Jacobs, John M.; Rhodes, M.; Brown, C. W.; Hood, Raleigh R.; Leight, A.; Long, Wen; Wood, R.

    2014-11-01

    The aim is to construct statistical models to predict the presence, abundance and potential virulence of Vibrio vulnificus in surface waters. A variety of statistical techniques were used in concert to identify water quality parameters associated with V. vulnificus presence, abundance and virulence markers in the interest of developing strong predictive models for use in regional oceanographic modeling systems. A suite of models are provided to represent the best model fit and alternatives using environmental variables that allow them to be put to immediate use in current ecological forecasting efforts. Conclusions: Environmental parameters such as temperature, salinity and turbidity are capable of accurately predicting abundance and distribution of V. vulnificus in Chesapeake Bay. Forcing these empirical models with output from ocean modeling systems allows for spatially explicit forecasts for up to 48 h in the future. This study uses one of the largest data sets compiled to model Vibrio in an estuary, enhances our understanding of environmental correlates with abundance, distribution and presence of potentially virulent strains and offers a method to forecast these pathogens that may be replicated in other regions.

  4. Turbulence-driven coronal heating and improvements to empirical forecasting of the solar wind

    SciTech Connect

    Woolsey, Lauren N.; Cranmer, Steven R.

    2014-06-01

    Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfvn waves on coronal heating and wind acceleration. The one-dimensional magnetohydrodynamic code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another one-dimensional code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that can be used to independently validate prediction models. ZEPHYR provides the foundation and calibration for TEMPEST, and ultimately we will use these models to predict observations and explain space weather created by the bulk solar wind. We are able to reproduce with both models the general anticorrelation seen in comparisons of observed wind speed at 1 AU and the flux tube expansion factor. There is significantly less spread than comparing the results of the two models than between ZEPHYR and a traditional flux tube expansion relation. We suggest that the new code, TEMPEST, will become a valuable tool in the forecasting of space weather.

  5. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

    DOE PAGES [OSTI]

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equationsmore » at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.« less

  6. Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting

    SciTech Connect

    Carlberg, Kevin; Ray, Jaideep; van Bloemen Waanders, Bart

    2015-02-14

    Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-like method, which incurs a sequence of linear-system solves. Most model-reduction techniques for nonlinear ODEs exploit knowledge of system's spatial behavior to reduce the computational complexity of each linear-system solve. However, the number of linear-system solves for the reduced-order simulation often remains roughly the same as that for the full-order simulation. We propose exploiting knowledge of the model's temporal behavior to (1) forecast the unknown variable of the reduced-order system of nonlinear equations at future time steps, and (2) use this forecast as an initial guess for the Newton-like solver during the reduced-order-model simulation. To compute the forecast, we propose using the Gappy POD technique. As a result, the goal is to generate an accurate initial guess so that the Newton solver requires many fewer iterations to converge, thereby decreasing the number of linear-system solves in the reduced-order-model simulation.

  7. National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

    SciTech Connect

    Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

    1982-03-31

    The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

  8. Evaluation of Forecasted Southeast Pacific Stratocumulus in the NCAR, GFDL and ECMWF Models

    SciTech Connect

    Hannay, C; Williamson, D L; Hack, J J; Kiehl, J T; Olson, J G; Klein, S A; Bretherton, C S; K?hler, M

    2008-01-24

    We examine forecasts of Southeast Pacific stratocumulus at 20S and 85W during the East Pacific Investigation of Climate (EPIC) cruise of October 2001 with the ECMWF model, the Atmospheric Model (AM) from GFDL, the Community Atmosphere Model (CAM) from NCAR, and the CAM with a revised atmospheric boundary layer formulation from the University of Washington (CAM-UW). The forecasts are initialized from ECMWF analyses and each model is run for 3 days to determine the differences with the EPIC field data. Observations during the EPIC cruise show a stable and well-mixed boundary layer under a sharp inversion. The inversion height and the cloud layer have a strong and regular diurnal cycle. A key problem common to the four models is that the forecasted planetary boundary layer (PBL) height is too low when compared to EPIC observations. All the models produce a strong diurnal cycle in the Liquid Water Path (LWP) but there are large differences in the amplitude and the phase compared to the EPIC observations. This, in turn, affects the radiative fluxes at the surface. There is a large spread in the surface energy budget terms amongst the models and large discrepancies with observational estimates. Single Column Model (SCM) experiments with the CAM show that the vertical pressure velocity has a large impact on the PBL height and LWP. Both the amplitude of the vertical pressure velocity field and its vertical structure play a significant role in the collapse or the maintenance of the PBL.

  9. Forecasting the Magnitude of Sustainable Biofeedstock Supplies: the Challenges and the Rewards

    SciTech Connect

    Graham, Robin Lambert

    2007-01-01

    Forecasting the magnitude of sustainable biofeedstock supplies is challenging because of 1) the myriad of potential feedstock types and their management 2) the need to account for the spatial variation of both the supplies and their environmental and economic consequences, and 3) the inherent challenges of optimizing across economic and environmental considerations. Over the last two decades U.S. biomass forecasts have become increasingly complex and sensitive to environmental and economic considerations. More model development and research is needed however, to capture the landscape and regional tradeoffs of differing biofeedstock supplies especially with regards water quality concerns and wildlife/biodiversity. Forecasts need to be done in the context of the direction of change and what the probable land use and attendant environmental and economic outcomes would be if biofeedstocks were not being produced. To evaluate sustainability, process-oriented models need to be coupled or used to inform sector models and more work needs to be done on developing environmental metrics that are useful for evaluating economic and environmental tradeoffs. These challenges are exciting and worthwhile as they will enable the bioenergy industry to capture environmental and social benefits of biofeedstock production and reduce risks.

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

    SciTech Connect

    McNamara, Laura A.

    2010-08-01

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

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

    SciTech Connect

    Templeton, K.J.

    1996-05-23

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

  12. Use of Data Denial Experiments to Evaluate ESA Forecast Sensitivity Patterns

    SciTech Connect

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

    2011-09-13

    The overall goal of this multi-phased research project known as WindSENSE is to develop an observation system deployment strategy that would improve wind power generation forecasts. The objective of the deployment strategy is to produce the maximum benefit for 1- to 6-hour ahead forecasts of wind speed at hub-height ({approx}80 m). In this phase of the project the focus is on the Mid-Columbia Basin region which encompasses the Bonneville Power Administration (BPA) wind generation area shown in Figure 1 that includes Klondike, Stateline, and Hopkins Ridge wind plants. The Ensemble Sensitivity Analysis (ESA) approach uses data generated by a set (ensemble) of perturbed numerical weather prediction (NWP) simulations for a sample time period to statistically diagnose the sensitivity of a specified forecast variable (metric) for a target location to parameters at other locations and prior times referred to as the initial condition (IC) or state variables. The ESA approach was tested on the large-scale atmospheric prediction problem by Ancell and Hakim 2007 and Torn and Hakim 2008. ESA was adapted and applied at the mesoscale by Zack et al. (2010a, b, and c) to the Tehachapi Pass, CA (warm and cools seasons) and Mid-Colombia Basin (warm season only) wind generation regions. In order to apply the ESA approach at the resolution needed at the mesoscale, Zack et al. (2010a, b, and c) developed the Multiple Observation Optimization Algorithm (MOOA). MOOA uses a multivariate regression on a few select IC parameters at one location to determine the incremental improvement of measuring multiple variables (representative of the IC parameters) at various locations. MOOA also determines how much information from each IC parameter contributes to the change in the metric variable at the target location. The Zack et al. studies (2010a, b, and c), demonstrated that forecast sensitivity can be characterized by well-defined, localized patterns for a number of IC variables such as 80-m

  13. Climatic Forecasting of Net Infiltration at Yucca Montain Using Analogue Meteororological Data

    SciTech Connect

    B. Faybishenko

    2006-09-11

    At Yucca Mountain, Nevada, future changes in climatic conditions will most likely alter net infiltration, or the drainage below the bottom of the evapotranspiration zone within the soil profile or flow across the interface between soil and the densely welded part of the Tiva Canyon Tuff. The objectives of this paper are to: (a) develop a semi-empirical model and forecast average net infiltration rates, using the limited meteorological data from analogue meteorological stations, for interglacial (present day), and future monsoon, glacial transition, and glacial climates over the Yucca Mountain region, and (b) corroborate the computed net-infiltration rates by comparing them with the empirically and numerically determined groundwater recharge and percolation rates through the unsaturated zone from published data. In this paper, the author presents an approach for calculations of net infiltration, aridity, and precipitation-effectiveness indices, using a modified Budyko's water-balance model, with reference-surface potential evapotranspiration determined from the radiation-based Penman (1948) formula. Results of calculations show that net infiltration rates are expected to generally increase from the present-day climate to monsoon climate, to glacial transition climate, and then to the glacial climate. The forecasting results indicate the overlap between the ranges of net infiltration for different climates. For example, the mean glacial net-infiltration rate corresponds to the upper-bound glacial transition net infiltration, and the lower-bound glacial net infiltration corresponds to the glacial transition mean net infiltration. Forecasting of net infiltration for different climate states is subject to numerous uncertainties-associated with selecting climate analogue sites, using relatively short analogue meteorological records, neglecting the effects of vegetation and surface runoff and runon on a local scale, as well as possible anthropogenic climate changes.

  14. Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010

    SciTech Connect

    Jacobs, David E. . E-mail: dejacobs@starpower.net; Nevin, Rick

    2006-11-15

    We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels {>=}10 {mu}g/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

  15. U.S. oil production forecast update reflects lower rig count

    Energy Information Administration (EIA) (indexed site)

    U.S. oil production forecast update reflects lower rig count Lower oil prices and fewer rigs drilling for crude oil are expected to slow U.S. oil production growth this year and in 2016. U.S. crude oil production is still expected to average 9.2 million barrels per day this year. That's up half a million barrels per day from last year and the highest output level in more than four decades. A substantial part of the year-over-year increase reflects rapid production growth throughout 2014.

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

    SciTech Connect

    Valero, O.J.

    1996-04-23

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

  17. Energy Savings Forecast of Solid-State Lighting in General Illumination Applications

    SciTech Connect

    none,

    2014-08-29

    With declining production costs and increasing technical capabilities, LED adoption has recently gained momentum in general illumination applications. This is a positive development for our energy infrastructure, as LEDs use significantly less electricity per lumen produced than many traditional lighting technologies. The U.S. Department of Energy’s Energy Savings Forecast of Solid-State Lighting in General Illumination Applications examines the expected market penetration and resulting energy savings of light-emitting diode, or LED, lamps and luminaires from today through 2030.

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

    SciTech Connect

    Xu, Lilai; Gao, Peiqing; Cui, Shenghui; Liu, Chun

    2013-06-15

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

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

    SciTech Connect

    Not Available

    1994-12-01

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

  20. RACORO Forecasting

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    7a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7 26.5

  1. Crude oil and alternate energy production forecasts for the twenty-first century: The end of the hydrocarbon era

    SciTech Connect

    Edwards, J.D.

    1997-08-01

    Predictions of production rates and ultimate recovery of crude oil are needed for intelligent planning and timely action to ensure the continuous flow of energy required by the world`s increasing population and expanding economies. Crude oil will be able to supply increasing demand until peak world production is reached. The energy gap caused by declining conventional oil production must then be filled by expanding production of coal, heavy oil and oil shales, nuclear and hydroelectric power, and renewable energy sources (solar, wind, and geothermal). Declining oil production forecasts are based on current estimated ultimate recoverable conventional crude oil resources of 329 billion barrels for the United States and close to 3 trillion barrels for the world. Peak world crude oil production is forecast to occur in 2020 at 90 million barrels per day. Conventional crude oil production in the United States is forecast to terminate by about 2090, and world production will be close to exhaustion by 2100.

  2. Incorporating Uncertainty of Wind Power Generation Forecast into Power System Operation, Dispatch, and Unit Commitment Procedures

    SciTech Connect

    Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian; Huang, Zhenyu; Subbarao, Krishnappa

    2011-06-23

    An approach to evaluate the uncertainties of the balancing capacity, ramping capability, and ramp duration requirements is proposed. The approach includes three steps: forecast data acquisition, statistical analysis of retrospective information, and prediction of grid balancing requirements for a specified time horizon and a given confidence level. An assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on histogram analysis, incorporating sources of uncertainty - both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures). A new method called the 'flying-brick' technique is developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation process is used to validate the accuracy of the confidence intervals. To demonstrate the validity of the developed uncertainty assessment methods and its impact on grid operation, a framework for integrating the proposed methods with an EMS system is developed. Demonstration through EMS integration illustrates the applicability of the proposed methodology and the developed tool for actual grid operation and paves the road for integration with EMS systems in control rooms.

  3. Incorporating Wind Generation Forecast Uncertainty into Power System Operation, Dispatch, and Unit Commitment Procedures

    SciTech Connect

    Makarov, Yuri V.; Etingov, Pavel V.; Huang, Zhenyu; Ma, Jian; Subbarao, Krishnappa

    2010-10-19

    In this paper, an approach to evaluate the uncertainties of the balancing capacity, ramping capability, and ramp duration requirements is proposed. The approach includes three steps: forecast data acquisition, statistical analysis of retrospective information, and prediction of grid balancing requirements for a specified time horizon and a given confidence level. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on histogram analysis, incorporating sources of uncertainty of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the "flying-brick" technique is developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation process is used to validate the accuracy of the confidence intervals. To demonstrate the validity of the developed uncertainty assessment methods and its impact on grid operation, a framework for integrating the proposed methods with an EMS system is developed. Demonstration through integration with an EMS system illustrates the applicability of the proposed methodology and the developed tool for actual grid operation and paves the road for integration with EMS systems from other vendors.

  4. Solid waste integrated forecast technical (SWIFT) report: FY1997 to FY 2070, Revision 1

    SciTech Connect

    Valero, O.J.; Templeton, K.J.; Morgan, J.

    1997-01-07

    This web site provides an up-to-date report on the radioactive solid waste expected to be managed by Hanford's Waste Management (WM) Project from onsite and offsite generators. It includes: an overview of Hanford-wide solid waste to be managed by the WM Project; program-level and waste class-specific estimates; background information on waste sources; and comparisons with previous forecasts and with other national data sources. This web site does not include: liquid waste (current or future generation); waste to be managed by the Environmental Restoration (EM-40) contractor (i.e., waste that will be disposed of at the Environmental Restoration Disposal Facility (ERDF)); or waste that has been received by the WM Project to date (i.e., inventory waste). The focus of this web site is on low-level mixed waste (LLMW), and transuranic waste (both non-mixed and mixed) (TRU(M)). Some details on low-level waste and hazardous waste are also provided. Currently, this web site is reporting data th at was requested on 10/14/96 and submitted on 10/25/96. The data represent a life cycle forecast covering all reported activities from FY97 through the end of each program's life cycle. Therefore, these data represent revisions from the previous FY97.0 Data Version, due primarily to revised estimates from PNNL. There is some useful information about the structure of this report in the SWIFT Report Web Site Overview.

  5. Developing an industrial end-use forecast: A case study at the Los Angeles department of water and power

    SciTech Connect

    Mureau, T.H.; Francis, D.M.

    1995-05-01

    The Los Angeles Department of Water and Power (LADWP) uses INFORM 1.0 to forecast industrial sector energy. INFORM 1.0 provides an end-use framework that can be used to forecast electricity, natural gas or other fuels consumption. Included with INFORM 1.0 is a default date set including the input data and equations necessary to solve each model. LADWP has substituted service area specific data for the default data wherever possible. This paper briefly describes the steps LADWP follows in developing those inputs and application in INFORM 1.0.

  6. Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

    SciTech Connect

    Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar; Marianno, Fernando J.; Shao, Xiaoyan; Zhang, Jie; Hodge, Bri-Mathias; Hamann, Hendrik F.

    2015-07-15

    With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

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

    SciTech Connect

    BARCOT, R.A.

    2000-08-31

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

  8. Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

    SciTech Connect

    Pennock, K.

    2012-10-01

    AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

  9. A comparison of water vapor quantities from model short-range forecasts and ARM observations

    SciTech Connect

    Hnilo, J J

    2006-03-17

    Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the 'Merged-sounding' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.

  10. A comparison of model short-range forecasts and the ARM Microbase data

    SciTech Connect

    Hnilo, J J

    2006-09-22

    For the fourth quarter ARM metric we will make use of new liquid water data that has become available, and called the 'Microbase' value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Tropical West Pacific (TWP) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both TWP and NSA. The Microbase data have been averaged to 35 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3hourly data for direct comparison to our model output.

  11. A Comparison of Water Vapor Quantities from Model Short-Range Forecasts and ARM Observations

    SciTech Connect

    Hnilo, J.

    2006-03-17

    Model evolution and improvement is complicated by the lack of high quality observational data. To address a major limitation of these measurements the Atmospheric Radiation Measurement (ARM) program was formed. For the second quarter ARM metric we will make use of new water vapor data that has become available, and called the “Mergedsounding” value added product (referred to as OBS, within the text) at three sites: the North Slope of Alaska (NSA), Darwin Australia (DAR) and the Southern Great Plains (SGP) and compare these observations to model forecast data. Two time periods will be analyzed March 2000 for the SGP and October 2004 for both DAR and NSA. The merged-sounding data have been interpolated to 37 pressure levels (e.g., from 1000hPa to 100hPa at 25hPa increments) and time averaged to 3 hourly data for direct comparison to our model output.

  12. Department of Energy award DE-SC0004164 Climate and National Security: Securing Better Forecasts

    SciTech Connect

    Reno Harnish

    2011-08-16

    The Climate and National Security: Securing Better Forecasts symposium was attended by senior policy makers and distinguished scientists. The juxtaposition of these communities was creative and fruitful. They acknowledged they were speaking past each other. Scientists were urged to tell policy makers about even improbable outcomes while articulating clearly the uncertainties around the outcomes. As one policy maker put it, we are accustomed to making these types of decisions. These points were captured clearly in an article that appeared on the New York Times website and can be found with other conference materials most easily on our website, www.scripps.ucsd.edu/cens/. The symposium, generously supported by the NOAA/JIMO, benefitted the public by promoting scientifically informed decision making and by the transmission of objective information regarding climate change and national security.

  13. DOE Climate Change Adaptation Training (Richland, WA)

    Energy.gov [DOE]

    This free training is a primer on climate science, identifying climate hazards, conducting vulnerability assessments, leveraging climate change data and tools and understanding the energy-water-food nexus.

  14. Sumas, WA LNG Imports from Canada

    Gasoline and Diesel Fuel Update

    332,358 313,922 312,236 333,050 359,343 429,642 1996-2015 Pipeline Prices 4.22 3.96 2.72 3.62 4.32 2.36 1996-2015 Liquefied Natural Gas Volumes 0 5 11 2013-2015 Liquefied Natural ...

  15. 2715-WA_Carpenter's_Shop_Refurbishment.pdf

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

  16. EIS-0346: Salmon Creek Project, WA

    Energy.gov [DOE]

    This EIS analyzes BPA's proposal to fund activities that would restore sufficient water flows to Salmon Creek and rehabilitate its streambed as necessary to provide adequate passage for summer steelhead (Oncorhynchus mykiss) and possibly spring chinook (O. tshawytscha).

  17. Sumas, WA LNG Imports from Canada

    Annual Energy Outlook

    12,530 7,769 9,768 6,016 10,409 3,547 1996-2014 Pipeline Prices 5.55 4.81 4.47 3.87 4.02 5.05 1996...

  18. Sumas, WA LNG Imports from Canada

    Annual Energy Outlook

    309,516 332,358 313,922 312,236 333,050 359,343 1996-2014 Pipeline Prices 3.99 4.22 3.96 2.72 3.62 4.32 1996-2014 Liquefied Natural Gas Volumes 0 5 2013-2014 Liquefied Natural Gas...

  19. Forecasting the northern African dust outbreak towards Europe in April 2011: A model intercomparison

    DOE PAGES [OSTI]

    Huneeus, N.; Basart, S.; Fiedler, S.; Morcrette, J. -J.; Benedetti, A.; Mulcahy, J.; Terradellas, E.; Garcia-Pando, C. Perez; Pejanovic, G.; Nickovic, S.; et al

    2016-04-21

    In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distributionmore » was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. In this paper, our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.« less

  20. Electric-utility DSM programs: 1990 data and forecasts to 2000

    SciTech Connect

    Hirst, E.

    1992-06-01

    In April 1992, the Energy Information Administration (EIA) released data on 1989 and 1990 electric-utility demand-site management (DMS) programs. These data represent a census of US utility DSM programs, with reports of utility expenditures, energy savings, and load reductions caused by these programs. In addition, EIA published utility estimates of the costs and effects of these programs from 1991 to 2000. These data provide the first comprehensive picture of what utilities are spending and accomplishing by utility, state, and region. This report presents, summarizes, and interprets the 1990 data and the utility forecasts of their DSM-program expenditures and impacts to the year 2000. Only utilities with annual sales greater than 120 GWh were required to report data on their DSM programs to EIA. Of the 1194 such utilities, 363 reported having a DSM program that year. These 363 electric utilities spent $1.2 billion on their DSM programs in 1990, up from $0.9 billion in 1989. Estimates of energy savings (17,100 GWh in 1990 and 14,800 GWh in 1989) and potential reductions in peak demand (24,400 MW in 1990 and about 19,400 MW in 1989) also showed substantial increases. Overall, utility DSM expenditures accounted for 0.7% of total US electric revenues, while the reductions in energy and demand accounted for 0.6% and 4.9% of their respective 1990 national totals. The investor-owned utilities accounted for 70 to 90% of the totals for DSM costs, energy savings, and demand reductions. The public utilities reported larger percentage reductions in peak demand and energy smaller percentage DSM expenditures. These averages hide tremendous variations across utilities. Utility forecasts of DSM expenditures and effects show substantial growth in both absolute and relative terms.

  1. Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

    SciTech Connect

    Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; Bierman, Paul R.; Breshears, David D.; Crosby, Benjamin T.; Ellis, Michael; Foufoula-Georgiou, Efi; Heimsath, Arjun M.; Houser, Chris; Lancaster, Nick; Marani, Marco; Merritts, Dorothy J.; Moore, Laura J.; Pederson, Joel L.; Poulos, Michael J.; Rittenour, Tammy M.; Rowland, Joel C.; Ruggiero, Peter; Ward, Dylan J.; Wickert, Andrew D.; Yager, Elowyn M.

    2015-07-14

    In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we have the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.

  2. Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

    DOE PAGES [OSTI]

    Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; Bierman, Paul R.; Breshears, David D.; Crosby, Benjamin T.; Ellis, Michael; Foufoula-Georgiou, Efi; Heimsath, Arjun M.; Houser, Chris; et al

    2015-07-14

    In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore » the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

  3. Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-09-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation) and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. In order to improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively, by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter unique

  4. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

    SciTech Connect

    Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

    2010-01-01

    The power system balancing process, which includes the scheduling, real time dispatch (load following) and regulation processes, is traditionally based on deterministic models. Since the conventional generation needs time to be committed and dispatched to a desired megawatt level, the scheduling and load following processes use load and wind and solar power production forecasts to achieve future balance between the conventional generation and energy storage on the one side, and system load, intermittent resources (such as wind and solar generation), and scheduled interchange on the other side. Although in real life the forecasting procedures imply some uncertainty around the load and wind/solar forecasts (caused by forecast errors), only their mean values are actually used in the generation dispatch and commitment procedures. Since the actual load and intermittent generation can deviate from their forecasts, it becomes increasingly unclear (especially, with the increasing penetration of renewable resources) whether the system would be actually able to meet the conventional generation requirements within the look-ahead horizon, what the additional balancing efforts would be needed as we get closer to the real time, and what additional costs would be incurred by those needs. To improve the system control performance characteristics, maintain system reliability, and minimize expenses related to the system balancing functions, it becomes necessary to incorporate the predicted uncertainty ranges into the scheduling, load following, and, in some extent, into the regulation processes. It is also important to address the uncertainty problem comprehensively by including all sources of uncertainty (load, intermittent generation, generators’ forced outages, etc.) into consideration. All aspects of uncertainty such as the imbalance size (which is the same as capacity needed to mitigate the imbalance) and generation ramping requirement must be taken into account. The latter

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

    SciTech Connect

    Bolinger, Mark; Wiser, Ryan

    2009-01-28

    On December 17, 2008, the reference-case projections from Annual Energy Outlook 2009 (AEO 2009) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof), differences in capital costs and O&M expenses, or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired or nuclear generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers; and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal, uranium, and

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

    SciTech Connect

    Bolinger, Mark A; Bolinger, Mark; Wiser, Ryan

    2008-01-07

    On December 12, 2007, the reference-case projections from Annual Energy Outlook 2008 (AEO 2008) were posted on the Energy Information Administration's (EIA) web site. We at LBNL have, in the past, compared the EIA's reference-case long-term natural gas price forecasts from the AEO series to contemporaneous natural gas prices that can be locked in through the forward market, with the goal of better understanding fuel price risk and the role that renewables can play in mitigating such risk. As such, we were curious to see how the latest AEO reference-case gas price forecast compares to the NYMEX natural gas futures strip. This brief memo presents our findings. Note that this memo pertains only to natural gas fuel price risk (i.e., the risk that natural gas prices might differ over the life of a gas-fired generation asset from what was expected when the decision to build the gas-fired unit was made). We do not take into consideration any of the other distinct attributes of gas-fired and renewable generation, such as dispatchability (or lack thereof) or environmental externalities. A comprehensive comparison of different resource types--which is well beyond the scope of this memo--would need to account for differences in all such attributes, including fuel price risk. Furthermore, our analysis focuses solely on natural-gas-fired generation (as opposed to coal-fired generation, for example), for several reasons: (1) price volatility has been more of a concern for natural gas than for other fuels used to generate power; (2) for environmental and other reasons, natural gas has, in recent years, been the fuel of choice among power plant developers (though its appeal has diminished somewhat as prices have increased); and (3) natural gas-fired generators often set the market clearing price in competitive wholesale power markets throughout the United States. That said, a more-complete analysis of how renewables mitigate fuel price risk would also need to consider coal and

  7. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People`s Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  8. Navy Mobility Fuels Forecasting System report: Navy fuel production in the year 2000

    SciTech Connect

    Hadder, G.R.; Davis, R.M.

    1991-09-01

    The Refinery Yield Model of the Navy Mobility Fuels Forecasting System has been used to study the feasibility and quality of Navy JP-5 jet fuel and F-76 marine diesel fuel for two scenarios in the year 2000. Both scenarios account for environmental regulations for fuels produced in the US and assume that Eastern Europe, the USSR, and the People's Republic of China have free market economies. One scenario is based on business-as-usual market conditions for the year 2000. The second scenario is similar to first except that USSR crude oil production is 24 percent lower. During lower oil production in the USSR., there are no adverse effects on Navy fuel availability, but JP-5 is generally a poorer quality fuel relative to business-as-usual in the year 2000. In comparison with 1990, there are two potential problems areas for future Navy fuel quality. The first problem is increased aromaticity of domestically produced Navy fuels. Higher percentages of aromatics could have adverse effects on storage, handling, and combustion characteristics of both JP-5 and F-76. The second, and related, problem is that highly aromatic light cycle oils are blended into F-76 at percentages which promote fuel instability. It is recommended that the Navy continue to monitor the projected trend toward increased aromaticity in JP-5 and F-76 and high percentages of light cycle oils in F-76. These potential problems should be important considerations in research and development for future Navy engines.

  9. Material World: Forecasting Household Appliance Ownership in a Growing Global Economy

    SciTech Connect

    Letschert, Virginie; McNeil, Michael A.

    2009-03-23

    Over the past years the Lawrence Berkeley National Laboratory (LBNL) has developed an econometric model that predicts appliance ownership at the household level based on macroeconomic variables such as household income (corrected for purchase power parity), electrification, urbanization and climate variables. Hundreds of data points from around the world were collected in order to understand trends in acquisition of new appliances by households, especially in developing countries. The appliances covered by this model are refrigerators, lighting fixtures, air conditioners, washing machines and televisions. The approach followed allows the modeler to construct a bottom-up analysis based at the end use and the household level. It captures the appliance uptake and the saturation effect which will affect the energy demand growth in the residential sector. With this approach, the modeler can also account for stock changes in technology and efficiency as a function of time. This serves two important functions with regard to evaluation of the impact of energy efficiency policies. First, it provides insight into which end uses will be responsible for the largest share of demand growth, and therefore should be policy priorities. Second, it provides a characterization of the rate at which policies affecting new equipment penetrate the appliance stock. Over the past 3 years, this method has been used to support the development of energy demand forecasts at the country, region or global level.

  10. Demand forecasting and revenue requirements, with implications for consideration in British Columbia

    SciTech Connect

    Acton, J.P.

    1983-05-01

    This paper was filed as an exhibit on behalf of The Consumers' Association of Canada (B.C. Branch), The Federated Anti-Poverty Groups of B.C., The Sierra Club of Western Canada, and the B.C. Old Age Pensioners' Organization. It was subjected to cross-examination on October 29, 1982, during Phase I of the hearings. The Utilities Commission had designated Phase I for consideration of (1) demand, (2) assets in service, (3) revenue requirements excluding return, and (4) financing and capital requirements. This paper presents a general discussion of the elements of a rate structure and their relationship to the demand for electricity, a systematic review of some 50 empirical studies of the demand for electricity as a function of price and other factors by the three principal classes of customers, and a discussion of the notion of revenue requirements. The paper should be of interest to utility regulators, rate specialists, and forecasters for its review of demand models and to academics concerned with the study of energy demand.

  11. Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets

    SciTech Connect

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

    2005-06-30

    The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

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

    SciTech Connect

    Koomey, J.G.; Brown, R.E.; Richey, R.

    1995-12-01

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

  13. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    SciTech Connect

    Vrugt, Jasper A; Wohling, Thomas

    2008-01-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  14. Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling

    SciTech Connect

    Dyson, Brian; Chang, N.-B. . E-mail: nchang@even.tamuk.edu

    2005-07-01

    Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach - system dynamics modeling - for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool - Stella[reg]. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues.

  15. Technology data characterizing water heating in commercial buildings: Application to end-use forecasting

    SciTech Connect

    Sezgen, O.; Koomey, J.G.

    1995-12-01

    Commercial-sector conservation analyses have traditionally focused on lighting and space conditioning because of their relatively-large shares of electricity and fuel consumption in commercial buildings. In this report we focus on water heating, which is one of the neglected end uses in the commercial sector. The share of the water-heating end use in commercial-sector electricity consumption is 3%, which corresponds to 0.3 quadrillion Btu (quads) of primary energy consumption. Water heating accounts for 15% of commercial-sector fuel use, which corresponds to 1.6 quads of primary energy consumption. Although smaller in absolute size than the savings associated with lighting and space conditioning, the potential cost-effective energy savings from water heaters are large enough in percentage terms to warrant closer attention. In addition, water heating is much more important in particular building types than in the commercial sector as a whole. Fuel consumption for water heating is highest in lodging establishments, hospitals, and restaurants (0.27, 0.22, and 0.19 quads, respectively); water heating`s share of fuel consumption for these building types is 35%, 18% and 32%, respectively. At the Lawrence Berkeley National Laboratory, we have developed and refined a base-year data set characterizing water heating technologies in commercial buildings as well as a modeling framework. We present the data and modeling framework in this report. The present commercial floorstock is characterized in terms of water heating requirements and technology saturations. Cost-efficiency data for water heating technologies are also developed. These data are intended to support models used for forecasting energy use of water heating in the commercial sector.

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

    SciTech Connect

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

    1994-05-01

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

  17. ON THE IMPACT OF SUPER RESOLUTION WSR-88D DOPPLER RADAR DATA ASSIMILATION ON HIGH RESOLUTION NUMERICAL MODEL FORECASTS

    SciTech Connect

    Chiswell, S

    2009-01-11

    Assimilation of radar velocity and precipitation fields into high-resolution model simulations can improve precipitation forecasts with decreased 'spin-up' time and improve short-term simulation of boundary layer winds (Benjamin, 2004 & 2007; Xiao, 2008) which is critical to improving plume transport forecasts. Accurate description of wind and turbulence fields is essential to useful atmospheric transport and dispersion results, and any improvement in the accuracy of these fields will make consequence assessment more valuable during both routine operation as well as potential emergency situations. During 2008, the United States National Weather Service (NWS) radars implemented a significant upgrade which increased the real-time level II data resolution to 8 times their previous 'legacy' resolution, from 1 km range gate and 1.0 degree azimuthal resolution to 'super resolution' 250 m range gate and 0.5 degree azimuthal resolution (Fig 1). These radar observations provide reflectivity, velocity and returned power spectra measurements at a range of up to 300 km (460 km for reflectivity) at a frequency of 4-5 minutes and yield up to 13.5 million point observations per level in super-resolution mode. The migration of National Weather Service (NWS) WSR-88D radars to super resolution is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current operational mesoscale model domains utilize grid spacing several times larger than the legacy data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of super resolution reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution is investigated here to determine the impact of the improved data resolution on model predictions.

  18. Energy consumption and expenditure projections by population group on the basis of the annual energy outlook 1999 forecast

    SciTech Connect

    Poyer, D.A.; Balsley, J.H.

    2000-01-07

    This report presents an analysis of the relative impact of the base-case scenario used in Annual Energy Outlook 1999 on different population groups. Projections of energy consumption and expenditures, as well as energy expenditure as a share of income, from 1996 to 2020 are given. The projected consumption of electricty, natural gas, distillate fuel, and liquefied petroleum gas during this period is also reported for each population group. In addition, this report compares the findings of the Annual Energy Outlook 1999 report with the 1998 report. Changes in certain indicators and information affect energy use forecasts, and these effects are analyzed and discussed.

  19. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    educational topics include simulation and risk assessment; monitoring, verification, and accounting (MVA); geology-related analytical tools; site characterization, methods to...

  20. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... Building on the President's Climate Action Plan, in July 2014 the White House and the ... emissions through common-sense standards, smart investments, and innovative research to ...

  1. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Act (ARRA) of 2009, to conduct geologic sequestration training and support funda- mental research projects for graduate and undergraduate students throughout the United...

  2. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    with a half-life of 3.82 days. 220 Rn is a decay product derived from the 232 Th (Thorium) decay series and has an even shorter half-life CONTACTS Traci Rodosta Carbon Storage ...

  3. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Enhanced Simulation Tools to Improve Predictions and Performance of Geologic Storage: Coupled Modeling of Fault Poromechanics, and High-Resolution Simulation of CO2 Migration and...

  4. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Complexity and Choice of Model Approaches for Practical Simulations of CO2 Injection, Migration, Leakage, and Long-term Fate Introduction The overall goal of the Department of...

  5. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... students from Norway's University of Stavanger. * STORE hosted a visit from Tim Dixon of the International Energy Agency's Greenhouse Gas Research and Development Programme. ...

  6. Albany, OR * Fairbanks, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    technologies overcoming the challenges posed by unconventional reservoirs. However, as with most unconventional plays, as Bakken development continues, questions ...

  7. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    to water for large volume hydraulic fracturing of shale formations * IMPLAN impact ... projections * Report to Congress on unconventional fossil energy resources and ...

  8. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Geomechanical Impacts of Shale Gas Activities Background During hydraulic fracturing of ... the likelihood of seismic events due to water disposal with shale gas is more prevalent. ...

  9. Albany, OR * Fairbanks, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    run at the Eastman Chemical Company's Kingsport, TN, site; at Tampa Electric Company's Polk Power Station in Lakeland, FL; and at the Wabash River Power Station in Terre Haute,...

  10. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    routes responsible for the observed catalytic effects. Such efforts will allow for the optimization of plasma systems so that they may be incorporated into a broad range of...

  11. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... Accomplishments * Selection of Horseshoe Lake Tree Kill, Mammoth Mountain, California as the first natural CO 2 leakage field site was made and an integrated 12 C, 13 C and 14 C ...

  12. WDR-PK-AK-018

    SciTech Connect

    Hollister, R

    2009-08-26

    Method - CES SOP-HW-P556 'Field and Bulk Gamma Analysis'. Detector - High-purity germanium, 40% relative efficiency. Calibration - The detector was calibrated on February 8, 2006 using a NIST-traceable sealed source, and the calibration was verified using an independent sealed source. Count Time and Geometry - The sample was counted for 20 minutes at 72 inches from the detector. A lead collimator was used to limit the field-of-view to the region of the sample. The drum was rotated 180 degrees halfway through the count time. Date and Location of Scans - June 1,2006 in Building 235 Room 1136. Spectral Analysis Spectra were analyzed with ORTEC GammaVision software. Matrix and geometry corrections were calculated using OR TEC Isotopic software. A background spectrum was measured at the counting location. No man-made radioactivity was observed in the background. Results were determined from the sample spectra without background subtraction. Minimum detectable activities were calculated by the Nureg 4.16 method. Results - Detected Pu-238, Pu-239, Am-241 and Am-243.

  13. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Assessing Reservoir Depositional Environments to Develop and Quantify Improvements in CO2 Storage Efficiency: A Reservoir Simulation Approach Background The overall goal of the...

  14. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    An Advanced Joint Inversion System for CO2 Storage Modeling with Large Date Sets for Characterization and Real- Time Monitoring - Enhancing Storage Performance and Reducing Failure...

  15. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Reactive Transport Models with Geomechanics to Mitigate Risks of CO2 Utilization and Storage Background The overall goal of the Department of Energy's (DOE) Carbon Storage Program...

  16. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    are an important target for studies seeking to positively affect both the efficiency and environmental impact of U.S. energy production. The diversity of available sources for...

  17. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... in the United States and Canada are deep saline formations in which formation water, if ... Produced water may also have applications for geothermal power generation, as recent ...

  18. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Verification, Accounting (MVA) and Assessment, (3) CO 2 Use and Re-Use, (4) Regional Carbon Sequestration Partnerships (RCSP), and (5) Focus Area for Sequestration Science....

  19. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Verification, Accounting (MVA) and Assessment, (3) CO2 Use and Re-Use, (4) Regional Carbon Sequestration Partnerships (RCSP), and (5) Focus Area for Sequestration Science....

  20. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Simplified Predictive Models for CO2 Sequestration Performance Assessment Background The overall goal of the Department of Energy's (DOE) Carbon Storage Program is to develop and...

  1. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    This project is part of the Core R&D GSRA Technology Area and works to develop technologies and simulation tools to ensure secure geologic storage of CO2. It is critical that these ...

  2. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    agencies, private companies, electric utilities, universities, and nonprofit organizations. ... oil bearing reservoirs in Illinois, Indiana, and Kentucky (G2-G5 on map below). ...

  3. Albany, OR * Fairbanks, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    oil, tar sands). * Recovery of oil from tight reservoirs and shale formations, as a percentage of the original oil in place, remains low in most cases. * Economic extraction ...

  4. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    to measure subtle surface displacements), seismology, and geochemistry in a straightforward series of procedures and algorithms, and assess the cost and efficacy of these...

  5. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    to develop a future generation of geologists, scientists, and engineers who possess the skills required for implementing and deploying CCUS technologies. The U.S. Department of...

  6. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... This project evaluated the use of snow fences to increase the extent that snow drifts form and Trans Alaska Pipeline System (TAPS) Oil production from known resources as well as ...

  7. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    receive more than 8.4 million in funding to develop regional carbon storage technology training centers in the United States. The majority of this funding is provided by the...

  8. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    between formations through a pathway along the cementearth interface or within the well cement (Figure 1). This three-year project will explore the development of a low-cost...

  9. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... at the porous microstructure scale of solid sorbent ... field simulation for microstructural evolution in alloys. ... environmentally benign Mg-based battery architecture with ...

  10. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... Safety, regulatory, and permitting requirements within the region are being ... PARTNERS (CONT.) National Coal Council National Mining Association Natural Resource Partners Norfolk ...

  11. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    verification, and accounting (MVA); geology-related analytical tools; site ... to develop regional carbon storage technology training centers in the United States. ...

  12. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    Carbon Capture and Storage in the Permian Basin, a Regional Technology Transfer and ... verification, and accounting (MVA); geology-related analytical tools; site ...

  13. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... brine pressurized by CCS injection operations in saline formations as the feedstock for desalination and water treatment technologies, including nanofiltration and reverse osmosis. ...

  14. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    The three projects are the Frio Brine Pilot (United States), Otway Basin Project (Australia), and the In Salah Industrial Scale CO2 Project (Algeria). ORNL is focusing on ...

  15. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... order to characterize the storage reservoir. * Analyzed produced water samples for characteristics similar to other oil field brines in order to effectively model fluid properties. ...

  16. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    results in decreased ultrasonic P- and S-wave velocities and increased porosity and permeability. Initial measurements on carbonate samples reveal as much as 30% decrease in...

  17. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    ... Students involved in the project have conducted an extensive literature review to identify representative samples and are also being trained on laboratory equipment, including a ...

  18. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    found in all of the major potential geological sequestration sites including oil and ... Objectives * Obtain representative strata samples per programproject constraints, working ...

  19. Albany, OR * Anchorage, AK * Morgantown...

    U.S. Department of Energy (DOE) - all webpages (Extended Search)

    gained from RCSP large-scale field projects- particularly from the Southeast Regional Carbon Sequestration Partnership (SECARB) to address knowledge gaps in the design and...

  20. Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms

    SciTech Connect

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

    2013-03-19

    Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (stochastic) model with the weather forecast model (deterministic) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.