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1

International Energy Module  

Reports and Publications (EIA)

Summarizes the overall structure of the International Energy Model and its interface with other NEMS modules, mathematical specifications of behavioral relationships, and data sources and estimation methods.

Adrian Geagla

2012-11-05T23:59:59.000Z

2

International Energy Module  

Reports and Publications (EIA)

Summarizes the overall structure of the International Energy Model and its interface with other NEMS modules, mathematical specifications of behavioral relationships, and data sources and estimation methods.

Adrian Geagla

2013-10-22T23:59:59.000Z

3

International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 23 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 International Energy Module The NEMS 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 NEMS IEM computes world oil prices, provides a supply curve of world crude-like liquids, generates a worldwide oil supply- demand balance with regional detail, and computes quantities of crude oil and light and heavy petroleum products imported into

4

International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 International Energy Module The NEMS 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 NEMS IEM computes oil prices, provides a supply curve of world crude-like liquids, generates a worldwide oil supply- demand balance with regional detail, and computes quantities of crude oil and light and heavy petroleum products imported into the United States by export region. Changes in the oil price (WTI), which is defined as the price of light, low-sulfur crude oil delivered to Cushing, Oklahoma in

5

International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

he International Energy Module determines changes in the world oil price and the supply prices of crude he 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).

6

Biomass Energy Technology Module | Open Energy Information  

Open Energy Info (EERE)

Biomass Energy Technology Module Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Biomass Energy Technology Module AgencyCompany Organization: World Bank Sector: Energy...

7

Flywheel Energy Storage Module  

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

kWh100 kW Flywheel Energy Storage Module * 100KWh - 18 cost KWh vs. current State of the Art * Bonded Magnetic Bearings on Rim ID * No Shaft Hub (which limits surface speed)...

8

Flywheel Energy Storage Module  

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

kWh/100 kW kWh/100 kW Flywheel Energy Storage Module * 100KWh - 1/8 cost / KWh vs. current State of the Art * Bonded Magnetic Bearings on Rim ID * No Shaft / Hub (which limits surface speed) * Flexible Motor Magnets on Rim ID * Develop Touch-down System for Earthquake Flying Rim Eliminate Shaft and Hub Levitate on Passive Magnetic Bearings Increase Rim Tip Speed Larger Diameter Thinner Rim Stores More Energy 4 X increase in Stored Energy with only 60% Increase in Weight Development of a 100 kWh/100 kW Flywheel Energy Storage Module High Speed, Low Cost, Composite Ring with Bore-Mounted Magnetics Current State of the Art Flywheel Limitations of Existing Flywheel * 15 Minutes of storage * Limited to Frequency Regulation Application * Rim Speed (Stored Energy) Limited by Hub Strain and Shaft Dynamics

9

Living Systems Energy Module  

DOE Green Energy (OSTI)

The Living Systems Energy Module, renamed Voyage from the Sun, is a twenty-lesson curriculum designed to introduce students to the major ways in which energy is important in living systems. Voyage from the Sun tells the story of energy, describing its solar origins, how it is incorporated into living terrestrial systems through photosynthesis, how it flows from plants to herbivorous animals, and from herbivores to carnivores. A significant part of the unit is devoted to examining how humans use energy, and how human impact on natural habitats affects ecosystems. As students proceed through the unit, they read chapters of Voyage from the Sun, a comic book that describes the flow of energy in story form (Appendix A). During the course of the unit, an ``Energy Pyramid`` is erected in the classroom. This three-dimensional structure serves as a classroom exhibit, reminding students daily of the importance of energy and of the fragile nature of our living planet. Interactive activities teach students about adaptations that allow plants and animals to acquire, to use and to conserve energy. A complete list of curricular materials and copies of all activity sheets appear in Appendix B.

NONE

1995-09-26T23:59:59.000Z

10

World Energy Projection System Plus (WEPS+): Global Activity Module  

Reports and Publications (EIA)

World Energy Projection System Plus Model Documentation: Global Activity Module Documents the objectives, analytical approach, and development of the World Energy Projection Plus (WEPS+) Global Activity Module (GAM) used to develop the International Energy Outlook for 2013 (IEO2013). The report catalogues and describes the module assumptions, computations, methodology, parameter estimation techniques, and mainframe source code

Vipin Arora

2013-10-23T23:59:59.000Z

11

Module Handbook Specialisation Wind Energy  

E-Print Network (OSTI)

of wind energy External costs Future price trends 3. Environmental Issues Environmental benefits of WT and Externalities Clculation methods Current plant costs Wind energy prices The value Module Handbook Specialisation Wind Energy 2nd Semester for the Master Programme

Habel, Annegret

12

Photovoltaic Energy Technology Module | Open Energy Information  

Open Energy Info (EERE)

Photovoltaic Energy Technology Module Photovoltaic Energy Technology Module Jump to: navigation, search Tool Summary Name: Photovoltaic Energy Technology Module Agency/Company /Organization: World Bank Sector: Energy Focus Area: Renewable Energy, Solar Topics: Technology characterizations Website: web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTENERGY2/EXTRENENERGYTK/0,, References: Photovoltaic Energy Technology Module[1] Resources Portable Solar Photovoltaic Lanterns: Performance and Certification Specification, and Type Approval, ESMAP TECHNICAL PAPER 078 Testing of Storage Batteries used in Stand Alone Photovoltaic Power Systems, Test procedures and examples of test results Technical Specifications for Solar Home Systems (SHS), Rural Electrification and Renewable Energy Development (PV Component) Project

13

Module Handbook Specialisation Biomass Energy  

E-Print Network (OSTI)

Target learning outcomes The module of Introduction and Basic concepts establishes the fundamental) Efficiency determination (I) Biofuels Management: prevention and protection against explosions Efficiency thermochemistry processes · Mass and energy balances · Efficiency calculation Target learning outcomes

Damm, Werner

14

Modulation Optimization under Energy Constraints  

E-Print Network (OSTI)

We consider radio applications where the nodes operate on batteries so that energy consumption must be minimized while satisfying given throughput and delay requirements. In this context, we analyze the best modulation strategy to minimize the total energy consumption required to send a given number of bits. The total energy consumption includes both the transmission energy and the circuit energy consumption. We show that for both MQAM and MFSK the transmission energy decreases with the product while the circuit energy consumption increases with , where is the modulation bandwidth and the transmission time. Thus, in short-range applications where the circuit energy consumption is nonnegligible compared with the transmission energy, the total energy consumption is minimized by using the maximum system bandwidth along with an optimized transmission time . We derive this optimal for MQAM and MFSK modulation in both AWGN channels and Rayleigh fading channels. Our optimization considers both delay and peak-power constraints. Numerical examples are given, where we exhibit up to 2 energy savings over modulation strategies that minimize the transmission energy alone.

Shuguang Cui Andrea; Andrea J. Goldsmith; Ahmad Bahai

2003-01-01T23:59:59.000Z

15

Assumptions to the Annual Energy Outlook 2002 - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The distinction between the two sets of manufacturing industries pertains to the level of modeling. 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 19). The Industrial Demand Module forecasts energy consumption at the four Census region levels; energy consumption at the Census Division level is allocated

16

Compact magnetic energy storage module  

DOE Patents (OSTI)

A superconducting compact magnetic energy storage module in which a plurality of superconducting toroids, each having a toroidally wound superconducting winding inside a poloidally wound superconducting winding, are stacked so that the flow of electricity in each toroidally wound superconducting winding is in a direction opposite from the direction of electrical flow in other contiguous superconducting toroids. This allows for minimal magnetic pollution outside of the module. 4 figures.

Prueitt, M.L.

1994-12-20T23:59:59.000Z

17

Compact magnetic energy storage module  

DOE Patents (OSTI)

A superconducting compact magnetic energy storage module in which a plurality of superconducting toroids, each having a toroidally wound superconducting winding inside a poloidally wound superconducting winding, are stacked so that the flow of electricity in each toroidally wound superconducting winding is in a direction opposite from the direction of electrical flow in other contiguous superconducting toroids. This allows for minimal magnetic pollution outside of the module.

Prueitt, Melvin L. (Los Alamos, NM)

1994-01-01T23:59:59.000Z

18

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

19

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 ...

20

Village Hydro Technology Module | Open Energy Information  

Open Energy Info (EERE)

Module AgencyCompany Organization: World Bank Sector: Energy Focus Area: Renewable Energy, Hydro Topics: Technology characterizations Website: web.worldbank.orgWBSITE...

Note: This page contains sample records for the topic "module estimates energy" 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

General Renewable Energy Technology Module | Open Energy Information  

Open Energy Info (EERE)

General Renewable Energy Technology Module General Renewable Energy Technology Module Jump to: navigation, search Tool Summary LAUNCH TOOL Name: General Renewable Energy Technology Module Agency/Company /Organization: World Bank Sector: Energy Focus Area: Renewable Energy Topics: Technology characterizations Website: web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTENERGY2/EXTRENENERGYTK/0,, References: General Renewable Energy Technology Module[1] Resource Generation and Transmission Interconnection Process Overview, PJM Manual, Transmission and Interconnection Planning Department, System Planning Division, PJM Interconnection, LLC References ↑ "General Renewable Energy Technology Module" Retrieved from "http://en.openei.org/w/index.php?title=General_Renewable_Energy_Technology_Module&oldid=328701

22

Wind Energy Technology Module | Open Energy Information  

Open Energy Info (EERE)

Wind Energy Technology Module Wind Energy Technology Module Jump to: navigation, search Tool Summary Name: Wind Energy Technology Module Agency/Company /Organization: World Bank Sector: Energy Focus Area: Renewable Energy, Wind Topics: Background analysis, Technology characterizations Website: web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTENERGY2/EXTRENENERGYTK/0,, Country: Russia, China Eastern Europe, Eastern Asia Coordinates: 54.5283298°, 112.9648819° 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":54.5283298,"lon":112.9648819,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

23

Photovoltaic module energy rating procedure. Final subcontract report  

DOE Green Energy (OSTI)

This document describes testing and computation procedures used to generate a photovoltaic Module Energy Rating (MER). The MER consists of 10 estimates of the amount of energy a single module of a particular type (make and model) will produce in one day. Module energy values are calculated for each of five different sets of weather conditions (defined by location and date) and two load types. Because reproduction of these exact testing conditions in the field or laboratory is not feasible, limited testing and modeling procedures and assumptions are specified.

Whitaker, C.M.; Newmiller, J.D. [Endecon Engineering (United States)

1998-01-01T23:59:59.000Z

24

Assumptions to the Annual Energy Outlook 2002 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module 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

25

Assumptions to the Annual Energy Outlook 2001 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module 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

26

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.

27

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.

28

International Energy Module of the National Energy Modeling System ...  

U.S. Energy Information Administration (EIA)

International Energy Module of the National Energy Modeling System Model Documentation 2012 November 2012 . Independent Statistics & Analysis . www.eia.gov

29

Estimating Renewable Energy Costs | Department of Energy  

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

Estimating Renewable Energy Costs Estimating Renewable Energy Costs Estimating Renewable Energy Costs October 16, 2013 - 4:40pm Addthis Some renewable energy measures, such as daylighting, passive solar heating, and cooling load avoidance, do not add much to the cost of a building. However, renewable energy technologies typically require large, additional capital investments with savings accruing over the project's life. It is crucial that these systems are considered early on in the budgeting process. Early budget requests need to include a set of technologies that could be used to meet the project's design requirements and their associated implementation costs. The design team may respond with a different set of feasible technologies, but it is wise to have an existing placeholder in the budget. Federal agencies can continue to update the budget as decisions

30

2007 Estimated International Energy Flows  

Science Conference Proceedings (OSTI)

An energy flow chart or 'atlas' for 136 countries has been constructed from data maintained by the International Energy Agency (IEA) and estimates of energy use patterns for the year 2007. Approximately 490 exajoules (460 quadrillion BTU) of primary energy are used in aggregate by these countries each year. While the basic structure of the energy system is consistent from country to country, patterns of resource use and consumption vary. Energy can be visualized as it flows from resources (i.e. coal, petroleum, natural gas) through transformations such as electricity generation to end uses (i.e. residential, commercial, industrial, transportation). These flow patterns are visualized in this atlas of 136 country-level energy flow charts.

Smith, C A; Belles, R D; Simon, A J

2011-03-10T23:59:59.000Z

31

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

32

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

33

Assumptions to the Annual Energy Outlook 2000 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

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. The Residential Demand Module also requires projections of available equipment 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.

34

Assumptions to the Annual Energy Outlook 1999 - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

residential.gif (5487 bytes) residential.gif (5487 bytes) 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. The Residential Demand Module also requires projections of available equipment 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.

35

Assumptions to the Annual Energy Outlook 2000 - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The distinction between the two sets of manufacturing industries pertains to the level of modeling. The energy-intensive industries are modeled through the use of a detailed process flow accounting procedure, whereas the nonenergy-intensive and the nonmanufacturing industries are modeled with substantially less detail (Table 14). The Industrial Demand Module forecasts energy consumption at the four Census region levels; energy consumption at the Census Division level is allocated by using the SEDS24 data.

36

Assumptions to the Annual Energy Outlook 2001 - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Comleted Copy in PDF Format Comleted Copy in PDF Format Related Links Annual Energy Outlook 2001 Supplemental Data to the AEO 2001 NEMS Conference To Forecasting Home Page EIA Homepage Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The distinction between the two sets of manufacturing industries pertains to the level of modeling. 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 19). The

37

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.

38

State energy data report 1994: Consumption estimates  

Science Conference Proceedings (OSTI)

This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA`s energy models. Division is made for each energy type and end use sector. Nuclear electric power is included.

NONE

1996-10-01T23:59:59.000Z

39

Table CT1. Energy Consumption Estimates for Major Energy Sources ...  

U.S. Energy Information Administration (EIA)

R A D O. U.S. Energy Information Administration State Energy Data 2011: Consumption 89 Table CT6. Industrial Sector Energy Consumption Estimates, Selected Years, 1960 ...

40

Table CT1. Energy Consumption Estimates for Major Energy ...  

U.S. Energy Information Administration (EIA)

U.S. Energy Information Administration State Energy Data 2011: Consumption 365 Table CT2. Primary Energy Consumption Estimates, Selected Years, 1960-2011, North ...

Note: This page contains sample records for the topic "module estimates energy" 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

Building Energy Software Tools Directory: Energy Estimation Software with  

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

Energy Estimation Software with Carbon Footprint Calculation Energy Estimation Software with Carbon Footprint Calculation Energy Estimation Software with Carbon Footprint Calculation logo. Energy Estimation Software for Fan and Pumping Applications estimates energy savings achieved when using a variable frequency drive instead of conventional control methods for fan and pumping applications. The results can be viewed in graphical format and text format and the software has built-in functions to generate an energy estimation report especially designed for consultants. The energy estimator software is available in two editions: Single System Edition This edition allows users to estimate energy savings for a single fan or pump system and generate a multi-page report based on the estimated savings. Project Edition This edition allows users to estimate energy savings of a single fan or

42

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

43

Assumptions to the Annual Energy Outlook 2000 - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

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(99), (Washington, DC, February 1999).

44

State energy data report 1993: Consumption estimates  

SciTech Connect

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public; and (2) to provide the historical series necessary for EIA`s energy models.

NONE

1995-07-01T23:59:59.000Z

45

State Energy Data Report, 1991: Consumption estimates  

DOE Green Energy (OSTI)

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to the Government, policy makers, and the public; and (2) to provide the historical series necessary for EIA`s energy models.

Not Available

1993-05-01T23:59:59.000Z

46

Modules for estimating solid waste from fossil-fuel technologies  

SciTech Connect

Solid waste has become a subject of increasing concern to energy industries for several reasons. Increasingly stringent air and water pollution regulations result in a larger fraction of residuals in the form of solid wastes. Control technologies, particularly flue gas desulfurization, can multiply the amount of waste. With the renewed emphasis on coal utilization and the likelihood of oil shale development, increased amounts of solid waste will be produced. In the past, solid waste residuals used for environmental assessment have tended only to include total quantities generated. To look at environmental impacts, however, data on the composition of the solid wastes are required. Computer modules for calculating the quantities and composition of solid waste from major fossil fuel technologies were therefore developed and are described in this report. Six modules have been produced covering physical coal cleaning, conventional coal combustion with flue gas desulfurization, atmospheric fluidized-bed combustion, coal gasification using the Lurgi process, coal liquefaction using the SRC-II process, and oil shale retorting. Total quantities of each solid waste stream are computed together with the major components and a number of trace elements and radionuclides.

Crowther, M.A.; Thode, H.C. Jr.; Morris, S.C.

1980-10-01T23:59:59.000Z

47

State energy data report 1995 - consumption estimates  

Science Conference Proceedings (OSTI)

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining SEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public, and (2) to provide the historical series necessary for EIA`s energy models.

NONE

1997-12-01T23:59:59.000Z

48

Interruption Cost Estimate Calculator | Open Energy Information  

Open Energy Info (EERE)

Interruption Cost Estimate Calculator Interruption Cost Estimate Calculator Jump to: navigation, search Tool Summary Name: Interruption Cost Estimate (ICE) Calculator Agency/Company /Organization: Freeman, Sullivan & Co. Sector: Energy Focus Area: Grid Assessment and Integration, Energy Efficiency Resource Type: Online calculator, Software/modeling tools User Interface: Website Website: icecalculator.com/ Country: United States Cost: Free Northern America References: [1] Logo: Interruption Cost Estimate (ICE) Calculator This calculator is a tool designed for electric reliability planners at utilities, government organizations or other entities that are interested in estimating interruption costs and/or the benefits associated with reliability improvements. About The Interruption Cost Estimate (ICE) Calculator is an electric reliability

49

Estimating Appliance and Home Electronic Energy Use | Department of Energy  

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

Estimating Appliance and Home Electronic Energy Use Estimating Appliance and Home Electronic Energy Use Estimating Appliance and Home Electronic Energy Use November 11, 2013 - 4:23pm Addthis Estimate the energy consumption and cost to operate an appliance when making a purchase. Investing in an energy-efficient product may save you money in the long run. | Photo courtesy of iStockphoto.com/wh1600. Estimate the energy consumption and cost to operate an appliance when making a purchase. Investing in an energy-efficient product may save you money in the long run. | Photo courtesy of iStockphoto.com/wh1600. If you're trying to decide whether to invest in a more energy-efficient appliance or you'd like to determine your electricity loads, you may want to estimate appliance energy consumption. Formula for Estimating Energy Consumption

50

Estimating Appliance and Home Electronic Energy Use | Department of Energy  

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

Estimating Appliance and Home Electronic Energy Use Estimating Appliance and Home Electronic Energy Use Estimating Appliance and Home Electronic Energy Use November 11, 2013 - 4:23pm Addthis Estimate the energy consumption and cost to operate an appliance when making a purchase. Investing in an energy-efficient product may save you money in the long run. | Photo courtesy of iStockphoto.com/wh1600. Estimate the energy consumption and cost to operate an appliance when making a purchase. Investing in an energy-efficient product may save you money in the long run. | Photo courtesy of iStockphoto.com/wh1600. If you're trying to decide whether to invest in a more energy-efficient appliance or you'd like to determine your electricity loads, you may want to estimate appliance energy consumption. Formula for Estimating Energy Consumption

51

Model documentation coal market module of the National Energy Modeling System  

SciTech Connect

This report documents the approaches used in developing the Annual Energy Outlook 1995 (AEO95). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of the coal market module`s three submodules. These are the Coal Production Submodule (CPS), the Coal Export Submodule (CES), the Coal Expert Submodule (CES), and the Coal Distribution Submodule (CDS).

1995-03-01T23:59:59.000Z

52

Residential Demand Module of the National Energy Modeling ...  

U.S. Energy Information Administration (EIA)

Residential Demand Module of the National Energy Modeling System: Model Documentation 2013 November 2013 Independent Statistics & Analysis ...

53

Integrating Module of the National Energy Modeling System ...  

U.S. Energy Information Administration (EIA)

Chapter 3 describes the NEMS global data structure, used for inter-module communication, ... technologies, representations of renewable energy technologies, ...

54

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

55

Coal Market Module of the National Energy Modeling System ...  

U.S. Energy Information Administration (EIA)

Coal Market Module of the National Energy Modeling System Model Documentation 2013 June 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy

56

State energy data report 1996: Consumption estimates  

Science Conference Proceedings (OSTI)

The State Energy Data Report (SEDR) provides annual time series estimates of State-level energy consumption by major economic sectors. The estimates are developed in the Combined State Energy Data System (CSEDS), which is maintained and operated by the Energy Information Administration (EIA). The goal in maintaining CSEDS is to create historical time series of energy consumption by State that are defined as consistently as possible over time and across sectors. CSEDS exists for two principal reasons: (1) to provide State energy consumption estimates to Members of Congress, Federal and State agencies, and the general public and (2) to provide the historical series necessary for EIA`s energy models. To the degree possible, energy consumption has been assigned to five sectors: residential, commercial, industrial, transportation, and electric utility sectors. Fuels covered are coal, natural gas, petroleum, nuclear electric power, hydroelectric power, biomass, and other, defined as electric power generated from geothermal, wind, photovoltaic, and solar thermal energy. 322 tabs.

NONE

1999-02-01T23:59:59.000Z

57

Retrofit Energy Savings Estimation Model | Open Energy Information  

Open Energy Info (EERE)

Retrofit Energy Savings Estimation Model Retrofit Energy Savings Estimation Model Jump to: navigation, search Tool Summary Name: Retrofit Energy Savings Estimation Model Agency/Company /Organization: Lawrence Berkeley National Laboratory Sector: Energy Focus Area: Buildings Topics: Resource assessment Resource Type: Software/modeling tools User Interface: Desktop Application Website: btech.lbl.gov/tools/resem/resem.htm Cost: Free Language: English References: Retrofit Energy Savings Estimation Model[1] Logo: Retrofit Energy Savings Estimation Model RESEM, the Retrofit Energy Savings Estimation Model, is a PC-based tool designed to allow Department of Energy (DOE) Institutional Conservation Program (ICP) staff and participants to reliably determine the energy savings directly caused by ICP-supported retrofit measures implemented in a

58

Estimating Appliance and Home Electronic Energy Use | Department of Energy  

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

Appliance and Home Electronic Energy Use Appliance and Home Electronic Energy Use Estimating Appliance and Home Electronic Energy Use November 11, 2013 - 4:23pm Addthis Estimate the energy consumption and cost to operate an appliance when making a purchase. Investing in an energy-efficient product may save you money in the long run. | Photo courtesy of iStockphoto.com/wh1600. Estimate the energy consumption and cost to operate an appliance when making a purchase. Investing in an energy-efficient product may save you money in the long run. | Photo courtesy of iStockphoto.com/wh1600. If you're trying to decide whether to invest in a more energy-efficient appliance or you'd like to determine your electricity loads, you may want to estimate appliance energy consumption. Formula for Estimating Energy Consumption

59

Estimates of US biomass energy consumption 1992  

DOE Green Energy (OSTI)

This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass-derived primary energy used by the US economy. It presents estimates of 1991 and 1992 consumption. The objective of this report is to provide updated estimates of biomass energy consumption for use by Congress, Federal and State agencies, biomass producers and end-use sectors, and the public at large.

Not Available

1994-05-06T23:59:59.000Z

60

SEDS: State Energy Production Estimates  

U.S. Energy Information Administration (EIA)

Exploration and reserves, storage, imports and exports, production, prices, sales. Electricity. ... Production. by state and for the United States; by energy source;

Note: This page contains sample records for the topic "module estimates energy" 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

Improved diagnostic model for estimating wind energy  

DOE Green Energy (OSTI)

Because wind data are available only at scattered locations, a quantitative method is needed to estimate the wind resource at specific sites where wind energy generation may be economically feasible. This report describes a computer model that makes such estimates. The model uses standard weather reports and terrain heights in deriving wind estimates; the method of computation has been changed from what has been used previously. The performance of the current model is compared with that of the earlier version at three sites; estimates of wind energy at four new sites are also presented.

Endlich, R.M.; Lee, J.D.

1983-03-01T23:59:59.000Z

62

State energy data report 1992: Consumption estimates  

SciTech Connect

This is a report of energy consumption by state for the years 1960 to 1992. The report contains summaries of energy consumption for the US and by state, consumption by source, comparisons to other energy use reports, consumption by energy use sector, and describes the estimation methodologies used in the preparation of the report. Some years are not listed specifically although they are included in the summary of data.

Not Available

1994-05-01T23:59:59.000Z

63

Model documentation Coal Market Module of the National Energy Modeling System  

SciTech Connect

This report documents objectives and conceptual and methodological approach used in the development of the National Energy Modeling System (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1996 (AEO96). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s three submodules: Coal Production Submodule, Coal Export Submodule, and Coal Distribution Submodule.

1996-04-30T23:59:59.000Z

64

Model documentation Renewable Fuels Module of the National Energy Modeling System  

DOE Green Energy (OSTI)

This report documents the objectives, analaytical approach and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the 1996 Annual Energy Outlook forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described.

NONE

1996-01-01T23:59:59.000Z

65

Figure 1.6 State-Level Energy Consumption Estimates and Estimated ...  

U.S. Energy Information Administration (EIA)

Figure 1.6 State-Level Energy Consumption Estimates and Estimated Consumption per Capita, 2010 Consumption Consumption per Capita

66

NETL: News Release - Great River Energy Unveils Prototype Module...  

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

August 9, 2005 Great River Energy Unveils Prototype Module Coal Dryer Novel Technology Expected to Improve Marketability and Environmental Performance of High-Moisture Coal...

67

Experimental Observation of Energy Modulation in Electron Beams...  

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

OBSERVATION OF ENERGY MODULATION IN ELECTRON BEAMS PASSING THROUGH TERAHERTZ DIELECTRIC WAKEFIELD STRUCTURES* S. Antipov , C. Jing, P. Schoessow, and A. Kanareykin, Euclid...

68

Commercial Demand Module of the National Energy Modeling System ...  

U.S. Energy Information Administration (EIA)

Commercial Demand Module of the National Energy Modeling System: Model Documentation 2012 November 2012 . Independent Statistics & Analysis . www.eia.gov

69

Integrating Module of the National Energy Modeling System  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2010-06-01T23:59:59.000Z

70

Geothermal Energy: National Estimate for Direct Use  

DOE Green Energy (OSTI)

The purpose of this report is to present the first national estimate of direct geothermal energy use based upon an aggregation of site-specific analyses of all known geothermal resources. The conclusions are: (1) Geothermal energy can make a significant contribution can to the nation's low temperature energy needs and lessen dependence on foreign energy sources. (2) Federal tax incentives and regulatory easement will enhance the development of geothermal energy in the U.S. (3) District heating applications will constitute the major portion of geothermal market penetration. (4) Most development will occur in the western U.S.

None

1980-12-01T23:59:59.000Z

71

Geothermal Energy: National Estimate for Direct Use  

SciTech Connect

The purpose of this report is to present the first national estimate of direct geothermal energy use based upon an aggregation of site-specific analyses of all known geothermal resources. The conclusions are: (1) Geothermal energy can make a significant contribution can to the nation's low temperature energy needs and lessen dependence on foreign energy sources. (2) Federal tax incentives and regulatory easement will enhance the development of geothermal energy in the U.S. (3) District heating applications will constitute the major portion of geothermal market penetration. (4) Most development will occur in the western U.S.

1980-12-01T23:59:59.000Z

72

Model documentation report: Residential sector demand module of the national energy modeling system  

SciTech Connect

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This reference document provides a detailed description for energy analysts, other users, and the public. The NEMS Residential Sector Demand Module is currently used for mid-term forecasting purposes and energy policy analysis over the forecast horizon of 1993 through 2020. The model generates forecasts of energy demand for the residential sector by service, fuel, and Census Division. Policy impacts resulting from new technologies, market incentives, and regulatory changes can be estimated using the module. 26 refs., 6 figs., 5 tabs.

NONE

1998-01-01T23:59:59.000Z

73

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.

74

Energy Basics: Flat-Plate Photovoltaic Modules  

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

PV module must have a high transmission in the wavelengths that can be used by the solar cells in the module. For example, for silicon solar cells, the top surface must have...

75

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

76

CHP Emissions Reduction Estimator | Open Energy Information  

Open Energy Info (EERE)

CHP Emissions Reduction Estimator CHP Emissions Reduction Estimator Jump to: navigation, search Tool Summary LAUNCH TOOL Name: CHP Emissions Reduction Estimator Agency/Company /Organization: United States Environmental Protection Agency Sector: Energy Focus Area: Buildings, Transportation, Industry Topics: GHG inventory, Co-benefits assessment Resource Type: Software/modeling tools User Interface: Spreadsheet Website: www.epa.gov/chp/basic/calculator.html Country: United States UN Region: Northern America CHP Emissions Reduction Estimator Screenshot References: http://www.epa.gov/chp/basic/calculator.html "This Emissions Estimator provides the amount of reduced emissions in terms of pounds of CO2, SO2, and NOX based on input from the User regarding the CHP technology being used. In turn the User will be provided with

77

Estimated United States Transportation Energy Use 2005  

DOE Green Energy (OSTI)

A flow chart depicting energy flow in the transportation sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 31,000 trillion British Thermal Units (trBTUs) of energy were used throughout the United States in transportation activities. Vehicles used in these activities include automobiles, motorcycles, trucks, buses, airplanes, rail, and ships. The transportation sector is powered primarily by petroleum-derived fuels (gasoline, diesel and jet fuel). Biomass-derived fuels, electricity and natural gas-derived fuels are also used. The flow patterns represent a comprehensive systems view of energy used within the transportation sector.

Smith, C A; Simon, A J; Belles, R D

2011-11-09T23:59:59.000Z

78

Definition: PV module | Open Energy Information  

Open Energy Info (EERE)

Definition Definition Edit with form History Facebook icon Twitter icon » Definition: PV module Jump to: navigation, search Dictionary.png PV module A unit comprised of several PV cells, and the principal unit of a PV array; it is intended to generate direct current power under un-concentrated sunlight.[1][2] View on Wikipedia Wikipedia Definition A solar panel is a set of solar photovoltaic modules electrically connected and mounted on a supporting structure. A photovoltaic module is a packaged, connected assembly of photovoltaic cells. The solar module can be used as a component of a larger photovoltaic system to generate and supply electricity in commercial and residential applications. Each module is rated by its DC output power under standard test conditions (STC), and

79

Estimating Marginal Residential Energy Prices in the Analysis...  

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

Marginal Residential Energy Prices in the Analysis of Proposed Appliance Energy Efficiency Standards Title Estimating Marginal Residential Energy Prices in the Analysis of Proposed...

80

Assumptions to the Annual Energy Outlook 2001 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. 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

Note: This page contains sample records for the topic "module estimates energy" 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

Assumptions to the Annual Energy Outlook 2002 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. 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

82

Utility Savings Estimators | Building Energy Codes Program  

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

the Utility Savings Estimators: Commercial Estimator | Residential Estimator (These *.zip files contain the Microsoft Excel macro-enabled (*.xlsm) estimator files. You will...

83

SHARP Physics Modules Updated | Department of Energy  

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

Physics Modules Updated Physics Modules Updated SHARP Physics Modules Updated January 29, 2013 - 12:37pm Addthis PROTEUS Development The SHARP neutronics module, PROTEUS, includes neutron and gamma transport solvers, cross-section processing tools, and tools for depletion and fuel cycle analysis. Efforts in the second quarter focused on three major priorities: multi-physics integration, intermediate-fidelity tool development, and demonstrations of applicability. Integration of the second-order, discrete ordinates (Sn method) solver of PROTEUS with the latest version of the MOAB framework (which represents and evaluates mesh data) was initiated to enable its use for multi-physics analysis. With these updates, PROTEUS can obtain the mesh specification from the MOAB framework and store its data on the MOAB mesh representation so that MOAB

84

Energy Basics: Flat-Plate Photovoltaic Modules  

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

Energy Basics Renewable Energy Printable Version Share this resource Biomass Geothermal Hydrogen Hydropower Ocean Solar Photovoltaics Cells Systems Concentrating Solar...

85

Federal Energy Management Program: Estimate and Analyze Greenhouse...  

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

Estimate and Analyze Greenhouse Gas Mitigation Strategy Implementation Costs to someone by E-mail Share Federal Energy Management Program: Estimate and Analyze Greenhouse Gas...

86

Selective chemical detection by energy modulation of sensors  

DOE Patents (OSTI)

A portable instrument for use in the field in detecting, identifying, and quantifying a component of a sampled fluid includes a sensor which chemically reacts with the component of interest or a derivative thereof, an electrical heating filament for heating the sample before it is applied to the sensor, and modulating means for continuously varying the temperature of the filament (and hence the reaction rate) between two values sufficient to produce the chemical reaction. In response to this thermal modulation, the sensor produces a modulated output signal, the modulation of which is a function of the activation energy of the chemical reaction, which activation energy is specific to the particular component of interest and its concentration. Microprocessor means compares the modulated output signal with standard responses for a plurality of components to identify and quantify the particular component of interest. 4 figs.

Stetter, J.R.; Otagawa, T.

1985-05-20T23:59:59.000Z

87

A MOOS MODULE FOR MONITORING ENERGY USAGE OF AUTONOMOUS VEHICLES  

E-Print Network (OSTI)

A MOOS MODULE FOR MONITORING ENERGY USAGE OF AUTONOMOUS VEHICLES Anthony Kanago, Kevin Roos, James--Tracking the energy usage of an autonomous underwater vehicle (AUV) and making accurate data available provides especially effectively in energy-aware systems, allowing inspection vehicles (which typically travel farther

Idaho, University of

88

Engineering a thermal squeezed reservoir by system energy-modulation  

E-Print Network (OSTI)

We show that a thermal reservoir can effectively act as a squeezed reservoir on atoms that are subject to energy-level modulation. For sufficiently fast and strong modulation, for which the rotating-wave-approximation is broken, the resulting squeezing persists at long times. These effects are analyzed by a master equation that is valid beyond the rotating wave approximation. As an example we consider a two-level-atom in a cavity with Lorentzian linewidth, subject to sinusoidal energy modulation. A possible realization of these effects is discussed for Rydberg atoms.

Shahmoon, Ephraim

2013-01-01T23:59:59.000Z

89

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,

90

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,

91

Direct Torque Control Based on Space Vector Modulation with Adaptive Neural Integrator for Stator Flux Estimation in Induction Motors  

Science Conference Proceedings (OSTI)

Direct torque control based on space vector modulation (SVM-DTC) preserve DTC transient merits, furthermore, produce better quality steady-state performance in a wide speed range. A new adaptive neural integration algorithm for estimating stator flux ... Keywords: DTC, space vector modulation, adaptive neural integrator, stator flux estimation

Chunhua Zang; Xianqing Cao

2009-08-01T23:59:59.000Z

92

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.

93

Assumptions to the Annual Energy Outlook 2002 - Electricity Market Module  

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 2002, DOE/EIA- M068(2002) January 2002. 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

94

Assumptions to the Annual Energy Outlook 2001 - Electricity Market Module  

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 2001, DOE/EIA- M068(2001) January 2001. 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

95

Estimating Risk to California Energy Infrastructure From Projected...  

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

Risk to California Energy Infrastructure From Projected Climate Change Title Estimating Risk to California Energy Infrastructure From Projected Climate Change Publication Type...

96

Table F28: Wind Energy Consumption Estimates, 2011  

U.S. Energy Information Administration (EIA)

Table F28: Wind Energy Consumption Estimates, 2011 State Commercial Industrial Electric Power Total Commercial Industrial Electric Power Total

97

Table 2.1d Industrial Sector Energy Consumption Estimates ...  

U.S. Energy Information Administration (EIA)

Table 2.1d Industrial Sector Energy Consumption Estimates, 1949-2011 (Trillion Btu) Year: Primary Consumption 1: Electricity

98

Table 2.1e Transportation Sector Energy Consumption Estimates ...  

U.S. Energy Information Administration (EIA)

Table 2.1e Transportation Sector Energy Consumption Estimates, 1949-2011 (Trillion Btu) Year: Primary Consumption 1: Electricity

99

Integrated energy analysis of error correcting codes and modulation for energy efficient wireless sensor nodes  

Science Conference Proceedings (OSTI)

Optimizing energy consumption is a key objective in designing wireless sensor nodes. It has been shown earlier [1] that the node energy is strongly influenced by the modulation and the error correcting code (ECC) used. The utility of using ECC from an ... Keywords: block codes, communication systems, energy management, modulation, reed-solomon codes

Sonali Chouhan; Ranjan Bose; M. Balakrishnan

2009-10-01T23:59:59.000Z

100

Swarm intelligence approaches to estimate electricity energy demand in Turkey  

Science Conference Proceedings (OSTI)

This paper proposes two new models based on artificial bee colony (ABC) and particle swarm optimization (PSO) techniques to estimate electricity energy demand in Turkey. ABC and PSO electricity energy estimation models (ABCEE and PSOEE) are developed ... Keywords: Ant colony optimization, Artificial bee colony, Electricity energy estimation, Particle swarm optimization, Swarm intelligence

Mustafa Servet K?Ran; Eren Zceylan; Mesut GNdZ; Turan Paksoy

2012-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Selective chemical detection by energy modulation of sensors  

DOE Patents (OSTI)

A portable instrument for use in the field in detecting, identifying, and quantifying a component of a sampled fluid includes a sensor which chemically reacts with the component of interest or a derivative thereof, an electrical heating filament for heating the sample before it is applied to the sensor, and modulator for continuously varying the temperature of the filament (and hence the reaction rate) between two values sufficient to produce the chemical reaction. In response to this thermal modulation, the sensor produces a modulated output signal, the modulation of which is a function of the activation energy of the chemical reaction, which activation energy is specific to the particular component of interest and its concentration. Microprocessor which compares the modulated output signal with standard responses for a plurality of components to identify and quantify the particular component of interest. In particular, the concentration of the component of interest is proportional to the amplitude of the modulated output signal, while the identifying activation output energy of the chemical interaction indicative of that component is proportional to a normalized parameter equal to the peak-to-peak amplitude divided by the height of the upper peaks above a base line signal level. 5 figures.

Stetter, J.R.; Otagawa, T.

1991-09-10T23:59:59.000Z

102

Green Modulations in Energy-Constrained Wireless Sensor Networks  

E-Print Network (OSTI)

Due to the unique characteristics of sensor devices, finding the energy-efficient modulation with a low-complexity implementation (refereed to as green modulation) poses significant challenges in the physical layer design of Wireless Sensor Networks (WSNs). Toward this goal, we present an in-depth analysis on the energy efficiency of various modulation schemes using realistic models in the IEEE 802.15.4 standard to find the optimum distance-based scheme in a WSN over Rayleigh and Rician fading channels with path-loss. We describe a proactive system model according to a flexible duty-cycling mechanism utilized in practical sensor apparatus. The present analysis includes the effect of the channel bandwidth and the active mode duration on the energy consumption of popular modulation designs. Path-loss exponent and DC-DC converter efficiency are also taken into consideration. In considering the energy efficiency and complexity, it is demonstrated that among various sinusoidal carrier-based modulations, the optimi...

Abouei, Jamshid; Pasupathy, Subbarayan

2010-01-01T23:59:59.000Z

103

ESTIMATING CONSUMER BEHAVIOUR IN AN ENERGY-ECONOMY POLICY MODEL  

E-Print Network (OSTI)

ESTIMATING CONSUMER BEHAVIOUR IN AN ENERGY-ECONOMY POLICY MODEL by Dale Beugin B.A.Sc., University-economy models; Calibration; Consumer behaviour; Uncertainty; Energy policy Subject Terms: Energy policy Degree: Master of Resource Management Title of Thesis: Estimating Consumer Behaviour in an Energy

104

Additional Resources for Estimating Building Energy and Cost Savings to  

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

Additional Resources for Estimating Building Energy and Cost Additional Resources for Estimating Building Energy and Cost Savings to Reduce Greenhouse Gases Additional Resources for Estimating Building Energy and Cost Savings to Reduce Greenhouse Gases October 7, 2013 - 11:06am Addthis For evaluating greenhouse gas reduction strategies and estimating costs, the following information resources can help Federal agencies estimate energy and cost savings potential by building type. When deciding what resource to use for developing energy- and cost-savings estimates, a program should consider items detailed in Table 1. Table 1.Resources for Estimating Energy Savings Resource Items to consider Advanced Energy Retrofit Guides Based on representative building models of commercial buildings. Guidance available for a limited number of building types using the most common technologies.

105

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.

106

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

107

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

108

Assumptions to the Annual Energy Outlook 1999 - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

commercial.gif (5196 bytes) commercial.gif (5196 bytes) The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2020. 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

109

A Buildings Module for the Stochastic Energy Deployment System  

SciTech Connect

The U.S. Department of Energy (USDOE) is building a new long-range (to 2050) forecasting model for use in budgetary and management applications called the Stochastic Energy Deployment System (SEDS), which explicitly incorporates uncertainty through its development within the Analytica(R) platform of Lumina Decision Systems. SEDS is designed to be a fast running (a few minutes), user-friendly model that analysts can readily run and modify in its entirety through a visual programming interface. Lawrence Berkeley National Laboratory is responsible for implementing the SEDS Buildings Module. The initial Lite version of the module is complete and integrated with a shared code library for modeling demand-side technology choice developed by the National Renewable Energy Laboratory (NREL) and Lumina. The module covers both commercial and residential buildings at the U.S. national level using an econometric forecast of floorspace requirement and a model of building stock turnover as the basis for forecasting overall demand for building services. Although the module is fundamentally an engineering-economic model with technology adoption decisions based on cost and energy performance characteristics of competing technologies, it differs from standard energy forecasting models by including considerations of passive building systems, interactions between technologies (such as internal heat gains), and on-site power generation.

Lacommare, Kristina S H; Marnay, Chris; Stadler, Michael; Borgeson, Sam; Coffey, Brian; Komiyama, Ryoichi; Lai, Judy

2008-05-15T23:59:59.000Z

110

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

111

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

112

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.

113

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)

114

Design Considerations for a Universal Smart Energy Module for Energy Harvesting in Wireless  

E-Print Network (OSTI)

;Harvester AC/DC Rectification Harvester Source Switching Energy Source Switching Voltage Regulation Node Voltage Regulation Other Load Energy Buffer Energy Storage Backup Battery Harvester AC/DC RectificationUniversalEnergy to energy efficient smaller types. The module supports active AC/DC rectification and maximum power point

Turau, Volker

115

Model documentation renewable fuels module of the National Energy Modeling System  

Science Conference Proceedings (OSTI)

This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the 1995 Annual Energy Outlook (AEO95) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. The RFM consists of six analytical submodules that represent each of the major renewable energy resources--wood, municipal solid waste (MSW), solar energy, wind energy, geothermal energy, and alcohol fuels. The RFM also reads in hydroelectric facility capacities and capacity factors from a data file for use by the NEMS Electricity Market Module (EMM). The purpose of the RFM is to define the technological, cost and resource size characteristics of renewable energy technologies. These characteristics are used to compute a levelized cost to be competed against other similarly derived costs from other energy sources and technologies. The competition of these energy sources over the NEMS time horizon determines the market penetration of these renewable energy technologies. The characteristics include available energy capacity, capital costs, fixed operating costs, variable operating costs, capacity factor, heat rate, construction lead time, and fuel product price.

NONE

1995-06-01T23:59:59.000Z

116

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).

117

Breaking the energy-bandwidth limit of electro-optic modulators: theory and a device proposal  

E-Print Network (OSTI)

In this paper, we quantitatively analyzed the trade-off between energy per bit for switching and modulation bandwidth of classical electro-optic modulators. A formally simple energy-bandwidth limit (Eq. 10) is derived for electro-optic modulators based on intra-cavity index modulation. To overcome this limit, we propose a dual cavity modulator device which uses a coupling modulation scheme operating at high bandwidth (> 200 GHz) not limited by cavity photon lifetime and simultaneously features an ultra-low switching energy of 0.26 aJ, representing over three orders of magnitude energy consumption reduction compared to state-of-the-art electro-optic modulators.

Lin, Hongtao; Liu, Jifeng; Zhang, Lin; Michel, Jurgen; Hu, Juejun

2013-01-01T23:59:59.000Z

118

Industrial Demand Module 1999, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

1999-01-01T23:59:59.000Z

119

Industrial Demand Module 2005, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2005-05-01T23:59:59.000Z

120

Industrial Demand Module 2006, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2006-07-01T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Industrial Demand Module 2009, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2009-05-20T23:59:59.000Z

122

Industrial Demand Module 2003, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2003-12-01T23:59:59.000Z

123

Industrial Demand Module 2007, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2007-03-21T23:59:59.000Z

124

Industrial Demand Module 2002, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2001-12-01T23:59:59.000Z

125

Industrial Demand Module 2001, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2000-12-01T23:59:59.000Z

126

Industrial Demand Module 2008, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. C. Honeycutt

2008-06-01T23:59:59.000Z

127

Industrial Demand Module 2000, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2000-01-01T23:59:59.000Z

128

Industrial Demand Module 2004, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

T. Crawford Honeycutt

2004-02-01T23:59:59.000Z

129

An Estimate of Residential Energy Savings From IECC Change Proposals  

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

An Estimate of Residential Energy Savings From IECC Change Proposals An Estimate of Residential Energy Savings From IECC Change Proposals Recommended for Approval at the ICC's Fall, 2009, Initial Action Hearings The U.S. Department of Energy (DOE) has established ambitious goals to improve the energy efficiency requirements of the International Energy Conservation Code (IECC) for residential buildings. DOE has established near- and long-term goals of 30% and 50% energy efficiency improvements, respectively, compared to the 2006 IECC. This report presents DOE's approach to calculating residential energy consumption for the purpose of estimating energy savings attributable to improvements in the code. This approach is then used to estimate the national average energy savings, relative to the 2006 IECC, resulting from the proposed improvements DOE submitted and supported for the 2012 IECC.

130

Table E1. Estimated Primary Energy Consumption in the United ...  

U.S. Energy Information Administration (EIA)

Table E1. Estimated Primary Energy Consumption in the United States, Selected Years, 1635-1945 (Quadrillion Btu) Year: Fossil Fuels

131

ESTIMATION OF ENERGY SAVINGS RESULTING FROM THE BESTPRACTICES...  

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

27 ESTIMATION OF ENERGY SAVINGS RESULTING FROM THE BESTPRACTICES PROGRAM, FISCAL YEAR 2002 September 2003 Lorena F. Truett Michaela A. Martin Bruce E. Tonn DOCUMENT AVAILABILITY...

132

An Estimate of Residential Energy Savings From IECC Change Proposals...  

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

Development Adoption Compliance Regulations Resource Center An Estimate of Residential Energy Savings From IECC Change Proposals Recommended for Approval at the ICC's Fall,...

133

Model documentation: Renewable Fuels Module of the National Energy Modeling System  

SciTech Connect

This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it related to the production of the 1994 Annual Energy Outlook (AEO94) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. This documentation report serves two purposes. First, it is a reference document for model analysts, model users, and the public interested in the construction and application of the RFM. Second, it meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. The RFM consists of six analytical submodules that represent each of the major renewable energy resources -- wood, municipal solid waste (MSW), solar energy, wind energy, geothermal energy, and alcohol fuels. Of these six, four are documented in the following chapters: municipal solid waste, wind, solar and biofuels. Geothermal and wood are not currently working components of NEMS. The purpose of the RFM is to define the technological and cost characteristics of renewable energy technologies, and to pass these characteristics to other NEMS modules for the determination of mid-term forecasted renewable energy demand.

Not Available

1994-04-01T23:59:59.000Z

134

Estimating the Meridional Energy Transports in the Atmosphere and Ocean  

Science Conference Proceedings (OSTI)

The poleward energy transports in the atmosphereocean system are estimated for the annual mean and the four seasons based on satellite measurements of the net radiation balance at the top of the atmosphere, atmospheric transports of energy at ...

B. C. Carissimo; A. H. Oort; T. H. Vonder Haar

1985-01-01T23:59:59.000Z

135

Estimating Internal Wave Energy Fluxes in the Ocean  

Science Conference Proceedings (OSTI)

Energy flux is a fundamental quantity for understanding internal wave generation, propagation, and dissipation. In this paper, the estimation of internal wave energy fluxes u?p? from ocean observations that may be sparse in either time or depth ...

Jonathan D. Nash; Matthew H. Alford; Eric Kunze

2005-10-01T23:59:59.000Z

136

Estimates of U.S. Biomass Energy Consumption 1992  

Reports and Publications (EIA)

This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass derived primary energy used by the U.S. economy. It presents estimates of 1991 and 1992 consumption.

Fred Mayes

1994-05-01T23:59:59.000Z

137

Table PT1. Energy Production Estimates in Physical Units, New ...  

U.S. Energy Information Administration (EIA)

Energy Production Estimates in Physical Units, New Mexico, 1960 - 2011 1960 295 798,928 107,380 NA 1961 412 789,662 112,553 NA ... Fossil Fuels Renewable Energy

138

Assumptions to the Annual Energy Outlook 2000 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module (RFM) consists of five distinct submodules that represent the major renewable energy technologies. Although it is described here, conventional hydroelectric is included in the Electricity Market Module (EMM) and is not part of the RFM. Similarly, ethanol modeling is included in the Petroleum Market Module (PMM). Some renewables, such as 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 require 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 wind, solar, and geothermal 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.

139

Assumptions to the Annual Energy Outlook 1999 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

renewable.gif (4875 bytes) renewable.gif (4875 bytes) The NEMS Renewable Fuels Module (RFM) consists of five distinct submodules that represent the major renewable energy technologies. Although it is described here, conventional hydroelectric is included in the Electricity Market Module (EMM) and is not part of the RFM. Similarly, ethanol modeling is included in the Petroleum Market Module (PMM). Some renewables, such as 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 require 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 wind, solar, and geothermal energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittence, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

140

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).

Note: This page contains sample records for the topic "module estimates energy" 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

2008 Status Report - Savings Estimates for the ENERGY STAR Voluntary  

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

Status Report - Savings Estimates for the ENERGY STAR Voluntary Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Title 2008 Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Publication Type Journal Article LBNL Report Number LBNL-56380 Year of Publication 2007 Authors Sanchez, Marla C., Carrie A. Webber, Richard E. Brown, and Gregory K. Homan Date Published 11/2007 Publisher Lawrence Berkeley National Laboratory ISBN Number LBNL-56380(2008) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR is a voluntary labeling program designed to identify and promote energy-efficient products, buildings and practices. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), ENERGY STAR includes more than thirty products, spanning office equipment, residential heating and cooling equipment, commercial and residential lighting, home electronics, and major appliances. This report presents savings estimates for ENERGY STAR labeled products. We present estimates of energy, dollar, and carbon savings achieved by the program in the year 2007, what we expect in 2008, and provide savings forecasts for the periods 2008 to 2015 and 2008 to 2025. The forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic ENERGY STAR unit sales for each of the products.

142

State energy data report: Consumption estimates, 1960--1990  

Science Conference Proceedings (OSTI)

The State Energy Data Report (SEDR) provides estimates of energy consumption by major end-use sectors developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). SEDS is a database for estimating the consumption of energy by end-use sectors (residential, commercial, industrial, and transportation) and electric utilities annually by state. The goal in maintaining SEDS is to produce historical data series of estimated end-use consumption by state, defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide state energy consumption estimates to the government, policy makers, and the public; and (2) to provide the historical series necessary for EIA`s energy models.

Not Available

1992-05-15T23:59:59.000Z

143

Estimating the Potential Impact of Renewable Energy on the Caribbean  

E-Print Network (OSTI)

and renewable energy incentive programs available within the territory and highlights the immediate need for specific policy related to VI energy strategy. The development of indigenous sources of clean energy may Estimating the Potential Impact of Renewable Energy on the Caribbean Job Sector Rebekah Shirley

Kammen, Daniel M.

144

Rapid Energy Estimation of Computations on FPGA based Soft Processors  

E-Print Network (OSTI)

such energy performance, we propose a methodology based on instruction level energy profiling. We first, techniques that can quickly and ac- curately obtain the energy dissipation of the software programs executing1 Rapid Energy Estimation of Computations on FPGA based Soft Processors Jingzhao Ou and Viktor K

Hwang, Kai

145

Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System  

Science Conference Proceedings (OSTI)

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. The NEMS Commercial Sector Demand Module is a simulation tool based upon economic and engineering relationships that models commercial sector energy demands at the nine Census Division level of detail for eleven distinct categories of commercial buildings. Commercial equipment selections are performed for the major fuels of electricity, natural gas, and distillate fuel, for the major services of space heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The algorithm also models demand for the minor fuels of residual oil, liquefied petroleum gas, steam coal, motor gasoline, and kerosene, the renewable fuel sources of wood and municipal solid waste, and the minor services of office equipment. Section 2 of this report discusses the purpose of the model, detailing its objectives, primary input and output quantities, and the relationship of the Commercial Module to the other modules of the NEMS system. Section 3 of the report describes the rationale behind the model design, providing insights into further assumptions utilized in the model development process to this point. Section 3 also reviews alternative commercial sector modeling methodologies drawn from existing literature, providing a comparison to the chosen approach. Section 4 details the model structure, using graphics and text to illustrate model flows and key computations.

NONE

1998-01-01T23:59:59.000Z

146

State Energy Price and Expenditure Estimates  

U.S. Energy Information Administration (EIA)

2010 Price and Expenditure Summary Tables. Table E1. Primary Energy, Electricity, ... Ranked by State, 2010 Rank Prices Expenditures Expenditures per Person State

147

Property:EstimatedTime | Open Energy Information  

Open Energy Info (EERE)

EstimatedTime EstimatedTime Jump to: navigation, search Property Name EstimatedTime Property Type Quantity Description An estimate of a particular chronological span, uses the Type:Epoch. Use this type to enumerate a length of time. The default unit is the year. Acceptable units (and their conversions) are: 8766 hours,hour,h,H,Hour,Hours,HOUR,HOURS 365.25 days,day,d,Day,Days,D,DAY,DAYS 52.17857 weeks,week,w,Week,Weeks,W,WEEK,WEEKS 12 months,month,m,Month,Months,M,MONTH,MONTHS 1 years,year,y,Year,Years,Y,YEAR,YEARS Subproperties This property has the following 35 subproperties: G GRR/Elements/14-CA-b.1 - NPDES Permit Application GRR/Elements/14-CA-b.12 - Were all EPA objections resolved GRR/Elements/14-CA-b.13 - NPDES Permit issued GRR/Elements/14-CA-b.3 - Is the application complete for the Regional Water Quality Control Board

148

Macroeconomic Activity Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Macroeconomic Activity Module (MAM) used to develop the Annual Energy Outlook for 2013 (AEO2013). The report catalogues and describes the module assumptions, computations, methodology, parameter estimation techniques, and mainframe source code

2013-04-10T23:59:59.000Z

149

Model documentation report: Residential sector demand module of the National Energy Modeling System  

SciTech Connect

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This document serves three purposes. First, it is a reference document providing a detailed description for energy analysts, other users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports according to Public Law 93-275, section 57(b)(1). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

NONE

1995-03-01T23:59:59.000Z

150

Model documentation report: Residential sector demand module of the National Energy Modeling System  

Science Conference Proceedings (OSTI)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code. This document serves three purposes. First, it is a reference document that provides a detailed description for energy analysts, other users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports according to Public Law 93-275, section 57(b)(1). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

NONE

1997-01-01T23:59:59.000Z

151

Rapid energy estimation of computations on FPGA based soft processors  

E-Print Network (OSTI)

Abstract FPGA based soft processors are an attractive option for implementing embedded applications. As energy efficiency has become a key performance metric, techniques that can quickly and accurately obtain the energy performance of these soft processors are needed. While low-level simulation based on traditional FPGA design flow is too time consuming for obtaining such energy performance, we propose a methodology based on instruction level energy profiling. We first analyze the energy dissipation of various instructions. An energy estimator is built using this information. To illustrate the effectiveness of our approach, the energy performance of several FFT and matrix multiplication software programs running on a state-of-the-art soft processor is evaluated using the estimator. Compared with the results obtained through low-level simulation, an average estimation error of 5.9 % is observed in our experiments. I.

Jingzhao Ou; Viktor K. Prasanna

2004-01-01T23:59:59.000Z

152

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

153

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).

154

Evaluation of an Incremental Ventilation Energy Model for Estimating  

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

Evaluation of an Incremental Ventilation Energy Model for Estimating Evaluation of an Incremental Ventilation Energy Model for Estimating Impacts of Air Sealing and Mechanical Ventilation Title Evaluation of an Incremental Ventilation Energy Model for Estimating Impacts of Air Sealing and Mechanical Ventilation Publication Type Report LBNL Report Number LBNL-5796E Year of Publication 2012 Authors Logue, Jennifer M., William J. N. Turner, Iain S. Walker, and Brett C. Singer Date Published 06/2012 Abstract Changing the rate of airflow through a home affects the annual thermal conditioning energy.Large-scale changes to airflow rates of the housing stock can significantly alter the energy consumption of the residential energy sector. However, the complexity of existing residential energy models hampers the ability to estimate the impact of policy changes on a state or nationwide level. The Incremental Ventilation Energy (IVE) model developed in this study was designed to combine the output of simple airflow models and a limited set of home characteristics to estimate the associated change in energy demand of homes. The IVE model was designed specifically to enable modelers to use existing databases of home characteristics to determine the impact of policy on ventilation at a population scale. In this report, we describe the IVE model and demonstrate that its estimates of energy change are comparable to the estimates of a well-validated, complex residential energy model when applied to homes with limited parameterization. Homes with extensive parameterization would be more accurately characterized by complex residential energy models. The demonstration included a range of home types, climates, and ventilation systems that cover a large fraction of the residential housing sector.

155

Derived Annual Estimates of Manufacturing Energy Consumption, 1974-1988  

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

Manufacturing > Derived Annual Estimates - Executive Summary Manufacturing > Derived Annual Estimates - Executive Summary Derived Annual Estimates of Manufacturing Energy Consumption, 1974-1988 Figure showing Derived Estimates Executive Summary This report presents a complete series of annual estimates of purchased energy used by the manufacturing sector of the U.S. economy, for the years 1974 to 1988. These estimates interpolate over gaps in the actual data collections, by deriving estimates for the missing years 1982-84 and 1986-87. For the purposes of this report, "purchased" energy is energy brought from offsite for use at manufacturing establishments, whether the energy is purchased from an energy vendor or procured from some other source. The actual data on purchased energy comes from two sources, the U.S. Department of Commerce Bureau of the Census's Annual Survey of Manufactures (ASM) and EIA's Manufacturing Energy Consumption Survey (MECS). The ASM provides annual estimates for the years 1974 to 1981. However, in 1982 (and subsequent years) the scope of the ASM energy data was reduced to collect only electricity consumption and expenditures and total expenditures for other purchased energy. In 1985, EIA initiated the triennial MECS collecting complete energy data. The series equivalent to the ASM is referred to in the MECS as "offsite-produced fuels." The completed annual series for 1974 to 1988 developed in this report links the ASM and MECS "offsite" series, estimating for the missing years. Estimates are provided for the manufacturing sector as a whole and at the two-digit Standard Industrial Classification (SIC) level for total energy consumption and for the consumption of individual fuels. There are no direct sources of data for the missing years (1982-1984 and 1986-1987). To derive consumption estimates, a comparison was made between the ASM, MECS, and other economic series to see whether there were any good predictors for the missing data. Various estimation schemes were analyzed to fill in the gaps in data after 1981 by trying to match known data for the 1974 to 1981 period.

156

Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings  

E-Print Network (OSTI)

Estimating Total Energy Consumption and Emissions of Chinasof Chinas total energy consumption mix. However, accuratelyof Chinas total energy consumption, while others estimate

Fridley, David G.

2008-01-01T23:59:59.000Z

157

EIA - Appendix B: Estimation Methodologies of Household Vehicles Energy  

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

If you have trouble viewing this page, contact the National Energy Informaiton Center at (202) 586-8800. Return to Energy Information Administration Home Page If you have trouble viewing this page, contact the National Energy Informaiton Center at (202) 586-8800. Return to Energy Information Administration Home Page EIA Home > Transportation Home Page > Appendix B Estimation MethodologiesIntroduction Appendix B Estimation Methodologies Introduction Statistics concerning vehicle miles traveled (VMT), vehicle fuel efficiency (given in terms of miles per gallon (MPG)), vehicle fuel consumption, and vehicle fuel expenditures are presented in this report. The methodology used to estimate these statistics relied on data from the 1993 Residential Energy Consumption Survey (RECS), the 1994 Residential Transportation Energy Consumption Survey (RTECS), the U.S. Environmental Protection Agency (EPA) fuel efficiency test results, the U.S. Bureau of Labor Statistics (BLS) retail pump price series, and the Lundberg Survey, Inc., price series for 1994.

158

2005 Status Report - Savings Estimates for the ENERGY STAR Voluntary  

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

5 Status Report - Savings Estimates for the ENERGY STAR Voluntary 5 Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Title 2005 Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Publication Type Journal Article LBNL Report Number LBNL-56380 Year of Publication 2007 Authors Sanchez, Marla C., Carrie A. Webber, Richard E. Brown, and Gregory K. Homan Pagination 35 Date Published 03/2006 Publisher Lawrence Berkeley National Laboratory ISBN Number LBNL-56380(2005) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR is a voluntary labeling program designed to identify and promote energy-efficient products, buildings and practices. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), Energy Star labels exist for more than forty products, spanning office equipment, residential heating and cooling equipment, commercial and residential lighting, home electronics, and major appliances. This report presents savings estimates for a subset of ENERGY STAR labeled products. We present estimates of the energy, dollar and carbon savings achieved by the program in the year 2004, what we expect in 2005, and provide savings forecasts for two market penetration scenarios for the periods 2005 to 2010 and 2005 to 2020. The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100 percent market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard efficiency products throughout the analysis period.

159

2007 Status Report - Savings Estimates for the ENERGY STAR Voluntary  

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

7 Status Report - Savings Estimates for the ENERGY STAR Voluntary 7 Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Title 2007 Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Publication Type Journal Article LBNL Report Number LBNL-56380 Year of Publication 2007 Authors Sanchez, Marla C., Carrie A. Webber, Richard E. Brown, and Gregory K. Homan Pagination 38 Date Published 03/2007 Publisher Lawrence Berkeley National Laboratory ISBN Number LBNL-56380(2007) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR® is a voluntary labeling program designed to identify and promote energy-efficient products, buildings and practices. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), ENERGY STAR labels exist for more than thirty products, spanning office equipment, residential heating and cooling equipment, commercial and residential lighting, home electronics, and major appliances. This report presents savings estimates for a subset of ENERGY STAR labeled products. We present estimates of the energy, dollar and carbon savings achieved by the program in the year 2006, what we expect in 2007, and provide savings forecasts for two market penetration scenarios for the periods 2007 to 2015 and 2007 to 2025. The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100 percent market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard efficiency products throughout the analysis period.

160

2006 Status Report - Savings Estimates for the ENERGY STAR Voluntary  

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

6 Status Report - Savings Estimates for the ENERGY STAR Voluntary 6 Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Title 2006 Status Report - Savings Estimates for the ENERGY STAR Voluntary Labeling Program Publication Type Journal Article LBNL Report Number LBNL-56380 Year of Publication 2007 Authors Sanchez, Marla C., Carrie A. Webber, Richard E. Brown, and Gregory K. Homan Pagination 38 Date Published March 2006 Publisher Lawrence Berkeley National Laboratory ISBN Number LBNL-56380(2006) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR® is a voluntary labeling program designed to identify and promote energy-efficient products, buildings and practices. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), ENERGY STAR labels exist for more than thirty products, spanning office equipment, residential heating and cooling equipment, commercial and residential lighting, home electronics, and major appliances. This report presents savings estimates for a subset of ENERGY STAR labeled products. We present estimates of the energy, dollar and carbon savings achieved by the program in the year 2005, what we expect in 2006, and provide savings forecasts for two market penetration scenarios for the periods 2006 to 2015 and 2006 to 2025. The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100 percent market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard efficiency products throughout the analysis period.

Note: This page contains sample records for the topic "module estimates energy" 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

Table PT1. Energy Production Estimates in Physical Units, Michigan ...  

U.S. Energy Information Administration (EIA)

Table PT1. Energy Production Estimates in Physical Units, Michigan, 1960 - 2011 1960 0 20,790 15,899 NA 1961 0 27,697 18,901 NA 1962 0 28,987 17,114 NA

162

Radiative Energy Budget Estimates for the 1979 Southwest Summer Monsoon  

Science Conference Proceedings (OSTI)

Obsemations of temperature moisture, cloud amount, cloud height and soil-derived aerosols are incorporated into radiative transfer models to yield estimates of the tropospheric and surface radiative energy budgets for the summer Monsoon of 1979. ...

Steven A. Ackerman; Stephen K. Cox

1987-10-01T23:59:59.000Z

163

Estimating disaggregated price elasticities in industrial energy demand  

Science Conference Proceedings (OSTI)

Econometric energy models are used to evaluate past policy experiences, assess the impact of future policies and forecast energy demand. This paper estimates an industrial energy demand model for the province of Ontario using a linear-logit specification for fuel type equations which are embedded in an aggregate energy demand equation. Short-term, long-term, own- and cross-price elasticities are estimated for electricity, natural gas, oil and coal. Own- and cross-price elasticities are disaggregated to show that overall price elasticities and the energy-constant price elasticities when aggregate energy use is held unchanged. These disaggregations suggest that a substantial part of energy conservation comes from the higher aggregate price of energy and not from interfuel substitution. 13 refs., 2 tabs.

Elkhafif, M.A.T. (Ontario Ministry of Energy, Toronto (Canada))

1992-01-01T23:59:59.000Z

164

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

Owen Comstock

2012-12-19T23:59:59.000Z

165

Industrial Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Module. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

Kelly Perl

2013-05-14T23:59:59.000Z

166

Industrial Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Module. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code.

Kelly Perl

2013-09-30T23:59:59.000Z

167

Residential Sector Demand Module  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

Owen Comstock

2013-11-05T23:59:59.000Z

168

Flat-Plate Photovoltaic Modules | Department of Energy  

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

Modules Flat-Plate Photovoltaic Modules August 20, 2013 - 4:25pm Addthis Flat-plate photovoltaic (PV) modules are made of several components, including the front surface materials,...

169

Model documentation report: Commercial Sector Demand Module of the National Energy Modeling System  

SciTech Connect

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components. This report serves three purposes. First, it is a reference document providing a detailed description for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports (Public Law 93-275, section 57(b)(1)). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

NONE

1995-02-01T23:59:59.000Z

170

Model documentation renewable fuels module of the National Energy Modeling System  

DOE Green Energy (OSTI)

This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the 1997 Annual Energy Outlook forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs. and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. This documentation report serves three purposes. First, it is a reference document for model analysts, model users, and the public interested in the construction and application of the RFM. Second, it meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in support of its models. Finally, such documentation facilitates continuity in EIA model development by providing information sufficient to perform model enhancements and data updates as part of EIA`s ongoing mission to provide analytical and forecasting information systems.

NONE

1997-04-01T23:59:59.000Z

171

EIA Renewable Energy- Shipments of Photovoltaic Cells and Modules ...  

U.S. Energy Information Administration (EIA)

Renewables and Alternate Fuels > Solar Photovoltaic Cell/Module Annual Report > Annual Shipments of Photovoltaic Cells and Modules by Source: Shipments of ...

172

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

173

Estimating Marginal Residential Energy Prices in the Analysis of Proposed  

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

Marginal Residential Energy Prices in the Analysis of Proposed Marginal Residential Energy Prices in the Analysis of Proposed Appliance Energy Efficiency Standards Title Estimating Marginal Residential Energy Prices in the Analysis of Proposed Appliance Energy Efficiency Standards Publication Type Report LBNL Report Number LBNL-44230 Year of Publication 2000 Authors Chaitkin, Stuart, James E. McMahon, Camilla Dunham Whitehead, Robert D. Van Buskirk, and James D. Lutz Document Number LBNL-44230 Date Published March 1 Publisher Lawrence Berkeley National Laboratory City Berkeley Abstract Use of marginal energy prices, instead of average energy prices, represents a theoretically valuable and challenging refinement to the usual life-cycle cost analysis conducted for proposed appliance energy efficiency standards. LBNL developed a method to estimate marginal residential energy prices using a regression analysis based on a nationally representative sample of actual consumer energy bills. Based on the 1997 Residential Energy Consumption Survey (RECS), national mean marginal electricity prices were estimated to be 2.5% less than average electricity prices in the summer and 10.0% less than average prices in the non-summer months. For natural gas, marginal prices were 4.4% less than average prices in the winter and 15.3% less than average prices in the non-winter months.

174

Econometric Estimation of the Aggregate Impacts of Energy Efficiency  

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

Econometric Estimation of the Aggregate Impacts of Energy Efficiency Econometric Estimation of the Aggregate Impacts of Energy Efficiency Programs: Report on a California Pilot Study Speaker(s): Marvin J. Horowitz Date: February 26, 2013 - 2:00pm Location: 90-3075 Seminar Host/Point of Contact: Alan Sanstad The California Public Utilities Commission (CPUC) recently funded a study in which local area aggregated electricity and natural gas consumption in California from 2006 through 2010 were analyzed econometrically. Data were available to estimate electricity energy efficiency program impacts for two of the three large California investor-owned utilities. The findings indicate that net savings from these programs for the 2006-2010 period amounted to 11,391 GWh, or 7.3 percent of annual electricity consumption. This point estimate is 50 percent higher than the CPUC

175

Order Module--THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) FAMILIAR  

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

Order Module--THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) Order Module--THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) FAMILIAR LEVEL Order Module--THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) FAMILIAR LEVEL The familiar level of this module is divided into two sections. In the first section, we will discuss the purpose of 29 CFR 1910.147 and the terms associated with the standard. In the second section, we will discuss the requirements in the standard. We have provided examples throughout the module to help familiarize you with the material. The examples will also help prepare you for the practice at the end of this module and the criterion test. Most of what you will need to know to complete this module is contained in the module. However, before continuing, you should obtain a copy of the standard. Copies of the standard are available at

176

On spatial estimation of wind energy potential in Malaysia  

Science Conference Proceedings (OSTI)

Statistical distribution for describing the wind speed at a particular location provides information about the wind energy potential which is available. In this paper, five different statistical distributions are fitted to the data of average hourly ... Keywords: inverse distance weighting method, kriging, semivariogram, spatial estimation, wind energy, wind speed distribution

Nurulkamal Masseran; Ahmad Mahir Razali; Kamarulzaman Ibrahim; Wan Zawiah Wan Zin; Azami Zaharim

2011-07-01T23:59:59.000Z

177

A comparison of photovoltaic module performance evaluation methodologies for energy ratings  

DOE Green Energy (OSTI)

The rating of photovoltaic (PV) modules has always been a controversial topic in the PV community. Currently, there is no industry standard methodology to evaluate PV modules for energy production. This issue must be discussed and resolved for the benefit of system planners, utilities, and other consumers. Several methodologies are available to rate a module`s peak power, but do any accurately predict energy output for flat-plate modules? This paper analyzes the energy performance of PV modules using six different energy calculation techniques and compares the results to the measured amount of energy produced. The results indicate which methods are the most effective for predicting energy output in Golden, Colorado, under prevailing meteorological conditions.

Kroposki, B.; Emery, K.; Myers, D.; Mrig, L.

1995-10-01T23:59:59.000Z

178

Energy estimation of peripheral devices in embedded systems  

E-Print Network (OSTI)

This paper introduces a methodology for estimation of energy consumption in peripherals such as audio and video devices. Peripherals can be responsible for significant amount of the energy consumption in current embedded systems. We introduce a cycleaccurate energy simulator and profiler capable of simulating peripheral devices. Our energy estimation tool for peripherals can be useful for hardware and software energy optimization of multimedia applications and device drivers. The simulator and profiler use cycleaccurate energy and performance models for peripheral devices with the cycle-accurate energy and performance models for computing, storage and power devices created in previous work. We also implemented I/O communication protocols such as polling, I/O interrupts and direct memory access (DMA). Using our energy simulator and estimator, we optimized an audio driver for an MP3 (MPEG-2 Layer 3) audio decoder application. Our optimization results show 44 % reduction in the total system energy consumption for the MP3 audio decoder when optimized audio driver is used.

Ozgur Celebican

2004-01-01T23:59:59.000Z

179

Integrating Module of the National Energy Modeling System 2007, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2007-05-23T23:59:59.000Z

180

Integrating Module of the National Energy Modeling System 1995, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

1995-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Integrating Module of the National Energy Modeling System 1997, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

1997-05-01T23:59:59.000Z

182

Integrating Module of the National Energy Modeling System 2004, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2004-02-01T23:59:59.000Z

183

Integrating Module of the National Energy Modeling System 2001, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2000-12-01T23:59:59.000Z

184

Integrating Module of the National Energy Modeling System 2009, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2009-05-01T23:59:59.000Z

185

Integrating Module of the National Energy Modeling System 1999, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

1998-12-01T23:59:59.000Z

186

Integrating Module of the National Energy Modeling System 2000, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

1999-12-01T23:59:59.000Z

187

Integrating Module of the National Energy Modeling System 2008, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2008-08-29T23:59:59.000Z

188

Integrating Module of the National Energy Modeling System 2002, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2001-12-01T23:59:59.000Z

189

Integrating Module of the National Energy Modeling System 2005, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2005-05-01T23:59:59.000Z

190

Integrating Module of the National Energy Modeling System 1996 Model Documentation - NOT PUBLISHED  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

191

Integrating Module of the National Energy Modeling System 2006, Model Documentation  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

Dan Skelly

2006-06-01T23:59:59.000Z

192

Integrating Module of the National Energy Modeling System 1998 Model Documentation - NOT PUBLISHED  

Reports and Publications (EIA)

Provides an overview of the complete National Energy Modeling System (NEMS) model, and includes brief descriptions of the modules with which the Integrating Module interacts. The emphasis and focus, however, is on the structure and function of the Integrating Module of NEMS.

John Maples

2013-09-05T23:59:59.000Z

193

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and 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 SEDS 27 data.

194

Energy savings estimates and cost benefit calculations for high performance relocatable classrooms  

E-Print Network (OSTI)

hybrid incremental cost estimates were developed based onsizing . Final incremental cost estimates ranged from $1,786Energy Savings Estimates and Cost Benefit Calculations for

Rainer, Leo I.; Hoeschele, Marc A.; Apte, Michael G.; Shendell, Derek G.; Fisk, William J.

2003-01-01T23:59:59.000Z

195

Estimate Greenhouse Gas Emissions by Building Type | Department of Energy  

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

Estimate Greenhouse Gas Emissions by Building Type Estimate Greenhouse Gas Emissions by Building Type Estimate Greenhouse Gas Emissions by Building Type October 7, 2013 - 10:51am Addthis YOU ARE HERE Step 2 Starting with the programs contributing the greatest proportion of building greenhouse gas (GHG) emissions, the agency should next determine which building types operated by those programs use the most energy (Figure 1). Energy intensity is evaluated instead of emissions in this approach because programs may not have access to emissions data by building type. Figure 1 - An image of an organizational-type chart. A rectangle labeled 'Program 1' has lines pointing to three other rectangles below it labeled 'Building Type 1,' 'Building Type 2,' and 'Building Type 3.' Next to the building types it says, 'Step 2. Estimate emissions by building type.

196

Industrial Demand Module 1998, National Energy Modeling System (NEMS)  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description ofthe NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirement of the Energy Information Administration (EIA) to provide adequate documentation in supportof its models (Public Law 94-385, section 57.b2). Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

T. Crawford Honeycutt

1998-01-01T23:59:59.000Z

197

Model documentation, Renewable Fuels Module of the National Energy Modeling System  

DOE Green Energy (OSTI)

This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the Annual Energy Outlook 1998 (AEO98) forecasts. The report catalogues and describes modeling assumptions, computational methodologies, data inputs, and parameter estimation techniques. A number of offline analyses used in lieu of RFM modeling components are also described. For AEO98, the RFM was modified in three principal ways, introducing capital cost elasticities of supply for new renewable energy technologies, modifying biomass supply curves, and revising assumptions for use of landfill gas from municipal solid waste (MSW). In addition, the RFM was modified in general to accommodate projections beyond 2015 through 2020. Two supply elasticities were introduced, the first reflecting short-term (annual) cost increases from manufacturing, siting, and installation bottlenecks incurred under conditions of rapid growth, and the second reflecting longer term natural resource, transmission and distribution upgrade, and market limitations increasing costs as more and more of the overall resource is used. Biomass supply curves were also modified, basing forest products supplies on production rather than on inventory, and expanding energy crop estimates to include states west of the Mississippi River using information developed by the Oak Ridge National Laboratory. Finally, for MSW, several assumptions for the use of landfill gas were revised and extended.

NONE

1998-01-01T23:59:59.000Z

198

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are further subdivided 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, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Demand Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Demand Module calculates energy consumption for the four Census Regions (see Figure 5) and disaggregates the energy consumption

199

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

200

Table PT1. Energy Production Estimates in Physical Units, Ohio ...  

U.S. Energy Information Administration (EIA)

Table PT1. Energy Production Estimates in Physical Units, Ohio, 1960 - 2011 1960 33,957 36,074 5,405 NA 1961 32,226 36,423 5,639 NA 1962 34,125 36,747 5,835 NA

Note: This page contains sample records for the topic "module estimates energy" 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

Table PT2. Energy Production Estimates in Trillion Btu, Oklahoma ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, Oklahoma, 1960 - 2011 1960 33.9 902.0 1,118.9 0.0 NA 17.8 17.8 2,072.6 1961 26.1 976.9 1,119.9 0.0 NA 20.2 20 ...

202

Table PT2. Energy Production Estimates in Trillion Btu, California ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, California, 1960 - 2011 1960 0.0 589.7 1,771.0 (s) NA 270.2 270.2 2,630.9 1961 0.0 633.8 1,737.7 0.1 NA 248.2 ...

203

Table PT2. Energy Production Estimates in Trillion Btu, Delaware ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, Delaware, 1960 - 2011 1960 0.0 0.0 0.0 0.0 NA 5.0 5.0 5.0 1961 0.0 0.0 0.0 0.0 NA 5.1 5.1 5.1

204

Table PT2. Energy Production Estimates in Trillion Btu, Texas ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, Texas, 1960 - 2011 1960 26.4 6,610.7 5,379.4 0.0 NA 50.2 50.2 12,066.6 1961 26.5 6,690.2 5,447.3 0.0 NA 52.0 ...

205

Table PT2. Energy Production Estimates in Trillion Btu, Indiana ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, Indiana, 1960 - 2011 1960 346.3 0.3 69.9 0.0 NA 24.6 24.6 441.1 1961 336.7 0.4 66.7 0.0 NA 24.2 24.2 428.0

206

Table PT2. Energy Production Estimates in Trillion Btu, Oregon ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, Oregon, 1960 - 2011 1960 0.0 0.0 0.0 0.0 NA 190.5 190.5 190.5 1961 0.0 0.0 0.0 0.0 NA 188.9 188.9 188.9

207

Table PT2. Energy Production Estimates in Trillion Btu, Arizona ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, Arizona, 1960 - 2011 1960 0.1 0.0 0.4 0.0 NA 36.2 36.2 36.7 1961 0.0 0.0 0.4 0.0 NA 35.1 35.1 35.5

208

Table PT1. Energy Production Estimates in Physical Units, Texas ...  

U.S. Energy Information Administration (EIA)

Table PT1. Energy Production Estimates in Physical Units, Texas, 1960 - 2011 1960 2,098 5,892,704 927,479 NA 1961 2,108 5,963,605 939,191 NA 1962 2,054 6,080,210 ...

209

The estimate of the wind energy potential and insolation  

E-Print Network (OSTI)

The concise letter points out that the estimates of the global potential of wind power exceeds the amount of kinetic energy in the relevant layer of atmosphere by far more than an order of magnitude. Originally submitted to the Letters section of the Proceedings of the National Academy of Sciences in August 2009.

Aoki, Kenichiro

2009-01-01T23:59:59.000Z

210

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

211

Estimating Demand Response Market Potential | Open Energy Information  

Open Energy Info (EERE)

Estimating Demand Response Market Potential Estimating Demand Response Market Potential Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Estimating Demand Response Market Potential Focus Area: Energy Efficiency, - Utility Topics: Socio-Economic Website: www.ieadsm.org/Files/Tasks/Task%20XIII%20-%20Demand%20Response%20Resou Equivalent URI: cleanenergysolutions.org/content/estimating-demand-response-market-pot Language: English Policies: "Deployment Programs,Regulations" is not in the list of possible values (Deployment Programs, Financial Incentives, Regulations) for this property. DeploymentPrograms: Demonstration & Implementation Regulations: Resource Integration Planning This resource presents demand response (DR) potential results from top-performing programs in the United States and Canada, as well as a DR

212

Estimating home energy decision parameters for a hybrid energyYeconomy policy model  

E-Print Network (OSTI)

Estimating home energy decision parameters for a hybrid energyYeconomy policy model Mark JaccardYenvironment policy models To meet the challenge of an energyYenvironment issue such as greenhouse gas (GHG) emission with respect to energy use, and to specific policies aimed at advancing certain technologies over others. While

213

Order Module--THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) FAMILIAR  

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

THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) FAMILIAR LEVEL Order Module--THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) FAMILIAR LEVEL The familiar level of this module is divided into two sections. In the first section, we will discuss the purpose of 29 CFR 1910.147 and the terms associated with the standard. In the second section, we will discuss the requirements in the standard. We have provided examples throughout the module to help familiarize you with the material. The examples will also help prepare you for the practice at the end of this module and the criterion test. Most of what you will need to know to complete this module is contained in the module. However, before continuing, you should obtain a copy of the standard. Copies of the standard are available at

214

Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

This page intentionally left blank This page intentionally left blank 51 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2011 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing 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 procedure, whereas the non- manufacturing industries are modeled with substantially less detail. The petroleum refining industry is not included in the Industrial Module, as it is simulated separately in the Petroleum Market Module of NEMS. The Industrial Module calculates

215

Flat-Plate Photovoltaic Module Basics | Department of Energy  

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

Module Basics Module Basics Flat-Plate Photovoltaic Module Basics August 20, 2013 - 4:25pm Addthis Flat-plate photovoltaic (PV) modules are made of several components, including the front surface materials, encapsulant, rear surface, and frame. Front Surface Materials The front surface of a flat-plate PV module must have a high transmission in the wavelengths that can be used by the solar cells in the module. For example, for silicon solar cells, the top surface must have high transmission of light with wavelengths from 350 to 1200 nm. Also, reflection from the front surface should be minimal. An antireflection coating added to the top surface can greatly reduce the reflection of sunlight, and texturing of the surface can cause light that strikes the surface to stay within the cells. Unfortunately, these textured

216

Integrated Modules for Bioassay (IMBA) | Department of Energy  

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

Integrated Modules for Bioassay (IMBA) Integrated Modules for Bioassay (IMBA) Integrated Modules for Bioassay (IMBA) Current Central Registry Toolbox Version(s): IMBA ExpertTM USDOE Edition version 4.0.28 Code Owner: UK Health Protection Agency (HPA) Description: IMBA ExpertTM (IX) software suite comprises a series of independent modules (referred to as sub-modules) that implement the International Commission on Radiological Protection (ICRP) Publication 66, Human Respiratory Tract Model (HRTM) and the ICRP Publications 30 (series), 67, 68, 69, and 71 biokinetic models. In 2001, the United Kingdom (UK) National Radiological Protection Board (NRPB), whose functions were absorbed later into the UK Health Protection Agency (HPA), and ACJ & Associates Inc., began development of an interface (referred to as a shell) for the IMBA modules. This effort was funded in

217

Low thermal resistance power module assembly - Energy Innovation ...  

A power module assembly (400) with low thermal resistance and enhanced heat dissipation to a cooling medium. The assembly includes a heat sink or spreader plate (410 ...

218

Table 2.1a Energy Consumption Estimates by Sector, Selected Years ...  

U.S. Energy Information Administration (EIA)

40 U.S. Energy Information Administration / Annual Energy Review 2011 Table 2.1a Energy Consumption Estimates by Sector, Selected Years, 1949-2011

219

In-line thermoelectric module - Energy Innovation Portal  

Energy Analysis; Energy Storage; Geothermal; ... TECHNICAL FIELD The present invention relates generally to means for converting thermal energy to ele ...

220

Order Module--NNSA OCCUPATIONAL RADIATION PROTECTION | Department of Energy  

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

Order Module--NNSA OCCUPATIONAL RADIATION PROTECTION Order Module--NNSA OCCUPATIONAL RADIATION PROTECTION Order Module--NNSA OCCUPATIONAL RADIATION PROTECTION The familiar level of this module is designed to provide the basic information to meet the requirements that are related to 10 CFR 835, "Occupational Radiation Protection," in the following DOE Functional Area Qualification Standards: DOE-STD-1177-2004, Emergency Management DOE-STD-1151-2002, Facility Representative DOE-STD-1146-2007, General Technical Base DOE-STD-1138-2007, Industrial Hygiene DOE-STD-1183-2007, Nuclear Safety Specialist DOE-STD-1174-2003, Radiation Protection DOE-STD-1175-2006, Senior Technical Safety Manager DOE-STD-1178-2004, Technical Program Manager DOE-STD-1155-2002, Transportation and Traffic Management DOE Order Self Study Modules - 10 CFR 835 Occupational Radiation Protection

Note: This page contains sample records for the topic "module estimates energy" 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

Estimated United States Residential Energy Use in 2005  

DOE Green Energy (OSTI)

A flow chart depicting energy flow in the residential sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 11,000 trillion British Thermal Units (trBTUs) of electricity and fuels were used throughout the United States residential sector in lighting, electronics, air conditioning, space heating, water heating, washing appliances, cooking appliances, refrigerators, and other appliances. The residential sector is powered mainly by electricity and natural gas. Other fuels used include petroleum products (fuel oil, liquefied petroleum gas and kerosene), biomass (wood), and on-premises solar, wind, and geothermal energy. The flow patterns represent a comprehensive systems view of energy used within the residential sector.

Smith, C A; Johnson, D M; Simon, A J; Belles, R D

2011-12-12T23:59:59.000Z

222

Joint Voltage and Modulation Scaling for Energy Harvesting Sensor Networks Bo Zhang Robert Simon Hakan Aydin  

E-Print Network (OSTI)

@, bv2152@, luca@cs., kinget@ee., johnkym@ee., gil@ee.]columbia.edu ABSTRACT Energy Harvesting Active-low-power ultra- wideband (UWB) communications and in organic semiconductor- based energy harvesting materials with a UWB Transceiver and an energy harvest- ing module (EHM) that allows demonstrating energy harvesting

Aydin, Hakan

223

"Order Module--DOE O 442.1, DEPARTMENT OF ENERGY EMPLOYEE CONCERNS PROGRAM  

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

"Order Module--DOE O 442.1, DEPARTMENT OF ENERGY EMPLOYEE CONCERNS "Order Module--DOE O 442.1, DEPARTMENT OF ENERGY EMPLOYEE CONCERNS PROGRAM "Order Module--DOE O 442.1, DEPARTMENT OF ENERGY EMPLOYEE CONCERNS PROGRAM The familiar level of this module is divided into two sections. In the first section, we will discuss the objectives, requirements, responsibilities, and definitions that are contained in the Order. In the second section, we will discuss the Guide. There is only one level of instruction for this Order. You will not be required to complete a practice for this module. Before continuing, you should obtain a copy of DOE O 442.1, DOE Employee Concerns Program, and its companion guide. Copies of these documents are available on the Office of Management and Administration's Web site at http://www.directives.doe.gov or though the course manager. It is not

224

Standard Review Plan (SRP) Modules | Department of Energy  

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

Quality Assurance » Standard Quality Assurance » Standard Review Plan (SRP) Modules Standard Review Plan (SRP) Modules Standard Review Plan - Critical Decision Handbook Overview Project Management Project Execution Plan Review Module (RM) Risk Management RM Integrated Project Team RM Earned Value Management System RM Acquisition Strategy RM Decommissioning Plan RM Site Transition Guidance Engineering and Design Conceptual Design RM Preliminary Design RM Final Design RM Construction Readiness RM Checkout, Testing, and Commissioning Plan RM Readiness Review RM Seismic Design Expectations Report Technology Readiness Assessment Report External Technical Review Report Preparation for Facility Operations RM Safety Safety Design Strategy RM Conceptual Safety Design RM Preliminary Safety Design RM Facility Disposition Safety Strategy RM

225

Actual and Estimated Energy Savings Comparison for Deep Energy Retrofits in the Pacific Northwest  

Science Conference Proceedings (OSTI)

Seven homes from the Pacific Northwest were selected to evaluate the differences between estimated and actual energy savings achieved from deep energy retrofits. The energy savings resulting from these retrofits were estimated, using energy modeling software, to save at least 30% on a whole-house basis. The modeled pre-retrofit energy use was trued against monthly utility bills. After the retrofits were completed, each of the homes was extensively monitored, with the exception of one home which was monitored pre-retrofit. This work is being conducted by Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy Building Technologies Program as part of the Building America Program. This work found many discrepancies between actual and estimated energy savings and identified the potential causes for the discrepancies. The differences between actual energy use and modeled energy use also suggest improvements to improve model accuracy. The difference between monthly whole-house actual and estimated energy savings ranged from 75% more energy saved than predicted by the model to 16% less energy saved for all the monitored homes. Similarly, the annual energy savings difference was between 36% and -14%, which was estimated based on existing monitored savings because an entire year of data is not available. Thus, on average, for all six monitored homes the actual energy use is consistently less than estimates, indicating home owners are saving more energy than estimated. The average estimated savings for the eight month monitoring period is 43%, compared to an estimated savings average of 31%. Though this average difference is only 12%, the range of inaccuracies found for specific end-uses is far greater and are the values used to directly estimate energy savings from specific retrofits. Specifically, the monthly post-retrofit energy use differences for specific end-uses (i.e., heating, cooling, hot water, appliances, etc.) ranged from 131% under-predicted to 77% over-predicted by the model with respect to monitored energy use. Many of the discrepancies were associated with occupant behavior which influences energy use, dramatically in some cases, actual versus modeled weather differences, modeling input limitations, and complex homes that are difficult to model. The discrepancy between actual and estimated energy use indicates a need for better modeling tools and assumptions. Despite the best efforts of researchers, the estimated energy savings are too inaccurate to determine reliable paybacks for retrofit projects. While the monitored data allows researchers to understand why these differences exist, it is not cost effective to monitor each home with the level of detail presented here. Therefore an appropriate balance between modeling and monitoring must be determined for more widespread application in retrofit programs and the home performance industry. Recommendations to address these deficiencies include: (1) improved tuning process for pre-retrofit energy use, which currently utilized broad-based monthly utility bills; (2) developing simple occupant-based energy models that better address the many different occupant types and their impact on energy use; (3) incorporating actual weather inputs to increase accuracy of the tuning process, which uses utility bills from specific time period; and (4) developing simple, cost-effective monitoring solutions for improved model tuning.

Blanchard, Jeremy; Widder, Sarah H.; Giever, Elisabeth L.; Baechler, Michael C.

2012-10-01T23:59:59.000Z

226

Order Module--RADIATION PROTECTION PROGRAMS GUIDE | Department of Energy  

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

RADIATION PROTECTION PROGRAMS GUIDE RADIATION PROTECTION PROGRAMS GUIDE Order Module--RADIATION PROTECTION PROGRAMS GUIDE The familiar level of this module is designed to provide the basic information related to DOE G 441.1-1C, Radiation Protection Programs Guide, as required in DOE-STD-1174-2003, Radiation Protection Functional Area Qualification Standard, December 2003. Completion of this module also meets certain requirements associated with the DOE Facility Representative Program and the DOE Intern Program. The information contained in this module addresses specific requirements and as such does not include the entire text of the source document. Before continuing, you should obtain a copy of the Order. Copies of the DOE Directives are available at http://www.directives.doe.gov/ or through the course manager. In March

227

A Buildings Module for the Stochastic Energy Deployment System  

E-Print Network (OSTI)

F.W. Dodge 1991: Building Stock Database Methodology andEnd-Use Flow Maps for the Buildings Sector, D.B. Belzer,N ATIONAL L ABORATORY A Buildings Module for the Stochastic

Marnay, Chris

2008-01-01T23:59:59.000Z

228

A Buildings Module for the Stochastic Energy Deployment System  

E-Print Network (OSTI)

indicators from the Energy Intensity Indicators. See alsoIndicators from the Energy Intensity Indicators, http://and residential energy intensity indicators, which in turn

Marnay, Chris

2008-01-01T23:59:59.000Z

229

Current-Induced Modulation of the Ocean Wave Spectrum and the Role of Nonlinear Energy Transfer  

Science Conference Proceedings (OSTI)

Numerical simulations were performed to investigate current-induced modulation of the spectral and statistical properties of ocean waves advected by idealized and realistic current fields. In particular, the role of nonlinear energy transfer ...

Hitoshi Tamura; Takuji Waseda; Yasumasa Miyazawa; Kosei Komatsu

2008-12-01T23:59:59.000Z

230

Dynamical systems in nanophotonics: From energy efficient modulators to light forces and optomechanics  

E-Print Network (OSTI)

We demonstrate novel device concepts based on rigorous design of the dynamics of resonant nanophotonic systems, such as dispersionless resonant switches and energy-efficient mo-dulator architectures, slow-light cells, and ...

Kaertner, Franz X.

231

Coal Market Module  

Reports and Publications (EIA)

Documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System's (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 2013 (AEO2013). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM's two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS).

Michael Mellish

2013-07-17T23:59:59.000Z

232

Final Design And Manufacturing of the PEP II High Energy Ring Arc Bellows Module  

SciTech Connect

A novel RF shield bellows module developed at SLAC has been successfully manufactured and installed in the PEP-II High Energy Ring (HER). Tests indicate that the module meets its performance and operational requirements. The primary function of the bellows module is to allow for thermal expansion of the chambers and for lateral, longitudinal and angular offsets due to tolerances and alignment, while providing RF continuity between adjoining chambers. An update on the Arc bellows module for the PEP-II High Energy Ring is presented. Final design, manufacturing issues, material and coating selection, and tribological and RF testing are discussed. Performance and operational requirements are also reviewed. The RF shield design has been proven during assembly to allow for large manufacturing tolerances without reducing the mechanical spring force below required values. In addition, the RF shield maintains electrical contact even with large misalignments across the module.

Kurita, Nadine R.; Kulikov, Artem; /SLAC; Corlett, John; /LBL, Berkeley

2011-09-01T23:59:59.000Z

233

COMPRESSED-AIR ENERGY STORAGE SYSTEMS FOR STAND-ALONE OFF-GRID PHOTOVOLTAIC MODULES  

E-Print Network (OSTI)

-storage materials, flywheels, pumped hydro (PH), superconducting magnetic energy storage (SMES) and compressed airCOMPRESSED-AIR ENERGY STORAGE SYSTEMS FOR STAND-ALONE OFF-GRID PHOTOVOLTAIC MODULES Dominique, USA ABSTRACT In this work, a low-cost, low-volume, low-maintenance, small-scale compressed-air energy

Deymier, Pierre

234

EK 131/132 module: Introduction to Wind Energy MW 3-5  

E-Print Network (OSTI)

EK 131/132 module: Introduction to Wind Energy MW 3-5 Course. This course provides an overview of wind turbine technology and energy concepts. The question of whether wind. Students will measure personal energy use and analyze wind turbine data from the Museum of Science's wind

235

CORONAL MASS EJECTION MASS, ENERGY, AND FORCE ESTIMATES USING STEREO  

Science Conference Proceedings (OSTI)

Understanding coronal mass ejection (CME) energetics and dynamics has been a long-standing problem, and although previous observational estimates have been made, such studies have been hindered by large uncertainties in CME mass. Here, the two vantage points of the Solar Terrestrial Relations Observatory (STEREO) COR1 and COR2 coronagraphs were used to accurately estimate the mass of the 2008 December 12 CME. Acceleration estimates derived from the position of the CME front in three dimensions were combined with the mass estimates to calculate the magnitude of the kinetic energy and driving force at different stages of the CME evolution. The CME asymptotically approaches a mass of 3.4 {+-} 1.0 Multiplication-Sign 10{sup 15} g beyond {approx}10 R{sub Sun }. The kinetic energy shows an initial rise toward 6.3 {+-} 3.7 Multiplication-Sign 10{sup 29} erg at {approx}3 R{sub Sun }, beyond which it rises steadily to 4.2 {+-} 2.5 Multiplication-Sign 10{sup 30} erg at {approx}18 R{sub Sun }. The dynamics are described by an early phase of strong acceleration, dominated by a force of peak magnitude of 3.4 {+-} 2.2 Multiplication-Sign 10{sup 14} N at {approx}3 R{sub Sun }, after which a force of 3.8 {+-} 5.4 Multiplication-Sign 10{sup 13} N takes effect between {approx}7 and 18 R{sub Sun }. These results are consistent with magnetic (Lorentz) forces acting at heliocentric distances of {approx}Sun }, while solar wind drag forces dominate at larger distances ({approx}>7 R{sub Sun }).

Carley, Eoin P.; Gallagher, Peter T. [Astrophysics Research Group, School of Physics, Trinity College Dublin, Dublin 2 (Ireland); McAteer, R. T. James [Department of Astronomy, New Mexico State University, Las Cruces, NM 88003-8001 (United States)

2012-06-10T23:59:59.000Z

236

GAO Cost Estimating and Assessment Guide | Department of Energy  

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

GAO Cost Estimating and Assessment Guide GAO Cost Estimating and Assessment Guide GAO 12-Step Estimating Process.pdf More Documents & Publications EIR SOP Septmebr 2010 Microsoft...

237

Average estimate for additive energy in prime field  

E-Print Network (OSTI)

Assume that $A\\subseteq \\Fp, B\\subseteq \\Fp^{*}$, $\\1/4\\leqslant\\frac{|B|}{|A|},$ $|A|=p^{\\alpha}, |B|=p^{\\beta}$. We will prove that for $p\\geqslant p_0(\\beta)$ one has $$\\sum_{b\\in B}E_{+}(A, bA)\\leqslant 15 p^{-\\frac{\\min\\{\\beta, 1-\\alpha\\}}{308}}|A|^3|B|.$$ Here $E_{+}(A, bA)$ is an additive energy between subset $A$ and it's multiplicative shift $bA$. This improves previously known estimates of this type.

Glibichuk, Alexey

2011-01-01T23:59:59.000Z

238

NREL-How to Estimate the Economic Impacts from Renewable Energy Webinar |  

Open Energy Info (EERE)

NREL-How to Estimate the Economic Impacts from Renewable Energy Webinar NREL-How to Estimate the Economic Impacts from Renewable Energy Webinar (Redirected from How to Estimate the Economic Impacts from Renewable Energy) Jump to: navigation, search Tool Summary LAUNCH TOOL Name: How to Estimate the Economic Impacts from Renewable Energy Agency/Company /Organization: National Renewable Energy Laboratory Sector: Energy Topics: Co-benefits assessment Resource Type: Webinar, Training materials Website: www.nrel.gov/applying_technologies/state_local_activities/webinar_2009 How to Estimate the Economic Impacts from Renewable Energy Screenshot References: How to Estimate the Economic Impacts from Renewable Energy[1] Logo: How to Estimate the Economic Impacts from Renewable Energy Sponsored by the U.S. Department of Energy Technical Assistance Project for

239

Estimating the Cost and Energy Efficiency of a Solar Water Heater...  

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

Estimating the Cost and Energy Efficiency of a Solar Water Heater Estimating the Cost and Energy Efficiency of a Solar Water Heater May 30, 2012 - 3:09pm Addthis Solar water...

240

Model documentation, Coal Market Module of the National Energy Modeling System  

Science Conference Proceedings (OSTI)

This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The international area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.

NONE

1998-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Estimates of Renewable Energy Capacity Serving U.S. Green Power...  

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

Estimates of Renewable Energy Capacity Serving U.S. Green Power Markets (as of December 2004) Lori Bird and Blair Swezey National Renewable Energy Laboratory September 2005 This...

242

A Buildings Module for the Stochastic Energy Deployment System  

E-Print Network (OSTI)

that SEDS does not address. Energy history may have turned aEnergy Deployment System (SEDS), which follows in a long history

Marnay, Chris

2008-01-01T23:59:59.000Z

243

Integrating Module of the National Energy Modeling System (INT)  

U.S. Energy Information Administration (EIA)

The National Energy Modeling System (NEMS) represents a general equilibrium solution of the interactions between the U.S. energy markets and the economy.

244

Estimating Energy Efficiency Impacts Using Climate Wise "Wise Rules"  

E-Print Network (OSTI)

Climate Wise is an industrial energy efficiency program sponsored by the U.S. EPA, and supported by the U.S. DOE, working in partnership with more than 400 industrial companies, representing approximately than 11 percent of U.S. industrial energy use. Climate Wise provides technical assistance in the form of efficiency check-lists, handbooks, and one-on-one support through a toll-free Wise Line to help partners identify efficiency measures and quantify project impacts. Climate Wise has developed the Wise Rules for Industrial Efficiency (Wise Rules Tool Kit) to provide partners with ''Wise Rules" for estimating potential energy, cost, and greenhouse gas emissions savings from key industrial energy efficiency measures. The Tool Kit includes information on the following end-uses: boilers, steam systems, furnaces, process heating, waste heat recovery, cogeneration, compressed air systems, and process cooling. This paper provides an overview of the Wise Rules Tool Kit and presents excerpts from the document and sample Wise Rules.

Milmoe, P. H.; Winkelman, S. R.

1998-04-01T23:59:59.000Z

245

Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

2 2 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 housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type

246

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

247

Commercial Demand Module of the National Energy Modeling ...  

U.S. Energy Information Administration (EIA)

Commercial Buildings Energy Consumption Survey ... space cooling, water heating, ventilation, cooking, refrigeration, and lighting. The market segment ...

248

NREL-How to Estimate the Economic Impacts from Renewable Energy Webinar |  

Open Energy Info (EERE)

NREL-How to Estimate the Economic Impacts from Renewable Energy Webinar NREL-How to Estimate the Economic Impacts from Renewable Energy Webinar Jump to: navigation, search Tool Summary LAUNCH TOOL Name: How to Estimate the Economic Impacts from Renewable Energy Agency/Company /Organization: National Renewable Energy Laboratory Sector: Energy Topics: Co-benefits assessment Resource Type: Webinar, Training materials Website: www.nrel.gov/applying_technologies/state_local_activities/webinar_2009 How to Estimate the Economic Impacts from Renewable Energy Screenshot References: How to Estimate the Economic Impacts from Renewable Energy[1] Logo: How to Estimate the Economic Impacts from Renewable Energy Sponsored by the U.S. Department of Energy Technical Assistance Project for state and local officials, this Webinar featured information for

249

Estimated Rare Earth Reserves and Deposits | Department of Energy  

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

Department of Energy Facilities Department of Energy Facilities Recovery Act Smart Grid Projects Recovery Act Smart Grid Projects 2009 Energy Expenditure Per Person 2009 Energy...

250

A conceptual framework to energy estimation in buildings using agent based modeling  

Science Conference Proceedings (OSTI)

Actual energy consumption in buildings is typically different from predictions during the design phase. While differences in occupant energy usage characteristics play an important role in this variation, actual energy estimation software do not account ...

Elie Azar; Carol Menassa

2010-12-01T23:59:59.000Z

251

Coal Market Module of the National Energy Modeling System Model ...  

U.S. Energy Information Administration (EIA)

Appendix 3.E. Optimization and Modeling Library (OML) ... Energy Outlook 2002 Projections of Coal Production, Distribution, and Prices for the National

252

Order Module--U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: GETTING  

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

Order Module--U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: Order Module--U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: GETTING STARTED Order Module--U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: GETTING STARTED This course was developed using the Criterion Referenced Instruction (CRI) method of training. That means the course contains only the information you need to perform your job. You will be shown the learning objectives at the beginning of the course. If you think you can demonstrate competency without additional instruction, you may complete the practice at any time. When you complete all of the practices successfully, you may ask the course manager for the criterion test. The familiar level requires that you understand and remember the material. The general level requires that you understand the applicability of the material. If you are unsure of the

253

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.

254

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.

255

ESTIMATING RISK TO CALIFORNIA ENERGY INFRASTRUCTURE FROM PROJECTED CLIMATE CHANGE  

E-Print Network (OSTI)

1), S57S73. CEC (California Energy Commission). 2009. GISGuido Franco. CEC (California Energy Commission). 2009. GISJacque Gilbreath. CEC (California Energy Commission). 2009.

Sathaye, Jayant

2011-01-01T23:59:59.000Z

256

Estimating energy-augmenting technological change in developing country industries  

E-Print Network (OSTI)

trend due to the constant energy price bias assumption. ThisIndian industries, Energy price bias (standard error)industries, 19801997 Energy price bias (standard error)

Sanstad, Alan H.; Roy, Joyashree; Sathaye, Jayant A.

2006-01-01T23:59:59.000Z

257

Energy Savings Estimates of Light Emitting Diodes in Niche Lighting...  

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

in Niche Lighting Applications Prepared for: Building Technologies Program Office of Energy Efficiency and Renewable Energy U.S. Department of Energy Prepared by: Navigant...

258

Integrating Module of the National Energy Modeling System: Model Documentation  

E-Print Network (OSTI)

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.

Www. Eia. Gov

2012-01-01T23:59:59.000Z

259

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2012-11-15T23:59:59.000Z

260

Commercial Sector Demand Module  

Reports and Publications (EIA)

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

Kevin Jarzomski

2013-10-10T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Regional Earth-Atmosphere Energy Balance Estimates Based on Assimilations with a GCM  

Science Conference Proceedings (OSTI)

The column budget technique described by Oort and Vonder Haar (1976) is used to assess the physical consistency and accuracy of estimates of the earth-atmosphere energy balance. Regional estimates of the atmospheric budget terms, the net ...

Michael A. Alexander; Siegfried D. Schubert

1990-01-01T23:59:59.000Z

262

Estimation of Energy Baseline by Simulation for On-going Commissioning and Energy Saving Retrofit  

E-Print Network (OSTI)

This paper proposes a method of estimating the adjusted energy baseline using simulation models, which can calculate the energy baseline with various conditions, such as conditions of weather, occupancy and equipment operations. Especially, this paper reveals what detailed data the calibration of the model needs and the change of accuracy caused by different calibration data. Using the operational data of a middle-scale office building in Osaka Japan, the simulation accuracies of three models, which are calibrated using monthly energy consumptions of whole building (Level 1), monthly energy consumptions of subsystems (Level 2) and the detailed operational data of equipments (Level 3) respectively, are compared. The result shows that the differences of daily-integrated energy consumptions between measured value and simulated value using the model of Level 1 and 2 are not much different. The model of Level 3 is about 3% more accurate than the model of Level 1 and 2.

Miyata, M.; Yoshida, H.; Asada, M.; Iwata, T.; Tanabe, Y.; Yanagisawa, T.

2006-01-01T23:59:59.000Z

263

Photon energy-modulated radiotherapy: Monte Carlo simulation and treatment planning study  

SciTech Connect

Purpose: To demonstrate the feasibility of photon energy-modulated radiotherapy during beam-on time. Methods: A cylindrical device made of aluminum was conceptually proposed as an energy modulator. The frame of the device was connected with 20 tubes through which mercury could be injected or drained to adjust the thickness of mercury along the beam axis. In Monte Carlo (MC) simulations, a flattening filter of 6 or 10 MV linac was replaced with the device. The thickness of mercury inside the device varied from 0 to 40 mm at the field sizes of 5 x 5 cm{sup 2} (FS5), 10 x 10 cm{sup 2} (FS10), and 20 x 20 cm{sup 2} (FS20). At least 5 billion histories were followed for each simulation to create phase space files at 100 cm source to surface distance (SSD). In-water beam data were acquired by additional MC simulations using the above phase space files. A treatment planning system (TPS) was commissioned to generate a virtual machine using the MC-generated beam data. Intensity modulated radiation therapy (IMRT) plans for six clinical cases were generated using conventional 6 MV, 6 MV flattening filter free, and energy-modulated photon beams of the virtual machine. Results: As increasing the thickness of mercury, Percentage depth doses (PDD) of modulated 6 and 10 MV after the depth of dose maximum were continuously increased. The amount of PDD increase at the depth of 10 and 20 cm for modulated 6 MV was 4.8% and 5.2% at FS5, 3.9% and 5.0% at FS10 and 3.2%-4.9% at FS20 as increasing the thickness of mercury from 0 to 20 mm. The same for modulated 10 MV was 4.5% and 5.0% at FS5, 3.8% and 4.7% at FS10 and 4.1% and 4.8% at FS20 as increasing the thickness of mercury from 0 to 25 mm. The outputs of modulated 6 MV with 20 mm mercury and of modulated 10 MV with 25 mm mercury were reduced into 30%, and 56% of conventional linac, respectively. The energy-modulated IMRT plans had less integral doses than 6 MV IMRT or 6 MV flattening filter free plans for tumors located in the periphery while maintaining the similar quality of target coverage, homogeneity, and conformity. Conclusions: The MC study for the designed energy modulator demonstrated the feasibility of energy-modulated photon beams available during beam-on time. The planning study showed an advantage of energy-and intensity modulated radiotherapy in terms of integral dose without sacrificing any quality of IMRT plan.

Park, Jong Min; Kim, Jung-in; Heon Choi, Chang; Chie, Eui Kyu; Kim, Il Han; Ye, Sung-Joon [Interdiciplinary Program in Radiation Applied Life Science, Seoul National University, Seoul, 110-744, Korea and Department of Radiation Oncology, Seoul National University Hospital, Seoul, 110-744 (Korea, Republic of); Interdiciplinary Program in Radiation Applied Life Science, Seoul National University, Seoul, 110-744 (Korea, Republic of); Department of Radiation Oncology, Seoul National University Hospital, Seoul, 110-744 (Korea, Republic of); Interdiciplinary Program in Radiation Applied Life Science, Seoul National University, Seoul, 110-744 (Korea, Republic of) and Department of Radiation Oncology, Seoul National University Hospital, Seoul, 110-744 (Korea, Republic of); Interdiciplinary Program in Radiation Applied Life Science, Seoul National University, Seoul, 110-744 (Korea, Republic of); Department of Radiation Oncology, Seoul National University Hospital, Seoul, 110-744 (Korea, Republic of) and Department of Intelligent Convergence Systems, Seoul National University, Seoul, 151-742 (Korea, Republic of)

2012-03-15T23:59:59.000Z

264

EIA Report Estimates Growth of U.S. Energy Economy Through 2040 |  

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

EIA Report Estimates Growth of U.S. Energy Economy Through 2040 EIA Report Estimates Growth of U.S. Energy Economy Through 2040 EIA Report Estimates Growth of U.S. Energy Economy Through 2040 December 5, 2012 - 3:43pm Addthis EIA Report Estimates Growth of U.S. Energy Economy Through 2040 Matthew Loveless Matthew Loveless Data Integration Specialist, Office of Public Affairs What are the key facts? Crude oil, natural gas and renewable energy production are expected to grow rapidly. Net energy imports are expected to decline, as production grows faster than consumption. Editor's Note: This article was originally posted as part of the Energy Information Administration's (EIA) Today in Energy series. EIA has just issued its Annual Energy Outlook 2013 (AEO2013) Reference case, which highlights a growth in total U.S. energy production that

265

Residential Sector Demand Module 2000, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

1999-12-01T23:59:59.000Z

266

Residential Sector Demand Module 2004, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2004-02-01T23:59:59.000Z

267

Residential Sector Demand Module 2001, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2000-12-01T23:59:59.000Z

268

Residential Sector Demand Module 2002, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2001-12-01T23:59:59.000Z

269

Residential Sector Demand Module 2005, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2005-04-01T23:59:59.000Z

270

Residential Sector Demand Module 2003, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2003-01-01T23:59:59.000Z

271

Residential Sector Demand Module 2008, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2008-10-10T23:59:59.000Z

272

Residential Sector Demand Module 2006, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2006-03-01T23:59:59.000Z

273

Residential Sector Demand Module 2009, Model Documentation  

Reports and Publications (EIA)

Model Documentation - Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2009-05-01T23:59:59.000Z

274

Residential Sector Demand Module 2007, Model Documentation  

Reports and Publications (EIA)

Documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Residential Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, and FORTRAN source code.

John H. Cymbalsky

2007-04-26T23:59:59.000Z

275

PART 2. MATHEMATICAL MODELS IN POLLUTION CHAPTER V. MATHEMATICAL MODELS TO ESTIMATE THE ENERGY -  

E-Print Network (OSTI)

methodology to estimate the energy ­ ecologic efficiency of thermopower plants (TPP) Presently, the analysis impact point of view and also of the efficiency of the electrical and/or thermal energy producedPART 2. MATHEMATICAL MODELS IN POLLUTION CHAPTER V. MATHEMATICAL MODELS TO ESTIMATE THE ENERGY

Baica, Malvina

276

Theory of binless multi-state free energy estimation with applications to protein-ligand binding  

E-Print Network (OSTI)

Theory of binless multi-state free energy estimation with applications to protein-ligand binding Method (WHAM) is routinely used for com- puting free energies and expectations from multiple ensembles method, like WHAM, can be used not only to estimate free energies and equilibrium expectations, but also

277

Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation  

E-Print Network (OSTI)

Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation a technique for approximating the free energy of protein structures using Generalized Belief Propagation (GBP, we show that the entropy component of our free energy estimates can useful in distinguishing native

Xing, Eric P.

278

Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation  

E-Print Network (OSTI)

Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation a technique for approximating the free energy of protein structures using Generalized Belief Propagation (GBP, we show that the entropy compo- nent of our free energy estimates can be useful in distinguishing

Langmead, Christopher James

279

A POSTERIORI ESTIMATES FOR THE CAHN--HILLIARD EQUATION WITH OBSTACLE FREE ENERGY  

E-Print Network (OSTI)

A POSTERIORI ESTIMATES FOR THE CAHN--HILLIARD EQUATION WITH OBSTACLE FREE ENERGY L'UBOMÍR BA#AS 1 of the standard Cahn--Hilliard equation with a double obstacle free energy. The derived estimates are robust and e algorithm. Keywords: Cahn--Hilliard equation, obstacle free energy, linear finite elements, a posteriori

Banas, Lubomir

280

INVENTORY OF SOLAR RADIATION/SOLAR ENERGY SYSTEMS ESTIMATORS, MODELS, SITE-SPECIFIC DATA, AND PUBLICATIONS  

E-Print Network (OSTI)

INVENTORY OF SOLAR RADIATION/SOLAR ENERGY SYSTEMS ESTIMATORS, MODELS, SITE-SPECIFIC DATA, and Buildings Systems Integration Center National Renewable Energy Laboratory 8 July 2009 SOLAR SYSTEM POTENTIAL/calculators/PVWATTS/version1/ http://rredc.nrel.gov/solar/calculators/PVWATTS/version2/ Estimates the electrical energy

Note: This page contains sample records for the topic "module estimates energy" 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

Uncertainties of estimates of inertio-gravity energy in the atmosphere  

E-Print Network (OSTI)

Uncertainties of estimates of inertio-gravity energy in the atmosphere N. Zagar, J. Tribbia, J percentage of energy contained in balanced (Rossby) and inertio- gravity (IG) motions. Estimate energy)analysis Unclear how large part of the global atmospheric energetics pertains to the divergent motion i.e. inertio-gravity

Zagar, Nedjeljka

282

Estimation of wind characteristics at potential wind energy conversion sites  

DOE Green Energy (OSTI)

A practical method has been developed and applied to the problem of determining wind characteristics at candidate wind energy conversion sites where there are no available historical data. The method uses a mass consistent wind flow model (called COMPLEX) to interpolate between stations where wind data are available. The COMPLEX model incorporates the effects of terrain features and airflow. The key to the practical application of COMPLEX to the derivation of wind statistics is the model's linearity. This allows the input data sets to be resolved into orthogonal components along the set of eigenvectors of the covariance matrix. The solution for each eigenvector is determined with COMPLEX; the hourly interpolated winds are then formed from linear combinations of these solutions. The procedure requires: acquisition and merger of wind data from three to five stations, application of COMPLEX to each of the seven to 11 (depending on the number of stations for which wind data are available) eigenvectors, reconstruction of the hourly interpolated winds at the site from the eigenvector solutions, and finally, estimating the wind characteristics from the simulated hourly values. The report describes the methodology and the underlying theory. Possible improvements to the procedure are also discussed.

Not Available

1979-10-01T23:59:59.000Z

283

Estimation of wind characteristics at potential wind energy conversion sites  

SciTech Connect

A practical method has been developed and applied to the problem of determining wind characteristics at candidate wind energy conversion sites where there are no available historical data. The method uses a mass consistent wind flow model (called COMPLEX) to interpolate between stations where wind data are available. The COMPLEX model incorporates the effects of terrain features and airflow. The key to the practical application of COMPLEX to the derivation of wind statistics is the model's linearity. This allows the input data sets to be resolved into orthogonal components along the set of eigenvectors of the covariance matrix. The solution for each eigenvector is determined with COMPLEX; the hourly interpolated winds are then formed from linear combinations of these solutions. The procedure requires: acquisition and merger of wind data from three to five stations, application of COMPLEX to each of the seven to 11 (depending on the number of stations for which wind data are available) eigenvectors, reconstruction of the hourly interpolated winds at the site from the eigenvector solutions, and finally, estimating the wind characteristics from the simulated hourly values. The report describes the methodology and the underlying theory. Possible improvements to the procedure are also discussed.

1979-10-01T23:59:59.000Z

284

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

285

In-Situ Measurement of Crystalline Silicon Modules Undergoing Potential-Induced Degradation in Damp Heat Stress Testing for Estimation of Low-Light Power Performance  

DOE Green Energy (OSTI)

The extent of potential-induced degradation of crystalline silicon modules in an environmental chamber is estimated using in-situ dark I-V measurements and applying superposition analysis. The dark I-V curves are shown to correctly give the module power performance at 200, 600 and 1,000 W/m2 irradiance conditions, as verified with a solar simulator. The onset of degradation measured in low light in relation to that under one sun irradiance can be clearly seen in the module design examined; the time to 5% relative degradation measured in low light (200 W/m2) was 28% less than that of full sun (1,000 W/m2 irradiance). The power of modules undergoing potential-induced degradation can therefore be characterized in the chamber, facilitating statistical analyses and lifetime forecasting.

Hacke, P.; Terwilliger, K.; Kurtz, S.

2013-08-01T23:59:59.000Z

286

Energy Savings Estimates of Light Emitting Diodes in Niche Lighting Applications  

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

Savings Estimates of Light Emitting Diodes Savings Estimates of Light Emitting Diodes in Niche Lighting Applications Prepared for: Building Technologies Program Office of Energy Efficiency and Renewable Energy U.S. Department of Energy Prepared by: Navigant Consulting Inc. 1801 K Street, NW Suite 500 Washington DC, 20006 September 2008 * Department of Energy Washington, DC 20585 Energy Savings Estimates of Light Emitting Diodes in Niche Lighting Applications Released: September 2008 Revised: October 2008 This DOE report presents research findings for twelve different niche markets where LEDs are competing or poised to compete with traditional light sources (e.g., incandescent and fluorescent). Estimates of the energy saved due to current levels of LED market penetration as well as estimates of potential energy savings if these markets switched completely to LEDs

287

Methodology and Estimation of the Welfare Impact of Energy Reforms on Households in Azerbaijan.  

E-Print Network (OSTI)

??ABSTRACT Title of Dissertation: METHODOLOGY AND ESTIMATION OF THE WELFARE IMPACT OF ENERGY REFORMS ON HOUSEHOLDS IN AZERBAIJAN Irina Klytchnikova, Doctor of Philosophy, 2006 Dissertation (more)

Klytchnikova, Irina

2006-01-01T23:59:59.000Z

288

Table 1.3 Primary Energy Consumption Estimates by Source, 1949 ...  

U.S. Energy Information Administration (EIA)

Table 1.3 Primary Energy Consumption Estimates by Source, 1949-2011 (Quadrillion Btu) Year: Fossil Fuels: Nuclear Electric Power

289

A Bottom-Up Model to Estimate the Energy Efficiency Improvement...  

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

Efficiency Improvement and CO2 Emission Reduction Potentials in the Chinese Iron and Steel Industry Title A Bottom-Up Model to Estimate the Energy Efficiency Improvement and...

290

Indian Country Solar Energy Potential Estimates & DOE IE Updates  

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

Pathways to Sustained Energy Pathways to Sustained Energy Development in Oklahoma Oklahoma Tribal Leader Forum - August 2012 Oklahoma City, Oklahoma 1 Office of Indian Energy Goals and Objectives * Promote Indian tribal energy development, efficiency and use * Reduce or stabilize energy costs * Enhance and strengthen Indian tribal energy and economic infrastructure relating to natural resource development and electrification * Bring electrical power and service to Indian land and the homes of tribal members Energy Policy Act of 2005, Title V, Sec. 502 2 Office of Indian Energy Programs 3 * START (Strategic Technical Assistance Response Teams) - Providing Expert Development Technical Assistance Directly to Tribal Staff/Leaders/Projects - Targeted energy development assistance - post feasibility & pre

291

Estimating energy-augmenting technological change in developing country industries  

E-Print Network (OSTI)

over time is calculated. Second, prices and the energy costTime averages of sectoral productivity and autonomous energy efficiency trend Industry Prices and energy costTime averages (in percent) of sectoral productivity and autonomous energy efficiency trend Prices and energy cost

Sanstad, Alan H.; Roy, Joyashree; Sathaye, Jayant A.

2006-01-01T23:59:59.000Z

292

Estimating wind energy using extrapolated data of Cameron highlands  

Science Conference Proceedings (OSTI)

Wind energy is an alternative clean energy source compared to fossil fuel, which can be harmful and pollutes the layer of the atmosphere. Recently, wind energy is given a lot of attention because of the focus on renewable energy all over the world. Apart ... Keywords: data extrapolation technique, wind energy, wind speed

Siti Khadijah Najid; Ahmad Mahir Razali; Kamaruzaman Ibrahim; Kamaruzzaman Sopian; Azami Zaharim

2011-07-01T23:59:59.000Z

293

Development of an Outdoor Concentrating Photovoltaic Module Testbed, Module Handling and Testing Procedures, and Initial Energy Production Results  

DOE Green Energy (OSTI)

This report addresses the various aspects of setting up a CPV testbed and procedures for handling and testing CPV modules.

Muller, M.

2009-09-01T23:59:59.000Z

294

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).

295

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).

296

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.

297

Solid-state energy storage module employing integrated interconnect board  

DOE Patents (OSTI)

The present invention is directed to an improved electrochemical energy storage device. The electrochemical energy storage device includes a number of solid-state, thin-film electrochemical cells which are selectively interconnected in series or parallel through use of an integrated interconnect board. The interconnect board is typically disposed within a sealed housing which also houses the electrochemical cells, and includes a first contact and a second contact respectively coupled to first and second power terminals of the energy storage device. The interconnect board advantageously provides for selective series or parallel connectivity with the electrochemical cells, irrespective of electrochemical cell position within the housing. In one embodiment, a sheet of conductive material is processed by employing a known milling, stamping, or chemical etching technique to include a connection pattern which provides for flexible and selective interconnecting of individual electrochemical cells within the housing, which may be a hermetically sealed housing. Fuses and various electrical and electro-mechanical devices, such as bypass, equalization, and communication devices for example, may also be mounted to the interconnect board and selectively connected to the electrochemical cells.

Rouillard, Jean (Saint-Luc, CA); Comte, Christophe (Montreal, CA); Daigle, Dominik (St-Hyacinthe, CA); Hagen, Ronald A. (Stillwater, MN); Knudson, Orlin B. (Vadnais Heights, MN); Morin, Andre (Longueuil, CA); Ranger, Michel (Lachine, CA); Ross, Guy (Beloeil, CA); Rouillard, Roger (Beloeil, CA); St-Germain, Philippe (Outremont, CA); Sudano, Anthony (Laval, CA); Turgeon, Thomas A. (Fridley, MN)

2000-01-01T23:59:59.000Z

298

An Observational Estimate of Inferred Ocean Energy Divergence  

Science Conference Proceedings (OSTI)

Monthly net surface energy fluxes (FS) over the oceans are computed as residuals of the atmospheric energy budget using top-of-atmosphere (TOA) net radiation (RT) and the complete atmospheric energy (AE) budget tendency (?AE/?t) and divergence ( ...

Kevin E. Trenberth; John T. Fasullo

2008-05-01T23:59:59.000Z

299

Table PT1. Energy Production Estimates in Physical Units, United ...  

U.S. Energy Information Administration (EIA)

a Beginning in 2001, includes refuse recovery. d Includes denaturant. Estimated using production b Marketed production. ... Coal a Natural Gas b Crude Oil c Fuel ...

300

Estimated Rare Earth Reserves and Deposits | Department of Energy  

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

Non-powered Dams U.S. Hydropower Potential from Existing Non-powered Dams Creating an Energy Innovation Ecosystem Creating an Energy Innovation Ecosystem Sunshot Rooftop Solar...

Note: This page contains sample records for the topic "module estimates energy" 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

Indian Country Solar Energy Potential Estimates & DOE IE Updates  

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

1 inter-tribal org) * Renewable Energy Commercialization - Technical Assistance Program - Solar Communities - Wind Powering America Energy Policy and Planning: Begin at the...

302

2007 Status Report - Savings Estimates for the ENERGY STAR Voluntary...  

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

Date Published 032007 ISBN Number LBNL-56380(2007) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR is a voluntary labeling program designed to identify...

303

2006 Status Report - Savings Estimates for the ENERGY STAR Voluntary...  

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

Published March 2006 ISBN Number LBNL-56380(2006) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR is a voluntary labeling program designed to identify...

304

Accuracy of Estimates of Atmospheric Large-Scale Energy Flux Divergence  

Science Conference Proceedings (OSTI)

A short review of atmospheric energy transport studies is given, and the importance of the Global Weather Experiment for such studies is emphasized. The accuracy of energy flux (divergence) estimates is then discussed, comparing results obtained ...

Eero Holopainen; Carl Fortelius

1986-10-01T23:59:59.000Z

305

Estimating the Energy Use and Efficiency Potential of U.S. Data...  

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

the Energy Use and Efficiency Potential of U.S. Data Centers Title Estimating the Energy Use and Efficiency Potential of U.S. Data Centers Publication Type Conference Paper LBNL...

306

Mandatory Photovoltaic System Cost Estimate | Department of Energy  

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

Mandatory Photovoltaic System Cost Estimate Mandatory Photovoltaic System Cost Estimate Mandatory Photovoltaic System Cost Estimate < Back Eligibility Utility Savings Category Solar Buying & Making Electricity Program Info State Colorado Program Type Line Extension Analysis Provider Colorado Public Utilities Commission At the request of a customer or a potential customer, Colorado electric utilities are required to conduct a cost comparison of a photovoltaic (PV) system to any proposed distribution line extension if the customer or potential customer provides the utility with load data (estimated monthly kilowatt-hour usage) requested by the utility to conduct the comparison, and if the customer's or potential customer's peak demand is estimated to be less than 25 kilowatts (kW). In performing the comparison analysis, the

307

State Energy Data System Consumption Estimates Technical Notes  

U.S. Energy Information Administration (EIA)

as street lighting and public services; and the Manufacturing Energy Consumption Survey covers only manufacturing establishments,

308

Japan's Long-term Energy Demand and Supply Scenario to 2050 - Estimation for the Potential of Massive CO2 Mitigation  

E-Print Network (OSTI)

of tons oil equivalent (Mtoe) 700 i Sources : Estimates byas nuclear, oil will be the most important energy sourcenuclear energy, oil will be the most important energy source

Komiyama, Ryoichi

2010-01-01T23:59:59.000Z

309

State energy data report: Consumption estimates, 1960--1990. [Contains glossary  

Science Conference Proceedings (OSTI)

The State Energy Data Report (SEDR) provides estimates of energy consumption by major end-use sectors developed in the State Energy Data System (SEDS), which is maintained and operated by the Energy Information Administration (EIA). SEDS is a database for estimating the consumption of energy by end-use sectors (residential, commercial, industrial, and transportation) and electric utilities annually by state. The goal in maintaining SEDS is to produce historical data series of estimated end-use consumption by state, defined as consistently as possible over time and across sectors. SEDS exists for two principal reasons: (1) to provide state energy consumption estimates to the government, policy makers, and the public; and (2) to provide the historical series necessary for EIA's energy models.

Not Available

1992-05-15T23:59:59.000Z

310

Property:EstimatedTimeExplained | Open Energy Information  

Open Energy Info (EERE)

EstimatedTimeExplained EstimatedTimeExplained Jump to: navigation, search Property Name EstimatedTimeExplained Property Type Text Description An explanation of the accompanying time estimate, including any influential factors or variables. Subproperties This property has the following 40 subproperties: G GRR/Elements/14-CA-b.1 - NPDES Permit Application GRR/Elements/14-CA-b.10 - Did majority of RWQCB approve the permit GRR/Elements/14-CA-b.11 - EPA Review of Adopted Permit GRR/Elements/14-CA-b.12 - Were all EPA objections resolved GRR/Elements/14-CA-b.13 - NPDES Permit issued GRR/Elements/14-CA-b.2 - Review of application for completeness GRR/Elements/14-CA-b.3 - Is the application complete for the Regional Water Quality Control Board GRR/Elements/14-CA-b.4 - EPA review for completeness

311

Estimating material and energy intensities of urban areas  

E-Print Network (OSTI)

The objective of this thesis is to develop methods to estimate, analyze and visualize the resource intensity of urban areas. Understanding the resource consumption of the built environment is particularly relevant in cities ...

Quinn, David James, Ph. D. Massachusetts Institute of Technology

2012-01-01T23:59:59.000Z

312

2005 Status Report - Savings Estimates for the ENERGY STAR Voluntary...  

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

Date Published 032006 ISBN Number LBNL-56380(2005) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR is a voluntary labeling program designed to identify and...

313

2008 Status Report - Savings Estimates for the ENERGY STAR Voluntary...  

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

Date Published 112007 ISBN Number LBNL-56380(2008) Keywords Enduse, Energy End-Use Forecasting, EUF Abstract ENERGY STAR is a voluntary labeling program designed to identify and...

314

Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use  

SciTech Connect

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. Anthropogenic heat discharge was estimated based on a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. Building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/ Energy Information Administration survey data, Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data.

Zhou, Yuyu; Weng, Qihao; Gurney, Kevin R.; Shuai, Yanmin; Hu, Xuefei

2012-01-01T23:59:59.000Z

315

Assumptions to the Annual Energy Outlook 1999 - Industrial Demand...  

Gasoline and Diesel Fuel Update (EIA)

industrial.gif (5205 bytes) The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 9 manufacturing and 6 nonmanufacturing...

316

Property:EstimatedCostMedianUSD | Open Energy Information  

Open Energy Info (EERE)

EstimatedCostMedianUSD EstimatedCostMedianUSD Jump to: navigation, search Property Name EstimatedCostMedianUSD Property Type Quantity Description the median estimate of cost in USD Use this type to express a monetary value in US Dollars. The default unit is one US Dollar. http://en.wikipedia.org/wiki/Area Acceptable units (and their conversions) are: 100 cent USD,cents USD,Cent USD,Cents USD .001 k USD,thousand USD,Thousand USD .000001 M USD,million USD,Million USD .000000001 T USD,trillion USD,Trillion USD Pages using the property "EstimatedCostMedianUSD" Showing 25 pages using this property. (previous 25) (next 25) 2 2-M Probe Survey + 30030,000 centUSD 0.3 kUSD 3.0e-4 MUSD 3.0e-7 TUSD + A Acoustic Logs + 4.62462 centUSD 0.00462 kUSD 4.62e-6 MUSD 4.62e-9 TUSD + Aerial Photography + 240.5424,054 centUSD

317

Property:EstimatedTimeMedian | Open Energy Information  

Open Energy Info (EERE)

EstimatedTimeMedian EstimatedTimeMedian Jump to: navigation, search Property Name EstimatedTimeMedian Property Type Quantity Description the median estimate of time required Use this type to enumerate a length of time. The default unit is the year. Acceptable units (and their conversions) are: 8766 hours,hour,h,H,Hour,Hours,HOUR,HOURS 365.25 days,day,d,Day,Days,D,DAY,DAYS 52.17857 weeks,week,w,Week,Weeks,W,WEEK,WEEKS 12 months,month,m,Month,Months,M,MONTH,MONTHS 1 years,year,y,Year,Years,Y,YEAR,YEARS Pages using the property "EstimatedTimeMedian" Showing 25 pages using this property. (previous 25) (next 25) 2 2-M Probe Survey + 2.281542e-4 years2 hours 0.0833 days 0.0119 weeks 0.00274 months + A Acoustic Logs + 0.044 years385.92 hours 16.08 days 2.297 weeks 0.528 months +

318

Property:EstimatedTimeHigh | Open Energy Information  

Open Energy Info (EERE)

EstimatedTimeHigh EstimatedTimeHigh Jump to: navigation, search Property Name EstimatedTimeHigh Property Type Quantity Description the high estimate of time required Use this type to enumerate a length of time. The default unit is the year. Acceptable units (and their conversions) are: 8766 hours,hour,h,H,Hour,Hours,HOUR,HOURS 365.25 days,day,d,Day,Days,D,DAY,DAYS 52.17857 weeks,week,w,Week,Weeks,W,WEEK,WEEKS 12 months,month,m,Month,Months,M,MONTH,MONTHS 1 years,year,y,Year,Years,Y,YEAR,YEARS Pages using the property "EstimatedTimeHigh" Showing 25 pages using this property. (previous 25) (next 25) 2 2-M Probe Survey + 3.422313e-4 years3 hours 0.125 days 0.0179 weeks 0.00411 months + A Acoustic Logs + 0.0881 years772.08 hours 32.17 days 4.596 weeks 1.057 months + Aerial Photography + 0.00548 years48 hours

319

Property:EstimatedCostHighUSD | Open Energy Information  

Open Energy Info (EERE)

EstimatedCostHighUSD EstimatedCostHighUSD Jump to: navigation, search Property Name EstimatedCostHighUSD Property Type Quantity Description the high estimate of cost in USD Use this type to express a monetary value in US Dollars. The default unit is one US Dollar. http://en.wikipedia.org/wiki/Area Acceptable units (and their conversions) are: 100 cent USD,cents USD,Cent USD,Cents USD .001 k USD,thousand USD,Thousand USD .000001 M USD,million USD,Million USD .000000001 T USD,trillion USD,Trillion USD Pages using the property "EstimatedCostHighUSD" Showing 25 pages using this property. (previous 25) (next 25) 2 2-M Probe Survey + 50050,000 centUSD 0.5 kUSD 5.0e-4 MUSD 5.0e-7 TUSD + A Acoustic Logs + 161,600 centUSD 0.016 kUSD 1.6e-5 MUSD 1.6e-8 TUSD + Aerial Photography + 2,360236,000 centUSD

320

Property:EstimatedCostLowUSD | Open Energy Information  

Open Energy Info (EERE)

EstimatedCostLowUSD EstimatedCostLowUSD Jump to: navigation, search Property Name EstimatedCostLowUSD Property Type Quantity Description the low estimate of cost in USD Use this type to express a monetary value in US Dollars. The default unit is one US Dollar. http://en.wikipedia.org/wiki/Area Acceptable units (and their conversions) are: 100 cent USD,cents USD,Cent USD,Cents USD .001 k USD,thousand USD,Thousand USD .000001 M USD,million USD,Million USD .000000001 T USD,trillion USD,Trillion USD Pages using the property "EstimatedCostLowUSD" Showing 25 pages using this property. (previous 25) (next 25) 2 2-M Probe Survey + 20020,000 centUSD 0.2 kUSD 2.0e-4 MUSD 2.0e-7 TUSD + A Acoustic Logs + 1100 centUSD 1.0e-3 kUSD 1.0e-6 MUSD 1.0e-9 TUSD + Aerial Photography + 100.3610,036 centUSD

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


321

Property:EstimatedTimeLow | Open Energy Information  

Open Energy Info (EERE)

EstimatedTimeLow EstimatedTimeLow Jump to: navigation, search Property Name EstimatedTimeLow Property Type Quantity Description the low estimate of time required Use this type to enumerate a length of time. The default unit is the year. Acceptable units (and their conversions) are: 8766 hours,hour,h,H,Hour,Hours,HOUR,HOURS 365.25 days,day,d,Day,Days,D,DAY,DAYS 52.17857 weeks,week,w,Week,Weeks,W,WEEK,WEEKS 12 months,month,m,Month,Months,M,MONTH,MONTHS 1 years,year,y,Year,Years,Y,YEAR,YEARS Pages using the property "EstimatedTimeLow" Showing 25 pages using this property. (previous 25) (next 25) 2 2-M Probe Survey + 1.711157e-4 years1.5 hours 0.0625 days 0.00893 weeks 0.00205 months + A Acoustic Logs + 0.023 years201.36 hours 8.39 days 1.199 weeks 0.276 months + Aerial Photography + 2.737851e-4 years2.4 hours

322

Wind Resource Estimation and Mapping at the National Renewable Energy Laboratory  

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

Resource Estimation and Resource Estimation and Mapping at the National Renewable Energy Laboratory April 1999 * NREL/CP-500-26245 M. Schwartz Presented at the ASES Solar '99 Conference Portland, Maine June 12-16, 1999 National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401-3393 NREL is a U.S. Department of Energy Laboratory Operated by Midwest Research Institute * * * * Battelle * * * * Bechtel

323

Today in Energy - Geology and technology drive estimates of ...  

U.S. Energy Information Administration (EIA)

Energy Information ... "Sweet spot" is an industry term for those select and limited areas within a shale ... Because of the continual improvement in technology, ...

324

Table PT2. Energy Production Estimates in Trillion Btu ...  

U.S. Energy Information Administration (EIA)

... includes refuse recovery. sources except biofuels. ... Coal a Natural Gas b Crude Oil c Biofuels d Other e Production U.S. Energy Information Administration

325

Table PT2. Energy Production Estimates in Trillion Btu, Minnesota ...  

U.S. Energy Information Administration (EIA)

... includes refuse recovery. sources except biofuels. ... Coal a Natural Gas b Crude Oil c Biofuels d Other e Production U.S. Energy Information Administration

326

ESTIMATING RISK TO CALIFORNIA ENERGY INFRASTRUCTURE FROM PROJECTED CLIMATE CHANGE  

E-Print Network (OSTI)

Future Residential Electricity Demand." Energy Institute atClimate change and electricity demand in California. Climate change and electricity demand in California.

Sathaye, Jayant

2011-01-01T23:59:59.000Z

327

Distributed Generation Renewable Energy Estimate of Costs and...  

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

Agarwal, P. and L. Manuel, The influence of the joint wind-wave environment on offshore wind turbine support structure loads. Journal of Solar Energy Engineering, Transactions of...

328

The effect of the geomagnetic field on cosmic ray energy estimates and large scale anisotropy searches on data from the Pierre Auger Observatory  

Science Conference Proceedings (OSTI)

We present a comprehensive study of the influence of the geomagnetic field on the energy estimation of extensive air showers with a zenith angle smaller than 60{sup o}, detected at the Pierre Auger Observatory. The geomagnetic field induces an azimuthal modulation of the estimated energy of cosmic rays up to the {approx} 2% level at large zenith angles. We present a method to account for this modulation of the reconstructed energy. We analyse the effect of the modulation on large scale anisotropy searches in the arrival direction distributions of cosmic rays. At a given energy, the geomagnetic effect is shown to induce a pseudo-dipolar pattern at the percent level in the declination distribution that needs to be accounted for. In this work, we have identified and quantified a systematic uncertainty affecting the energy determination of cosmic rays detected by the surface detector array of the Pierre Auger Observatory. This systematic uncertainty, induced by the influence of the geomagnetic field on the shower development, has a strength which depends on both the zenith and the azimuthal angles. Consequently, we have shown that it induces distortions of the estimated cosmic ray event rate at a given energy at the percent level in both the azimuthal and the declination distributions, the latter of which mimics an almost dipolar pattern. We have also shown that the induced distortions are already at the level of the statistical uncertainties for a number of events N {approx_equal} 32 000 (we note that the full Auger surface detector array collects about 6500 events per year with energies above 3 EeV). Accounting for these effects is thus essential with regard to the correct interpretation of large scale anisotropy measurements taking explicitly profit from the declination distribution.

Abreu, P.; /Lisbon, IST; Aglietta, M.; /IFSI, Turin; Ahn, E.J.; /Fermilab; Albuquerque, I.F.M.; /Sao Paulo U.; Allard, D.; /APC, Paris; Allekotte, I.; /Centro Atomico Bariloche; Allen, J.; /New York U.; Allison, P.; /Ohio State U.; Alvarez Castillo, J.; /Mexico U., ICN; Alvarez-Muniz, J.; /Santiago de Compostela U.; Ambrosio, M.; /Naples U. /INFN, Naples /Nijmegen U., IMAPP

2011-11-01T23:59:59.000Z

329

EVALUATION AND CALIBRATION OF SOFTWARE-BASED ENERGY ESTIMATION  

E-Print Network (OSTI)

the power consumption. The article does not provide any results in form of error rates, instead it states widely used estimation model, which incorporates the state transitions of the radio transceiver. The im. To quantify the error rates, we determined the power consumption of a wireless sensor node through hardware

Braun, Torsten

330

Pumping System Measurements To Estimate Energy Savings: Why and How  

E-Print Network (OSTI)

Measuring performance parameters (flow rate, pressures, and power) for existing systems is essential to understanding how both the pump(s) and system are actually performing. Examples of reasons why actual measurements are critical and practical means of getting and using the measured data to estimate savings potential using DOE tools are discussed.

Casada, D.

2007-01-01T23:59:59.000Z

331

Estimation of the two-dimensional presampled modulation transfer function of digital radiography devices using one-dimensional test objects  

SciTech Connect

Purpose: The modulation transfer function (MTF) of medical imaging devices is commonly reported in the form of orthogonal one-dimensional (1D) measurements made near the vertical and horizontal axes with a slit or edge test device. A more complete description is found by measuring the two-dimensional (2D) MTF. Some 2D test devices have been proposed, but there are some issues associated with their use: (1) they are not generally available; (2) they may require many images; (3) the results may have diminished accuracy; and (4) their implementation may be particularly cumbersome. This current work proposes the application of commonly available 1D test devices for practical and accurate estimation of the 2D presampled MTF of digital imaging systems. Methods: Theory was developed and applied to ensure adequate fine sampling of the system line spread function for 1D test devices at orientations other than approximately vertical and horizontal. Methods were also derived and tested for slit nonuniformity correction at arbitrary angle. Techniques were validated with experimental measurements at ten angles using an edge test object and three angles using a slit test device on an indirect-detection flat-panel system [GE Revolution XQ/i (GE Healthcare, Waukesha, WI)]. The 2D MTF was estimated through a simple surface fit with interpolation based on Delaunay triangulation of the 1D edge-based MTF measurements. Validation by synthesis was also performed with simulated images from a hypothetical direct-detection flat-panel device. Results: The 2D MTF derived from physical measurements yielded an average relative precision error of 0.26% for frequencies below the cutoff (2.5 mm{sup -1}) and approximate circular symmetry at frequencies below 4 mm{sup -1}. While slit analysis generally agreed with the results of edge analysis, the two showed subtle differences at frequencies above 4 mm{sup -1}. Slit measurement near 45 Degree-Sign revealed radial asymmetry in the MTF resulting from the square pixel aperture (0.2 mm Multiplication-Sign 0.2 mm), a characteristic which was not necessarily appreciated with the orthogonal 1D MTF measurements. In simulation experiments, both slit- and edge-based measurements resolved the radial asymmetries in the 2D MTF. The average absolute relative accuracy error in the 2D MTF between the DC and cutoff (2.5 mm{sup -1}) frequencies was 0.13% with average relative precision error of 0.11%. Other simulation results were similar to those derived from physical data. Conclusions: Overall, the general availability, acceptance, accuracy, and ease of implementation of 1D test devices for MTF assessment make this a valuable technique for 2D MTF estimation.

Wells, Jered R.; Dobbins, James T. III [Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705 and Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 (United States); Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705 (United States); Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705 (United States); Department of Biomedical Engineering, Duke University, Durham, North Carolina 27705 (United States); Department of Physics, Duke University, Durham, North Carolina 27705 (United States) and Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 (United States)

2012-10-15T23:59:59.000Z

332

Smart Sensing, Estimation, and Prediction for Efficient Building Energy Management  

E-Print Network (OSTI)

occupancy by creating agent models of the occupants. These predictions enable the HVAC system increase is accounted for in heating, ventilation, and air conditioning (HVAC) systems. Smart sensing and adaptive energy management software can greatly decrease the energy usage of HVAC systems in many building

Chang, Yu-Han

333

Developing an environment for embedded software energy estimation  

Science Conference Proceedings (OSTI)

The paper presents the results of a novel method for the instruction-level energy consumption measurement and the corresponding modeling approach for embedded microprocessors. According to the proposed method the base and inter-instruction energy costs ... Keywords: Embedded microprocessors, Low power systems, Power consumption, Software modeling tools

S. Nikolaidis; A. Chatzigeorgiou; T. Laopoulos

2005-12-01T23:59:59.000Z

334

Bread Basket: a gaming model for estimating home-energy costs  

SciTech Connect

An instructional manual for answering the twenty variables on COLORADO ENERGY's computerized program estimating home energy costs. The program will generate home-energy cost estimates based on individual household data, such as total square footage, number of windows and doors, number and variety of appliances, heating system design, etc., and will print out detailed costs, showing the percentages of the total household budget that energy costs will amount to over a twenty-year span. Using the program, homeowners and policymakers alike can predict the effects of rising energy prices on total spending by Colorado households.

1982-01-01T23:59:59.000Z

335

Estimating the Cost and Energy Efficiency of a Solar Water Heater |  

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

Estimating the Cost and Energy Efficiency of a Solar Water Heater Estimating the Cost and Energy Efficiency of a Solar Water Heater Estimating the Cost and Energy Efficiency of a Solar Water Heater May 30, 2012 - 3:09pm Addthis Solar water heaters are more efficient the gas or electric heaters. | Chart credit ENERGY STAR Solar water heaters are more efficient the gas or electric heaters. | Chart credit ENERGY STAR What does this mean for me? Solar water heaters cost more to purchase and install but may save you money in the long run. Estimate the annual operating costs and compare several solar water heaters to determine whether it is worth investing in a more efficient system. Solar water heating systems usually cost more to purchase and install than conventional water heating systems. However, a solar water heater can

336

Estimating the Cost and Energy Efficiency of a Solar Water Heater |  

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

Estimating the Cost and Energy Efficiency of a Solar Water Heater Estimating the Cost and Energy Efficiency of a Solar Water Heater Estimating the Cost and Energy Efficiency of a Solar Water Heater May 30, 2012 - 3:09pm Addthis Solar water heaters are more efficient the gas or electric heaters. | Chart credit ENERGY STAR Solar water heaters are more efficient the gas or electric heaters. | Chart credit ENERGY STAR What does this mean for me? Solar water heaters cost more to purchase and install but may save you money in the long run. Estimate the annual operating costs and compare several solar water heaters to determine whether it is worth investing in a more efficient system. Solar water heating systems usually cost more to purchase and install than conventional water heating systems. However, a solar water heater can

337

Equilibrium free energy estimates based on nonequilibrium work relations and extended dynamics  

E-Print Network (OSTI)

Equilibrium free energy estimates based on nonequilibrium work relations and extended dynamics the equilibrium free energy and the nonequilibrium work is useful for computer simulations. In this paper, we exploit the fact that the free energy is a state function, independent of the pathway taken to change

Sun, Sean

338

Free Energy Estimates of All-atom Protein Structures Using Generalized Belief  

E-Print Network (OSTI)

Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation H Detection, Free Energy, Probabilistic Graphical Models #12;Abstract We present a technique for approximating the free energy of protein structures using Generalized Belief Propagation (GBP). The accuracy and utility

339

Context-aware parameter estimation for forecast models in the energy domain  

Science Conference Proceedings (OSTI)

Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability and efficiency of energy grids. This balancing task requires accurate forecasts of future electricity consumption and production at any point in time. For ... Keywords: energy, forecasting, maintenance, parameter estimation

Lars Dannecker; Robert Schulze; Matthias Bhm; Wolfgang Lehner; Gregor Hackenbroich

2011-07-01T23:59:59.000Z

340

Binding Energy Distribution Analysis Method (BEDAM) for Estimation of Protein-Ligand Binding Affinities  

E-Print Network (OSTI)

Binding Energy Distribution Analysis Method (BEDAM) for Estimation of Protein-Ligand Binding Jersey 08854 Received June 2, 2010 Abstract: The binding energy distribution analysis method (BEDAM of the probability distribution of the binding energy obtained in the canonical ensemble in which the ligand

Note: This page contains sample records for the topic "module estimates energy" 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

Revised equipartition & minimum energy formula for magnetic field strength estimates from radio synchrotron observations  

E-Print Network (OSTI)

The commonly used classical equipartition or minimum-energy estimate of total magnetic fields strengths from radio synchrotron intensities is of limited practical use because it is based on the hardly known ratio K of the total energies of cosmic ray protons and electrons and also has inherent problems. We present a revised formula, using the number density ratio K for which we give estimates. For particle acceleration in strong shocks K is about 40 and increases with decreasing shock strength. Our revised estimate for the field strength gives larger values than the classical estimate for flat radio spectra with spectral indices of about 0.5-0.6, but smaller values for steep spectra and total fields stronger than about 10 muG. In very young supernova remnants, for example, the classical estimate may be too large by up to 10x. On the other hand, if energy losses of cosmic ray electrons are important, K increases with particle energy and the equipartition field may be underestimated significantly. Our revised larger equipartition estimates in galaxy clusters and radio lobes are consistent with independent estimates from Faraday rotation measures, while estimates from the ratio between radio synchrotron and X-ray inverse Compton intensities generally give much weaker fields. This may be explained e.g. by a concentration of the field in filaments. Our revised field strengths may also lead to major revisions of electron lifetimes in jets and radio lobes estimated from the synchrotron break frequency in the radio spectrum.

Rainer Beck; Marita Krause

2005-07-15T23:59:59.000Z

342

Indian Country Solar Energy Potential Estimates & DOE IE Updates  

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

Potential for Renewable Energy Potential for Renewable Energy Development on Tribal Lands October 2012 1 Introduction * The DOE Office of Indian Energy Policy and Programs (OI) requested ICF International (ICF) to identify areas within Tribal Lands that have a strong potential for renewable energy generation (solar and wind) as a source of Tribal revenue within the geographic area covered by the Western Electric Coordination Council (WECC) transmission grid * ICF used a combination of geospatial modeling and power flow modeling to identify sites where: - Conditions are optimal for solar or wind generation - Access to high-voltage transmission lines is favorable - Transmission upgrade costs would be minimal 2 Geospatial Analysis Phase I: Identify Potential Sites * Obtained GIS layers (e.g., wind/solar resources, transmission

343

Estimates of Kinetic Energy Dissipation under Breaking Waves  

Science Conference Proceedings (OSTI)

The dissipation of kinetic energy at the surface of natural water bodies has important consequences for many Physical and biochemical processes including wave dynamics, gas transfer, mixing of nutrients and pollutants, and photosynthetic ...

E.A. Terray; M.A. Donelan; Y.C. Agrawal; W.M. Drennan; K.K. Kahma; A.J. Williams; P.A. Hwang; S.A. Kitaigorodskii

1996-05-01T23:59:59.000Z

344

Procedure for estimating fracture energy from fracture surface roughness  

DOE Patents (OSTI)

The fracture energy of a material is determined by first measuring the length of a profile of a section through a fractured surface of the material taken on a plane perpendicular to the mean plane of that surface, then determining the fractal dimensionality of the surface. From this, the yield strength of the material, and the Young's Modulus of that material, the fracture energy is calculated.

Williford, Ralph E. (Kennewick, WA)

1989-01-01T23:59:59.000Z

345

Development and testing of thermal-energy-storage modules for use in active solar heating and cooling systems. Final report  

DOE Green Energy (OSTI)

Additional development work on thermal-energy-storage modules for use with active solar heating and cooling systems is summarized. Performance testing, problems, and recommendations are discussed. Installation, operation, and maintenance instructions are included. (MHR)

Parker, J.C.

1981-04-01T23:59:59.000Z

346

Development of reduced-variable master curves for estimating tensile stresses of encapsulated solar cells caused by module deflection or thermal expansion  

DOE Green Energy (OSTI)

Complex computer programs are being used by Spectrolab, Inc., to achieve encapsulation engineering optimization of photovoltaic modules. Optimization involves structural adequacy, electrical isolation (safety), maximum optical transmission, and minimum module temperature, at the lowest life-cycle energy cost. A goal of this activity is the generation, where possible, of encapsulation engineering generalities, principles, and design aids (tables or graphs) that would permit a ready, desktop capability of an engineering evaluation of encapsulation options involving materials or designs. The first efforts to generate reduced-variable mater curves for thermal expansion and deflection stress to serve as structural-analysis design aids are reported.

Cuddihy, E.F.

1981-10-01T23:59:59.000Z

347

Comparison of Energy Production and Performance from Flat-Plate Photovoltaic Module Technologies Deployed at Fixed Tilt: Preprint  

DOE Green Energy (OSTI)

This conference paper describes the performance data for 14 photovoltaic modules deployed at fixed-latitude tilt in the field are presented and compared. Module performance is monitored continuously for optimum power characteristics. Flat-plate module technologies representative of crystalline, amorphous, and polycrystalline silicon, and cadmium telluride and copper indium diselenide, are scrutinized for energy production, effective efficiency and performance ratio-ratio of effective to reference efficiency. Most performance ratios exhibit seasonal fluctuations largely correlated to air or module temperatures, varying between 80% and 100%. These ratios tend toward larger values during winter and vise versa, except for amorphous silicon and cadmium telluride modules. In a-Si cases, the situation appears reversed: better performance ratios are exhibited during late summer. The effective efficiency and average daily and yearly energy production are analyzed and quantified.

del Cueto, J. A.

2002-05-01T23:59:59.000Z

348

Thermal to Electrical Energy Conversion of Skutterudite-Based Thermoelectric Modules  

SciTech Connect

The performance of thermoelectric (TE) materials has improved tremendously over the past decade. The intrinsic thermal and electrical properties of state-of-the-art TE materials demonstrate that the potential for widespread practical TE applications is very large and includes TE generators (TEGs) for automotive waste heat recovery. TE materials for automotive TEG applications must have good intrinsic performance, be thermomechanically compatible, and be chemically stable in the 400 K to 850 K temperature range. Both n-type and p-type varieties must be available at low cost, easily fabricated, and durable. They must also form robust junctions and develop good interfaces with other materials to permit efficient flows of electrical and thermal energy. Among the TE materials of interest for automotive waste heat recovery systems are the skutterudite compounds, which are the antimony-based transition-metal compounds RTE4Sb12, where R can be an alkali metal (e.g., Na, K), alkaline earth (e.g., Ba), or rare earth (e.g., La, Ce, Yb), and TE can be a transition metal (e.g., Co, Fe). We synthesized a considerable quantity of n-type and p-type skutterudites, fabricated TE modules, incorporated these modules into a prototype TEG, and tested the TEG on a production General Motors (GM) vehicle. We discuss our progress on skutterudite TE module fabrication and present module performance data for electrical power output under simulated operating conditions for automotive waste heat recovery systems. We also present preliminary durability results on our skutterudite modules.

Salvador, James R. [GM R& D and Planning, Warren, Michigan; Cho, Jung Y [GM R& D and Planning, Warren, Michigan; Ye, Zuxin [GM Research and Development Center; Moczygemba, Joshua E. [Marlow Industries, Inc; Thompson, Alan [Marlow Industries, Inc; Sharp, Jeff W. [Marlow Industries, Inc; Konig, Jan [Fraunhofer-Institute, Freiburg, Germany; Maloney, Ryan [Michigan State University; Thompson, Travis [Michigan State University; Sakamoto, Jeff [Michigan State University; Wang, Hsin [ORNL; Wereszczak, Andrew A [ORNL; Meisner, G P [General Motors Corporation-R& D

2013-01-01T23:59:59.000Z

349

Estimating Meridional Energy Transports by the Atmospheric and Oceanic General Circulations Using Boundary Fluxes  

Science Conference Proceedings (OSTI)

The annual-mean meridional energy transport in the atmosphereocean system (total transport) is estimated using 4-yr mean net radiative fluxes at the top of the atmosphere (TOA) calculated from the International Satellite Cloud Climatology ...

Y-C. Zhang; W. B. Rossow

1997-09-01T23:59:59.000Z

350

Satellite-Derived Surface Energy Balance Estimates in the Alaskan Sub-Arctic  

Science Conference Proceedings (OSTI)

Heat Capacity Mapping Mission (HCMM) data for 12 May 1978 were used in an energy balance modelto estimate evapotranspiration in sub-Arctic Alaska following snowmelt. The HCMM scene contained severalareas of melting snow as well as an actively ...

R. J. Gurney; D. K. Hall

1983-01-01T23:59:59.000Z

351

The Smart Grid: An Estimation of the Energy and Carbon Dioxide...  

Open Energy Info (EERE)

Login | Sign Up Search Page Edit with form History Facebook icon Twitter icon The Smart Grid: An Estimation of the Energy and Carbon Dioxide (CO2) Benefits Jump to:...

352

B-Spline Image Model for Energy Minimization-Based Optical Flow Estimation  

Science Conference Proceedings (OSTI)

Robust estimation of the optical flow is addressed through a multiresolution energy minimization. It involves repeated evaluation of spatial and temporal gradients of image intensity which rely usually on bilinear interpolation and image filtering. We ... Keywords: Optical flow (OF), robust estimation, splines

G. Le Besnerais; F. Champagnat

2006-10-01T23:59:59.000Z

353

Comments on Estimates of Kinetic Energy Dissipation under Breaking Waves  

Science Conference Proceedings (OSTI)

It is noted that the results of recent experiments on the enhancement of turbulent kinetic energy (TKE) dissipation below surface waves can be stated as follows. TKE dissipation is enhanced by a factor 15Hws/z at depths 0.5Hws < z < 20Hws with ...

Gerrit Burgers

1997-10-01T23:59:59.000Z

354

Localized energy estimates for wave equations on high dimensional Schwarzschild space-times  

E-Print Network (OSTI)

The localized energy estimate for the wave equation is known to be a fairly robust measure of dispersion. Recent analogs on the $(1+3)$-dimensional Schwarzschild space-time have played a key role in a number of subsequent results, including a proof of Price's law. In this article, we explore similar localized energy estimates for wave equations on $(1+n)$-dimensional hyperspherical Schwarzschild space-times.

Laul, Parul

2010-01-01T23:59:59.000Z

355

Order Module--U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: GETTING  

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

U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: GETTING STARTED Order Module--U.S. DEPARTMENT OF ENERGY ORDERS SELF-STUDY PROGRAM: GETTING STARTED This course was developed using the Criterion Referenced Instruction (CRI) method of training. That means the course contains only the information you need to perform your job. You will be shown the learning objectives at the beginning of the course. If you think you can demonstrate competency without additional instruction, you may complete the practice at any time. When you complete all of the practices successfully, you may ask the course manager for the criterion test. The familiar level requires that you understand and remember the material. The general level requires that you understand the applicability of the material. If you are unsure of the

356

Modeling, Estimation, and Control in Energy Systems: Batteries & Demand Response  

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

Modeling, Modeling, Estimation, and Control in Energy Systems: Batteries & Demand Response Scott Moura Assistant Professor Civl & Environmental Engineering University of California, Berkeley EETD | LBNL Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 1 Source: Vaclav Smil Estimates from Energy Transitions Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 2 Energy Initiatives Denmark 50% wind penetration by 2025 Brazil uses 86% renewables China's aggressive energy/carbon intensity reduction EV Everywhere SunShot Green Button Zero emissions vehicle (ZEV) 33% renewables by 2020 Go Solar California Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 3 Energy Systems of Interest Energy storage Smart Grids (e.g., batteries) (e.g., demand response) Scott Moura | UC Berkeley Control, Batts, DR December 4, 2013 | Slide 4 Energy

357

Model documentation report: Industrial sector demand module of the national energy modeling system  

SciTech Connect

This report documents the objectives, analytical approach, and development of the National Energy Modeling System (NEMS) Industrial Demand Model. The report catalogues and describes model assumptions, computational methodology, parameter estimation techniques, and model source code. This document serves three purposes. First, it is a reference document providing a detailed description of the NEMS Industrial Model for model analysts, users, and the public. Second, this report meets the legal requirements of the Energy Information Administration (EIA) to provide adequate documentation in support of its model. Third, it facilitates continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements as future projects.

NONE

1998-01-01T23:59:59.000Z

358

Experimental Estimation Of Energy Damping During Free Rocking Of Unreinforced Masonry Walls. First Results  

Science Conference Proceedings (OSTI)

This paper presents an ongoing experimental program on unreinforced masonry walls undergoing free rocking. Aim of the laboratory campaign is the estimation of kinetic energy damping exhibited by walls released with non?zero initial conditions of motion. Such energy damping is necessary for dynamic modelling of unreinforced masonry local mechanisms. After a brief review of the literature on this topic

Luigi Sorrentino; Renato Masiani; Stefano Benedetti

2008-01-01T23:59:59.000Z

359

Electrical power inverter having a phase modulated, twin-inverter, high frequency link and an energy storage module  

SciTech Connect

The present invention provides an electrical power inverter method and apparatus, which includes a high frequency link, for converting DC power into AC power. Generally stated, the apparatus includes a first high frequency module which produces an AC voltage at a first output frequency, and a second high frequency inverter module which produces an AC voltage at a second output frequency that is substantially the same as the first output frequency. The second AC voltage is out of phase with the first AC voltage by a selected angular phase displacement. A mixer mixes the first and second output voltages to produce a high frequency carrier which has a selected base frequency impressed on the sidebands thereof. A rectifier rectifies the carrier, and a filter filters the rectified carrier. An output inverter inverts the filtered carrier to produce an AC line voltage at the selected base frequency. A phase modulator adjusts the relative angular phase displacement between the outputs of the first and second high frequency modules to control the base frequency and magnitude of the AC line voltage.

Pitel, Ira J. (Whippany, NJ)

1987-02-03T23:59:59.000Z

360

Electrical power inverter having a phase modulated, twin-inverter, high frequency link and an energy storage module  

SciTech Connect

The present invention provides an electrical power inverter method and apparatus, which includes a high frequency link, for converting DC power into AC power. Generally stated, the apparatus includes a first high frequency module which produces an AC voltage at a first output frequency, and a second high frequency inverter module which produces an AC voltage at a second output frequency that is substantially the same as the first output frequency. The second AC voltage is out of phase with the first AC voltage by a selected angular phase displacement. A mixer mixes the first and second output voltages to produce a high frequency carrier which has a selected base frequency impressed on the sidebands thereof. A rectifier rectifies the carrier, and a filter filters the rectified carrier. An output inverter inverts the filtered carrier to produce an AC line voltage at the selected base frequency. A phase modulator adjusts the relative angular phase displacement between the outputs of the first and second high frequency modules to control the base frequency and magnitude of the AC line voltage. 19 figs.

Pitel, I.J.

1987-02-03T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Study Estimates Energy Savings From Bringing All U.S. Homes Up to Code  

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

Study Estimates Energy Savings From Bringing All U.S. Homes Up to Code Study Estimates Energy Savings From Bringing All U.S. Homes Up to Code October 2013 October-November Special Focus: Energy Efficiency, Buildings, the Electric Grid Throughout October and November, EETD presents a special series of articles, research highlights, and social media posts addressing some of its recent research on energy efficiency and other buildings-related topics, and the electric grid. Tightening the envelope of homes should save energy by reducing the loss of heat when the exterior is cold, and the loss of cooled air when it is hot. Until now, it has not been clear for the current housing stock how much potential for energy savings exists in the U.S. stock of homes if all were brought up to codes requiring the tightening of the building envelope.

362

Estimating the impacts of federal efforts to improve energy efficiency: The case of buildings  

Science Conference Proceedings (OSTI)

The US Department of Energy`s Office of Energy Efficiency and Renewable Energy (EE) has for more than a decade focused its efforts on research to develop new technologies for improving the efficiency of energy use and increasing the role of renewable energy; success has usually been measured in term of energy saved or displaced. Estimates of future energy savings remain an important factor in program planning and prioritization. A variety of internal and external factors are now radically changing the planning process, and in turn the composition and thrust of the EE program. The Energy Policy Act of 1992, the Framework Convention on Climate Change (and the Administration`s Climate Change Action Plan), and concerns for the future of the economy (especially employment and international competitiveness) are increasing emphasis on technology deployment and near-term results. The Reinventing Government Initiative, the Government Performance and Results Act, and the Executive Order on Environmental Justice are all forcing Federal programs to demonstrate that they are producing desired results in a cost-effective manner. The application of Total Quality management principles has increased the scope and importance of producing quantified measures of benefit. EE has established a process for estimating the benefits of DOE`s energy efficiency and renewable energy programs called ``Quality Metrics`` (QM). The ``metrics`` are: energy, employment, equity, environment, risk, economics. This paper describes the approach taken by EE`s Office of Building Technologies to prepare estimates of program benefits in terms of these metrics, presents the estimates, discusses their implications, and explores possible improvements to the QM process as it is currently configured.

LaMontagne, J; Jones, R; Nicholls, A; Shankle, S [Brookhaven National Lab., Upton, NY (United States). Energy Efficiency and Conservation Div.

1994-09-01T23:59:59.000Z

363

ESTIMATING RISK TO CALIFORNIA ENERGY INFRASTRUCTURE FROM PROJECTED CLIMATE CHANGE  

SciTech Connect

This report outlines the results of a study of the impact of climate change on the energy infrastructure of California and the San Francisco Bay region, including impacts on power plant generation; transmission line and substation capacity during heat spells; wildfires near transmission lines; sea level encroachment upon power plants, substations, and natural gas facilities; and peak electrical demand. Some end-of-century impacts were projected:Expected warming will decrease gas-fired generator efficiency. The maximum statewide coincident loss is projected at 10.3 gigawatts (with current power plant infrastructure and population), an increase of 6.2 percent over current temperature-induced losses. By the end of the century, electricity demand for almost all summer days is expected to exceed the current ninetieth percentile per-capita peak load. As much as 21 percent growth is expected in ninetieth percentile peak demand (per-capita, exclusive of population growth). When generator losses are included in the demand, the ninetieth percentile peaks may increase up to 25 percent. As the climate warms, California's peak supply capacity will need to grow faster than the population.Substation capacity is projected to decrease an average of 2.7 percent. A 5C (9F) air temperature increase (the average increase predicted for hot days in August) will diminish the capacity of a fully-loaded transmission line by an average of 7.5 percent.The potential exposure of transmission lines to wildfire is expected to increase with time. We have identified some lines whose probability of exposure to fire are expected to increase by as much as 40 percent. Up to 25 coastal power plants and 86 substations are at risk of flooding (or partial flooding) due to sea level rise.

Sathaye, Jayant; Dale, Larry; Larsen, Peter; Fitts, Gary; Koy, Kevin; Lewis, Sarah; Lucena, Andre

2011-06-22T23:59:59.000Z

364

Estimate of Geothermal Energy Resource in Major U.S. Sedimentary Basins (Presentation), NREL (National Renewable Energy Laboratory)  

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

ESTIMATE OF GEOTHERMAL ENERGY RESOURCE IN ESTIMATE OF GEOTHERMAL ENERGY RESOURCE IN MAJOR U.S. SEDIMENTARY BASINS Colleen Porro and Chad Augustine April 24, 2012 National Renewable Energy Lab, Golden, CO NREL/PR-6A20-55017 NATIONAL RENEWABLE ENERGY LABORATORY Sedimentary Basin Geothermal WHAT IS SEDIMENTARY BASIN GEOTHERMAL? 2 Geothermal Energy from Sedimentary Rock - Using 'hot" geothermal fluids (>100 o C) produced from sedimentary basins to generate electricity - Advantages: * Reservoirs are porous, permeable, and well characterized * Known/proven temperature gradients from oil and gas well records * Drilling and reservoir fracturing techniques proven in sedimentary environment - Disadvantages: * Great depths required to encounter high temperatures * Emerging industry Photo by Warren Gretz, NREL/PIX 00450

365

2003 status report savings estimates for the energy star(R)voluntary labeling program  

SciTech Connect

ENERGY STAR(R) is a voluntary labeling program designed to identify and promote energy-efficient products, buildings and practices. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), ENERGY STAR labels exist for more than thirty products, spanning office equipment, residential heating and cooling equipment, commercial and residential lighting, home electronics, and major appliances. This report presents savings estimates for a subset of ENERGY STAR program activities, focused primarily on labeled products. We present estimates of the energy, dollar and carbon savings achieved by the program in the year 2002, what we expect in 2003, and provide savings forecasts for two market penetration scenarios for the period 2003 to 2020. The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100 percent market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard efficiency products throughout the analysis period.

Webber, Carrie A.; Brown, Richard E.; McWhinney, Marla

2004-11-09T23:59:59.000Z

366

2007 Status Report: Savings Estimates for the ENERGY STAR(R)VoluntaryLabeling Program  

SciTech Connect

ENERGY STAR(R) is a voluntary labeling program designed toidentify and promote energy-efficient products, buildings and practices.Operated jointly by the Environmental Protection Agency (EPA) and theU.S. Department of Energy (DOE), ENERGY STAR labels exist for more thanthirty products, spanning office equipment, residential heating andcooling equipment, commercial and residential lighting, home electronics,and major appliances. This report presents savings estimates for a subsetof ENERGY STAR labeled products. We present estimates of the energy,dollar and carbon savings achieved by the program in the year 2006, whatwe expect in 2007, and provide savings forecasts for two marketpenetration scenarios for the periods 2007 to 2015 and 2007 to 2025. Thetarget market penetration forecast represents our best estimate of futureENERGY STAR savings. It is based on realistic market penetration goalsfor each of the products. We also provide a forecast under the assumptionof 100 percent market penetration; that is, we assume that all purchasersbuy ENERGY STAR-compliant products instead of standard efficiencyproducts throughout the analysis period.

Sanchez, Marla; Webber, Carrie A.; Brown, Richard E.; Homan,Gregory K.

2007-03-23T23:59:59.000Z

367

2006 Status Report Savings Estimates for the ENERGY STAR(R)Voluntary Labeling Program  

SciTech Connect

ENERGY STAR(R) is a voluntary labeling program designed toidentify and promote energy-efficient products, buildings and practices.Operated jointly by the Environmental Protection Agency (EPA) and theU.S. Department of Energy (DOE), ENERGY STAR labels exist for more thanthirty products, spanning office equipment, residential heating andcooling equipment, commercial and residential lighting, home electronics,and major appliances. This report presents savings estimates for a subsetof ENERGY STAR labeled products. We present estimates of the energy,dollar and carbon savings achieved by the program in the year 2005, whatwe expect in 2006, and provide savings forecasts for two marketpenetration scenarios for the periods 2006 to 2015 and 2006 to 2025. Thetarget market penetration forecast represents our best estimate of futureENERGY STAR savings. It is based on realistic market penetration goalsfor each of the products. We also provide a forecast under the assumptionof 100 percent market penetration; that is, we assume that all purchasersbuy ENERGY STAR-compliant products instead of standard efficiencyproducts throughout the analysis period.

Webber, Carrie A.; Brown, Richard E.; Sanchez, Marla; Homan,Gregory K.

2006-03-07T23:59:59.000Z

368

2002 status report: Savings estimates for the ENERGY STAR(R) voluntary labeling program  

SciTech Connect

ENERGY STAR [registered trademark] is a voluntary labeling program designed to identify and promote energy-efficient products, buildings and practices. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), ENERGY STAR labels exist for more than thirty products, spanning office equipment, residential heating and cooling equipment, commercial and residential lighting, home electronics, and major appliances. This report presents savings estimates for a subset of ENERGY STAR program activities, focused primarily on labeled products. We present estimates of the energy, dollar and carbon savings achieved by the program in the year 2001, what we expect in 2002, and provide savings forecasts for two market penetration scenarios for the period 2002 to 2020. The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100 percent market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard efficiency products throughout the analysis period.

Webber, Carrie A.; Brown, Richard E.; McWhinney, Marla; Koomey, Jonathan

2003-03-03T23:59:59.000Z

369

Savings estimates for the ENERGY STAR (registered trademark) voluntary labeling program: 2001 status report  

SciTech Connect

ENERGY STAR(Registered Trademark) is a voluntary labeling program designed to identify and promote energy-efficient products, buildings and practices. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), ENERGY STAR labels exist for more than thirty products, spanning office equipment, residential heating and cooling equipment, commercial and residential lighting, home electronics, and major appliances. This report presents savings estimates for a subset of ENERGY STAR program activities, focused primarily on labeled products. We present estimates of the energy, dollar and carbon savings achieved by the program in the year 2000, what we expect in 2001, and provide savings forecasts for two market penetration scenarios for the period 2001 to 2020. The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100 percent market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard efficiency products throughout the analysis period.

Webber, Carrie A.; Brown, Richard E.; Mahajan, Akshay; Koomey, Jonathan G.

2002-02-15T23:59:59.000Z

370

2004 status report: Savings estimates for the Energy Star(R)voluntarylabeling program  

SciTech Connect

ENERGY STAR(R) is a voluntary labeling program designed toidentify and promote energy-efficient products, buildings and practices.Operated jointly by the Environmental Protection Agency (EPA) and theU.S. Department of Energy (DOE), ENERGY STAR labels exist for more thanthirty products, spanning office equipment, residential heating andcooling equipment, commercial and residential lighting, home electronics,and major appliances. This report presents savings estimates for a subsetof ENERGY STAR labeled products. We present estimates of the energy,dollar and carbon savings achieved by the program in the year 2003, whatwe expect in 2004, and provide savings forecasts for two marketpenetration scenarios for the periods 2004 to 2010 and 2004 to 2020. Thetarget market penetration forecast represents our best estimate of futureENERGY STAR savings. It is based on realistic market penetration goalsfor each of the products. We also provide a forecast under the assumptionof 100 percent market penetration; that is, we assume that all purchasersbuy ENERGY STAR-compliant products instead of standard efficiencyproducts throughout the analysis period.

Webber, Carrie A.; Brown, Richard E.; McWhinney, Marla

2004-03-09T23:59:59.000Z

371

2005 Status Report Savings Estimates for the ENERGY STAR(R)Voluntary Labeling Program  

SciTech Connect

ENERGY STAR(R) is a voluntary labeling program designed toidentify and promote energy-efficient products, buildings and practices.Operated jointly by the Environmental Protection Agency (EPA) and theU.S. Department of Energy (DOE), Energy Star labels exist for more thanforty products, spanning office equipment, residential heating andcooling equipment, commercial and residential lighting, home electronics,and major appliances. This report presents savings estimates for a subsetof ENERGY STAR labeled products. We present estimates of the energy,dollar and carbon savings achieved by the program in the year 2004, whatwe expect in 2005, and provide savings forecasts for two marketpenetration scenarios for the periods 2005 to 2010 and 2005 to 2020. Thetarget market penetration forecast represents our best estimate of futureENERGY STAR savings. It is based on realistic market penetration goalsfor each of the products. We also provide a forecast under the assumptionof 100 percent market penetration; that is, we assume that all purchasersbuy ENERGY STAR-compliant products instead of standard efficiencyproducts throughout the analysis period.

Webber, Carrie A.; Brown, Richard E.; Sanchez, Marla

2006-03-07T23:59:59.000Z

372

Analysis of the Department of Energy's Clinch River Breeder Reactor cost estimate  

SciTech Connect

Much of the current congressional debate about the Clinch River Breeder Reactor (CRBR) centers around the estimated cost of designing, constructing, and operating it for a 5-year demonstration period. The Department of Energy (DOE) recently linked the revenue-generating potential of the CRBR beyond the demonstration period to the justification for continued funding. GAO presents information that points out many uncertainties in DOE's estimates of revenue and cost. GAO believes that because these estimates are based on numerous assumptions and calculations concerning events as far as 37 years in the future, they should be viewed with caution. Changes in the underlying assumptions could produce wide variance in the cost estimates. Further, GAO points out that CRBR is a research and development project and that judging its merits solely on cost and revenue estimates projected far into the future may not be appropriate.

Bowsher, C.A.

1982-12-10T23:59:59.000Z

373

Energy savings estimates and cost benefit calculations for high performance relocatable classrooms  

SciTech Connect

This report addresses the results of detailed monitoring completed under Program Element 6 of Lawrence Berkeley National Laboratory's High Performance Commercial Building Systems (HPCBS) PIER program. The purpose of the Energy Simulations and Projected State-Wide Energy Savings project is to develop reasonable energy performance and cost models for high performance relocatable classrooms (RCs) across California climates. A key objective of the energy monitoring was to validate DOE2 simulations for comparison to initial DOE2 performance projections. The validated DOE2 model was then used to develop statewide savings projections by modeling base case and high performance RC operation in the 16 California climate zones. The primary objective of this phase of work was to utilize detailed field monitoring data to modify DOE2 inputs and generate performance projections based on a validated simulation model. Additional objectives include the following: (1) Obtain comparative performance data on base case and high performance HVAC systems to determine how they are operated, how they perform, and how the occupants respond to the advanced systems. This was accomplished by installing both HVAC systems side-by-side (i.e., one per module of a standard two module, 24 ft by 40 ft RC) on the study RCs and switching HVAC operating modes on a weekly basis. (2) Develop projected statewide energy and demand impacts based on the validated DOE2 model. (3) Develop cost effectiveness projections for the high performance HVAC system in the 16 California climate zones.

Rainer, Leo I.; Hoeschele, Marc A.; Apte, Michael G.; Shendell, Derek G.; Fisk, Wlliam J.

2003-12-01T23:59:59.000Z

374

Absolute free energies estimated by combining pre-calculated molecular fragment libraries  

E-Print Network (OSTI)

The absolute free energy -- or partition function, equivalently -- of a molecule can be estimated computationally using a suitable reference system. Here, we demonstrate a practical method for staging such calculations by growing a molecule based on a series of fragments. Significant computer time is saved by pre-calculating fragment configurations and interactions for re-use in a variety of molecules. We employ such fragment libraries and interaction tables for amino acids and capping groups to estimate free energies for small peptides. Equilibrium ensembles for the molecules are generated at no additional computational cost, and are used to check our results by comparison to standard dynamics simulation.

Zhang, Xin; Zuckerman, Daniel M

2009-01-01T23:59:59.000Z

375

The Smart Grid: An Estimation of the Energy and Carbon Dioxide (CO2)  

Open Energy Info (EERE)

The Smart Grid: An Estimation of the Energy and Carbon Dioxide (CO2) The Smart Grid: An Estimation of the Energy and Carbon Dioxide (CO2) Benefits Jump to: navigation, search Tool Summary LAUNCH TOOL Name: The Smart Grid: An Estimation of the Energy and Carbon Dioxide (CO2) Benefits Focus Area: Crosscutting Topics: Market Analysis Website: energyenvironment.pnl.gov/news/pdf/PNNL-19112_Revision_1_Final.pdf Equivalent URI: cleanenergysolutions.org/content/smart-grid-estimation-energy-and-carb Language: English Policies: "Deployment Programs,Regulations,Financial Incentives" is not in the list of possible values (Deployment Programs, Financial Incentives, Regulations) for this property. DeploymentPrograms: Public-Private Partnerships Regulations: "Resource Integration Planning,Mandates/Targets,Enabling Legislation,Appliance & Equipment Standards and Required Labeling" is not in the list of possible values (Agriculture Efficiency Requirements, Appliance & Equipment Standards and Required Labeling, Audit Requirements, Building Certification, Building Codes, Cost Recovery/Allocation, Emissions Mitigation Scheme, Emissions Standards, Enabling Legislation, Energy Standards, Feebates, Feed-in Tariffs, Fuel Efficiency Standards, Incandescent Phase-Out, Mandates/Targets, Net Metering & Interconnection, Resource Integration Planning, Safety Standards, Upgrade Requirements, Utility/Electricity Service Costs) for this property.

376

End-use energy consumption estimates for US commercial buildings, 1989  

SciTech Connect

An accurate picture of how energy is used in the nation`s stock of commercial buildings can serve a variety of program planning and policy needs within the Department of Energy, by utilities, and other groups seeking to improve the efficiency of energy use in the building sector. This report describes an estimation of energy consumption by end use based upon data from the 1989 Commercial Building Energy Consumption Survey (CBECS). The methodology used in the study combines elements of engineering simulations and statistical analysis to estimate end-use intensities for heating, cooling, ventilation, lighting, refrigeration, hot water, cooking, and miscellaneous equipment. Billing data for electricity and natural gas were first decomposed into weather and nonweather dependent loads. Subsequently, Statistical Adjusted Engineering (SAE) models were estimated by building type with annual data. The SAE models used variables such as building size, vintage, climate region, weekly operating hours, and employee density to adjust the engineering model predicted loads to the observed consumption. End-use consumption by fuel was estimated for each of the 5,876 buildings in the 1989 CBECS. The report displays the summary results for eleven separate building types as well as for the total US commercial building stock.

Belzer, D.B.; Wrench, L.E.; Marsh, T.L. [Pacific Northwest Lab., Richland, WA (United States)

1993-11-01T23:59:59.000Z

377

End-use energy consumption estimates for U.S. commercial buildings, 1992  

SciTech Connect

An accurate picture of how energy is used in the nation`s stock of commercial buildings can serve a variety of program planning and policy needs of the US Department of Energy, utilities, and other groups seeking to improve the efficiency of energy use in the building sector. This report describes an estimation of energy consumption by end use based upon data from the 1992 Commercial Building Energy Consumption Survey (CBECS). The methodology used in the study combines elements of engineering simulations and statistical analysis to estimate end-use intensities for heating, cooling, ventilation, lighting, refrigeration, hot water, cooking, and miscellaneous equipment. Statistical Adjusted Engineering (SAE) models were estimated by building type. The nonlinear SAE models used variables such as building size, vintage, climate region, weekly operating hours, and employee density to adjust the engineering model predicted loads to the observed consumption (based upon utility billing information). End-use consumption by fuel was estimated for each of the 6,751 buildings in the 1992 CBECS. The report displays the summary results for 11 separate building types as well as for the total US commercial building stock. 4 figs., 15 tabs.

Belzer, D.B.; Wrench, L.E.

1997-03-01T23:59:59.000Z

378

Fine Adjustment of Large Scale Air-Sea Energy Flux Parameterizations by Direct Estimates of Ocean Heat Transport  

Science Conference Proceedings (OSTI)

An inverse technique is used to adjust uncertain coefficients and parameters in the bulk formulae of climatological air-sea energy fluxes in order to obtain an agreement of indirect estimates of meridional heat transport with direct estimates in ...

Hans-Jrg Isemer; Jrgen Willebrand; Lutz Hasse

1989-10-01T23:59:59.000Z

379

Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings  

SciTech Connect

Buildings represent an increasingly important component of China's total energy consumption mix. However, accurately assessing the total volume of energy consumed in buildings is difficult owing to deficiencies in China's statistical collection system and a lack of national surveys. Official statistics suggest that buildings account for about 19% of China's total energy consumption, while others estimate the proportion at 23%, rising to 30% over the next few years. In addition to operational energy, buildings embody the energy used in the in the mining, extraction, harvesting, processing, manufacturing and transport of building materials as well as the energy used in the construction and decommissioning of buildings. This embodied energy, along with a building's operational energy, constitutes the building's life-cycle energy and emissions footprint. This report first provides a review of international studies on commercial building life-cycle energy use from which data are derived to develop an assessment of Chinese commercial building life-cycle energy use, then examines in detail two cases for the development of office building operational energy consumption to 2020. Finally, the energy and emissions implications of the two cases are presented.

Fridley, David; Fridley, David G.; Zheng, Nina; Zhou, Nan

2008-03-01T23:59:59.000Z

380

DOE Order Self Study Modules - DOE O 442.1 Department of energy Employee Concerns Program  

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

2.1 2.1 DEPARTMENT OF ENERGY EMPLOYEE CONCERNS PROGRAM ALBUQUERQUE OPERATIONS OFFICE Change No: 0 DOE O 442.1 Level: Familiar Date: 6/15/01 1 DOE O 442.1 DOE EMPLOYEE CONCERNS PROGRAM FAMILIAR LEVEL _________________________________________________________________________ OBJECTIVES Given the familiar level of this module and the resources listed below, you will be able to perform the following: 1. State three examples of criteria that should be used to assess the significance of an employee's concern. 2. Describe three classifications of employee occupational safety and health concerns that are used for determining safety significance. 3. Discuss the objective of implementing DOE O 442.1, DOE Employee Concerns Program. 4. Discuss the four alternatives for processing concerns.

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381

Figure ES1. Schema for Estimating Energy and Energy-Related ...  

U.S. Energy Information Administration (EIA)

Home > Households, Buildings & Industry >Transportation Surveys > Household Vehicles Energy Use > Figure ES1

382

Development of an energy-use estimation methodology for the revised Navy Manual MO-303  

SciTech Connect

The U.S. Navy commissioned Pacific Northwest Laboratory (PNL) to revise and/or update the Navy Utilities Targets Manual, NAVFAC MO-303 (U.S. Navy 1972b). The purpose of the project was to produce a current, applicable, and easy-to-use version of the manual for use by energy and facility engineers and staff at all Navy Public Works Centers (PWCs), Public Works Departments (PWDs), Engineering Field Divisions (EFDs), and other related organizations. The revision of the MO-303 manual involved developing a methodology for estimating energy consumption in buildings and ships. This methodology can account for, and equitably allocate, energy consumption within Navy installations. The analyses used to develop this methodology included developing end-use intensities (EUIs) from a vast collection of Navy base metering and billing data. A statistical analysis of the metering data, weather data, and building energy-use characteristics was used to develop appropriate EUI values for use at all Navy bases. A complete Navy base energy reconciliation process was also created for use in allocating all known energy consumption. Initial attempts to use total Navy base consumption values did not produce usable results. A parallel effort using individual building consumption data provided an estimating method that incorporated weather effects. This method produced a set of building EUI values and weather adjustments for use in estimating building energy use. A method of reconciling total site energy consumption was developed based on a {open_quotes}zero-sum{close_quotes} principle. This method provides a way to account for all energy use and apportion part or all of it to buildings and other energy uses when actual consumption is not known. The entire text of the manual was also revised to present a more easily read understood and usable document.

Richman, E.E.; Keller, J.M.; Wood, A.G.; Dittmer, A.L.

1995-01-01T23:59:59.000Z

383

Estimation of Energy Savings Resulting From the BestPractices Program, Fiscal Year 2002  

SciTech Connect

Within the U.S. Department of Energy (DOE), the Office of Energy Efficiency and Renewable Energy (EERE) has a vision of a future with clean, abundant, reliable, and affordable energy. Within EERE, the Industrial Technologies Program (ITP), formerly the Office of Industrial Technologies, works in partnership with industry to increase energy efficiency, improve environmental performance, and boost productivity. The BestPractices (BP) Program, within ITP, works directly with industries to encourage energy efficiency. The purpose of the BP Program is to improve energy utilization and management practices in the industrial sector. The program targets distinct technology areas, including pumps, process heating, steam, compressed air, motors, and insulation. This targeting is accomplished with a variety of delivery channels, such as computer software, printed publications, Internet-based resources, technical training, technical assessments, and other technical assistance. A team of program evaluators from Oak Ridge National Laboratory (ORNL) was tasked to evaluate the fiscal year 2002 (FY02) energy savings of the program. The ORNL assessment enumerates levels of program activity for technology areas across delivery channels. In addition, several mechanisms that target multiple technology areas--e.g., Plant-wide Assessments (PWAs), the ''Energy Matters'' newsletter, and special events--are also evaluated for their impacts. When possible, the assessment relies on published reports and the Industrial Assessment Center (IAC) database for estimates of energy savings that result from particular actions. Data were also provided by ORNL, Lawrence Berkeley National Laboratory (LBNL) and Project Performance Corporation (PPC), the ITP Clearinghouse at Washington State University, the National Renewable Energy Laboratory (NREL), Energetics Inc., and the Industrial Technologies Program Office. The estimated energy savings in FY02 resulting from activities of the BP Program are almost 81.9 trillion Btu (0.0819 Quad), which is about 0.25% of the 32.5 Quads of energy consumed during FY02 by the industrial sector in the United States. The technology area with the largest estimated savings is steam, with 32% of the total energy savings. The delivery mechanism with the largest savings is that of software systems distribution, encompassing 44% of the total savings. Training results in an energy savings of 33%. Energy savings from PWAs and PWA replications equal 10%. Sources of overestimation of energy savings might derive from (1) a possible overlap of energy savings resulting from separate events (delivery channels) occurring in conjunction with one another (e.g., a training event and CTA at the same plant), and (2) a possible issue with the use of the average CTA value to assess savings for training and software distribution. Any overestimation attributable to these sources probably is outweighed by underestimations caused by the exclusion of savings resulting from general awareness workshops, data not submitted to the ITP Tracking Database, omission of savings attributable to web downloads of publications, use of BP products by participants over multiple years, and the continued utilization of equipment installed or replaced in previous years. Next steps in improving these energy savings estimates include continuing to enhance the design of the ITP Tracking Database and to improve reporting of program activities for the distribution of products and services; obtaining more detailed information on implementation rates and savings estimates for software training, tools, and assessments; continuing attempts to quantify savings based on Qualified Specialist activities; defining a methodology for assessing savings based on web downloads of publications; establishing a protocol for evaluating savings from other BP-sponsored events and activities; and continuing to refine the estimation methodology and reduction factors.

Truett, LF

2003-09-24T23:59:59.000Z

384

Estimation of Energy Savings Resulting From the BestPractices Program, Fiscal Year 2002  

DOE Green Energy (OSTI)

Within the U.S. Department of Energy (DOE), the Office of Energy Efficiency and Renewable Energy (EERE) has a vision of a future with clean, abundant, reliable, and affordable energy. Within EERE, the Industrial Technologies Program (ITP), formerly the Office of Industrial Technologies, works in partnership with industry to increase energy efficiency, improve environmental performance, and boost productivity. The BestPractices (BP) Program, within ITP, works directly with industries to encourage energy efficiency. The purpose of the BP Program is to improve energy utilization and management practices in the industrial sector. The program targets distinct technology areas, including pumps, process heating, steam, compressed air, motors, and insulation. This targeting is accomplished with a variety of delivery channels, such as computer software, printed publications, Internet-based resources, technical training, technical assessments, and other technical assistance. A team of program evaluators from Oak Ridge National Laboratory (ORNL) was tasked to evaluate the fiscal year 2002 (FY02) energy savings of the program. The ORNL assessment enumerates levels of program activity for technology areas across delivery channels. In addition, several mechanisms that target multiple technology areas--e.g., Plant-wide Assessments (PWAs), the ''Energy Matters'' newsletter, and special events--are also evaluated for their impacts. When possible, the assessment relies on published reports and the Industrial Assessment Center (IAC) database for estimates of energy savings that result from particular actions. Data were also provided by ORNL, Lawrence Berkeley National Laboratory (LBNL) and Project Performance Corporation (PPC), the ITP Clearinghouse at Washington State University, the National Renewable Energy Laboratory (NREL), Energetics Inc., and the Industrial Technologies Program Office. The estimated energy savings in FY02 resulting from activities of the BP Program are almost 81.9 trillion Btu (0.0819 Quad), which is about 0.25% of the 32.5 Quads of energy consumed during FY02 by the industrial sector in the United States. The technology area with the largest estimated savings is steam, with 32% of the total energy savings. The delivery mechanism with the largest savings is that of software systems distribution, encompassing 44% of the total savings. Training results in an energy savings of 33%. Energy savings from PWAs and PWA replications equal 10%. Sources of overestimation of energy savings might derive from (1) a possible overlap of energy savings resulting from separate events (delivery channels) occurring in conjunction with one another (e.g., a training event and CTA at the same plant), and (2) a possible issue with the use of the average CTA value to assess savings for training and software distribution. Any overestimation attributable to these sources probably is outweighed by underestimations caused by the exclusion of savings resulting from general awareness workshops, data not submitted to the ITP Tracking Database, omission of savings attributable to web downloads of publications, use of BP products by participants over multiple years, and the continued utilization of equipment installed or replaced in previous years. Next steps in improving these energy savings estimates include continuing to enhance the design of the ITP Tracking Database and to improve reporting of program activities for the distribution of products and services; obtaining more detailed information on implementation rates and savings estimates for software training, tools, and assessments; continuing attempts to quantify savings based on Qualified Specialist activities; defining a methodology for assessing savings based on web downloads of publications; establishing a protocol for evaluating savings from other BP-sponsored events and activities; and continuing to refine the estimation methodology and reduction factors.

Truett, LF

2003-09-24T23:59:59.000Z

385

Accurate location estimation of moving object with energy constraint & adaptive update algorithms to save data  

E-Print Network (OSTI)

In research paper "Accurate estimation of the target location of object with energy constraint & Adaptive Update Algorithms to Save Data" one of the central issues in sensor networks is track the location, of moving object which have overhead of saving data, an accurate estimation of the target location of object with energy constraint .We do not have any mechanism which control and maintain data .The wireless communication bandwidth is also very limited. Some field which is using this technique are flood and typhoon detection, forest fire detection, temperature and humidity and ones we have these information use these information back to a central air conditioning and ventilation system. In this research paper, we propose protocol based on the prediction and adaptive based algorithm which is using less sensor node reduced by an accurate estimation of the target location. we are using minimum three sensor node to get the accurate position .We can extend it upto four or five to find more accurate location ...

Semwal, Vijay Bhaskar; Bhaskar, Vinay S; Sati, Meenakshi

2011-01-01T23:59:59.000Z

386

Estimates of Mass, Momentum and Kinetic Energy Fluxes of the Gulf Stream  

Science Conference Proceedings (OSTI)

Mass, momentum and kinetic-energy fluxes in the Gulf Stream have been estimated from hydrographic data taken by Fuglister in the Gulf Stream 60 project; the data cover the Stream as it flows eastward, from south of Georges Bank to the Grand ...

N. P. Fofonoff; M. M. Hall

1983-10-01T23:59:59.000Z

387

Table PT2. Energy Production Estimates in Trillion Btu, Ohio, 1960 ...  

U.S. Energy Information Administration (EIA)

Table PT2. Energy Production Estimates in Trillion Btu, Ohio, 1960 - 2011 1960 796.6 36.9 31.3 0.0 NA 37.0 37.0 901.9 1961 756.0 37.3 32.7 0.0 NA 36.4 36.4 862.4

388

Comparison of Energy Source Estimates Derived from Atmospheric Circulation Data with Satellite Measurements of Net Radiation  

Science Conference Proceedings (OSTI)

The distributions of the net sources of atmospheric dry and latent energy are evaluated by the residual technique using the reanalyzed ECMWF FGGE level IIIb data for February and July 1979. Their sum (i.e., the residual estimate of the source of ...

Carl Fortelius; Eero Holopainen

1990-06-01T23:59:59.000Z

389

Power in the wind. [Techniques for estimation of wind potential energy  

SciTech Connect

Techniques are described which can be used by engineers, technicians and homeowners for the estimation of potential energy in wind and in particular wind machines. They are suitable for onsite calculations with the use of nothing more than a pocket calculator. (JMT)

Gipe, P.

1981-04-01T23:59:59.000Z

390

Savings estimates for the Energy Star(registered trademark) voluntary labeling program  

Science Conference Proceedings (OSTI)

ENERGY STAR7 is a voluntary labeling program designed to identify and promote energy-efficient products. Operated jointly by the Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE), ENERGY STAR labels exist for more than twenty products, spanning office equipment, residential heating and cooling equipment, new homes, commercial and residential lighting, home electronics, and major appliances. We present estimates of the energy, dollar and carbon savings already achieved by the program and provide savings forecasts for several market penetration scenarios for the period 2001 to 2010. The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100 percent market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard efficiency products throughout the analysis period. Finally, we assess the sensitivity of our target penetration case forecasts to greater or lesser marketing success by EPA and DOE, lower-than-expected future energy prices, and higher or lower rates of carbon emissions by electricity generators.

Webber, Carrie A.; Brown, Richard E.; Koomey, Jonathan G.

2000-07-13T23:59:59.000Z

391

Recoverable Resource Estimate of Identified Onshore Geopressured Geothermal Energy in Texas and Louisiana (Presentation), NREL (National Renewable Energy Laboratory)  

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

Recoverable Resource Estimate of Identified Recoverable Resource Estimate of Identified Onshore Geopressured Geothermal Energy in Texas and Louisiana AAPG 2012 Annual Convention and Exhibition Ariel Esposito and Chad Augustine April 24, 2012 NREL/PR-6A20-54999 2 * Geopressured Geothermal o Reservoirs characterized by pore fluids under high confining pressures and high temperatures with correspondingly large quantities of dissolved methane o Soft geopressure: Hydrostatic to 15.83 kPa/m o Hard geopressure: 15.83- 22.61 kPa/m (lithostatic pressure gradient) * Common Geopressured Geothermal Reservoir Structure o Upper thick low permeability shale o Thin sandstone layer o Lower thick low permeability shale * Three Potential Sources of Energy o Thermal energy (Temperature > 100°C - geothermal electricity generation)

392

Analysis of the residential-energy-conservation tax credits: concepts and numerical estimates  

SciTech Connect

The purposes of the study were to develop an analytical framework for examining the effects of residential energy conservation tax credits, to identify the data needed to obtain numerical analyses of these effects, and to provide numerical estimates and sensitivity analyses of these effects. Investment in both energy-saving devices and renewable energy sources is examined. The variables analyzed are: oil import savings, their time path, revenue costs to the U.S. Treasury, and the net economic welfare gain or loss. The results of the study are summarized. (MCW)

Not Available

1982-06-01T23:59:59.000Z

393

Estimating the Benefits of Government-Sponsored Energy R&D: Synthesis of Conference Discussions  

SciTech Connect

In 2001, a National Research Council (NRC) committee conducted a retrospective study of the benefits of some of the energy efficiency and fossil energy programs in the U.S. Department of Energy (DOE). As part of its study, the NRC committee developed a methodological framework for estimating these benefits. Following the NRC report, a conference was organized by Oak Ridge National Laboratory to discuss ways of adapting and refining the NRC framework for possible use by DOE offices to help plan and manage their R&D. This report is a synthesis of the discussions at the conference.

Lee, R.

2003-11-14T23:59:59.000Z

394

Energy loss estimates at several beam intensities in the Fermilab Booster  

SciTech Connect

The difference between the effective rf voltage and the accelerating voltage required to match the rate of change of the Booster magnetic field can be used to estimate the energy loss per beam turn. Although the effective rf voltage (RFSUM) and the synchronous phase can be experimentally measured and used to calculate the accelerating voltage, the calibration of the signals during the fast change of the Booster rf frequency is difficult and appears to introduce some offset to the beam energy loss estimation. An observed linear relationship between energy loss and beam intensity is used to evaluate the offset, which is then applied to the experimental data. This approach, rather than recalibrating the signals, is simple and suitable for minimizing the error in the data.

Xi Yang and James MacLachlan

2004-06-08T23:59:59.000Z

395

Modeling renewable portfolio standards for the annual energy outlook 1998 - electricity market module  

SciTech Connect

The Electricity Market Module (EMM) is the electricity supply component of the National Energy Modeling System (NEMS). The EMM represents the generation, transmission, and pricing of electricity. It consists of four submodules: the Electricity Capacity Planning (ECP) Submodule, the Electricity Fuel Dispatch (EFD) Submodule, the Electricity Finance and Pricing (EFP) Submodule, and the Load and Demand-Side Management (LDSM) Submodule. For the Annual Energy Outlook 1998 (AEO98), the EMM has been modified to represent Renewable Portfolio Standards (RPS), which are included in many of the Federal and state proposals for deregulating the electric power industry. A RPS specifies that electricity suppliers must produce a minimum level of generation using renewable technologies. Producers with insufficient renewable generating capacity can either build new plants or purchase {open_quotes}credits{close_quotes} from other suppliers with excess renewable generation. The representation of a RPS involves revisions to the ECP, EFD, and the EFP. The ECP projects capacity additions required to meet the minimum renewable generation levels in future years. The EFD determines the sales and purchases of renewable credits for the current year. The EFP incorporates the cost of building capacity and trading credits into the price of electricity.

1998-02-01T23:59:59.000Z

396

T ti E St S tTetiaroa Energy Storage System Estimated ZBB Zinc Bromide Battery Performance and Costs  

E-Print Network (OSTI)

T ti E St S tTetiaroa Energy Storage System Estimated ZBB Zinc Bromide Battery Performance and Costs Prull / KammenPrull / Kammen Renewable and Appropriate Energy Lab, UC Berkeley 7/26/2010 http

Kammen, Daniel M.

397

An Estimate of Energy Use in Laboratories, Cleanrooms, and Data Centers in New York  

SciTech Connect

Laboratories, cleanrooms and data centers are very energy-intensive. For example, laboratories are typically three to eight times as energy intensive as a typical office building, and a data center may be as much as 20-50 times as energy intensive as a typical office building. This technical note presents an estimate of the total energy use in laboratories, cleanrooms and data centers in New York. There is generally very limited data on energy use in the high tech sector, both at the national and state level. Since it was beyond the scope of this project to develop primary data through surveys, the analysis relied primarily on the use of proxy indicators and extrapolation from national data where available. The results for each building type are summarized below in table E-1 and figure E-1.

Mathew, Paul

2008-10-01T23:59:59.000Z

398

Macroeconomic Activity Module  

Annual Energy Outlook 2012 (EIA)

d022412A. U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2012 18 Macroeconomic Activity Module To reflect uncertainty in the projection of...

399

NREL Study Finds U.S. Wind Energy Potential Triples Previous Estimates (Fact Sheet)  

DOE Green Energy (OSTI)

The maximum potential to generate wind power in the contiguous United States is more than three times greater than previously estimated, according to a National Renewable Energy Laboratory (NREL) study. The new analysis is based on the latest computer models and examines the wind potential at wind turbine hub heights of 80 meters and 100 meters. These hub heights, which reflect current and future models of wind turbines, are higher than those used in previous national estimates and are mainly responsible for the increased wind potential in the study.

Not Available

2011-02-01T23:59:59.000Z

400

Embedded DRAM (eDRAM) Power-Energy Estimation for System-on-a-chip (SoC) Applications  

Science Conference Proceedings (OSTI)

EmbeddedDRAM (eDRAM) power-energy estimation is presented for system-on-a-chip (SOC) applications. The main feature is the signal swing based analytic (SSBA) model, which improves the accuracy of the conventional SRAM power-energy models. The SSBA model ... Keywords: embedded DRAM, power estimation

Yong-Ha Park; Hoi-Jun Yoo; Jeonghoon Kook

2002-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Assumptions to the Annual Energy Outlook 2002 - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply. 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(2002), (Washington, DC, January 2002). 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 gas from domestic fields throughout the United States, acquire natural gas from foreign producers for resale in the United States, or sell U.S. gas to foreign consumers. OGSM encompasses domestic crude oil and natural gas supply by both

402

Assumptions to the Annual Energy Outlook 2001 - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply. 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(2001), (Washington, DC, January 2001). 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 gas from domestic fields throughout the United States, acquire natural gas from foreign producers for resale in the United States, or sell U.S. gas to foreign consumers. OGSM encompasses domestic crude oil and natural gas supply by both

403

MHK Technologies/Small power take off module | Open Energy Information  

Open Energy Info (EERE)

power take off module power take off module < MHK Technologies Jump to: navigation, search << Return to the MHK database homepage Small power take off module.jpg Technology Profile Primary Organization Wavegen subsidiary of Voith Siemens Hydro Power Generation Technology Resource Click here Wave Technology Readiness Level Click here TRL 1 3 Discovery Concept Def Early Stage Dev Design Engineering Technology Description The 18 5kW power modules consist of a 5th generation Wells turbine valve and noise attenuator The complete modules weigh less than a tonne so installation or removal is easily achievable using a small mobile crane The modules are very simple and rugged the blades are fixed onto the rotor have no pitching mechanism no gearbox and have no contact with seawater

404

Recoverable Resource Estimate of Identified Onshore Geopressured Geothermal Energy in Texas and Louisiana (Presentation)  

Science Conference Proceedings (OSTI)

Geopressured geothermal reservoirs are characterized by high temperatures and high pressures with correspondingly large quantities of dissolved methane. Due to these characteristics, the reservoirs provide two sources of energy: chemical energy from the recovered methane, and thermal energy from the recovered fluid at temperatures high enough to operate a binary power plant for electricity production. Formations with the greatest potential for recoverable energy are located in the gulf coastal region of Texas and Louisiana where significantly overpressured and hot formations are abundant. This study estimates the total recoverable onshore geopressured geothermal resource for identified sites in Texas and Louisiana. In this study a geopressured geothermal resource is defined as a brine reservoir with fluid temperature greater than 212 degrees F and a pressure gradient greater than 0.7 psi/ft.

Esposito, A.; Augustine, C.

2012-04-01T23:59:59.000Z

405

Evaluation of a Two-Source Snow-Vegetation Energy Balance Model for Estimating Surface Energy Fluxes in a Rangeland Ecosystem  

Science Conference Proceedings (OSTI)

The utility of a snow-vegetation energy balance model for estimating surface energy fluxes is evaluated with field measurements at two sites in a rangeland ecosystem in southwestern Idaho during the winter of 2007: one site dominated by aspen ...

Cezar Kongoli; William P. Kustas; Martha C. Anderson; John M. Norman; Joseph G. Alfieri; Gerald N. Flerchinger; Danny Marks

406

Review of Photovoltaic Energy Production Using CdTe Thin-Film Modules: Extended Abstract Preprint  

DOE Green Energy (OSTI)

CdTe has near-optimum bandgap, excellent deposition traits, and leads other technologies in commercial PV module production volume. Better understanding materials properties will accelerate deployment.

Gessert, T. A.

2008-09-01T23:59:59.000Z

407

Well-posed initial-boundary value problem for the harmonic Einstein equations using energy estimates  

E-Print Network (OSTI)

In recent work, we used pseudo-differential theory to establish conditions that the initial-boundary value problem for second order systems of wave equations be strongly well-posed in a generalized sense. The applications included the harmonic version of the Einstein equations. Here we show that these results can also be obtained via standard energy estimates, thus establishing strong well-posedness of the harmonic Einstein problem in the classical sense.

H. -O. Kreiss; O. Reula; O. Sarbach; J. Winicour

2007-07-27T23:59:59.000Z

408

The tradeoff between energy efficiency and user state estimation accuracy in mobile sensing  

E-Print Network (OSTI)

Abstract. People-centric sensing and user state recognition can provide rich contextual information for various mobile applications and services. However, continuously capturing this contextual information on mobile devices drains device battery very quickly. In this paper, we study the tradeoff between device energy consumption and user state recognition accuracy from a novel perspective. We assume the user state evolves as a hidden discrete time Markov chain (DTMC) and an embedded sensor on mobile device discovers user state by performing a sensing observation. We investigate a stationary deterministic sensor sampling policy which assigns different sensor duty cycles based on different user states, and propose two state estimation mechanisms providing the best guess of user state sequence when observations are missing. We analyze the effect of varying sensor duty cycles on (a) device energy consumption and (b) user state estimation error, and visualize the tradeoff between the two numerically for a two-state setting. Key words: mobile sensing, energy efficiency, user state estimation accuracy, tradeoff 1

Yi Wang; Bhaskar Krishnamachari; Qing Zhao; Murali Annavaram

2009-01-01T23:59:59.000Z

409

Estimating the Impact (Energy, Emissions and Economics) of the US Fluid Power Industry  

Science Conference Proceedings (OSTI)

The objective of this report is to estimate the impact (energy, emissions and economics) of United Fluid power (hydraulic and pneumatic actuation) is the generation, control, and application of pumped or compressed fluids when this power is used to provide force and motion to mechanisms. This form of mechanical power is an integral part of United States (U.S.) manufacturing and transportation. In 2008, according to the U.S. Census Bureau, sales of fluid power components exceeded $17.7B, sales of systems using fluid power exceeded $226B. As large as the industry is, it has had little fundamental research that could lead to improved efficiency since the late 1960s (prior to the 1970 energy crisis). While there have been some attempts to replace fluid powered components with electric systems, its performance and rugged operating condition limit the impact of simple part replacement. Oak Ridge National Laboratory and the National Fluid Power Association (NFPA) collaborated with 31 industrial partners to collect and consolidate energy specific measurements (consumption, emissions, efficiency) of deployed fluid power systems. The objective of this study was to establish a rudimentary order of magnitude estimate of the energy consumed by fluid powered systems. The analysis conducted in this study shows that fluid powered systems consumed between 2.0 and 2.9 Quadrillion (1015) Btus (Quads) of energy per year; producing between 310 and 380 million metric tons (MMT) of Carbon Dioxide (CO2). In terms of efficiency, the study indicates that, across all industries, fluid power system efficiencies range from less than 9% to as high as 60% (depending upon the application), with an average efficiency of 22%. A review of case studies shows that there are many opportunities to impact energy savings in both the manufacturing and transportation sectors by the development and deployment of energy efficient fluid power components and systems.

Love, Lonnie J [ORNL

2012-12-01T23:59:59.000Z

410

Overview of the PV Module Model in PVWatts (Presentation)  

DOE Green Energy (OSTI)

Overview of the PV module model. PVWatts module power estimates were compared with those using the Sandia model for three modules and data sets.

Marion, B.

2010-09-22T23:59:59.000Z

411

Detailed Course Module Description  

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

Course Module Description Course Module Description Module/Learning Objectives Level of Detail in Module by Audience Consumers Gen Ed/ Community College Trades 1. Energy Issues and Building Solutions High High High Learning Objectives: * Define terms of building science, ecological systems, economics of consumption * Relate building science perspective, ecology, social science * Explain historical energy and environmental issues related to buildings * Compare Site and source energy * Examine the health, safety and comfort issues in buildings * Examine the general context for building solutions (zero energy green home with durability as the goal) * Explain a basic overview of alternative energy (total solar flux) - do we have enough energy * Examine cash flow to homeowners

412

PRISM 2.0: Personal Transportation Module of the U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN) Model: A Guide to Operation and Development  

Science Conference Proceedings (OSTI)

The personal transportation/electric vehicle penetration module (Transportation Module) of the U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN) integrated regional macroeconomic model is a structural economic model of personal vehicle purchase and driving behaviors that focuses on the adoption of electric vehicles. The module employs a representation of consumers demographics, existing vehicles, vehicle choices, and preferences for vehicle characteristics to model personal vehicle ...

2013-09-26T23:59:59.000Z

413

Optimal estimation of free energies and stationary densities from multiple biased simulations  

E-Print Network (OSTI)

When studying high-dimensional dynamical systems such as macromolecules, quantum systems and polymers, a prime concern is the identification of the most probable states and their stationary probabilities or free energies. Often, these systems have metastable regions or phases, prohibiting to estimate the stationary probabilities by direct simulation. Efficient sampling methods such as umbrella sampling, metadynamics and conformational flooding have developed that perform a number of simulations where the system's potential is biased such as to accelerate the rare barrier crossing events. A joint free energy profile or stationary density can then be obtained from these biased simulations with weighted histogram analysis method (WHAM). This approach (a) requires a few essential order parameters to be defined in which the histogram is set up, and (b) assumes that each simulation is in global equilibrium. Both assumptions make the investigation of high-dimensional systems with previously unknown energy landscape ...

Wu, Hao

2013-01-01T23:59:59.000Z

414

Cost and energy consumption estimates for the aluminum-air battery anode fuel cycle  

DOE Green Energy (OSTI)

At the request of DOE's Office of Energy Storage and Distribution (OESD), Pacific Northwest Laboratory (PNL) conducted a study to generate estimates of the energy use and costs associated with the aluminum anode fuel cycle of the aluminum-air (Al-air) battery. The results of this analysis indicate that the cost and energy consumption characteristics of the mechanically rechargeable Al-air battery system are not as attractive as some other electrically rechargeable electric vehicle battery systems being developed by OESD. However, there are distinct advantages to mechanically rechargeable batteries, which may make the Al-air battery (or other mechanically rechargeable batteries) attractive for other uses, such as stand-alone applications. Fuel cells, such as the proton exchange membrane (PEM), and advanced secondary batteries may be better suited to electric vehicle applications. 26 refs., 3 figs., 25 tabs.

Humphreys, K.K.; Brown, D.R.

1990-01-01T23:59:59.000Z

415

The Smart Grid: An Estimation of the Energy and CO2 Benefits  

Science Conference Proceedings (OSTI)

This report articulates nine mechanisms by which the smart grid can reduce energy use and carbon impacts associated with electricity generation and delivery. The quantitative estimates of potential reductions in electricity sector energy and associated CO2 emissions presented are based on a survey of published results and simple analyses. This report does not attempt to justify the cost effectiveness of the smart grid, which to date has been based primarily upon the twin pillars of cost-effective operation and improved reliability. Rather, it attempts to quantify the additional energy and CO2 emission benefits inherent in the smart grids potential contribution to the nations goal of mitigating climate change by reducing the carbon footprint of the electric power system.

Pratt, Robert G.; Balducci, Patrick J.; Gerkensmeyer, Clint; Katipamula, Srinivas; Kintner-Meyer, Michael CW; Sanquist, Thomas F.; Schneider, Kevin P.; Secrest, Thomas J.

2010-01-27T23:59:59.000Z

416

The Smart Grid: An Estimation of the Energy and CO2 Benefits  

Science Conference Proceedings (OSTI)

This report articulates nine mechanisms by which the smart grid can reduce energy use and carbon impacts associated with electricity generation and delivery. The quantitative estimates of potential reductions in electricity sector energy and associated CO2 emissions presented are based on a survey of published results and simple analyses. This report does not attempt to justify the cost effectiveness of the smart grid, which to date has been based primarily upon the twin pillars of cost-effective operation and improved reliability. Rather, it attempts to quantify the additional energy and CO2 emission benefits inherent in the smart grids potential contribution to the nations goal of mitigating climate change by reducing the carbon footprint of the electric power system.

Pratt, Robert G.; Balducci, Patrick J.; Gerkensmeyer, Clint; Katipamula, Srinivas; Kintner-Meyer, Michael CW; Sanquist, Thomas F.; Schneider, Kevin P.; Secrest, Thomas J.

2010-01-15T23:59:59.000Z

417

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Energy Module Oil and Gas Supply Module Household Expenditures Module Natural Gas Transmission and Distribution Module Residential Demand Module Petroleum Market Module...

418

Stability estimates for an inverse problem for the Schr\\"odinger equation at negative energy in two dimensions  

E-Print Network (OSTI)

We study the inverse problem of determining a real-valued potential in the two-dimensional Schr\\"odinger equation at negative energy from the Dirichlet-to-Neumann map. It is known that the problem is ill-posed and a stability estimate of logarithmic type holds. In this paper we prove three new stability estimates. The main feature of the first one is that the stability increases exponentially with respect to the smoothness of the potential, in a sense to be made precise. The others show how the first estimate depends on the energy, for low and high energies (in modulus). In particular it is found that for high energies the stability estimate changes, in some sense, from logarithmic type to Lipschitz type: in this sense the ill-posedness of the problem decreases when increasing the energy (in modulus).

Santacesaria, Matteo

2012-01-01T23:59:59.000Z

419

Estimates of wind energy input to the Ekman layer in the Southern Ocean from surface drifter data  

E-Print Network (OSTI)

Estimates of wind energy input to the Ekman layer in the Southern Ocean from surface drifter data the contribution from the anticyclonic frequencies dominate the wind energy input. The latitudinal and seasonal variations of the wind energy input to the Ekman layer are closely related to the variations of the wind

Gille, Sarah T.

420

Practical method for estimating wind characteristics at potential wind-energy-conversion sites  

DOE Green Energy (OSTI)

Terrain features and variations in the depth of the atmospheric boundary layer produce local variations in wind, and these variations are not depicted well by standard weather reports. A method is developed to compute local winds for use in estimating the wind energy available at any potential site for a wind turbine. The method uses the terrain heights for an area surrounding the site and a series of wind and pressure reports from the nearest four or five national Weather Service stations. An initial estimate of the winds in the atmospheric boundary layer is made, then these winds are adjusted to satisfy the continuity equation. In this manner the flow is made to reflect the influences of the terrain and the shape of the boundary-layer top. This report describes in detail the methodology and results, and provides descriptions of the computer programs, instructions for using them, and complete program listings.

Endlich, R. M.; Ludwig, F. L.; Bhumralkar, C. M.; Estoque, M. A.

1980-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

Bayesian Estimates of Free Energies from Nonequilibrium Work Data in the Presence of Instrument Noise  

Science Conference Proceedings (OSTI)

The Jarzynski equality and the fluctuation theorem relate equilibrium free energy differences to nonequilibrium measurements of the work. These relations extend to single-molecule experiments that have probed the finite-time thermodynamics of proteins and nucleic acids. The effects of experimental error and instrument noise have not been considered previously. Here, we present a Bayesian formalism for estimating free energy changes from nonequilibrium work measurements that compensates for instrument noise and combines data from multiple driving protocols. We reanalyze a recent set of experiments in which a single RNA hairpin is unfolded and refolded using optical tweezers at three different rates. Interestingly, the fastest and farthest-from-equilibrium measurements contain the least instrumental noise and, therefore, provide a more accurate estimate of the free energies than a few slow, more noisy, near-equilibrium measurements. The methods we propose here will extend the scope of single-molecule experiments; they can be used in the analysis of data from measurements with atomic force microscopy, optical, and magnetic tweezers

Maragakis, Paul; Ritort, Felix; Bustamante, Carlos; Karplus, Martin; Crooks, Gavin E.

2008-07-08T23:59:59.000Z

422

Appendix model performance - model documentation renewable fuels module of the National Energy Modeling System  

DOE Green Energy (OSTI)

This appendix discusses performance aspects of the Renewable Fuels Module (RFM). It is intended to present the pattern of response of the RFM to typical changes in its major inputs from other NEMS modules. The overall approach of this document, with the particular statistics presented, is designed to be comparable with similar analyses conducted for all of the modules of NEMS. While not always applicable, the overall approach has been to produce analyses and statistics that are as comparable as possible with model developer`s reports for other NEMS modules. Those areas where the analysis is somewhat limited or constrained are discussed. Because the RFM consists of independent submodules, this appendix is broken down by submodule.

Not Available

1994-09-01T23:59:59.000Z

423

A new approach to estimate commercial sector end-use load shapes and energy use intensities  

SciTech Connect

We discuss the application of an end-use load shape estimation technique to develop annual energy use intensities (EUIs) and hourly end-use load shapes (LSs) for commercial buildings in the Pacific Gas and Electric Company (PG&E) service territory. Results will update inputs for the commercial sector energy and peak demand forecasting models used by PG&E and the California Energy Commission (CEC). EUIs were estimated for 11 building types, up to 10 end uses, 3 fuel types, 2 building vintages, and up to 5 climate regions. The integrated methodology consists of two major parts. The first part is the reconciliation of initial end-use load-shape estimates with measured whole-building load data to produce intermediate EUIs and load shapes, using LBL`s End-use Disaggregation Algorithm, EDA. EDA is a deterministic hourly algorithm that relies on the observed characteristics of the measured hourly whole-building electricity use and disaggregates it into major end-use components. The end-use EUIs developed through the EDA procedure represent a snap-shot of electricity use by building type and end-use for two regions of the PG&E service territory, for the year that disaggregation is performed. In the second part of the methodology, we adjust the EUIs for direct application to forecasting models based on factors such as climatic impacts on space-conditioning EUIs, fuel saturation effects, building and equipment vintage, and price impacts. Core data for the project are detailed on-site surveys for about 800 buildings, mail surveys ({approximately}6000), load research data for over 1000 accounts, and hourly weather data for five climate regions.

Akbari, H.; Eto, J.; Konopacki, S.; Afzal, A.; Heinemeier, K.; Rainer, L.

1994-08-01T23:59:59.000Z

424

NREL Triples Previous Estimates of U.S. Wind Power Potential (Fact Sheet), The Spectrum of Clean Energy Innovation, NREL (National Renewable Energy Laboratory)  

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

Triples Previous Estimates of Triples Previous Estimates of U.S. Wind Power Potential The National Renewable Energy Laboratory (NREL) recently released new estimates of the U.S. potential for wind-generated electricity, using advanced wind mapping and validation techniques that triple previous estimates of the size of the nation's wind resources. The new study, conducted by NREL and AWS TruePower, finds that the contiguous 48 states have the potential to generate up to 37 million gigawatt-hours annually. In comparison, the total U.S. electricity generation from all sources was roughly 4 million gigawatt-hours in 2009. Detailed state-by-state estimates of wind energy potential for the United States show the estimated average wind speeds at an 80-meter height. The wind resource maps and estimates

425

Efficient estimation of energy transfer efficiency in light-harvesting complexes  

E-Print Network (OSTI)

The fundamental physical mechanisms of energy transfer in photosynthetic complexes is not yet fully understood. In particular, the degree of efficiency or sensitivity of these systems for energy transfer is not known given their non-perturbative and non-Markovian interactions with proteins backbone and surrounding photonic and phononic environments. One major problem in studying light-harvesting complexes has been the lack of an efficient method for simulation of their dynamics in biological environments. To this end, here we revisit the second-order time-convolution (TC2) master equation and examine its reliability beyond extreme Markovian and perturbative limits. In particular, we present a derivation of TC2 without making the usual weak system-bath coupling assumption. Using this equation, we explore the long time behaviour of exciton dynamics of Fenna-Matthews-Olson (FMO) protein complex. Moreover, we introduce a constructive error analysis to estimate the accuracy of TC2 equation in calculating energy transfer efficiency, exhibiting reliable performance for environments with weak and intermediate memory and strength. Furthermore, we numerically show that energy transfer efficiency is optimal and robust for the FMO protein complex of green sulphur bacteria with respect to variations in reorganization energy and bath correlation time-scales.

Alireza Shabani; Masoud Mohseni; Herschel Rabitz; Seth Lloyd

2011-03-20T23:59:59.000Z

426

Estimates of the Global Indirect Energy-Use Emission Impacts of USA Biofuel Policy  

Science Conference Proceedings (OSTI)

This paper evaluates the indirect energy-use emission implications of increases in the use of biofuels in the USA between 2001 and 2010 as mandates within a dynamic global computable general equilibrium model. The study incorporates explicit markets for biofuels, petroleum and other fossil fuels, and accounts for interactions among all sectors of an 18-region global economy. It considers bilateral trade, as well as the dynamics of capital allocation and investment. Simulation results show that the biofuel mandates in the USA generate an overall reduction in global energy use and emissions over the simulation period from 2001 to 2030. Consequently, the indirect energy-use emission change or emission leakage under the mandate is negative. That is, global emission reductions are larger than the direct emission savings from replacing petroleum with biofuels under the USA RFS2 over the last decade. Under our principal scenario this enhanced the direct emission reduction from biofuels by about 66%. The global change in lifecycle energy-use emissions for this scenario was estimated to be about 93 million tons of CO2e in 2010, 45 million tons of CO2e in 2020, and an increase of 5 million tons of CO2e in 2030, relative to the baseline scenario. Sensitivity results of six alternative scenarios provided additional insights into the pattern of the regional and global effects of biofuel mandates on energy-use emissions.

Oladosu, Gbadebo A [ORNL

2012-01-01T23:59:59.000Z

427

Reliability estimation for multiunit nuclear and fossil-fired industrial energy systems  

SciTech Connect

The purpose of this paper is twofold: to report industrial experience with process energy system reliability, and to assess the reliability of multiunit nuclear and fossil-fired energy systems in an industrial setting. Reliability here refers to the percentage of clock time that sufficient amounts of steam energy were available to permit desired production quotas to be met at a particular plant. A nationwide survey was conducted to obtain data relative to energy system reliabilities during 1973--74, and these data for 29 plants from chemicals and allied products (S.I.C. 28), petroleum refining and related industries (S.I.C. 29) and primary metals industries (S.I.C. 33) are reported here. A simulation model in which various operating characteristics of the energy systems were taken into account was developed to obtain estimates of reliabilities of proposed multiunit nuclear and fossil-fired systems. Based on several example problems evaluated with the simulation model, study results indicated that multiple nuclear units or a combination of nuclear and fossil-fired units could provide adequate reliability to meet large-scale industrial requirements for continuity of service.

Sullivan, W.G.; Wilson, J.V.; Klepper, O.H.

1978-01-01T23:59:59.000Z

428

Geothermal -- The Energy Under Our Feet: Geothermal Resource Estimates for the United States  

DOE Green Energy (OSTI)

On May 16, 2006, the National Renewable Energy Laboratory (NREL) in Golden, Colorado hosted a geothermal resources workshop with experts from the geothermal community. The purpose of the workshop was to re-examine domestic geothermal resource estimates. The participating experts were organized into five working groups based on their primary area of expertise in the following types of geothermal resource or application: (1) Hydrothermal, (2) Deep Geothermal Systems, (3) Direct Use, (4) Geothermal Heat Pumps (GHPs), and (5) Co-Produced and Geopressured. The workshop found that the domestic geothermal resource is very large, with significant benefits.

Green, B. D.; Nix, R. G.

2006-11-01T23:59:59.000Z

429

Modulation of Root Microbiome Community Assembly by the Plant Immune Response (2013 DOE JGI Genomics of Energy and Environment 8th Annual User Meeting)  

SciTech Connect

Sarah Lebeis of University of North Carolina on "Modulation of root microbiome community assembly by the plant immune response" at the 8th Annual Genomics of Energy & Environment Meeting on March 28, 2013 in Walnut Creek, Calif.

Lebeis, Sarah [University of North Carolina

2013-03-01T23:59:59.000Z

430

Decision support system for estimating the technically and economically exploitable renewable energy sources potential in wide areas for connection to high voltage networks  

Science Conference Proceedings (OSTI)

A decision support information system for estimating the technically and economically exploitable renewable energy sources (RES) potential in wide areas is presented in this paper. The system estimates the RES potential in specific areas, examines ... Keywords: DSS, GIS, biomass, decision support systeoms, distributed generation, geographical information systems, high voltage network, renewable energy estimation, renewable energy forecasting, renewable energy potential, renewable energy sources, small hydro, wind energy, wind power

Michael Psalidas; Demosthenes Agoris; Vassilis Kilias; Kostas Tigas; Panagiotis Stratis; Giannis Vlachos

2005-04-01T23:59:59.000Z

431

Twilight Irradiance Reflected by the Earth Estimated from Clouds and the Earth's Radiant Energy System (CERES) Measurements  

Science Conference Proceedings (OSTI)

The upward shortwave irradiance at the top of the atmosphere when the solar zenith angle is greater than 90 (twilight irradiance) is estimated from radiance measurements by the Clouds and the Earth's Radiant Energy System (CERES) instrument on ...

Seiji Kato; Norman G. Loeb

2003-08-01T23:59:59.000Z

432

Estimation of costs for applications of remediation technologies for the Department of Energy`s Programmatic Environmental Impact Statement  

SciTech Connect

The Programmatic Environmental impact Statement (PEIS) being developed by the US Department of Energy (DOE) for environmental restoration (ER) and waste management (WM) activities expected to be carried out across the DOE`s nationwide complex of facilities is assessing the impacts of removing, transporting, treating, storing, and disposing of waste from these ER and WM activities. Factors being considered include health and safety impacts to the public and to workers, impacts on the environment, costs and socio-economic impacts, and near-term and residual risk during those ER and WM operations. The purpose of this paper is to discuss the methodology developed specifically for the PEIS to estimate costs associated with the deployment and application of individual remediation technologies. These individual costs are used in developing order-of-magnitude cost estimates for the total remediation activities. Costs are developed on a per-unit-of-material-to-be-treated basis (i.e., $/m{sup 3}) to accommodate remediation projects of varying sizes. The primary focus of this cost-estimating effort was the development of capital and operating unit cost factors based on the amount of primary media to be removed, handled, and treated. The unit costs for individual treatment technologies were developed using information from a variety of sources, mainly from periodicals, EPA documentation, handbooks, vendor contacts, and cost models. The unit cost factors for individual technologies were adjusted to 1991 dollars.

Villegas, A.J.; Hansen, R.I.; Humphreys, K.K.; Paananen, J.M.; Gildea, L.F.

1994-03-01T23:59:59.000Z

433

Scintillation counter and wire chamber front end modules for high energy physics experiments  

SciTech Connect

This document describes two front-end modules developed for the proposed MIPP upgrade (P-960) experiment at Fermilab. The scintillation counter module was developed for the Plastic Ball detector time and charge measurements. The module has eight LEMO 00 input connectors terminated with 50 ohms and accepts negative photomultiplier signals in the range 0.25...1000 pC with the maximum input voltage of 4.0 V. Each input has a passive splitter with integration and differentiation times of {approx}20 ns. The integrated portion of the signal is digitized at 26.55 MHz by Analog Devices AD9229 12-bit pipelined 4-channel ADC. The differentiated signal is discriminated for time measurement and sent to one of the four TMC304 inputs. The 4-channel TMC304 chip allows high precision time measurement of rising and falling edges with {approx}100 ps resolution and has internal digital pipeline. The ADC data is also pipelined which allows deadtime-less operation with trigger decision times of {approx}4 {micro}s. The wire chamber module was developed for MIPP EMCal detector charge measurements. The 32-channel digitizer accepts differential analog signals from four 8-channel integrating wire amplifiers. The connection between wire amplifier and digitizer is provided via 26-wire twist-n-flat cable. The wire amplifier integrates input wire current and has sensitivity of 275 mV/pC and the noise level of {approx}0.013 pC. The digitizer uses the same 12-bit AD9229 ADC chip as the scintillator counter module. The wire amplifier has a built-in test pulser with a mask register to provide testing of the individual channels. Both modules are implemented as a 6Ux220 mm VME size board with 48-pin power connector. A custom europack (VME) 21-slot crate is developed for housing these front-end modules.

Baldin, Boris; DalMonte, Lou; /Fermilab

2011-01-01T23:59:59.000Z

434

Estimate of Geothermal Energy Resource in Major U.S. Sedimentary Basins (Presentation)  

Science Conference Proceedings (OSTI)

This study estimates the magnitude of geothermal energy from fifteen major known US sedimentary basins and ranks these basins relative to their potential. Because most sedimentary basins have been explored for oil and gas, well logs, temperatures at depth, and reservoir properties are known. This reduces exploration risk and allows development of geologic exploration models for each basin as well as a relative assessment of geologic risk elements for each play. The total available thermal resource for each basin was estimated using the volumetric heat-in-place method originally proposed by Muffler (USGS). Total sedimentary thickness maps, stratigraphic columns, cross sections, and temperature gradient Information were gathered for each basin from published articles, USGS reports, and state geological survey reports. When published data was insufficient, thermal gradients and reservoir properties were derived from oil and gas well logs obtained on oil and gas commission websites. Basin stratigraphy, structural history, and groundwater circulation patterns were studied in order to develop a model that estimates resource size and temperature distribution, and to qualitatively assess reservoir productivity.

Porro, C.; Augustine, C.

2012-04-01T23:59:59.000Z

435

Estimate of the Geothermal Energy Resource in the Major Sedimentary Basins in the United States (Presentation)  

Science Conference Proceedings (OSTI)

Because most sedimentary basins have been explored for oil and gas, well logs, temperatures at depth, and reservoir properties such as depth to basement and formation thickness are well known. The availability of this data reduces exploration risk and allows development of geologic exploration models for each basin. This study estimates the magnitude of recoverable geothermal energy from 15 major known U.S. sedimentary basins and ranks these basins relative to their potential. The total available thermal resource for each basin was estimated using the volumetric heat-in-place method originally proposed by (Muffler, 1979). A qualitative recovery factor was determined for each basin based on data on flow volume, hydrothermal recharge, and vertical and horizontal permeability. Total sedimentary thickness maps, stratigraphic columns, cross sections, and temperature gradient information was gathered for each basin from published articles, USGS reports, and state geological survey reports. When published data were insufficient, thermal gradients and reservoir properties were derived from oil and gas well logs obtained on oil and gas commission databases. Basin stratigraphy, structural history, and groundwater circulation patterns were studied in order to develop a model that estimates resource size, temperature distribution, and a probable quantitative recovery factor.

Esposito, A.; Porro, C.; Augustine, C.; Roberts, B.

2012-09-01T23:59:59.000Z

436

Thermionic modules  

DOE Patents (OSTI)

Modules of assembled microminiature thermionic converters (MTCs) having high energy-conversion efficiencies and variable operating temperatures manufactured using MEMS manufacturing techniques including chemical vapor deposition. The MTCs incorporate cathode to anode spacing of about 1 micron or less and use cathode and anode materials having work functions ranging from about 1 eV to about 3 eV. The MTCs also exhibit maximum efficiencies of just under 30%, and thousands of the devices and modules can be fabricated at modest costs.

King, Donald B. (Albuquerque, NM); Sadwick, Laurence P. (Salt Lake City, UT); Wernsman, Bernard R. (Clairton, PA)

2002-06-18T23:59:59.000Z

437

Estimating the ground state energy of the Schrdinger equation for convex potentials  

E-Print Network (OSTI)

In 2011, the fundamental gap conjecture for Schr\\"odinger operators was proven. This can be used to estimate the ground state energy of the time-independent Schr\\"odinger equation with a convex potential and relative error \\epsilon. Classical deterministic algorithms solving this problem have cost exponential in the number of its degrees of freedom d. We show a quantum algorithm, that is based on a perturbation method, for estimating the ground state energy with relative error \\epsilon. The cost of the algorithm is polynomial in d and \\epsilon^{-1}, while the number of qubits is polynomial in d and \\log\\epsilon^{-1}. In addition, we present an algorithm for preparing a quantum state that overlaps within 1-\\delta, \\delta \\in (0,1), with the ground state eigenvector of the discretized Hamiltonian. This algorithm also approximates the ground state with relative error \\epsilon. The cost of the algorithm is polynomial in d, \\epsilon^{-1} and \\delta^{-1}, while the number of qubits is polynomial in d, \\log\\epsilon^{-1} and \\log\\delta^{-1}.

Anargyros Papageorgiou; Iasonas Petras

2013-09-25T23:59:59.000Z

438

Estimating Energy and Water Losses in Residential Hot WaterDistribution Systems  

DOE Green Energy (OSTI)

Residential single family building practice currently ignores the losses of energy and water caused by the poor design of hot water systems. These losses include; the waste of water while waiting for hot water to get to the point of use; the wasted heat as water cools down in the distribution system after a draw; and the energy needed to reheat water that was already heated once before. Average losses of water are estimated to be 6.35 gallons (24.0 L) per day. (This is water that is rundown the drain without being used while waiting for hot water.) The amount of wasted hot water has been calculated to be 10.9 gallons (41.3L) per day. (This is water that was heated, but either is not used or issued after it has cooled off.) A check on the reasonableness of this estimate is made by showing that total residential hot water use averages about 52.6 gallons (199 L) per day. This indicates about 20 percent of average daily hot water is wasted.

Lutz, James

2005-02-26T23:59:59.000Z

439

PHEV Energy Use Estimation: Validating the Gamma Distribution for Representing the Random Daily Driving Distance  

SciTech Connect

The petroleum and electricity consumptions of plug-in hybrid electric vehicles (PHEVs) are sensitive to the variation of daily vehicle miles traveled (DVMT). Some studies assume DVMT to follow a Gamma distribution, but such a Gamma assumption is yet to be validated. This study finds the Gamma assumption valid in the context of PHEV energy analysis, based on continuous GPS travel data of 382 vehicles, each tracked for at least 183 days. The validity conclusion is based on the found small prediction errors, resulting from the Gamma assumption, in PHEV petroleum use, electricity use, and energy cost. The finding that the Gamma distribution is valid and reliable is important. It paves the way for the Gamma distribution to be assumed for analyzing energy uses of PHEVs in the real world. The Gamma distribution can be easily specified with very few pieces of driver information and is relatively easy for mathematical manipulation. Given the validation in this study, the Gamma distribution can now be used with better confidence in a variety of applications, such as improving vehicle consumer choice models, quantifying range anxiety for battery electric vehicles, investigating roles of charging infrastructure, and constructing online calculators that provide personal estimates of PHEV energy use.

Lin, Zhenhong [ORNL; Dong, Jing [ORNL; Liu, Changzheng [ORNL; Greene, David L [ORNL

2012-01-01T23:59:59.000Z

440

Table ET1. Primary Energy, Electricity, and Total Energy Price and Expenditure Estimates, Selected Years, 1970-2011, United States  

Gasoline and Diesel Fuel Update (EIA)

ET1. Primary Energy, Electricity, and Total Energy Price and Expenditure Estimates, Selected Years, 1970-2011, United States ET1. Primary Energy, Electricity, and Total Energy Price and Expenditure Estimates, Selected Years, 1970-2011, United States Year Primary Energy Electric Power Sector h,j Retail Electricity Total Energy g,h,i Coal Coal Coke Natural Gas a Petroleum Nuclear Fuel Biomass Total g,h,i,j Coking Coal Steam Coal Total Exports Imports Distillate Fuel Oil Jet Fuel b LPG c Motor Gasoline d Residual Fuel Oil Other e Total Wood and Waste f,g Prices in Dollars per Million Btu 1970 0.45 0.36 0.38 1.27 0.93 0.59 1.16 0.73 1.43 2.85 0.42 1.38 1.71 0.18 1.29 1.08 0.32 4.98 1.65 1975 1.65 0.90 1.03 2.37 3.47 1.18 2.60 2.05 2.96 4.65 1.93 2.94 3.35 0.24 1.50 2.19 0.97 8.61 3.33 1980 2.10 1.38 1.46 2.54 3.19 2.86 6.70 6.36 5.64 9.84 3.88 7.04 7.40 0.43 2.26 4.57 1.77 13.95 6.89 1985 2.03 1.67 1.69 2.76 2.99 4.61 7.22 5.91 6.63 9.01 4.30 R 7.62 R 7.64 0.71 2.47 4.93 1.91 19.05

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441

The National Energy Modeling System: An Overview 1998 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

COAL MARKET MODULE COAL MARKET MODULE blueball.gif (205 bytes) Coal Production Submodule blueball.gif (205 bytes) Coal Distribution Submodule blueball.gif (205 bytes) Coal Export Component The coal market module (CMM) represents the mining, transportation, and pricing of coal, subject to end-use demand. Coal supplies are differentiated by heat and sulfur content. The CMM also determines the minimum cost pattern of coal supply to meet exogenously defined U.S. coal export demands as a part of the world coal market. Coal supply is projected on a cost-minimizing basis, constrained by existing contracts. Twelve different coal types are differentiated with respect to thermal grade, sulfur content, and underground or surface mining. The domestic production and distribution of coal is forecast for 13 demand regions and 11 supply

442

Order Module--DOE O 420.1B, FACILITY SAFETY | Department of Energy  

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

DOE O 420.1B, FACILITY SAFETY DOE O 420.1B, FACILITY SAFETY Order Module--DOE O 420.1B, FACILITY SAFETY To ensure that new DOE hazard category 1, 2, and 3 nuclear facilities are designed and constructed in a manner that ensures adequate protection to the public, workers, and the environment from nuclear hazards. To ensure that major modifications to hazard category 1, 2, and 3 nuclear facilities comply with the design and construction requirements for new hazard category 1, 2, and 3 nuclear facilities. To ensure that new DOE nuclear reactors comply with the requirements of DOE O 420.1B and the design requirements of DOE O 5480.30, Nuclear Reactor Safety Design Criteria. DOE Order Self Study Modules - DOE O 420.1B Facility Safety More Documents & Publications Order Module--DOE O 420.2B, SAFETY OF ACCELERATOR FACILITIES

443

Outdoor PV Module Degradation of Current-Voltage Parameters: Preprint  

DOE Green Energy (OSTI)

Photovoltaic (PV) module degradation rate analysis quantifies the loss of PV power output over time and is useful for estimating the impact of degradation on the cost of energy. An understanding of the degradation of all current-voltage (I-V) parameters helps to determine the cause of the degradation and also gives useful information for the design of the system. This study reports on data collected from 12 distinct mono- and poly-crystalline modules deployed at the National Renewable Energy Laboratory (NREL) in Golden, Colorado. Most modules investigated showed < 0.5%/year decrease in maximum power due to short-circuit current decline.

Smith, R. M.; Jordan, D. C.; Kurtz, S. R.

2012-04-01T23:59:59.000Z

444

Savings estimates for the ENERGY STAR (registered trademark) voluntary labeling program: 2001 status report  

E-Print Network (OSTI)

Washington, DC. : US Department of Energy, Energy EfficiencyAir Conditioners. US Department of Energy, Energy Efficiency1997. Energy Data Sourcebook for the U.S. Residential

Webber, Carrie A.; Brown, Richard E.; Mahajan, Akshay; Koomey, Jonathan G.

2002-01-01T23:59:59.000Z

445

2003 status report savings estimates for the energy star(R) voluntary labeling program  

E-Print Network (OSTI)

Washington, DC. : US Department of Energy, Energy Efficiency1997. Energy Data Sourcebook for the U.S. ResidentialAir Conditioners. US Department of Energy, Energy Efficiency

Webber, Carrie A.; Brown, Richard E.; McWhinney, Marla

2004-01-01T23:59:59.000Z

446

Assumptions to the Annual Energy Outlook 1999 - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

oil.gif (4836 bytes) oil.gif (4836 bytes) The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply. 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(99), (Washington, DC, January 1999). 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 gas from domestic fields throughout the United States, acquire natural gas from foreign producers for resale in the United States, or sell U.S. gas to foreign consumers. OGSM encompasses domestic crude oil and natural gas supply by both conventional and nonconventional recovery techniques. Nonconventional recovery includes enhanced oil recovery and unconventional gas recovery from tight gas formations, gas shale, and coalbeds. Foreign gas transactions may occur via either pipeline (Canada or Mexico) or transport ships as liquefied natural gas (LNG).

447

The National Energy Modeling System: An Overview 2000 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

coal market module (CMM) represents the mining, transportation, and pricing of coal, subject to end-use demand. Coal supplies are differentiated by heat and sulfur content. CMM also determines the minimum cost pattern of coal supply to meet exogenously defined U.S. coal export demands as a part of the world coal market. Coal supply is projected on a cost-minimizing basis, constrained by existing contracts. Twelve different coal types are differentiated with respect to thermal grade, sulfur content, and underground or surface mining. The domestic production and distribution of coal is forecast for 13 demand regions and 11 supply regions (Figures 19 and 20). coal market module (CMM) represents the mining, transportation, and pricing of coal, subject to end-use demand. Coal supplies are differentiated by heat and sulfur content. CMM also determines the minimum cost pattern of coal supply to meet exogenously defined U.S. coal export demands as a part of the world coal market. Coal supply is projected on a cost-minimizing basis, constrained by existing contracts. Twelve different coal types are differentiated with respect to thermal grade, sulfur content, and underground or surface mining. The domestic production and distribution of coal is forecast for 13 demand regions and 11 supply regions (Figures 19 and 20). Figure 19. Coal Market Module Demand Regions Figure 20. Coal Market Module Supply Regions

448

Model documentation coal market module of the National Energy Modeling System  

SciTech Connect

This report documents the objectives and the conceptual and methodological approach used in the development of the Coal Production Submodule (CPS). It provides a description of the CPS for model analysts and the public. The Coal Market Module provides annual forecasts of prices, production, and consumption of coal.

1997-02-01T23:59:59.000Z

449

Self-oscillating modulators for direct energy conversion audio power amplifiers  

E-Print Network (OSTI)

component count and eventually low cost. This paper presents how self-oscillating modulators can be used application areas, from the smallest low-end portable devices with extended battery life to the large high by cutting production costs and introducing cheap products of satisfactory quality, can be answered

450

An Estimate of the Lorenz Energy Cycle for the World Ocean Based on the STORM/NCEP Simulation  

Science Conference Proceedings (OSTI)

This paper presents an estimate of the oceanic Lorenz energy cycle derived from a simulation forced by 6-hourly fluxes obtained from NCEPNCAR reanalysis-1. The total rate of energy generation amounts to 6.6 TW, of which 1.9 TW is generated by ...

Jin-Song von Storch; Carsten Eden; Irina Fast; Helmuth Haak; Daniel Hernndez-Deckers; Ernst Maier-Reimer; Jochem Marotzke; Detlef Stammer

2012-12-01T23:59:59.000Z

451

Capital requirements for the transportation of energy materials: 1979 ARC estimates. Draft final report  

SciTech Connect

This report contains TERA's estimates of capital requirements to transport natural gas, crude oil, petroleum products, and coal in the United States by 1990. The low, medium, and high world-oil-price scenarios from the EIA's Mid-range Energy Forecasting System (MEFS), as used in the 1979 Annual Report to Congress (ARC), were provided as a basis for the analysis and represent three alternative futures. TERA's approach varies by energy commodity to make best use of the information and analytical tools available. Summaries of transportation investment requirements through 1990 are given. Total investment requirements for three modes (pipelines, rails, waterways and the three energy commodities can accumulate to a $49.9 to $50.9 billion range depending on the scenario. The scenarios are distinguished primarily by the world price of oil which, given deregulation of domestic oil prices, affects US oil prices even more profoundly than in the past. The high price of oil, following the evidence of the last year, is projected to hold demand for oil below the recent past.

Not Available

1980-08-13T23:59:59.000Z

452

Capital requirements for the transportation of energy materials: 1979 arc estimates  

Science Conference Proceedings (OSTI)

Summaries of transportation investment requirements through 1990 are given for the low, medium and high scenarios. Total investment requirements for the three modes and the three energy commodities can accumulate to a $46.3 to $47.0 billion range depending on the scenario. The high price of oil, following the evidence of the last year, is projected to hold demand for oil below the recent past. Despite the overall decrease in traffic some investment in crude oil and LPG pipelines is necessary to reach new sources of supply. Although natural gas production and consumption is projected to decline through 1990, new investments in carrying capacity also are required due to locational shifts in supply. The Alaska Natural Gas Transportation System is the dominant investment for energy transportation in the next ten years. This year's report focuses attention on waterborne coal transportation to the northeast states in keeping with a return to significant coal consumption projected for this area. A resumption of such shipments will require a completely new fleet. The investment estimates given in this report identify capital required to transport projected energy supplies to market. The requirement is strategic in the sense that other reasonable alternatives do not exist or that a shared load of new growth can be expected. Not analyzed or forecasted are investments in transportation facilities made in response to local conditions. The total investment figures, therefore, represent a minimum necessary capital improvement to respond to changes in interregional supply conditions.

Not Available

1980-08-29T23:59:59.000Z

453

Estimates of Energy Consumption by Building Type and End Use at U.S. Army Installations  

E-Print Network (OSTI)

5-5. 1993 Electricity Consumption Estimates by End Use forft ) 1993 Electricity Consumption Estimates by End Use forTotal) 1993 Electricity Consumption Estimates by End Use for

Konopacki, S.J.

2010-01-01T23:59:59.000Z

454

Modulation-Aware Energy Balancing in Hierarchical Wireless Sensor Networks1  

E-Print Network (OSTI)

Approach to Dynamic Energy Minimization in Wireless Transceivers", A. Iranli, H. Fatemi, M. Pedram, Int;17 Optimization problem · Minimize total energy consumption for a link ­ (A) Minimize energy at transmitter ­ (B of SNR and mod. level Vector upper bound on overall Energy consumption #12;20 Some performance results

Pedram, Massoud

455

Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings  

E-Print Network (OSTI)

construction, Energy and Buildings 20: 205217. Chau 2007.management in China, Energy and Buildings (forthcoming).addition to operational energy, buildings embody the energy

Fridley, David G.

2008-01-01T23:59:59.000Z

456

Developing a Cost Model and Methodology to Estimate Capital Costs for Thermal Energy Storage  

DOE Green Energy (OSTI)

This report provides an update on the previous cost model for thermal energy storage (TES) systems. The update allows NREL to estimate the costs of such systems that are compatible with the higher operating temperatures associated with advanced power cycles. The goal of the Department of Energy (DOE) Solar Energy Technology Program is to develop solar technologies that can make a significant contribution to the United States domestic energy supply. The recent DOE SunShot Initiative sets a very aggressive cost goal to reach a Levelized Cost of Energy (LCOE) of 6 cents/kWh by 2020 with no incentives or credits for all solar-to-electricity technologies.1 As this goal is reached, the share of utility power generation that is provided by renewable energy sources is expected to increase dramatically. Because Concentrating Solar Power (CSP) is currently the only renewable technology that is capable of integrating cost-effective energy storage, it is positioned to play a key role in providing renewable, dispatchable power to utilities as the share of power generation from renewable sources increases. Because of this role, future CSP plants will likely have as much as 15 hours of Thermal Energy Storage (TES) included in their design and operation. As such, the cost and performance of the TES system is critical to meeting the SunShot goal for solar technologies. The cost of electricity from a CSP plant depends strongly on its overall efficiency, which is a product of two components - the collection and conversion efficiencies. The collection efficiency determines the portion of incident solar energy that is captured as high-temperature thermal energy. The conversion efficiency determines the portion of thermal energy that is converted to electricity. The operating temperature at which the overall efficiency reaches its maximum depends on many factors, including material properties of the CSP plant components. Increasing the operating temperature of the power generation system leads to higher thermal-to-electric conversion efficiency. However, in a CSP system, higher operating temperature also leads to greater thermal losses. These two effects combine to give an optimal system-level operating temperature that may be less than the upper operating temperature limit of system components. The overall efficiency may be improved by developing materials, power cycles, and system-integration strategies that enable operation at elevated temperature while limiting thermal losses. This is particularly true for the TES system and its components. Meeting the SunShot cost target will require cost and performance improvements in all systems and components within a CSP plant. Solar collector field hardware will need to decrease significantly in cost with no loss in performance and possibly with performance improvements. As higher temperatures are considered for the power block, new working fluids, heat-transfer fluids (HTFs), and storage fluids will all need to be identified to meet these new operating conditions. Figure 1 shows thermodynamic conversion efficiency as a function of temperature for the ideal Carnot cycle and 75% Carnot, which is considered to be the practical efficiency attainable by current power cycles. Current conversion efficiencies for the parabolic trough steam cycle, power tower steam cycle, parabolic dish/Stirling, Ericsson, and air-Brayton/steam Rankine combined cycles are shown at their corresponding operating temperatures. Efficiencies for supercritical steam and carbon dioxide (CO{sub 2}) are also shown for their operating temperature ranges.

Glatzmaier, G.

2011-12-01T23:59:59.000Z

457

DOE Order Self Study Modules - 29 CFR 1910.147, The Control Of Hazardous Energy (Lockout/Tagout)  

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

CFR 1910.147 CFR 1910.147 Familiar Level June 2011 1 June 2011 29 CFR 1910.147 THE CONTROL OF HAZARDOUS ENERGY (LOCKOUT/TAGOUT) FAMILIAR LEVEL ___________________________________________________________________________ OBJECTIVES Given the familiar level of this module and the resources, you will be able to answer the following questions: 1. What is the purpose of implementing 29 CFR 1910.147? 2. What is the definition of the following terms?  authorized employee  hot tap  tagout  lockout  lockout device  energy isolating device  tagout device 3. What are the conditions that require a contractor to retrain employees in lockout/tagout procedures? 4. What are the contractor requirements for personnel lockout/tagout training, and what

458

Experimental Estimation Of Energy Damping During Free Rocking Of Unreinforced Masonry Walls. First Results  

SciTech Connect

This paper presents an ongoing experimental program on unreinforced masonry walls undergoing free rocking. Aim of the laboratory campaign is the estimation of kinetic energy damping exhibited by walls released with non-zero initial conditions of motion. Such energy damping is necessary for dynamic modelling of unreinforced masonry local mechanisms. After a brief review of the literature on this topic, the main features of the laboratory tests are presented. The program involves the experimental investigation of several parameters: 1) unit material (brick or tuff), 2) wall aspect ratio (ranging between 14.5 and 7.1), 3) restraint condition (two-sided or one-sided rocking), and 4) depth of the contact surface between facade and transverse walls (one-sided rocking only). All walls are single wythe and the mortar is pozzuolanic. The campaign is still in progress. However, it is possible to present the results on most of the mechanical properties of mortar and bricks. Moreover, a few time histories are reported, already indicating the need to correct some of the assumptions frequent in the literature.

Sorrentino, Luigi; Masiani, Renato; Benedetti, Stefano [Dipartimento di Ingegneria Strutturale e Geotecnica, Sapienza Universita di Roma, via Antonio Gramsci, 53-00197 Roma (Italy)

2008-07-08T23:59:59.000Z

459

Fossil Asset and Project Evaluator (Energy Book System Modules) Methodologies: Highlighting the Strengths, Weaknesses and Unique Capabilities of Option Evaluation Techniques  

Science Conference Proceedings (OSTI)

In support of EPRI's fossil generation asset management activities, two important analysis software products have been developed in recent years: Generation/Fossil Asset Evaluator and its companion Project Evaluator (CM-113198-P3-R2 and CM-113198-P4-R2). These tools are modules of EPRI's Energy Book System (EBS), which consists of modules for o Generation (Asset and Project Evaluator) o Wholesale trading (Contract Evaluator) o Retail (Product Mix) o Risk management (Risk Manager) Fossil Asset Evaluator e...

2001-05-23T23:59:59.000Z

460

Renewable Fuels Module  

Reports and Publications (EIA)

This report documents the objectives, analytical approach, and design of the National Energy Modeling System (NEMS) Renewable Fuels Module (RFM) as it relates to the production of the Annual Energy Outlook forecasts.

Chris Namovicz

2013-07-03T23:59:59.000Z

Note: This page contains sample records for the topic "module estimates energy" 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

CALCULATING SEPARATE MAGNETIC FREE ENERGY ESTIMATES FOR ACTIVE REGIONS PRODUCING MULTIPLE FLARES: NOAA AR11158  

SciTech Connect

It is well known that photospheric flux emergence is an important process for stressing coronal fields and storing magnetic free energy, which may then be released during a flare. The Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) captured the entire emergence of NOAA AR 11158. This region emerged as two distinct bipoles, possibly connected underneath the photosphere, yet characterized by different photospheric field evolutions and fluxes. The combined active region complex produced 15 GOES C-class, two M-class, and the X2.2 Valentine's Day Flare during the four days after initial emergence on 2011 February 12. The M and X class flares are of particular interest because they are nonhomologous, involving different subregions of the active region. We use a Magnetic Charge Topology together with the Minimum Current Corona model of the coronal field to model field evolution of the complex. Combining this with observations of flare ribbons in the 1600 A channel of the Atmospheric Imaging Assembly on board SDO, we propose a minimization algorithm for estimating the amount of reconnected flux and resulting drop in magnetic free energy during a flare. For the M6.6, M2.2, and X2.2 flares, we find a flux exchange of 4.2 Multiplication-Sign 10{sup 20} Mx, 2.0 Multiplication-Sign 10{sup 20} Mx, and 21.0 Multiplication-Sign 10{sup 20} Mx, respectively, resulting in free energy drops of 3.89 Multiplication-Sign 10{sup 30} erg, 2.62 Multiplication-Sign 10{sup 30} erg, and 1.68 Multiplication-Sign 10{sup 32} erg.

Tarr, Lucas; Longcope, Dana; Millhouse, Margaret [Department of Physics, Montana State University, Bozeman, MT 59717 (United States)

2013-06-10T23:59:59.000Z

462

Electricity Market Module  

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

Market Module Market Module This page inTenTionally lefT blank 101 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 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, electricity load and demand, 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 2013, DOE/EIA-M068(2013). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most

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Review of Evaluation, Measurement and Verification Approaches Used to Estimate the Load Impacts and Effectiveness of Energy Efficiency Programs  

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

LBNL-3277E LBNL-3277E ERNEST ORLANDO LAWRENCE BERKELEY NATIONAL LABORATORY Review of Evaluation, Measurement and Verification Approaches Used to Estimate the Load Impacts and Effectiveness of Energy Efficiency Programs Mike Messenger*, Ranjit Bharvirkar*, Bill Golemboski*, Charles A. Goldman**, Steven R. Schiller*** *Itron, Inc., **Lawrence Berkeley National Laboratory, ***Schiller Consulting, Inc. Environmental Energy Technologies Division April 2010 The work described in this report was funded by the Office of Electricity Delivery and Energy Reliability (OE); Permitting, Siting, and Analysis Division of the U.S. Department of Energy under Contract No. DE-AC02- 05CH11231. Disclaimer This document was prepared as an account of work sponsored by the United

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Climate Science for a Sustainable Energy Future Atmospheric Radiation Measurement Best Estimate (CSSEFARMBE)  

SciTech Connect

The Climate Science for a Sustainable Energy Future (CSSEF) project is working to improve the representation of the hydrological cycle in global climate models, critical information necessary for decision-makers to respond appropriately to predictions of future climate. In order to accomplish this objective, CSSEF is building testbeds to implement uncertainty quantification (UQ) techniques to objectively calibrate and diagnose climate model parameterizations and predictions with respect to local, process-scale observations. In order to quantify the agreement between models and observations accurately, uncertainty estimates on these observations are needed. The DOE Atmospheric Radiation Measurement (ARM) program takes atmospheric and climate related measurements at three permanent locations worldwide. The ARM VAP called the ARM Best Estimate (ARMBE) [Xie et al., 2010] collects a subset of ARM observations, performs quality control checks, averages them to one hour temporal resolution, and puts them in a standard format for ease of use by climate modelers. ARMBE has been widely used by the climate modeling community as a summary product of many of the ARM observations. However, the ARMBE product does not include uncertainty estimates on the data values. Thus, to meet the objectives of the CSSEF project and enable better use of this data with UQ techniques, we created the CSSEFARMBE data set. Only a subset of the variables contained in ARMBE is included in CSSEFARMBE. Currently only surface meteorological observations are included, though this may be expanded to include other variables in the future. The CSSEFARMBE VAP is produced for all extended facilities at the ARM Southern Great Plains (SGP) site that contain surface meteorological equipment. This extension of the ARMBE data set to multiple facilities at SGP allows for better comparison between model grid boxes and the ARM point observations. In the future, CSSEFARMBE may also be created for other ARM sites. As each site has slightly different instrumentation, this will require additional development to understand the uncertainty characterization associated with instrumentation at those sites. The uncertainty assignment process is implemented into the ARM programs new Integrated Software Development Environment (ISDE) so that many of the key steps can be used in the future to screen data based on ARM Data Quality Reports (DQRs), propagate uncertainties when transforming data from one time scale into another, and convert names and units into NetCDF Climate and Forecast (CF) standards. These processes are described in more detail in the following sections.

Riihimaki, Laura D.; Gaustad, Krista L.; McFarlane, Sally A.

2012-09-28T23:59:59.000Z

465

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . 99 Petroleum Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Coal Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Renewable Fuels Module . . . . . . . . . . .

466

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.

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How the Carbon Emissions Were Estimated - U.S. Energy Information ...  

U.S. Energy Information Administration (EIA)

How the Carbon Emissions Were Estimated. Carbon dioxide emissions are the main component of greenhouse gas emissions caused by human ...

468

Table 3.5 Consumer Expenditure Estimates for Energy by Source ...  

U.S. Energy Information Administration (EIA)

Short-Term Energy Outlook Annual Energy Outlook Energy Disruptions International Energy Outlook ... 1984: 29,025-22: 77,169: 44,668: 15,097: R 14,197:

469

Performance analysis of energy efficient asymmetric coding and modulation schemes for wireless sensor networks  

Science Conference Proceedings (OSTI)

Wireless Sensor Networks generally operate under severe energy constraints. In many cases, the networks are star connected with battery-powered nodes sensing data and sending it to a centrally-powered base station, whose energy constraints are more fore-bearing ...

Govinda M. Kamath; Yogesh Shekar; G. Abhijith Kini; U. Sripati; Muralidhar Kulkarni

2010-05-01T23:59:59.000Z

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