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1

Assumptions to Annual Energy Outlook - Energy Information Administration  

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

Assumptions to AEO2013 Assumptions to AEO2013 Release Date: May 14, 2013 | Next Release Date: May 2014 | full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S. Energy Information Administration (EIA). In addition to its use in developing the Annual

2

Assumptions to Annual Energy Outlook - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to AEO2012 Assumptions to AEO2012 Release Date: August 2, 2012 | Next Release Date: August 2013 | Full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2012 [1] (AEO2012), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in AEO2012 are generated using the NEMS, developed and maintained by the Office of Energy Analysis (OEA) of the U.S. Energy Information Administration (EIA). In addition to its use in developing the

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Energy Information Administration (EIA) - Assumptions to the...  

Gasoline and Diesel Fuel Update (EIA)

of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs...

4

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

to the to the Annual Energy Outlook 1998 December 1997 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Household Expenditures Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Oil and Gas Supply Module

5

Assumptions to Annual Energy Outlook - Energy Information ...  

U.S. Energy Information Administration (EIA)

Energy Information Administration - EIA ... Financial market analysis and financial data for major energy companies. Environment. Greenhouse gas data, ...

6

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

6 6 Assumptions to the Annual Energy Outlook 2006 This report presents major assumptions of NEMS that are used to generate the projections in the AEO2006. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800.

7

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2006 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20061 (AEO2006), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every few years. The National Energy Modeling System

8

Assumptions to Annual Energy Outlook - Energy Information Administrati...  

Annual Energy Outlook 2012 (EIA)

Assumptions to AEO2013 Release Date: May 14, 2013 | Next Release Date: May 2014 | full report Introduction This report presents the major assumptions of the National Energy...

9

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2006 The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2006, DOE/EIA- M068(2006). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

10

PUBLIC INFORMATION AND INPUT ON WIPP  

E-Print Network (OSTI)

PUBLIC INFORMATION AND INPUT ON WIPP Get The Information You Need 1. Check the EPA Website, Fact Sheets and Issue Papers. EPA will make sure that key information is available on its WIPP Website. EPA the EPA WIPP Information Line at 1-800-331-WIPP (1-800-331-9477) to obtain information on upcoming events

11

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

12

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2006 Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment

13

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2006 Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

14

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

15

Assumption Parish, Louisiana: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

Assumption Parish, Louisiana: Energy Resources Assumption Parish, Louisiana: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 29.9232544°, -91.09694° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":29.9232544,"lon":-91.09694,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

16

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS27 data.

17

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2006 The International Energy Module determines changes in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market clearing method is used to determine the price at which worldwide demand for oil is equal to the worldwide supply. The module determines new values for oil production and demand for regions outside the United States, along with a new world oil price that balances supply and demand in the international oil market. A detailed description of the International Energy Module is provided in the EIA publication, Model Documentation Report: The International Energy Module of the National Energy Modeling System, DOE/EIA-M071(06), (Washington, DC, February 2006).

18

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

19

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.14

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

E-Print Network (OSTI)

A comparison of national energy consumption by fuel typeenergy consumption in homes under differing assumptions, scenarios, and policies. At the national

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

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


21

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

E-Print Network (OSTI)

G. Koomey. 1994. Residential Appliance Data, Assumptions andunits) Table A 3 : Number of Appliances in Existing Homes (sector, including appliances and heating, ventilation, and

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

22

Energy Input Output Calculator | Open Energy Information  

Open Energy Info (EERE)

Input Output Calculator Input Output Calculator Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Energy Input-Output Calculator Agency/Company /Organization: Department of Energy Sector: Energy Focus Area: Energy Efficiency Resource Type: Online calculator User Interface: Website Website: www2.eere.energy.gov/analysis/iocalc/Default.aspx Web Application Link: www2.eere.energy.gov/analysis/iocalc/Default.aspx OpenEI Keyword(s): Energy Efficiency and Renewable Energy (EERE) Tools Language: English References: EERE Energy Input-Output Calculator[1] The Energy Input-Output Calculator (IO Calculator) allows users to estimate the economic development impacts from investments in alternate electricity generating technologies. About the Calculator The Energy Input-Output Calculator (IO Calculator) allows users to estimate

23

V-192: Symantec Security Information Manager Input Validation Flaws Permit  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

92: Symantec Security Information Manager Input Validation Flaws 92: Symantec Security Information Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Information Disclosure Attacks V-192: Symantec Security Information Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Information Disclosure Attacks July 4, 2013 - 6:00am Addthis PROBLEM: Several vulnerabilities were reported in Symantec Security Information Manager PLATFORM: Symantec Security Information Manager Appliance Version 4.7.x and 4.8.0 ABSTRACT: Symantec was notified of multiple security issues impacting the SSIM management console REFERENCE LINKS: SecurityTracker Alert ID: 1028727 Symantec Security Advisory SYM13-006 CVE-2013-1613 CVE-2013-1614 CVE-2013-1615 IMPACT ASSESSMENT: Medium DISCUSSION: The console does not properly filter HTML code from user-supplied input

24

AEO Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for the for the Annual Energy Outlook 1997 December 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Energy Information Administration/Assumptions for the Annual Energy Outlook 1997 Contents Page Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Commercial Demand Module . . . . . . . . . . . . . . . . . .

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

SciTech Connect

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

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

1995-12-01T23:59:59.000Z

26

Annual Energy Outlook 96 Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for for the Annual Energy Outlook 1996 January 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 1996 (AEO96). In this context, assumptions include general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports listed in the Appendix. 1 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview. The National Energy Modeling System The projections

27

US Nuclear Regulatory Commission Input to DOE Request for Information Smart  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

US Nuclear Regulatory Commission Input to DOE Request for US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input. Comments relevant to the following two sections of the RFI: "Long Term Issues: Managing a Grid with High Penetration of New Technologies" and "Reliability and Cyber-Security," US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input More Documents & Publications Comments of DRSG to DOE Smart Grid RFI: Addressing Policy and Logistical Challenges Reply Comments of Entergy Services, Inc. Progress Energy draft regarding Smart Grid RFI: Addressing Policy and

28

OECD Input-Output Tables | Open Energy Information  

Open Energy Info (EERE)

OECD Input-Output Tables OECD Input-Output Tables Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Input-Output Tables Agency/Company /Organization: Organisation for Economic Co-Operation and Development Topics: Co-benefits assessment, Market analysis, Co-benefits assessment, Pathways analysis Resource Type: Dataset Website: www.oecd.org/document/3/0,3343,en_2649_34445_38071427_1_1_1_1,00.html Country: Sweden, Finland, Japan, South Korea, Argentina, Australia, China, Israel, United Kingdom, Portugal, Romania, Greece, Poland, Slovakia, Chile, India, Canada, New Zealand, United States, Denmark, Norway, Spain, Austria, Italy, Netherlands, Ireland, France, Belgium, Brazil, Czech Republic, Estonia, Germany, Hungary, Luxembourg, Mexico, Slovenia, South Africa, Turkey, Indonesia, Switzerland, Taiwan, Russia

29

V-192: Symantec Security Information Manager Input Validation...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

in Symantec Security Information Manager PLATFORM: Symantec Security Information Manager Appliance Version 4.7.x and 4.8.0 ABSTRACT: Symantec was notified of multiple security...

30

Assumptions to the Annual Energy Outlook 2013  

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

Introduction Introduction This page inTenTionally lefT blank 3 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S.

31

DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF A  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF A PREFERRED ALTERNATIVE FOR DISPOSAL OF GREATER-THAN-CLASS C WASTE DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF A PREFERRED ALTERNATIVE FOR DISPOSAL OF GREATER-THAN-CLASS C WASTE March 1, 2011 - 12:00pm Addthis During the months of April and May, 2011 the Department of Energy's Office of Environmental Management will be holding nine public hearings on the Draft Environmental Impact Statement (EIS) for the Disposal of Greater-Than-Class C (GTCC) Low-Level Radioactive Waste and GTCC-Like Waste. Hearings will be held at the each of the sites being considered for disposal of GTCC wastes and in Washington, DC. DOE does not have a preferred alternative at this time. These hearings will

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Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

States. States. OGSM encompasses domestic crude oil and natural gas supply by both conventional and nonconventional recovery techniques. Nonconventional recovery includes unconventional gas recovery from low permeability formations of sandstone and shale, and coalbeds. Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 93 Figure 7. Oil and Gas Supply Model Regions Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Report #:DOE/EIA-0554(2007) Release date: April 2007 Next release date: March 2008 Primary inputs for the module are varied. One set of key assumptions concerns estimates of domestic technically recoverable oil and gas resources. Other factors affecting the projection include the assumed

33

Assumptions to the Annual Energy Outlook 2000 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Key Assumptions Key Assumptions The historical input data used to develop the HEM version for the AEO2000 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2000 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and transportation sectors as inputs to the disaggregation algorithm that results in the direct fuel expenditure analysis. Household end-use and personal transportation service consumption are obtained by HEM from the NEMS Residential and Transportation Demand Modules. Household disposable income is adjusted with forecasts of total disposable income from the NEMS Macroeconomic Activity Module.

34

Assumptions to the Annual Energy Outlook - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumption to the Annual Energy Outlook Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20041 (AEO2004), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every two years. The National Energy Modeling System The projections in the AEO2004 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers in the U.S. Congress, the Administration, including DOE Program Offices, and other government agencies.

35

Summary of Input to DOE Request for Information DE-FOA-0000225  

NLE Websites -- All DOE Office Websites (Extended Search)

& & Renewable Energy Summary of Input to DOE Request for Information DE FOA 0000225 DE-FOA-0000225 Greg Kleen Greg Kleen Golden Golden Field Field O Office ffice Golden Golden Field Field Office Office US DOE Fuel Cells T US DOE Fuel Cells Technology echnology Program Program Lakewood, Colorado Lakewood, Colorado March 16, 2010 March 16, 2010 83 ses o 6 o a at o s RFI Summary Release Date: 12/29/2009 Release Date: 12/29/2009 Closing Date: 1/29/2010 Purpose: Obtain Feedback from the Fuel Cell Community Purpose: Obtain Feedback from the Fuel Cell Community Planned Funding Opportunity Announcement Tentative Release: Summer 2010 Tentative Release: Summer 2010 Tentative Awards Made: Fiscal Year 2011 183 responses from 61 org ganizations espo Responses covered: Most fuel cell types and applications

36

EIA - Assumptions to the Annual Energy Outlook 2009 - Coal Market...  

Annual Energy Outlook 2012 (EIA)

of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply costs. The key assumptions underlying the coal production modeling are: As capacity...

37

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20031 (AEO2003), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2003 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of Energy’s Office of Policy and International Affairs, other DOE offices, and other government agencies.

38

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division. Key Assumptions The historical input data used to develop the HEM version for the AEO2003 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2003 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS).

39

EIA - Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

7 7 Assumptions to the Annual Energy Outlook 2007 This report summarizes the major assumptions used in the NEMS to generate the AEO2007 projections. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800.

40

Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumption to the Annual Energy Outlook Petroleum Market Module Figure 8. Petroleum Administration for Defense Districts. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohols, ethers, and bioesters natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of U.S. refining

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


41

Assumptions to the Annual Energy Outlook 2001 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Completed Copy in PDF Format Completed Copy in PDF Format Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Household Expenditures Module Key Assumptions The historical input data used to develop the HEM version for the AEO2001 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2001 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and

42

Summary of Input to DOE Request for Information DE-PS36-08GO38002 (Presentation)  

NLE Websites -- All DOE Office Websites (Extended Search)

Input to Input to DOE Request for Information DE-PS36-08GO38002 David Peterson Golden Field Office Department of Energy Fuel Cell Pre-Solicitation Workshop January 23, 2008 2 Purpose * Release date: 11/20/07; Close date: 1/14/08 * Obtain feedback from the fuel cell community. * Planned Funding Opportunity Announcement for RD&D of fuel cell technologies for automotive, stationary, portable power and early market applications * Tentative Release: Spring '08 * Tentative Awards Made: FY09 3 Response Areas * Technical topic areas * Catalysts (durability and reduced loading) and supports. * Catalyst layer. * Water management. * Membrane electrode assembly (MEA) optimization. * Accelerated durability testing. * Balance of plant component development. * Impurity effects.

43

EIA - Assumptions to the Annual Energy Outlook 2009 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2009 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2009 (AEO2009),1 including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2009 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

44

EIA - Assumptions to the Annual Energy Outlook 2010 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2010 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2010 [1] (AEO2010), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in the AEO2010 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

45

EIA - Assumptions to the Annual Energy Outlook 2008 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2008 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20081 (AEO2008), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2008 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The AEO projections are also used by analysts and planners in other government agencies and outside organizations.

46

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

1 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Petroleum Market Module. . . . . . . . . . . . .

47

EIA - Assumptions to the Annual Energy Outlook 2009 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2009 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy.

48

EIA - Assumptions to the Annual Energy Outlook 2008 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2008 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

49

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7 7 1 (AEO2007), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant to formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports. 2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview 3 , which is updated once every few years. The National Energy Modeling System The projections in the AEO2007 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and

50

Assumptions to the Annual Energy Outlook 2013  

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

Assumptions to the Annual Assumptions to the Annual Energy Outlook 2013 May 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Table of Contents Introduction .................................................................................................................................................. 3

51

Assumptions to the Annual Energy Outlook 2000 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20001 (AEO2000), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2000 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of Energy’s Office of Policy, other DOE offices, and other government agencies.

52

Assumptions to Annual Energy Outlook - Energy Information ...  

U.S. Energy Information Administration (EIA)

Analysis & Projections. Monthly and yearly energy forecasts, analysis of energy topics, financial analysis, Congressional reports. Markets & ...

53

Energy Information Administration (EIA) - Assumptions to the...  

Gasoline and Diesel Fuel Update (EIA)

Nonfarm labor productivity is expected to diminish from its current high level to a more sustainable level between 1.8 and 2.6 percent for the remainder of the forecast period...

54

Energy Information Administration (EIA) - Assumptions to the...  

Gasoline and Diesel Fuel Update (EIA)

density, housing values, income values, and availability of deepwater ports. The production costs reflect assumed market prices entering the liquefaction facility for...

55

Research Note---Does Technological Progress Alter the Nature of Information Technology as a Production Input? New Evidence and New Results  

Science Conference Proceedings (OSTI)

Prior research at the firm level finds information technology (IT) to be a net substitute for both labor and non-IT capital inputs. However, it is unclear whether these results hold, given recent IT innovations and continued price declines. In this study ... Keywords: IT business value, capital services, complement, hedonic, organizational decentralization, price index, productivity, rental price, substitute, technological change

Paul Chwelos; Ronald Ramirez; Kenneth L. Kraemer; Nigel P. Melville

2010-06-01T23:59:59.000Z

56

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.106

57

Assumptions to the Annual Energy Outlook 2013  

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

Energy Module Energy Module This page inTenTionally lefT blank 21 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 International Energy Module The LFMM International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the LFMM IEM computes BRENT and WTI prices, provides a supply curve of world crude-like liquids, and generates a worldwide oil supply- demand balance with regional detail. The IEM also provides, for each year of the projection period, endogenous and

58

EIA - Assumptions to the Annual Energy Outlook 2010  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2010 This report summarizes the major assumptions used in the NEMS to generate the AEO2010 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions Download the Report Assumptions to the Annual Energy Outlook 2010 Report Cover. Need help, contact the National Energy Information Center at 202-586-8800.

59

NGNP: High Temperature Gas-Cooled Reactor Key Definitions, Plant Capabilities, and Assumptions  

SciTech Connect

This document is intended to provide a Next Generation Nuclear Plant (NGNP) Project tool in which to collect and identify key definitions, plant capabilities, and inputs and assumptions to be used in ongoing efforts related to the licensing and deployment of a high temperature gas-cooled reactor (HTGR). These definitions, capabilities, and assumptions are extracted from a number of sources, including NGNP Project documents such as licensing related white papers [References 1-11] and previously issued requirement documents [References 13-15]. Also included is information agreed upon by the NGNP Regulatory Affairs group's Licensing Working Group and Configuration Council. The NGNP Project approach to licensing an HTGR plant via a combined license (COL) is defined within the referenced white papers and reference [12], and is not duplicated here.

Phillip Mills

2012-02-01T23:59:59.000Z

60

Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumption to the Annual Energy Outlook Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).109 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

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


61

EIA - Assumptions to the Annual Energy Outlook 2008 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2008 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

62

Sensory information to motor cortices: Effects of motor execution in the upper-limb contralateral to sensory input.  

E-Print Network (OSTI)

??Performance of efficient and precise motor output requires proper planning of movement parameters as well as integration of sensory feedback. Peripheral sensory information is projected (more)

Legon, Wynn

2009-01-01T23:59:59.000Z

63

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2003, DOE/EIA-M060(2003) (Washington, DC, January 2003). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

64

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2003, DOE/EIA-M068(2003) April 2003. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

65

Assumptions to the Annual Energy Outlook 1999 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

link.gif (1946 bytes) link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Supplemental Tables to the AEO99 bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage introduction.gif (4117 bytes) This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 19991 (AEO99), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3

66

The disciplined use of simplifying assumptions  

Science Conference Proceedings (OSTI)

Simplifying assumptions --- everyone uses them but no one's programming tool explicitly supports them. In programming, as in other kinds of engineering design, simplifying assumptions are an important method for dealing with complexity. Given a complex ...

Charles Rich; Richard C. Waters

1982-04-01T23:59:59.000Z

67

Assumptions to the Annual Energy Outlook 2013  

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

Demand Module Demand Module This page inTenTionally lefT blank 39 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2040. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial.

68

Assumptions to the Annual Energy Outlook - Contacts  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts Assumption to the Annual Energy Outlook Contacts Specific questions about the information in this report may be directed to: Introduction Paul D. Holtberg 202/586-1284 Macroeconomic Activity Module Ronald F. Earley Yvonne Taylor 202/586-1398 202/586-1398 International Energy Module G. Daniel Butler 202/586-9503 Household Expenditures Module/ Residential Demand Module John H. Cymbalsky 202/586-4815 Commercial Demand Module Erin E. Boedecker 202/586-4791 Industrial Demand Module T. Crawford Honeycutt 202/586-1420 Transportation Demand Module John D. Maples 202/586-1757 Electricity Market Module Laura Martin 202/586-1494 Oil and Gas Supply Module/Natural Gas Transmission and Distribution Module Joseph Benneche 202/586-6132 Petroleum Market Module Bill Brown 202/586-8181

69

Assumptions to the Annual Energy Outlook 2013  

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

Demand Module Demand Module This page inTenTionally lefT blank 27 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" (UEC) by appliance (in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing

70

Assumptions to the Annual Energy Outlook 2013  

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

Industrial Demand Module Industrial Demand Module This page inTenTionally lefT blank 53 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Industrial Demand Module The NEMS Industrial Demand Module (IDM) estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are subdivided further into the energy- intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure. The non-manufacturing industries are modeled with less detail because processes are simpler and there is less available data. The petroleum refining

71

Assumptions to the Annual Energy Outlook 2013  

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

Macroeconomic Activity Module Macroeconomic Activity Module This page inTenTionally lefT blank 17 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents interactions between the U.S. economy and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP), is a key determinant of growth in the demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected

72

Assumptions to the Annual Energy Outlook 2012  

U.S. Energy Information Administration (EIA)

Assumptions to the Annual Energy Outlook 2012 August 2012 www.eia.gov U.S. Department of Energy Washington, DC 20585

73

Assumptions to the Annual Energy Outlook 2002 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20021 (AEO2002), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2002 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of

74

Assumptions to the Annual Energy Outlook 2001 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Outlook2001 Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20011 (AEO2001), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2001 were produced with the National Energy

75

Assumptions to the Annual Energy Outlook 2000 - Errata  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2000 Assumptions to the Annual Energy Outlook 2000 as of 4/4/2000 1. On table 20 "the fractional fuel efficiency change for 4-Speed Automatic" should be .045 instead of .030. On table 20 "the fractional fuel efficiency change for 5-Speed Automatic" should be .065 instead of .045. (Change made on 3/6/2000) 2. Table 28 should be labeled: "Alternative-Fuel Vehicle Attribute Inputs for Compact Cars for Two Stage Logit Model". (Change made on 3/6/2000) 3. The capital costs in Table 29 should read 1998 dollars not 1988 dollars. (Change made on 3/6/2000) 4. Table 37 changed the label "Year Available" to "First Year Completed." Changed the second sentence of Footnote 1 to read "these estimates are costs of new projects

76

Effects of internal gain assumptions in building energy calculations  

DOE Green Energy (OSTI)

The utilization of direct solar gains in buildings can be affected by operating profiles, such as schedules for internal gains, thermostat controls, and ventilation rates. Building energy analysis methods use various assumptions about these profiles. The effects of typical internal gain assumptions in energy calculations are described. Heating and cooling loads from simulations using the DOE 2.1 computer code are compared for various internal-gain inputs: typical hourly profiles, constant average profiles, and zero gain profiles. Prototype single-family-detached and multi-family-attached residential units are studied with various levels of insulation and infiltration. Small detached commercial buildings and attached zones in large commercial buildings are studied with various levels of internal gains. The results of this study indicate that calculations of annual heating and cooling loads are sensitive to internal gains, but in most cases are relatively insensitive to hourly variations in internal gains.

Christensen, C.; Perkins, R.

1981-01-01T23:59:59.000Z

77

EIA - Assumptions to the Annual Energy Outlook 2009 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2009 Petroleum Market Module Figure 9., Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. Table 11.1. Petroleum Product Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.2. Year Round Gasoline Specifications by Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.3. Gasolline Sulfur Content Assumptions, by Region and Gasoline Type, Parts per Million (PPM). Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version

78

EIA-Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2007 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

79

Cost and Performance Assumptions for Modeling Electricity Generation Technologies  

Science Conference Proceedings (OSTI)

The goal of this project was to compare and contrast utility scale power plant characteristics used in data sets that support energy market models. Characteristics include both technology cost and technology performance projections to the year 2050. Cost parameters include installed capital costs and operation and maintenance (O&M) costs. Performance parameters include plant size, heat rate, capacity factor or availability factor, and plant lifetime. Conventional, renewable, and emerging electricity generating technologies were considered. Six data sets, each associated with a different model, were selected. Two of the data sets represent modeled results, not direct model inputs. These two data sets include cost and performance improvements that result from increased deployment as well as resulting capacity factors estimated from particular model runs; other data sets represent model input data. For the technologies contained in each data set, the levelized cost of energy (LCOE) was also evaluated, according to published cost, performance, and fuel assumptions.

Tidball, R.; Bluestein, J.; Rodriguez, N.; Knoke, S.

2010-11-01T23:59:59.000Z

80

Climate Action Planning Tool Formulas and Assumptions  

NLE Websites -- All DOE Office Websites (Extended Search)

CLIMATE ACTION PLANNING TOOL FORMULAS AND ASSUMPTIONS Climate Action Planning Tool Formulas and Assumptions The Climate Action Planning Tool calculations use the following formulas and assumptions to generate the business-as-usual scenario and the greenhouse gas emissions reduction goals for the technology options. Business-as-Usual Scenario All Scope 1 (gas, oil, coal, fleet, and electricity) and Scope 2 calculations increase at a rate equal to the building growth rate. Scope 3 calculations (commuters and business travel) increase at a rate equal to the population growth rate. Assumptions New buildings will consume energy at the same rate (energy use intensity) as existing campus buildings. Fleet operations will be proportional to total building area.

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


81

Hierarchy of Mesoscale Flow Assumptions and Equations  

Science Conference Proceedings (OSTI)

The present research proposes a standard nomenclature for mesoscale meteorological concepts and integrates existing concepts of atmospheric space scales, flow assumptions, governing equations, and resulting motions into a hierarchy useful in ...

P. Thunis; R. Bornstein

1996-02-01T23:59:59.000Z

82

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).119 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

83

NGNP: High Temperature Gas-Cooled Reactor Key Definitions, Plant Capabilities, and Assumptions  

SciTech Connect

This document provides key definitions, plant capabilities, and inputs and assumptions related to the Next Generation Nuclear Plant to be used in ongoing efforts related to the licensing and deployment of a high temperature gas-cooled reactor. These definitions, capabilities, and assumptions were extracted from a number of NGNP Project sources such as licensing related white papers, previously issued requirement documents, and preapplication interactions with the Nuclear Regulatory Commission (NRC).

Wayne Moe

2013-05-01T23:59:59.000Z

84

Assumptions to the Annual Energy Outlook 2008  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Release date: June 2008 Next release date: March 2009 Assumptions to the Annual Energy Outlook 2008 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 113 Petroleum Market Module

85

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2004 Assumptions to the Annual Energy Outlook 2004 143 Appendix A: Handling of Federal and Selected State Legislation and Regulation in the Annual Energy Outlook Legislation Brief Description AEO Handling Basis Residential Sector A. National Appliance Energy Conservation Act of 1987 Requires Secretary of Energy to set minimum efficiency standards for 10 appliance categories a. Room Air Conditioners Current standard of 8.82 EER Federal Register Notice of Final Rulemaking, b. Other Air Conditioners (<5.4 tons) Current standard 10 SEER for central air conditioner and heat pumps, increasing to 12 SEER in 2006. Federal Register Notice of Final Rulemaking, c. Water Heaters Electric: Current standard .86 EF, incr easing to .90 EF in 2004. Gas: Curren

86

Assumptions to the Annual Energy Outlook 1999 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

petroleum.gif (4999 bytes) petroleum.gif (4999 bytes) The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below. 75

87

EIA - Assumptions to the Annual Energy Outlook 2010 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2010 Petroleum Market Module The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. Figure 9. Petroleum Administration for Defense Districts. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9),

88

Assumptions to the Annual Energy Outlook 1999 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

coal.gif (4423 bytes) coal.gif (4423 bytes) The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Model Documentation: Coal Market Module of the National Energy Modeling System, DOE/EIA-MO60. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

89

Assumptions to the Annual Energy Outlook 2001 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

90

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA-M060(2007) (Washington, 7, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

91

TART input manual  

Science Conference Proceedings (OSTI)

The TART code is a Monte Carlo neutron/photon transport code that is only on the CRAY computer. All the input cards for the TART code are listed, and definitions for all input parameters are given. The execution and limitations of the code are described, and input for two sample problems are given. (WHK)

Kimlinger, J.R.; Plechaty, E.F.

1982-04-01T23:59:59.000Z

92

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA- 7, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described. EMM Regions The supply regions used in EMM are based on the North American Electric Reliability Council regions and

93

Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumption to the Annual Energy Outlook Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2004, DOE/EIA-M060(2004) (Washington, DC, 2004). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

94

USDA, Departments of Energy and Navy Seek Input from Industry to Advance  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

USDA, Departments of Energy and Navy Seek Input from Industry to USDA, Departments of Energy and Navy Seek Input from Industry to Advance Biofuels for Military and Commercial Transportation USDA, Departments of Energy and Navy Seek Input from Industry to Advance Biofuels for Military and Commercial Transportation August 30, 2011 - 12:23pm Addthis WASHINGTON, Aug. 30, 2011 -Secretary of Agriculture Tom Vilsack, Secretary of Energy Steven Chu, and Secretary of the Navy Ray Mabus today announced the next step in the creation of a public-private partnership to develop drop-in advanced biofuels. The Secretaries issued a Request for Information (RFI) laying out the Administration's goals, assumptions, and tools and requesting from industry specific ideas for how to leverage private capital markets to establish a commercially viable drop-in biofuels

95

Assumptions to the Annual Energy Outlook 2000 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.100

96

Assumptions to the Annual Energy Outlook - Table 41  

Annual Energy Outlook 2012 (EIA)

> Forecasts >Assumptions to the Annual Energy Outlook> Download Report Assumption to the Annual Energy Outlook Adobe Acrobat Reader Logo Adobe Acrobat Reader is required for PDF...

97

Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumption to the Annual Energy Outlook Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2004, DOE/EIA- M068(2004). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

98

EIA - Assumptions to the Annual Energy Outlook 2008 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2008 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2008, DOE/EIA-M068(2008). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

99

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, and (4) the implementation of recent regulatory reform. A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2003, DOE/EIA- M062(2003) (Washington, DC, January 2003).

100

EIA - Assumptions to the Annual Energy Outlook 2010 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2010 International Energy Module Figure 2. World Oil Prices in Three Cases, 1995-2035 Figure 2. World Oil Prices in three Cases, 1995-2035 (2008 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800.

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


101

EIA - Assumptions to the Annual Energy Outlook 2009 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2009 International Energy Module Figure 2. World Oil Prices in three Cases, 1995-2030 (2006 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids

102

Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumption to the Annual Energy Outlook International Energy Module Figure 2. World Oil Prices in three Cases, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 3. OPEC Oil Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 4. Non-OPEC Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Table 4. Worldwide Oil Reserves as of January 1, 2002 (Billion Barrels) Printer Friendly Version Region Proved Oil Reserves Western Hemisphere 313.6 Western‘Europe 18.1 Asia-Pacific 38.7

103

Assumptions to the Annual Energy Outlook - Household Expenditures Module  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module Assumption to the Annual Energy Outlook Household Expenditures Module Figure 5. United States Census Divisions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division (see

104

Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and  

Alternative Fuels and Advanced Vehicles Data Center (EERE)

Tools Tools Printable Version Share this resource Send a link to Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology to someone by E-mail Share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Facebook Tweet about Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Twitter Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Google Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Delicious Rank Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Digg Find More places to share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on AddThis.com...

105

Deriving input syntactic structure from execution  

Science Conference Proceedings (OSTI)

Program input syntactic structure is essential for a wide range of applications such as test case generation, software debugging and network security. However, such important information is often not available (e.g., most malware programs make use of ... Keywords: bottom-up grammar, control dependence, input lineage, reverse engineering, syntax tree, top-down grammar

Zhiqiang Lin; Xiangyu Zhang

2008-11-01T23:59:59.000Z

106

EIA - Assumptions to the Annual Energy Outlook 2009 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2009 Industrial Demand Module Table 6.1. Industry Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 6.2.Retirement Rates. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process flow or end use accounting

107

EIA-Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2007 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind.112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

108

Assumptions to the Annual Energy Outlook 2000 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

109

Assumptions to the Annual Energy Outlook 2000 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

110

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

clothes drying, ceiling fans, coffee makers, spas, home security clothes drying, ceiling fans, coffee makers, spas, home security systems, microwave ovens, set-top boxes, home audio equipment, rechargeable electronics, and VCR/DVDs. In addition to the major equipment-driven end-uses, the average energy consumption per household is projected for other electric and nonelectric appliances. The module's output includes number Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 19 Pacific East South Central South Atlantic Middle Atlantic New England West South Central West North Central East North Central Mountain AK WA MT WY ID NV UT CO AZ NM TX OK IA KS MO IL IN KY TN MS AL FL GA SC NC WV PA NJ MD DE NY CT VT ME RI MA NH VA WI MI OH NE SD MN ND AR LA OR CA HI Middle Atlantic New England East North Central West North Central Pacific West South Central East South Central

111

EIA - Assumptions to the Annual Energy Outlook 2010 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2010 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and

112

EIA - Assumptions to the Annual Energy Outlook 2009 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2009 Electricity Market Module figure 6. Electricity Market Model Supply Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2009, DOE/EIA-M068(2009). Based on fuel prices and electricity demands provided by the other modules

113

EIA - Assumptions to the Annual Energy Outlook 2008 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2008 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

114

EIA - Assumptions to the Annual Energy Outlook 2009 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2009 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

115

EIA - Assumptions to the Annual Energy Outlook 2010 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2010 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock,

116

Assumptions to the Annual Energy Outlook - Natural Gas Transmission and  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumption to the Annual Energy Outlook Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

117

Repeat on input for data flow computers  

DOE Patents (OSTI)

A processing node for a data flow parallel processing computer is activated by an input token from the system. The token or the stored information in the node includes information to cause the node to repeat a specified sequence of operations upon initiation by the token, thereby increasing the efficiency system for some computing operations.

Grafe, V.G.; Hoch, J.E.

1989-12-27T23:59:59.000Z

118

SWAT 2012 Input/Output Documentation  

E-Print Network (OSTI)

The Soil and Water Assessment Tool (SWAT) is a comprehensive model that requires a diversity of information in order to run. Novice users may feel overwhelmed by the variety and number of inputs when they first begin to use the model. This document provides a full description of model inputs. The inputs are organized by topic and emphasis is given to differentiating required inputs from optional inputs. The first chapter focuses on assisting the user in identifying inputs that must be defined for their particular dataset. The remaining chapters list variables by file and discuss methods used to measure or calculate values for the input parameters. SWAT is a public domain model jointly developed by USDA Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research, part of The Texas A&M University System. SWAT is a small watershed to river basin-scale model to simulate the quality and quantity of surface and ground water and predict the environmental impact of land use, land management practices, and climate change. SWAT is widely used in assessing soil erosion prevention and control, non-point source pollution control and regional management in watersheds. Download the SWAT model, or read more information at the SWAT website.

Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; Williams, J.R.; Haney, E.B.; Neitsch, S.L.

2013-03-04T23:59:59.000Z

119

A survey of design issues in spatial input  

Science Conference Proceedings (OSTI)

We present a survey of design issues for developing effective free-space three-dimensional (3D) user interfaces. Our survey is based upon previous work in 3D interaction, our experience in developing free-space interfaces, and our informal observations ... Keywords: 3D interaction, ergonomics of virtual manipulation, haptic input, spatial input, two-handed input, virtual reality

Ken Hinckley; Randy Pausch; John C. Goble; Neal F. Kassell

1994-11-01T23:59:59.000Z

120

Assumptions to the Annual Energy Outlook - Macroeconomic Activity...  

Annual Energy Outlook 2012 (EIA)

Macroeconomic Activity Module Assumption to the Annual Energy Outlook Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the...

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


121

Assumptions to the Annual Energy Outlook 2002 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).117 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

122

Assumptions to the Annual Energy Outlook 2001 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

123

Assumption-Commitment Support for CSP Model Checking  

E-Print Network (OSTI)

AVoCS 2006 Assumption-Commitment Support for CSP Model Checking Nick Moffat1 Systems Assurance using CSP. In our formulation, an assumption-commitment style property of a process SYS takes the form-Guarantee, CSP, Model Checking, Compositional Reasoning 1 Introduction The principle of compositional program

Paris-Sud XI, Université de

124

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

E-Print Network (OSTI)

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

125

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

E-Print Network (OSTI)

Homes End-Use Equipment Type Equipment Market Shares Index Heating ElecFurnace Gas Furnace LPG Furnace OilHomes (millions) End-Use Equipment Type Appliance stock in millions of units Index Heating FJec Furnace Gas Furnace L P G Furnace OilHomes End-Use Equipment Type Units Efficiency for Stock Equipment Index Heating Elec Furnace Btu.out/Wh.in Gas Furnace AFUE LPG Furnace AFUE Oil

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

126

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

E-Print Network (OSTI)

Natural Gas Oil Lighting 0-1 hrs 1-2 his 2-3 hrs Usage levelgas Oil Dishwasher End-Use Lighting 0-1 hrs 1-2 hrs Usagegas Oil Dishwasher End-Use Lighting 0-1 hrs 1-2 hrs Usage

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

127

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

E-Print Network (OSTI)

year consumption estimates. DISCUSSION The Importance of Miscellaneous ElectricityConsumption of New Equipment Index kWh/Year MMBtu/Year MMBtu/Year ElectricityConsumption of Equipment in Existing Homes Index kWh/Year MMBtu/Year MMBtu/Year Electricity

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

128

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

E-Print Network (OSTI)

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

129

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

E-Print Network (OSTI)

=1 Index 1990=1 Lighting 0-1 hrs 1-2 hrs Usage level 2-3 hrsMiscellaneous Lighting 0-1 hrs 1-2 hrs Usage level 2-3 hrsMiscellaneous Lighting 0-1 hrs 1-2 hrs Usage level 2-3 hrs

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

130

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

E-Print Network (OSTI)

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

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

131

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

E-Print Network (OSTI)

gas Oil Secondary Heating Wood Stove Secondary Cooling RoomTotal Secondary Heating Wood Stove Secondary Cooling Room ACConsumption Secondary Heating Wood Stove Secondary Cooling

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

132

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

E-Print Network (OSTI)

Development of a Residential Forecasting Database. Lawrenceand Methodology for End-Use Forecasting with EPRI-REEPS 2.1.and Methodology for End-Use Forecasting with EPRI-REEPS 2.1.

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

133

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

E-Print Network (OSTI)

heaters, clothes washers, dishwashers, lighting, cooking,Refrigerators Water Heaters Dishwashers Clothes Washersof its useful life. For dishwashers, clothes washers, and

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

134

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

E-Print Network (OSTI)

light bulbs having designated usage level in the average house. (3) Refrigerator marketlight bulbs having designated usage level in the average house. (3) Refrigerator market

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

135

EIA - Assumptions to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2009 The Early Release for next year's Annual Energy Outlook will be presented at the John Hopkins Kenney Auditorium on December 14th This report summarizes the major assumptions used in the NEMS to generate the AEO2009 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions

136

Notes 01. The fundamental assumptions and equations of lubrication theory  

E-Print Network (OSTI)

The fundamental assumption in Lubrication Theory. Derivation of thin film flow equations from Navier-Stokes equations. Importance of fluid inertia effects in thin film flows. Some fluid physical properties

San Andres, Luis

2009-01-01T23:59:59.000Z

137

Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations  

Science Conference Proceedings (OSTI)

Using a spectral-type cumulus parameterization that includes moist downdrafts within a three-dimensional mesoscale model, various disparate closure assumptions are systematically tested within the generalized framework of dynamic control, static ...

Georg A. Grell

1993-03-01T23:59:59.000Z

138

Computational soundness for standard assumptions of formal cryptography  

E-Print Network (OSTI)

This implementation is conceptually simple, and relies only on general assumptions. Specifically, it can be thought of as a 'self-referential' variation on a well-known encryption scheme. 4. Lastly, we show how the ...

Herzog, Jonathan, 1975-

2004-01-01T23:59:59.000Z

139

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology  

NLE Websites -- All DOE Office Websites (Extended Search)

5 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1 Francis X. Johnson, Richard E. Brown, James W. Hanford, Alan H. Sanstad and...

140

Assumptions to the Annual Energy Outlook 1999 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

bullet1.gif (843 bytes) Feedback link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Interactive Data Queries to the AEO99 bullet1.gif...

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


141

Idaho National Engineering Laboratory installation roadmap assumptions document. Revision 1  

SciTech Connect

This document is a composite of roadmap assumptions developed for the Idaho National Engineering Laboratory (INEL) by the US Department of Energy Idaho Field Office and subcontractor personnel as a key element in the implementation of the Roadmap Methodology for the INEL Site. The development and identification of these assumptions in an important factor in planning basis development and establishes the planning baseline for all subsequent roadmap analysis at the INEL.

Not Available

1993-05-01T23:59:59.000Z

142

Opportunities for Public Input Into DOE Projects  

NLE Websites -- All DOE Office Websites (Extended Search)

Opportunities for Public Input Into DOE Projects Opportunities for Public Input Into DOE Projects There are currently several DOE-proposed activities that citizens can comment on in the near future. Here is a summary of each, as well as a description of how to provide your input into the project: Hanford Draft Closure and Waste Management Environmental Impact Statement Idahoans might be interested in this document because one of the proposed actions involves sending a small amount of radioactive waste (approximately 5 cubic meters of special reactor components) to the Idaho Nuclear Technology and Engineering Center on DOE's Idaho Site for treatment. Here is a link to more information about the document: http://www.hanford.gov . A public hearing on the draft EIS will be held in Boise on Tuesday, Feb. 2 at the Owyhee Plaza Hotel. It begins at 6 p.m.

143

Research Input Form  

NLE Websites -- All DOE Office Websites (Extended Search)

HighlightsSubmit HighlightsSubmit Form Submit a New Research Highlight Sort Highlights Submitter Title Research Area Working Group Submission Date DOE Progress Reports Notable Research Findings for 2001-2006 Biological and Environmental Research Abstracts Database Research Highlights Summaries Research Highlight Submittal Form Tell us about your research! This form is designed to collect summary information about working group research results. If you have any questions or comments, please contact the administrators. Journal or Book Reference(s) (if applicable): Look Up Your reference from the Publications Database. Limit two references. If you have not submitted the references, please Add it now. Area of Research: Radiation Processes Cloud Distributions/Characterizations Surface Properties General Circulation and Single Column

144

PERSPECTIVES ON A DOE CONSEQUENCE INPUTS FOR ACCIDENT ANALYSIS APPLICATIONS  

Science Conference Proceedings (OSTI)

Department of Energy (DOE) accident analysis for establishing the required control sets for nuclear facility safety applies a series of simplifying, reasonably conservative assumptions regarding inputs and methodologies for quantifying dose consequences. Most of the analytical practices are conservative, have a technical basis, and are based on regulatory precedent. However, others are judgmental and based on older understanding of phenomenology. The latter type of practices can be found in modeling hypothetical releases into the atmosphere and the subsequent exposure. Often the judgments applied are not based on current technical understanding but on work that has been superseded. The objective of this paper is to review the technical basis for the major inputs and assumptions in the quantification of consequence estimates supporting DOE accident analysis, and to identify those that could be reassessed in light of current understanding of atmospheric dispersion and radiological exposure. Inputs and assumptions of interest include: Meteorological data basis; Breathing rate; and Inhalation dose conversion factor. A simple dose calculation is provided to show the relative difference achieved by improving the technical bases.

(NOEMAIL), K; Jonathan Lowrie, J; David Thoman (NOEMAIL), D; Austin Keller (NOEMAIL), A

2008-07-30T23:59:59.000Z

145

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7) 7) Release date: April 2007 Next release date: March 2008 Assumptions to the Annual Energy Outlook 2007 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 107 Petroleum Market Module

146

A Comparison of the Free Ride and CISK Assumptions  

Science Conference Proceedings (OSTI)

In a recent paper Fraedrich and McBride have studied the relation between the free ride and CISK (conditional instability of the second kind) assumptions in a well-known two-layer model. Here the comparison is extended to a more general case. ...

Torben Strunge Pedersen

1991-08-01T23:59:59.000Z

147

Kernel principal component analysis for stochastic input model generation  

Science Conference Proceedings (OSTI)

Stochastic analysis of random heterogeneous media provides useful information only if realistic input models of the material property variations are used. These input models are often constructed from a set of experimental samples of the underlying random ... Keywords: Data-driven models, Flow in random porous media, Kernel principal component analysis, Non-linear model reduction, Stochastic partial differential equations

Xiang Ma; Nicholas Zabaras

2011-08-01T23:59:59.000Z

148

EIA - Assumptions to the Annual Energy Outlook 2009 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2009 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.1. Crude Oil Technically Recoverable Resources. Need help, contact the Naitonal Energy Information Center at 202-586-8800. printer-friendly version Table 9.2. Natural Gas Technically Recoverable Resources. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.2. Continued printer-friendly version Table 9.3. Assumed Size and Initial Production year of Major Announced Deepwater Discoveries. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 9.4. Assumed Annual Rates of Technological Progress for Conventional Crude Oil and Natural Gas Sources. Need help, contact the National Energy Information Center at 202-586-8800.

149

V-139: Cisco Network Admission Control Input Validation Flaw...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Sensitive Information U-270:Trend Micro Control Manager Input Validation Flaw in Ad Hoc Query Module Lets Remote Users Inject SQL Commands U-015: CiscoWorks Common Services Home...

150

Code Completion From Abbreviated Input  

E-Print Network (OSTI)

Abbreviation Completion is a novel technique to improve the efficiency of code-writing by supporting code completion of multiple keywords based on non-predefined abbreviated input - a different approach from conventional ...

Miller, Robert C.

151

PROJECT MANGEMENT PLAN EXAMPLES Policy & Operational Decisions, Assumptions  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Policy & Operational Decisions, Assumptions Policy & Operational Decisions, Assumptions and Strategies Examples 1 & 2 Example 1 1.0 Summary The 322-M Metallurgical Laboratory is currently categorized as a Radiological Facility. It is inactive with no future DOE mission. In May of 1998 it was ranked Number 45 in the Inactive Facilities Risk Ranking database which the Facilities Decommissioning Division maintains. A short-term surveillance and maintenance program is in-place while the facility awaits final deactivation. Completion of the end points described in this deactivation project plan will place the 322-M facility into an End State that can be described as "cold and dark". The facility will be made passively safe requiring minimal surveillance and no scheduled maintenance.

152

Cost and Performance Assumptions for Modeling Electricity Generation Technologies  

NLE Websites -- All DOE Office Websites (Extended Search)

Cost and Performance Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia Subcontract Report NREL/SR-6A20-48595 November 2010 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401 303-275-3000 * www.nrel.gov Contract No. DE-AC36-08GO28308 Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia NREL Technical Monitor: Jordan Macknick

153

Paducah DUF6 Conversion Final EIS - Chapter 4: Environmental Impact Assessment Approach, Assumptions, and Methodology  

NLE Websites -- All DOE Office Websites (Extended Search)

Paducah DUF Paducah DUF 6 Conversion Final EIS 4 ENVIRONMENTAL IMPACT ASSESSMENT APPROACH, ASSUMPTIONS, AND METHODOLOGY This EIS evaluates potential impacts on human health and the natural environment from building and operating a DUF 6 conversion facility at three alternative locations at the Paducah site and for a no action alternative. These impacts might be positive, in that they would improve conditions in the human or natural environment, or negative, in that they would cause a decline in those conditions. This chapter provides an overview of the methods used to estimate the potential impacts associated with the EIS alternatives, summarizes the major assumptions that formed the basis of the evaluation, and provides some background information on human health

154

EIA - Assumptions to the Annual Energy Outlook 2008 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2008 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2008, DOE/EIA-M060(2008) (Washington, DC, 2008). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

155

EIA-Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2007 Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2007, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

156

EIA - Assumptions to the Annual Energy Outlook 2010 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2010 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2010, DOE/EIA-M060(2010) (Washington, DC, 2010). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply costs.

157

Integrating surprisal and uncertain-input models in online sentence comprehension: formal techniques and empirical results  

Science Conference Proceedings (OSTI)

A system making optimal use of available information in incremental language comprehension might be expected to use linguistic knowledge together with current input to revise beliefs about previous input. Under some circumstances, such an error-correction ...

Roger Levy

2011-06-01T23:59:59.000Z

158

Standard assumptions and methods for solar heating and cooling systems analysis  

DOE Green Energy (OSTI)

A set of inputs, assumptions, analytical methods, and a reporting format is presented to help compare the results of residential and commercial solar system analyses being performed by different investigators. By the common use of load data, meteorological data, economic parameters, and reporting format, researchers examining, for example, two types of collectors may more easily compare their results. For residential heating and cooling systems, three locations were selected. The weather data chosen to characterize these cities are the Typical Meteorological Year (TMY). A house for each location was defined that is typical of new construction in that locale. Hourly loads for each location were calculated using a computerized load model that interacts with the system specified inputs characterizing each house. Four locations for commercial cooling analyses were selected from among the existing sites for which TMYs were available. A light commercial (nominal 25-ton cooling load) office building was defined and is used in all four locations. Hourly cooling and heating loads were computed for each city and are available on magnetic tape from the Solar Energy Research Insititute (SERI).

Leboeuf, C.M.

1980-01-01T23:59:59.000Z

159

EIA-Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2007 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules-electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2007, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

160

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

unfinished oil imports, other refinery inputs (including alcohols, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9). The LP model is created by aggregating individual refineries within a PADD into one representative refinery, and linking all five PADD's via crude and product transit links. This representation provides the marginal costs of production for a number of conventional and new petroleum products. In order to interact with other NEMS modules with different regional representations,

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


161

Assumptions to the Annual Energy Outlook 2000 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2000, DOE/EIA-M062(2000), January 2000.

162

Assumptions to the Annual Energy Outlook 2000 - Electricity Market Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. The major assumptions are summarized below.

163

Assumptions to the Annual Energy Outlook 1999 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by obtaining market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation Report: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, DOE/EIA-MO62/1, January 1999.

164

Refinery and Blender Net Inputs  

Annual Energy Outlook 2012 (EIA)

Refinery and Blender Net Inputs Crude OIl ... 14.54 15.14 15.26 15.08 14.51 15.30 15.70 14.93 14.47 15.30 15.54 14.97 15.01...

165

Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumption to the Annual Energy Outlook Industrial Demand Module Table 17. Industry Categories Printer Friendly Version Energy-Intensive Manufacturing Nonenergy-Intensive Manufacturing Nonmanufacturing Industries Food and Kindred Products (NAICS 311) Metals-Based Durables (NAICS 332-336) Agricultural Production -Crops (NAICS 111) Paper and Allied Products (NAICS 322) Balance of Manufacturing (all remaining manufacturing NAICS) Other Agriculture Including Livestock (NAICS112- 115) Bulk Chemicals (NAICS 32B) Coal Mining (NAICS 2121) Glass and Glass Products (NAICS 3272) Oil and Gas Extraction (NAICS 211) Hydraulic Cement (NAICS 32731) Metal and Other Nonmetallic Mining (NAICS 2122- 2123) Blast Furnaces and Basic Steel (NAICS 331111) Construction (NAICS233-235)

166

EIA - Assumptions to the Annual Energy Outlook 2009 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

167

EIA - Assumptions to the Annual Energy Outlook 2009 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2009 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight, rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

168

EIA - Assumptions to the Annual Energy Outlook 2010 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services [1].

169

Assumptions to the Annual Energy Outlook 1999 - Table 1  

Gasoline and Diesel Fuel Update (EIA)

Summary of AEO99 Cases Summary of AEO99 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.5 percent, compared to the reference case growth of 2.1 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.1 percent. Fully Integrated Low World Oil Price World oil prices are $14.57 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated High World Oil Price World oil prices are $29.35 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated Residential: 1999 Technology

170

Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumption to the Annual Energy Outlook Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, sport utility vehicles and vans), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

171

EIA-Assumptions to the Annual Energy Outlook - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2007 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

172

EIA - Assumptions to the Annual Energy Outlook 2008 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2008 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

173

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2010 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Document>ation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2009), (Washington, DC, January 2009).

174

Assumptions to the Annual Energy Outlook 2001 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2001, DOE/EIA-M060(2001) January 2001. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

175

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7), 7), (Washington, DC, January 2007). Key Assumptions The output of the U.S. economy, measured by GDP, is expected to increase by 2.9 percent between 2005 and 2030 in the reference case. Two key factors help explain the growth in GDP: the growth rate of nonfarm employment and the rate of productivity change associated with employment. As Table 3 indicates, for the Reference Case GDP growth slows down in each of the periods identified, from 3.0 percent between 2005 and 2010, to 2.9 percent between 2010 and 2020, to 2.8 percent in the between 2020 and 2030. In the near term from 2005 through 2010, the growth in nonfarm employment is low at 1.2 percent compared with 2.4 percent in the second half of the 1990s, while the economy is expected to experiencing relatively strong

176

EIA - Assumptions to the Annual Energy Outlook 2008 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2008 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module projects energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region projection using the SEDS1 data.

177

EIA - Assumptions to the Annual Energy Outlook 2008 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2008 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

178

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2009 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2008), (Washington, DC, January 2008).

179

EIA - Assumptions to the Annual Energy Outlook 2008 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2008 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

180

Assumptions to the Annual Energy Outlook 2002 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2002, DOE/EIA-M060(2002) (Washington, DC, January 2002). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

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


181

Truncated predictor feedback for linear systems with long time-varying input delays  

Science Conference Proceedings (OSTI)

In this paper we study the problem of stabilizing a linear system with a single long time-varying delay in the input. Under the assumption that the open-loop system is stabilizable and not exponentially unstable, a finite dimensional static time-varying ... Keywords: Actuator saturation, Energy constraints, Semi-global stabilization, Stabilization, Time-varying delay, Truncated predictor feedback

Bin Zhou; Zongli Lin; Guang-Ren Duan

2012-10-01T23:59:59.000Z

182

DOE Seeks Public Input on an Integrated, Interagency Pre-Application...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

on an Integrated, Interagency Pre-Application Process for Transmission Authorizations August 29, 2013 - 9:09am Addthis A Request for Information (RFI) seeking public input for...

183

Assumptions to the Annual Energy Outlook 2001 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

184

Assumptions to the Annual Energy Outlook 2002 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

185

Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumption to the Annual Energy Outlook Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

186

EIA - Assumptions to the Annual Energy Outlook 2009 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2009 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the projection horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the projection horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

187

EIA - Assumptions to the Annual Energy Outlook 2008 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2008 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids supply and demand curves (1 curve per year; 2008-2030; approximated, isoelastic fit to previous NEMS results). These quantities are not modeled directly in NEMS. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2008 with IEO2007 and the STEO some functionality was removed from IEM while a new algorithm was implemented. Based on the difference between U.S. total petroleum liquids production (consumption) and the expected U.S. total liquids production (consumption) at the current WTI price, curves for global petroleum liquids consumption (production) were adjusted for each year. According to previous operations, a new WTI price path was generated. An exogenous oil supply module, Generate World Oil Balances (GWOB), was also used in IEM to provide annual regional (country) level production detail for conventional and unconventional liquids.

188

Risk Neutral Investors Do Not Acquire Information  

E-Print Network (OSTI)

attempt to extract market information from asset price (more than investors market information. Market clearing orasset price omits market information. Unrelated assumptions

Muendler, Marc-Andreas

2005-01-01T23:59:59.000Z

189

DOE Seeks Industry Input on Nickel Disposition Strategy | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Industry Input on Nickel Disposition Strategy Industry Input on Nickel Disposition Strategy DOE Seeks Industry Input on Nickel Disposition Strategy March 23, 2012 - 12:00pm Addthis WASHINGTON, D.C. - The Energy Department's prime contractor, Fluor-B&W Portsmouth (FBP), managing the Portsmouth Gaseous Diffusion Plant (GDP), issued a request for Expressions of Interest (EOI) seeking industry input to support the development of an acquisition strategy for potential disposition of DOE nickel. The EOI requests technical, financial, and product market information to review the feasibility of technologies capable of decontaminating the nickel to a level indistinguishable from what is commercially available, such that it could be safely recycled and reused. The EOI scope is for 6,400 tons of nickel to be recovered from the uranium enrichment process

190

DOE Seeks Industry Input on Nickel Disposition Strategy | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

DOE Seeks Industry Input on Nickel Disposition Strategy DOE Seeks Industry Input on Nickel Disposition Strategy DOE Seeks Industry Input on Nickel Disposition Strategy March 23, 2012 - 12:00pm Addthis WASHINGTON, D.C. - The Energy Department's prime contractor, Fluor-B&W Portsmouth (FBP), managing the Portsmouth Gaseous Diffusion Plant (GDP), issued a request for Expressions of Interest (EOI) seeking industry input to support the development of an acquisition strategy for potential disposition of DOE nickel. The EOI requests technical, financial, and product market information to review the feasibility of technologies capable of decontaminating the nickel to a level indistinguishable from what is commercially available, such that it could be safely recycled and reused. The EOI scope is for 6,400 tons of nickel to be recovered from the uranium enrichment process

191

Input to the 2012-2021 Strategic Plan  

NLE Websites -- All DOE Office Websites (Extended Search)

Related Federal Climate Efforts Related Federal Climate Efforts Input to the 2012-2021 Strategic Plan Print E-mail Engaging Stakeholders The USGCRP is dedicated to engaging stakeholders in strategic planning efforts. Our community outreach activities created a dialogue with our stakeholders through various communication channels, such as opportunities for interagency collaboration, town hall meetings, public presentations and listening sessions. These channels alongside our 60 day public comment period enabled the program to incorporate stakeholder input int the process of drafting this decadal plan. In addition, we welcome input - particularly on the future direction of USGCRP and on the climate information you need and use. Please send your comments to input@usgcrp.gov. Listening Sessions

192

Assumptions to the Annual Energy Outlook 1999 - Footnotes  

Gasoline and Diesel Fuel Update (EIA)

footnote.gif (3505 bytes) footnote.gif (3505 bytes) [1] Energy Information Administration, Annual Energy Outlook 1999 (AEO99), DOE/EIA-0383(99), (Washington, DC, December 1998). [2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986. [3] Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98), (Washington, DC, February 1998). [4] The underlying macroeconomic growth cases use DRI/McGraw-Hill’s August 1998 T250898 and February TO250298 and TP250298. [5] EIA, International Energy Outlook 1998, DOE/EIA-0484(98) (Washington DC, April 1998).

193

Assumptions to the Annual Energy Outlook 2001 - Footnotes  

Gasoline and Diesel Fuel Update (EIA)

Feedback Feedback Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage FOOTNOTES [1] Energy Information Administration, Annual Energy Outlook 2001 (AEO2001), DOE/EIA-0383(2001), (Washington, DC, December 2000). [2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986. [3] Energy Information Administration, The National Energy Modeling System: An Overview 2000, DOE/EIA-0581(2000), (Washington, DC, March 2000). [4] The underlying macroeconomic growth cases use Standard and Poor’s DRI February 2000 T250200 and February TO250299 and TP250299.

194

Assumptions to the Annual Energy Outlook 2000 - Footnote  

Gasoline and Diesel Fuel Update (EIA)

[1] Energy Information Administration, Annual Energy Outlook 2000 (AEO2000), DOE/EIA-0383(2000), (Washington, DC, December 1999). [1] Energy Information Administration, Annual Energy Outlook 2000 (AEO2000), DOE/EIA-0383(2000), (Washington, DC, December 1999). [2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986. [3] Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98), (Washington, DC, February 1998). [4] The underlying macroeconomic growth cases use Standard and Poor’s DRI August 1999 T250899 and February TO250299 and TP250299. [5] PennWell Publishing Co., International Petroleum Encyclopedia, (Tulsa, OK, 1999). [6] EIA, EIA Model Documentation: World Oil Refining Logistics Demand Model, “WORLD” Reference Manual, DOE/EIA-M058, (Washington, DC, March 1994).

195

A Statistical Analysis of the Dependency of Closure Assumptions in Cumulus Parameterization on the Horizontal Resolution  

Science Conference Proceedings (OSTI)

Simulated data from the UCLA cumulus ensemble model are used to investigate the quasi-universal validity of closure assumptions used in existing cumulus parameterizations. A closure assumption is quasi-universally valid if it is sensitive neither ...

Kuan-Man Xu

1994-12-01T23:59:59.000Z

196

Table 8. Capacity and Fresh Feed Input to Selected Downstream ...  

U.S. Energy Information Administration (EIA)

Capacity Inputs CapacityInputs Capacity Inputs Table 8. ... (EIA) Form EIA-820, "Annual Refinery Report." Inputs are from the form EIA-810, "Monthly Refinery Report."

197

inform  

Science Conference Proceedings (OSTI)

The monthly member publication of AOCS. inform Inform Magazine Membership Merchandise Subscriptions Journals Membership Merchandise 8C5A902BB64F1A5D499524EFF5918AE0 INFORM-NM 2008

198

EIA-Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2007 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

199

EIA - Assumptions to the Annual Energy Outlook 2008 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2008 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Module. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2007), (Washington, DC, 2007). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

200

EIA-Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2007 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new

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


201

EIA-Assumptions to the Annual Energy Outlook - National Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2007 National Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

202

Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumption to the Annual Energy Outlook Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. Table 50. Crude Oil Technically Recoverable Resources (Billion barrels) Printer Friendly Version Crude Oil Resource Category As of January 1, 2002 Undiscovered 56.02 Onshore 19.33 Northeast 1.47 Gulf Coast 4.76 Midcontinent 1.12 Southwest 3.25 Rocky Moutain 5.73 West Coast 3.00 Offshore 36.69 Deep (>200 meter W.D.) 35.01 Shallow (0-200 meter W.D.) 1.69 Inferred Reserves 49.14 Onshore 37.78 Northeast 0.79 Gulf Coast 0.80 Midcontinent 3.73 Southwest 14.61 Rocky Mountain 9.91 West Coast 7.94

203

DOE-2 Input File From WINDOW  

NLE Websites -- All DOE Office Websites (Extended Search)

an EnergyPlus input file from WINDOW 5 Last update: 12232008 01:54 PM Creating an EnergyPlus Input File for One Window In the WINDOW Window Library, which defines a complete...

204

DOE-2 Input File From WINDOW  

NLE Websites -- All DOE Office Websites (Extended Search)

a DOE2 input file from WINDOW 5 Last update: 02012008 01:19 PM Creating a DOE-2 Input File for One Window In the WINDOW Window Library, which defines a complete window including...

205

A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data  

SciTech Connect

Bayesian networks have attained widespread use in data analysis and decision making. Well studied topics include: efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating Bayesian network probabilities, and structure learning. It is not uncommon for the researcher to assume the structure of the Bayesian network or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted Bayesian networks; this is the contribution of this paper. We key on the definition of (conditional) independence to develop a graphical diagnostic method which indicates if the conditional independence assumptions imposed when one assumes the structure of the Bayesian network are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data with conditional independence assumptions under the model structure. We describe how this theoretical information and the data are presented in a graphical diagnostic tool. We demonstrate the approach through data simulated from Bayesian networks under different conditional independence assumptions. We also apply the diagnostic to a real world data set. The results indicate that our approach is a reasonable way of visualizing and inspecting the conditional independence assumption of a Bayesian network given data.

Walsh, Stephen J.; Whitney, Paul D.

2012-12-14T23:59:59.000Z

206

Assumptions to the Annual Energy Outlook 1999 - Acronyms  

Gasoline and Diesel Fuel Update (EIA)

acronyms.gif (3143 bytes) acronyms.gif (3143 bytes) AEO Annual Energy Outlook AEO98 Annual Energy Outlook 1998 AEO99 Annual Energy Outlook 1999 AFV AFV Alternative-Fuel Vehicle AGA American Gas Association ANGTS Alaskan Natural Gas Transportation System BEA Bureau of Economic Analysis BSC Boiler/Steam/Cogeneration BTU British Thermal Unit CAAA90 Clean Air Act Amendments of 1990 CBECS Commercial Buildings Energy Consumption Surveys CCAP Climate Change Action Plan CDD Cooling Degree-Days CNG Compressed natural gas DOE U.S. Department of Energy DRB Demonstrated Reserve Base DRI Data Resources, Inc./McGraw Hill EER Energy Efficiency Ratio EIA Energy Information Administration EIS Environmental Impact Statement EPA U.S. Environmental Protection Agency EPACT Energy Policy Act of 1992 EWG Exempt Wholesale Generator FAA Federal Aviation Administration

207

Design and analysis of MIMO systems with practical channel state information assumptions  

E-Print Network (OSTI)

frequency division duplex (FDD) systems are perfect examplesliteratures. However, for an FDD system without channel

Zheng, Jun

2006-01-01T23:59:59.000Z

208

Comparison of risk-dominant scenario assumptions for several TRU waste facilities in the DOE complex  

Science Conference Proceedings (OSTI)

In order to gain a risk management perspective, the DOE Rocky Flats Field Office (RFFO) initiated a survey of other DOE sites regarding risks from potential accidents associated with transuranic (TRU) storage and/or processing facilities. Recently-approved authorization basis documents at the Rocky Flats Environmental Technology Site (RFETS) have been based on the DOE Standard 3011 risk assessment methodology with three qualitative estimates of frequency of occurrence and quantitative estimates of radiological consequences to the collocated worker and the public binned into three severity levels. Risk Class 1 and 2 events after application of controls to prevent or mitigate the accident are designated as risk-dominant scenarios. Accident Evaluation Guidelines for selection of Technical Safety Requirements (TSRs) are based on the frequency and consequence bin assignments to identify controls that can be credited to reduce risk to Risk Class 3 or 4, or that are credited for Risk Class 1 and 2 scenarios that cannot be further reduced. This methodology resulted in several risk-dominant scenarios for either the collocated worker or the public that warranted consideration on whether additional controls should be implemented. RFFO requested the survey because of these high estimates of risks that are primarily due to design characteristics of RFETS TRU waste facilities (i.e., Butler-type buildings without a ventilation and filtration system, and a relatively short distance to the Site boundary). Accident analysis methodologies and key assumptions are being compared for the DOE sites responding to the survey. This includes type of accidents that are risk dominant (e.g., drum explosion, material handling breach, fires, natural phenomena, external events, etc.), source term evaluation (e.g., radionuclide material-at-risk, chemical and physical form, damage ratio, airborne release fraction, respirable fraction, leakpath factors), dispersion analysis (e.g., meteorological assumptions, distance to receptors, plume meander, deposition, and other factors affecting the calculated {chi}/Q), dose assessments (specific activities, inhalation dose conversion factors, breathing rates), designated frequency of occurrence, and risk assignment per the DOE Standard 3011 methodology. Information from the sites is being recorded on a spreadsheet to facilitate comparisons. The first response from Westinghouse Safety Management Solutions for the Savannah River Site (SRS) also provided a detailed analysis of the major differences in methods and assumptions between RFETS and SRS, which forms much of the basis for this paper. Other sites responding to the survey include the Idaho National Engineering and Environmental Laboratory (INEEL), Hanford, and the Los Alamos National Laboratory (LANL).

Foppe, T.L. [Foppe and Associates, Inc., Golden, CO (United States); Marx, D.R. [Westinghouse Safety Management Solutions, Inc., Aiken, SC (United States)

1999-06-01T23:59:59.000Z

209

Input apparatus for dynamic signature verification systems  

DOE Patents (OSTI)

The disclosure relates to signature verification input apparatus comprising a writing instrument and platen containing piezoelectric transducers which generate signals in response to writing pressures.

EerNisse, Errol P. (Albuquerque, NM); Land, Cecil E. (Albuquerque, NM); Snelling, Jay B. (Albuquerque, NM)

1978-01-01T23:59:59.000Z

210

A Review of Electric Vehicle Cost Studies: Assumptions, Methodologies, and Results  

E-Print Network (OSTI)

assumptions Battery costs and capacities: Lead acid batteryElectricity cost Battery cost and capacity: Lead acidElectricity cost Battery cost and capacity: N i C d

Lipman, Timothy

1999-01-01T23:59:59.000Z

211

U.S. Weekly Inputs & Utilization  

U.S. Energy Information Administration (EIA)

Crude Oil Inputs: 16,237: 16,031: 15,965: 15,893: 15,611: 15,845: 1982-2013: Gross Inputs: 16,539: 16,448: 16,257: 16,200: 15,927: 16,209: 1990-2013: Operable ...

212

Designating required vs. optional input fields  

Science Conference Proceedings (OSTI)

This paper describes a study comparing different techniques for visually distingishing required from optional input fields in a form-filling application. Seven techniques were studied: no indication, bold field labels, chevrons in front of the labels, ... Keywords: data input, optional fields, required fields, visual design

Thomas S. Tullis; Ana Pons

1997-03-01T23:59:59.000Z

213

The political life of information: "Information" and the practice of governance in India  

E-Print Network (OSTI)

the Right To Information (RTI) campaigns basic assumption.hearings and RTI dharna .78Rupees (Indian currency) RTI Right to Information SC

Srinivasan, Janaki

2011-01-01T23:59:59.000Z

214

PADD 5 Refinery Net Input - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Area: Period-Unit: Download ... 51: 54: 50: 57: 59: 2005-2013: Pentanes Plus: 23: 21: 17: 13: 17: 18: 2005-2013: Liquefied Petroleum Gases: 40: 30: 37: 37: 40: 41 ...

215

U.S. Refinery & Blender Net Input - Energy Information Administration  

U.S. Energy Information Administration (EIA)

413: 353: 340: 289: 2008-2013: RBOB for Blending with Alcohol : 2005-2009: RBOB for Blending with Ether : 2005-2009: GTAB : 2005-2009: Conventional: 173: 117: 246 ...

216

U.S. Refinery Net Input - Energy Information Administration  

U.S. Energy Information Administration (EIA)

413: 420: 2005-2013: Pentanes Plus: 166: 168: 156: 130: 148: 151: 2005-2013: Liquefied Petroleum Gases: 300: 281: 241: 238: 265: 270: 2005-2013: Normal Butane: 132 ...

217

Refinery Inputs of Crude Oil - Energy Information Administration  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Finished motor gasoline ...

218

National Climate Assessment: Available Technical Inputs  

NLE Websites -- All DOE Office Websites (Extended Search)

Available Technical Inputs Print E-mail Available Technical Inputs Print E-mail Technical inputs for the 2013 National Climate Assessment were due March 1, 2012. Please note that these reports were submitted independently to the National Climate Assessment for consideration and have not been reviewed by the National Climate Assessment Development and Advisory Committee. Links to agency-sponsored reports will be posted here as they are made available. Sectors National Climate Assessment Health Sector Literature Review and Bibliography. Technical Input for the Interagency Climate Change and Human Health Group, September 2012. Overview Bibliography Bibliography User's Guide Search Strategy and Results Walthall et al. 2012. Climate Change and Agriculture in the United States: Effects and Adaptation. USDA Technical Bulletin 1935. Washington, DC. 186 pages. | Report FAQs

219

Wind Energy Input to the Ekman Layer  

Science Conference Proceedings (OSTI)

Wind stress energy input through the surface ageostrophic currents is studied. The surface ageostrophic velocity is calculated using the classical formula of the Ekman spiral, with the Ekman depth determined from an empirical formula. The total ...

Wei Wang; Rui Xin Huang

2004-05-01T23:59:59.000Z

220

Oak Ridge's EM Program Seeks Public Input on Cleanup | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Seeks Public Input on Cleanup Seeks Public Input on Cleanup Oak Ridge's EM Program Seeks Public Input on Cleanup April 25, 2013 - 12:00pm Addthis Oak Ridge’s EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Oak Ridge's EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Oak Ridge EM Manager Mark Whitney addresses participants on EM’s mission and priorities.

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


221

Oak Ridge's EM Program Seeks Public Input on Cleanup | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Oak Ridge's EM Program Seeks Public Input on Cleanup Oak Ridge's EM Program Seeks Public Input on Cleanup Oak Ridge's EM Program Seeks Public Input on Cleanup April 25, 2013 - 12:00pm Addthis Oak Ridge’s EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Oak Ridge's EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Oak Ridge EM Manager Mark Whitney addresses participants on EM’s mission and priorities.

222

Heterogeneous Correlation Modeling Based on the Wavelet Diagonal Assumption and on the Diffusion Operator  

Science Conference Proceedings (OSTI)

This article discusses several models for background error correlation matrices using the wavelet diagonal assumption and the diffusion operator. The most general properties of filtering local correlation functions, with wavelet formulations, are ...

Olivier Pannekoucke

2009-09-01T23:59:59.000Z

223

Microwave Properties of Ice-Phase Hydrometeors for Radar and Radiometers: Sensitivity to Model Assumptions  

Science Conference Proceedings (OSTI)

A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase ...

Benjamin T. Johnson; Grant W. Petty; Gail Skofronick-Jackson

2012-12-01T23:59:59.000Z

224

Documentation of Calculation Methodology, Input Data, and Infrastructure  

NLE Websites -- All DOE Office Websites (Extended Search)

Documentation of Calculation Methodology, Input Data, and Infrastructure Documentation of Calculation Methodology, Input Data, and Infrastructure for the Home Energy Saver Web Site Title Documentation of Calculation Methodology, Input Data, and Infrastructure for the Home Energy Saver Web Site Publication Type Report LBNL Report Number LBNL-51938 Year of Publication 2005 Authors Pinckard, Margaret J., Richard E. Brown, Evan Mills, James D. Lutz, Mithra M. Moezzi, Celina S. Atkinson, Christopher A. Bolduc, Gregory K. Homan, and Katie Coughlin Document Number LBNL-51938 Pagination 108 Date Published July 13 Publisher Lawrence Berkeley National Laboratory City Berkeley Abstract The Home Energy Saver (HES, http://HomeEnergySaver.lbl.gov) is an interactive web site designed to help residential consumers make decisions about energy use in their homes. This report describes the underlying methods and data for estimating energy consumption. Using engineering models, the site estimates energy consumption for six major categories (end uses); heating, cooling, water heating, major appliances, lighting, and miscellaneous equipment. The approach taken by the Home Energy Saver is to provide users with initial results based on a minimum of user input, allowing progressively greater control in specifying the characteristics of the house and energy consuming appliances. Outputs include energy consumption (by fuel and end use), energy-related emissions (carbon dioxide), energy bills (total and by fuel and end use), and energy saving recommendations. Real-world electricity tariffs are used for many locations, making the bill estimates even more accurate. Where information about the house is not available from the user, default values are used based on end-use surveys and engineering studies. An extensive body of qualitative decision-support information augments the analytical results.

225

Total Blender Net Input of Petroleum Products  

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

Input Input Product: Total Input Natural Gas Plant Liquids and Liquefied Refinery Gases Pentanes Plus Liquid Petroleum Gases Normal Butane Isobutane Other Liquids Oxygenates/Renewables Methyl Tertiary Butyl Ether (MTBE) Renewable Fuels (incl. Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Unfinished Oils (net) Unfinished Oils, Naphthas and Lighter Unfinished Oils, Kerosene and Light Gas Oils Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Components (MGBC) (net) MGBC - Reformulated MGBC - Reformulated - RBOB MGBC - Reformulated, RBOB for Blending w/ Alcohol MGBC - Reformulated, RBOB for Blending w/ Ether MGBC - Reformulated, GTAB MGBC - Conventional MGBC - Conventional, CBOB MGBC - Conventional, GTAB MGBC - Other Conventional Period-Unit: Monthly-Thousand Barrels Monthly-Thousand Barrels per Day Annual-Thousand Barrels Annual-Thousand Barrels per Day

226

Characterization of industrial process waste heat and input heat streams  

SciTech Connect

The nature and extent of industrial waste heat associated with the manufacturing sector of the US economy are identified. Industry energy information is reviewed and the energy content in waste heat streams emanating from 108 energy-intensive industrial processes is estimated. Generic types of process equipment are identified and the energy content in gaseous, liquid, and steam waste streams emanating from this equipment is evaluated. Matchups between the energy content of waste heat streams and candidate uses are identified. The resultant matrix identifies 256 source/sink (waste heat/candidate input heat) temperature combinations. (MHR)

Wilfert, G.L.; Huber, H.B.; Dodge, R.E.; Garrett-Price, B.A.; Fassbender, L.L.; Griffin, E.A.; Brown, D.R.; Moore, N.L.

1984-05-01T23:59:59.000Z

227

Efficient concurrency-bug detection across inputs  

Science Conference Proceedings (OSTI)

In the multi-core era, it is critical to efficiently test multi-threaded software and expose concurrency bugs before software release. Previous work has made significant progress in detecting and validating concurrency bugs under a given input. Unfortunately, ... Keywords: bug detection, concurrency bugs, multi-threaded software, software testing

Dongdong Deng, Wei Zhang, Shan Lu

2013-10-01T23:59:59.000Z

228

Gravity Transform for Input Conditioning in  

E-Print Network (OSTI)

Gravity Transform for Input Conditioning in Brain Machine Interfaces António R. C. Paiva, José C. Motivation 2. Methods i. Gravity Transform ii. Modeling and output sensitivity analysis 3. Data Analysis #12;3 Outline 1. Motivation 2. Methods i. Gravity Transform ii. Modeling and output sensitivity analysis 3. Data

Paiva, António R. C.

229

Wind Energy Input to the Surface Waves  

Science Conference Proceedings (OSTI)

Wind energy input into the ocean is primarily produced through surface waves. The total rate of this energy source, integrated over the World Ocean, is estimated at 60 TW, based on empirical formulas and results from a numerical model of surface ...

Wei Wang; Rui Xin Huang

2004-05-01T23:59:59.000Z

230

Hydrogen Generation Rate Model Calculation Input Data  

DOE Green Energy (OSTI)

This report documents the procedures and techniques utilized in the collection and analysis of analyte input data values in support of the flammable gas hazard safety analyses. This document represents the analyses of data current at the time of its writing and does not account for data available since then.

KUFAHL, M.A.

2000-04-27T23:59:59.000Z

231

Multiple Input Microcantilever Sensor with Capacitive Readout  

DOE Green Energy (OSTI)

A surface-micromachined MEMS process has been used to demonstrate multiple-input chemical sensing using selectively coated cantilever arrays. Combined hydrogen and mercury-vapor detection was achieved with a palm-sized, self-powered module with spread-spectrum telemetry reporting.

Britton, C.L., Jr.; Brown, G.M.; Bryan, W.L.; Clonts, L.G.; DePriest, J.C.; Emergy, M.S.; Ericson, M.N.; Hu, Z.; Jones, R.L.; Moore, M.R.; Oden, P.I.; Rochelle, J.M.; Smith, S.F.; Threatt, T.D.; Thundat, T.; Turner, G.W.; Warmack, R.J.; Wintenberg, A.L.

1999-03-11T23:59:59.000Z

232

On the Input Problem for Massive Modularity  

Science Conference Proceedings (OSTI)

Jerry Fodor argues that the massive modularity thesis -- the claim that (human) cognition is wholly served by domain specific, autonomous computational devices, i.e., modules -- is a priori ... Keywords: Fodor, Sperber, input problem, language faculty, massive modularity, theory of mind

J. Collins

2005-02-01T23:59:59.000Z

233

Evaluating capacitive touch input on clothes  

Science Conference Proceedings (OSTI)

Wearable computing and smart clothing have attracted a lot of attention in the last years. For a variety of applications, it can be seen as potential future direction of mobile user interfaces. In this paper, we concentrate on usability and applicability ... Keywords: capacitive touch, design guidelines, input on textiles, wearable controls

Paul Holleis; Albrecht Schmidt; Susanna Paasovaara; Arto Puikkonen; Jonna Hkkil

2008-09-01T23:59:59.000Z

234

Annual Energy Outlook 2001-Appendix G: Major Assumptions for the Forecasts  

Gasoline and Diesel Fuel Update (EIA)

Forecasts Forecasts Summary of the AEO2001 Cases/ Scenarios - Appendix Table G1 bullet1.gif (843 bytes) Model Results (Formats - PDF, ZIP) - Appendix Tables - Reference Case - 1998 to 2020 bullet1.gif (843 bytes) Download Report - Entire AEO2001 (PDF) - AEO2001 by Chapters (PDF) bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts Related Links bullet1.gif (843 bytes) Assumptions to the AEO2001 bullet1.gif (843 bytes) Supplemental Data to the AEO2001 (Only available on the Web) - Regional and more detailed AEO 2001 Reference Case Results - 1998, 2000 to 2020 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Forecast Homepage bullet1.gif (843 bytes) EIA Homepage Appendix G Major Assumptions for the Forecasts Component Modules Major Assumptions for the Annual Energy Outlook 2001

235

On the use of the parabolic concentration profile assumption for a rotary desiccant dehumidifier  

SciTech Connect

The current work describes a model for a desiccant dehumidifier which uses a parabolic concentration profile assumption to model the diffusion resistance inside the desiccant particle. The relative merits of the parabolic concentration profile model compared with widely utilized rotary desiccant wheel models are discussed. The periodic steady-state parabolic concentration profile model developed is efficient and can accommodate a variety of materials. These features make it an excellent tool for design studies requiring repetitive desiccant wheel simulations. A quartic concentration profile assumption was also investigated which yielded a 2.8 percent average improvement in prediction error over the parabolic model.

Chant, E.E. [Univ. of Turabo, Gurabo (Puerto Rico); Jeter, S.M. [Georgia Inst. of Technology, Atlanta, GA (United States). George W. Woodruff School of Mechanical Engineering

1995-02-01T23:59:59.000Z

236

Sensitivity of Rooftop PV Projections in the SunShot Vision Study to Market Assumptions  

Science Conference Proceedings (OSTI)

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The SolarDS model was used to simulate rooftop PV demand for this study, based on several PV market assumptions--future electricity rates, customer access to financing, and others--in addition to the SunShot PV price projections. This paper finds that modeled PV demand is highly sensitive to several non-price market assumptions, particularly PV financing parameters.

Drury, E.; Denholm, P.; Margolis, R.

2013-01-01T23:59:59.000Z

237

DOE Seeks Public Input on an Integrated, Interagency Pre-Application  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Seeks Public Input on an Integrated, Interagency Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations DOE Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations August 29, 2013 - 9:09am Addthis A Request for Information (RFI) seeking public input for a draft Integrated, Interagency Pre-application (IIP) Process was published in the Federal Register on August 29, 2013. The Federal Register Notice is available now for downloading. Comments must be received on or before September 30, 2013. As comments are received, they will be posted online. The proposed IIP Process is intended to improve interagency and intergovernmental coordination focused on ensuring that project proponents develop and submit accurate and complete information early in the project

238

Neural Network Input Representations that Produce Accurate Consensus Sequences from DNA Fragment Assemblies  

E-Print Network (OSTI)

Motivation: Given inputs extracted from an aligned column of DNA bases and the underlying Perkin Elmer Applied Biosystems (ABI) fluorescent traces, our goal is to train a neural network to correctly determine the consensus base for the column. Choosing an appropriate network input representation is critical to success in this task. We empirically compare five representations; one uses only base calls and the others include trace information. Results: We attained the most accurate results from networks that incorporate trace information into their input representations. Based on estimates derived from using 10-fold cross-validation, the best network topology produces consensus accuracies ranging from 99.26% to over 99.98% for coverages from two to six aligned sequences. With a coverage of six, it makes only three errors in 20,000 consensus calls. In contrast, the network that only uses base calls in its input representation has over double that error rate -- eight errors in 20,000 cons...

C.F. Allex; J.W. Shavlik; F.R. Blattner

1999-01-01T23:59:59.000Z

239

DOE Seeks Public Input on an Integrated, Interagency Pre-Application  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

DOE Seeks Public Input on an Integrated, Interagency DOE Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations DOE Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations August 29, 2013 - 9:09am Addthis A Request for Information (RFI) seeking public input for a draft Integrated, Interagency Pre-application (IIP) Process was published in the Federal Register on August 29, 2013. The Federal Register Notice is available now for downloading. Comments must be received on or before September 30, 2013. As comments are received, they will be posted online. The proposed IIP Process is intended to improve interagency and intergovernmental coordination focused on ensuring that project proponents develop and submit accurate and complete information early in the project

240

T-623: HP Business Availability Center Input Validation Hole...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

3: HP Business Availability Center Input Validation Hole Permits Cross-Site Scripting Attacks T-623: HP Business Availability Center Input Validation Hole Permits Cross-Site...

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


241

U-252: Barracuda Web Filter Input Validation Flaws Permit Cross...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

2: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks U-252: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks September...

242

U-219: Symantec Web Gateway Input Validation Flaws Lets Remote...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

9: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords U-219: Symantec Web Gateway Input...

243

DOE Seeks Input On Addressing Contractor Pension and Medical...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Seeks Input On Addressing Contractor Pension and Medical Benefits Liabilities DOE Seeks Input On Addressing Contractor Pension and Medical Benefits Liabilities March 27, 2007 -...

244

USDA, Departments of Energy and Navy Seek Input from Industry...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Departments of Energy and Navy Seek Input from Industry to Advance Biofuels for Military and Commercial Transportation USDA, Departments of Energy and Navy Seek Input from Industry...

245

Documentation of Calculation Methodology, Input Data, and Infrastructu...  

NLE Websites -- All DOE Office Websites (Extended Search)

Documentation of Calculation Methodology, Input Data, and Infrastructure for the Home Energy Saver Web Site Title Documentation of Calculation Methodology, Input Data, and...

246

,"Arkansas Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Arkansas Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sar_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sar_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:49 AM"

247

,"Illinois Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Illinois Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sil_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sil_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

248

,"Catalytic Reforming Downstream Processing of Fresh Feed Input"  

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

Catalytic Reforming Downstream Processing of Fresh Feed Input" Catalytic Reforming Downstream Processing of Fresh Feed Input" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Catalytic Reforming Downstream Processing of Fresh Feed Input",16,"Monthly","9/2013","1/15/2010" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_dwns_a_(na)_ydr_mbblpd_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_dwns_a_(na)_ydr_mbblpd_m.htm" ,"Source:","Energy Information Administration"

249

,"Pennsylvania Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Pennsylvania Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_spa_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_spa_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:55 AM"

250

,"Iowa Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Iowa Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sia_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sia_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

251

,"Alabama Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Alabama Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sal_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sal_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:49 AM"

252

,"Maryland Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Maryland Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_smd_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_smd_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

253

,"New Jersey Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","New Jersey Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snj_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snj_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

254

,"Hawaii Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Hawaii Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_shi_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_shi_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

255

,"Rhode Island Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Rhode Island Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sri_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sri_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:55 AM"

256

,"Louisiana Natural Gas Input Supplemental Fuels (Million Cubic Feet)"  

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

Input Supplemental Fuels (Million Cubic Feet)" Input Supplemental Fuels (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Louisiana Natural Gas Input Supplemental Fuels (Million Cubic Feet)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","nga_epg0_ovi_sla_mmcfa.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/nga_epg0_ovi_sla_mmcfa.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov"

257

,"North Carolina Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","North Carolina Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snc_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snc_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

258

,"Alaska Natural Gas Input Supplemental Fuels (Million Cubic Feet)"  

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

Input Supplemental Fuels (Million Cubic Feet)" Input Supplemental Fuels (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Alaska Natural Gas Input Supplemental Fuels (Million Cubic Feet)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na_epg0_ovi_sak_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na_epg0_ovi_sak_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov"

259

,"Connecticut Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Connecticut Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sct_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sct_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

260

,"Minnesota Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Minnesota Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_smn_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_smn_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

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


261

,"New Mexico Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","New Mexico Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snm_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snm_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

262

,"Wyoming Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Wyoming Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_swy_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_swy_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

263

,"Washington Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Washington Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_swa_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_swa_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

264

,"Wisconsin Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Wisconsin Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_swi_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_swi_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

265

,"New Hampshire Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","New Hampshire Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snh_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snh_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

266

,"Kentucky Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Kentucky Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sky_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sky_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

267

,"Tennessee Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Tennessee Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_stn_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_stn_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

268

,"Indiana Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Indiana Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sin_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sin_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

269

,"Michigan Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Michigan Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_smi_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_smi_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

270

,"Virginia Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Virginia Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sva_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sva_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

271

,"Georgia Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Georgia Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sga_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sga_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

272

,"South Dakota Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","South Dakota Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_ssd_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_ssd_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

273

,"Nebraska Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Nebraska Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sne_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sne_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

274

,"Delaware Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Delaware Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sde_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sde_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

275

,"North Dakota Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","North Dakota Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snd_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snd_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

276

,"South Carolina Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","South Carolina Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_ssc_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_ssc_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

277

,"Massachusetts Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Massachusetts Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sma_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sma_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

278

,"Nevada Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Nevada Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snv_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snv_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

279

,"Texas Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_stx_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_stx_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

280

,"U.S. Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","U.S. Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","n9090us2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/n9090us2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:57:08 AM"

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


281

,"Colorado Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Colorado Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sco_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sco_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:49 AM"

282

,"Oregon Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Oregon Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sor_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sor_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:55 AM"

283

,"Florida Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Florida Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sfl_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sfl_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

284

,"Vermont Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Vermont Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_svt_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_svt_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

285

,"Maine Natural Gas Input Supplemental Fuels (MMcf)"  

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

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Maine Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sme_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sme_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

286

Petroleum Market Module - Energy Information Administration  

U.S. Energy Information Administration (EIA)

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2012 137 Petroleum Market Module Table 11.2. Year-round gasoline ...

287

Reconciling Simulated Moisture Fluxes Resulting from Alternate Hydrologic Model Time Steps and Energy Budget Closure Assumptions  

Science Conference Proceedings (OSTI)

Hydrological model predictions are sensitive to model forcings, input parameters, and the parameterizations of physical processes. Analyses performed for the Variable Infiltration Capacity model show that the resulting moisture fluxes are ...

Ingjerd Haddeland; Dennis P. Lettenmaier; Thomas Skaugen

2006-06-01T23:59:59.000Z

288

Multimodal interfaces with voice and gesture input  

SciTech Connect

The modalities of speech and gesture have different strengths and weaknesses, but combined they create synergy where each modality corrects the weaknesses of the other. We believe that a multimodal system such a one interwining speech and gesture must start from a different foundation than ones which are based solely on pen input. In order to provide a basis for the design of a speech and gesture system, we have examined the research in other disciplines such as anthropology and linguistics. The result of this investigation was a taxonomy that gave us material for the incorporation of gestures whose meanings are largely transparent to the users. This study describes the taxonomy and gives examples of applications to pen input systems.

Milota, A.D.; Blattner, M.M.

1995-07-20T23:59:59.000Z

289

Biennial Assessment of the Fifth Power Plan Gas Turbine Power Plant Planning Assumptions  

E-Print Network (OSTI)

Biennial Assessment of the Fifth Power Plan Gas Turbine Power Plant Planning Assumptions October 17, 2006 Simple- and combined-cycle gas turbine power plants fuelled by natural gas are among the bulk-emission and efficient gas turbine technology made combined-cycle gas turbine power plants the "resource of choice

290

External review of the thermal energy storage (TES) cogeneration study assumptions. Final report  

DOE Green Energy (OSTI)

This report is to provide a detailed review of the basic assumptions made in the design, sizing, performance, and economic models used in the thermal energy storage (TES)/cogeneration feasibility studies conducted by Pacific Northwest Laboratory (PNL) staff. This report is the deliverable required under the contract.

Lai, B.Y.; Poirier, R.N. [Chicago Bridge and Iron Technical Services Co., Plainfield, IL (United States)

1996-08-01T23:59:59.000Z

291

,"U.S. Blender Net Input"  

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

,"Available from Web Page:","http:www.eia.govdnavpetpetpnpinpt3dcnusmbbla.htm" ,"Source:","Energy Information Administration" ,"For Help,...

292

Increased Software Reliability Through Input Validation Analysis and Testing  

Science Conference Proceedings (OSTI)

The Input Validation Testing (IVT) technique has been developed to address the problem of statically analyzing input command syntax as defined in English textual interface and requirements specifications and then generating test cases for input validation ... Keywords: Software reliability, requirements analysis, system testing, quality control and assurance, interfaces, input validation

Jane Huffman Hayes; A. Jefferson Offutt

1999-11-01T23:59:59.000Z

293

East Coast (PADD 1) Gross Inputs to Atmospheric Crude Oil ...  

U.S. Energy Information Administration (EIA)

East Coast (PADD 1) Gross Inputs to Atmospheric Crude Oil Distillation Units (Thousand Barrels per Day)

294

Rocky Mountains (PADD 4) Gross Inputs to Refineries (Thousand ...  

U.S. Energy Information Administration (EIA)

Gross Input to Atmospheric Crude Oil Distillation Units ; PAD District 4 Refinery Utilization and Capacity ...

295

Refining District New Mexico Gross Inputs to Atmospheric Crude Oil ...  

U.S. Energy Information Administration (EIA)

Refining District New Mexico Gross Inputs to Atmospheric Crude Oil Distillation Units (Thousand Barrels per Day)

296

EIA-Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2007 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously derived supply curves, initial price paths and international regional supply and demand levels into NEMS. These quantities are not modeled directly in NEMS because NEMS is not an international model. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2007 with the IEO2006 and the STEO some functionality was removed from the IEM. More analyst time was devoted to analyzing price relationships between marker crude oils and refined products. A new exogenous oil supply model, Generate World Oil Balances (GWOB), was also developed to incorporate actual investment occurring in the international oil market through 2015 and resource assumptions through 2030. The GWOB model provides annual country level oil production detail for eight conventional and unconventional oils.

297

GRI baseline projection: Methodology and assumptions 1996 edition. Topical report, January-December 1995  

Science Conference Proceedings (OSTI)

The report documents the methodology employed in producing the 1996 Edition of the GRI Baseline Projection. DRI/McGraw-Hill`s Energy Group (DRI) maintains an energy modeling system for the Gas Research Institute (GRI) that is used to produce an annual projection of the supply and demand for energy by regions in the United States. The 1996 Edition of the GRI Baseline Projection is produced using several different models. The models analyze various pieces of the U.S. energy markets and their solutions are based on a framework of exogenous assumptions provided by GRI. The report describes the integration and solution procedures of the models and the assumptions used to produce the final projection results.

Rhodes, M.R.; Baxter, R.P.; Nottingham, R.P.

1996-04-01T23:59:59.000Z

298

GRI baseline projection: Methodology and assumptions 1995 edition. Topical report, January-December 1994  

SciTech Connect

The report documents the methodology employed in producing the 1995 Edition of the GRI Baseline Projection. DRI/McGraw-Hill`s Energy Group (DRI) maintains an energy modeling system for the Gas Research Institute (GRI) that is used to produce an annual projection of the supply and demand for energy by regions in the United States. The 1995 Edition of the GRI Baseline Projection is produced using several different models. The models analyze various pieces of the U.S. energy markets and their solutions are based on a framework of exogeneous assumptions provided by GRI. The report describes the integration and solution procedures of the models and the assumptions used to produce the final projection results.

Baxter, R.P.; Silveira, T.S.; Harshbarger, S.L.

1995-02-01T23:59:59.000Z

299

Assumptions and Criteria for Performing a Feasability Study of the Conversion of the High Flux Isotope Reactor Core to Use Low-Enriched Uranium Fuel  

SciTech Connect

A computational study will be initiated during fiscal year 2006 to examine the feasibility of converting the High Flux Isotope Reactor from highly enriched uranium fuel to low-enriched uranium. The study will be limited to steady-state, nominal operation, reactor physics and thermal-hydraulic analyses of a uranium-molybdenum alloy that would be substituted for the current fuel powder--U{sub 3}O{sub 8} mixed with aluminum. The purposes of this document are to (1) define the scope of studies to be conducted, (2) define the methodologies to be used to conduct the studies, (3) define the assumptions that serve as input to the methodologies, (4) provide an efficient means for communication with the Department of Energy and American research reactor operators, and (5) expedite review and commentary by those parties.

Primm, R.T., III; Ellis, R.J.; Gehin, J.C.; Moses, D.L.; Binder, J.L.; Xoubi, N. (U. of Cincinnati)

2006-02-01T23:59:59.000Z

300

Ground motion input in seismic evaluation studies  

Science Conference Proceedings (OSTI)

This report documents research pertaining to conservatism and variability in seismic risk estimates. Specifically, it examines whether or not artificial motions produce unrealistic evaluation demands, i.e., demands significantly inconsistent with those expected from real earthquake motions. To study these issues, two types of artificial motions are considered: (a) motions with smooth response spectra, and (b) motions with realistic variations in spectral amplitude across vibration frequency. For both types of artificial motion, time histories are generated to match target spectral shapes. For comparison, empirical motions representative of those that might result from strong earthquakes in the Eastern U.S. are also considered. The study findings suggest that artificial motions resulting from typical simulation approaches (aimed at matching a given target spectrum) are generally adequate and appropriate in representing the peak-response demands that may be induced in linear structures and equipment responding to real earthquake motions. Also, given similar input Fourier energies at high-frequencies, levels of input Fourier energy at low frequencies observed for artificial motions are substantially similar to those levels noted in real earthquake motions. In addition, the study reveals specific problems resulting from the application of Western U.S. type motions for seismic evaluation of Eastern U.S. nuclear power plants.

Sewell, R.T.; Wu, S.C.

1996-07-01T23:59:59.000Z

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


301

DOE Seeks Additional Input on Next Generation Nuclear Plant | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Seeks Additional Input on Next Generation Nuclear Plant Seeks Additional Input on Next Generation Nuclear Plant DOE Seeks Additional Input on Next Generation Nuclear Plant April 17, 2008 - 10:49am Addthis WASHINGTON, DC -The U.S. Department of Energy (DOE) today announced it is seeking public and industry input on how to best achieve the goals and meet the requirements for the Next Generation Nuclear Plant (NGNP) demonstration project work at DOE's Idaho National Laboratory. DOE today issued a Request for Information and Expressions of Interest from prospective participants and interested parties on utilizing cutting-edge high temperature gas reactor technology in the effort to reduce greenhouse gas emissions by enabling nuclear energy to replace fossil fuels used by industry for process heat. "This is an opportunity to advance the development of safe, reliable, and

302

On the Wind Power Input to the Ocean General Circulation  

Science Conference Proceedings (OSTI)

The wind power input to the ocean general circulation is usually calculated from the time-averaged wind products. Here, this wind power input is reexamined using available observations, focusing on the role of the synoptically varying wind. Power ...

Xiaoming Zhai; Helen L. Johnson; David P. Marshall; Carl Wunsch

2012-08-01T23:59:59.000Z

303

On the Wind Power Input to the Ocean General Circulation  

E-Print Network (OSTI)

The wind power input to the ocean general circulation is usually calculated from the time-averaged wind products. Here, this wind power input is reexamined using available observations, focusing on the role of the synoptically ...

Zhai, Xiaoming

304

Wisconsin Natural Gas Input Supplemental Fuels (Million Cubic...  

Annual Energy Outlook 2012 (EIA)

Input Supplemental Fuels (Million Cubic Feet) Wisconsin Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

305

Vermont Natural Gas Input Supplemental Fuels (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Input Supplemental Fuels (Million Cubic Feet) Vermont Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

306

Estimation of time-dependent input from neuronal membrane potential  

Science Conference Proceedings (OSTI)

The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For ...

Ryota Kobayashi; Shigeru Shinomoto; Petr Lansky

2011-12-01T23:59:59.000Z

307

New Mexico Natural Gas Input Supplemental Fuels (Million Cubic...  

Gasoline and Diesel Fuel Update (EIA)

Input Supplemental Fuels (Million Cubic Feet) New Mexico Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

308

Texas Natural Gas Input Supplemental Fuels (Million Cubic Feet...  

Gasoline and Diesel Fuel Update (EIA)

Input Supplemental Fuels (Million Cubic Feet) Texas Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

309

Bases, Assumptions, and Results of the Flowsheet Calculations for the Decision Phase Salt Disposition Alternatives  

SciTech Connect

The High Level Waste (HLW) Salt Disposition Systems Engineering Team was formed on March 13, 1998, and chartered to identify options, evaluate alternatives, and recommend a selected alternative(s) for processing HLW salt to a permitted wasteform. This requirement arises because the existing In-Tank Precipitation process at the Savannah River Site, as currently configured, cannot simultaneously meet the HLW production and Authorization Basis safety requirements. This engineering study was performed in four phases. This document provides the technical bases, assumptions, and results of this engineering study.

Dimenna, R.A.; Jacobs, R.A.; Taylor, G.A.; Durate, O.E.; Paul, P.K.; Elder, H.H.; Pike, J.A.; Fowler, J.R.; Rutland, P.L.; Gregory, M.V.; Smith III, F.G.; Hang, T.; Subosits, S.G.; Campbell, S.G.

2001-03-26T23:59:59.000Z

310

Science with the Square Kilometer Array: Motivation, Key Science Projects, Standards and Assumptions  

E-Print Network (OSTI)

The Square Kilometer Array (SKA) represents the next major, and natural, step in radio astronomical facilities, providing two orders of magnitude increase in collecting area over existing telescopes. In a series of meetings, starting in Groningen, the Netherlands (August 2002) and culminating in a `science retreat' in Leiden (November 2003), the SKA International Science Advisory Committee (ISAC), conceived of, and carried-out, a complete revision of the SKA science case (to appear in New Astronomy Reviews). This preface includes: (i) general introductory material, (ii) summaries of the key science programs, and (iii) a detailed listing of standards and assumptions used in the revised science case.

C. Carilli; S. Rawlings

2004-09-12T23:59:59.000Z

311

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

E-Print Network (OSTI)

LBL-34046 UC-350 Residential Appliance Data, Assumptions and Methodology for End-Use Forecasting. DE-AC03-76SF00098 #12;i ABSTRACT This report details the data, assumptions and methodology for end-use provided by the Appliance Model in the Residential End-Use Energy Planning System (REEPS), which

312

Assumptions to the Annual Energy Outlook 2001 - Table 3. Coal-Related  

Gasoline and Diesel Fuel Update (EIA)

Coal-Related Methane Assumptions Coal-Related Methane Assumptions Northern Appalachia Central Appalachia Southern Appalachia Eastern Interior Western Fraction of underground coal production at: Gassy mines 0.885 0.368 0.971 0.876 0.681 Nongassy mines 0.115 0.632 0.029 0.124 0.319 Production from mines with degasification systems (fraction of underground production) 0.541 0.074 0.810 0.067 0.056 Emission factors (kilograms methane per short ton of coal produced) Underground Mining Gassy mines 6.047 5.641 27.346 2.988 6.027 Nongassy mines 0.362 0.076 15.959 0.285 0.245 Degassified mines 4.085 37.724 22.025 0.310 0.000 Surface Mining 0.706 0.706 0.706 0.706 0.706 Post-Mining, underground-mined 1.505 1.505 1.505 1.505 1.505 Post-Mining, surface-mined 0.061 0.061 0.061 0.061 0.061 Methane recovery at active coal mines

313

U.S. Blender Net Input  

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

2007 2008 2009 2010 2011 2012 View 2007 2008 2009 2010 2011 2012 View History Total Input 1,184,435 1,522,193 1,850,204 2,166,784 2,331,109 2,399,318 2005-2012 Natural Gas Plant Liquids and Liquefied Refinery Gases 3,445 5,686 6,538 7,810 10,663 2008-2012 Pentanes Plus 2,012 474 1,808 1,989 2,326 4,164 2005-2012 Liquid Petroleum Gases 2,971 3,878 4,549 5,484 6,499 2008-2012 Normal Butane 2,943 2,971 3,878 4,549 5,484 6,499 2005-2012 Isobutane 2005-2006 Other Liquids 1,518,748 1,844,518 2,160,246 2,323,299 2,388,655 2008-2012 Oxygenates/Renewables 234,047 274,974 286,837 295,004 2009-2012 Methyl Tertiary Butyl Ether (MTBE) 2005-2006 Renewable Fuels (incl. Fuel Ethanol) 234,047 274,974 286,837 295,004 2009-2012 Fuel Ethanol 131,810 182,772 232,677 273,107 281,507 287,433 2005-2012

314

U.S. Blender Net Input  

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

Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History Total Input 206,541 217,867 212,114 216,075 219,783 208,203 2005-2013 Natural Gas Plant Liquids and Liquefied Refinery Gases 891 352 376 196 383 1,397 2008-2013 Pentanes Plus 261 301 313 67 287 393 2005-2013 Liquid Petroleum Gases 630 51 63 129 96 1,004 2008-2013 Normal Butane 630 51 63 129 96 1,004 2005-2013 Isobutane 2005-2006 Other Liquids 205,650 217,515 211,738 215,879 219,400 206,806 2008-2013 Oxygenates/Renewables 25,156 26,576 26,253 26,905 27,788 25,795 2009-2013 Methyl Tertiary Butyl Ether (MTBE) 2005-2006 Renewable Fuels (incl. Fuel Ethanol) 25,156 26,576 26,253 26,905 27,788 25,795 2009-2013 Fuel Ethanol 24,163 25,526 24,804 25,491 25,970 24,116 2005-2013

315

Precoding by Pairing Subchannels to Increase MIMO Capacity With Discrete Input Alphabets  

E-Print Network (OSTI)

AbstractWe consider Gaussian multiple-input multiple-output (MIMO) channels with discrete input alphabets. We propose a nondiagonal precoder based on the X-Codes in [1] to increase the mutual information. The MIMO channel is transformed into a set of parallel subchannels using singular value decomposition (SVD) and X-Codes are then used to pair the subchannels. X-Codes are fully characterized by the pairings and a 2 2 2 real rotation matrix for each pair (parameterized with a single angle). This precoding structure enables us to express the total mutual information as a sum of the mutual information of all the pairs. The problem of finding the optimal precoder with the above structure, which maximizes the total mutual information, is solved by: i) optimizing the rotation angle and the power allocation within each pair and ii) finding the optimal pairing and power allocation among the pairs. It is shown that the mutual information achieved with the proposed pairing scheme is very close to that achieved with the optimal precoder by Cruz et al., and is significantly better than Mercury/waterfilling strategy by Lozano et al. Our approach greatly simplifies both the precoder optimization and the detection complexity, making it suitable for practical applications. Index TermsCondition number, multiple-input multiple-output (MIMO), mutual information, orthogonal frequency division multiplexing (OFDM), precoding, singular value decomposition (SVD). I.

Saif Khan Mohammed; Emanuele Viterbo; Yi Hong; Senior Member; Ananthanarayanan Chockalingam; Senior Member

2010-01-01T23:59:59.000Z

316

EIA-Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2007 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS25 data.

317

EIA-Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2007 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

318

EIA-Assumptions to the Annual Energy Outlook - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2007 Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

319

Assumptions to the Annual Energy Outlook 2000-Table 1. Summary of the  

Gasoline and Diesel Fuel Update (EIA)

0 Cases 0 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.7 percent, compared to the reference case growth of 2.2 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.2 percent. Fully Integrated Low World Oil Price World oil prices are $14.90 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated High World Oil Price World oil prices are $28.04 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated Residential: 2000 Technology

320

Assumptions to the Annual Energy Outlook 2001 - Table 1. Summary of AEO2001  

Gasoline and Diesel Fuel Update (EIA)

1 Cases 1 Cases Case name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully integrated Low Economic Growth Gross domestic product grows at an average annual rate of 2.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated High Economic Growth Gross domestic product grows at an average annual rate of 3.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated Low World Oil Price World oil prices are $15.10 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated High World Oil Price World oil prices are $28.42 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated Residential: 2001 Technology

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


321

EIA - Assumptions to the Annual Energy Outlook 2010 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2010 Oil and Gas Supply Module Figure 8. Natural Gas Transmission and Distribution Model Regions. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas natural gas exploration and development on a regional basis (Figure 7). The OGSM is organized into 4 submodules: Onshore Lower 48 Oil and Gas Supply Submodule, Offshore Oil and Gas Supply Submodule, Oil Shale Supply submodule, and Alaska Oil and Gas Supply Submodule. A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2010), (Washington, DC, 2010). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural

322

Washington International Renewable Energy Conference 2008 Pledges: Methodology and Assumptions Summary  

Science Conference Proceedings (OSTI)

The 2008 Washington International Renewable Energy Conference (WIREC) was held in Washington, D.C., from March 4-6, 2008, and involved nearly 9,000 people from 125 countries. The event brought together worldwide leaders in renewable energy (RE) from governments, international organizations, nongovernmental organizations, and the private sector to discuss the role that renewables can play in alleviating poverty, growing economies, and passing on a healthy planet to future generations. The conference concluded with more than 140 governments, international organizations, and private-sector representatives pledging to advance the uptake of renewable energy. The U.S. government authorized the National Renewable Energy Laboratory (NREL) to estimate the carbon dioxide (CO2) savings that would result from the pledges made at the 2008 conference. This report describes the methodology and assumptions used by NREL in quantifying the potential CO2 reductions derived from those pledges.

Babiuch, B.; Bilello, D. E.; Cowlin, S. C.; Mann, M.; Wise, A.

2008-08-01T23:59:59.000Z

323

Design assumptions and bases for small D-T-fueled spherical tokamak (ST) fusion core  

SciTech Connect

Recent progress in defining the assumptions and clarifying the bases for a small D-T-fueled ST fusion core are presented. The paper covers several issues in the physics of ST plasmas, the technology of neutral beam injection, the engineering design configuration, and the center leg material under intense neutron irradiation. This progress was driven by the exciting data from pioneering ST experiments, a heightened interest in proof-of-principle experiments at the MA level in plasma current, and the initiation of the first conceptual design study of the small ST fusion core. The needs recently identified for a restructured fusion energy sciences program have provided a timely impetus for examining the subject of this paper. Our results, though preliminary in nature, strengthen the case for the potential realism and attractiveness of the ST approach.

Peng, Yueng Kay Martin [ORNL; Haines, J.R. [Oak Ridge National Laboratory (ORNL)

1996-01-01T23:59:59.000Z

324

CRITICAL ASSUMPTIONS IN THE F-TANK FARM CLOSURE OPERATIONAL DOCUMENTATION REGARDING WASTE TANK INTERNAL CONFIGURATIONS  

SciTech Connect

The intent of this document is to provide clarification of critical assumptions regarding the internal configurations of liquid waste tanks at operational closure, with respect to F-Tank Farm (FTF) closure documentation. For the purposes of this document, FTF closure documentation includes: (1) Performance Assessment for the F-Tank Farm at the Savannah River Site (hereafter referred to as the FTF PA) (SRS-REG-2007-00002), (2) Basis for Section 3116 Determination for Closure of F-Tank Farm at the Savannah River Site (DOE/SRS-WD-2012-001), (3) Tier 1 Closure Plan for the F-Area Waste Tank Systems at the Savannah River Site (SRR-CWDA-2010-00147), (4) F-Tank Farm Tanks 18 and 19 DOE Manual 435.1-1 Tier 2 Closure Plan Savannah River Site (SRR-CWDA-2011-00015), (5) Industrial Wastewater Closure Module for the Liquid Waste Tanks 18 and 19 (SRRCWDA-2010-00003), and (6) Tank 18/Tank 19 Special Analysis for the Performance Assessment for the F-Tank Farm at the Savannah River Site (hereafter referred to as the Tank 18/Tank 19 Special Analysis) (SRR-CWDA-2010-00124). Note that the first three FTF closure documents listed apply to the entire FTF, whereas the last three FTF closure documents listed are specific to Tanks 18 and 19. These two waste tanks are expected to be the first two tanks to be grouted and operationally closed under the current suite of FTF closure documents and many of the assumptions and approaches that apply to these two tanks are also applicable to the other FTF waste tanks and operational closure processes.

Hommel, S.; Fountain, D.

2012-03-28T23:59:59.000Z

325

,"Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities"  

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

Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities" Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities",16,"Monthly","9/2013","1/15/1985" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_crq_a_epc0_ycs_pct_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_crq_a_epc0_ycs_pct_m.htm" ,"Source:","Energy Information Administration"

326

DOE Seeks Further Public Input on How Best To Streamline Existing  

NLE Websites -- All DOE Office Websites (Extended Search)

Further Public Input on How Best To Streamline Existing Further Public Input on How Best To Streamline Existing Regulations DOE Seeks Further Public Input on How Best To Streamline Existing Regulations December 7, 2011 - 12:34pm Addthis The Department of Energy (DOE) has announced a further step to implementing the President's Executive Order on Improving Regulatory Review. The Executive Order directs federal agencies to review existing regulations and determine whether they are still necessary and crafted effectively to solve current problems. Engaging the public in an open, transparent process is a crucial step in DOE's regulatory review process. Because public comments in response to the Request for Information (RFI) issued in January were important in the development of DOE's plan for retrospective regulatory review, DOE issued a second RFI this week asking the public how

327

Procedure for developing biological input for the design, location, or modification of water-intake structures  

Science Conference Proceedings (OSTI)

To minimize adverse impact on aquatic ecosystems resulting from the operation of water intake structures, design engineers must have relevant information on the behavior, physiology and ecology of local fish and shellfish. Identification of stimulus/response relationships and the environmental factors that influence them is the first step in incorporating biological information in the design, location or modification of water intake structures. A procedure is presented in this document for providing biological input to engineers who are designing, locating or modifying a water intake structure. The authors discuss sources of stimuli at water intakes, historical approaches in assessing potential/actual impact and review biological information needed for intake design.

Neitzel, D.A.; McKenzie, D.H.

1981-12-01T23:59:59.000Z

328

,"U.S. Downstream Processing of Fresh Feed Input"  

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

Annual",2012,"6/30/1987" Annual",2012,"6/30/1987" ,"Release Date:","9/27/2013" ,"Next Release Date:","9/26/2014" ,"Excel File Name:","pet_pnp_dwns_dc_nus_mbblpd_a.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_dwns_dc_nus_mbblpd_a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:17:28 AM" "Back to Contents","Data 1: U.S. Downstream Processing of Fresh Feed Input" "Sourcekey","M_NA_YDR_NUS_MBBLD","MCRCCUS2","MCRCHUS2","MCRDFUS2" "Date","U.S. Downstream Processing of Fresh Feed Input by Catalytic Reforming Units (Thousand Barrels per Day)","U.S. Downstream Processing of Fresh Feed Input by Catalytic Cracking Units (Thousand Barrels per Day)","U.S. Downstream Processing of Fresh Feed Input by Catalytic Hydrocracking Units (Thousand Barrels per Day)","U.S. Downstream Processing of Fresh Feed Input by Delayed and Fluid Coking Units (Thousand Barrels per Day)"

329

Press Release: DOE Seeks Public Input for Depleted Uranium Hexafluorid...  

NLE Websites -- All DOE Office Websites (Extended Search)

Perry, (865) 576-0885 September 24, 2001 www.oakridge.doe.gov DOE SEEKS PUBLIC INPUT FOR DEPLETED URANIUM HEXAFLUORIDE ENVIRONMENTAL IMPACT STATEMENT Public Meetings Planned in...

330

,"U.S. Blender Net Input"  

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

Monthly","9/2013","1/15/2005" Monthly","9/2013","1/15/2005" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_inpt3_dc_nus_mbbl_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_inpt3_dc_nus_mbbl_m.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:22:43 AM" "Back to Contents","Data 1: U.S. Blender Net Input" "Sourcekey","MTXRB_NUS_1","M_EPL0_YIB_NUS_MBBL","MPPRB_NUS_1","M_EPLL_YIB_NUS_MBBL","MBNRB_NUS_1","MBIRB_NUS_1","M_EPOL_YIB_NUS_MBBL","M_EPOOXR_YIB_NUS_MBBL","MMTRB_NUS_1","M_EPOOR_YIB_NUS_MBBL","MFERB_NUS_1","M_EPOORD_YIB_NUS_MBBL","M_EPOORO_YIB_NUS_MBBL","M_EPPU_YIB_NUS_MBBL","M_EPOUN_YIB_NUS_MBBL","M_EPOUK_YIB_NUS_MBBL","M_EPOUH_YIB_NUS_MBBL","M_EPOUR_YIB_NUS_MBBL","MBCRB_NUS_1","MO1RB_NUS_1","M_EPOBGRR_YIB_NUS_MBBL","MO3RB_NUS_1","MO4RB_NUS_1","MO2RB_NUS_1","MO5RB_NUS_1","MO6RB_NUS_1","MO7RB_NUS_1","MO9RB_NUS_1"

331

Single family heating and cooling requirements: Assumptions, methods, and summary results  

Science Conference Proceedings (OSTI)

The research has created a data base of hourly building loads using a state-of-the-art building simulation code (DOE-2.ID) for 8 prototypes, representing pre-1940s to 1990s building practices, in 16 US climates. The report describes the assumed modeling inputs and building operations, defines the building prototypes and selection of base cities, compares the simulation results to both surveyed and measured data sources, and discusses the results. The full data base with hourly space conditioning, water heating, and non-HVAC electricity consumption is available from GRI. In addition, the estimated loads on a per square foot basis are included as well as the peak heating and cooling loads.

Ritschard, R.L.; Hanford, J.W.; Sezgen, A.O. [Lawrence Berkeley Lab., CA (United States)

1992-03-01T23:59:59.000Z

332

50-year-old assumptions about strength muscled aside | Argonne National  

NLE Websites -- All DOE Office Websites (Extended Search)

C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. To view a larger, downloadable version of the image, click on it. To view a larger, downloadable version of the image, click on it. 50-year-old assumptions about strength muscled aside July 11, 2013 Tweet EmailPrint LEMONT, Ill. - Doctors have a new way of thinking about how to treat heart and skeletal muscle diseases. Body builders have a new way of

333

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting  

SciTech Connect

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associated with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.

Zhang, Xuesong; Liang, Faming; Yu, Beibei; Zong, Ziliang

2011-11-09T23:59:59.000Z

334

[Faculty of Science Information and Computing Sciences  

E-Print Network (OSTI)

Set Parser r = Input [r ? Input] #12;[Faculty of Science Information and Computing Sciences] 13) :: [ ] #12;[Faculty of Science Information and Computing Sciences] 13 Applicative interface fail : {r Parser r Parser r (p q) inp = p inp ++ q inp #12;[Faculty of Science Information and Computing Sciences

Löh, Andres

335

Input--output capital coefficients for energy technologies. [Input-output model  

DOE Green Energy (OSTI)

Input-output capital coefficients are presented for five electric and seven non-electric energy technologies. They describe the durable goods and structures purchases (at a 110 sector level of detail) that are necessary to expand productive capacity in each of twelve energy source sectors. Coefficients are defined in terms of 1967 dollar purchases per 10/sup 6/ Btu of output from new capacity, and original data sources include Battelle Memorial Institute, the Harvard Economic Research Project, The Mitre Corp., and Bechtel Corp. The twelve energy sectors are coal, crude oil and gas, shale oil, methane from coal, solvent refined coal, refined oil products, pipeline gas, coal combined-cycle electric, fossil electric, LWR electric, HTGR electric, and hydroelectric.

Tessmer, R.G. Jr.

1976-12-01T23:59:59.000Z

336

Semi-valid input coverage for fuzz testing  

Science Conference Proceedings (OSTI)

We define semi-valid input coverage (SVCov), the first coverage criterion for fuzz testing. Our criterion is applicable whenever the valid inputs can be defined by a finite set of constraints. SVCov measures to what extent the tests cover the domain ... Keywords: coverage criteria, fuzz testing, security testing

Petar Tsankov, Mohammad Torabi Dashti, David Basin

2013-07-01T23:59:59.000Z

337

Finding input sub-spaces for polymorphic fuzzy signatures  

Science Conference Proceedings (OSTI)

A significant feature of fuzzy signatures is its applicability for complex and sparse data. To create Polymorphic Fuzzy Signatures (PFS) for sparse data, sparse input sub-spaces (ISSs) should be considered. Finding the optimal ISSs manually is not a ... Keywords: WRAO, fuzzy C-means, fuzzy signatures, input subspace clustering, polymorphic fuzzy signatures, trapezoidal approximation

A. H. Hadad; T. D. Gedeon; B. S. U. Mendis

2009-08-01T23:59:59.000Z

338

Ancient runes: using text input for interaction in mobile games  

Science Conference Proceedings (OSTI)

Mobile phones are often carried in the pocket making them available for gaming any time. Mobile games typically rely on the joystick for input, but quality of the joystick is very different in the different devices. This paper presents Ancient Runes, ... Keywords: mobile multiplayer gaming, playability, text input

Elina M. I. Koivisto; Riku Suomela; Ari Koivisto

2006-07-01T23:59:59.000Z

339

Manual deskterity: an exploration of simultaneous pen + touch direct input  

Science Conference Proceedings (OSTI)

Manual Deskterity is a prototype digital drafting table that supports both pen and touch input. We explore a division of labor between pen and touch that flows from natural human skill and differentiation of roles of the hands. We also explore the simultaneous ... Keywords: bimanual input, gestures, pen, tabletop, tablets, touch

Ken Hinckley; Koji Yatani; Michel Pahud; Nicole Coddington; Jenny Rodenhouse; Andy Wilson; Hrvoje Benko; Bill Buxton

2010-04-01T23:59:59.000Z

340

Skeletal input for user interaction in X3D  

Science Conference Proceedings (OSTI)

Recent developments in depth sensor technology enable developers to use skeletal input in interactive 3D environments with high user fluctuation like museum exhibits. However, the question of how to use natural user input and body movement to control ... Keywords: Kinect, X3D, natural interaction

Manuel Olbrich; Tobias Franke; Jens Keil; Sven Hertling

2013-06-01T23:59:59.000Z

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


341

BeThere: 3D mobile collaboration with spatial input  

Science Conference Proceedings (OSTI)

We present BeThere, a proof-of-concept system designed to explore 3D input for mobile collaborative interactions. With BeThere, we explore 3D gestures and spatial input which allow remote users to perform a variety of virtual interactions ... Keywords: around device interaction, augmented reality, collaboration, depth sensors

Rajinder S. Sodhi; Brett R. Jones; David Forsyth; Brian P. Bailey; Giuliano Maciocci

2013-04-01T23:59:59.000Z

342

Twinkle box: a three-dimensional computer input device  

Science Conference Proceedings (OSTI)

During the past fifteen years, use of two-dimensional computer input/output devices has become commonplace. Since the earliest uses of the light pen for target identification in air defense systems it has been obvious that two-dimensional input would ...

Robert P. Burton; Ivan E. Sutherland

1974-05-01T23:59:59.000Z

343

T-623: HP Business Availability Center Input Validation Hole Permits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

3: HP Business Availability Center Input Validation Hole 3: HP Business Availability Center Input Validation Hole Permits Cross-Site Scripting Attacks T-623: HP Business Availability Center Input Validation Hole Permits Cross-Site Scripting Attacks May 16, 2011 - 3:05pm Addthis PROBLEM: A vulnerability was reported in HP Business Availability Center. A remote user can conduct cross-site scripting attacks. PLATFORM: HP Business Availability Center software 8.06 and prior versions ABSTRACT: The software does not properly filter HTML code from user-supplied input before displaying the input. reference LINKS: SecurityTracker Alert ID:1025535 HP Knowledge Base CVE-2011-1856 Secunia ID: SA44569 HP Document ID:c02823184 | ESB-2011.0525 IMPACT ASSESSMENT: High Discussion: A remote user can cause arbitrary scripting code to be executed by the

344

Crude Oil Watch - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Crude Oil Watch April 19, 2000 Energy Information Administration Office of Oil & Gas A large stockbuild in crude oil inventories contributed to blunt crude oil inputs ...

345

PREDICTING THE TIME RESPONSE OF A BUILDING UNDER HEAT INPUT CONDITIONS FOR ACTIVE SOLAR HEATING SYSTEMS  

E-Print Network (OSTI)

solar space heating system with heat input and building loadBUILDING UNDER HEAT INPUT CONDITIONS FOR ACTIVE SOLAR HEATINGBUILDING UNDER HEAT INPUT CONDITIONS FOR ACTIVE SOLAR HEATING

Warren, Mashuri L.

2013-01-01T23:59:59.000Z

346

Development of the fundamental attributes and inputs for proliferation resistance assessments of nuclear fuel cycles  

E-Print Network (OSTI)

Robust and reliable quantitative proliferation resistance assessment tools are critical to a strengthened nonproliferation regime and to the future deployment of nuclear fuel cycle technologies. Efforts to quantify proliferation resistance have thus far met with limited success due to the inherent subjectivity of the problem and interdependencies between attributes that contribute to proliferation resistance. This work focuses on the diversion of nuclear material by a state and defers other threats such as theft or terrorism to future work. A new approach is presented that assesses the problem through four stages of proliferation: the diversion of nuclear material, the transportation of nuclear material from an internationally safeguarded nuclear facility to an undeclared facility, the transformation of material into a weapons-usable metal, and weapon fabrication. A complete and concise set of intrinsic and extrinsic attributes of the nation, facility and material that could impede proliferation are identified. Quantifiable inputs for each of these attributes are defined. For example, the difficulty of handling the diverted material is captured with inputs like mass and bulk, radiation dose, heating rate and others. Aggregating these measurements into an overall value for proliferation resistance can be done in multiple ways based on well-developed decision theory. A preliminary aggregation scheme is provided along with results obtained from analyzing a small spent fuel reprocessing plant to demonstrate quantification of the attributes and inputs. This quantification effort shows that the majority of the inputs presented are relatively straightforward to work with while a few are not. These few difficult inputs will only be useful in special cases where the analyst has access to privileged, detailed or classified information. The stages, attributes and inputs of proliferation presented in this work provide a foundation for proliferation resistance assessments which may use multiple types of aggregation schemes. The overall results of these assessments are useful in comparing nuclear technologies and aiding decisions about development and deployment of that technology.

Giannangeli, Donald D. J., III

2003-05-01T23:59:59.000Z

347

,"U.S. Refinery Net Input"  

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

2,"Annual",2012,"6/30/2005" 2,"Annual",2012,"6/30/2005" ,"Data 2","Alaskan Crude Oil Receipts",1,"Annual",2012,"6/30/1986" ,"Release Date:","9/27/2013" ,"Next Release Date:","9/26/2014" ,"Excel File Name:","pet_pnp_inpt2_dc_nus_mbbl_a.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_inpt2_dc_nus_mbbl_a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:21:04 AM" "Back to Contents","Data 1: Refinery Net Input" "Sourcekey","MTTRO_NUS_1","MCRRO_NUS_1","MNGRO_NUS_1","MPPRO_NUS_1","MLPRO_NUS_1","MBNRO_NUS_1","MBIRO_NUS_1","MOLRO_NUS_1","MOHRO_NUS_1","M_EPOOOH_YIY_NUS_MBBL","M_EPOOXXFE_YIY_NUS_MBBL","MMTRO_NUS_1","MOORO_NUS_1","M_EPOOR_YIY_NUS_MBBL","MFERO_NUS_1","M_EPOORD_YIY_NUS_MBBL","M_EPOOOXH_YIY_NUS_MBBL","MUORO_NUS_1","MNLRO_NUS_1","MKORO_NUS_1","MH1RO_NUS_1","MRURO_NUS_1","MBCRO_NUS_1","MO1RO_NUS_1","M_EPOBGRR_YIY_NUS_MBBL","MO3RO_NUS_1","MO4RO_NUS_1","MO5RO_NUS_1","MO6RO_NUS_1","MO7RO_NUS_1","MO9RO_NUS_1","MBARO_NUS_1"

348

,"U.S. Refinery Net Input"  

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

3,"Monthly","9/2013","1/15/2005" 3,"Monthly","9/2013","1/15/2005" ,"Data 2","Alaskan Crude Oil Receipts",1,"Monthly","9/2013","1/15/1986" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_inpt2_dc_nus_mbbl_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_inpt2_dc_nus_mbbl_m.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:21:05 AM" "Back to Contents","Data 1: Refinery Net Input" "Sourcekey","MTTRO_NUS_1","MCRRO_NUS_1","MNGRO_NUS_1","MPPRO_NUS_1","MLPRO_NUS_1","MBNRO_NUS_1","MBIRO_NUS_1","MOLRO_NUS_1","MOHRO_NUS_1","M_EPOOOH_YIY_NUS_MBBL","M_EPOOXXFE_YIY_NUS_MBBL","MMTRO_NUS_1","MOORO_NUS_1","M_EPOOR_YIY_NUS_MBBL","MFERO_NUS_1","M_EPOORD_YIY_NUS_MBBL","M_EPOORO_YIY_NUS_MBBL","M_EPOOOXH_YIY_NUS_MBBL","MUORO_NUS_1","MNLRO_NUS_1","MKORO_NUS_1","MH1RO_NUS_1","MRURO_NUS_1","MBCRO_NUS_1","MO1RO_NUS_1","M_EPOBGRR_YIY_NUS_MBBL","MO3RO_NUS_1","MO4RO_NUS_1","MO5RO_NUS_1","MO6RO_NUS_1","MO7RO_NUS_1","MO9RO_NUS_1","MBARO_NUS_1"

349

Abandoned Uranium Mines Report to Congress: LM Wants Your Input |  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Abandoned Uranium Mines Report to Congress: LM Wants Your Input Abandoned Uranium Mines Report to Congress: LM Wants Your Input Abandoned Uranium Mines Report to Congress: LM Wants Your Input April 11, 2013 - 1:33pm Addthis C-SR-10 Uintah Mine, Colorado, LM Uranium Lease Tracts C-SR-10 Uintah Mine, Colorado, LM Uranium Lease Tracts What does this project do? Goal 4. Optimize the use of land and assets Abandoned Uranium Mines Report to Congress The U.S. Department of Energy (DOE) Office of Legacy Management (LM) is seeking stakeholder input on an abandoned uranium mines report to Congress. On January 2, 2013, President Obama signed into law the National Defense Authorization Act for Fiscal Year 2013, which requires the Secretary of Energy, in consultation with the Secretary of the U.S Department of the Interior (DOI) and the Administrator

350

,"U.S. Refinery Crude Oil Input Qualities"  

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

,,"(202) 586-8800",,,"7242013 11:46:42 PM" "Back to Contents","Data 1: U.S. Refinery Crude Oil Input Qualities" "Sourcekey","MCRS1US2","MCRAPUS2" "Date","U.S. Sulfur...

351

Speech recognition as a computer graphics input technique (Panel Session)  

Science Conference Proceedings (OSTI)

Richard Rabin Interactive graphics systems typically require intense hands busy/eyes busy and brains busy activity on the part of the system user/operator. Voice input by means of automatic speech recognition equipment, offers major potential ...

Alan R. Strass; Mark Robillard; Sue Schedler; Matthew Peterson / Richard Rabin

1982-07-01T23:59:59.000Z

352

Comparison of wind stress algorithms, datasets and oceanic power input  

E-Print Network (OSTI)

If the ocean is in a statistically steady state, energy balance is a strong constraint, suggesting that the energy input into the world ocean is dissipated simultaneously at the same rate. Energy conservation is one of the ...

Yuan, Shaoyu

2009-01-01T23:59:59.000Z

353

Constructing Verifiable Random Functions with Large Input Spaces Susan Hohenberger  

E-Print Network (OSTI)

idea is to apply a simulation technique where the large space of VRF inputs is collapsed into a small, the verification should remain secure even if the public commitment were setup in a malicious manner. The VRF

354

On the Energy Input from Wind to Surface Waves  

Science Conference Proceedings (OSTI)

A basic model relating the energy dissipation in the ocean mixed layer to the energy input into the surface wave field is combined with recent measurements of turbulent kinetic energy dissipation to determine the average phase speed of the waves ...

J. R. Gemmrich; T. D. Mudge; V. D. Polonichko

1994-11-01T23:59:59.000Z

355

Eclat : automatic generation and classification of test inputs  

E-Print Network (OSTI)

This thesis describes a technique that selects, from a large set of test inputs, a small subset likely to reveal faults in the software under test. The technique takes a program or software component, plus a set of correct ...

Pacheco, Carlos, S.M. Massachusetts Institute of Technology

2005-01-01T23:59:59.000Z

356

IMPACT OF HIGH-INPUT PRODUCTION PRACTICES ON SOYBEAN YIELD.  

E-Print Network (OSTI)

??High-input management practices are often heavily marketed to producers to increase soybean [Glycine max (L) Merr.] yield in already high-yielding environments. Field research was conducted (more)

Jordan, Daniel L.

2010-01-01T23:59:59.000Z

357

T-693: Symantec Endpoint Protection Manager Input Validation Hole Permits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

3: Symantec Endpoint Protection Manager Input Validation Hole 3: Symantec Endpoint Protection Manager Input Validation Hole Permits Cross-Site Scripting and Cross-Site Request Forgery Attacks T-693: Symantec Endpoint Protection Manager Input Validation Hole Permits Cross-Site Scripting and Cross-Site Request Forgery Attacks August 15, 2011 - 3:42pm Addthis PROBLEM: Two vulnerabilities were reported in Symantec Endpoint Protection Manager. A remote user can conduct cross-site scripting attacks. A remote user can conduct cross-site request forgery attacks. PLATFORM: Version(s): 11.0 RU6(11.0.600x), 11.0 RU6-MP1(11.0.6100), 11.0 RU6-MP2(11.0.6200), 11.0 RU6-MP3(11.0.6300) ABSTRACT: Symantec Endpoint Protection Manager Input Validation Hole Permits Cross-Site Scripting and Cross-Site Request Forgery Attacks. reference LINKS:

358

Indiana, Illinois, Kentucky Refinery District Gross Inputs to ...  

U.S. Energy Information Administration (EIA)

Indiana, Illinois, Kentucky Refinery District Gross Inputs to Refineries (Thousand Barrels per Day) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec; 1985: 1,739 ...

359

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

E-Print Network (OSTI)

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting-uses include Heating, Ventilation and Air Conditioning (HVAC). Our analysis uses the modeling framework provided by the HVAC module in the Residential End-Use Energy Planning System (REEPS), which was developed

360

Preliminary Review of Models, Assumptions, and Key Data used in Performance Assessments and Composite Analysis at the Idaho National Laboratory  

SciTech Connect

This document is in response to a request by Ming Zhu, DOE-EM to provide a preliminary review of existing models and data used in completed or soon to be completed Performance Assessments and Composite Analyses (PA/CA) documents, to identify codes, methodologies, main assumptions, and key data sets used.

Arthur S. Rood; Swen O. Magnuson

2009-07-01T23:59:59.000Z

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


361

What are the Starting Points? Evaluating Base-Year Assumptions in the Asian Modeling Exercise  

SciTech Connect

A common feature of model inter-comparison efforts is that the base year numbers for important parameters such as population and GDP can differ substantially across models. This paper explores the sources and implications of this variation in Asian countries across the models participating in the Asian Modeling Exercise (AME). Because the models do not all have a common base year, each team was required to provide data for 2005 for comparison purposes. This paper compares the year 2005 information for different models, noting the degree of variation in important parameters, including population, GDP, primary energy, electricity, and CO2 emissions. It then explores the difference in these key parameters across different sources of base-year information. The analysis confirms that the sources provide different values for many key parameters. This variation across data sources and additional reasons why models might provide different base-year numbers, including differences in regional definitions, differences in model base year, and differences in GDP transformation methodologies, are then discussed in the context of the AME scenarios. Finally, the paper explores the implications of base-year variation on long-term model results.

Chaturvedi, Vaibhav; Waldhoff, Stephanie; Clarke, Leon E.; Fujimori, Shinichiro

2012-12-01T23:59:59.000Z

362

Simplifying DPDA using supplementary information  

Science Conference Proceedings (OSTI)

We study the effect of using supplementary information on the complexity of deterministic pushdown automata. This continues the study of assisted problem solving initiated in [Gai, Rovan 2008]. We study deterministic PDA that can assume its input ...

Pavel Labath; Branislav Rovan

2011-05-01T23:59:59.000Z

363

Assumptions to the Annual Energy Outlook 2001 - Table 4. Coefficients of  

Gasoline and Diesel Fuel Update (EIA)

Coefficients of Linear Equations for Natural Gas- and Coefficients of Linear Equations for Natural Gas- and Oil-Related Methane Emissions Emissions Sources Intercept Variable Name and Units Coefficient Variable Name and Units Coefficient Natural Gas -38.77 Time trend (calendar year) .02003 Dry gas production (thousand cubic feet .02186 Natural Gas Processing -0.9454 Natural gas liquids production (million barrels per day) .9350 Not applicable Natural Gas Transmission and Storage 2.503 Pipeline fuel use (thousand cubic feet) 1.249 Dry gas production (thousand cubic feet) -0.06614 Natural Gas Distribution -58.16 Time trend (calendar year) .0297 Natural gas consumption (quadrillion Btu) .0196 Oil production, Refining, and Transport 0.03190 Oil consumption (quadrillion Btu) .002764 Not applicable Source: Derived from data used in Energy Information Administration, Emissions of Greenhouse Gases in the United States 1999, DOE/EIA-0573(99), (Washington, DC, October 2000).

364

The SCALE Verified, Archived Library of Inputs and Data - VALID  

SciTech Connect

The Verified, Archived Library of Inputs and Data (VALID) at ORNL contains high quality, independently reviewed models and results that improve confidence in analysis. VALID is developed and maintained according to a procedure of the SCALE quality assurance (QA) plan. This paper reviews the origins of the procedure and its intended purpose, the philosophy of the procedure, some highlights of its implementation, and the future of the procedure and associated VALID library. The original focus of the procedure was the generation of high-quality models that could be archived at ORNL and applied to many studies. The review process associated with model generation minimized the chances of errors in these archived models. Subsequently, the scope of the library and procedure was expanded to provide high quality, reviewed sensitivity data files for deployment through the International Handbook of Evaluated Criticality Safety Benchmark Experiments (IHECSBE). Sensitivity data files for approximately 400 such models are currently available. The VALID procedure and library continue fulfilling these multiple roles. The VALID procedure is based on the quality assurance principles of ISO 9001 and nuclear safety analysis. Some of these key concepts include: independent generation and review of information, generation and review by qualified individuals, use of appropriate references for design data and documentation, and retrievability of the models, results, and documentation associated with entries in the library. Some highlights of the detailed procedure are discussed to provide background on its implementation and to indicate limitations of data extracted from VALID for use by the broader community. Specifically, external users of data generated within VALID must take responsibility for ensuring that the files are used within the QA framework of their organization and that use is appropriate. The future plans for the VALID library include expansion to include additional experiments from the IHECSBE, to include experiments from areas beyond criticality safety, such as reactor physics and shielding, and to include application models. In the future, external SCALE users may also obtain qualification under the VALID procedure and be involved in expanding the library. The VALID library provides a pathway for the criticality safety community to leverage modeling and analysis expertise at ORNL.

Marshall, William BJ J [ORNL; Rearden, Bradley T [ORNL

2013-01-01T23:59:59.000Z

365

New Hampshire Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) New Hampshire Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 774 720 582 328 681 509 362 464 492 592 1990's 205 128 96 154 160 90 147 102 103 111 2000's 180 86 66 58 91 84 92 9 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas New Hampshire Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

366

Washington Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Washington Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 15 13 15 11 11 9 10 21 79 154 1990's 181 154 180 4 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Washington Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

367

Minnesota Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Minnesota Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 48 106 337 1 3 11 2 1 385 315 1990's 56 49 52 78 289 194 709 172 50 64 2000's 101 118 13 42 71 154 13 54 46 47 2010's 12 20 9 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Minnesota Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

368

District of Columbia Natural Gas Input Supplemental Fuels (Million Cubic  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) District of Columbia Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 2 1 46 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas District of Columbia Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition)

369

Maryland Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Maryland Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 484 498 984 352 332 373 155 136 743 899 1990's 24 72 126 418 987 609 882 178 80 498 2000's 319 186 48 160 124 382 41 245 181 170 2010's 115 89 116 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Maryland Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

370

Iowa Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Iowa Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 57 64 68 23 53 45 44 40 34 82 1990's 81 46 45 84 123 96 301 137 17 12 2000's 44 39 23 143 30 31 46 40 27 3 2010's 2 1 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Iowa Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

371

Pennsylvania Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Pennsylvania Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 3,127 10,532 5,621 3,844 82 221 196 247 254 305 1990's 220 222 132 110 252 75 266 135 80 119 2000's 261 107 103 126 131 132 124 145 123 205 2010's 4 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Pennsylvania Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

372

Possible Magmatic Input to the Dixie Valley Geothermal Field, and  

Open Energy Info (EERE)

Possible Magmatic Input to the Dixie Valley Geothermal Field, and Possible Magmatic Input to the Dixie Valley Geothermal Field, and Implications for District-Scale Resource Exploration, Inferred from Magnetotelluric (MT) Resistivity Surveying Jump to: navigation, search OpenEI Reference LibraryAdd to library Journal Article: Possible Magmatic Input to the Dixie Valley Geothermal Field, and Implications for District-Scale Resource Exploration, Inferred from Magnetotelluric (MT) Resistivity Surveying Abstract Magnetotelluric (MT) profiling in northwestern Nevadais used to test hypotheses on the main sources of heat andhydrothermal fluid for the Dixie Valley-Central NevadaSeismic Belt area. The transect reveals families of resistivitystructures commonly dominated by steeply-dipping features,some of which may be of key geothermal significance. Mostnotably, 2-D inversion

373

Missouri Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Missouri Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 65 60 2,129 1,278 326 351 1 1 2 1,875 1990's 0 0 0 0 371 4 785 719 40 207 2000's 972 31 62 1,056 917 15 78 66 6 10 2010's 18 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Missouri Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

374

Rhode Island Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Rhode Island Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 257 951 718 594 102 130 182 109 391 219 1990's 51 92 155 126 0 27 42 18 1 1 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Rhode Island Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

375

DOE Seeks Input On Addressing Contractor Pension and Medical Benefits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Input On Addressing Contractor Pension and Medical Input On Addressing Contractor Pension and Medical Benefits Liabilities DOE Seeks Input On Addressing Contractor Pension and Medical Benefits Liabilities March 27, 2007 - 12:10pm Addthis WASHINGTON, DC - The U.S. Department of Energy (DOE) today announced in the Federal Register that it is seeking public comment on how to address the increasing costs and liabilities of contractor employee pension and medical benefits. Under the Department of Energy's unique Management and Operating and other site management contracts, DOE reimburses its contractors for allowable costs incurred in providing contractor employee pension and medical benefits to current employees and retirees. In FY2006, these costs reached approximately $1.1 billion - a more than 226 percent increase since FY2000 - and are expected to grow in future years.

376

Georgia Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Georgia Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 24 57 151 84 28 121 124 248 241 292 1990's 209 185 166 199 123 130 94 14 16 12 2000's 73 51 7 14 5 0 3 2 52 2010's 732 701 660 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Georgia Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

377

Delaware Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Delaware Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 55 135 56 20 13 12 9 0 2 18 1990's 4,410 4,262 3,665 3,597 3,032 1 1 2 0 0 2000's 6 0 0 7 17 0 W 5 2 2 2010's 1 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Delaware Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

378

South Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) South Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9 24 50 1 0 0 0 0 10 16 1990's 10 3 10 9 61 37 87 30 4 5 2000's 13 5 3 57 5 4 0 1 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas South Dakota Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

379

Incorporating uncertainty in RADTRAN 6.0 input files.  

SciTech Connect

Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine is required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.

Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John (Alion Science and Technology)

2010-02-01T23:59:59.000Z

380

Optical device with conical input and output prism faces  

DOE Patents (OSTI)

A device for radially translating radiation in which a right circular cylinder is provided at each end thereof with conical prism faces. The faces are oppositely extending and the device may be severed in the middle and separated to allow access to the central part of the beam. Radiation entering the input end of the device is radially translated such that radiation entering the input end at the perimeter is concentrated toward the output central axis and radiation at the input central axis is dispersed toward the output perimeter. Devices are disclosed for compressing beam energy to enhance drilling techniques, for beam manipulation of optical spatial frequencies in the Fourier plane and for simplification of dark field and color contrast microscopy. Both refracting and reflecting devices are disclosed.

Brunsden, Barry S. (Chicago, IL)

1981-01-01T23:59:59.000Z

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


381

Connecticut Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Connecticut Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 144 1,584 1,077 291 239 343 298 180 245 251 1990's 111 146 40 94 29 68 48 37 33 31 2000's 20 6 6 57 191 273 91 0 0 1 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Connecticut Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

382

South Carolina Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) South Carolina Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 74 184 63 73 62 87 31 22 191 201 1990's 17 47 26 34 154 62 178 10 0 18 2000's 63 6 3 15 2 86 75 0 2010's 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas South Carolina Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

383

Tennessee Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Tennessee Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 12 42 90 39 25 36 13 26 36 78 1990's 3 8 12 13 84 33 73 19 4 11 2000's 13 0 1 1 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Tennessee Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

384

Information to iteration : using information and communication technologies [ICT] in design for remote regions  

E-Print Network (OSTI)

Remote design comes with significant challenges. A major barrier to designing in remote regions is the lack of communication between designers and users. As a result, the lack of information flow leads to assumptions about ...

Griffith, Kenfield A. (Kenfield Allistair)

2012-01-01T23:59:59.000Z

385

Table 3. U.S. Inputs to Biodiesel Production  

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

U.S. Inputs to Biodiesel Production U.S. Inputs to Biodiesel Production (million pounds) 2011 January 8 17 - W 150 W 14 11 February 26 13 - W 150 W 14 11 March 68 14 - W 190 W 19 27 April 88 20 - W 236 W 15 47 May 113 21 - W 264 W 16 36 June 75 34 - W 311 W 23 49 July 77 35 - W 367 W 26 64 August 84 37 W W 398 W 34 38 September 84 27 W W 430 W

386

Environmental issues of material input in CDTE-module manufacturing  

DOE Green Energy (OSTI)

The goal of a low-cost and high-volume photovoltaic (PV) module fabrication demands an optimized process sequence to guarantee product quality and module stability on a long-term basis. Nevertheless, large-scale module manufacturing uses several input and auxiliary materials and generates waste from processing output materials. The mining and refining of the PV manufacturing material consumes input and auxiliary material and also creates waste. Therefore, investigations into these materials were conducted with respect to their risk potential for environment and health.

Steinberger, H.; Hochwimmer, R.; Schmid, H. [Fraunhofer Inst. fuer Festkoerpertechnologie, Muenchen (Germany); Thumm, W.; Kettrup, A. [GSF, Oberschleissheim (Germany). Inst. fuer Oekologische Chemie; Moskowitz, P. [Brookhaven National Lab., Upton, NY (United States). Biomedical and Environmental Assessment Group

1995-12-31T23:59:59.000Z

387

Electricity Regulation in California and Input Market Distortions  

E-Print Network (OSTI)

We provide an analysis of the soft price cap regulation that occurred in Californias electricity market between December 2000 and June 2001. We demonstrate the incentive it created to distort the prices of electricity inputs. After introducing a theoretical model of the incentive, we present empirical data from two important input markets: pollution emissions permits and natural gas. We find substantial evidence that generators manipulated these costs in a way that allowed them to justify bids in excess of the price cap and earn higher rents than they could otherwise. Our analysis suggests that the potential benefits of soft price cap regulation were likely undone by such behavior. 1

Mark R. Jacobsen; Azeem M. Shaikh

2004-01-01T23:59:59.000Z

388

A toolbox for calculating net anthropogenic nitrogen inputs (NANI)  

Science Conference Proceedings (OSTI)

The ''Net Anthropogenic Nitrogen Input'' (NANI) to a region represents an estimate of anthropogenic net nitrogen (N) fluxes across its boundaries, and is thus a measure of the effect of human activity on the regional nitrogen cycle. NANI accounts for ... Keywords: Anthropogenic, Nitrogen, Synthesis, Toolbox, Watershed

Bongghi Hong; Dennis P. Swaney; Robert W. Howarth

2011-05-01T23:59:59.000Z

389

On the Information Loss in Static Systems  

E-Print Network (OSTI)

In this work we give a concise definition of information loss from a system-theoretic point of view. Based on this definition, we analyze the information loss in static input-output systems subject to a continuous-valued input. For a certain class of multiple-input, multiple-output systems the information loss is quantified. An interpretation of this loss is accompanied by upper bounds which are simple to evaluate. Finally, a class of systems is identified for which the information loss is necessarily infinite. Quantizers and limiters are shown to belong to this class.

Geiger, Bernhard C

2011-01-01T23:59:59.000Z

390

The Effect of Changing Input and Product Prices on the Demand for Irrigation Water in Texas  

E-Print Network (OSTI)

Agriculture is a major income-producing sector in the Texas economy and a large part of this economic activity originates in irrigated crop production. For example, in 1973, 50% of all grain sorghum and 46% of all cotton in Texas were produced on irrigated acreage [Texas Crop and Livestock Reporting Service]. These two crops alone produced 26% of the cash receipts from the sale of Texas farm commodities in 1973 [Texas Crop and Livestock Reporting Service]. There are several other crops in Texas including vegetables which generate significant levels of income and rely heavily on irrigation. Further there are several associated industries which rely on production from irrigated agriculture, such as the cattle feeding industry in the Texas Panhandle. It is evident from this rather cursory examination of statistics that irrigation plays a large role in Texas agriculture. Both producers and policy-makers have found themselves faced in the past two years with many uncertainties. The U.S., plagued in the past with surplus production and supply control problems, now finds itself in a world shortage of food products. The long range signals seem to call for increased production, yet the policy-maker faces decisions concerning not only how to increase production, but more basically, how to maintain current levels of production. Groundwater resources in many areas are being diminished and annual irrigation water supplies fully committed in other areas. Long run planning for Texas agriculture requires that interbasin transfers of water be evaluated. Texas holds a position of prominence in the production of U.S. food and fiber products, and the evaluation of these alternatives has implications not only for Texas, but for the U.S. and possibly the world. To objectively evaluate water transfer proposals, it is necessary that the value of irrigation water in different regions of Texas be established. The producer faces the same call for maintaining or increasing production as the policy-maker, but he does so with many uncertainties which often have not disturbed the policy-maker in evaluating alternatives. Product prices have risen and fallen at an unprecedented rate while input prices have steadily risen at rates which preclude realistic budgeting. For example, during the recent energy crisis, the prices of fuel and fertilizer have more than doubled. These variable input and product prices weigh heavily upon production decisions by the producer, and likewise must receive serious consideration in evaluation of resource allocation alternatives by policy-makers. The demand for irrigation water is derived from the production of crops and any change in production patterns, input prices or availability, and product prices directly affects this demand. Current and future water resources planning requires an estimate of the various quantities of water which will be used for irrigation under differing assumptions concerning price of water, other input prices, and product prices. Of particular importance are shifts in cropping patterns, changes in level of agricultural production and net effect on producers income. Since many policy decisions are made in relatively short periods of time, there is an urgent need for a capability to evaluate alternative policies and change input or product prices in a timely fashion.

Lacewell, R. D.; Condra, G. D.

1976-06-01T23:59:59.000Z

391

Total Refinery Net Input of Crude Oil and Petroleum Products  

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

Input Input Product: Total Crude Oil & Petroleum Products Crude Oil Natural Gas Plant Liquids Pentanes Plus Liquefied Petroleum Gases Normal Butane Isobutane Other Liquids Hydrogen/Oxygenates/Renewables/Other Hydrocarbons Hydrogen Oxygenates (excl. Fuel Ethanol) Methyl Tertiary Butyl Ether (MTBE) All Other Oxygenates Renewable Fuels (incl. Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Other Hydrocarbons Unfinished Oils (net) Unfinished Oils, Naphthas and Lighter Unfinished Oils, Kerosene and Light Gas Oils Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Components (MGBC) (net) MGBC - Reformulated MGBC - Reformulated - RBOB MGBC - Reformulated, RBOB for Blending w/ Alcohol MGBC - Reformulated, RBOB for Blending w/ Ether MGBC - Conventional MGBC - CBOB MGBC - Conventional, GTAB MGBC - Other Conventional Aviation Gasoline Blending Components (net) Alaskan Crude Oil Receipts Period-Unit: Monthly-Thousand Barrels Monthly-Thousand Barrels per Day Annual-Thousand Barrels Annual-Thousand Barrels per Day

392

Agricultural and Environmental Input Parameters for the Biosphere Model  

SciTech Connect

This analysis is one of 10 technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model). It documents development of agricultural and environmental input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters.

K. Rasmuson; K. Rautenstrauch

2004-09-14T23:59:59.000Z

393

Refinery & Blenders Net Input of Crude Oil  

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

Input Input Product: Total Crude Oil & Petroleum Products Crude Oil Natural Gas Plant Liquids and Liquefied Refinery Gases Pentanes Plus Liquefied Petroleum Gases Ethane Normal Butane Isobutane Other Liquids Hydrogen/Oxygenates/Renewables/Other Hydrocarbons Hydrogen Oxygenates (excl. Fuel Ethanol) Methyl Tertiary Butyl Ether (MTBE) All Other Oxygenates Renewable Fuels (incl. Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Other Hydrocarbons Unfinished Oils (net) Unfinished Oils, Naphthas and Lighter Unfinished Oils, Kerosene and Light Gas Oils Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Components (MGBC) (net) MGBC - Reformulated MGBC - Reformulated - RBOB MGBC - Reformulated, RBOB for Blending w/ Alcohol MGBC - Reformulated, RBOB for Blending w/ Ether MGBC - Reformulated, GTAB MGBC - Conventional MGBC - CBOB MGBC - Conventional, GTAB MGBC - Other Conventional Aviation Gasoline Blending Components (net) Period-Unit: Monthly-Thousand Barrels Monthly-Thousand Barrels per Day Annual-Thousand Barrels Annual-Thousand Barrels per Day

394

North Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) North Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 196 417 102 0 8,335 40,370 49,847 51,543 49,014 54,408 1990's 53,144 52,557 58,496 57,680 57,127 57,393 55,867 53,179 54,672 53,185 2000's 49,190 51,004 53,184 53,192 47,362 51,329 54,361 51,103 50,536 53,495 2010's 54,813 51,303 52,541 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas

395

New Jersey Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) New Jersey Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9,574 11,504 9,786 9,896 8,616 13,421 12,099 13,774 14,846 14,539 1990's 9,962 14,789 14,362 14,950 7,737 7,291 6,778 6,464 9,082 5,761 2000's 8,296 12,330 3,526 473 530 435 175 379 489 454 2010's 457 392 139 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas New Jersey Supplemental Supplies of Natural Gas

396

Nebraska Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Nebraska Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9 1,838 63 2,006 2,470 2,689 2,142 2,199 1,948 2,088 1990's 2,361 2,032 1,437 791 890 15 315 134 11 4 2000's 339 6 1 13 39 16 19 33 28 18 2010's 12 9 4 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Nebraska Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

397

Michigan Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Michigan Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 3 3,038 2,473 2,956 2,773 2,789 2,754 2,483 2,402 2,402 1990's 19,106 15,016 14,694 12,795 13,688 21,378 21,848 22,238 21,967 20,896 2000's 12,423 4,054 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Michigan Supplemental Supplies of Natural Gas

398

Colorado Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Colorado Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9,868 9,133 8,877 7,927 9,137 8,934 8,095 8,612 10,322 9,190 1990's 15,379 6,778 7,158 8,456 8,168 7,170 6,787 6,314 5,292 4,526 2000's 4,772 5,625 5,771 5,409 5,308 5,285 6,149 6,869 6,258 7,527 2010's 5,148 4,268 4,412 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Colorado Supplemental Supplies of Natural Gas

399

Ohio Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Ohio Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 69,169 69,850 64,812 62,032 43,866 24,444 5,182 18 44 348 1990's 849 891 1,051 992 1,432 904 1,828 1,423 1,194 1,200 2000's 1,442 1,149 79 1,002 492 579 423 608 460 522 2010's 353 296 366 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Ohio Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

400

Hawaii Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Hawaii Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,190 2,993 2,899 2,775 2,449 2,655 2,630 2,461 2,801 2,844 1990's 2,817 2,725 2,711 2,705 2,831 2,793 2,761 2,617 2,715 2,752 2000's 2,769 2,689 2,602 2,602 2,626 2,606 2,613 2,683 2,559 2,447 2010's 2,472 2,467 2,510 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Hawaii Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

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


401

Massachusetts Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Massachusetts Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 15,366 21,828 17,586 10,732 6,545 3,668 2,379 1,404 876 692 1990's 317 120 105 61 154 420 426 147 68 134 2000's 26 16 137 324 80 46 51 15 13 10 2010's 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Massachusetts Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

402

Indiana Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Indiana Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 1,602 5,056 3,496 4,142 4,027 2,711 2,351 3,890 4,243 3,512 1990's 3,015 3,077 3,507 3,232 2,457 3,199 3,194 3,580 3,149 5,442 2000's 5,583 5,219 1,748 2,376 2,164 1,988 1,642 635 30 1 2010's 1 5 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Indiana Supplemental Supplies of Natural Gas

403

Illinois Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

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

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Illinois Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 36,713 29,509 19,005 19,734 17,308 19,805 22,980 12,514 9,803 9,477 1990's 8,140 6,869 8,042 9,760 7,871 6,256 3,912 4,165 2,736 2,527 2000's 1,955 763 456 52 14 15 13 11 15 20 2010's 17 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Illinois Supplemental Supplies of Natural Gas

404

CCN predictions using simplified assumptions of organic aerosol composition and mixing state: A synthesis from six different locations  

SciTech Connect

An accurate but simple quantification of the fraction of aerosol particles that can act as cloud condensation nuclei (CCN) is needed for implementation in large-scale models. Data on aerosol size distribution, chemical composition, and CCN concentration from six different locations have been analyzed to explore the extent to which simple assumptions of composition and mixing state of the organic fraction can reproduce measured CCN number concentrations. Fresher pollution aerosol as encountered in Riverside, CA, and the ship channel in Houston, TX, cannot be represented without knowledge of more complex (size-resolved) composition. For aerosol that has experienced processing (Mexico City, Holme Moss (UK), Point Reyes (CA), and Chebogue Point (Canada)), CCN can be predicted within a factor of two assuming either externally or internally mixed soluble organics although these simplified compositions/mixing states might not represent the actual properties of ambient aerosol populations, in agreement with many previous CCN studies in the literature. Under typical conditions, a factor of two uncertainty in CCN concentration due to composition assumptions translates to an uncertainty of {approx}15% in cloud drop concentration, which might be adequate for large-scale models given the much larger uncertainty in cloudiness.

Ervens, B.; Wang, J.; Cubison, M. J.; Andrews, E.; Feingold, G.; Ogren, J. A.; Jimenez, J. L.; Quinn, P. K.; Bates, T. S.; Zhang, Q.; Coe, H.; Flynn, M.; Allan, J. D.

2010-05-01T23:59:59.000Z

405

Statistical Approaches and Assumptions  

Science Conference Proceedings (OSTI)

... during the PCR amplification process This is highly affected by DNA quantity and quality ... PCR inhibitors present in the sample may reduce PCR ...

2012-10-16T23:59:59.000Z

406

Analysis of the Effects of Compositional and Configurational Assumptions on Product Costs for the Thermochemical Conversion of Lignocellulosic Biomass to Mixed Alcohols FY 2007 Progress Report  

DOE Green Energy (OSTI)

The purpose of this study was to examine alternative biomass-to-ethanol conversion process assumptions and configuration options to determine their relative effects on overall process economics. A process-flow-sheet computer model was used to determine the heat and material balance for each configuration that was studied. The heat and material balance was then fed to a costing spreadsheet to determine the impact on the ethanol selling price. By examining a number of operational and configuration alternatives and comparing the results to the base flow sheet, alternatives having the greatest impact the performance and cost of the overall system were identified and used to make decisions on research priorities. This report, which was originally published in December 2008, has been revised primarily to correct information presented in Appendix B -- Base Case Flow Sheets and Model Results. The corrections to Appendix B include replacement of several pages in Table B.1 that duplicated previous pages of the table. Other changes were made in Appendix B to correct inconsistencies between stream labels presented in the tables and the stream labels in the figures.

Zhu, Yunhua; Gerber, Mark A.; Jones, Susanne B.; Stevens, Don J.

2009-02-01T23:59:59.000Z

407

NIST Seeks Private-Sector Input at Cybersecurity Framework ...  

Science Conference Proceedings (OSTI)

... economy, security and health such as finance, energy, transportation, food and ... from the previous workshops and information on future workshops. ...

2013-06-25T23:59:59.000Z

408

Current mode instrumentation amplifier with rail-to-rail input and output  

Science Conference Proceedings (OSTI)

A Current Mode Instrumentation Amplifier with rail-to-rail input and output is presented. It is based on constant gm input stages, and cascode output stages. Although this CMIA structure has a good Input Common Mode Voltage, it suffers from a poor output ... Keywords: analog integrated circuits, current mode instrumentation amplifier, rail-to-rail input and output

Filipe Costa Beber Vieira; Cesar Augusto Prior; Cesar Ramos Rodrigues; Leonardo Perin; Joao Baptista dos Santos Martins

2007-09-01T23:59:59.000Z

409

PADD 5 Refinery & Blender Net Input - U.S. Energy Information ...  

U.S. Energy Information Administration (EIA)

Area: Period-Unit: Download Series History: Definitions, Sources & Notes: Show Data By: Product: Area: Apr ... 51: 32: 75: 78: 2005-2013: Reformulated - RBOB: 104: 53 ...

410

Summary of Input to DOE Request for Information DE-FOA-0000225  

NLE Websites -- All DOE Office Websites (Extended Search)

stack component and system BOP RD&D for automotive, stationary, portable power, and early market applications. Comments on the existing DOE targets and justification for any...

411

Simultaneous cognitive origin of life and information  

Science Conference Proceedings (OSTI)

Shannon's information quantity I(E) = log(1/P(E)) is defined under an assumption of the existence of a "cognitive subjective entity" capable of judging yes/no or occurred/not-occurred of an event E ... Keywords: Minimum cognitive system, Origin of information, Semiogenesis, Teacher sign

Koji Ohnishi

2012-02-01T23:59:59.000Z

412

Mutual information aspects of scale space images  

Science Conference Proceedings (OSTI)

In image registration, mutual information is a well-performing measure based on principles of uncertainty. Similarly, in image analysis the Gaussian scale space, based on minimal assumptions of the image, is used to derive intrinsic properties of an ... Keywords: Entropy, Image analysis, Image structure, Multiresolution, Mutual information, Registration, Scale space

A. Kuijper

2004-12-01T23:59:59.000Z

413

Sensitivity of Utility-Scale Solar Deployment Projections in the SunShot Vision Study to Market and Performance Assumptions  

SciTech Connect

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The ReEDS model was used to simulate utility PV and CSP deployment for this present study, based on several market and performance assumptions - electricity demand, natural gas prices, coal retirements, cost and performance of non-solar renewable technologies, PV resource variability, distributed PV deployment, and solar market supply growth - in addition to the SunShot solar price projections. This study finds that utility-scale solar deployment is highly sensitive to solar prices. Other factors can have significant impacts, particularly electricity demand and natural gas prices.

Eurek, K.; Denholm, P.; Margolis, R.; Mowers, M.

2013-04-01T23:59:59.000Z

414

Guide: a graphics-utilizing input deck editor program for the RELAP5/MOD2 thermal-hydraulic code  

SciTech Connect

Inherent to modern-day analysis of thermal systems for nuclear power plants is the extensive use of thermal-hydraulic codes, such as TRAC, RETRAN, RELAP5/MOD2, etc., that quantitatively model individual components and their connectivity to represent a thermal-hydraulic system. The application of these codes is essential in the areas of design, safety, research and development, and operation and maintenance. Results can give detailed information on plant conditions during operational and accident situations, thus giving system analysis a better understanding of plant conditions and insights and improvements. Considering the complexity inherent in these systems, the codes that model these systems are similarly very involved; likewise, using these codes requires the creation of a large data input deck, which can take as much as a year to create and modify for correction. To make this job easier, a preprocessor for the code could be developed that would make the job of creating an input deck more one of providing the information the code needs rather than researching the manuals to ensure that you have provided all the necessary information to the code. This program would also be instrumental in making quick changes that may be necessary to correct a model. Guide, an advanced prototype preprocessor program for the RELAP5/MOD2 code, is a code developed to demonstrate the potential of these ideas and research other methods for aiding thermal-hydraulic code input deck developers.

Martin, R.P.

1988-01-01T23:59:59.000Z

415

Economic Effect on Agricultural Production of Alternative Energy Input Prices: Texas High Plains  

E-Print Network (OSTI)

The Arab oil embargo of 1973 awakened the world to the reality of energy shortages and higher fuel prices. Agriculture in the United States is highly mechanized and thus energy intensive. This study seeks to develop an evaluative capability to readily determine the short-run effect of rising energy prices on agricultural production. The results are measured in terms of demand schedules for each input investigated, net revenue adjustments, cropping pattern shifts, and changes in agricultural output. The High Plains of Texas was selected as a study area due to the heterogeneous nature of agricultural production in the region and highly energy intensive methods of production employed. The region is associated with a diversity in crops and production practices as well as a high degree of mechanization and irrigation, which means agriculture is very dependent upon energy inputs and, in turn, is significantly affected by energy price changes. The study area was defined by the Texas Agricultural Extension subregions of High Plains II, High Plains III, and High Plains IV. The crops chosen for study were cotton, grain sorghum, wheat, corn, and soybeans. The energy and energy-related inputs under investigation were diesel, herbicide, natural gas, nitrogen fertilizer, and water. Mathematical linear programming was used as the analytical technique with parametric programming techniques incorporated into the LP model to evaluate effect of varying input price parameters over a specified range. Thus, demand schedules were estimated. The objective function was constructed using variable costs only; no fixed costs are considered. Therefore, the objective function maximizes net revenue above variable costs and thus limits the study to the short run. The data bases for the model were crop enterprise budgets developed by the Texas Agricultural Extension Service. These budgets were modified to adapt them to the study. Particularly important was the substitution of owner-operated harvesting equipment for custom-harvesting costs. This procedure made possible the delineation of fuel use by crop and production alternative which was necessary information in the accounting of costs. The completed LP model was applied to 16 alternative situations made up of various input and product price combinations which are considered as feasible in the short run future. The results reveal that diesel consumption would change very little in the short run unless commodity prices simultaneously decline below the lowest prices since 1971 or unless diesel price approaches $2.00 per gallon. Under average commodity price conditions, natural gas consumption would not decline appreciably until the price rose above $4.00 per 1000 cubic feet (mcf). Even when using the least product prices since 1971, natural gas would be consumed in substantial amounts as long as the price was below $1.28 per Mcf. The findings regarding nitrogen indicate that present nitrogen prices are within a critical range such that consumption would be immediately affected by nitrogen price increases. Water price was considered as the price a farmer can afford to pay for water above pumping and distribution costs. Application of water was defined as the price that would be paid for imported water. Under average commodity price conditions, the study results show that as water price rises from zero dollars to $22 per acre foot there would be less than a 4 percent reduction in consumption. However, as the price continues to rise, consumption would decline dramatically reaching zero at a water price of $71.75 per acre foot. This study indicates that rising input prices would cause acreage shifts from irrigated to dryland; however, with average commodity prices, these shifts do not occur until diesel reaches $2.69 per gallon, or natural gas sells for $1.92 per Mcf, or nitrogen price is $.41 per pound, or water price reaches $14.69 per acre foot. In general, the first crops that would shift out of production as energy input prices rise woul

Adams, B. M.; Lacewell, R. D.; Condra, G. D.

1976-06-01T23:59:59.000Z

416

Informing design decisions : an approach to corporate building design  

E-Print Network (OSTI)

This thesis is an investigation into design methodologies. How do we, as designers, prepare ourselves for decision making and evaluate our assumptions and decisions? The intent is to employ this information as a basis for ...

Maxwell, Marc A

1985-01-01T23:59:59.000Z

417

How are spandrel systems input in COMcheck? | Building Energy...  

NLE Websites -- All DOE Office Websites (Extended Search)

& Offices Consumer Information Building Energy Codes Search Search Search Help Building Energy Codes Program Home News Events About DOE EERE BTO BECP Resource Center...

418

Residential oil burners with low input and two stages firing  

SciTech Connect

The residential oil burner market is currently dominated by the pressure-atomized, retention head burner. At low firing rates pressure atomizing nozzles suffer rapid fouling of the small internal passages, leading to bad spray patterns and poor combustion performance. To overcome the low input limitations of conventional burners, a low pressure air-atomized burner has been developed watch can operate at fining rates as low as 0.25 gallons of oil per hour (10 kW). In addition, the burner can be operated in a high/low fining rate mode. Field tests with this burner have been conducted at a fixed input rate of 0.35 gph (14 kW) with a side-wall vented boiler/water storage tank combination. At the test home, instrumentation was installed to measure fuel and energy flows and record trends in system temperatures. Laboratory efficiency testing with water heaters and boilers has been completed using standard single purpose and combined appliance test procedures. The tests quantify benefits due to low firing rates and other burner features. A two stage oil burner gains a strong advantage in rated efficiency while maintaining capacity for high domestic hot water and space heating loads.

Butcher, T.; Krajewski, R.; Leigh, R. [and others

1997-12-31T23:59:59.000Z

419

Design of the spoke cavity ED&D input coupler.  

DOE Green Energy (OSTI)

The current design of the Accelerator Driven Test Facility (ADTF) accelerator contains multiple {beta}, superconducting, resonant cavities. Spoke-type resonators ({beta} = 0.175 and {beta} = 0.34) are proposed for the low energy linac immediately following the radio frequency quadrupole. A continuous wave power requirement of 8.5 - 211.8 kW, 350 MHz has been established for the input couplers of these spoke cavities. The coupler design approach was to have a single input coupler design for beam currents of 13.3 mA and 100 mA and both cavity {beta}'s. The baseline design consists of a half-height WR2300 waveguide section merged with a shorted coaxial conductor. At the transition is a 4.8-mm thick cylindrical ceramic window creating the air/vacuum barrier. The coax is 103-mm inner diameter, 75 Ohm. The coax extends from the short through the waveguide and terminates with an antenna tip in the sidewall of the cavity. A full diameter pumping port is located in the quarter-wave stub to facilitate good vacuum. The coaxial geometry chosen was based on multipacting and thermal design considerations. The coupling coefficient is adjusted by statically adjusting the outer conductor length. The RF-physics, thermal, vacuum, and structural design considerations will be discussed in this paper, in addition to future room temperature testing plans.

Schmierer, E. N. (Eric N.); Chan, K. D. (Kwok-Chi D.); Gentzlinger, R.C. (Robert C.); Haynes, W. B. (William B.); Krawczyk, F. L. (Frank L.); Montoya, D. I. (Debbie I.); Roybal, P. L. (Phillip L.); Schrage, D. L. (Dale L.); Tajima, T. (Tsuyoshi)

2001-01-01T23:59:59.000Z

420

Maintaining secrecy when information leakage is unavoidable  

E-Print Network (OSTI)

(cont.) We apply the framework to get new results, creating (a) encryption schemes with very short keys, and (b) hash functions that leak no information about their input, yet-paradoxically-allow testing if a candidate ...

Smith, Adam (Adam Davidson), 1977-

2004-01-01T23:59:59.000Z

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


421

Weekly Petroleum Status Report - Energy Information Administration  

U.S. Energy Information Administration (EIA)

Weekly Petroleum Status Report/Energy Information Administration v U.S. crude oil refinery inputs averaged over 14.5 million barrels per day during the week ending ...

422

Form:SampleForm | Open Energy Information  

Open Energy Info (EERE)

SampleForm Jump to: navigation, search Input the name of a Test Page below. If the resource already exists, you will be able to edit its information. AddEdit a Test Page The text...

423

,"U.S. Downstream Processing of Fresh Feed Input"  

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

Monthly","9/2013","1/15/1987" Monthly","9/2013","1/15/1987" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_dwns_dc_nus_mbblpd_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_dwns_dc_nus_mbblpd_m.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:17:28 AM" "Back to Contents","Data 1: U.S. Downstream Processing of Fresh Feed Input" "Sourcekey","M_NA_YDR_NUS_MBBLD","MCRCCUS2","MCRCHUS2","MCRDFUS2"

424

MULTIPLE INPUT BINARY ADDER EMPLOYING MAGNETIC DRUM DIGITAL COMPUTING APPARATUS  

DOE Patents (OSTI)

A digital computing apparatus is described for adding a plurality of multi-digit binary numbers. The apparatus comprises a rotating magnetic drum, a recording head, first and second reading heads disposed adjacent to the first and second recording tracks, and a series of timing signals recorded on the first track. A series of N groups of digit-representing signals is delivered to the recording head at time intervals corresponding to the timing signals, each group consisting of digits of the same significance in the numbers, and the signal series is recorded on the second track of the drum in synchronism with the timing signals on the first track. The multistage registers are stepped cyclically through all positions, and each of the multistage registers is coupled to the control lead of a separate gate circuit to open the corresponding gate at only one selected position in each cycle. One of the gates has its input coupled to the bistable element to receive the sum digit, and the output lead of this gate is coupled to the recording device. The inputs of the other gates receive the digits to be added from the second reading head, and the outputs of these gates are coupled to the adding register. A phase-setting pulse source is connected to each of the multistage registers individually to step the multistage registers to different initial positions in the cycle, and the phase-setting pulse source is actuated each N time interval to shift a sum digit to the bistable element, where the multistage register coupled to bistable element is operated by the phase- setting pulse source to that position in its cycle N steps before opening the first gate, so that this gate opens in synchronism with each of the shifts to pass the sum digits to the recording head.

Cooke-Yarborough, E.H.

1960-12-01T23:59:59.000Z

425

Input modeling for hospital simulation models using electronic messages  

Science Conference Proceedings (OSTI)

Health care organizations function in a complex, non-integrated setting, yet the coordination of information, tasks, and equipment across multiple units is essential for productive operations. A variety of simulation models of hospitals exist; however, ...

Renata A. Konrad; Mark A. Lawley

2009-12-01T23:59:59.000Z

426

V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

8: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks May 31, 2013 - 6:00am Addthis...

427

T-602: BlackBerry Enterprise Server Input Validation Flaw in...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

02: BlackBerry Enterprise Server Input Validation Flaw in BlackBerry Web Desktop Manager Permits Cross-Site Scripting Attacks T-602: BlackBerry Enterprise Server Input Validation...

428

V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

4: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks April 2, 2013 - 1:13am Addthis...

429

U.S. Natural Gas Input Supplemental Fuels (Million Cubic Feet...  

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

Input Supplemental Fuels (Million Cubic Feet) U.S. Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

430

U-270:Trend Micro Control Manager Input Validation Flaw in Ad...  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

0:Trend Micro Control Manager Input Validation Flaw in Ad Hoc Query Module Lets Remote Users Inject SQL Commands U-270:Trend Micro Control Manager Input Validation Flaw in Ad Hoc...

431

A CMOS Voltage Comparator with Rail-to-Rail Input-Range  

Science Conference Proceedings (OSTI)

A simple new continuous-time CMOS comparator circuit with rail-to-rail input common-mode range and rail-to-rail output is presented. This design uses parallel complementary decision paths to accommodate power-supply-valued inputs. The 2 decision results ... Keywords: CMOS continuous-time voltage comparator, rail-to-rail input range

Wei-Shang Chu; K. Wayne Current

1999-05-01T23:59:59.000Z

432

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

Science Conference Proceedings (OSTI)

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

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

1994-05-01T23:59:59.000Z

433

The Role Of Modeling Assumptions And Policy Instruments in Evaluating The Global Implications Of U.S. Biofuel Policies  

Science Conference Proceedings (OSTI)

The primary objective of current U.S. biofuel law the Energy Independence and Security Act of 2007 (EISA) is to reduce dependence on imported oil, but the law also requires biofuels to meet carbon emission reduction thresholds relative to petroleum fuels. EISA created a renewable fuel standard with annual targets for U.S. biofuel use that climb gradually from 9 billion gallons per year in 2008 to 36 billion gallons (or about 136 billion liters) of biofuels per year by 2022. The most controversial aspects of the biofuel policy have centered on the global social and environmental implications of its potential land use effects. In particular, there is an ongoing debate about whether indirect land use change (ILUC) make biofuels a net source, rather sink, of carbon emissions. However, estimates of ILUC induced by biofuel production and use can only be inferred through modeling. This paper evaluates how model structure, underlying assumptions, and the representation of policy instruments influence the results of U.S. biofuel policy simulations. The analysis shows that differences in these factors can lead to divergent model estimates of land use and economic effects. Estimates of the net conversion of forests and grasslands induced by U.S. biofuel policy range from 0.09 ha/1000 gallons described in this paper to 0.73 ha/1000 gallons from early studies in the ILUC change debate. We note that several important factors governing LUC change remain to be examined. Challenges that must be addressed to improve global land use change modeling are highlighted.

Oladosu, Gbadebo A [ORNL; Kline, Keith L [ORNL

2010-01-01T23:59:59.000Z

434

Gross Input to Atmospheric Crude Oil Distillation Units  

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

Day) Day) Process: Gross Input to Atmospheric Crude Oil Dist. Units Operable Capacity (Calendar Day) Operating Capacity Idle Operable Capacity Operable Utilization Rate Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Process Area Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History U.S. 15,283 15,709 16,327 16,490 16,306 16,162 1985-2013 PADD 1 1,134 1,188 1,178 1,142 1,122 1,130 1985-2013 East Coast 1,077 1,103 1,080 1,058 1,031 1,032 1985-2013 Appalachian No. 1 57 85 98 84 90 97 1985-2013 PADD 2 3,151 3,087 3,336 3,572 3,538 3,420 1985-2013 Ind., Ill. and Ky. 2,044 1,947 2,069 2,299 2,330 2,266 1985-2013

435

Interface module for transverse energy input to dye laser modules  

SciTech Connect

An interface module (10) for transverse energy input to dye laser modules is provided particularly for the purpose of delivering enhancing transverse energy beams (36) in the form of illumination bar (54) to the lasing zone (18) of a dye laser device, in particular to a dye laser amplifier (12). The preferred interface module (10) includes an optical fiber array (30) having a plurality of optical fibers (38) arrayed in a co-planar fashion with their distal ends (44) receiving coherent laser energy from an enhancing laser source (46), and their proximal ends (4) delivered into a relay structure (3). The proximal ends (42) of the optical fibers (38) are arrayed so as to be coplanar and to be aimed generally at a common point. The transverse energy beam array (36) delivered from the optical fiber array (30) is acted upon by an optical element array (34) to produce an illumination bar (54) which has a cross section in the form of a elongated rectangle at the position of the lasing window (18). The illumination bar (54) is selected to have substantially uniform intensity throughout.

English, Jr., Ronald E. (Tracy, CA); Johnson, Steve A. (Tracy, CA)

1994-01-01T23:59:59.000Z

436

Interface module for transverse energy input to dye laser modules  

DOE Patents (OSTI)

An interface module for transverse energy input to dye laser modules is provided particularly for the purpose of delivering enhancing transverse energy beams in the form of illumination bar to the lasing zone of a dye laser device, in particular to a dye laser amplifier. The preferred interface module includes an optical fiber array having a plurality of optical fibers arrayed in a co-planar fashion with their distal ends receiving coherent laser energy from an enhancing laser source, and their proximal ends delivered into a relay structure. The proximal ends of the optical fibers are arrayed so as to be coplanar and to be aimed generally at a common point. The transverse energy beam array delivered from the optical fiber array is acted upon by an optical element array to produce an illumination bar which has a cross section in the form of a elongated rectangle at the position of the lasing window. The illumination bar is selected to have substantially uniform intensity throughout. 5 figs.

English, R.E. Jr.; Johnson, S.A.

1994-10-11T23:59:59.000Z

437

KEPLER INPUT CATALOG: PHOTOMETRIC CALIBRATION AND STELLAR CLASSIFICATION  

Science Conference Proceedings (OSTI)

We describe the photometric calibration and stellar classification methods used by the Stellar Classification Project to produce the Kepler Input Catalog (KIC). The KIC is a catalog containing photometric and physical data for sources in the Kepler mission field of view; it is used by the mission to select optimal targets. Four of the visible-light (g, r, i, z) magnitudes used in the KIC are tied to Sloan Digital Sky Survey magnitudes; the fifth (D51) is an AB magnitude calibrated to be consistent with Castelli and Kurucz (CK) model atmosphere fluxes. We derived atmospheric extinction corrections from hourly observations of secondary standard fields within the Kepler field of view. For these filters and extinction estimates, repeatability of absolute photometry for stars brighter than magnitude 15 is typically 2%. We estimated stellar parameters {l_brace}T{sub eff}, log (g), log (Z), E{sub B-V}{r_brace} using Bayesian posterior probability maximization to match observed colors to CK stellar atmosphere models. We applied Bayesian priors describing the distribution of solar-neighborhood stars in the color-magnitude diagram, in log (Z), and in height above the galactic plane. Several comparisons with samples of stars classified by other means indicate that for 4500 K {data archive.

Brown, Timothy M. [Las Cumbres Observatory Global Telescope, Goleta, CA 93117 (United States); Latham, David W.; Esquerdo, Gilbert A. [Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138 (United States); Everett, Mark E., E-mail: tbrown@lcogt.net, E-mail: latham@cfa.harvard.edu, E-mail: gesquerd@cfa.harvard.edu, E-mail: everett@noao.edu [National Optical Astronomy Observatories, Tucson, AZ 85721 (United States)

2011-10-15T23:59:59.000Z

438

Proper input phase-space filling for accurate beam-dynamics codes  

Science Conference Proceedings (OSTI)

In the future, more attention will be required concerning the filling of the input phase space used by particle-simulation codes. The prospect of greatly improved particle-tracking codes implies that code input distributions must be accurate models of real input distributions. Much of present simulation work is done using artificial phase-space distributions (K-V, waterbag, etc.). Real beams can differ dramatically from such ideal input. We have already developed a method for deriving code input distributions from measurements. This paper addresses the problem of determining the number of pseudoparticles needed to model the measured distribution properly.

Boicourt, G.P.; Vasquez, M.C.

1986-01-01T23:59:59.000Z

439

Documentation of Calculation Methodology, Input data, and Infrastructure for the Home Energy Saver Web Site  

SciTech Connect

The Home Energy Saver (HES, http://HomeEnergySaver.lbl.gov) is an interactive web site designed to help residential consumers make decisions about energy use in their homes. This report describes the underlying methods and data for estimating energy consumption. Using engineering models, the site estimates energy consumption for six major categories (end uses); heating, cooling, water heating, major appliances, lighting, and miscellaneous equipment. The approach taken by the Home Energy Saver is to provide users with initial results based on a minimum of user input, allowing progressively greater control in specifying the characteristics of the house and energy consuming appliances. Outputs include energy consumption (by fuel and end use), energy-related emissions (carbon dioxide), energy bills (total and by fuel and end use), and energy saving recommendations. Real-world electricity tariffs are used for many locations, making the bill estimates even more accurate. Where information about the house is not available from the user, default values are used based on end-use surveys and engineering studies. An extensive body of qualitative decision-support information augments the analytical results.

Pinckard, Margaret J.; Brown, Richard E.; Mills, Evan; Lutz, James D.; Moezzi, Mithra M.; Atkinson, Celina; Bolduc, Chris; Homan, Gregory K.; Coughlin, Katie

2005-07-13T23:59:59.000Z

440

Risk-Informed Asset Management  

Science Conference Proceedings (OSTI)

This report contains business requirements for Risk-Informed Asset Management (RIAM) software. The requirements pertain to both a full-blown version of RIAM (including uncertainty analysis of the economic and safety risk of a proposed equipment improvement project) and for RIAM Level 1 project screening software. The RIAM Level 1 analysis is a bounding process intended to estimate the most optimistic effect that the proposed investment would have on plant safety, cost, and revenue. The optimistic assumpt...

2006-02-24T23:59:59.000Z

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


441

On the Value of Input-Efficiency, Capacity-Efficiency, and the Flexibility to Rebalance Them  

E-Print Network (OSTI)

Abstract: A common characteristic of basic material manufacturers (which account for 85 % of all industrial energy use) and of cleantech manufacturers is that they are price-takers in their input and output markets. Variability in those prices has implications for how much a manufacturer should invest in three fundamental types of process improvement. Input price variability reduces the value of improving input-efficiency (output produced per unit input) but increases that of capacityefficiency (the rate at which a production facility can convert input into output). Output price variability increases the value of capacity-efficiency, but it increases the value of input-efficiency if and only if the expected margin is small. Moreover, as the expected input cost rises, the value of input-efficiency decreases. A third type of process improvement is to develop flexibility in inputefficiency versus capacity-efficiency (the ability to respond to a rise in input cost or fall in output price by increasing input-efficiency at the expense of capacity-efficiency). The value of this flexibility decreases with variability in input and output prices, if and only if the expected margin is thin. Together, these results suggest that a carbon tax or cap-and-trade system may reduce investment by basic material manufacturers in improving energy-efficiency.

Erica L. Plambeck; Terry A. Taylor

2013-01-01T23:59:59.000Z

442

U-001:Symantec IM Manager Input Validation Flaws | Department of Energy  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

U-001:Symantec IM Manager Input Validation Flaws U-001:Symantec IM Manager Input Validation Flaws U-001:Symantec IM Manager Input Validation Flaws October 3, 2011 - 12:45pm Addthis PROBLEM: Symantec IM Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Code Execution Attacks. PLATFORM: Version(s): prior to 8.4.18 ABSTRACT: Symantec IM Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Code Execution Attacks. reference LINKS: Security Advisory: SYM11-012 SecurityTracker Alert ID: 1026130 IMPACT ASSESSMENT: Medium Discussion: Several vulnerabilities were reported in Symantec IM Manager. A remote user can conduct cross-site scripting attacks. A remote user can inject SQL commands. Several scripts do not properly filter HTML code from user-supplied input before displaying the input [CVE-2011-0552]. A remote user can create a

443

Mutual Entropy in Quantum Information and Information Genetics  

E-Print Network (OSTI)

After Shannon, entropy becomes a fundamental quantity to describe not only uncertainity or chaos of a system but also information carried by the system. Shannon's important discovery is to give a mathematical expression of the mutual entropy (information), information transmitted from an input system to an output system, by which communication processes could be analyzed on the stage of mathematical science. In this paper, first we review the quantum mutual entropy and discuss its uses in quantum information theory, and secondly we show how the classical mutual entropy can be used to analyze genomes, in particular, those of HIV.

Masanori Ohya

2004-06-30T23:59:59.000Z

444

Evaluation of severe accident risks: Quantification of major input parameters  

DOE Green Energy (OSTI)

This report records part of the vast amount of information received during the expert judgment elicitation process that took place in support of the NUREG-1150 effort sponsored by the U.S. Nuclear Regulatory Commission. The results of the Containment Loads and Molten Core/Containment Interaction Expert Panel Elicitation are presented in this part of Volume 2 of NUREG/CR-4551. The Containment Loads Expert Panel considered seven issues: (1) hydrogen phenomena at Grand Gulf; (2) hydrogen burn at vessel breach at Sequoyah; (3) BWR reactor building failure due to hydrogen; (4) Grand Gulf containment loads at vessel breach; (5) pressure increment in the Sequoyah containment at vessel breach; (6) loads at vessel breach: Surry; and (7) pressure increment in the Zion containment at vessel breach. The report begins with a brief discussion of the methods used to elicit the information from the experts. The information for each issue is then presented in five sections: (1) a brief definition of the issue, (2) a brief summary of the technical rationale supporting the distributions developed by each of the experts, (3) a brief description of the operations that the project staff performed on the raw elicitation results in order to aggregate the distributions, (4) the aggregated distributions, and (5) the individual expert elicitation summaries. The Molten Core/Containment Interaction Panel considered three issues. The results of the following two of these issues are presented in this document: (1) Peach Bottom drywell shell meltthrough; and (2) Grand Gulf pedestal erosion. 89 figs., 154 tabs.

Harper, F.T.; Payne, A.C.; Breeding, R.J.; Gorham, E.D.; Brown, T.D.; Rightley, G.S.; Gregory, J.J. (Sandia National Labs., Albuquerque, NM (USA)); Murfin, W. (Technadyne Engineering Consultants, Inc., Albuquerque, NM (USA)); Amos, C.N. (Science Applications International Corp., Albuquerque, NM (USA))

1991-04-01T23:59:59.000Z

445

This book adds an important nuance to the traditional historiographical assumption that trade in the Early Modern period was mostly conducted between family and those of the same  

E-Print Network (OSTI)

This book adds an important nuance to the traditional historiographical assumption that trade group. Rather, it is the assertion of this book, that there were very real and quite important trade relationships between merchants of different groups, and the book uses a case study of the Sephardim

van den Brink, Jeroen

446

T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' 8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks August 22, 2011 - 3:54pm Addthis PROBLEM: A vulnerability was reported in Adobe ColdFusion. A remote user can conduct cross-site scripting attacks. PLATFORM: Adobe ColdFusion 9.x ABSTRACT: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks. reference LINKS: Adobe Vulnerability Report Adobe Security Bulletin ColdFusion Support SecurityTracker Alert ID: 1025957 IMPACT ASSESSMENT: Medium Discussion: The 'probe.cfm' script does not properly filter HTML code from user-supplied input in the 'name' parameter before displaying the input. A remote user can create a specially crafted URL that, when loaded by a

447

T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

0: Skype Input Validation Flaw in 'mobile phone' Profile Entry 0: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks July 18, 2011 - 7:09am Addthis PROBLEM: A vulnerability was reported in Skype. A remote user can conduct cross-site scripting attacks. PLATFORM: 5.3.0.120 and prior versions ABSTRACT: The software does not properly filter HTML code from user-supplied input in the The "mobile phone" profile entry before displaying the input. reference LINKS: SecurityTracker Alert ID: 1025789 Skype Security Advisory KoreSecure News H Security ID: 1279864 IMPACT ASSESSMENT: High Discussion: Skype suffers from a persistent Cross-Site Scripting vulnerability due to a lack of input validation and output sanitization of the "mobile phone"

448

U-132: Apache Wicket Input Validation Flaw in 'wicket:pageMapName'  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

2: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' 2: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' Parameter Permits Cross-Site Scripting Attacks U-132: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' Parameter Permits Cross-Site Scripting Attacks March 23, 2012 - 7:42am Addthis PROBLEM: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' Parameter Permits Cross-Site Scripting Attacks PLATFORM: Apache Wicket 1.4.x ABSTRACT: A remote user can conduct cross-site scripting attacks. reference LINKS: Apache Wicket CVE-2012-0047 SecurityTracker Alert ID: 1026839 IMPACT ASSESSMENT: High Discussion: The software does not properly filter HTML code from user-supplied input in the 'wicket:pageMapName' request parameter before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target

449

V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

4: Splunk Web Input Validation Flaw Permits Cross-Site 4: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks April 2, 2013 - 1:13am Addthis PROBLEM: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 4.3.0 through 4.3.5 ABSTRACT: A vulnerability was reported in Splunk Web. REFERENCE LINKS: SecurityTracker Alert ID: 1028371 Splunk IMPACT ASSESSMENT: High DISCUSSION: Splunk Web does not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target user's browser. The code will originate from the site running the Splunk Web software and will run in the security context of that site. As a result, the code will be able to access the

450

U-252: Barracuda Web Filter Input Validation Flaws Permit Cross-Site  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

2: Barracuda Web Filter Input Validation Flaws Permit 2: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks U-252: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks September 6, 2012 - 6:00am Addthis PROBLEM: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks PLATFORM: Barracuda Web Filter 5.0.015 is vulnerable; other versions may also be affected. ABSTRACT: Barracuda Web Filter Authentication Module Multiple HTML Injection Vulnerabilities reference LINKS: Barracuda Networks Barracuda Networks Security ID: BNSEC-279/BNYF-5533 SecurityTracker Alert ID: 1027500 Bugtraq ID: 55394 seclists.org IMPACT ASSESSMENT: Medium Discussion: Two scripts not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to

451

T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

70: Skype Input Validation Flaw in 'mobile phone' Profile Entry 70: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks July 18, 2011 - 7:09am Addthis PROBLEM: A vulnerability was reported in Skype. A remote user can conduct cross-site scripting attacks. PLATFORM: 5.3.0.120 and prior versions ABSTRACT: The software does not properly filter HTML code from user-supplied input in the The "mobile phone" profile entry before displaying the input. reference LINKS: SecurityTracker Alert ID: 1025789 Skype Security Advisory KoreSecure News H Security ID: 1279864 IMPACT ASSESSMENT: High Discussion: Skype suffers from a persistent Cross-Site Scripting vulnerability due to a lack of input validation and output sanitization of the "mobile phone"

452

U-050: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

0: Adobe Flex SDK Input Validation Flaw Permits Cross-Site 0: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting Attacks U-050: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting Attacks December 2, 2011 - 5:24am Addthis PROBLEM: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting Attacks. PLATFORM: Adobe Flex SDK 4.5.1 and earlier 4.x versions for Windows, Macintosh and Linux Adobe Flex SDK 3.6 and earlier 3.x versions for Windows, Macintosh and Linux ABSTRACT: Flex applications created using the Flex SDK may not properly filter HTML code from user-supplied input before displaying the input. reference LINKS: Adobe Security Bulletin CVE-2011-2461 SecurityTracker Alert ID: 1026361 IMPACT ASSESSMENT: High Discussion: A remote user may be able to cause arbitrary scripting code to be executed

453

T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' 8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks August 22, 2011 - 3:54pm Addthis PROBLEM: A vulnerability was reported in Adobe ColdFusion. A remote user can conduct cross-site scripting attacks. PLATFORM: Adobe ColdFusion 9.x ABSTRACT: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks. reference LINKS: Adobe Vulnerability Report Adobe Security Bulletin ColdFusion Support SecurityTracker Alert ID: 1025957 IMPACT ASSESSMENT: Medium Discussion: The 'probe.cfm' script does not properly filter HTML code from user-supplied input in the 'name' parameter before displaying the input. A remote user can create a specially crafted URL that, when loaded by a

454

Biotrans: Cost Optimization Model | Open Energy Information  

Open Energy Info (EERE)

Biotrans: Cost Optimization Model Biotrans: Cost Optimization Model Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Biotrans: Cost Optimization Model Focus Area: Ethanol Topics: Market Analysis Website: www.ecn.nl/units/ps/models-and-tools/biotrans/ Equivalent URI: cleanenergysolutions.org/content/biotrans-cost-optimization-model,http Language: English Policies: Deployment Programs DeploymentPrograms: Demonstration & Implementation BIOTRANS optimizes the biofuel supply chain allocation by finding the least-cost configuration of resources and trade to meet a specified biofuel demand in the European transportation sector. The user can constrain the optimization by inputting a number of economic and technological assumptions for a specific target year. References Retrieved from

455

[Composite analysis E-area vaults and saltstone disposal facilities]. PORFLOW and FACT input files  

Science Conference Proceedings (OSTI)

This diskette contains the PORFLOW and FACT input files described in Appendix B of the accompanying report `Composite Analysis E-Area Vaults and Saltstone Disposal Facilities`.

Cook, J.R.

1997-09-01T23:59:59.000Z

456

SRTC input to DOE-HQ R and D database for FY99  

SciTech Connect

This is a database of the Savannah River Site input to the DOE Research and Development database. The report contains approximately 50 project abstracts.

Chandler, L.R. Jr.

2000-01-05T23:59:59.000Z

457

Table A4. Total Inputs of Energy for Heat, Power, and Electricity...  

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

"Table A4. Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Census Region, Census Division, Industry Group, and Selected Industries, 1994: Part 2" "...

458

Table A36. Total Inputs of Energy for Heat, Power, and Electricity  

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

"Table A36. Total Inputs of Energy for Heat, Power, and Electricity" " Generation by Fuel Type, Industry Group, Selected Industries, and End Use, 1991:" " Part 2" " (Estimates in...

459

Table A10. Total Inputs of Energy for Heat, Power, and Electricity...  

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

"Table A10. Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Fuel Type, Industry Group, Selected Industries, and End Use, 1994:" " Part 2" " (Estimates in...

460

Use of probabilistic inversion to model qualitative expert input when selecting a new nuclear reactor technology.  

E-Print Network (OSTI)

?? Complex investment decisions by corporate executives often require the comparison of dissimilar attributes and competing technologies. A technique to evaluate qualitative input from experts (more)

Merritt, Charles R., Jr.

2008-01-01T23:59:59.000Z

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


461

Table A12. Total Inputs of Energy for Heat, Power, and Electricity...  

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

2. Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Census Region and Economic Characteristics of the Establishment, 1991" " (Estimates in Btu or Physical...

462

Calibration of a distributed flood forecasting model with input uncertainty using a Bayesian framework  

E-Print Network (OSTI)

Calibrated probabilistic forecasting using ensemble modelSutcliffe (1970), River flow forecasting through conceptuala Distributed Flood Forecasting Model with Input Uncertainty

Li, M.

2013-01-01T23:59:59.000Z

463

Fossil energy use in conventional and low-external-input cropping systems.  

E-Print Network (OSTI)

??The production of fossil fuels will crest within the next decade and with reliance of modern conventional agriculture on fossil fuel energy inputs, food production (more)

Cruse, Michael James

2009-01-01T23:59:59.000Z

464

Inform Editors  

Science Conference Proceedings (OSTI)

Inform editorial board Inform Editors inform Magazine algae algal AOCS biomass business chemistry cottonseed date detergents fats filing first history inform inform Magazine international inventor law magazine member members monthly news oil oils

465

Subscription Information  

Science Conference Proceedings (OSTI)

Inform subscription rates. Subscription Information inform Magazine algae algal AOCS biomass business chemistry cottonseed date detergents fats filing first history inform inform Magazine international inventor law magazine member members monthly

466

Predicting information diffusion on social networks with partial knowledge  

Science Conference Proceedings (OSTI)

Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure ... Keywords: diffusion, machine learning, social networks

Anis Najar; Ludovic Denoyer; Patrick Gallinari

2012-04-01T23:59:59.000Z

467

Arbitrary blade section design based on viscous considerations. Background information  

SciTech Connect

Background information is presented on an arbitrary blade section design method which is outlined in a joint paper. This information concerns the assumptions, the development, and the predictive capabilities of the viscous flow calculation tool used in the design procedure. General properties of laminar and turbulent, unseparated or separated compressible shear layers, necessary for the blade optimization procedure, are discussed.

Bouras, B.; Karagiannis, F.; Leoutsakos, G.; Giannakoglou, K.C.; Papailiou, K.D. [National Technical Univ. of Athens (Greece). Thermal Turbomachinery Lab.

1996-06-01T23:59:59.000Z

468

Input-output theory for waveguide QED with an ensemble of inhomogeneous atoms  

E-Print Network (OSTI)

We study the collective effects that emerge in waveguide quantum electrodynamics where several (artificial) atoms are coupled to a one-dimensional (1D) superconducting transmission line. Since single microwave photons can travel without loss for a long distance along the line, real and virtual photons emitted by one atom can be reabsorbed or scattered by a second atom. Depending on the distance between the atoms, this collective effect can lead to super- and subradiance or to a coherent exchange-type interaction between the atoms. Changing the artificial atoms transition frequencies, something which can be easily done with superconducting qubits (two levels artificial atoms), is equivalent to changing the atom-atom separation and thereby opens the possibility to study the characteristics of these collective effects. To study this waveguide quantum electrodynamics system, we extend previous work and present an effective master equation valid for an ensemble of inhomogeneous atoms. Using input-output theory, we compute analytically and numerically the elastic and inelastic scattering and show how these quantities reveal information about collective effects. These theoretical results are compatible with recent experimental results using transmon qubits coupled to a superconducting one-dimensional transmission line [A.F. van Loo {\\it et al.} (2013)].

Kevin Lalumire; Barry C. Sanders; Arjan F. van Loo; Arkady Fedorov; Andreas Wallraff; Alexandre Blais

2013-05-30T23:59:59.000Z

469

Employment structures of information systems professionals: a comparative study of the United States and Singapore  

Science Conference Proceedings (OSTI)

Prior research into information systems (IS) personnel has been primarily directed from the perspective of an internal labor market. The assumption underlying that body of research is that agents, i.e., IS workers, form long-term employment relationships ...

Sandra Slaughter; Soon Ang

1994-04-01T23:59:59.000Z

470

Can the conventional models apply? The microeconomics of the information revolution  

Science Conference Proceedings (OSTI)

Operating with incorrect assumptions concerning information firms and how they conduct commerce has significant public policy implications. One possible consequence is inappropriate anti-trust action (or inaction) by government regulators. Because the ...

Bruce Don; Dave Frelinger

1995-07-01T23:59:59.000Z

471

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought  

Science Conference Proceedings (OSTI)

Crop growth models are increasingly used as part of research into areas such as climate change and bioenergy, so it is particularly important to understand the effects of environmental inputs on model results. Rather than investigating the effects of ... Keywords: Crop growth model, Drought, Input data, Parameterisation, Sensitivity analysis, Soil water

Mark Pogson; Astley Hastings; Pete Smith

2012-03-01T23:59:59.000Z

472

Technical communication: Extending the analog input capabilities of the DS1102 DSP controller board  

Science Conference Proceedings (OSTI)

The paper deals with an extention of the number of analog inputs of the DS1102 controller board which is commonly used in the area of electric machines. Manufactured with just four analog inputs, the DS1102 has been found inadequate for the implementation ... Keywords: Analog multiplexing, Analog to digital converters, Digital signal processor, Doubly-fed machine, Field oriented control

Badreddine Louhichi; Ahmed Masmoudi; Luc Loron

2005-01-01T23:59:59.000Z

473

Simulation for Performance Analysis of Grid-Connected Induction Generators with Input Voltage Control  

Science Conference Proceedings (OSTI)

With the increasing application of wind energy, various technologies are developed for analyzing the performance of grid-connected induction generator (GIG) based wind energy conversion systems (WECSs). Input voltage control is one among them. In the ... Keywords: grid-connected induction generators (GIGs), wind energy conversion systems (WECSs), input voltage control, performance analysis, MATLAB

Farhad Ilahi Bakhsh, Shirazul Islam, Sayeed Ahmad

2013-04-01T23:59:59.000Z

474

Call for White Papers: Soliciting Community Input for Alternate Science Investigations for the Kepler Spacecraft  

E-Print Network (OSTI)

Call for White Papers: Soliciting Community Input for Alternate Science Investigations of this call for white papers is to solicit community input for alternate science investigations that may project office personnel and expertise already in place. All white papers submitted in response

Rodriguez, Carlos

475

Inform App  

Science Conference Proceedings (OSTI)

Access INFORM anytime with our free app available on iPad, iPhone, Android and Kindle. Inform App Inform App

476

U-255: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

5: Apache Wicket Input Validation Flaw Permits Cross-Site 5: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting Attacks U-255: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting Attacks September 11, 2012 - 6:00am Addthis PROBLEM: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: Apache Software Foundation Apache Wicket 1.5.5 Apache Software Foundation Apache Wicket 1.5-RC5.1 Apache Software Foundation Apache Wicket 1.4.20 Apache Software Foundation Apache Wicket 1.4.18 Apache Software Foundation Apache Wicket 1.4.17 Apache Software Foundation Apache Wicket 1.4.16 ABSTRACT: A vulnerability was reported in Apache Wicket reference LINKS: Apache Wicket SecurityTracker Alert ID: 1027508 Bugtraq ID: 55445 CVE-2012-3373 IMPACT ASSESSMENT: Medium Discussion: The software does not properly filter HTML code from user-supplied input in

477

U-139: IBM Tivoli Directory Server Input Validation Flaw | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

39: IBM Tivoli Directory Server Input Validation Flaw 39: IBM Tivoli Directory Server Input Validation Flaw U-139: IBM Tivoli Directory Server Input Validation Flaw April 3, 2012 - 7:00am Addthis PROBLEM: A vulnerability was reported in IBM Tivoli Directory Server. A remote user can conduct cross-site scripting attacks PLATFORM: Version(s): 6.2, 6.3 ABSTRACT: The Web Admin Tool does not properly filter HTML code from user-supplied input before displaying the input. Reference LINKS: Vendor Advisory Security Tracker ID 1026880 CVE-2012-0740 IMPACT ASSESSMENT: Medium Discussion: A remote user can create a specially crafted URL that, when loaded by a target user, will cause arbitrary scripting code to be executed by the target user's browser. The code will originate from the site running the IBM Tivoli Directory Server software and will run in the security context

478

V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site Scripting  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site Scripting Attacks V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site Scripting Attacks August 28, 2013 - 6:00am Addthis PROBLEM: Several vulnerabilities were reported in IBM Lotus iNotes PLATFORM: IBM Lotus iNotes 8.5.x ABSTRACT: IBM Lotus iNotes 8.5.x contains four cross-site scripting vulnerabilities REFERENCE LINKS: Security Tracker Alert ID 1028954 IBM Security Bulletin 1647740 Seclist.org CVE-2013-0590 CVE-2013-0591 CVE-2013-0595 IMPACT ASSESSMENT: Medium DISCUSSION: The software does not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target user's browser. The code will originate

479

U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

204: HP Network Node Manager i Input Validation Hole Permits 204: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks July 3, 2012 - 7:00am Addthis PROBLEM: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 8.x, 9.0x, 9.1x ABSTRACT: Potential security vulnerabilities have been identified with HP Network Node Manager I (NNMi) for HP-UX, Linux, Solaris, and Windows. The vulnerabilities could be remotely exploited resulting in cross site scripting (XSS). reference LINKS: The Vendor's Advisory SecurityTracker Alert ID: 1027215 CVE-2012-2018 IMPACT ASSESSMENT: Medium Discussion: A vulnerability was reported in HP Network Node Manager i. The software does not properly filter HTML code from user-supplied input before

480

DOE Seeking Input on Alternative Uses of Nickel Inventory | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

Seeking Input on Alternative Uses of Nickel Inventory Seeking Input on Alternative Uses of Nickel Inventory DOE Seeking Input on Alternative Uses of Nickel Inventory March 9, 2007 - 10:28am Addthis WASHINGTON, DC - The U.S. Department of Energy (DOE) is seeking input from industry representatives on the safe disposition of approximately 15,300 tons of nickel scrap recovered from uranium enrichment process equipment at the Department's Oak Ridge, TN, and Paducah, KY, facilities. The Expression of Interest (EOI), released today, will assist in DOE's evaluation of restricted uses of its nickel material for controlled radiological applications. These restricted uses could include use in commercial nuclear power plants, DOE nuclear facilities, or by the U.S. Navy. The Department will solicit input through May 8, 2007.

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


481

DOE Seeking Input on Alternative Uses of Nickel Inventory | Department of  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

DOE Seeking Input on Alternative Uses of Nickel Inventory DOE Seeking Input on Alternative Uses of Nickel Inventory DOE Seeking Input on Alternative Uses of Nickel Inventory March 9, 2007 - 10:28am Addthis WASHINGTON, DC - The U.S. Department of Energy (DOE) is seeking input from industry representatives on the safe disposition of approximately 15,300 tons of nickel scrap recovered from uranium enrichment process equipment at the Department's Oak Ridge, TN, and Paducah, KY, facilities. The Expression of Interest (EOI), released today, will assist in DOE's evaluation of restricted uses of its nickel material for controlled radiological applications. These restricted uses could include use in commercial nuclear power plants, DOE nuclear facilities, or by the U.S. Navy. The Department will solicit input through May 8, 2007.

482

U-102: Cisco IronPort Encryption Appliance Input Validation Flaw Permits  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

2: Cisco IronPort Encryption Appliance Input Validation Flaw 2: Cisco IronPort Encryption Appliance Input Validation Flaw Permits Cross-Site Scripting Attacks U-102: Cisco IronPort Encryption Appliance Input Validation Flaw Permits Cross-Site Scripting Attacks February 14, 2012 - 8:00am Addthis PROBLEM: A vulnerability was reported in Cisco IronPort Encryption Appliance. PLATFORM: Version(s): prior to 6.5.3 ABSTRACT: A remote user can conduct cross-site scripting reference LINKS: Vendor URL CVE-2012-0340 Security Tracker ID:1026669 IMPACT ASSESSMENT: Medium Discussion: The interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can create a specially crafted URL that, when loaded by a target user, will cause arbitrary scripting code to be executed by the target user's browser. The code will originate from

483

V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

8: Splunk Web Input Validation Flaw Permits Cross-Site 8: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks May 31, 2013 - 6:00am Addthis PROBLEM: A vulnerability was reported in Splunk Web PLATFORM: Version(s) prior to 5.0.3 ABSTRACT: A reflected cross-site scripting vulnerability was identified in Splunk Web REFERENCE LINKS: SecurityTracker Alert ID: 1028605 Splunk Security Advisory SPL-59895 CVE-2012-6447 IMPACT ASSESSMENT: Medium DISCUSSION: The web interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can create a specially crafted URL that, when loaded by a target user, will cause arbitrary scripting code to be executed by the target user's browser. The code will

484

U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

4: HP Network Node Manager i Input Validation Hole Permits 4: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks July 3, 2012 - 7:00am Addthis PROBLEM: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 8.x, 9.0x, 9.1x ABSTRACT: Potential security vulnerabilities have been identified with HP Network Node Manager I (NNMi) for HP-UX, Linux, Solaris, and Windows. The vulnerabilities could be remotely exploited resulting in cross site scripting (XSS). reference LINKS: The Vendor's Advisory SecurityTracker Alert ID: 1027215 CVE-2012-2018 IMPACT ASSESSMENT: Medium Discussion: A vulnerability was reported in HP Network Node Manager i. The software does not properly filter HTML code from user-supplied input before

485

V-139: Cisco Network Admission Control Input Validation Flaw Lets Remote  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

9: Cisco Network Admission Control Input Validation Flaw Lets 9: Cisco Network Admission Control Input Validation Flaw Lets Remote Users Inject SQL Commands V-139: Cisco Network Admission Control Input Validation Flaw Lets Remote Users Inject SQL Commands April 21, 2013 - 11:50pm Addthis PROBLEM: Cisco Network Admission Control Input Validation Flaw Lets Remote Users Inject SQL Commands PLATFORM: Cisco NAC Manager versions prior to 4.8.3.1 and 4.9.2 ABSTRACT: A vulnerability was reported in Cisco Network Admission Control. REFERENCE LINKS: SecurityTracker Alert ID: 1028451 Cisco Advisory ID: cisco-sa-20130417-nac CVE-2013-1177 IMPACT ASSESSMENT: High DISCUSSION: The Cisco Network Admission Control (NAC) Manager does not properly validate user-supplied input. A remote user can supply a specially crafted parameter value to execute SQL commands on the underlying database.

486

U-144:Juniper Secure Access Input Validation Flaw Permits Cross-Site  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

4:Juniper Secure Access Input Validation Flaw Permits 4:Juniper Secure Access Input Validation Flaw Permits Cross-Site Scripting Attacks U-144:Juniper Secure Access Input Validation Flaw Permits Cross-Site Scripting Attacks April 10, 2012 - 7:30am Addthis PROBLEM: A vulnerability was reported in Juniper Secure Access/Instant Virtual Extranet (IVE). A remote user can conduct cross-site scripting attacks. PLATFORM: Version(s): prior to 7.0R9 and 7.1R ABSTRACT: The VPN management interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target user's browser. reference LINKS: Vendor URL SecurityTracker Alert ID: 1026893 IMPACT ASSESSMENT: High Discussion: The code will originate from the interface and will run in the security

487

How are basement walls input in REScheck? | Building Energy Codes Program  

NLE Websites -- All DOE Office Websites (Extended Search)

basement walls input in REScheck? basement walls input in REScheck? After selecting a basement wall type, a basement wall illustration will appear with input boxes for the basement wall height, depth below grade, and depth of insulation. The illustration helps identify the dimensions being requested. You may enter basement wall dimensions directly into this illustration and select the OK button to have them transferred to the corresponding row in the table on the Envelope screen. If you prefer to enter the dimensions directly into the table on the Envelope screen, you can select Cancel to remove the illustration without entering dimensions. To view the basement wall illustration and inputs at a later time, click the right-mouse button anywhere on the basement row and select Edit Basement Inputs from the popup menu.

488

V-193: Barracuda SSL VPN Input Validation Hole Permits Cross-Site Scripting  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

93: Barracuda SSL VPN Input Validation Hole Permits Cross-Site 93: Barracuda SSL VPN Input Validation Hole Permits Cross-Site Scripting Attacks V-193: Barracuda SSL VPN Input Validation Hole Permits Cross-Site Scripting Attacks July 5, 2013 - 6:00am Addthis PROBLEM: A vulnerability was reported in Barracuda SSL VPN PLATFORM: Version(s) prior to 2.3.3.216 ABSTRACT: Several scripts do not properly filter HTML code from user-supplied input before displaying the input via several parameters REFERENCE LINKS: SecurityTracker Alert ID: 1028736 Barracuda SSL VPN Release Notes Zero Science Lab IMPACT ASSESSMENT: Medium DISCUSSION: The code will originate from the Barracuda SSL VPN interface and will run in the security context of that site. As a result, the code will be able to access the target user's cookies (including authentication cookies), if

489

V-153: Symantec Brightmail Gateway Input Validation Flaw Permits Cross-Site  

Energy.gov (U.S. Department of Energy (DOE)) Indexed Site

3: Symantec Brightmail Gateway Input Validation Flaw Permits 3: Symantec Brightmail Gateway Input Validation Flaw Permits Cross-Site Scripting Attacks V-153: Symantec Brightmail Gateway Input Validation Flaw Permits Cross-Site Scripting Attacks May 10, 2013 - 6:00am Addthis PROBLEM: A vulnerability was reported in Symantec Brightmail Gateway PLATFORM: The vulnerabilities are reported in versions prior to 9.5.x ABSTRACT: Symantec's Brightmail Gateway management console is susceptible to stored c