National Library of Energy BETA

Sample records for household characteristics rse

  1. Characteristics RSE Column Factor: Total

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

    and 1994 Vehicle Characteristics RSE Column Factor: Total 1993 Family Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factor: Less than 5,000 5,000...

  2. Characteristics RSE Column Factor: Households with Children Households...

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

    ... 7.6 2.1 3.3 2.2 11.5 Q Q Q 1.4 6.9 2.8 18.8 Below Poverty Line 100 Percent ... 6.6 1.6 3.6 1.3 5.8 0.3 0.7...

  3. Table HC1-3a. Housing Unit Characteristics by Household Income...

    Gasoline and Diesel Fuel Update (EIA)

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  4. appl_household2001.pdf

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

    2a. Appliances by West Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.5 1.0 1.7 1.2 Total .............................................................. 107.0 23.3 6.7 16.6 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 22.1 6.6 15.5 1.1 1

  5. ac_household2001.pdf

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

    0a. Air Conditioning by Midwest Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 20.5 13.6 6.8 2.2 Air Conditioners Not Used ........................... 2.1 0.3 Q Q 27.5 Households Using Electric Air-Conditioning 1

  6. ac_household2001.pdf

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

    1a. Air Conditioning by South Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.2 1.3 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 37.2 19.3 6.4 11.5 1.5 Air Conditioners Not Used ........................... 2.1 0.4 Q Q Q 28.2 Households Using Electric Air-Conditioning 1

  7. ac_household2001.pdf

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

    2a. Air Conditioning by West Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.4 1.2 1.7 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 10.7 3.4 7.2 7.1 Air Conditioners Not Used ........................... 2.1 1.1 0.2 0.9 15.5 Households Using Electric Air-Conditioning 1 ........................................ 80.8 9.6 3.2

  8. ac_household2001.pdf

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

    4a. Air Conditioning by Type of Housing Unit, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.6 1.5 1.4 1.8 Households With Electric Air-Conditioning Equipment ........ 82.9 58.7 6.5 12.4 5.3 4.9 Air Conditioners Not Used ............ 2.1 1.1 Q 0.6 Q 21.8 Households Using Electric Air-Conditioning 1

  9. ac_household2001.pdf

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

    8a. Air Conditioning by Urban/Rural Location, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Urban/Rural Location 1 RSE Row Factors City Town Suburbs Rural 0.5 0.8 1.4 1.3 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 36.8 13.6 18.9 13.6 4.3 Air Conditioners Not Used ........................... 2.1 1.2 0.2 0.4 0.3 21.4 Households Using Electric Air-Conditioning 2 ........................................ 80.8 35.6 13.4

  10. ac_household2001.pdf

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

    9a. Air Conditioning by Northeast Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.0 1.2 1.8 Households With Electric Air-Conditioning Equipment ...................... 82.9 14.5 11.3 3.2 3.3 Air Conditioners Not Used ........................... 2.1 0.3 0.3 Q 28.3 Households Using Electric Air-Conditioning 1

  11. housingunit_household2001.pdf

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

    ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  12. appl_household2001.pdf

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

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  13. ac_household2001.pdf

    Gasoline and Diesel Fuel Update (EIA)

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  14. spaceheat_household2001.pdf

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

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  15. appl_household2001.pdf

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

    0a. Appliances by Midwest Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.5 Total .............................................................. 107.0 24.5 17.1 7.4 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 23.8 16.6 7.2 NE 1

  16. appl_household2001.pdf

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

    1a. Appliances by South Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.1 1.4 1.5 Total .............................................................. 107.0 38.9 20.3 6.8 11.8 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 36.2 19.4 6.4 10.3 1.5 1

  17. appl_household2001.pdf

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

    4a. Appliances by Type of Housing Unit, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.5 1.7 1.6 1.9 Total ............................................... 107.0 73.7 9.5 17.0 6.8 4.2 Kitchen Appliances Cooking Appliances Oven ........................................... 101.7 69.1 9.4 16.7 6.6 4.3 1

  18. appl_household2001.pdf

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

    5a. Appliances by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.3 0.4 2.1 3.1 1.3 Total ............................................... 72.7 63.2 2.1 1.8 5.7 6.7 Kitchen Appliances Cooking Appliances Oven ...........................................

  19. appl_household2001.pdf

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

    6a. Appliances by Type of Rented Housing Unit, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.8 1.1 0.9 2.5 Total ............................................... 34.3 10.5 7.4 15.2 1.1 6.9 Kitchen Appliances Cooking Appliances Oven ........................................... 33.4 10.1 7.3 14.9 1.1

  20. appl_household2001.pdf

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

    8a. Appliances by Urban/Rural Location, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Urban/Rural Location 1 RSE Row Factors City Town Suburbs Rural 0.5 0.9 1.4 1.2 1.3 Total .............................................................. 107.0 49.9 18.0 21.2 17.9 4.1 Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 47.5 17.5 19.9 16.8 4.2 1

  1. appl_household2001.pdf

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

    9a. Appliances by Northeast Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.0 1.3 1.6 Total .............................................................. 107.0 20.3 14.8 5.4 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 19.6 14.5 5.2 1.1 1

  2. spaceheat_household2001.pdf

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

    0a. Space Heating by Midwest Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.6 Total .............................................................. 107.0 24.5 17.1 7.4 NE Heat Home .................................................... 106.0 24.5 17.1 7.4 NE Do Not Heat Home ....................................... 1.0 Q Q Q 19.8 No

  3. spaceheat_household2001.pdf

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

    1a. Space Heating by South Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.9 1.2 1.4 1.3 Total .............................................................. 107.0 38.9 20.3 6.8 11.8 NE Heat Home .................................................... 106.0 38.8 20.2 6.8 11.8 NE Do Not Heat Home

  4. spaceheat_household2001.pdf

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

    2a. Space Heating by West Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.6 1.0 1.6 1.2 Total .............................................................. 107.0 23.3 6.7 16.6 NE Heat Home .................................................... 106.0 22.6 6.7 15.9 NE Do Not Heat Home ....................................... 1.0 0.7 Q 0.7 10.6 No Heating Equipment

  5. spaceheat_household2001.pdf

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

    5a. Space Heating by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.4 1.9 3.0 1.3 Total ............................................... 72.7 63.2 2.1 1.8 5.7 6.7 Heat Home ..................................... 72.4 63.0 2.0 1.7 5.7 6.7 Do Not Heat Home

  6. spaceheat_household2001.pdf

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

    6a. Space Heating by Type of Rented Housing Unit, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.8 1.1 0.9 2.5 Total ............................................... 34.3 10.5 7.4 15.2 1.1 6.9 Heat Home ..................................... 33.7 10.4 7.4 14.8 1.1 6.9 Do Not Heat Home

  7. spaceheat_household2001.pdf

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

    8a. Space Heating by Urban/Rural Location, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Urban/Rural Location 1 RSE Row Factors City Town Suburbs Rural 0.6 0.9 1.3 1.3 1.2 Total .............................................................. 107.0 49.9 18.0 21.2 17.9 4.3 Heat Home .................................................... 106.0 49.1 18.0 21.2 17.8 4.3 Do Not Heat Home ....................................... 1.0 0.7 0.1 0.1 0.1 25.8 No Heating

  8. spaceheat_household2001.pdf

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

    9a. Space Heating by Northeast Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.0 1.2 1.7 Total .............................................................. 107.0 20.3 14.8 5.4 NE Heat Home .................................................... 106.0 20.1 14.7 5.4 NE Do Not Heat Home ....................................... 1.0 Q Q Q 19.9 No

  9. ac_household2001.pdf

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

    2a. Air Conditioning by Year of Construction, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.4 1.6 1.2 1.1 1.2 1.1 0.9 Households With Electric Air-Conditioning Equipment ........ 82.9 13.6 16.0 14.7 10.4 10.5 17.6 4.7 Air Conditioners Not Used ............ 2.1 Q 0.3 0.5 0.3 0.4 0.5 27.2 Households Using Electric Air-Conditioning 2

  10. ac_household2001.pdf

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

    5a. Air Conditioning by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.5 1.5 1.4 1.8 Households With Electric Air-Conditioning Equipment ........ 59.5 58.7 6.5 12.4 5.3 5.2 Air Conditioners Not Used ............ 1.2 1.1 Q 0.6 Q 23.3 Households Using

  11. ac_household2001.pdf

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

    6a. Air Conditioning by Type of Rented Housing Unit, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.8 0.5 1.4 1.2 1.6 Households With Electric Air-Conditioning Equipment ........ 23.4 58.7 6.5 12.4 5.3 6.1 Air Conditioners Not Used ............ 0.9 1.1 Q 0.6 Q 23.0 Households Using Electric Air-Conditioning

  12. ac_household2001.pdf

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

    2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.7 1.2 1.2 Households With Electric Air-Conditioning Equipment ...................... 82.9 4.9 6.0 7.4 6.2 2.4 Air Conditioners Not Used ........................... 2.1 0.1 0.8 Q 0.1 23.2 Households Using Electric Air-Conditioning 1 ........................................ 80.8 4.7 5.2 7.4 6.1 2.6 Type of Electric Air-Conditioning Used Central

  13. 1997 Housing Characteristics Tables Housing Unit Tables

    Gasoline and Diesel Fuel Update (EIA)

    ... RSE Column Factor: Total 1997 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 1997 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  14. appl_household2001.pdf

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

    a. Appliances by Climate Zone, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Climate Zone 1 RSE Row Factors Fewer than 2,000 CDD and -- 2,000 CDD or More and Fewer than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Fewer than 4,000 HDD 0.4 1.9 1.1 1.1 1.2 1.1 Total .................................................. 107.0 9.2 28.6 24.0 21.0 24.1 7.8 Kitchen Appliances Cooking Appliances Oven

  15. appl_household2001.pdf

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

    2a. Appliances by Year of Construction, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.4 1.5 1.2 1.1 1.2 1.1 0.9 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.2 Kitchen Appliances Cooking Appliances Oven ........................................... 101.7 14.3 17.2 17.8 12.9 13.7 25.9 4.2 1

  16. spaceheat_household2001.pdf

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

    2a. Space Heating by Year of Construction, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.5 1.5 1.1 1.1 1.1 1.1 0.9 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.3 Heat Home ..................................... 106.0 15.4 18.2 18.6 13.6 13.9 26.4 4.3 Do Not Heat Home ........................

  17. spaceheat_household2001.pdf

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

    4a. Space Heating by Type of Housing Unit, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.5 1.5 1.4 1.7 Total ............................................... 107.0 73.7 9.5 17.0 6.8 4.4 Heat Home ..................................... 106.0 73.4 9.4 16.4 6.8 4.5 Do Not Heat Home ........................ 1.0 0.3 Q 0.6 Q 19.0 No

  18. homeoffice_household2001.pdf

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

    ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... 29.1 5.3 22.7 3.8 1 Below 150 percent of poverty line or 60 percent of median State ...

  19. char_household2001.pdf

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

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... Income Relative to Poverty Line Below 100 Percent ...... 15.0 13.2 1.8 Q ...

  20. Household and environmental characteristics related to household energy-consumption change: A human ecological approach

    SciTech Connect (OSTI)

    Guerin, D.A.

    1988-01-01

    This study focused on the family household as an organism and on its interaction with the three environments of the human ecosystem (natural, behavioral, and constructed) as these influence energy consumption and energy-consumption change. A secondary statistical analysis of data from the US Department of Energy Residential Energy Consumption Surveys (RECS) was completed. The 1980 and 1983 RECS were used as the data base. Longitudinal data, including household, environmental, and energy-consumption measures, were available for over 800 households. The households were selected from a national sample of owner-occupied housing units surveyed in both years. Results showed a significant( p = <.05) relationship between the dependent-variable energy-consumption change and the predictor variables heating degree days, addition of insulation, addition of a wood-burning stove, year the housing unit was built, and weighted number of appliances. A significant (p = <.05) relationship was found between the criterion variable energy-consumption change and the discriminating variables of age of the head of the household, cooling degree days, heating degree days, year the housing unit was built, and number of stories in the housing unit.

  1. Ventilation Behavior and Household Characteristics in NewCalifornia Houses

    SciTech Connect (OSTI)

    Price, Phillip N.; Sherman, Max H.

    2006-02-01

    A survey was conducted to determine occupant use of windows and mechanical ventilation devices; barriers that inhibit their use; satisfaction with indoor air quality (IAQ); and the relationship between these factors. A questionnaire was mailed to a stratified random sample of 4,972 single-family detached homes built in 2003, and 1,448 responses were received. A convenience sample of 230 houses known to have mechanical ventilation systems resulted in another 67 completed interviews. Some results are: (1) Many houses are under-ventilated: depending on season, only 10-50% of houses meet the standard recommendation of 0.35 air changes per hour. (2) Local exhaust fans are under-utilized. For instance, about 30% of households rarely or never use their bathroom fan. (3) More than 95% of households report that indoor air quality is ''very'' or ''somewhat'' acceptable, although about 1/3 of households also report dustiness, dry air, or stagnant or humid air. (4) Except households where people cook several hours per week, there is no evidence that households with significant indoor pollutant sources get more ventilation. (5) Except households containing asthmatics, there is no evidence that health issues motivate ventilation behavior. (6) Security and energy saving are the two main reasons people close windows or keep them closed.

  2. homeoffice_household2001.pdf

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

    0a. Home Office Equipment by Midwest Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.6 Total .............................................................. 107.0 24.5 17.1 7.4 NE Households Using Office Equipment ......................................... 96.2 22.4 15.7 6.7 1.3 Personal Computers 1 ................................. 60.0

  3. homeoffice_household2001.pdf

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

    1a. Home Office Equipment by South Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.2 1.3 1.6 Total .............................................................. 107.0 38.9 20.3 6.8 11.8 NE Households Using Office Equipment ......................................... 96.2 34.6 18.4 6.0 10.1 1.2 Personal Computers 1

  4. homeoffice_household2001.pdf

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

    2a. Home Office Equipment by West Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.5 1.0 1.6 1.2 Total .............................................................. 107.0 23.3 6.7 16.6 NE Households Using Office Equipment ......................................... 96.2 21.4 6.2 15.2 1.0 Personal Computers 1 ................................. 60.0 14.3 4.0 10.4 3.7 Number of

  5. homeoffice_household2001.pdf

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

    9a. Home Office Equipment by Northeast Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.1 1.4 1.2 Total .............................................................. 107.0 20.3 14.8 5.4 NE Households Using Office Equipment ......................................... 96.2 17.9 12.8 5.0 1.3 Personal Computers 1 ................................. 60.0 10.9

  6. Characteristics RSE Column Factor: All Vehicle Types

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

    or More ... 19.1 13.0 12.3 0.7 1.0 1.7 Q 2.7 Q 21.8 Below Poverty Line 100 Percent ... 12.4 9.5 8.9 0.5 Q Q Q 1.8 Q...

  7. homeoffice_household2001.pdf

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

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... 29.1 5.3 22.7 3.8 1 Below 150 percent of poverty line or 60 percent of median State income

  8. Improving Demographic Components of Integrated Assessment Models: The Effect of Changes in Population Composition by Household Characteristics

    SciTech Connect (OSTI)

    Brian C. O'Neill

    2006-08-09

    This report describes results of the research project on "Improving Demographic Components of Integrated Assessment Models: The Effect of Changes in Population Composition by Household Characteristics". The overall objective of this project was to improve projections of energy demand and associated greenhouse gas emissions by taking into account demographic factors currently not incorporated in Integrated Assessment Models (IAMs) of global climate change. We proposed to examine the potential magnitude of effects on energy demand of changes in the composition of populations by household characteristics for three countries: the U.S., China, and Indonesia. For each country, we planned to analyze household energy use survey data to estimate relationships between household characteristics and energy use; develop a new set of detailed household projections for each country; and combine these analyses to produce new projections of energy demand illustrating the potential importance of consideration of households.

  9. homeoffice_household2001.pdf

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

    2001 Home Office Equipment RSE Column Factor: Total U.S. Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.0 1.5 1.5 Total .............................................................. 107.0 7.1 12.3 7.7 6.3 NE Households Using Office Equipment ......................................... 96.2 6.2 11.4 6.7 5.9 1.7 Personal Computers 1 ................................. 60.0 3.4 7.9 4.1 3.8 4.4 Number of Desktop PCs 1

  10. Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay

    SciTech Connect (OSTI)

    Oribe-Garcia, Iraia Kamara-Esteban, Oihane; Martin, Cristina; Macarulla-Arenaza, Ana M.; Alonso-Vicario, Ainhoa

    2015-05-15

    Highlights: • We have modelled household waste generation in Biscay municipalities. • We have identified relevant characteristics regarding household waste generation. • Factor models are used in order to identify the best subset of explicative variables. • Biscay’s municipalities are grouped by means of hierarchical clustering. - Abstract: The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.

  11. Re: NBP RFI: CommunicationRse quirements | Department of Energy

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

    CommunicationRse quirements Re: NBP RFI: CommunicationRse quirements Pepco Holdings, Inc. (PHI) is pleased to respond to the U.S Department of Energy request for comments regarding the communications requirements of electric utilities deploying the Smart Grid. Re: NBP RFI: CommunicationRse quirements (589.11 KB) More Documents & Publications Re: NBP RFI: Communications Requirements Re: NBP RFI-Implementing the National Broadband Plan by Studying the Communications Requirements of Electric

  12. Table HC6.11 Home Electronics Characteristics by Number of Household Members, 2005

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

    1 Home Electronics Characteristics by Number of Household Members, 2005 Total...................................................................... 111.1 30.0 34.8 18.4 15.9 12.0 Personal Computers Do Not Use a Personal Computer ................... 35.5 16.3 9.4 4.0 2.7 3.2 Use a Personal Computer................................ 75.6 13.8 25.4 14.4 13.2 8.8 Number of Desktop PCs 1.................................................................. 50.3 11.9 17.4 8.5 7.3 5.2

  13. Table HC6.2 Living Space Characteristics by Number of Household Members, 2005

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

    2 Living Space Characteristics by Number of Household Members, 2005 Total...................................................................... 111.1 30.0 34.8 18.4 15.9 12.0 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500............................................... 3.2 1.7 0.8 0.4 0.3 Q 500 to 999....................................................... 23.8 10.2 6.4 3.4 2.3 1.5 1,000 to 1,499................................................. 20.8 5.5 6.3 3.0 3.3 2.6 1,500 to

  14. Table HC6.9 Home Appliances Characteristics by Number of Household Members, 2005

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

    HC6.9 Home Appliances Characteristics by Number of Household Members, 2005 Total U.S.............................................................. 111.1 30.0 34.8 18.4 15.9 12.0 Cooking Appliances Conventional Ovens Use an Oven.................................................. 109.6 29.5 34.4 18.2 15.7 11.8 1................................................................. 103.3 28.4 32.0 17.3 14.7 11.0 2 or More.................................................... 6.2 1.1 2.5 1.0 0.9 0.8 Do Not

  15. homeoffice_household2001.pdf

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

    2a. Home Office Equipment by Year of Construction, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.4 1.4 1.1 1.1 1.2 1.2 1.0 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.2 Households Using Office Equipment .......................... 96.2 14.9 16.7 17.0 12.2 13.0 22.4 4.4 Personal Computers 2

  16. Table HC6.4 Space Heating Characteristics by Number of Household Members, 2005

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

    4 Space Heating Characteristics by Number of Household Members, 2005 Total..................................................................... 111.1 30.0 34.8 18.4 15.9 12.0 Do Not Have Space Heating Equipment............ 1.2 0.3 0.3 Q 0.2 0.2 Have Main Space Heating Equipment............... 109.8 29.7 34.5 18.2 15.6 11.8 Use Main Space Heating Equipment................. 109.1 29.5 34.4 18.1 15.5 11.6 Have Equipment But Do Not Use It................... 0.8 Q Q Q Q Q Main Heating Fuel and

  17. appl_household2001.pdf

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

    2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.2 1.1 1.4 1.3 Total .............................................................. 107.0 7.1 12.3 7.7 6.3 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 6.9 11.4 6.7 6.1 1.6 1 .............................................................. 95.2 6.2 10.7 6.3 6.0 2.1 2 or More

  18. spaceheat_household2001.pdf

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

    2001 Space Heating Characteristics RSE Column Factor: Total U.S. Four Most Populated States RSE Row Factors New York California Texas Florida 0.5 1.1 1.0 1.2 1.6 Total .............................................................. 107.0 7.1 12.3 7.7 6.3 NE Heat Home .................................................... 106.0 7.1 12.0 7.7 6.2 NE Do Not Heat Home ....................................... 1.0 Q 0.3 Q 0.1 20.7 No Heating Equipment ................................ 0.5 Q 0.1 Q Q 41.3

  19. CBECS Buildings Characteristics --Revised Tables

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

    Totals and Means of Floorspace, Number of Workers, and Hours of Operation, 1995 Building Characteristics RSE Column Factor: All Buildings (thousand) Total Floorspace (million...

  20. Table HC1-2a. Housing Unit Characteristics by Year of Construction,

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

    2a. Housing Unit Characteristics by Year of Construction, Million U.S. Households, 2001 Housing Unit Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.5 1.6 1.2 1.0 1.1 1.1 0.8 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.3 Census Region and Division Northeast ...................................... 20.3 1.5 2.4 2.1 2.8 3.0 8.5 8.8 New

  1. 1997 Housing Characteristics Tables Home Office Equipment Tables

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

    ... RSE Column Factor: Total 1997 Household Income Below Poverty Line Eli- gible for Fed- eral ... 32.3 39.0 2.7 1 Below 150 percent of poverty line or 60 percent of median State income. ...

  2. Characteristics RSE Column Factor: All Model Years Model Year

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

    ... 19.1 1.4 2.0 2.2 5.0 4.4 2.1 0.6 Q 0.9 14.3 Below Poverty Line 100 Percent ... 12.4 Q Q 0.6 2.1 2.1 2.4 1.7...

  3. RSE Pulp & Chemical, LLC (Subsidiary of Red Shield Environmental, LLC) |

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

    Department of Energy RSE Pulp & Chemical, LLC (Subsidiary of Red Shield Environmental, LLC) RSE Pulp & Chemical, LLC (Subsidiary of Red Shield Environmental, LLC) A fact sheet detailling a proposal of a biorefinery facility in an existing pulp mill to demonstrate the production of cellulosic ethanol from lignocellulosic (wood) extract. RSE Pulp & Chemical, LLC (Subsidiary of Red Shield Environmental, LLC) (19.31 KB) More Documents & Publications Pacific Ethanol, Inc EA-1888:

  4. char_household2001.pdf

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

    Contact: Stephanie J. Battles, Survey Manager (stephanie.battles@eia.doe.gov) World Wide Web: http:www.eia.doe.govemeuconsumption Table HC2-1a. Household Characteristics by ...

  5. Table 5.10. U.S. Average Vehicle Fuel Consumption by Family...

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

    1993 Household Characteristics RSE Column Factor: Total 1993 Family Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factor: Less than 5,000 5,000...

  6. Table 5.9. U.S. Average Vehicle-Miles Traveled by Family Income...

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

    1993 Household Characteristics RSE Column Factor: Total 1993 Family Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factor: Less than 5,000 5,000...

  7. Household magnets

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

    Household magnets Chances are very good that you have experimented with magnets. People have been fascinated with magnetism for thousands of years. As familiar to us as they may be, magnets still have some surprises for us. Here is a small collection of some of our favorite magnet experiments. What happens when we break a magnet in half? Radio Shack sells cheap ceramic magnets in several shapes. Get a ring shaped magnet and break it with pliers or a tap with a hammer. Try to put it back

  8. Household vehicles energy consumption 1994

    SciTech Connect (OSTI)

    1997-08-01

    Household Vehicles Energy Consumption 1994 reports on the results of the 1994 Residential Transportation Energy Consumption Survey (RTECS). The RTECS is a national sample survey that has been conducted every 3 years since 1985. For the 1994 survey, more than 3,000 households that own or use some 6,000 vehicles provided information to describe vehicle stock, vehicle-miles traveled, energy end-use consumption, and energy expenditures for personal vehicles. The survey results represent the characteristics of the 84.9 million households that used or had access to vehicles in 1994 nationwide. (An additional 12 million households neither owned or had access to vehicles during the survey year.) To be included in then RTECS survey, vehicles must be either owned or used by household members on a regular basis for personal transportation, or owned by a company rather than a household, but kept at home, regularly available for the use of household members. Most vehicles included in the RTECS are classified as {open_quotes}light-duty vehicles{close_quotes} (weighing less than 8,500 pounds). However, the RTECS also includes a very small number of {open_quotes}other{close_quotes} vehicles, such as motor homes and larger trucks that are available for personal use.

  9. Household energy consumption and expenditures, 1990

    SciTech Connect (OSTI)

    Not Available

    1993-03-02

    This report, Household Energy Consumption and Expenditures 1990, is based upon data from the 1990 Residential Energy Consumption Survey (RECS). Focusing on energy end-use consumption and expenditures of households, the 1990 RECS is the eighth in a series conducted since 1978 by the Energy Information Administration (EIA). Over 5,000 households were surveyed, providing information on their housing units, housing characteristics, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information provided represents the characteristics and energy consumption of 94 million households nationwide.

  10. " Million U.S. Housing Units" ,,"2005 Household...

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

    8 Water Heating Characteristics by Household Income, 2005" " Million U.S. Housing Units" ... to 79,999","80,000 or More" "Water Heating Characteristics" ...

  11. EIA - Household Transportation report: Household Vehicles Energy...

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

    logo printer-friendly version logo for Portable Document Format file Household Vehicles Energy Consumption 1994 August 1997 Release Next Update: EIA has discontinued this series....

  12. Table 4

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

    4. Light Usage by Total Number of Rooms, Percent of U.S. Households, 1993 Total Number of Rooms Housing Unit and Household Characteristics Total 1 or 2 3 to 5 6 to 8 9 or More RSE...

  13. Household energy consumption and expenditures, 1990. [Contains Glossary

    SciTech Connect (OSTI)

    Not Available

    1993-03-02

    This report, Household Energy Consumption and Expenditures 1990, is based upon data from the 1990 Residential Energy Consumption Survey (RECS). Focusing on energy end-use consumption and expenditures of households, the 1990 RECS is the eighth in a series conducted since 1978 by the Energy Information Administration (EIA). Over 5,000 households were surveyed, providing information on their housing units, housing characteristics, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information provided represents the characteristics and energy consumption of 94 million households nationwide.

  14. appl_household2001.pdf

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

    Appliances Tables (Million U.S. Households; 60 pages, 240 kb) Contents Pages HC5-1a. Appliances by Climate Zone, Million U.S. Households, 2001 5 HC5-2a. Appliances by Year of Construction, Million U.S. Households, 2001 5 HC5-3a. Appliances by Household Income, Million U.S. Households, 2001 5 HC5-4a. Appliances by Type of Housing Unit, Million U.S. Households, 2001 5 HC5-5a. Appliances by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 5 HC5-6a. Appliances by Type of Rented

  15. 1997 Housing Characteristics Tables Housing Unit Tables

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

    Million U.S. Households; 45 pages, 128 kb) Contents Pages HC1-1a. Housing Unit Characteristics by Climate Zone, Million U.S. Households, 1997 4 HC1-2a. Housing Unit Characteristics by Year of Construction, Million U.S. Households, 1997 4 HC1-3a. Housing Unit Characteristics by Household Income, Million U.S. Households, 1997 4 HC1-4a. Housing Unit Characteristics by Type of Housing Unit, Million U.S. Households, 1997 3 HC1-5a. Housing Unit Characteristics by Type of Owner-Occupied Housing Unit,

  16. 1997 Housing Characteristics Tables Housing Unit Tables

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

    Percent of U.S. Households; 45 pages, 121 kb) Contents Pages HC1-1b. Housing Unit Characteristics by Climate Zone, Percent of U.S. Households, 1997 4 HC1-2b. Housing Unit Characteristics by Year of Construction, Percent of U.S. Households, 1997 4 HC1-3b. Housing Unit Characteristics by Household Income, Percent of U.S. Households, 1997 4 HC1-4b. Housing Unit Characteristics by Type of Housing Unit, Percent of U.S. Households, 1997 3 HC1-5b. Housing Unit Characteristics by Type of Owner-Occupied

  17. Housing characteristics 1993

    SciTech Connect (OSTI)

    1995-06-01

    This report, Housing Characteristics 1993, presents statistics about the energy-related characteristics of US households. These data were collected in the 1993 Residential Energy Consumption Survey (RECS) -- the ninth in a series of nationwide energy consumption surveys conducted since 1978 by the Energy Information Administration of the US Department of Energy. Over 7 thousand households were surveyed, representing 97 million households nationwide. A second report, to be released in late 1995, will present statistics on residential energy consumption and expenditures.

  18. Table 5.1. U.S. Number of Vehicles, Vehicle-Miles, Motor Fuel...

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

    Table 5.1. U.S. Number of Vehicles, Vehicle-Miles, Motor Fuel Consumption and Expenditures, 1994 (Continued) 1993 Household and 1994 Vehicle Characteristics RSE Column Factor:...

  19. Household Vehicles Energy Consumption 1991

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

    or commercial trucks (See Table 1). Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 5 The 1991 RTECS count includes vehicles that were owned or used...

  20. Household Vehicles Energy Consumption 1991

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

    logo printer-friendly version logo for Portable Document Format file Household Vehicles Energy Consumption 1991 December 1993 Release Next Update: August 1997. Based on the 1991...

  1. R93HC.PDF

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

    3. Total Air-Conditioning in U.S. Households, 1993 Housing Unit and Household Characteristics RSE Column Factor: Total Households (millions) Cooled Floorspace (square feet per household) Number of Cooling Degree-Days per Household Air-Conditioner Use in Summer 1993 1 (percent of households) RSE Row Factors 1993 Normal Total Not at All Only a Few Times Quite a Bit All Summer 0.8 0.6 0.6 0.6 3.5 0.9 1.4 1.2 Total .................................................... 66.1 1,416 1,536 1,438 100.0 3.4

  2. RSE Table 7.4 Relative Standard Errors for Table 7.4

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

    4 Relative Standard Errors for Table 7.4;" " Unit: Percents." " ",," "," ",," "," " "Economic",,"Residual","Distillate","Natural ","LPG and" "Characteristic(a)","Electricity","Fuel Oil","Fuel Oil(b)","Gas(c)","NGL(d)","Coal" ,"Total United States" "Value of Shipments and Receipts"

  3. RSE Table 7.5 Relative Standard Errors for Table 7.5

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

    5 Relative Standard Errors for Table 7.5;" " Unit: Percents." " ",," "," ",," "," " "Economic",,"Residual","Distillate","Natural ","LPG and" "Characteristic(a)","Electricity","Fuel Oil","Fuel Oil(b)","Gas(c)","NGL(d)","Coal" ,"Total United States" "Value of Shipments and Receipts"

  4. Perceptions of risk among households in two Australian coastal communities

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Elrick-Barr, Carmen E.; Smith, Timothy F.; Thomsen, Dana C.; Preston, Benjamin L.

    2015-04-20

    There is limited knowledge of risk perceptions in coastal communities despite their vulnerability to a range of risks including the impacts of climate change. A survey of 400 households in two Australian coastal communities, combined with semi-structured interviews, provides insight into household perceptions of the relative importance of climatic and non-climatic risks and the subsequent risk priorities that may inform household adaptive action. In contrast to previous research, the results demonstrated that geographic location and household characteristics might not affect perceptions of vulnerability to environmental hazards. However, past experience was a significant influence, raising the priority of environmental concerns. Overall,more » the results highlight the priority concerns of coastal households (from finance, to health and environment) and suggest to increase the profile of climate issues in coastal communities climate change strategies need to better demonstrate links between climate vulnerability and other household concerns. Moreover, promoting generic capacities in isolation from understanding the context in which households construe climate risks is unlikely to yield the changes required to decrease the vulnerability of coastal communities.« less

  5. Perceptions of risk among households in two Australian coastal communities

    SciTech Connect (OSTI)

    Elrick-Barr, Carmen E.; Smith, Timothy F.; Thomsen, Dana C.; Preston, Benjamin L.

    2015-04-20

    There is limited knowledge of risk perceptions in coastal communities despite their vulnerability to a range of risks including the impacts of climate change. A survey of 400 households in two Australian coastal communities, combined with semi-structured interviews, provides insight into household perceptions of the relative importance of climatic and non-climatic risks and the subsequent risk priorities that may inform household adaptive action. In contrast to previous research, the results demonstrated that geographic location and household characteristics might not affect perceptions of vulnerability to environmental hazards. However, past experience was a significant influence, raising the priority of environmental concerns. Overall, the results highlight the priority concerns of coastal households (from finance, to health and environment) and suggest to increase the profile of climate issues in coastal communities climate change strategies need to better demonstrate links between climate vulnerability and other household concerns. Moreover, promoting generic capacities in isolation from understanding the context in which households construe climate risks is unlikely to yield the changes required to decrease the vulnerability of coastal communities.

  6. Household Vehicles Energy Use Cover Page

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

    Energy Use Cover Page Glossary Home > Households, Buildings & Industry >Transportation Surveys > Household Vehicles Energy Use Cover Page Contact Us * Feedback * PrivacySecurity *...

  7. Strategies for Collecting Household Energy Data | Department...

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

    Collecting Household Energy Data Strategies for Collecting Household Energy Data Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for ...

  8. Next Generation Household Refrigerator | Department of Energy

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

    Next Generation Household Refrigerator Next Generation Household Refrigerator Embraco's high efficiency, oil-free linear compressor.
    Credit: Whirlpool Embraco's high ...

  9. Household Vehicles Energy Consumption 1991

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

    16.8 17.4 18.6 18.9 1.7 2.2 0.6 1.5 Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 15 Vehicle Miles Traveled per Vehicle (Thousand) . . . . . . . . ....

  10. Cover Page of Household Vehicles Energy Use: Latest Data & Trends

    Gasoline and Diesel Fuel Update (EIA)

    Household Vehicles Energy Use Cover Page Cover Page of Household Vehicles Energy Use: Latest Data & Trends...

  11. CBECS 1992 - Building Characteristics, Detailed Tables

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

    major topics of each table. Directions for calculating an approximate relative standard error (RSE) for each estimate in the tables are presented in Figure A1, "Use of RSE Row...

  12. 2003 CBECS RSE Tables

    Gasoline and Diesel Fuel Update (EIA)

    Dec 2006 Next CBECS will be conducted in 2007 Standard error is a measure of the reliability or precision of the survey statistic. The value for the standard error can be used...

  13. S:\VM3\RX97\TBL_LIST.WPD

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

    b. Household Characteristics by Four Most Populated States, Percent of U.S. Households, 1997 Household Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.2 1.4 1.4 Total .............................................................. 100.0 100.0 100.0 100.0 100.0 0.0 1997 Household Income Category Less than $5,000 ......................................... 3.7 4.3 2.8 4.8 1.9 16.2 $5,000 to $9,999

  14. 1989 CBECS EUI

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

    5. Electricity Consumption and Expenditure Intensities, 1992 Building Characteristics RSE Column Factor: Electricity Consumption Electricity Expenditures RSE Row Factor per...

  15. Fact #748: October 8, 2012 Components of Household Expenditures...

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

    Household Expenditures on Transportation, 1984-2010 Fact 748: October 8, 2012 Components of Household Expenditures on Transportation, 1984-2010 The overall share of annual household ...

  16. " Sources by Industry Group, Selected Industries, and Selected Characteristics,"

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

    6. Capability to Switch from Residual Fuel Oil to Alternative Energy" " Sources by Industry Group, Selected Industries, and Selected Characteristics," 1991 " (Estimates in Thousand Barrels)" ,," Residual Fuel Oil",,," Alternative Types of Energy(b)" ," ","-","-","-------------","-","-","-","-","-","-","-","RSE"

  17. This publication is available from the Superintendent of Documents, U.S. Governm

    Gasoline and Diesel Fuel Update (EIA)

    0a. Housing Unit Characteristics by Midwest Census Region, Million U.S. Households, 2001 Housing Unit Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.8 Total .............................................................. 107.0 24.5 17.1 7.4 NE Census Region and Division Northeast ..................................................... 20.3 -- -- -- NF New England

  18. S:\VM3\RX97\TBL_LIST.WPD

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

    b. Housing Unit Characteristics by Four Most Populated States, Percent of U.S. Households, 1997 Housing Unit Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.1 1.2 1.7 Total .............................................................. 100.0 100.0 100.0 100.0 100.0 0.0 Census Region and Division Northeast ..................................................... 19.4 100.0 -- -- -- NF New England

  19. Energy-efficient housing alternatives: a predictive model of factors affecting household perceptions

    SciTech Connect (OSTI)

    Schreckengost, R.L.

    1985-01-01

    The major purpose of this investigation was to assess the impact of household socio-economic factors, dwelling characteristics, energy conservation behavior, and energy attitudes on the perceptions of energy-efficient housing alternatives. Perceptions of passive solar, active solar, earth sheltered, and retrofitted housing were examined. Data used were from the Southern Regional Research Project, S-141, Housing for Low and Moderate Income Families. Responses from 1804 households living in seven southern states were analyzed. A conceptual model was proposed to test the hypothesized relationships which were examined by path analysis. Perceptions of energy efficient housing alternatives were found to be a function of selected household and dwelling characteristics, energy attitude, household economic factors, and household conservation behavior. Age and education of the respondent, family size, housing-income ratio, utility income ratio, energy attitude, and size of the dwelling unit were found to have direct and indirect effects on perceptions of energy-efficient housing alternatives. Energy conservation behavior made a significant direct impact with behavioral energy conservation changes having the most profound influence. Conservation behavior was influenced by selected household and dwelling characteristics, energy attitude, and household economic factors.

  20. S:\VM3\RX97\TBL_LIST.WPD

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

    Million U.S. Households, 1997 Housing Unit Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.1 1.2 1.7 Total .............................................................. 101.5 6.8 11.5 7.0 5.9 NF Census Region and Division Northeast ..................................................... 19.7 6.8 -- -- -- NF New England ............................................. 5.3 Q -- -- -- NF Middle Atlantic

  1. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    b. Air Conditioning by Four Most Populated States, Percent of U.S. Households, 1997 Air Conditioning Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.3 1.4 1.2 1.2 Households With Electric Air-Conditioning Equipment ...................... 100.0 100.0 100.0 100.0 100.0 0.0 Central Equipment Not Used ....................... 0.5 Q 2.9 0.6 1.2 28.9 Room Air Conditioners Not Used ................ 1.0 Q Q Q 1.2 40.5 Households

  2. "RSE Table E13.2. Relative Standard Errors for Table E13.2;"

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

    2. Relative Standard Errors for Table E13.2;" " Unit: Percents." " ",,,"Renewable Energy" ,,,"(excluding Wood" "Economic","Total Onsite",,"and" "Characteristic(a)","Generation","Cogeneration(b)","Other Biomass)(c)","Other(d)" ,"Total United States" "Value of Shipments and Receipts" "(million dollars)" " Under 20",15,15,58,37 "

  3. "RSE Table E13.3. Relative Standard Errors for Table E13.3;"

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

    3. Relative Standard Errors for Table E13.3;" " Unit: Percents." ,"Total of" "Economic","Sales and","Utility","Nonutility" "Characteristic(a)","Transfers Offsite","Purchaser(b)","Purchaser(c)" ,"Total United States" "Value of Shipments and Receipts" "(million dollars)" " Under 20",4,4,10 " 20-49",33,35,70 " 50-99",10,12,10 "

  4. "RSE Table E7.1. Relative Standard Errors for Table E7.1;"

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

    1. Relative Standard Errors for Table E7.1;" " Unit: Percents." ,,,"Consumption" " ",,"Consumption","per Dollar" "Economic","Consumption","per Dollar","of Value" "Characteristic(a)","per Employee","of Value Added","of Shipments" ,"Total United States" "Value of Shipments and Receipts" "(million dollars)" " Under 20",2,2,2

  5. "RSE Table E7.2. Relative Standard Errors for Table E7.2;"

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

    2. Relative Standard Errors for Table E7.2;" " Unit: Percents." " "," ",,,"Consumption" " "," ",,"Consumption","per Dollar" "NAICS",,"Consumption","per Dollar","of Value" "Code(a)","Economic Characteristic(b)","per Employee","of Value Added","of Shipments" ,,"Total United States" " 311 - 339","ALL

  6. Fact #565: April 6, 2009 Household Gasoline Expenditures by Income |

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

    Department of Energy 5: April 6, 2009 Household Gasoline Expenditures by Income Fact #565: April 6, 2009 Household Gasoline Expenditures by Income In the annual Consumer Expenditure Survey, household incomes are grouped into five equal parts called quintiles (each quintile is 20%). Households in the second and third quintiles consistently have a higher share of spending on gasoline each year than households in the other quintiles. Household Gasoline Expenditures by Income Quintile Bar graph

  7. Transferring 2001 National Household Travel Survey

    SciTech Connect (OSTI)

    Hu, Patricia S; Reuscher, Tim; Schmoyer, Richard L; Chin, Shih-Miao

    2007-05-01

    Policy makers rely on transportation statistics, including data on personal travel behavior, to formulate strategic transportation policies, and to improve the safety and efficiency of the U.S. transportation system. Data on personal travel trends are needed to examine the reliability, efficiency, capacity, and flexibility of the Nation's transportation system to meet current demands and to accommodate future demand. These data are also needed to assess the feasibility and efficiency of alternative congestion-mitigating technologies (e.g., high-speed rail, magnetically levitated trains, and intelligent vehicle and highway systems); to evaluate the merits of alternative transportation investment programs; and to assess the energy-use and air-quality impacts of various policies. To address these data needs, the U.S. Department of Transportation (USDOT) initiated an effort in 1969 to collect detailed data on personal travel. The 1969 survey was the first Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990, 1995, and 2001. Data on daily travel were collected in 1969, 1977, 1983, 1990 and 1995. In 2001, the survey was renamed the National Household Travel Survey (NHTS) and it collected both daily and long-distance trips. The 2001 survey was sponsored by three USDOT agencies: Federal Highway Administration (FHWA), Bureau of Transportation Statistics (BTS), and National Highway Traffic Safety Administration (NHTSA). The primary objective of the survey was to collect trip-based data on the nature and characteristics of personal travel so that the relationships between the characteristics of personal travel and the demographics of the traveler can be established. Commercial and institutional travel were not part of the survey. Due to the survey's design, data in the NHTS survey series were not recommended for estimating travel statistics for categories smaller than the combination of Census division (e.g., New England, Middle

  8. ASSESSMENT OF HOUSEHOLD CARBON FOOTPRINT REDUCTION POTENTIALS

    SciTech Connect (OSTI)

    Kramer, Klaas Jan; Homan, Greg; Brown, Rich; Worrell, Ernst; Masanet, Eric

    2009-04-15

    The term ?household carbon footprint? refers to the total annual carbon emissions associated with household consumption of energy, goods, and services. In this project, Lawrence Berkeley National Laboratory developed a carbon footprint modeling framework that characterizes the key underlying technologies and processes that contribute to household carbon footprints in California and the United States. The approach breaks down the carbon footprint by 35 different household fuel end uses and 32 different supply chain fuel end uses. This level of end use detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the carbon footprint of California households. The modeling framework was applied to estimate the annual home energy and supply chain carbon footprints of a prototypical California household. A preliminary assessment of parameter uncertainty associated with key model input data was also conducted. To illustrate the policy-relevance of this modeling framework, a case study was conducted that analyzed the achievable carbon footprint reductions associated with the adoption of energy efficient household and supply chain technologies.

  9. Kingston Creek Hydro Project Powers 100 Households | Department...

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

    Kingston Creek Hydro Project Powers 100 Households Kingston Creek Hydro Project Powers 100 Households August 21, 2013 - 12:00am Addthis Nevada-based contracting firm Nevada ...

  10. Energy Information Administration/Household Vehicles Energy Consumptio...

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

    , Energy Information AdministrationHousehold Vehicles Energy Consumption 1994 ix Household Vehicles Energy Consumption 1994 presents statistics about energy-related...

  11. Loan Programs for Low- and Moderate-Income Households | Department...

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

    Programs for Low- and Moderate-Income Households Loan Programs for Low- and Moderate-Income Households Better Buildings Residential Network Multifamily and Low-Income Housing Peer ...

  12. Household Response To Dynamic Pricing Of Electricity: A Survey...

    Open Energy Info (EERE)

    Household Response To Dynamic Pricing Of Electricity: A Survey Of The Experimental Evidence Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Household Response To Dynamic...

  13. Microsoft Word - Household Energy Use CA

    Gasoline and Diesel Fuel Update (EIA)

    0 20 40 60 80 100 US PAC CA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US PAC CA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US PAC CA Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US PAC CA Expenditures dollars ELECTRICITY ONLY average per household  California households use 62 million Btu of energy per home, 31% less than the U.S. average. The lower than average site

  14. Microsoft Word - Household Energy Use CA

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

    0 20 40 60 80 100 US PAC CA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US PAC CA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US PAC CA Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US PAC CA Expenditures dollars ELECTRICITY ONLY average per household  California households use 62 million Btu of energy per home, 31% less than the U.S. average. The lower than average site

  15. Household energy consumption and expenditures 1993

    SciTech Connect (OSTI)

    1995-10-05

    This presents information about household end-use consumption of energy and expenditures for that energy. These data were collected in the 1993 Residential Energy Consumption Survey; more than 7,000 households were surveyed for information on their housing units, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information represents all households nationwide (97 million). Key findings: National residential energy consumption was 10.0 quadrillion Btu in 1993, a 9% increase over 1990. Weather has a significant effect on energy consumption. Consumption of electricity for appliances is increasing. Houses that use electricity for space heating have lower overall energy expenditures than households that heat with other fuels. RECS collected data for the 4 most populous states: CA, FL, NY, TX.

  16. The changing character of household waste in the Czech Republic between 1999 and 2009 as a function of home heating methods

    SciTech Connect (OSTI)

    Dolealov, Markta; Beneov, Libue; Zvodsk, Anita

    2013-09-15

    Highlights: The character of household waste in the three different types of households were assesed. The quantity, density and composition of household waste were determined. The physicochemical characteristics were determined. The changing character of household waste during past 10 years was described. The potential of energy recovery of household waste in Czech republic was assesed. - Abstract: The authors of this paper report on the changing character of household waste, in the Czech Republic between 1999 and 2009 in households differentiated by their heating methods. The data presented are the result of two projects, financed by the Czech Ministry of Environment, which were undertaken during this time period with the aim of focusing on the waste characterisation and complete analysis of the physicochemical properties of the household waste. In the Czech Republic, the composition of household waste varies significantly between different types of households based on the methods of home heating employed. For the purposes of these studies, the types of homes were divided into three categories urban, mixed and rural. Some of the biggest differences were found in the quantities of certain subsample categories, especially fine residue (matter smaller than 20 mm), between urban households with central heating and rural households that primarily employ solid fuel such coal or wood. The use of these solid fuels increases the fraction of the finer categories because of the higher presence of ash. Heating values of the residual household waste from the three categories varied very significantly, ranging from 6.8 MJ/kg to 14.2 MJ/kg in 1999 and from 6.8 MJ/kg to 10.5 MJ/kg in 2009 depending on the type of household and season. The same factors affect moisture of residual household waste which varied from 23.2% to 33.3%. The chemical parameters also varied significantly, especially in the quantities of Tl, As, Cr, Zn, Fe and Mn, which were higher in rural

  17. SNOiioaroad A9U3N3

    Gasoline and Diesel Fuel Update (EIA)

    1997 Household Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.2 1.4 1.4 Total .............................................................. 101.5 6.8 11.5 7.0 5.9 NF 1997 Household Income Category Less than $5,000 ......................................... 3.8 0.3 0.3 0.3 0.1 16.2 $5,000 to $9,999 ......................................... 9.6 0.9 1.1 0.6 0.7 14.2 $10,000 to $14,999

  18. S:\VM3\RX97\TBL_LIST.WPD

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

    1997 Household Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.2 1.4 1.4 Total .............................................................. 101.5 6.8 11.5 7.0 5.9 NF 1997 Household Income Category Less than $5,000 ......................................... 3.8 0.3 0.3 0.3 0.1 16.2 $5,000 to $9,999 ......................................... 9.6 0.9 1.1 0.6 0.7 14.2 $10,000 to $14,999

  19. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    1997 Air Conditioning Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.2 1.9 1.1 1.0 Households With Electric Air-Conditioning Equipment ...................... 73.6 4.3 4.8 6.4 5.7 3.5 Central Equipment Not Used ....................... 0.3 Q 0.1 (*) 0.1 29.0 Room Air Conditioners Not Used ................ 0.7 Q Q Q 0.1 43.1 Households Using Electric Air-Conditioning 1 ........................................ 72.6 4.2 4.6

  20. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    b. Appliances by Four Most Populated States, Percent of U.S. Households, 1997 Appliance Types and Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.3 1.2 1.2 1.4 Total .............................................................. 100.0 100.0 100.0 100.0 100.0 0.0 Households With Electric Air-Conditioning Equipment ...................... 72.5 62.6 41.4 91.7 96.0 3.5 Central Equipment Not Used ....................... 0.3 Q

  1. Determinants of Household Use of Selected Energy Star Appliances - Energy

    Gasoline and Diesel Fuel Update (EIA)

    Information Administration Determinants of Household Use of Selected Energy Star Appliances Release date: May 25, 2016 Introduction According to the 2009 Residential Energy Consumption Survey (RECS), household appliances1accounted for 35% of U.S. household energy consumption, up from 24% in 1993. Thus, improvements in the energy performance of residential appliances as well as increases in the use of more efficient appliances can be effective in reducing household energy consumption and

  2. Comparison of energy expenditures by elderly and non-elderly households: 1975 and 1985

    SciTech Connect (OSTI)

    Siler, A.

    1980-05-01

    The relative position of the elderly in the population is examined and their characteristic use of energy in relation to the total population and their non-elderly counterparts is observed. The 1985 projections are based on demographic, economic, and socio-economic, and energy data assumptions contained in the 1978 Annual Report to Congress. The model used for estimating household energy expenditure is MATH/CHRDS - Micro-Analysis of Transfers to Households/Comprehensive Human Resources Data System. Characteristics used include households disposable income, poverty status, location by DOE region and Standard Metropolitan Statistical Area (SMSA), and race and sex of the household head as well as age. Energy use by fuel type will be identified for total home fuels, including electricity, natural gas, bottled gas and fuel oil, and for all fuels, where gasoline use is also included. Throughout the analysis, both income and expenditure-dollar amounts for 1975 and 1985 are expressed in constant 1978 dollars. Two appendices contain statistical information.

  3. 1995 CECS C&E Tables

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

    Major Fuel, 1995 Building Characteristics RSE Column Factor: All Buildings Total Energy Consumption (trillion Btu) Primary Electricity (trillion Btu) RSE Row Factor Number of...

  4. 1989 CBECS EUI

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

    Table 3.2. Total Energy Consumption by Major Fuel, 1992 Building Characteristics RSE Column Factor: All Buildings Total Energy Consumption (trillion Btu) RSE Row Factor Number of...

  5. 1989 CBECS EUI

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

    . Consumption for Sum of Major Fuels, 1992 Building Characteristics RSE Column Factor: All Buildings Sum of Major Fuel Consumption RSE Row Factor Number of Buildings (thousand)...

  6. 1989 CBECS EUI

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

    . Expenditures for Sum of Major Fuels, 1992 Building Characteristics RSE Column Factor: All Buildings Sum of Major Fuel Expenditures RSE Row Factor Number of Buildings (thousand)...

  7. 1989 CBECS EUI

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

    . Total Energy Consumption by Major Fuel, 1992 Building Characteristics RSE Column Factor: All Buildings Total Energy Consumption (trillion Btu) RSE Row Factor Number of Buildings...

  8. Household Energy Consumption Segmentation Using Hourly Data

    SciTech Connect (OSTI)

    Kwac, J; Flora, J; Rajagopal, R

    2014-01-01

    The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.

  9. Household energy consumption and expenditures, 1987

    SciTech Connect (OSTI)

    Not Available

    1989-10-10

    Household Energy Consumption and Expenditures 1987, Part 1: National Data is the second publication in a series from the 1987 Residential Energy Consumption Survey (RECS). It is prepared by the Energy End Use Division (EEUD) of the Office of Energy Markets and End Use (EMEU), Energy Information Administration (EIA). The EIA collects and publishes comprehensive data on energy consumption in occupied housing units in the residential sector through the RECS. 15 figs., 50 tabs.

  10. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-10a

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

    0a. Usage Indicators by Midwest Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | Midwest Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | | | | | | Total | | East North| West North| Usage Indicators | U.S. | Total | Central | Central | |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 |

  11. file://C:\MyFiles\TeamWorks%20Website\index.htm

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

    0a. Usage Indicators by Midwest Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | Midwest Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | | | | | | Total | | East North| West North| Usage Indicators | U.S. | Total | Central | Central | |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 |

  12. h:prjq496 ext intext.pdf

    Gasoline and Diesel Fuel Update (EIA)

    0a. Usage Indicators by Midwest Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | Midwest Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | | | | | | Total | | East North| West North| Usage Indicators | U.S. | Total | Central | Central | |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 |

  13. Strategies for Collecting Household Energy Data | Department of Energy

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

    Collecting Household Energy Data Strategies for Collecting Household Energy Data Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for Collecting Household Energy Data, Call Slides and Discussion Summary, July 19, 2012. Call Slides and Discussion Summary (700.06 KB) More Documents & Publications Homeowner and Contractor Surveys Mastermind: Jim Mikel, Spirit Foundation Generating Energy Efficiency Project Leads and Allocating Leads to Contractors

  14. Household energy consumption and expenditures 1987

    SciTech Connect (OSTI)

    Not Available

    1990-01-22

    This report is the third in the series of reports presenting data from the 1987 Residential Energy Consumption Survey (RECS). The 1987 RECS, seventh in a series of national surveys of households and their energy suppliers, provides baseline information on household energy use in the United States. Data from the seven RECS and its companion survey, the Residential Transportation Energy Consumption Survey (RTECS), are made available to the public in published reports such as this one, and on public use data files. This report presents data for the four Census regions and nine Census divisions on the consumption of and expenditures for electricity, natural gas, fuel oil and kerosene (as a single category), and liquefied petroleum gas (LPG). Data are also presented on consumption of wood at the Census region level. The emphasis in this report is on graphic depiction of the data. Data from previous RECS surveys are provided in the graphics, which indicate the regional trends in consumption, expenditures, and uses of energy. These graphs present data for the United States and each Census division. 12 figs., 71 tabs.

  15. Appliance Commitment for Household Load Scheduling

    SciTech Connect (OSTI)

    Du, Pengwei; Lu, Ning

    2011-06-30

    This paper presents a novel appliance commitment algorithm that schedules thermostatically-controlled household loads based on price and consumption forecasts considering users comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem was described in the paper using an electrical water heater load as an example. The thermal dynamics of heating and coasting of the water heater load was modeled by physical models; random hot water consumption was modeled with statistical methods. The models were used to predict the appliance operation over the scheduling time horizon. User comfort was transformed to a set of linear constraints. Then, a novel linear, sequential, optimization process was used to solve the appliance commitment problem. The simulation results demonstrate that the algorithm is fast, robust, and flexible. The algorithm can be used in home/building energy-management systems to help household owners or building managers to automatically create optimal load operation schedules based on different cost and comfort settings and compare cost/benefits among schedules.

  16. Delivering Energy Efficiency to Middle Income Single Family Households

    SciTech Connect (OSTI)

    none,

    2011-12-01

    Provides state and local policymakers with information on successful approaches to the design and implementation of residential efficiency programs for households ineligible for low-income programs.

  17. Barriers to household investment in residential energy conservation: preliminary assessment

    SciTech Connect (OSTI)

    Hoffman, W.L.

    1982-12-01

    A general assessment of the range of barriers which impede household investments in weatherization and other energy efficiency improvements for their homes is provided. The relationship of similar factors to households' interest in receiving a free energy audits examined. Rates of return that underly household investments in major conservation improvements are assessed. A special analysis of household knowledge of economically attractive investments is provided that compares high payback improvements specified by the energy audit with the list of needed or desirable conservation improvements identified by respondents. (LEW)

  18. Characterization of household hazardous waste from Marin County, California, and New Orleans, Louisiana

    SciTech Connect (OSTI)

    Rathje, W.L.; Wilson, D.C.; Lambou, V.W.; Herndon, R.C.

    1987-09-01

    There is a growing concern that certain constituents of common household products, that are discarded in residential garbage, may be potentially harmful to human health and the environment by adversely affecting the quality of ground and surface water. A survey of hazardous wastes in residential garbage from Marin County, California, and New Orleans, Louisiana, was conducted in order to determine the amount and characteristics of such wastes that are entering municipal landfills. The results of the survey indicate that approximately 642 metric tons of hazardous waste are discarded per year for the New Orleans study area and approximately 259 metric tons are discarded per year for the Marin County study area. Even though the percent of hazardous household waste in the garbage discarded in both study areas was less than 1%, it represents a significant quantity of hazardous waste because of the large volume of garbage involved.

  19. speakers bureau.indd

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

    0a. Space Heating by Midwest Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.6 Total .............................................................. 107.0 24.5 17.1 7.4 NE Heat Home .................................................... 106.0 24.5 17.1 7.4 NE Do Not Heat Home ....................................... 1.0 Q Q Q 19.8 No

  20. S:\VM3\RX97\TBL_LIST.WPD

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

    b. Space Heating by Four Most Populated States, Percent of U.S. Households, 1997 Space Heating Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.5 1.2 0.9 1.3 1.5 Total .............................................................. 100.0 100.0 100.0 100.0 100.0 0.0 Main Heating Fuel and Equipment Natural Gas ................................................. 52.7 49.8 68.2 54.1 11.0 8.6 Central Warm-Air Furnace

  1. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    1997 Appliance Types and Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.3 1.2 1.2 1.4 Total .............................................................. 101.5 6.8 11.5 7.0 5.9 NF Households With Electric Air-Conditioning Equipment ...................... 73.6 4.3 4.8 6.4 5.7 3.5 Central Equipment Not Used ....................... 0.3 Q 0.1 (*) 0.1 29.3 Room Air Conditioners Not Used ................ 0.7 Q Q Q 0.1 36.9

  2. RSE Table E8.1 and E8.2. Relative Standard Errors for Tables E8.1 and E8.2

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

    E8.1 and E8.2. Relative Standard Errors for Tables E8.1 and E8.2;" " Unit: Percents." " ",," "," ",," "," " "Economic",,"Residual","Distillate",,"LPG and" "Characteristic(a)","Electricity","Fuel Oil","Fuel Oil(b)","Natural Gas(c)","NGL(d)","Coal" ,"Total United States" "Value of Shipments and Receipts"

  3. Reconstructing householder vectors from Tall-Skinny QR

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Ballard, Grey Malone; Demmel, James; Grigori, Laura; Jacquelin, Mathias; Knight, Nicholas; Nguyen, Hong Diep

    2015-08-05

    The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. However, TSQR produces a different representation of the orthogonal factor and therefore requires more software development to support the new representation. Further, implicitly applying the orthogonal factor to the trailing matrix in the context of factoring a square matrix is more complicated and costly than with the Householder representation. We show how to perform TSQR and then reconstruct the Householder vector representation with the same asymptotic communication efficiency and little extra computational cost. We demonstratemore » the high performance and numerical stability of this algorithm both theoretically and empirically. The new Householder reconstruction algorithm allows us to design more efficient parallel QR algorithms, with significantly lower latency cost compared to Householder QR and lower bandwidth and latency costs compared with Communication-Avoiding QR (CAQR) algorithm. Experiments on supercomputers demonstrate the benefits of the communication cost improvements: in particular, our experiments show substantial improvements over tuned library implementations for tall-and-skinny matrices. Furthermore, we also provide algorithmic improvements to the Householder QR and CAQR algorithms, and we investigate several alternatives to the Householder reconstruction algorithm that sacrifice guarantees on numerical stability in some cases in order to obtain higher performance.« less

  4. Reconstructing householder vectors from Tall-Skinny QR

    SciTech Connect (OSTI)

    Ballard, Grey Malone; Demmel, James; Grigori, Laura; Jacquelin, Mathias; Knight, Nicholas; Nguyen, Hong Diep

    2015-08-05

    The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. However, TSQR produces a different representation of the orthogonal factor and therefore requires more software development to support the new representation. Further, implicitly applying the orthogonal factor to the trailing matrix in the context of factoring a square matrix is more complicated and costly than with the Householder representation. We show how to perform TSQR and then reconstruct the Householder vector representation with the same asymptotic communication efficiency and little extra computational cost. We demonstrate the high performance and numerical stability of this algorithm both theoretically and empirically. The new Householder reconstruction algorithm allows us to design more efficient parallel QR algorithms, with significantly lower latency cost compared to Householder QR and lower bandwidth and latency costs compared with Communication-Avoiding QR (CAQR) algorithm. Experiments on supercomputers demonstrate the benefits of the communication cost improvements: in particular, our experiments show substantial improvements over tuned library implementations for tall-and-skinny matrices. Furthermore, we also provide algorithmic improvements to the Householder QR and CAQR algorithms, and we investigate several alternatives to the Householder reconstruction algorithm that sacrifice guarantees on numerical stability in some cases in order to obtain higher performance.

  5. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-01-01

    Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

  6. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-12-31

    Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

  7. Fact #618: April 12, 2010 Vehicles per Household and Other Demographic...

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

    per Household and Other Demographic Statistics Fact 618: April 12, 2010 Vehicles per Household and Other Demographic Statistics Since 1969, the number of vehicles per ...

  8. "Table HC7.5 Space Heating Usage Indicators by Household Income...

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

    ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ... for 2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ...

  9. "Table HC7.12 Home Electronics Usage Indicators by Household...

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

    ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ... for 2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ...

  10. "Table HC7.10 Home Appliances Usage Indicators by Household...

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

    ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ... for 2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ...

  11. Fact #748: October 8, 2012 Components of Household Expenditures on

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

    Transportation, 1984-2010 | Department of Energy 8: October 8, 2012 Components of Household Expenditures on Transportation, 1984-2010 Fact #748: October 8, 2012 Components of Household Expenditures on Transportation, 1984-2010 The overall share of annual household expenditures for transportation was lower in 2010 than it was in 1984, reaching its lowest point in 2009 at 15.5%. In the early to mid-1980s when oil prices were high, gasoline and motor oil made up a larger share of transportation

  12. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

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

    6 Air Conditioning Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Air Conditioning Characteristics"

  13. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

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

    HC7.9 Home Appliances Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Home Appliances Characteristics" "Total

  14. Table 2. Percent of Households with Vehicles, Selected Survey...

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

    Percent of Households with Vehicles, Selected Survey Years " ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",85.5450237,89.00343643,88.75545852,89.42917548,87.25590956,92.08...

  15. Fact #618: April 12, 2010 Vehicles per Household and Other Demographic

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

    Statistics | Department of Energy 8: April 12, 2010 Vehicles per Household and Other Demographic Statistics Fact #618: April 12, 2010 Vehicles per Household and Other Demographic Statistics Since 1969, the number of vehicles per household has increased by 66% and the number of vehicles per licensed driver has increased by 47%. The number of workers per household has changed the least of the statistics shown here. There has been a decline in the number of persons per household from 1969 to

  16. Buildings and Energy in the 1980's (TABLES)

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

    than 10 households were sampled. Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding column and row factors. * Because of rounding, data may...

  17. Modeling patterns of hot water use in households

    SciTech Connect (OSTI)

    Lutz, J.D.; Liu, Xiaomin; McMahon, J.E.

    1996-11-01

    This report presents a detailed model of hot water use patterns in individual household. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies. 21 refs., 3 figs., 10 tabs.

  18. Modeling patterns of hot water use in households

    SciTech Connect (OSTI)

    Lutz, James D.; Liu, Xiaomin; McMahon, James E.; Dunham, Camilla; Shown, Leslie J.; McCure, Quandra T.

    1996-01-01

    This report presents a detailed model of hot water use patterns in individual households. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies.

  19. New York Household Travel Patterns: A Comparison Analysis

    SciTech Connect (OSTI)

    Hu, Patricia S; Reuscher, Tim

    2007-05-01

    In 1969, the U. S. Department of Transportation began collecting detailed data on personal travel to address various transportation planning issues. These issues range from assessing transportation investment programs to developing new technologies to alleviate congestion. This 1969 survey was the birth of the Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990 and 1995. Longer-distance travel was collected in 1977 and 1995. In 2001, the survey was renamed to the National Household Travel Survey (NHTS) and collected both daily and longer-distance trips in one survey. In addition to the number of sample households that the national NPTS/NHTS survey allotted to New York State (NYS), the state procured an additional sample of households in both the 1995 and 2001 surveys. In the 1995 survey, NYS procured an addition sample of more than 9,000 households, increasing the final NY NPTS sample size to a total of 11,004 households. Again in 2001, NYS procured 12,000 additional sample households, increasing the final New York NHTS sample size to a total of 13,423 households with usable data. These additional sample households allowed NYS to address transportation planning issues pertinent to geographic areas significantly smaller than for what the national NPTS and NHTS data are intended. Specifically, these larger sample sizes enable detailed analysis of twelve individual Metropolitan Planning Organizations (MPOs). Furthermore, they allowed NYS to address trends in travel behavior over time. In this report, travel data for the entire NYS were compared to those of the rest of the country with respect to personal travel behavior and key travel determinants. The influence of New York City (NYC) data on the comparisons of the state of New York to the rest of the country was also examined. Moreover, the analysis examined the relationship between population density and travel patterns, and the similarities and differences among New

  20. A Glance at China’s Household Consumption

    SciTech Connect (OSTI)

    Shui, Bin

    2009-10-01

    Known for its scale, China is the most populous country with the world’s third largest economy. In the context of rising living standards, a relatively lower share of household consumption in its GDP, a strong domestic market and globalization, China is witnessing an unavoidable increase in household consumption, related energy consumption and carbon emissions. Chinese policy decision makers and researchers are well aware of these challenges and keen to promote green lifestyles. China has developed a series of energy policies and programs, and launched a wide-range social marketing activities to promote energy conservation.

  1. Shared Solar Projects Powering Households Throughout America | Department

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

    of Energy Shared Solar Projects Powering Households Throughout America Shared Solar Projects Powering Households Throughout America January 31, 2014 - 2:30pm Addthis Shared solar projects allow consumers to take advantage of solar energy’s myriad benefits, even though the system is not located on the consumer’s own rooftop. | Photo courtesy of the Vote Solar Initiative Shared solar projects allow consumers to take advantage of solar energy's myriad benefits, even though the system

  2. Household heating bills expected to be lower this winter

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

    Household heating bills expected to be lower this winter U.S. consumers are expected to pay less this winter on their home heating bills because of lower oil and natural gas prices and projected milder temperatures than last winter. In its new forecast, the U.S. Energy Information Administration said households that rely on heating oil which are mainly located in the Northeast will pay the lowest heating expenditures in 9 years down 25% from last winter as consumers are expected to save about

  3. Heating oil and propane households bills to be lower this winter...

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

    Heating oil and propane households bills to be lower this winter despite recent cold spell Despite the recent cold weather, households that use heating oil or propane as their main ...

  4. 1989 CBECS EUI

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

    . Electricity Consumption and Conditional Energy Intensity for Buildings Cooled with Electricity, 1992 Building Characteristics RSE Column Factor: Total Electricity Consumption...

  5. Fact #727: May 14, 2012 Nearly Twenty Percent of Households Own Three or

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

    More Vehicles | Department of Energy 7: May 14, 2012 Nearly Twenty Percent of Households Own Three or More Vehicles Fact #727: May 14, 2012 Nearly Twenty Percent of Households Own Three or More Vehicles Household vehicle ownership has changed over the last six decades. In 1960, over twenty percent of households did not own a vehicle, but by 2010, that number fell to less than 10%. The number of households with three or more vehicles grew from 2% in 1960 to nearly 20% in 2010. Before 1990,

  6. Fact #729: May 28, 2012 Secondary Household Vehicles Travel Fewer Miles |

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

    Department of Energy 9: May 28, 2012 Secondary Household Vehicles Travel Fewer Miles Fact #729: May 28, 2012 Secondary Household Vehicles Travel Fewer Miles When a household has more than one vehicle, the secondary vehicles travel fewer miles than the primary vehicle. In a two-vehicle household, the second vehicle travels less than half of the miles that the primary vehicle travels in a day. In a six-vehicle household, the sixth vehicle travels fewer than five miles a day. Daily Vehicle

  7. "Table HC15.3 Household Characteristics by Four Most Populated...

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

    "Income Relative to Poverty Line" "Below 100 Percent",16.6,1.5,1,1.5,... " 1. Below 150 percent of poverty line or 60 percent of median State ...

  8. 1992 CBECS C&E Table 3.29

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

    per Square Foot and Load Factors, 1992 Building Characteristics RSE Column Factor: All Demand-Metered Buildings Peak Watts per Square Foot Load Factor RSE Row Factor Number of...

  9. 1992 CBECS C & E

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

    of District Heat by End Use, 1989 District Heat Consumption (trillion Btu) Space Water a Total Heating Heating Other RSE Building Row Characteristics Factor 1.0 NF NF NF RSE...

  10. 1992 CBECS C & E

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

    0. Consumption of Fuel Oil by End Use, 1989 Fuel Oil Consumption (trillion Btu) Space Water a Total Heating Heating Other RSE Building Row Characteristics Factor 1.0 NF NF NF RSE...

  11. Table 5.8. U.S. Vehicle Fuel Consumption by Family Income, 1994

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

    and 1994 Vehicle Characteristics RSE Column Factor: Total 1993 Family Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factor: Less than 5,000 5,000...

  12. Loan Programs for Low- and Moderate-Income Households | Department of

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

    Energy Programs for Low- and Moderate-Income Households Loan Programs for Low- and Moderate-Income Households Better Buildings Residential Network Multifamily and Low-Income Housing Peer Exchange Call Series: Loan Programs for Low- and Moderate-Income Households, March 13, 2014. Call Slides and Discussion Summary (919.64 KB) More Documents & Publications EcoHouse Program Overview Strengthening Relationships Between Energy Programs and Housing Programs Targeted Marketing and Program

  13. Fact #614: March 15, 2010 Average Age of Household Vehicles | Department of

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

    Energy 4: March 15, 2010 Average Age of Household Vehicles Fact #614: March 15, 2010 Average Age of Household Vehicles The average age of household vehicles has increased from 6.6 years in 1977 to 9.2 years in 2009. Pickup trucks have the oldest average age in every year listed. Sport utility vehicles (SUVs), first reported in the 1995 survey, have the youngest average age. Average Vehicle Age by Vehicle Type Graph showing the average vehicle age by type (car, van, pickup, SUV, all household

  14. Effect of Income on Appliances in U.S. Households, The

    Reports and Publications (EIA)

    2004-01-01

    Entails how people live, the factors that cause the most differences in home lifestyle, including energy use in geographic location, socioeconomics and household income.

  15. Forum on Enhancing the Delivery of Energy Efficiency to Middle Income Households: Discussion Summary

    SciTech Connect (OSTI)

    none,

    2012-09-20

    Summarizes discussions and recommendations from a forum for practitioners and policymakers aiming to strengthen residential energy efficiency program design and delivery for middle income households.

  16. A material flow analysis on current electrical and electronic waste disposal from Hong Kong households

    SciTech Connect (OSTI)

    Lau, Winifred Ka-Yan; Chung, Shan-Shan; Zhang, Chan

    2013-03-15

    Highlights: ► Most household TWARC waste is sold directly to private e-waste collectors in HK. ► The current e-waste recycling network is popular with HK households. ► About 80% of household generated TWARC is exported overseas each year. ► Over 7000 tonnes/yr of household generated TWARC reach landfills. ► It is necessary to upgrade safety and awareness in HK’s e-waste recycling industry. - Abstract: A material flow study on five types of household electrical and electronic equipment, namely television, washing machine, air conditioner, refrigerator and personal computer (TWARC) was conducted to assist the Government of Hong Kong to establish an e-waste take-back system. This study is the first systematic attempt on identifying key TWARC waste disposal outlets and trade practices of key parties involved in Hong Kong. Results from two questionnaire surveys, on local households and private e-waste traders, were used to establish the material flow of household TWARC waste. The study revealed that the majority of obsolete TWARC were sold by households to private e-waste collectors and that the current e-waste collection network is efficient and popular with local households. However, about 65,000 tonnes/yr or 80% of household generated TWARC waste are being exported overseas by private e-waste traders, with some believed to be imported into developing countries where crude recycling methods are practiced. Should Hong Kong establish a formal recycling network with tight regulatory control on imports and exports, the potential risks of current e-waste recycling practices on e-waste recycling workers, local residents and the environment can be greatly reduced.

  17. Commercial viability of hybrid vehicles : best household use and cross national considerations.

    SciTech Connect (OSTI)

    Santini, D. J.; Vyas, A. D.

    1999-07-16

    Japanese automakers have introduced hybrid passenger cars in Japan and will soon do so in the US. In this paper, we report how we used early computer simulation model results to compare the commercial viability of a hypothetical near-term (next decade) hybrid mid-size passenger car configuration under varying fuel price and driving patterns. The fuel prices and driving patterns evaluated are designed to span likely values for major OECD nations. Two types of models are used. One allows the ''design'' of a hybrid to a specified set of performance requirements and the prediction of fuel economy under a number of possible driving patterns (called driving cycles). Another provides an estimate of the incremental cost of the hybrid in comparison to a comparably performing conventional vehicle. In this paper, the models are applied to predict the NPV cost of conventional gasoline-fueled vehicles vs. parallel hybrid vehicles. The parallel hybrids are assumed to (1) be produced at high volume, (2) use nickel metal hydride battery packs, and (3) have high-strength steel bodies. The conventional vehicle also is assumed to have a high-strength steel body. The simulated vehicles are held constant in many respects, including 0-60 time, engine type, aerodynamic drag coefficient, tire rolling resistance, and frontal area. The hybrids analyzed use the minimum size battery pack and motor to meet specified 0-60 times. A key characteristic affecting commercial viability is noted and quantified: that hybrids achieve the most pronounced fuel economy increase (best use) in slow, average-speed, stop-and-go driving, but when households consistently drive these vehicles under these conditions, they tend to travel fewer miles than average vehicles. We find that hours driven is a more valuable measure than miles. Estimates are developed concerning hours of use of household vehicles versus driving cycle, and the pattern of minimum NPV incremental cost (or benefit) of selecting the hybrid over

  18. Laboratory Testing of Demand-Response Enabled Household Appliances

    SciTech Connect (OSTI)

    Sparn, B.; Jin, X.; Earle, L.

    2013-10-01

    With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses. The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

  19. Laboratory Testing of Demand-Response Enabled Household Appliances

    SciTech Connect (OSTI)

    Sparn, B.; Jin, X.; Earle, L.

    2013-10-01

    With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses.The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

  20. Fact #616: March 29, 2010 Household Vehicle-Miles of Travel by Trip Purpose

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

    | Department of Energy 6: March 29, 2010 Household Vehicle-Miles of Travel by Trip Purpose Fact #616: March 29, 2010 Household Vehicle-Miles of Travel by Trip Purpose In 2009, getting to and from work accounted for about 27% of household vehicle-miles of travel (VMT). Work-related business was 8.4% of VMT in 2001, but declined to 6.7% in 2009, possibly due to advancements in computing technology making it possible for more business to be handled electronically. VMT for shopping was almost

  1. Rail Coal Transportation Rates

    Gasoline and Diesel Fuel Update (EIA)

    7a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7 26.5

  2. R93HC.PDF

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

    a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7 26.5 22.5

  3. R93HC.PDF

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

    6a. Appliances by Census Region and Climate Zone, Million U.S. Households, 1993 Appliance Types and Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.4 0.9 0.8 0.7 0.8 2.3 1.3 1.3 1.4 1.1 Total ..................................................... 96.6 19.5 23.3 33.5 20.4 8.7

  4. R93HC.PDF

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

    Table 3.7a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7

  5. RACORO Forecasting

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

    7a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7 26.5

  6. Table 2.5 Household Energy Consumption and Expenditures by End...

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

    5 Household 1 Energy Consumption and Expenditures by End Use, Selected Years, 1978-2005 Year Space ... 3 Fuel Oil 4 LPG 5 Total Electricity 3 Natural Gas Elec- tricity 3 ...

  7. How Do You Encourage Everyone in Your Household to Save Energy?

    Broader source: Energy.gov [DOE]

    Anyone who has decided to save energy at home knows that the entire household needs to be involved if you really want to see savings. Some people—be they roommates, spouses, children, or maybe even...

  8. EPA Webinar: Bringing Energy Efficiency and Renewable Housing to Low-Income Households

    Broader source: Energy.gov [DOE]

    Hosted by the U.S. Environmental Protection Agency, this webinar will explore the topic of linking and leveraging energy efficiency and renewable energy programs for limited-income households, including the need to coordinate with other energy assistance programs.

  9. Competition Helps Kids Learn About Energy and Save Their Households Some

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

    Money | Department of Energy Competition Helps Kids Learn About Energy and Save Their Households Some Money Competition Helps Kids Learn About Energy and Save Their Households Some Money May 21, 2013 - 2:40pm Addthis Students can register now to save energy and win prizes with the Home Energy Challenge. Students can register now to save energy and win prizes with the Home Energy Challenge. Eric Barendsen Energy Technology Program Specialist, Office of Energy Efficiency and Renewable Energy

  10. Residential Network Members Impact More Than 42,000 Households | Department

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

    of Energy Impact More Than 42,000 Households Residential Network Members Impact More Than 42,000 Households Photo of a row of townhomes. Eligible Better Buildings Residential Network members reported completing 27,563 home energy upgrades during 2013 as part of the Residential Network's first reporting cycle. In addition, 13 Better Buildings Neighborhood Program partners completed 12,166 home energy upgrades, and six Home Performance with ENERGY STAR® Sponsors completed 2,540 home energy

  11. A Mixed Nordic Experience: Implementing Competitive Retail Electricity Markets for Household Customers

    SciTech Connect (OSTI)

    Olsen, Ole Jess; Johnsen, Tor Arnt; Lewis, Philip

    2006-11-15

    Although the Nordic countries were among the first to develop competition in the electricity industry, it took a long time to make retail competition work. In Norway and Sweden a considerable number of households are actively using the market but very few households are active in Finland and Denmark. One problem has been institutional barriers involving metering, limited unbundling of distribution and supply, and limited access to reliable information on contracts and prices. (author)

  12. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-12a

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

    2a. Usage Indicators by West Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | West Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | Total | | | | Usage Indicators | U.S. | Total | Mountain | Pacific | |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 | 1.0 | 1.6 | 1.2 |Factors

  13. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-7a_

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

    Million U.S. Households, 2001 _____________________________________________________________________________________________ | | | | | Four Most Populated States | | |_______________________________________| | Total | | | | | Usage Indicators | U.S. |New York| California | Texas | Florida| |________|________|____________|________|________| RSE | | | | | | Row RSE Column Factor: | 0.4 | 1.1 | 1.0 | 1.3 | 1.6 |Factors

  14. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-8a_

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

    8a. Usage Indicators by Urban/Rural Location, Million U.S. Households, 2001 ______________________________________________________________________________________________ | | | | | Urban/Rural Location 1 | | |_______________________________________| | | | | | | Usage Indicators | Total | City | Town | Suburbs | Rural | |_________|_________|_________|_________|_________| RSE | | | | | | Row RSE Column Factor: | 0.5 | 0.8 | 1.4 | 1.3 | 1.4 |Factors

  15. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-9a_

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

    9a. Usage Indicators by Northeast Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | Northeast Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | | | | | | Total | | Middle | | Usage Indicators | U.S. | Total | Atlantic |New England| |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 | 1.0 |

  16. Development of the household sample for furnace and boilerlife-cycle cost analysis

    SciTech Connect (OSTI)

    Whitehead, Camilla Dunham; Franco, Victor; Lekov, Alex; Lutz, Jim

    2005-05-31

    Residential household space heating energy use comprises close to half of all residential energy consumption. Currently, average space heating use by household is 43.9 Mbtu for a year. An average, however, does not reflect regional variation in heating practices, energy costs, or fuel type. Indeed, a national average does not capture regional or consumer group cost impacts from changing efficiency levels of heating equipment. The US Department of Energy sets energy standards for residential appliances in, what is called, a rulemaking process. The residential furnace and boiler efficiency rulemaking process investigates the costs and benefits of possible updates to the current minimum efficiency regulations. Lawrence Berkeley National Laboratory (LBNL) selected the sample used in the residential furnace and boiler efficiency rulemaking from publically available data representing United States residences. The sample represents 107 million households in the country. The data sample provides the household energy consumption and energy price inputs to the life-cycle cost analysis segment of the furnace and boiler rulemaking. This paper describes the choice of criteria to select the sample of houses used in the rulemaking process. The process of data extraction is detailed in the appendices and is easily duplicated. The life-cycle cost is calculated in two ways with a household marginal energy price and a national average energy price. The LCC results show that using an national average energy price produces higher LCC savings but does not reflect regional differences in energy price.

  17. Household energy use in urban Venezuela: Implications from surveys in Maracaibo, Valencia, Merida, and Barcelona-Puerto La Cruz

    SciTech Connect (OSTI)

    Figueroa, M.J.; Sathaye, J.

    1993-08-01

    This report identifies the most important results of a comparative analysis of household commercial energy use in Venezuelan urban cities. The use of modern fuels is widespread among all cities. Cooking consumes the largest share of urban household energy use. The survey documents no use of biomass and a negligible use of kerosene for cooking. LPG, natural gas, and kerosene are the main fuels available. LPG is the fuel choice of low-income households in all cities except Maracaibo, where 40% of all households use natural gas. Electricity consumption in Venezuela`s urban households is remarkably high compared with the levels used in households in comparable Latin American countries and in households of industrialized nations which confront harsher climatic conditions and, therefore, use electricity for water and space heating. The penetration of appliances in Venezuela`s urban households is very high. The appliances available on the market are inefficient, and there are inefficient patterns of energy use among the population. Climate conditions and the urban built form all play important roles in determining the high level of energy consumption in Venezuelan urban households. It is important to acknowledge the opportunities for introducing energy efficiency and conservation in Venezuela`s residential sector, particularly given current economic and financial constraints, which may hamper the future provision of energy services.

  18. NYSERDA's Green Jobs-Green New York Program: Extending Energy Efficiency Financing To Underserved Households

    SciTech Connect (OSTI)

    Zimring, Mark; Fuller, Merrian

    2011-01-24

    The New York legislature passed the Green Jobs-Green New York (GJGNY) Act in 2009. Administered by the New York State Energy Research and Development Authority (NYSERDA), GJGNY programs provide New Yorkers with access to free or low-cost energy assessments,1 energy upgrade services,2 low-cost financing, and training for various 'green-collar' careers. Launched in November 2010, GJGNY's residential initiative is notable for its use of novel underwriting criteria to expand access to energy efficiency financing for households seeking to participate in New York's Home Performance with Energy Star (HPwES) program.3 The GJGNY financing program is a valuable test of whether alternatives to credit scores can be used to responsibly expand credit opportunities for households that do not qualify for traditional lending products and, in doing so, enable more households to make energy efficiency upgrades.

  19. Average household expected to save $675 at the pump in 2015

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

    Average household expected to save $675 at the pump in 2015 Although retail gasoline prices have risen in recent weeks U.S. consumers are still expected to save about $675 per household in motor fuel costs this year. In its new monthly forecast, the U.S. Energy Information Administration says the average pump price for regular grade gasoline in 2015 will be $2.43 per gallon. That's about 93 cents lower than last year's average. The savings for consumers will be even bigger during the

  20. EERE Success Story-Kingston Creek Hydro Project Powers 100 Households |

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

    Department of Energy Kingston Creek Hydro Project Powers 100 Households EERE Success Story-Kingston Creek Hydro Project Powers 100 Households August 21, 2013 - 12:00am Addthis Nevada-based contracting firm Nevada Controls, LLC used a low-interest loan from the Nevada State Office of Energy's Revolving Loan Fund to help construct a hydropower project in the small Nevada town of Kingston. The Kingston Creek Project-benefitting the Young Brothers Ranch-is a 175-kilowatt hydro generation plant

  1. Drivers of U.S. Household Energy Consumption, 1980-2009

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

    Drivers of U.S. Household Energy Consumption, 1980-2009 February 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Drivers of U.S. Household Energy Consumption, 1980-2009 i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any

  2. Households to pay more than expected to stay warm this winter

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

    Households to pay more than expected to stay warm this winter Following a colder-than-expected November, U.S. households are forecast to consume more heating fuels than previously expected....resulting in higher heating bills. Homeowners that rely on natural gas will see their total winter expenses rise nearly 13 percent from last winter....while users of electric heat will see a 2.6 percent increase in costs. That's the latest forecast from the U.S. Energy Information Administration. Propane

  3. Residential Energy Consumption Survey: Housing Characteristics...

    Gasoline and Diesel Fuel Update (EIA)

    either air or liquid as the working fluid. It does not refer :<: passive collection of solar thermal energy. Fuel Oil Paid by Household: The household paid directly to the fuel...

  4. 2003 Commercial Buildings Energy Consumption - What is an RSE

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

    the estimates differ from the true population values. However, the sample design permits us to estimate the sampling error in each value. It is important to...

  5. A Multi Agent-Based Framework for Simulating Household PHEV Distribution and Electric Distribution Network Impact

    SciTech Connect (OSTI)

    Cui, Xiaohui; Liu, Cheng; Kim, Hoe Kyoung; Kao, Shih-Chieh; Tuttle, Mark A; Bhaduri, Budhendra L

    2011-01-01

    The variation of household attributes such as income, travel distance, age, household member, and education for different residential areas may generate different market penetration rates for plug-in hybrid electric vehicle (PHEV). Residential areas with higher PHEV ownership could increase peak electric demand locally and require utilities to upgrade the electric distribution infrastructure even though the capacity of the regional power grid is under-utilized. Estimating the future PHEV ownership distribution at the residential household level can help us understand the impact of PHEV fleet on power line congestion, transformer overload and other unforeseen problems at the local residential distribution network level. It can also help utilities manage the timing of recharging demand to maximize load factors and utilization of existing distribution resources. This paper presents a multi agent-based simulation framework for 1) modeling spatial distribution of PHEV ownership at local residential household level, 2) discovering PHEV hot zones where PHEV ownership may quickly increase in the near future, and 3) estimating the impacts of the increasing PHEV ownership on the local electric distribution network with different charging strategies. In this paper, we use Knox County, TN as a case study to show the simulation results of the agent-based model (ABM) framework. However, the framework can be easily applied to other local areas in the US.

  6. Material World: Forecasting Household Appliance Ownership in a Growing Global Economy

    SciTech Connect (OSTI)

    Letschert, Virginie; McNeil, Michael A.

    2009-03-23

    Over the past years the Lawrence Berkeley National Laboratory (LBNL) has developed an econometric model that predicts appliance ownership at the household level based on macroeconomic variables such as household income (corrected for purchase power parity), electrification, urbanization and climate variables. Hundreds of data points from around the world were collected in order to understand trends in acquisition of new appliances by households, especially in developing countries. The appliances covered by this model are refrigerators, lighting fixtures, air conditioners, washing machines and televisions. The approach followed allows the modeler to construct a bottom-up analysis based at the end use and the household level. It captures the appliance uptake and the saturation effect which will affect the energy demand growth in the residential sector. With this approach, the modeler can also account for stock changes in technology and efficiency as a function of time. This serves two important functions with regard to evaluation of the impact of energy efficiency policies. First, it provides insight into which end uses will be responsible for the largest share of demand growth, and therefore should be policy priorities. Second, it provides a characterization of the rate at which policies affecting new equipment penetrate the appliance stock. Over the past 3 years, this method has been used to support the development of energy demand forecasts at the country, region or global level.

  7. 1989 CBECS EUI

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

    4. Electricity Consumption and Conditional Energy Intensity for Buildings Heated with Electricity, 1992 Building Characteristics RSE Column Factor: Total Electricity Consumption...

  8. 1989 CBECS EUI

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

    9. Consumption and Gross Energy Intensity by Building Size for Sum of Major Fuels, 1992 Building Characteristics RSE Column Factor: Sum of Major Fuel Consumption (trillion Btu)...

  9. 1989 CBECS EUI

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

    Energy Intensity for Sum of Major Fuels for Mercantile and Office Buildings, 1992 Building Characteristics RSE Column Factor: Sum of Major Fuel Consumption (trillion Btu) Total...

  10. 1989 CBECS EUI

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

    Energy Intensity for Sum of Major Fuels in Older Buildings by Year Constructed, 1992 Building Characteristics RSE Column Factor: Sum of Major Fuel Consumption (trillion Btu) Total...

  11. 1989 CBECS EUI

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

    Consumption and Gross Energy Intensity by Census Region for Sum of Major Fuels, 1992 Building Characteristics RSE Column Factor: Sum of Major Fuel Consumption (trillion Btu) Total...

  12. 1989 CBECS EUI

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

    Expenditures by Census Region for Sum of Major Fuels, 1992 Building Characteristics RSE Column Factor: Sum of Major Fuel Expenditures (million dollars) Sum of Major Fuel...

  13. 1989 CBECS EUI

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

    Season of Peak Electricity Demand, Number of Buildings and Floorspace, 1992 Building Characteristics RSE Column Factor: Number of Buildings (thousand) Total Floorspace (million...

  14. Buildings and Energy in the 1980's (TABLES)

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

    in Residential Buildings, 1984 End Uses RSE Row Fac- tors All End Uses Space Heating Water Heating Air Conditioning Appliances Building Characteristics Buildings (thou- sand)...

  15. Buildings and Energy in the 1980's (TABLES)

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

    in Residential Buildings, 1987 End Uses RSE Row Fac- tors All End Uses Space Heating Water Heating Air Conditioning Appliances Building Characteristics Buildings (thou- sand)...

  16. Buildings and Energy in the 1980's (TABLES)

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

    1982 End Uses RSE Row Fac- tors All End Uses Space Heating Water Heating Air Conditioning Appliances Building Characteristics Buildings (thou- sand) Consump- tion...

  17. Buildings and Energy in the 1980's (TABLES)

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

    1980 End Uses RSE Row Fac- tors All End Uses Space Heating Water Heating Air Conditioning Appliances Building Characteristics Buildings (thou- sand) Consump- tion...

  18. Buildings and Energy in the 1980's (TABLES)

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

    1981 End Uses RSE Row Fac- tors All End Uses Space Heating Water Heating Air Conditioning Appliances Building Characteristics Buildings (thou- sand) Consump- tion...

  19. Mitigating Carbon Emissions: the Potential of Improving Efficiencyof Household Appliances in China

    SciTech Connect (OSTI)

    Lin, Jiang

    2006-07-10

    China is already the second's largest energy consumer in the world after the United States, and its demand for energy is expected to continue to grow rapidly in the foreseeable future, due to its fast economic growth and its low level of energy use per capita. From 2001 to 2005, the growth rate of energy consumption in China has exceeded the growth rate of its economy (NBS, 2006), raising serious concerns about the consequences of such energy use on local environment and global climate. It is widely expected that China is likely to overtake the US in energy consumption and greenhouse gas (GHG) emissions during the first half of the 21st century. Therefore, there is considerable interest in the international community in searching for options that may help China slow down its growth in energy consumption and GHG emissions through improving energy efficiency and adopting more environmentally friendly fuel supplies such as renewable energy. This study examines the energy saving potential of three major residential energy end uses: household refrigeration, air-conditioning, and water heating. China is already the largest consumer market in the world for household appliances, and increasingly the global production base for consumer appliances. Sales of household refrigerators, room air-conditioners, and water heaters are growing rapidly due to rising incomes and booming housing market. At the same time, the energy use of Chinese appliances is relatively inefficient compared to similar products in the developed economies. Therefore, the potential for energy savings through improving appliance efficiency is substantial. This study focuses particularly on the impact of more stringent energy efficiency standards for household appliances, given that such policies are found to be very effective in improving the efficiency of household appliances, and are well established both in China and around world (CLASP, 2006).

  20. Household-level dynamics of food waste production and related beliefs, attitudes, and behaviours in Guelph, Ontario

    SciTech Connect (OSTI)

    Parizeau, Kate; Massow, Mike von; Martin, Ralph

    2015-01-15

    Highlights: • We combined household waste stream weights with survey data. • We examine relationships between waste and food-related practices and beliefs. • Families and large households produced more total waste, but less waste per capita. • Food awareness and waste awareness were related to reduced food waste. • Convenience lifestyles were differentially associated with food waste. - Abstract: It has been estimated that Canadians waste $27 billion of food annually, and that half of that waste occurs at the household level (Gooch et al., 2010). There are social, environmental, and economic implications for this scale of food waste, and source separation of organic waste is an increasingly common municipal intervention. There is relatively little research that assesses the dynamics of household food waste (particularly in Canada). The purpose of this study is to combine observations of organic, recyclable, and garbage waste production rates to survey results of food waste-related beliefs, attitudes, and behaviours at the household level in the mid-sized municipality of Guelph, Ontario. Waste weights and surveys were obtained from 68 households in the summer of 2013. The results of this study indicate multiple relationships between food waste production and household shopping practices, food preparation behaviours, household waste management practices, and food-related attitudes, beliefs, and lifestyles. Notably, we observed that food awareness, waste awareness, family lifestyles, and convenience lifestyles were related to food waste production. We conclude that it is important to understand the diversity of factors that can influence food wasting behaviours at the household level in order to design waste management systems and policies to reduce food waste.

  1. Table 2.6 Household End Uses: Fuel Types, Appliances, and Electronics, Selected Years, 1978-2009

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

    6 Household End Uses: Fuel Types, Appliances, and Electronics, Selected Years, 1978-2009 Appliance Year Change 1978 1979 1980 1981 1982 1984 1987 1990 1993 1997 2001 2005 2009 1980 to 2009 Total Households (millions) 77 78 82 83 84 86 91 94 97 101 107 111 114 32 Percent of Households<//td> Space Heating - Main Fuel 1 Natural Gas 55 55 55 56 57 55 55 55 53 52 55 52 50 -5 Electricity 2 16 17 18 17 16 17 20 23 26 29 29 30 35 17 Liquefied Petroleum Gases 4 5 5 4 5 5 5 5 5 5 5 5 5 0 Distillate

  2. The importance of China's household sector for black carbon emissions - article no. L12708

    SciTech Connect (OSTI)

    Streets, D.G.; Aunan, K.

    2005-06-30

    The combustion of coal and biofuels in Chinese households is a large source of black carbon (BC), representing about 10-15% of total global emissions during the past two decades, depending on the year. How the Chinese household sector develops during the next 50 years will have an important bearing on future aerosol concentrations, because the range of possible outcomes (about 550 Gg yr{sup -1}) is greater than total BC emissions in either the United States or Europe (each about 400-500 Gg yr{sup -1}). In some Intergovernmental Panel on Climate Change scenarios biofuels persist in rural China for at least the next 50 years, whereas in other scenarios a transition to cleaner fuels and technologies effectively mitigates BC emissions. This paper discusses measures and policies that would help this transition and also raises the possibility of including BC emission reductions as a post-Kyoto option for China and other developing countries.

  3. 1999 Commercial Buildings Characteristics

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

    Data Reports > 2003 Building Characteristics Overview 1999 Commercial Buildings Energy Consumption SurveyCommercial Buildings Characteristics Released: May 2002 Topics: Energy...

  4. Evaluation of bulk paint worker exposure to solvents at household hazardous waste collection events

    SciTech Connect (OSTI)

    Cameron, M.

    1995-09-01

    In fiscal year 93/94, over 250 governmental agencies were involved in the collection of household hazardous wastes in the State of California. During that time, over 3,237,000 lbs. of oil based paint were collected in 9,640 drums. Most of this was in lab pack drums, which can only hold up to 20 one gallon cans. Cost for disposal of such drums is approximately $1000. In contrast, during the same year, 1,228,000 lbs. of flammable liquid were collected in 2,098 drums in bulk form. Incineration of bulked flammable liquids is approximately $135 per drum. Clearly, it is most cost effective to bulk flammable liquids at household hazardous waste events. Currently, this is the procedure used at most Temporary Household Hazardous Waste Collection Facilities (THHWCFs). THHWCFs are regulated by the Department of Toxic Substances Control (DTSC) under the new Permit-by Rule Regulations. These regulations specify certain requirements regarding traffic flow, emergency response notifications and prevention of exposure to the public. The regulations require that THHWCF operators bulk wastes only when the public is not present. [22 CCR, section 67450.4 (e) (2) (A)].Santa Clara County Environmental Health Department sponsors local THHWCF`s and does it`s own bulking. In order to save time and money, a variance from the regulation was requested and an employee monitoring program was initiated to determine actual exposure to workers. Results are presented.

  5. Household`s choices of efficiency levels for appliances: Using stated- and revealed-preference data to identify the importance of rebates and financing arrangements

    SciTech Connect (OSTI)

    Train, K.; Atherton, T.

    1994-11-01

    We examine customers` choice between standard and high-efficiency equipment, and the impact of utility incentives such as rebates and loans on this decision. Using data from interviews with 400 households, we identify the factors that customers consider in their choice of efficiency level for appliances and the relative importance of these factors. We build a model that describes customers` choices and can be used to predict choices in future situations under changes in the attributes of appliances and in the utility`s DSM and as part of the appliance-choice component of utilities` end-use forecasting systems. As examples, the model is used to predict the impacts of: doubling the size of rebates, replacing rebates with financing programs, and offering loans and rebates as alternative options for customers.

  6. The Impact of Carbon Control on Low-Income Household Electricity and Gasoline Expenditures

    SciTech Connect (OSTI)

    Eisenberg, Joel Fred

    2008-06-01

    In July of 2007 The Department of Energy's (DOE's) Energy Information Administration (EIA) released its impact analysis of 'The Climate Stewardship And Innovation Act of 2007,' known as S.280. This legislation, cosponsored by Senators Joseph Lieberman and John McCain, was designed to significantly cut U.S. greenhouse gas emissions over time through a 'cap-and-trade' system, briefly described below, that would gradually but extensively reduce such emissions over many decades. S.280 is one of several proposals that have emerged in recent years to come to grips with the nation's role in causing human-induced global climate change. EIA produced an analysis of this proposal using the National Energy Modeling System (NEMS) to generate price projections for electricity and gasoline under the proposed cap-and-trade system. Oak Ridge National Laboratory integrated those price projections into a data base derived from the EIA Residential Energy Consumption Survey (RECS) for 2001 and the EIA public use files from the National Household Transportation Survey (NHTS) for 2001 to develop a preliminary assessment of impact of these types of policies on low-income consumers. ORNL will analyze the impacts of other specific proposals as EIA makes its projections for them available. The EIA price projections for electricity and gasoline under the S.280 climate change proposal, integrated with RECS and NHTS for 2001, help identify the potential effects on household electric bills and gasoline expenditures, which represent S.280's two largest direct impacts on low-income household budgets in the proposed legislation. The analysis may prove useful in understanding the needs and remedies for the distributive impacts of such policies and how these may vary based on patterns of location, housing and vehicle stock, and energy usage.

  7. Table HC6.10 Home Appliances Usage Indicators by Number of Household Members, 2005

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

    0 Home Appliances Usage Indicators by Number of Household Members, 2005 Total.............................................................................. 111.1 30.0 34.8 18.4 15.9 12.0 Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day........................................... 8.2 1.4 1.9 1.4 1.0 2.4 2 Times A Day........................................................ 24.6 4.3 7.6 4.3 4.8 3.7 Once a Day............................................................ 42.3 9.9

  8. Table HC6.12 Home Electronics Usage Indicators by Number of Household Members, 2005

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

    2 Home Electronics Usage Indicators by Number of Household Members, 2005 Total................................................................................ 111.1 30.0 34.8 18.4 15.9 12.0 Personal Computers Do Not Use a Personal Computer............................. 35.5 16.3 9.4 4.0 2.7 3.2 Use a Personal Computer.......................................... 75.6 13.8 25.4 14.4 13.2 8.8 Most-Used Personal Computer Type of PC Desk-top Model.....................................................

  9. Table HC6.7 Air-Conditioning Usage Indicators by Number of Household Members, 2005

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

    7 Air-Conditioning Usage Indicators by Number of Household Members, 2005 Total........................................................................ 111.1 30.0 34.8 18.4 15.9 12.0 Do Not Have Cooling Equipment.......................... 17.8 5.4 5.3 2.7 2.5 2.0 Have Cooling Equipment...................................... 93.3 24.6 29.6 15.7 13.4 10.0 Use Cooling Equipment....................................... 91.4 24.0 29.1 15.5 13.2 9.7 Have Equipment But Do Not Use it......................

  10. Assessment of lead contamination in Bahrain environment. I. Analysis of household paint

    SciTech Connect (OSTI)

    Madany, I.M.; Ali, S.M.; Akhter, M.S.

    1987-01-01

    The analysis of lead in household paint collected from various old buildings in Bahrain is reported. The atomic absorption spectrophotometric method, both flame and flameless (graphite furnace) techniques, were used for the analysis. The concentrations of lead in paint were found in the range 200 to 5700 mg/kg, which are low compared to the limit of 0.5% in UK and 0.06% in USA. Nevertheless, these are hazardous. Recommendations are reported in order to avoid paint containing lead. 17 references, 1 table.

  11. WEEE and portable batteries in residual household waste: Quantification and characterisation of misplaced waste

    SciTech Connect (OSTI)

    Bigum, Marianne; Petersen, Claus; Scheutz, Charlotte

    2013-11-15

    Highlights: • We analyse 26.1 Mg of residual waste from 3129 Danish households. • We quantify and characterise misplaced WEEE and portable batteries. • We compare misplaced WEEE and batteries to collection through dedicated schemes. • Characterisation showed that primarily small WEEE and light sources are misplaced. • Significant amounts of misplaced batteries were discarded as built-in WEEE. - Abstract: A total of 26.1 Mg of residual waste from 3129 households in 12 Danish municipalities was analysed and revealed that 89.6 kg of Waste Electrical and Electronic Equipment (WEEE), 11 kg of batteries, 2.2 kg of toners and 16 kg of cables had been wrongfully discarded. This corresponds to a Danish household discarding 29 g of WEEE (7 items per year), 4 g of batteries (9 batteries per year), 1 g of toners and 7 g of unidentifiable cables on average per week, constituting 0.34% (w/w), 0.04% (w/w), 0.01% (w/w) and 0.09% (w/w), respectively, of residual waste. The study also found that misplaced WEEE and batteries in the residual waste constituted 16% and 39%, respectively, of what is being collected properly through the dedicated special waste collection schemes. This shows that a large amount of batteries are being discarded with the residual waste, whereas WEEE seems to be collected relatively successfully through the dedicated special waste collection schemes. Characterisation of the misplaced batteries showed that 20% (w/w) of the discarded batteries were discarded as part of WEEE (built-in). Primarily alkaline batteries, carbon zinc batteries and alkaline button cell batteries were found to be discarded with the residual household waste. Characterisation of WEEE showed that primarily small WEEE (WEEE directive categories 2, 5a, 6, 7 and 9) and light sources (WEEE directive category 5b) were misplaced. Electric tooth brushes, watches, clocks, headphones, flashlights, bicycle lights, and cables were items most frequently found. It is recommended that these

  12. Table HC1.1.2 Housing Unit Characteristics by Average Floorspace, 2005

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

    2 Housing Unit Characteristics by Average Floorspace, 2005 " ,,"Average Square Feet per--" ," Housing Units (millions)" ,,"Housing Unit",,,"Household Member" "Housing Unit Characteristics",,"Total1","Heated","Cooled","Total","Heated","Cooled" "Total",111.1,2171,1618,1031,845,630,401 "Census Region and Division" "Northeast",20.6,2334,1664,562,911,649,220

  13. Recovery and separation of high-value plastics from discarded household appliances

    SciTech Connect (OSTI)

    Karvelas, D.E.; Jody, B.J.; Poykala, J.A. Jr.; Daniels, E.J.; Arman, B. |

    1996-03-01

    Argonne National Laboratory is conducting research to develop a cost- effective and environmentally acceptable process for the separation of high-value plastics from discarded household appliances. The process under development has separated individual high purity (greater than 99.5%) acrylonitrile-butadiene-styrene (ABS) and high- impact polystyrene (HIPS) from commingled plastics generated by appliance-shredding and metal-recovery operations. The process consists of size-reduction steps for the commingled plastics, followed by a series of gravity-separation techniques to separate plastic materials of different densities. Individual plastics of similar densities, such as ABS and HIPS, are further separated by using a chemical solution. By controlling the surface tension, the density, and the temperature of the chemical solution we are able to selectively float/separate plastics that have different surface energies. This separation technique has proven to be highly effective in recovering high-purity plastics materials from discarded household appliances. A conceptual design of a continuous process to recover high-value plastics from discarded appliances is also discussed. In addition to plastics separation research, Argonne National Laboratory is conducting research to develop cost-effective techniques for improving the mechanical properties of plastics recovered from appliances.

  14. A Method for Modeling Household Occupant Behavior to Simulate Residential Energy Consumption

    SciTech Connect (OSTI)

    Johnson, Brandon J; Starke, Michael R; Abdelaziz, Omar; Jackson, Roderick K; Tolbert, Leon M

    2014-01-01

    This paper presents a statistical method for modeling the behavior of household occupants to estimate residential energy consumption. Using data gathered by the U.S. Census Bureau in the American Time Use Survey (ATUS), actions carried out by survey respondents are categorized into ten distinct activities. These activities are defined to correspond to the major energy consuming loads commonly found within the residential sector. Next, time varying minute resolution Markov chain based statistical models of different occupant types are developed. Using these behavioral models, individual occupants are simulated to show how an occupant interacts with the major residential energy consuming loads throughout the day. From these simulations, the minimum number of occupants, and consequently the minimum number of multiple occupant households, needing to be simulated to produce a statistically accurate representation of aggregate residential behavior can be determined. Finally, future work will involve the use of these occupant models along side residential load models to produce a high-resolution energy consumption profile and estimate the potential for demand response from residential loads.

  15. 1997 Housing Characteristics Tables Home Office Equipment Tables

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

    Percent of U.S. Households; 13 pages, 48 kb) Contents Pages HC7-1b. Home Office Equipment by Climate Zone, Percent of U.S. Households, 1997 1 HC7-2b. Home Office Equipment by Year of Construction, Percent of U.S. Households, 1997 1 HC7-3b. Home Office Equipment by Household Income, Percent of U.S. Households, 1997 1 HC7-4b. Home Office Equipment by Type of Housing Unit, Percent of U.S. Households, 1997 1 HC7-5b. Home Office Equipment by Type of Owner-Occupied Housing Unit, Percent of U.S.

  16. Survey of Recipients of WAP Services Assessment of Household Budget and Energy Behaviors Pre to Post Weatherization DOE

    SciTech Connect (OSTI)

    Tonn, Bruce Edward; Rose, Erin M.; Hawkins, Beth A.

    2015-10-01

    This report presents results from the national survey of weatherization recipients. This research was one component of the retrospective and Recovery Act evaluations of the U.S. Department of Energy s Weatherization Assistance Program. Survey respondents were randomly selected from a nationally representative sample of weatherization recipients. The respondents and a comparison group were surveyed just prior to receiving their energy audits and then again approximately 18 months post-weatherization. This report focuses on budget issues faced by WAP households pre- and post-weatherization, whether household energy behaviors changed from pre- to post, the effectiveness of approaches to client energy education, and use and knowledge about thermostats.

  17. Commercial Buildings Characteristics 1992

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

    schedules and the number of workers across all shifts as well as the main shift. * Energy Management Characteristics - Energy management questions were expanded to ask whether or...

  18. Table HC6.5 Space Heating Usage Indicators by Number of Household Members, 2005

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

    5 Space Heating Usage Indicators by Number of Household Members, 2005 Total U.S. Housing Units.................................. 111.1 30.0 34.8 18.4 15.9 12.0 Do Not Have Heating Equipment..................... 1.2 0.3 0.3 Q 0.2 0.2 Have Space Heating Equipment....................... 109.8 29.7 34.5 18.2 15.6 11.8 Use Space Heating Equipment........................ 109.1 29.5 34.4 18.1 15.5 11.6 Have But Do Not Use Equipment.................... 0.8 Q Q Q Q Q Space Heating Usage During 2005

  19. Particle and gas emissions from a simulated coal-burning household fire pit

    SciTech Connect (OSTI)

    Linwei Tian; Donald Lucas; Susan L. Fischer; S. C. Lee; S. Katharine Hammond; Catherine P. Koshland

    2008-04-01

    An open fire was assembled with firebricks to simulate the household fire pit used in rural China, and 15 different coals from this area were burned to measure the gaseous and particulate emissions. Particle size distribution was studied with a microorifice uniform-deposit impactor (MOUDI). Over 90% of the particulate mass was attributed to sub-micrometer particles. The carbon balance method was used to calculate the emission factors. Emission factors for four pollutants (particulate matter, CO{sub 2}, total hydrocarbons, and NOx) were 2-4 times higher for bituminous coals than for anthracites. In past inventories of carbonaceous emissions used for climate modeling, these two types of coal were not treated separately. The dramatic emission factor difference between the two types of coal warrants attention in the future development of emission inventories. 25 refs., 8 figs., 1 tab.

  20. An Analysis of the Price Elasticity of Demand for Household Appliances

    SciTech Connect (OSTI)

    Fujita, Kimberly; Dale, Larry; Fujita, K. Sydny

    2008-01-25

    This report summarizes our study of the price elasticity of demand for home appliances, including refrigerators, clothes washers, and dishwashers. In the context of increasingly stringent appliance standards, we are interested in what kind of impact the increased manufacturing costs caused by higher efficiency requirements will have on appliance sales. We begin with a review of existing economics literature describing the impact of economic variables on the sale of durable goods.We then describe the market for home appliances and changes in this market over the past 20 years, performing regression analysis on the shipments of home appliances and relevant economic variables including changes to operating cost and household income. Based on our analysis, we conclude that the demand for home appliances is price inelastic.

  1. Household energy conservation attitudes and behaviors in the Northwest: Tracking changes between 1983 and 1985

    SciTech Connect (OSTI)

    Fang, J.M.; Hattrup, M.P.; Nordi, R.T.; Shankle, S.A.; Ivey, D.L.

    1987-05-01

    Pacific Northwest Laboratory (PNL) has analyzed the changes in consumer energy conservation attitudes and behaviors in the Pacific Northwest between 1983 and 1985. The information was collected through stratified random telephone surveys on 2000 and 1058 households, respectively, for 1983 and 1985 in the Bonneville Power Administration (BPA) service area in Idaho, Oregon, Washington and Western Montana. This report covers four topic areas and tests two hypotheses. The topics are as follows: consumer perceptions and attitudes of energy use and conservation in the home; consumer perceptions of energy institutions and other entities; past and intended conservation actions and investments; and segmentation of homeowners into market prospect groups. The hypotheses tested are as follows: (1) There has been no change in the size and psychographic make-up of the original three market segments found in the 1983 survey analysis; and (2) image profiles of institutions with respect to familiarity, overall impression, and believability as sources of energy conservation information remain unchanged since 1983.

  2. A Green Approach to SNF Reprocessing: Are Common Household Reagents the Answer?

    SciTech Connect (OSTI)

    Peper, Shane M.; McNamara, Bruce K.; O'Hara, Matthew J.; Douglas, Matthew

    2008-04-03

    It has been discovered that UO2, the principal component of spent nuclear fuel (SNF), can efficiently be dissolved at room temperature using a combination of common household reagents, namely hydrogen peroxide, baking soda, and ammonia. This rather serendipitous discovery opens up the possibility, for the first time, of considering a non-acidic process for recycling U from SNF. Albeit at the early stages of development, our unconventional dissolution approach possesses many attractive features that could make it a reality in the future. With dissolution byproducts of water and oxygen, our approach poses a minimal threat to the environment. Moreover, the use of common household reagents to afford actinide oxide dissolution suggests a certain degree of economic favorability. With the use of a “closed” digestion vessel as a reaction chamber, our approach has substantial versatility with the option of using either aqueous or gaseous reactant feeds or a combination of both. Our approach distinguishes itself from all existing reprocessing technologies in two important ways. First and foremost, it is an alkaline rather than an acidic process, using mild non-corrosive chemicals under ambient conditions to effect actinide separations. Secondly, it does not dissolve the entire SNF matrix, but rather selectively solubilizes U and other light actinides for subsequent separation, resulting in potentially faster head-end dissolution and fewer downstream separation steps. From a safeguards perspective, the use of oxidizing alkaline solutions to effect actinide separations also potentially offers a degree of inherent proliferation resistance, by allowing the U to be selectively removed from the remaining dissolver solution while keeping Pu grouped with the other minor actinides and fission products. This paper will describe the design and general experimental setup of a “closed” digestion vessel for performing uranium oxide dissolutions under alkaline conditions using

  3. Residential energy use and conservation in Venezuela: Results and implications of a household survey in Caracas

    SciTech Connect (OSTI)

    Figueroa, M.J.; Ketoff, A.; Masera, O.

    1992-10-01

    This document presents the final report of a study of residential energy use in Caracas, the capital of Venezuela. It contains the findings of a household energy-use survey held in Caracas in 1988 and examines options for introducing energy conservation measures in the Venezuelan residential sector. Oil exports form the backbone of the Venezuelan economy. Improving energy efficiency in Venezuela will help free domestic oil resources that can be sold to the rest of the world. Energy conservation will also contribute to a faster recovery of the economy by reducing the need for major investments in new energy facilities, allowing the Venezuelan government to direct its financial investments towards other areas of development. Local environmental benefits will constitute an important additional by-product of implementing energy-efficiency policies in Venezuela. Caracas`s residential sector shows great potential for energy conservation. The sector is characterized by high saturation levels of major appliances, inefficiency of appliances available in the market, and by careless patterns of energy use. Household energy use per capita average 6.5 GJ/per year which is higher than most cities in developing countries; most of this energy is used for cooking. Electricity accounts for 41% of all energy use, while LPG and natural gas constitute the remainder. Specific options for inducing energy conservation and energy efficiency in Caracas`s residential sector include energy-pricing policies, fuel switching, particularly from electricity to gas, improving the energy performance of new appliances and customer information. To ensure the accomplishment of an energy-efficiency strategy, a concerted effort by energy users, manufacturers, utility companies, government agencies, and research institutions will be needed.

  4. 1992 CBECS BC

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

    A68. Principal Building Activity, Number of Buildings and Floorspace, 1992 Building Characteristics RSE Column Factor: All Buildings (thousand) Total Floorspace (million square feet) RSE Row Factor 0.9 1.1 All Buildings ........................................................ 4,806 67,876 3.7 Principal Building Activity Education ............................................................ 301 8,470 7.5 Food Sales ......................................................... 130 757 14.5 Food

  5. Crude Oil Characteristics Research

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

    SAE Plan June 29, 2015 Page 1 Crude Oil Characteristics Research Sampling, Analysis and Experiment (SAE) Plan The U.S. is experiencing a renaissance in oil and gas production. The ...

  6. ARM - Measurement - Soil characteristics

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

    characteristics ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Soil characteristics Includes available water capacity, bulk density, permeability, porosity, rock fragment classification, rock fragment volume, percent clay, percent sand, and texture classification Categories Surface Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer

  7. Household mold and dust allergens: Exposure, sensitization and childhood asthma morbidity

    SciTech Connect (OSTI)

    Gent, Janneane F.; Kezik, Julie M.; Hill, Melissa E.; Tsai, Eling; Li, De-Wei; Leaderer, Brian P.

    2012-10-15

    Background: Few studies address concurrent exposures to common household allergens, specific allergen sensitization and childhood asthma morbidity. Objective: To identify levels of allergen exposures that trigger asthma exacerbations in sensitized individuals. Methods: We sampled homes for common indoor allergens (fungi, dust mites (Der p 1, Der f 1), cat (Fel d 1), dog (Can f 1) and cockroach (Bla g 1)) for levels associated with respiratory responses among school-aged children with asthma (N=1233) in a month-long study. Blood samples for allergy testing and samples of airborne fungi and settled dust were collected at enrollment. Symptoms and medication use were recorded on calendars. Combined effects of specific allergen sensitization and level of exposure on wheeze, persistent cough, rescue medication use and a 5-level asthma severity score were examined using ordered logistic regression. Results: Children sensitized and exposed to any Penicillium experienced increased risk of wheeze (odds ratio [OR] 2.12 95% confidence interval [CI] 1.12, 4.04), persistent cough (OR 2.01 95% CI 1.05, 3.85) and higher asthma severity score (OR 1.99 95% CI 1.06, 3.72) compared to those not sensitized or sensitized but unexposed. Children sensitized and exposed to pet allergen were at significantly increased risk of wheeze (by 39% and 53% for Fel d 1>0.12 {mu}g/g and Can f 1>1.2 {mu}g/g, respectively). Increased rescue medication use was significantly associated with sensitization and exposure to Der p 1>0.10 {mu}g/g (by 47%) and Fel d 1>0.12 {mu}g/g (by 32%). Conclusion: Asthmatic children sensitized and exposed to low levels of common household allergens Penicillium, Der p 1, Fel d 1 and Can f 1 are at significant risk for increased morbidity. - Highlights: Black-Right-Pointing-Pointer Few studies address concurrent allergen exposures, sensitization and asthma morbidity. Black-Right-Pointing-Pointer Children with asthma were tested for sensitivity to common indoor allergens

  8. Commercial Buildings Characteristics, 1992

    SciTech Connect (OSTI)

    Not Available

    1994-04-29

    Commercial Buildings Characteristics 1992 presents statistics about the number, type, and size of commercial buildings in the United States as well as their energy-related characteristics. These data are collected in the Commercial Buildings Energy Consumption Survey (CBECS), a national survey of buildings in the commercial sector. The 1992 CBECS is the fifth in a series conducted since 1979 by the Energy Information Administration. Approximately 6,600 commercial buildings were surveyed, representing the characteristics and energy consumption of 4.8 million commercial buildings and 67.9 billion square feet of commercial floorspace nationwide. Overall, the amount of commercial floorspace in the United States increased an average of 2.4 percent annually between 1989 and 1992, while the number of commercial buildings increased an average of 2.0 percent annually.

  9. Influence of assumptions about household waste composition in waste management LCAs

    SciTech Connect (OSTI)

    Slagstad, Helene; Brattebo, Helge

    2013-01-15

    Highlights: Black-Right-Pointing-Pointer Uncertainty in waste composition of household waste. Black-Right-Pointing-Pointer Systematically changed waste composition in a constructed waste management system. Black-Right-Pointing-Pointer Waste composition important for the results of accounting LCA. Black-Right-Pointing-Pointer Robust results for comparative LCA. - Abstract: This article takes a detailed look at an uncertainty factor in waste management LCA that has not been widely discussed previously, namely the uncertainty in waste composition. Waste composition is influenced by many factors; it can vary from year to year, seasonally, and with location, for example. The data publicly available at a municipal level can be highly aggregated and sometimes incomplete, and performing composition analysis is technically challenging. Uncertainty is therefore always present in waste composition. This article performs uncertainty analysis on a systematically modified waste composition using a constructed waste management system. In addition the environmental impacts of several waste management strategies are compared when applied to five different cities. We thus discuss the effect of uncertainty in both accounting LCA and comparative LCA. We found the waste composition to be important for the total environmental impact of the system, especially for the global warming, nutrient enrichment and human toxicity via water impact categories.

  10. Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel

    SciTech Connect (OSTI)

    Lebersorger, S.; Beigl, P.

    2011-09-15

    Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions are met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation).

  11. Estimating household fuel oil/kerosine, natural gas, and LPG prices by census region

    SciTech Connect (OSTI)

    Poyer, D.A.; Teotia, A.P.S.

    1994-08-01

    The purpose of this research is to estimate individual fuel prices within the residential sector. The data from four US Department of Energy, Energy Information Administration, residential energy consumption surveys were used to estimate the models. For a number of important fuel types - fuel oil, natural gas, and liquefied petroleum gas - the estimation presents a problem because these fuels are not used by all households. Estimates obtained by using only data in which observed fuel prices are present would be biased. A correction for this self-selection bias is needed for estimating prices of these fuels. A literature search identified no past studies on application of the selectivity model for estimating prices of residential fuel oil/kerosine, natural gas, and liquefied petroleum gas. This report describes selectivity models that utilize the Dubin/McFadden correction method for estimating prices of residential fuel oil/kerosine, natural gas, and liquefied petroleum gas in the Northeast, Midwest, South, and West census regions. Statistically significant explanatory variables are identified and discussed in each of the models. This new application of the selectivity model should be of interest to energy policy makers, researchers, and academicians.

  12. Weatherization assistance for low-income households: An evaluation of local program performance

    SciTech Connect (OSTI)

    Schweitzer, M.; Rayner, S.; Wolfe, A.K.; Mason, T.W.; Ragins, B.R.; Cartor, R.A.

    1987-08-01

    The US Department of Energy's Weatherization Assistance Program (WAP) funds local agencies to provide weatherization services to low-income households. This report describes the most salient features of this program, examines relationships between organization and program outcomes, and presents recommendations for the program's further development. Data were collected by written surveys administered to local weatherization agencies, a telephone survey of 38 states and eight DOE support offices, and site visits to selected local agencies. Locally controlled factors found to be significantly related to program performance include the amount of the weatherization director's time spent on program administration, the use of established client selection criteria, the frequency of evaluation of local goal attainment, and the type of weatherization crews used. Factors controlled at the state or federal levels that influence program performance include delays in state reimbursements of local agency expenditures and local flexibility in the choice of weatherization measures. Data-gathering difficulties experienced during this project indicate a need for possible improvements in goal-setting and record-keeping procedures.

  13. The evolving price of household LED lamps: Recent trends and historical comparisons for the US market

    SciTech Connect (OSTI)

    Gerke, Brian F.; Ngo, Allison T.; Alstone, Andrea L.; Fisseha, Kibret S.

    2014-10-14

    In recent years, household LED light bulbs (LED A lamps) have undergone a dramatic price decline. Since late 2011, we have been collecting data, on a weekly basis, for retail offerings of LED A lamps on the Internet. The resulting data set allows us to track the recent price decline in detail. LED A lamp prices declined roughly exponentially with time in 2011-2014, with decline rates of 28percent to 44percent per year depending on lumen output, and with higher-lumen lamps exhibiting more rapid price declines. By combining the Internet price data with publicly available lamp shipments indices for the US market, it is also possible to correlate LED A lamp prices against cumulative production, yielding an experience curve for LED A lamps. In 2012-2013, LED A lamp prices declined by 20-25percent for each doubling in cumulative shipments. Similar analysis of historical data for other lighting technologies reveals that LED prices have fallen significantly more rapidly with cumulative production than did their technological predecessors, which exhibited a historical decline of 14-15percent per doubling of production.

  14. LCA for household waste management when planning a new urban settlement

    SciTech Connect (OSTI)

    Slagstad, Helene; Brattebo, Helge

    2012-07-15

    Highlights: Black-Right-Pointing-Pointer Household waste management of a new carbon neutral settlement. Black-Right-Pointing-Pointer EASEWASTE as a LCA tool to compare different centralised and decentralised solutions. Black-Right-Pointing-Pointer Environmental benefit or close to zero impact in most of the categories. Black-Right-Pointing-Pointer Paper and metal recycling important for the outcome. Black-Right-Pointing-Pointer Discusses the challenges of waste prevention planning. - Abstract: When planning for a new urban settlement, industrial ecology tools like scenario building and life cycle assessment can be used to assess the environmental quality of different infrastructure solutions. In Trondheim, a new greenfield settlement with carbon-neutral ambitions is being planned and five different scenarios for the waste management system of the new settlement have been compared. The results show small differences among the scenarios, however, some benefits from increased source separation of paper and metal could be found. The settlement should connect to the existing waste management system of the city, and not resort to decentralised waste treatment or recovery methods. However, as this is an urban development project with ambitious goals for lifestyle changes, effort should be put into research and initiatives for proactive waste prevention and reuse issues.

  15. Status of not-in-kind refrigeration technologies for household space conditioning, water heating and food refrigeration

    SciTech Connect (OSTI)

    Bansal, Pradeep; Vineyard, Edward Allan; Abdelaziz, Omar

    2012-07-19

    This paper presents a review of the next generation not-in-kind technologies to replace conventional vapor compression refrigeration technology for household applications. Such technologies are sought to provide energy savings or other environmental benefits for space conditioning, water heating and refrigeration for domestic use. These alternative technologies include: thermoacoustic refrigeration, thermoelectric refrigeration, thermotunneling, magnetic refrigeration, Stirling cycle refrigeration, pulse tube refrigeration, Malone cycle refrigeration, absorption refrigeration, adsorption refrigeration, and compressor driven metal hydride heat pumps. Furthermore, heat pump water heating and integrated heat pump systems are also discussed due to their significant energy saving potential for water heating and space conditioning in households. The paper provides a snapshot of the future R&D needs for each of the technologies along with the associated barriers. Both thermoelectric and magnetic technologies look relatively attractive due to recent developments in the materials and prototypes being manufactured.

  16. Cost comparison between private and public collection of residual household waste: Multiple case studies in the Flemish region of Belgium

    SciTech Connect (OSTI)

    Jacobsen, R.; Buysse, J.; Gellynck, X.

    2013-01-15

    Highlights: Black-Right-Pointing-Pointer The goal is to compare collection costs for residual household waste. Black-Right-Pointing-Pointer We have clustered all municipalities in order to find mutual comparable pairs. Black-Right-Pointing-Pointer Each pair consists of one private and one public operating waste collection program. Black-Right-Pointing-Pointer All cases show that private service has lower costs than public service. Black-Right-Pointing-Pointer Municipalities were contacted to identify the deeper causes for the waste management program. - Abstract: The rising pressure in terms of cost efficiency on public services pushes governments to transfer part of those services to the private sector. A trend towards more privatizing can be noticed in the collection of municipal household waste. This paper reports the findings of a research project aiming to compare the cost between the service of private and public collection of residual household waste. Multiple case studies of municipalities about the Flemish region of Belgium were conducted. Data concerning the year 2009 were gathered through in-depth interviews in 2010. In total 12 municipalities were investigated, divided into three mutual comparable pairs with a weekly and three mutual comparable pairs with a fortnightly residual waste collection. The results give a rough indication that in all cases the cost of private service is lower than public service in the collection of household waste. Albeit that there is an interest in establishing whether there are differences in the costs and service levels between public and private waste collection services, there are clear difficulties in establishing comparisons that can be made without having to rely on a large number of assumptions and corrections. However, given the cost difference, it remains the responsibility of the municipalities to decide upon the service they offer their citizens, regardless the cost efficiency: public or private.

  17. Insights from Smart Meters. Identifying Specific Actions, Behaviors and Characteristics that drive savings in Behavior-Based Programs

    SciTech Connect (OSTI)

    Todd, Annika; Perry, Michael; Smith, Brian; Sullivan, Michael; Cappers, Peter; Goldman, Charles A.

    2014-12-01

    In this report, we use smart meter data to analyze specific actions, behaviors, and characteristics that drive energy savings in a behavior-based (BB) program. Specifically, we examine a Home Energy Report (HER) program. These programs typically obtain 1% to 3% annual savings, and recent studies have shown hourly savings of between 0.5% and 3%. But what is driving these savings? What types of households tend to be “high-savers”, and what behaviors are they adopting? There are several possibilities: one-time behaviors (e.g., changing thermostat settings); reoccurring habitual behaviors (e.g., turning off lights); and equipment purchase behaviors (e.g., energy efficient appliances), and these may vary across households, regions, and over time.

  18. A life cycle approach to the management of household food waste - A Swedish full-scale case study

    SciTech Connect (OSTI)

    Bernstad, A.; Cour Jansen, J. la

    2011-08-15

    Research Highlights: > The comparison of three different methods for management of household food waste show that anaerobic digestion provides greater environmental benefits in relation to global warming potential, acidification and ozone depilation compared to incineration and composting of food waste. Use of produced biogas as car fuel provides larger environmental benefits compared to a use of biogas for heat and power production. > The use of produced digestate from the anaerobic digestion as substitution for chemical fertilizer on farmland provides avoidance of environmental burdens in the same ratio as the substitution of fossil fuels with produced biogas. > Sensitivity analyses show that results are highly sensitive to assumptions regarding the environmental burdens connected to heat and energy supposedly substituted by the waste treatment. - Abstract: Environmental impacts from incineration, decentralised composting and centralised anaerobic digestion of solid organic household waste are compared using the EASEWASTE LCA-tool. The comparison is based on a full scale case study in southern Sweden and used input-data related to aspects such as source-separation behaviour, transport distances, etc. are site-specific. Results show that biological treatment methods - both anaerobic and aerobic, result in net avoidance of GHG-emissions, but give a larger contribution both to nutrient enrichment and acidification when compared to incineration. Results are to a high degree dependent on energy substitution and emissions during biological processes. It was seen that if it is assumed that produced biogas substitute electricity based on Danish coal power, this is preferable before use of biogas as car fuel. Use of biogas for Danish electricity substitution was also determined to be more beneficial compared to incineration of organic household waste. This is a result mainly of the use of plastic bags in the incineration alternative (compared to paper bags in the

  19. Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for Collecting Household Energy Data, Call Slides and Discussion Summary, July 19, 2012

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

    19, 2012 Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for Collecting Household Energy Data Call Slides and Discussion Summary Agenda * Call Logistics and Attendance  Is your program getting household energy data? How? * Program Experience and Lessons:  Janelle Beverly and Jeff Hughes, University of North Carolina Environmental Finance Center (http://www.efc.unc.edu/index.html) * Discussion:  What are successful strategies for obtaining

  20. Better Buildings Residential Network Multi-Family & Low-Income Housing Peer Exchange Call Series: Loan Programs for Low- and Moderate-Income Households, March 13, 2014

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

    Family & Low-Income Housing Peer Exchange Call Series: Loan Programs for Low- and Moderate-Income Households March 13, 2014 Agenda  Call Logistics and Introductions  Featured Participants  Becca Harmon Murphy (Indianapolis Neighborhood Housing Partnership)  Discussion:  What strategies or approaches has your program used to build interest in your loan programs for moderate- and low-income households? What has worked well, and why do you think it was effective?  What

  1. Table HC7-5a. Home Office Equipment by Type of Owner-Occupied Housing Unit,

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

    5a. Home Office Equipment by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.3 0.3 2.1 3.0 1.6 Total ............................................... 72.7 63.2 2.1 1.8 5.7 6.7 Households Using Office Equipment .......................... 67.5 59.0 2.0 1.7 4.8 7.0

  2. Table HC7-6a. Home Office Equipment by Type of Rented Housing Unit,

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

    6a. Home Office Equipment by Type of Rented Housing Unit, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.8 1.0 0.9 3.0 Total ............................................... 34.3 10.5 7.4 15.2 1.1 6.9 Households Using Office Equipment .......................... 28.7 9.2 6.5 12.1 0.9 7.5 Personal Computers 1

  3. Table HC7-8a. Home Office Equipment by Urban/Rural Location,

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

    8a. Home Office Equipment by Urban/Rural Location, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total Urban/Rural Location 1 RSE Row Factors City Town Suburbs Rural 0.5 0.9 1.3 1.2 1.4 Total .............................................................. 107.0 49.9 18.0 21.2 17.9 4.1 Households Using Office Equipment ......................................... 96.2 43.9 16.0 20.2 16.1 4.1 Personal Computers 2 ................................. 60.0 25.6 9.3 15.0 10.1 4.7

  4. untitled

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

    4a. Home Office Equipment by Type of Housing Unit, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.5 1.7 1.5 2.2 Total ............................................... 107.0 73.7 9.5 17.0 6.8 4.1 Households Using Office Equipment .......................... 96.2 68.2 8.5 13.8 5.8 4.5 Personal Computers 1 ................... 60.0 46.4

  5. untitled

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

    4a. Home Office Equipment by Type of Housing Unit, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.5 1.7 1.5 2.2 Total ............................................... 107.0 73.7 9.5 17.0 6.8 4.1 Households Using Office Equipment .......................... 96.2 68.2 8.5 13.8 5.8 4.5 Personal Computers 1 ................... 60.0 46.4

  6. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    b. Home Office Equipment by Four Most Populated States, Percent of U.S. Households, 1997 Home Office Equipment RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.1 1.5 1.3 Total .............................................................. 100.0 100.0 100.0 100.0 100.0 0.0 Households Using Office Equipment ......................................... 79.3 79.4 77.6 75.1 79.4 1.9 Personal Computers ................................... 35.1

  7. LED Color Characteristics

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

    LED Color Characteristics Color quality is an important consideration when evaluating lighting products. This fact sheet reviews the fundamentals regarding light and color, summarizing the most important color issues related to white-light LED systems, including color consistency, stability, tuning, and rendering, as well as chromaticity. LED Emission Attributes Individual LED dies, often referred to as chips, emit light in a narrow range of wavelengths, giving the appearance of a monochromatic

  8. Residential energy consumption across different population groups: Comparative analysis for Latino and non-Latino households in U.S.A.

    SciTech Connect (OSTI)

    Poyer, D.A.; Teotia, A.P.S.; Henderson, L.

    1998-05-01

    Residential energy cost, an important part of the household budget, varies significantly across different population groups. In the United States, researchers have conducted many studies of household fuel consumption by fuel type -- electricity, natural gas, fuel oil, and liquefied petroleum gas (LPG) -- and by geographic areas. The results of past research have also demonstrated significant variation in residential energy use across various population groups, including white, black, and Latino. However, research shows that residential energy demand by fuel type for Latinos, the fastest-growing population group in the United States, has not been explained by economic and noneconomic factors in any available statistical model. This paper presents a discussion of energy demand and expenditure patterns for Latino and non-Latino households in the United States. The statistical model developed to explain fuel consumption and expenditures for Latino households is based on Stone and Geary`s linear expenditure system model. For comparison, the authors also developed models for energy consumption in non-Latino, black, and nonblack households. These models estimate consumption of and expenditures for electricity, natural gas, fuel oil, and LPG by various households at the national level. The study revealed significant variations in the patterns of fuel consumption for Latinos and non-Latinos. The model methodology and results of this research should be useful to energy policymakers in government and industry, researchers, and academicians who are concerned with economic and energy issues related to various population groups.

  9. Crude Oil Characteristics Research

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

    SAE Plan June 29, 2015 Page 1 Crude Oil Characteristics Research Sampling, Analysis and Experiment (SAE) Plan The U.S. is experiencing a renaissance in oil and gas production. The Energy Information Administration projects that U.S. oil production will reach 9.3 million barrels per day in 2015 - the highest annual average level of oil production since 1972. This domestic energy boom is due primarily to new unconventional production of light sweet crude oil from tight-oil formations like the

  10. Housing characteristics, 1987: Residential Energy Consumption Survey

    SciTech Connect (OSTI)

    Not Available

    1989-05-26

    This report is the first of a series of reports based on data from the 1987 RECS. The 1987 RECS is the seventh in the series of national surveys of households and their energy suppliers. These surveys provide baseline information on how households in the United States use energy. A cross section of housing types such as single-family detached homes, townhouses, large and small apartment buildings, condominiums, and mobile homes were included in the survey. Data from the RECS and a companion survey, the Residential Transportation Energy Consumption Survey (RTECS), are available to the public in published reports such as this one and on public use tapes. 10 figs., 69 tabs.

  11. Wafer characteristics via reflectometry

    DOE Patents [OSTI]

    Sopori, Bhushan L.

    2010-10-19

    Various exemplary methods (800, 900, 1000, 1100) are directed to determining wafer thickness and/or wafer surface characteristics. An exemplary method (900) includes measuring reflectance of a wafer and comparing the measured reflectance to a calculated reflectance or a reflectance stored in a database. Another exemplary method (800) includes positioning a wafer on a reflecting support to extend a reflectance range. An exemplary device (200) has an input (210), analysis modules (222-228) and optionally a database (230). Various exemplary reflectometer chambers (1300, 1400) include radiation sources positioned at a first altitudinal angle (1308, 1408) and at a second altitudinal angle (1312, 1412). An exemplary method includes selecting radiation sources positioned at various altitudinal angles. An exemplary element (1650, 1850) includes a first aperture (1654, 1854) and a second aperture (1658, 1858) that can transmit reflected radiation to a fiber and an imager, respectfully.

  12. Modern technical solutions of gas-fired heating devices of household and communal use and analysis of their testing

    SciTech Connect (OSTI)

    Bodzon, L.; Radwan, W.

    1995-12-31

    A review of technical solutions for gas-fired heating devices for household and communal use in Poland is presented. Based upon the analysis it is stated that the power output of Polish and foreign boilers ranges between 9 and 35 kW. The carbon monoxide content in flue gases reaches (on average) 0.005 vol.%, i.e., it is much lower than the maximum permissible level. Temperature of flue gases (excluding condensation boilers and those with air-tight combustion chamber) ranges between 150 and 200{degrees}C and their heating efficiency reaches 87-93%. The best parameters are given for condensation boilers, however they are still not widespread in Poland for the high cost of the equipment and assembling works. Among the heaters, the most safe are convection devices with closed combustion chamber; their efficiency is also the highest. Thus, it is concluded that a wide spectrum of high efficiency heating devices with good combustion parameters are available. The range of output is sufficient to meet household and communal requirement. They are however - predominantly - units manufactured abroad. It is difficult to formulate the program aimed at the improvement of the technique of heating devices made in Poland, and its implementation is uncertain because the production process is broken up into small handicraft workshops.

  13. Sensor Characteristics Reference Guide

    SciTech Connect (OSTI)

    Cree, Johnathan V.; Dansu, A.; Fuhr, P.; Lanzisera, Steven M.; McIntyre, T.; Muehleisen, Ralph T.; Starke, M.; Banerjee, Pranab; Kuruganti, T.; Castello, C.

    2013-04-01

    The Buildings Technologies Office (BTO), within the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), is initiating a new program in Sensor and Controls. The vision of this program is: • Buildings operating automatically and continuously at peak energy efficiency over their lifetimes and interoperating effectively with the electric power grid. • Buildings that are self-configuring, self-commissioning, self-learning, self-diagnosing, self-healing, and self-transacting to enable continuous peak performance. • Lower overall building operating costs and higher asset valuation. The overarching goal is to capture 30% energy savings by enhanced management of energy consuming assets and systems through development of cost-effective sensors and controls. One step in achieving this vision is the publication of this Sensor Characteristics Reference Guide. The purpose of the guide is to inform building owners and operators of the current status, capabilities, and limitations of sensor technologies. It is hoped that this guide will aid in the design and procurement process and result in successful implementation of building sensor and control systems. DOE will also use this guide to identify research priorities, develop future specifications for potential market adoption, and provide market clarity through unbiased information

  14. 1989 CBECS EUI

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

    . Peak Electricity Demand Category, Number of Buildings, 1992 (Thousand) Building Characteristics RSE Column Factor: Demand- Metered Buildings 10 kW or Less 11 to 25 kW 26 to 50 kW...

  15. 1992 CBECS C & E

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

    of Natural Gas by End Use, 1989 Natural Gas Consumption (trillion Btu) Space Water a Total Heating Heating Cooking Other RSE Building Row Characteristics Factor 1.0 NF...

  16. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

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

    3 Lighting Usage Indicators by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Lighting Usage Indicators" "Total U.S. Housing

  17. Table A20. Components of Onsite Electricity Generation by Census Region and

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

    Components of Onsite Electricity Generation by Census Region and" " Economic Characteristics of the Establishment, 1991" " (Estimates in Million Kilowatthours)" ,,,,,"RSE" " "," "," "," "," ","Row" "Economic Characteristics(a)","Total","Cogeneration","Renewables","Other(b)","Factors" ,"Total United States" "RSE Column

  18. Separate collection of household food waste for anaerobic degradation - Comparison of different techniques from a systems perspective

    SciTech Connect (OSTI)

    Bernstad, A.; Cour Jansen, J. la

    2012-05-15

    Highlight: Black-Right-Pointing-Pointer Four modern and innovative systems for household food waste collection are compared. Black-Right-Pointing-Pointer Direct emissions and resource use were based on full-scale data. Black-Right-Pointing-Pointer Conservation of nutrients/energy content over the system was considered. Black-Right-Pointing-Pointer Systems with high energy/nutrient recovery are most environmentally beneficial. - Abstract: Four systems for household food waste collection are compared in relation the environmental impact categories eutrophication potential, acidification potential, global warming potential as well as energy use. Also, a hotspot analysis is performed in order to suggest improvements in each of the compared collection systems. Separate collection of household food waste in paper bags (with and without drying prior to collection) with use of kitchen grinders and with use of vacuum system in kitchen sinks were compared. In all cases, food waste was used for anaerobic digestion with energy and nutrient recovery in all cases. Compared systems all resulted in net avoidance of assessed environmental impact categories; eutrophication potential (-0.1 to -2.4 kg NO{sub 3}{sup -}eq/ton food waste), acidification potential (-0.4 to -1.0 kg SO{sub 2}{sup -}eq/ton food waste), global warming potential (-790 to -960 kg CO{sub 2}{sup -}eq/ton food waste) and primary energy use (-1.7 to -3.6 GJ/ton food waste). Collection with vacuum system results in the largest net avoidance of primary energy use, while disposal of food waste in paper bags for decentralized drying before collection result in a larger net avoidance of global warming, eutrophication and acidification. However, both these systems not have been taken into use in large scale systems yet and further investigations are needed in order to confirm the outcomes from the comparison. Ranking of scenarios differ largely if considering only emissions in the foreground system, indicating the

  19. Property:Other Characteristics | Open Energy Information

    Open Energy Info (EERE)

    Characteristics Jump to: navigation, search Property Name Other Characteristics Property Type String Pages using the property "Other Characteristics" Showing 8 pages using this...

  20. Process for the utilization of household rubbish or garbage and other organic waste products for the production of methane gas

    SciTech Connect (OSTI)

    Hunziker, M.; Schildknecht, A.

    1985-04-16

    Non-organic substances are separated from household garbage and the organic substances are fed in proportioned manner into a mixing tank and converted into slurry by adding liquid. The slurry is crushed for homogenization purposes in a crushing means and passed into a closed holding container. It is then fed over a heat exchanger and heated to 55/sup 0/ to 60/sup 0/ C. The slurry passes into a plurality of reaction vessels in which the methane gas and carbon dioxide are produced. In a separating plant, the mixture of gaseous products is broken down into its components and some of the methane gas is recycled by bubbling it through both the holding tank and the reaction tank, the remainder being stored in gasholders. The organic substances are degraded much more rapidly through increasing the degradation temperature and as a result constructional expenditure can be reduced.

  1. Emissions of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans from the open burning of household waste in barrels

    SciTech Connect (OSTI)

    Lemieux, P.M.; Lutes, C.C.; Abbott, J.A.; Aldous, K.M.

    2000-02-01

    Backyard burning of household waste in barrels is a common waste disposal practice for which pollutant emissions have not been well characterized. This study measured the emissions of several pollutants, including polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDDs/PCDFs), from burning mixtures designed to simulate waste generated by a recycling and a nonrecycling family in a 208-L (55-gal) burn barrel at the EPA's Open Burning Test Facility. This paper focuses on the PCDD/PCDF emissions and discusses the factors influencing PCDD/PCDF formation for different test burns. Four test burns were made in which the amount of waste placed in the barrel varied from 6.4 to 13.6 kg and the amount actually burned varied from 46.6% to 68.1%. Emissions of total PCDDs/PCDFs ranged between 0.0046 and 0.48 mg/kg of waste burned. Emissions are also presented in terms of 2,3,7,8-TCDD toxic equivalents. Emissions of PCDDs/PCDFs appear to correlate with both copper and hydrochloric acid emissions. The results of this study indicate that backyard burning emits more PCDDs/PCDFs on a mass of refuse burned basis than various types of municipal waste combustors (MWCs). Comparison of burn barrel emissions to emissions from a hypothetical modern MWC equipped with high-efficiency flue gas cleaning technology indicates that about 2--40 households burning their trash daily in barrels can produce average PCDD/PCDF emissions comparable to a 182,000 kg/day (200 ton/day) MWC facility. This study provides important data on a potentially significant source of emissions of PCDDs/PCDFs.

  2. Travel Patterns And Characteristics Of Transit Users In New York State

    SciTech Connect (OSTI)

    Hwang, Ho-Ling; Wilson, Daniel W.; Reuscher, Tim; Chin, Shih-Miao; Taylor, Rob D.

    2015-12-01

    This research is a detailed examination of the travel behaviors and patterns of transit users within New York State (NYS), primarily based on travel data provided by the National Household Travel Survey (NHTS) in 2009 and the associated Add-on sample households purchased by the New York State Department of Transportation (NYSDOT). Other data sources analyzed in this study include: NYS General Transit Feed Specification (GTFS) to assist in analyzing spatial relationships for access to transit and the creation of Transit Shed geographic areas of 1, 2.5, and 5 miles from transit stop locations, LandScan population database to understand transit coverage, and Census Bureau s American Community Survey (ACS) data to examine general transit patterns and trends in NYS over time. The majority of analyses performed in this research aimed at identifying transit trip locations, understanding differences in transit usage by traveler demographics, as well as producing trip/mode-specific summary statistics including travel distance, trip duration, time of trip, and travel purpose of transit trips made by NYS residents, while also analyzing regional differences and unique travel characteristics and patterns. The analysis was divided into two aggregated geographic regions: New York Metropolitan Transportation Council (NYMTC) and NYS minus NYMTC (Rest of NYS). The inclusion of NYMTC in all analysis would likely produce misleading conclusions for other regions in NYS. TRANSIT COVERAGE The NYS transit network has significant coverage in terms of transit stop locations across the state s population. Out of the 19.3 million NYS population in 2011, about 15.3 million (or 79%) resided within the 1-mile transit shed. This NYS population transit coverage increased to 16.9 million (or 88%) when a 2.5-mile transit shed was considered; and raised to 17.7 million (or 92%) when the 5-mile transit shed was applied. KEY FINDINGS Based on 2009 NHTS data, about 40% of NYMTC households used transit

  3. The impact of rising energy prices on household energy consumption and expenditure patterns: The Persian Gulf crisis as a case example

    SciTech Connect (OSTI)

    Henderson, L.J. ); Poyer, D.A.; Teotia, A.P.S. . Energy Systems Div.)

    1992-09-01

    The Iraqi invasion of Kuwait and the subsequent war between Iraq and an international alliance led by the United States triggered immediate increases in world oil prices. Increases in world petroleum prices and in US petroleum imports resulted in higher petroleum prices for US customers. In this report, the effects of the Persian Gulf War and its aftermath are used to demonstrate the potential impacts of petroleum price changes on majority, black, and Hispanic households, as well as on poor and nonpoor households. The analysis is done by using the Minority Energy Assessment Model developed by Argonne National Laboratory for the US Department of Energy (DOE). The differential impacts of these price increases and fluctuations on poor and minority households raise significant issues for a variety of government agencies, including DOE. Although the Persian Gulf crisis is now over and world oil prices have returned to their prewar levels, the differential impacts of rising energy prices on poor and minority households as a result of any future crisis in the world oil market remains a significant long-term issue.

  4. EIA - Household Transportation report: Household Vehicles Energy...

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

    National Research Council, Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards (Washington, DC: National Academy of Sciences, 2002), p. 85. 4 8.3 million...

  5. "RSE Table C10.1. Relative Standard Errors for Table C10.1;...

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

    Know" ,,"Total United States" , 311,"Food",3,1,4,2,1,2... 324110," Petroleum Refineries",15,10,36,15,25,44,15,3... Know" ,,"Total United States" , ...

  6. "RSE Table N5.1. Relative Standard Errors for Table N5.1;...

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

    ","FurnaceCoke"," ","Petroleum","or","Wood ... ,,"Total United States" , 311,"Food",2,0,1,0,0,0... 324110," Petroleum Refineries",4,0,3,6,0,0,24 ...

  7. "RSE Table N7.1. Relative Standard Errors for Table N7.1;...

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

    Shipments" ,,"Total United States" , 311,"Food",1,1,1 311221," ... Printing",4,5,4 324,"Petroleum and Coal Products",4,3,3 324110," Petroleum Refineries",3,3,3 ...

  8. "RSE Table C2.1. Relative Standard Errors for Table C2.1;...

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

    ,,"Total United States" , 311,"Food",4,0,3,0,1,0... 324,"Petroleum and Coal Products ... "produced at refineries or natural gas ...

  9. "RSE Table E2.1. Relative Standard Errors for Table E2.1;...

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

    by petroleum" "refineries (e.g., crude oil ... ,"Total United States" "Value of Shipments and ... Examples of Liquefied Petroleum Gases '(LPG)' are ...

  10. "RSE Table N11.2. Relative Standard Errors for Table N11.2;...

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

    ... by" "petroleum refineries, rather than purchased ... ,,"Total United States" , 311,"Food",1,1,3,3,1,1... 324,"Petroleum and Coal ...

  11. "RSE Table C12.1. Relative Standard Errors for Table C12.1;...

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

    ,,"Total United States" , 311,"Food",2,0,2,1,1 ... 324110," Petroleum Refineries",4,0,15,5,12 ... Establishment" ,,"Total United States" , ...

  12. "RSE Table C4.1. Relative Standard Errors for Table C4.1;...

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

    ,,"Total United States" , 311,"Food",0,0,3,4,1,3... 324,"Petroleum and Coal ... "produced at refineries or natural gas ...

  13. RSE Table 1.1 Relative Standard Errors for Table 1.1

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

    Oil","Fuel Oil(d)","Gas(e)","NGL(f)","Coal","Breeze","Other(g)","Produced Onsite(h)" ,,"Total United States" 311,"Food",4,5,25,20,5,27,6,0,10,0 311221," Wet Corn ...

  14. RSE Table 1.2 Relative Standard Errors for Table 1.2

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

    Oil","Fuel Oil(d)","Gas(e)","NGL(f)","Coal","Breeze","Other(g)","Produced Onsite(h)" ,,"Total United States" 311,"Food",4,5,25,20,5,27,6,0,10,0 311221," Wet Corn ...

  15. RSE Table 4.2 Relative Standard Errors for Table 4.2

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

    Corn Milling",1,0,0,1,3,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ... Corn Milling",0,0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ...

  16. RSE Table 7.10 Relative Standard Errors for Table 7.10

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

    Corn Milling",1,1,0,3,0,4,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ... Corn Milling",0,0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ...

  17. RSE Table 7.7 Relative Standard Errors for Table 7.7

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

    Corn Milling",0,0,0,3,0,3,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ... Corn Milling",0,0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ...

  18. RSE Table 7.3 Relative Standard Errors for Table 7.3

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

    Corn Milling",0,0,0,3,0,3,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ... Corn Milling",0,0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ...

  19. RSE Table 3.1 Relative Standard Errors for Table 3.1

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

    Corn Milling",1,2,0,1,3,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ... Corn Milling",0,0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ...

  20. RSE Table 3.2 Relative Standard Errors for Table 3.2

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

    Corn Milling",1,2,0,1,3,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ... Corn Milling",0,0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ...

  1. RSE Table 4.1 Relative Standard Errors for Table 4.1

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

    Corn Milling",1,0,0,1,3,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ... Corn Milling",0,0,0,0,0,0,0,0,0 31131," Sugar ",0,0,0,0,0,0,0,0,0 311421," Fruit and ...

  2. "RSE Table C10.3. Relative Standard Errors for Table C10.3;...

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

    ," Membrane Hyperfiltration to Separate Water from Food Products",4,1,3 311221," Wet ... ," Membrane Hyperfiltration to Separate Water from Food Products",0,0,0 312,"BEVERAGE ...

  3. "RSE Table C3.1. Relative Standard Errors for Table C3.1;...

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

    ... and Office of Oil and Gas, Petroleum" "Supply Division, Form EIA-810, 'Monthly Refinery Report' for 1998." ... and",,"Coke"," " "Code(a)","Subsector and ...

  4. "RSE Table E13.1. Relative Standard Errors for Table E13.1;...

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

    ... for which" "payment was not made, quantities purchased centrally within the company but separate" "from the reporting establishment, and quantities for which payment was made ...

  5. "RSE Table N11.3. Relative Standard Errors for Table N11.3;...

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

    ... for which" "payment was not made, quantities purchased centrally within the company but separate" "from the reporting establishment, and quantities for which payment was made ...

  6. "RSE Table C11.3. Relative Standard Errors for Table C11.3;...

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

    ... for which" "payment was not made, quantities purchased centrally within the company but separate" "from the reporting establishment, and quantities for which payment was made ...

  7. "RSE Table N11.1. Relative Standard Errors for Table N11.1;...

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

    ... office offsite, and quantities for which payment" "is made in-kind." " Source: Energy ... by a central purchasing office offsite, and quantities for which payment" "is made in-kind

  8. "RSE Table N11.4. Relative Standard Errors for Table N11.4;...

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

    ... for which" "payment was not made, quantities purchased centrally within the company but separate" "from the reporting establishment, and quantities for which payment was made ...

  9. "RSE Table N8.3. Relative Standard Errors for Table N8.3;...

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

    ... for which" "payment was not made, quantities purchased centrally within the company but separate" "from the reporting establishment, and quantities for which payment was made ...

  10. "RSE Table N13.1. Relative Standard Errors for Table N13.1;...

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

    ... for which" "payment was not made, quantities purchased centrally within the company but separate" "from the reporting establishment, and quantities for which payment was made ...

  11. RSE Table 10.10 Relative Standard Errors for Table 10.10

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

    0 Relative Standard Errors for Table 10.10;" " Unit: Percents." ,,"Coal",,,"Alternative Energy Sources(b)" "NAICS"," ","Total"," ","Not","Electricity","Natural","Distillate","Residual" "Code(a)","Subsector and Industry","Consumed(c)","Switchable","Switchable","Receipts(d)","Gas","Fuel

  12. RSE Table 10.11 Relative Standard Errors for Table 10.11

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

    1 Relative Standard Errors for Table 10.11;" " Unit: Percents." ,,"Coal(b)",,,"Alternative Energy Sources(c)" "NAICS"," ","Total"," ","Not","Electricity","Natural","Distillate","Residual" "Code(a)","Subsector and Industry","Consumed(d)","Switchable","Switchable","Receipts(e)","Gas","Fuel

  13. RSE Table 10.12 Relative Standard Errors for Table 10.12

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

    2 Relative Standard Errors for Table 10.12;" " Unit: Percents." ,,"LPG",,,"Alternative Energy Sources(b)" ,,,,,,,,,,"Coal Coke" "NAICS"," ","Total"," ","Not","Electricity","Natural","Distillate","Residual",,"and" "Code(a)","Subsector and

  14. RSE Table 10.13 Relative Standard Errors for Table 10.13

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

    3 Relative Standard Errors for Table 10.13;" " Unit: Percents." ,,"LPG(b)",,,"Alternative Energy Sources(c)" ,,,,,,,,,,"Coal Coke" "NAICS"," ","Total"," ","Not","Electricity","Natural","Distillate","Residual",,"and" "Code(a)","Subsector and

  15. RSE Table 2.1 Relative Standard Errors for Table 2.1

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

    2.1 Relative Standard Errors for Table 2.1;" " Unit: Percents." " "," " " "," " "NAICS"," "," ","Residual","Distillate","Natural ","LPG and",,"Coke"," " "Code(a)","Subsector and Industry","Total","Fuel Oil","Fuel Oil(b)","Gas(c)","NGL(d)","Coal","and

  16. RSE Table 3.5 Relative Standard Errors for Table 3.5

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

    5 Relative Standard Errors for Table 3.5;" " Unit: Percents." " "," "," "," "," "," "," "," ","Waste",," " " "," "," ","Blast"," "," ","Pulping Liquor"," ","Oils/Tars" "NAICS"," ","

  17. RSE Table 5.1 Relative Standard Errors for Table 5.1

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

    1 Relative Standard Errors for Table 5.1;" " Unit: Percents." " "," " " "," "," ",," ","Distillate"," "," ",," " " "," ",,,,"Fuel Oil",,,"Coal" "NAICS"," "," ","Net","Residual","and","Natural ","LPG and","(excluding Coal"," "

  18. RSE Table 5.2 Relative Standard Errors for Table 5.2

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

    2 Relative Standard Errors for Table 5.2;" " Unit: Percents." " "," "," ",," ","Distillate"," "," ",," " " "," ",,,,"Fuel Oil",,,"Coal" "NAICS"," "," ","Net","Residual","and","Natural ","LPG and","(excluding Coal"," " "Code(a)","End

  19. RSE Table 5.4 Relative Standard Errors for Table 5.4

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

    4 Relative Standard Errors for Table 5.4;" " Unit: Percents." " "," ",," ","Distillate"," "," " " "," ","Net Demand",,"Fuel Oil",,,"Coal" "NAICS"," ","for ","Residual","and","Natural ","LPG and","(excluding Coal" "Code(a)","End Use","Electricity(b)","Fuel

  20. RSE Table 5.5 Relative Standard Errors for Table 5.5

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

    5 Relative Standard Errors for Table 5.5;" " Unit: Percents." " "," ",," ",," "," ",," " " ",,,,"Distillate" " "," ",,,"Fuel Oil",,,"Coal"," " " ",,"Net","Residual","and","Natural","LPG and","(excluding Coal" "End Use","Total","Electricity(a)","Fuel

  1. RSE Table 5.6 Relative Standard Errors for Table 5.6

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

    6 Relative Standard Errors for Table 5.6;" " Unit: Percents." " "," ",," ","Distillate"," "," ",," " " ",,,,"Fuel Oil",,,"Coal" " "," ","Net","Residual","and","Natural","LPG and","(excluding Coal"," " "End Use","Total","Electricity(a)","Fuel

  2. RSE Table 5.7 Relative Standard Errors for Table 5.7

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

    7 Relative Standard Errors for Table 5.7;" " Unit: Percents." " ",,,"Distillate" " ","Net Demand",,"Fuel Oil",,,"Coal" " ","for ","Residual","and","Natural ","LPG and","(excluding Coal" "End Use","Electricity(a)","Fuel Oil","Diesel Fuel(b)","Gas(c)","NGL(d)","Coke and Breeze)"

  3. RSE Table 5.8 Relative Standard Errors for Table 5.8

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

    8 Relative Standard Errors for Table 5.8;" " Unit: Percents." " ",," ","Distillate"," "," " " ","Net Demand",,"Fuel Oil",,,"Coal" " ","for ","Residual","and","Natural ","LPG and","(excluding Coal" "End Use","Electricity(a)","Fuel Oil","Diesel

  4. RSE Table 7.6 Relative Standard Errors for Table 7.6

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

    6 Relative Standard Errors for Table 7.6;" " Unit: Percents." " "," " " "," ",,,,,,,,," " "NAICS"," "," ",,"Residual","Distillate","Natural ","LPG and",,"Coke" "Code(a)","Subsector and Industry","Total","Electricity","Fuel Oil","Fuel

  5. RSE Table 7.9 Relative Standard Errors for Table 7.9

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

    9 Relative Standard Errors for Table 7.9;" " Unit: Percents." " "," "," ",," "," "," "," "," "," "," ",," " " "," " "NAICS"," "," ",,"Residual","Distillate","Natural ","LPG and",,"Coke"," " "Code(a)","Subsector and

  6. RSE Table 8.2 Relative Standard Errors for Table 8.2

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

    2 Relative Standard Errors for Table 8.2;" " Unit: Percents." " "," ",,"Computer Control of Building Wide Evironment(c)",,,"Computer Control of Processes or Major Energy-Using Equipment(d)",,,"Waste Heat Recovery",,,"Adjustable - Speed Motors",,,"Oxy - Fuel Firing" " "," " "NAICS"," " "Code(a)","Subsector and

  7. "RSE Table C9.1. Relative Standard Errors for Table C9.1;...

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

    ," U.S. Environmental Protection Agency's Energy Star Program",1,10,0,0,0,0 ," U.S. Environmental Protection Agency's Green Lights Program",1,9,0,0,0,0 ," U.S. Department of ...

  8. Boundary Layer Cloud Turbulence Characteristics

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

    Boundary Layer Cloud Turbulence Characteristics Virendra Ghate Bruce Albrecht Parameter Observational Readiness (/10) Modeling Need (/10) Cloud Boundaries 9 9 Cloud Fraction Variance Skewness Up/Downdraft coverage Dominant Freq. signal Dissipation rate ??? Observation-Modeling Interface

  9. Travel Patterns and Characteristics of Elderly Subpopulation in New York State

    SciTech Connect (OSTI)

    Hwang, Ho-Ling; Wilson, Daniel W.; Reuscher, Tim; Yang, Jianjiang; Taylor, Rob D.; Chin, Shih-Miao

    2015-03-01

    With the increasing demographic shift towards a larger population of elderly (individuals 65 years and older), it is essential for policy makers and planners to have an understanding of transportation issues that affect the elderly. These issues include livability of the community, factors impacting travel behavior and mobility, transportation safety, etc. In this study, Oak Ridge National Laboratory was tasked by the New York State (NYS) Department of Transportation to conduct a detailed examination of travel behaviors, and identify patterns and trends of the elderly within NYS. The National Household Travel Survey (NHTS) was used as the primary data source to analyze subjects and address questions such as: Are there differences in traveler demographics between the elderly population and those of younger age groups who live in various NYS regions; e.g., New York City, other urban areas of NYS, or other parts of the country? How do they compare with the population at large? Are there any regional differences (e.g., urban versus rural)? Gender differences? Do any unique travel characteristics or patterns exist within the elderly group? In addition to analysis of NHTS data, roadway travel safety concerns associated with elderly travelers were also investigated in this study. Specifically, data on accidents involving the elderly (including drivers, passengers, and others) as captured in the Fatal Analysis Reporting System (FARS) database was analyzed to examine elderly driver and elderly pedestrian travel safety issues in NYS. The analyses of these data sets provide a greater understanding of the elderly within NYS and their associated transportation issues. Through this study, various key findings on elderly population size, household characteristics, and travel patterns were produced and are report herein this report.

  10. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    1997 Home Office Equipment RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.1 1.5 1.3 Total .............................................................. 101.5 6.8 11.5 7.0 5.9 NF Households Using Office Equipment ......................................... 80.5 5.4 8.9 5.2 4.7 1.9 Personal Computers ................................... 35.6 2.2 4.6 2.6 2.0 5.9 Number of PCs 1

  11. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    Million U.S. Households, 1997 Usage Indicators RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.2 1.1 1.3 1.5 Total .............................................................. 101.5 6.8 11.5 7.0 5.9 NF Weekday Home Activities Home Used for Business Yes ............................................................ 7.4 0.5 0.9 0.4 0.4 13.5 No .............................................................. 94.1 6.3 10.6 6.5 5.6 2.2

  12. S:\VM3\RX97\TBL_LIST.WPD [PFP#201331587]

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

    b. Usage Indicators by Four Most Populated States, Percent of U.S. Households, 1997 Usage Indicators RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.2 1.1 1.3 1.5 Total .............................................................. 100.0 100.0 100.0 100.0 100.0 0.0 Weekday Home Activities Home Used for Business Yes ............................................................ 7.2 7.4 7.5 6.0 6.4 13.5 No

  13. Table 2.4 Household Energy Consumption by Census Region, Selected Years, 1978-2009 (Quadrillion Btu, Except as Noted)

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

    Household 1 Energy Consumption by Census Region, Selected Years, 1978-2009 (Quadrillion Btu, Except as Noted) Census Region 2 1978 1979 1980 1981 1982 1984 1987 1990 1993 1997 2001 2005 2009 United States Total (does not include wood) 10.56 9.74 9.32 9.29 8.58 9.04 9.13 9.22 10.01 10.25 9.86 10.55 10.18 Natural Gas 5.58 5.31 4.97 5.27 4.74 4.98 4.83 4.86 5.27 5.28 4.84 4.79 4.69 Electricity 3 2.47 2.42 2.48 2.42 2.35 2.48 2.76 3.03 3.28 3.54 3.89 4.35 4.39 Distillate Fuel Oil and Kerosene 2.19

  14. Characterizing Walk Trips in communities by Using Data from 2009 National Household Travel Survey, American Community Survey, and Other Sources

    SciTech Connect (OSTI)

    Hwang, Ho-Ling; Reuscher, Tim; Wilson, Daniel W; Murakami, Elaine

    2013-01-01

    Non-motorized travel (i.e. walking and bicycling) are of increasing interest to the transportation profession, especially in context with energy consumption, reducing vehicular congestion, urban development patterns, and promotion of healthier life styles. This research project aimed to identify factors impacting the amount of travel for both walk and bike trips at the Census block group or tract level, using several public and private data sources. The key survey of travel behavior is the 2009 National Household Travel Survey (NHTS) which had over 87,000 walk trips for persons 16 and over, and over 6000 bike trips for persons 16 and over. The NHTS, in conjunction with the Census Bureau s American Community Survey, street density measures using Census Bureau TIGER, WalkScore , Nielsen Claritas employment estimates, and several other sources were used for this study. Stepwise Logistic Regression modeling techniques as well as Discriminant Analysis were applied using the integrated data set. While the models performed reasonably well for walk trips, travel by bike was abandoned due to sparseness of data. This paper discusses data sources utilized and modeling processes conducted under this study. It also presents a summary of findings and addresses data challenges and lesson-learned from this research effort.

  15. User interface in ORACLE for the Worldwide Household Goods Information System for Transportation Modernization (WHIST-MOD)

    SciTech Connect (OSTI)

    James, T. ); Loftis, J. )

    1990-07-01

    The Directorate of Personal Property of the Military Traffic Management Command (MTMC) requested that Oak Ridge National laboratory (ORNL) design a prototype decision support system, the Worldwide Household Goods Information System for Transportation Modernization (WHIST-MOD). This decision support system will automate current tasks and provide analysis tools for evaluating the Personal Property Program, predicting impacts to the program, and planning modifications to the program to meet the evolving needs of military service members and the transportation industry. The system designed by ORNL consists of three application modules: system dictionary applications, data acquisition and administration applications, and user applications. The development of the user applications module is divided into two phases. Round 1 is the data selection front-end interface, and Round 2 is the output or back-end interface. This report describes the prototyped front-end interface for the user application module. It discusses user requirements and the prototype design. The information contained in this report is the product of in-depth interviews with MTMC staff, prototype meetings with the users, and the research and design work conducted at ORNL. 18 figs., 2 tabs.

  16. Lands with Wilderness Characteristics | Open Energy Information

    Open Energy Info (EERE)

    with Wilderness Characteristics Jump to: navigation, search Retrieved from "http:en.openei.orgwindex.php?titleLandswithWildernessCharacteristics&oldid647799...

  17. Health Care Buildings : Basic Characteristics Tables

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

    Basic Characteristics Tables Buildings and Size Data by Basic Characteristics for Health Care Buildings Number of Buildings (thousand) Percent of Buildings Floorspace (million...

  18. LED Color Characteristics | Department of Energy

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

    Color Characteristics LED Color Characteristics Reviews the fundamentals regarding light and color, summarizing the most important color issues related to white-light LED systems. (6 pages, April 2016) led-color-characteristics-factsheet.pdf (1.77 MB) More Documents & Publications Evaluating Color Rendition Using IES TM-30-15 LED Color Characteristics presentation slides: UNDERSTANDING AND APPLYING TM-30-15

  19. charlock-98.pdf

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

    Household Tables (Million U.S. Households; 24 pages, 122 kb) Contents Pages HC2-1a. Household Characteristics by Climate Zone, Million U.S. Households, 2001 2 HC2-2a. Household Characteristics by Year of Construction, Million U.S. Households, 2001 2 HC2-3a. Household Characteristics by Household Income, Million U.S. Households, 2001 2 HC2-4a. Household Characteristics by Type of Housing Unit, Million U.S. Households, 2001 2 HC2-5a. Household Characteristics by Type of Owner-Occupied Housing

  20. Next Generation Household Refrigerator

    Broader source: Energy.gov [DOE]

    Lead Performer: Oak Ridge National Laboratory - Oak Ridge, TN Partner: Whirlpool - Benton Harbor, MI

  1. Try This: Household Magnets

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

    Now which is stronger, gravity or magnetism? What is going on? How do flexible refrigerator magnets work? Get two of these magnets, they are often the size of a business card....

  2. 1989 CBECS EUI

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

    reported for fewer than 20 buildings. Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding RSE column and RSE row factors. * See Glossary for...

  3. 1995 CECS C&E Tables

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

    reported for fewer than 20 buildings. Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding RSE column and RSE row factors. * See Glossary for...

  4. Doppler characteristics of sea clutter.

    SciTech Connect (OSTI)

    Raynal, Ann Marie; Doerry, Armin Walter

    2010-06-01

    Doppler radars can distinguish targets from clutter if the target's velocity along the radar line of sight is beyond that of the clutter. Some targets of interest may have a Doppler shift similar to that of clutter. The nature of sea clutter is different in the clutter and exo-clutter regions. This behavior requires special consideration regarding where a radar can expect to find sea-clutter returns in Doppler space and what detection algorithms are most appropriate to help mitigate false alarms and increase probability of detection of a target. This paper studies the existing state-of-the-art in the understanding of Doppler characteristics of sea clutter and scattering from the ocean to better understand the design and performance choices of a radar in differentiating targets from clutter under prevailing sea conditions.

  5. Tier identification (TID) for tiered memory characteristics

    SciTech Connect (OSTI)

    Chang, Jichuan; Lim, Kevin T; Ranganathan, Parthasarathy

    2014-03-25

    A tier identification (TID) is to indicate a characteristic of a memory region associated with a virtual address in a tiered memory system. A thread may be serviced according to a first path based on the TID indicating a first characteristic. The thread may be serviced according to a second path based on the TID indicating a second characteristic.

  6. "Table HC11.6 Air Conditioning Characteristics by Northeast...

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

    "20 Years or More",6.5,0.7,0.7,"Q" "Don't Know",4.5,0.3,"Q","Q" "Used by Two or More ... "20 Years or More",0.3,"Q","Q","N" "Don't Know",0.7,"Q","N","Q" "Household Pays for ...

  7. Characteristics of Strong Programs | Department of Energy

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

    Characteristics of Strong Programs Characteristics of Strong Programs Existing financing programs offer a number of important lessons on effective program design. Some characteristics of strong financing programs drawn from past program experience are described below. Engage Contractor Networks The programs with the highest volume of loans have strong contractor networks and regular program communication with those contractors. Significant time and effort are often expended to make sure the

  8. BIOENERGIZEME INFOGRAPHIC CHALLENGE: Biomass: Types/Characteristics |

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

    Department of Energy Biomass: Types/Characteristics BIOENERGIZEME INFOGRAPHIC CHALLENGE: Biomass: Types/Characteristics BIOENERGIZEME INFOGRAPHIC CHALLENGE: Biomass: Types/Characteristics This infographic was created by students from Albany Academies and Academy of the Holy Names in Albany, NY, as part of the U.S. Department of Energy-BioenergizeME Infographic Challenge. The BioenergizeME Infographic Challenge encourages young people to improve their foundational understanding of bioenergy,

  9. CASL - The Michigan Parallel Characteristics Transport Code

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

    The Michigan Parallel Characteristics Transport Code Verification of MPACT: The Michigan Parallel Characteristics Transport Code Benjamin Collins, Brendan Kochunas, Daniel Jabbay, Thomas Downar, William Martin Department of Nuclear Engineering and Radiological Sciences University of Michigan Andrew Godfrey Oak Ridge National Laboroatory MPACT (Michigan PArallel Characteristics Transport Code) is a new reactor analysis tool being developed at the University of Michigan as an advanced pin-resolved

  10. Commercial Buildings Characteristics 1995 - Index Page

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

    >Commercial Buildings Home > 1995 Characteristics Data 1995 Data Executive Summary Table of Contents Overview to Detailed Tables Detailed Tables 1995 national and Census region...

  11. 1999 Commercial Buildings Characteristics--Census Region

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

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  12. 1999 Commercial Buildings Characteristics--Year Constructed

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

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  13. 1999 Commercial Buildings Characteristics--Building Size

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

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  14. 1999 Commercial Buildings Characteristics--Disaggregated Principal...

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

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  15. Principal Characteristics of a Modern Grid

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

    ... MODERN GRID S T R A T E G Y 5 5 Principal Characteristics The Smart Grid is "transactive" ... Utility Operational Benefits Operational improvements Metering and billing Outage ...

  16. Principal Characteristics of a Modern Grid

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

    peaking units builds *Cancelled baseload coal plants pressure pressure 5 Office of ... Today's Grid Principal Characteristic Modern Grid Consumers are uninformed and ...

  17. Mechanical properties and energy absorption characteristics of...

    Office of Scientific and Technical Information (OSTI)

    Mechanical properties and energy absorption characteristics of a polyurethane foam Citation Details In-Document Search Title: Mechanical properties and energy absorption ...

  18. DERIVATION OF STOCHASTIC ACCELERATION MODEL CHARACTERISTICS FOR...

    Office of Scientific and Technical Information (OSTI)

    FOR SOLAR FLARES FROM RHESSI HARD X-RAY OBSERVATIONS Citation Details In-Document Search Title: DERIVATION OF STOCHASTIC ACCELERATION MODEL CHARACTERISTICS FOR SOLAR FLARES ...

  19. Fracture characteristics and their relationships to producing...

    Open Energy Info (EERE)

    characteristics and their relationships to producing zones in deep wells, Raft River geothermal area Jump to: navigation, search OpenEI Reference LibraryAdd to library Book:...

  20. " Row: NAICS Codes;"

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

    ,,"Total United States" ,"RSE Column ... 324110," Petroleum Refineries",44,240,337696.4,4578,2... ,,"Total United States" ,"RSE Column ...

  1. LED Color Characteristics | Department of Energy

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

    DOE EERE Building Technologies Program Solid-State Lighting fact sheet led-color-characteristics-factsheet.pdf (804.38 KB) More Documents & Publications LED Color Characteristics Evaluating Color Rendition Using IES TM-30-15 Report 23: Photometric Testing of White Tunable LED Luminaires

  2. Table A21. Quantity of Electricity Sold to Utility and Nonutility Purchasers

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

    1. Quantity of Electricity Sold to Utility and Nonutility Purchasers" " by Census Region and Economic Characteristics of the Establishment, 1991" " (Estimates in Million Kilowatthours)" ,,,,"RSE" " "," ","Utility ","Nonutility","Row" "Economic Characteristics(a)","Total Sold","Purchaser(b)","Purchaser(c)","Factors" ,"Total United States",,, "RSE

  3. "RSE Table C1.1. Relative Standard Errors for Table C1.1;"

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

    .1. Relative Standard Errors for Table C1.1;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," ","Any",," "," ",," "," ",," ","Shipments" "NAICS"," ","Energy","Net","Residual","Distillate",,"LPG

  4. "RSE Table C10.2. Relative Standard Errors for Table C10.2;"

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

    2. Relative Standard Errors for Table C10.2;" " Unit: Percents." ,,,"Establishments" " "," ",,"with Any"," Steam Turbines","Supplied","by Either","Conventional","Combustion","Turbines"," "," "," ","Internal","Combustion","Engines"," Steam Turbines","Supplied","by Heat",," " "

  5. "RSE Table E1.1. Relative Standard Errors for Table E1.1;"

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

    .1. Relative Standard Errors for Table E1.1;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," " " "," ",," "," ",," "," ",," ","Shipments" "Economic",,"Net","Residual","Distillate",,"LPG and",,"Coke and"," ","of Energy

  6. "RSE Table N1.3. Relative Standard Errors for Table N1.3;"

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

    .3. Relative Standard Errors for Table N1.3;" " Unit: Percents." " "," " ,"Total" "Energy Source","First Use" ,"Total United States" "Coal ",3 "Natural Gas",1 "Net Electricity",1 " Purchases",1 " Transfers In",9 " Onsite Generation from Noncombustible Renewable Energy",15 " Sales and Transfers Offsite",3 "Coke and Breeze",2 "Residual Fuel

  7. "RSE Table N13.3. Relative Standard Errors for Table N13.3;"

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

    3. Relative Standard Errors for Table N13.3;" " Unit: Percents." " "," ","Total of" "NAICS"," ","Sales and","Utility","Nonutility" "Code(a)","Subsector and Industry","Transfers Offsite","Purchaser(b)","Purchaser(c)" ,,"Total United States" , 311,"Food",8,9,0 311221," Wet Corn Milling",0,0,0 312,"Beverage and Tobacco

  8. "RSE Table N5.2. Relative Standard Errors for Table N5.2;"

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

    2. Relative Standard Errors for Table N5.2;" " Unit: Percents." ,,"S e l e c t e d","W o o d","a n d","W o o d -","R e l a t e d","P r o d u c t s" ,,,,,"B i o m a s s" ,,,,,,"Wood Residues" ,,,,,,"and","Wood-Related" " "," ","Pulping Liquor"," "," ","Wood","Byproducts","and",," "

  9. Principal Characteristics of a Modern Grid

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

    ... MODERN GRID S T R A T E G Y 24 24 The Smart Grid Gap Characteristic Today's Grid Smart ... MODERN GRID S T R A T E G Y Utility Benefits Operational efficiencies Metering and billing ...

  10. NREL: Wind Research - Site Wind Resource Characteristics

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

    Site Wind Resource Characteristics A graphic showing the location of National Wind Technology Center and its wind power class 2. Click on the image to view a larger version. ...

  11. Probability-theoretic characteristics of solar batteries

    SciTech Connect (OSTI)

    Lidorenko, N.S.; Asharin, L.N.; Borisova, N.A.; Evdokimov, V.M.; Ryabikov, S.V.

    1980-01-01

    Results are reported for an investigation into the characteristics of solar batteries on the basis of probability theory with the photocells treated as current generators; methods for reducing solar-battery circuit losses are considered.

  12. 1997 Housing Characteristics Tables Housing Unit Tables

    Gasoline and Diesel Fuel Update (EIA)

    Contact: Robert Latta, Survey Manager (rlatta@eia.doe.gov) World Wide Web: http:www.eia.doe.govemeuconsumption Table HC1-1a. Housing Unit Characteristics by Climate Zone, ...

  13. Crude Oil Characteristics Research | Department of Energy

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

    Crude Oil Characteristics Research Crude Oil Characteristics Research July 9, 2015 - 1:00pm Addthis Paula Gant Paula Gant Principal Deputy Assistant Secretary The DOE Office of Fossil Energy wanted to identify the actions needed to obtain a science-based understanding of outstanding questions associated with the production, treatment, and transportation of various types of crude oil, including Bakken crude oil. In support of that effort, DOE - in collaboration with the Department of

  14. 1992 CBECS BC

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

    . Census Region, Number of Buildings and Floorspace, 1992 Building Characteristics RSE Column Factor: Number of Buildings (thousand) Total Floorspace (million square feet) RSE Row Factor All Buildings Northeast Midwest South West All Buildings Northeast Midwest South West 0.6 1.2 1.1 1.0 1.3 0.6 1.3 1.1 1.1 1.2 All Buildings ................................... 4,806 771 1,202 1,963 870 67,876 13,400 17,280 24,577 12,619 6.3 Building Floorspace (square feet) 1,001 to 5,000

  15. 1992 CBECS BC

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

    7. Employment Size Category, Number of Buildings, 1992 (Thousand) Building Characteristics RSE Column Factor: All Buildings Buildings by Number of Workers RSE Row Factor Less than 5 Workers 5 to 9 Workers 10 to 19 Workers 20 to 49 Workers 50 to 99 Workers 100 to 249 Workers 250 or More Workers 0.5 0.8 0.9 1.1 1.0 1.2 1.3 1.4 All Buildings ................................... 4,806 2,718 895 561 405 130 64 31 5.9 Building Floorspace (square feet) 1,001 to 5,000 ................................

  16. 1992 CBECS BC

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

    9. Energy Sources, Number of Buildings, 1992 (Thousand) Building Characteristics RSE Column Factor: All Buildings All Buildings Using Any Energy Source Energy Sources Used (more than one may apply) RSE Row Factor Electricity Natural Gas Fuel Oil District Heat District Chilled Water Propane Wood 0.5 0.5 0.5 0.6 1.1 1.6 2.2 1.6 2.0 All Buildings ..................................... 4,806 4,620 4,616 2,665 559 95 28 337 103 7.7 Building Floorspace (Square Feet) 1,001 to 5,000

  17. 1992 CBECS BC

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

    4. Percent of Floorspace Heated, Number of Buildings and Floorspace, 1992 Building Characteristics RSE Column Factor: Number of Buildings (thousand) Total Floorspace (million square feet) RSE Row Factor All Buildings Not Heated Less than 51 Percent Heated 51 to 99 Percent Heated 100 Percent Heated All Buildings Total Heated Floorspace in All Buildings Not Heated Less than 51 Percent Heated 51 to 99 Percent Heated 100 Percent Heated 0.6 1.6 1.2 1.1 0.7 0.6 0.6 2.2 1.6 1.2 0.7 All Buildings

  18. 1992 CBECS BC

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

    7. Heating Equipment, Number of Buildings, 1992 (Thousand) Building Characteristics RSE Column Factor: All Buildings All Heated Buildings Heating Equipment (more than one may apply) RSE Row Factor Heat Pumps Furnaces Individual Space Heaters District Heat Boilers Packaged Heating Units Other 0.5 0.5 1.3 0.8 0.8 1.7 0.9 1.0 3.1 All Buildings ..................................... 4,806 4,178 449 1,692 1,464 93 624 870 42 6.7 Building Floorspace (square feet) 1,001 to 5,000

  19. 1992 CBECS BC

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

    A57. Energy Conservation Features, Number of Buildings and Floorspace, 1992 Building Characteristics RSE Column Factor: Number of Buildings (thousand) Total Floorspace (million square feet) RSE Row Factor All Buildings Any Conser- vation Features Build- ing Shell HVAC Light- ing Other All Buildings Any Conser- vation Features Build- ing Shell HVAC Light- ing Other 0.8 0.8 0.8 0.9 1.0 1.9 0.8 0.9 0.9 0.9 1.2 1.7 All Buildings ................................... 4,806 4,357 4,223 2,604 1,178 264

  20. 1992 CBECS BC

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

    . Summary Table of Square Feet, Hours of Operation and Age of Building, 1992 Building Characteristics RSE Column Factor: All Buildings (thousand) Total Floorspace (million square feet) Total Workers in All Buildings (thousand) Mean Square Feet per Building (thousand) Median Square Feet per Building (thousand) Mean Square Feet per Worker Median Square Feet per Worker Mean Hours per Week Median Hours per Week Median Age of Buildings (years) RSE Row Factor 1.1 1.2 1.5 1.0 -- 1.3 -- 0.4 -- -- All

  1. table1.3_02.xls

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

    3 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources and Shipments; Unit: Trillion Btu. Shipments RSE Economic Net Residual Distillate Natural LPG and Coke and of Energy Sources Row Characteristic(a) Total(b) Electricity(c) Fuel Oil Fuel Oil(d) Gas(e) NGL(f) Coal Breeze Other(g) Produced Onsite(h) Factors Total United States RSE Column Factors: 0.8 0.9 1.4 2.7 0.8 0.6 2 1.4 1.1

  2. table11.4_02.xls

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

    Electricity: Components of Onsite Generation, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Onsite-Generation Components; Unit: Million Kilowatthours. Renewable Energy (excluding Wood RSE Economic Total Onsite and Row Characteristic(a) Generation Cogeneration(b) Other Biomass)(c) Other(d) Factors Total United States RSE Column Factors: 0.8 0.8 1.1 1.4 Value of Shipments and Receipts (million dollars) Under 20 609 379 W W 25.2 20-49 4,155 4,071 27

  3. table11.6_02.xls

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

    .6 Electricity: Sales to Utility and Nonutility Purchasers, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Utility and Nonutility Purchasers; Unit: Million Kilowatthours. Total of RSE Economic Sales and Utility Nonutility Row Characteristic(a) Transfers Offsite Purchaser(b) Purchaser(c) Factors Total United States RSE Column Factors: 0.9 1.3 0.9 Value of Shipments and Receipts (million dollars) Under 20 251 99 152 11.3 20-49 2,975 372 2,602 1.6

  4. table2.3_02.xls

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

    Nonfuel (Feedstock) Use of Combustible Energy, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: Trillion Btu. RSE Economic Residual Distillate Natural LPG and Coke and Row Characteristic(a) Total Fuel Oil Fuel Oil(b) Gas(c) NGL(d) Coal Breeze Other(e) Factors Total United States RSE Column Factors: 1 0.4 6.4 0.6 0.5 1.1 1.7 0.8 Value of Shipments and Receipts (million dollars) Under 20 94 * 6 19 W W W W 9 20-49 135 19 3 8 W W

  5. table7.4_02.xls

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

    4 Average Prices of Selected Purchased Energy Sources, 2002; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: U.S. Dollars per Physical Units. Residual Distillate Natural LPG and RSE Economic Electricity Fuel Oil Fuel Oil(b) Gas(c) NGL(d) Coal Row Characteristic(a) (kWh) (gallons) (gallons) (1000 cu ft) (gallons) (short tons) Factors Total United States RSE Column Factors: 0.7 1.2 2.2 0.7 0.5 1.6 Value of Shipments and Receipts

  6. S:\VM3\RX97\TBL_LIST.WPD

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

    1997 Space Heating Characteristics RSE Column Factor: Total Four Most Populated States RSE Row Factors New York California Texas Florida 0.5 1.2 0.9 1.3 1.5 Total .............................................................. 101.5 6.8 11.5 7.0 5.9 NF Main Heating Fuel and Equipment Natural Gas ................................................. 53.5 3.4 7.8 3.8 0.7 8.6 Central Warm-Air Furnace ........................ 38.4 1.8 4.7 2.3 0.6 12.2 For One Housing Unit ..............................

  7. Superconducting wire with improved strain characteristics

    DOE Patents [OSTI]

    Luhman, Thomas; Klamut, Carl J.; Suenaga, Masaki; Welch, David

    1982-01-01

    A superconducting wire comprising a superconducting filament and a beryllium strengthened bronze matrix in which the addition of beryllium to the matrix permits a low volume matrix to exhibit reduced elastic deformation after heat treating which increases the compression of the superconducting filament on cooling and thereby improves the strain characteristics of the wire.

  8. Superconducting wire with improved strain characteristics

    DOE Patents [OSTI]

    Luhman, Thomas; Klamut, Carl J.; Suenaga, Masaki; Welch, David

    1982-01-01

    A superconducting wire comprising a superconducting filament and a beryllium strengthened bronze matrix in which the addition of beryllium to the matrix permits a low volume matrix to exhibit reduced elastic deformation after heat treating which increases the compression of the superconducting filament on cooling and thereby improve the strain characteristics of the wire.

  9. Superconducting wire with improved strain characteristics

    DOE Patents [OSTI]

    Luhman, T.; Klamut, C.J.; Suenaga, M.; Welch, D.

    1979-12-19

    A superconducting wire comprising a superconducting filament and a beryllium strengthened bronze matrix in which the addition of beryllium to the matrix permits a low volume matrix to exhibit reduced elastic deformation after heat treating which increases the compression of the superconducting filament on cooling and thereby improve the strain characteristics of the wire.

  10. Table HC1.2.1. Living Space Characteristics by

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

    Space Characteristics by" " Total, Heated, and Cooled Floorspace, 2005" ,,,"Total Square Footage" ,"Housing Units",,"Total1",,"Heated",,"Cooled" "Living Space Characteristics","Mil...

  11. Gas Electron Multiplier (GEM) Chamber Characteristics Test (Technical...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Gas Electron Multiplier (GEM) Chamber Characteristics Test Citation Details In-Document Search Title: Gas Electron Multiplier (GEM) Chamber Characteristics Test ...

  12. Origins of optical absorption characteristics of Cu2+ complexes...

    Office of Scientific and Technical Information (OSTI)

    Origins of optical absorption characteristics of Cu2+ complexes in solutions Citation Details In-Document Search Title: Origins of optical absorption characteristics of Cu2+ ...

  13. Spectral characteristics of time resolved magnonic spin Seebeck...

    Office of Scientific and Technical Information (OSTI)

    Spectral characteristics of time resolved magnonic spin Seebeck effect Citation Details In-Document Search Title: Spectral characteristics of time resolved magnonic spin Seebeck ...

  14. Plant Root Characteristics and Dynamics in Arctic Tundra Ecosystems...

    Office of Scientific and Technical Information (OSTI)

    Dataset: Plant Root Characteristics and Dynamics in Arctic Tundra Ecosystems, 1960-2012 Citation Details In-Document Search Title: Plant Root Characteristics and Dynamics in Arctic...

  15. Oxidation characteristics of gasoline direct-injection (GDI)...

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

    characteristics of gasoline direct-injection (GDI) engine soot: Catalytic effects of ash and modified kinetic correlation Title Oxidation characteristics of gasoline...

  16. Characteristics of seismic waves from Soviet peaceful nuclear...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Characteristics of seismic waves from Soviet peaceful nuclear explosions in salt Citation Details In-Document Search Title: Characteristics of seismic waves from...

  17. A comparison between characteristics of atmospheric-pressure...

    Office of Scientific and Technical Information (OSTI)

    A comparison between characteristics of atmospheric-pressure plasma jets sustained by ... Title: A comparison between characteristics of atmospheric-pressure plasma jets sustained ...

  18. Forward and reverse characteristics of irradiated MOSFETs

    SciTech Connect (OSTI)

    Paccagnella, A.; Ceschia, M.; Verzellesi, G.; Dalla Betta, G.F.; Soncini, G.; Bellutti, P.; Fuochi, P.G.

    1996-06-01

    pMOSFETs biased with V{sub gs} < V{sub gd} during Co{sup 60} {gamma} irradiation have shown substantial differences between the forward and reverse subthreshold characteristics, induced by a non-uniform charge distribution in the gate oxide. Correspondingly, modest differences have been observed in the over-threshold I-V characteristics. After irradiation, the forward subthreshold curves can shift at higher or lower gate voltages than the reverse ones. The former behavior has been observed in long-channel devices, in agreement with the classical MOS theory and numerical simulations. The latter result has been obtained in short-channel devices, and it has been correlated to a parasitic punch-through conduction mechanism.

  19. Characteristics of potential repository wastes. Volume 1

    SciTech Connect (OSTI)

    Not Available

    1992-07-01

    This document, and its associated appendices and microcomputer (PC) data bases, constitutes the reference OCRWM data base of physical and radiological characteristics data of radioactive wastes. This Characteristics Data Base (CDB) system includes data on spent nuclear fuel and high-level waste (HLW), which clearly require geologic disposal, and other wastes which may require long-term isolation, such as sealed radioisotope sources. The data base system was developed for OCRWM by the CDB Project at Oak Ridge National Laboratory. Various principal or official sources of these data provided primary information to the CDB Project which then used the ORIGEN2 computer code to calculate radiological properties. The data have been qualified by an OCRWM-sponsored peer review as suitable for quality-affecting work meeting the requirements of OCRWM`s Quality Assurance Program. The wastes characterized in this report include: light-water reactor (LWR) spent fuel and immobilized HLW.

  20. Measuring spatial variability in soil characteristics

    DOE Patents [OSTI]

    Hoskinson, Reed L.; Svoboda, John M.; Sawyer, J. Wayne; Hess, John R.; Hess, J. Richard

    2002-01-01

    The present invention provides systems and methods for measuring a load force associated with pulling a farm implement through soil that is used to generate a spatially variable map that represents the spatial variability of the physical characteristics of the soil. An instrumented hitch pin configured to measure a load force is provided that measures the load force generated by a farm implement when the farm implement is connected with a tractor and pulled through or across soil. Each time a load force is measured, a global positioning system identifies the location of the measurement. This data is stored and analyzed to generate a spatially variable map of the soil. This map is representative of the physical characteristics of the soil, which are inferred from the magnitude of the load force.

  1. homeoffice_household2001.pdf

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

    ... For this report, the heating or cooling degree-days are a measure of how cold or how hot a location is over a period of one year, relative to a base temperature of 65 degrees ...

  2. ac_household2001.pdf

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

    ... For this report, the heating or cooling degree-days are a measure of how cold or how hot a location is over a period of one year, relative to a base temperature of 65 degrees ...

  3. spaceheat_household2001.pdf

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

    ... For this report, the heating or cooling degree-days are a measure of how cold or how hot a location is over a period of one year, relative to a base temperature of 65 degrees ...

  4. char_household2001.pdf

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent ...... 9.8 2.8 2.1 4.4 0.5 11.6 100 to 150 Percent ...... 5.1 1.4 1.1 2.3 Q 14.2 Above 150 ...

  5. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 0.9 0.5 0.6 13.0 1 Below 150 percent of poverty line or 60 percent of median State ...

  6. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 0.6 0.5 Q 17.4 1 Below 150 percent of poverty line or 60 percent of median State ...

  7. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 5.2 3.9 Q Q 1.1 21.9 100 to 150 Percent ...... 6.4 5.2 0.2 Q 0.9 16.5 Above 150 Percent ...

  8. char_household2001.pdf

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent ...... definition. 2 Below 150 percent of poverty line or 60 percent of median State ...

  9. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 15.0 1.4 2.3 ... were conducted. 2 Below 150 percent of poverty line or 60 percent of median State ...

  10. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 0.7 0.4 0.2 18.4 1 Below 150 percent of poverty line or 60 percent of median State ...

  11. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 15.0 1.0 3.4 ... weather station. 2 Below 150 percent of poverty line or 60 percent of median State ...

  12. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 1.2 0.7 0.5 11.3 1 Below 150 percent of poverty line or 60 percent of median State ...

  13. char_household2001.pdf

    Gasoline and Diesel Fuel Update (EIA)

    Income Relative to Poverty Line Below 100 Percent ...... 1.5 0.5 1.0 14.6 1 Below 150 percent of poverty line or 60 percent of median State ...

  14. char_household2001.pdf

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

    Income Relative to Poverty Line Below 100 Percent ...... 15.0 6.7 2.3 ... 4.9 Q Q 0.2 14.8 1 Below 150 percent of poverty line or 60 percent of median State ...

  15. Household Vehicles Energy Consumption 1991

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

    selected tabulations were produced using two different software programs, Table Producing Language (TPL) and Statistical Analysis System (SAS). Energy Information Administration...

  16. Household Vehicles Energy Consumption 1991

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

    Matching: A model-based procedure used to impute for item nonresponse. This method uses logistic models to compute predicted means that are used to statistically match each...

  17. Household Vehicles Energy Consumption 1991

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

    were imputed as disposed vehicles. To impute vehicle stock changes in the 1991 RTECS, logistic regression equations were used to compute a predicted probability (or propensity)...

  18. Household Vehicles Energy Consumption 1991

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

    production vehicles in order to assess compliance with Corporate Average Fuel Economy (CAFE) standards. The EPA Composite MPG is based on the assumption of a "typical" vehicle-use...

  19. Household Vehicles Energy Consumption 1991

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

    more fuel-efficient vehicles, and the implementation of Corporate Average Fuel Economy (CAFE) 6 standards. Figure 13. Average Fuel Efficiency of All Vehicles, by Model Year 6...

  20. Household Vehicles Energy Consumption 1991

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

    of vehicles in the residential sector. Data are from the 1991 Residential Transportation Energy Consumption Survey. The "Glossary" contains the definitions of terms used in the...

  1. Household Vehicles Energy Consumption 1991

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

    for 1994, will continue the 3-year cycle. The RTECS, a subsample of the Residential Energy Consumption Survey (RECS), is an integral part of a series of surveys designed by...

  2. Household Vehicles Energy Consumption 1991

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

    a comparison between the 1991 and previous years RTECS designs; (2) the sample design; (3) the data-collection procedures; (4) the Vehicle Identification Number (VIN); (5)...

  3. Household Vehicles Energy Consumption 1994

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

    DC, October 1995), Table DL-1B. 5. "Chained dollars" is a measure used to express real prices. Real prices are those that have been adjusted to remove the effect of changes...

  4. ac_household2001.pdf

    Gasoline and Diesel Fuel Update (EIA)

    Contact: Stephanie J. Battles, Survey Manager (stephanie.battles@eia.doe.gov) World Wide Web: http:www.eia.doe.govemeuconsumption Table HC4-1a. Air Conditioning by Climate ...

  5. usage_household2001.pdf

    Gasoline and Diesel Fuel Update (EIA)

    Contact: Stephanie J. Battles, Survey Manager (stephanie.battles@eia.doe.gov) World Wide Web: http:www.eia.doe.govemeuconsumption Table HC6-1a. Usage Indicators by Climate ...

  6. Buildings Energy Data Book: 2.2 Residential Sector Characteristics

    Buildings Energy Data Book [EERE]

    1 Total Number of Households and Buildings, Floorspace, and Household Size, by Year 1980 80 N.A. 227 2.9 1981 83 N.A. 229 2.8 1982 84 N.A. 232 2.8 1983 85 N.A. 234 2.8 1984 86 N.A. 236 2.7 1985 88 N.A. 238 2.7 1986 89 N.A. 240 2.7 1987 91 N.A. 242 2.7 1988 92 N.A. 244 2.7 1989 93 N.A. 247 2.6 1990 94 N.A. 250 2.6 1991 95 N.A. 253 2.7 1992 96 N.A. 257 2.7 1993 98 N.A. 260 2.7 1994 99 N.A. 263 2.7 1995 100 N.A. 266 2.7 1996 101 N.A. 269 2.7 1997 102 N.A. 273 2.7 1998 104 N.A. 276 2.7 1999 105 N.A.

  7. Building and occupant characteristics as determinants of residential energy consumption

    SciTech Connect (OSTI)

    Nieves, L.A.; Nieves, A.L.

    1981-10-01

    The major goals of the research are to gain insight into the probable effects of building energy performance standards on energy consumption; to obtain observations of actual residential energy consumption that could affirm or disaffirm comsumption estimates of the DOE 2.0A simulation model; and to investigate home owner's conservation investments and home purchase decisions. The first chapter covers the investigation of determinants of household energy consumption. The presentation begins with the underlying economic theory and its implications, and continues with a description of the data collection procedures, the formulation of variables, and then of data analysis and findings. In the second chapter the assumptions and limitations of the energy use projections generated by the DOE 2.0A model are discussed. Actual electricity data for the houses are then compared with results of the simulation. The third chapter contains information regarding households' willingness to make energy conserving investments and their ranking of various conservation features. In the final chapter conclusions and recommendations are presented with an emphasis on the policy implications of this study. (MCW)

  8. R93HC.PDF

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

    0a. Light Usage by Census Region and Climate Zone, Million U.S. Households, 1993 Light Usage RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.4 0.8 0.8 0.6 0.8 2.4 1.4 1.3 1.4 1.1 Total ..................................................... 96.6 19.5 23.3 33.5 20.4 8.7 26.5 22.5 17.8 21.2 6.6

  9. R93HC.PDF

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

    3a. Usage Indicators by Census Region and Climate Zone, Million U.S. Households, 1993 Usage Indicators RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.4 0.8 0.8 0.7 0.7 2.5 1.4 1.3 1.5 1.2 Total ..................................................... 96.6 19.5 23.3 33.5 20.4 8.7 26.5 22.5 17.8

  10. R93HC.PDF

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

    7a. Conservation by Census Region and Climate Zone, Million U.S. Households, 1993 Conservation-Related Items RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.4 0.8 0.8 0.7 0.8 2.3 1.4 1.3 1.5 1.2 Total ..................................................... 96.6 19.5 23.3 33.5 20.4 8.7 26.5 22.5

  11. R93HC.PDF

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

    31a. Equipment Purchase by Census Region and Climate Zone, Million U.S. Households, 1993 Equipment Purchase and Purchase Considerations RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 1.0 0.8 0.7 0.9 2.1 1.1 1.1 1.4 1.1 Total ..................................................... 96.6 19.5

  12. Nuclear reactor characteristics and operational history

    Gasoline and Diesel Fuel Update (EIA)

    2. Ownership Data, Table 3. Characteristics and Operational History Table 1. Nuclear Reactor, State, Type, Net Capacity, Generation, and Capacity Factor PDF XLS Plant/Reactor Name Generator ID State Type 2009 Summer Capacity Net MW(e)1 2010 Annual Generation Net MWh2 Capacity Factor Percent3 Arkansas Nuclear One 1 AR PWR 842 6,607,090 90 Arkansas Nuclear One 2 AR PWR 993 8,415,588 97 Beaver Valley 1 PA PWR 892 7,119,413 91 Beaver Valley 2 PA PWR 885 7,874,151 102 Braidwood Generation Station 1

  13. Coal-water slurry atomization characteristics

    SciTech Connect (OSTI)

    Caton, J.A.; Kihm, K.D.

    1994-04-01

    The overall objective of this work was to fully characterize the CWS fuel sprays of a medium-speed diesel engine injection system. Specifically, the spray plume penetration as a function of time was determined for a positive-displacement fuel injection system. The penetration was determined as a function of orifice diameter, coal loading, gas density in the engine, and fuel line pressure. Preliminary droplet information also was obtained. The results of this study will assist CWS engine development by providing much needed insight about the fuel spray. In addition, the results will aid the development and use of CWS engine cycle simulations which require information on the fuel spray characteristics.

  14. An Experimental Study of PM Emission Characteristics of Commercial...

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

    Study of PM Emission Characteristics of Commercial Diesel Engine with Urea-SCR System An Experimental Study of PM Emission Characteristics of Commercial Diesel Engine with Urea-SCR ...

  15. Characteristics of transverse waves in chromospheric mottles

    SciTech Connect (OSTI)

    Kuridze, D.; Mathioudakis, M.; Jess, D. B.; Keenan, F. P. [Astrophysics Research Center, School of Mathematics and Physics, Queen's University, Belfast BT7 1NN (United Kingdom); Verth, G.; Erdlyi, R. [Solar Physics and Space Plasma Research Center (SP2RC), University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH (United Kingdom); Morton, R. J. [Mathematics and Information Science, Northumbria University, Camden Street, Newcastle Upon Tyne NE1 8ST (United Kingdom); Christian, D. J., E-mail: dkuridze01@qub.ac.uk [Department of Physics and Astronomy, California State University, Northridge, CA 91330 (United States)

    2013-12-10

    Using data obtained by the high temporal and spatial resolution Rapid Oscillations in the Solar Atmosphere instrument on the Dunn Solar Telescope, we investigate at an unprecedented level of detail transverse oscillations in chromospheric fine structures near the solar disk center. The oscillations are interpreted in terms of propagating and standing magnetohydrodynamic kink waves. Wave characteristics including the maximum transverse velocity amplitude and the phase speed are measured as a function of distance along the structure's length. Solar magnetoseismology is applied to these measured parameters to obtain diagnostic information on key plasma parameters (e.g., magnetic field, density, temperature, flow speed) of these localized waveguides. The magnetic field strength of the mottle along the ?2 Mm length is found to decrease by a factor of 12, while the local plasma density scale height is ?280 80 km.

  16. Gas sensor with attenuated drift characteristic

    DOE Patents [OSTI]

    Chen, Ing-Shin [Danbury, CT; Chen, Philip S. H. [Bethel, CT; Neuner, Jeffrey W. [Bethel, CT; Welch, James [Fairfield, CT; Hendrix, Bryan [Danbury, CT; Dimeo, Jr., Frank [Danbury, CT

    2008-05-13

    A sensor with an attenuated drift characteristic, including a layer structure in which a sensing layer has a layer of diffusional barrier material on at least one of its faces. The sensor may for example be constituted as a hydrogen gas sensor including a palladium/yttrium layer structure formed on a micro-hotplate base, with a chromium barrier layer between the yttrium layer and the micro-hotplate, and with a tantalum barrier layer between the yttrium layer and an overlying palladium protective layer. The gas sensor is useful for detection of a target gas in environments susceptible to generation or incursion of such gas, and achieves substantial (e.g., >90%) reduction of signal drift from the gas sensor in extended operation, relative to a corresponding gas sensor lacking the diffusional barrier structure of the invention

  17. Buildings and Energy in the 1980s

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

    No. PB83-199554, 220. Residential Energy Consumption Survey: Household Transportation Panel Monthly Gas Purchases and Vehicle and Household Characteristics, 679-981; Order...

  18. DOE/EIA-0516(85) Energy Information Administration Manufacturing...

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

    Order No. PB83- 199554HAA Residential Energy Consumption Survey: HouseholdTransportation Panel Monthly Gas Purchases and Vehicle and Household Characteristics, 6179-9181 * Order...

  19. Buildings and Energy in the 1980s

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

    conducted in two stages: (1) A Household (RECS)Building (CBECS) Survey and an Energy Suppliers Survey. The HouseholdBuilding Characteristics Survey consists of personal...

  20. U.S. Energy Information Administration (EIA) - Data

    Gasoline and Diesel Fuel Update (EIA)

    Residential Energy Consumption Survey (RECS) Home energy use & costs Release Date: ... Household characteristics Home energy use & costs Detailed household microdata Commercial ...

  1. Foaming characteristics of refigerant/lubricant mixtures

    SciTech Connect (OSTI)

    Goswami, D.Y.; Shah, D.O.; Jotshi, C.K.; Bhagwat, S.; Leung, M.; Gregory, A.

    1997-04-01

    The air-conditioning and refrigeration industry has moved to HFC refrigerants which have zero ozone depletion and low global warming potential due to regulations on CFC and HCFC refrigerants and concerns for the environment. The change in refrigerants has prompted the switch from mineral oil and alkylbenzene lubricants to polyolester-based lubricants. This change has also brought about a desire for lubricant, refrigerant and compressor manufacturers to understand the foaming properties of alternative refrigerant/ lubricant mixtures, as well as the mechanisms which affect these properties. The objectives of this investigation are to experimentally determine the foaming absorption and desorption rates of HFC and blended refrigerants in polyolester lubricant and to define the characteristics of the foam formed when the refrigerant leaves the refrigerant/ lubricant mixture after being exposed to a pressure drop. The refrigerants being examined include baseline refrigerants: CFC-12 (R-12) and HCFC-22 (R-22); alternative refrigerants: HFC-32 (R-32), R-125, R-134a, and R-143a; and blended refrigerants: R-404A, R-407C, and R-410A. The baseline refrigerants are tested with ISO 32 (Witco 3GS) and ISO 68 (4GS) mineral oils while the alternative and blended refrigerants are tested with two ISO 68 polyolesters (Witco SL68 and ICI RL68H).

  2. "Table HC14.6 Air Conditioning Characteristics by West Census...

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

    "20 Years or More",6.5,1.3,0.3,1 "Don't Know",4.5,0.6,"Q",0.5 "Used by Two or More ... "20 Years or More",0.3,"Q","N","Q" "Don't Know",0.7,"Q","N","Q" "Household Pays for ...

  3. "Table HC12.6 Air Conditioning Characteristics by Midwest Census...

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

    "20 Years or More",6.5,1.5,1,0.5 "Don't Know",4.5,1.3,0.7,0.6 "Used by Two or More ... "20 Years or More",0.3,"Q","Q","N" "Don't Know",0.7,"Q","Q","Q" "Household Pays for ...

  4. "Table HC15.6 Air Conditioning Characteristics by Four Most...

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

    "20 Years or More",6.5,"Q",0.5,0.8,0.9 "Don't Know",4.5,"Q",0.4,0.7,0.4 "Used by Two or ... "20 Years or More",0.3,"N","Q","Q","Q" "Don't Know",0.7,"N","Q","Q","Q" "Household Pays ...

  5. TableHC1.1.1.xls

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

    ... Housing Unit Housing Unit Characteristics Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Household Member ...

  6. Sooting characteristics of surrogates for jet fuels

    SciTech Connect (OSTI)

    Mensch, Amy; Santoro, Robert J.; Litzinger, Thomas A.; Lee, S.-Y.

    2010-06-15

    Currently, modeling the combustion of aviation fuels, such as JP-8 and JetA, is not feasible due to the complexity and compositional variation of these practical fuels. Surrogate fuel mixtures, composed of a few pure hydrocarbon compounds, are a key step toward modeling the combustion of practical aviation fuels. For the surrogate to simulate the practical fuel, the composition must be designed to reproduce certain pre-designated chemical parameters such as sooting tendency, H/C ratio, autoignition, as well as physical parameters such as boiling range and density. In this study, we focused only on the sooting characteristics based on the Threshold Soot Index (TSI). New measurements of TSI values derived from the smoke point along with other sooting tendency data from the literature have been combined to develop a set of recommended TSI values for pure compounds used to make surrogate mixtures. When formulating the surrogate fuel mixtures, the TSI values of the components are used to predict the TSI of the mixture. To verify the empirical mixture rule for TSI, the TSI values of several binary mixtures of candidate surrogate components were measured. Binary mixtures were also used to derive a TSI for iso-cetane, which had not previously been measured, and to verify the TSI for 1-methylnaphthalene, which had a low smoke point and large relative uncertainty as a pure compound. Lastly, surrogate mixtures containing three components were tested to see how well the measured TSI values matched the predicted values, and to demonstrate that a target value for TSI can be maintained using various components, while also holding the H/C ratio constant. (author)

  7. Primary Characteristics of Loan Loss Reserve Funds | Department of Energy

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

    Primary Characteristics of Loan Loss Reserve Funds Primary Characteristics of Loan Loss Reserve Funds Loan loss reserve (LLR) funds have four primary characteristics, detailed here. Portfolio approach to credit Leverage Financial institution partner Secondary market support Portfolio Approach to Credit LLRs take a "portfolio approach," meaning that state and local governments setting up LLRs do so on the basis of the entire portfolio of loans they support. For example, a 5% loss

  8. About the Rhythms of Variability of the Submicron Aerosol Characterist...

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

    of the distribution of the aerosol characteristics were considered. The periodograms (Fourier spectra of the discrete data set) were calculated for all data arrays using...

  9. Characteristics and Effects of Lubricant Additive Chemistry and...

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

    Additive Chemistry and Exhaust Conditions on Diesel Particulate Filter Service Life and Vehicle Fuel Economy Characteristics and Effects of Lubricant Additive Chemistry and ...

  10. Effects of discharge voltage waveform on the discharge characteristics...

    Office of Scientific and Technical Information (OSTI)

    atmospheric plasma jet Citation Details In-Document Search Title: Effects of discharge voltage waveform on the discharge characteristics in a helium atmospheric plasma jet We ...

  11. Receiver Operating Characteristic (ROC) Curves: An Analysis Tool...

    Office of Scientific and Technical Information (OSTI)

    for Detection Performance Citation Details In-Document Search Title: Receiver Operating Characteristic (ROC) Curves: An Analysis Tool for Detection Performance You are ...

  12. Understanding Fault Characteristics And Sediment Depth For Geothermal...

    Open Energy Info (EERE)

    Understanding Fault Characteristics And Sediment Depth For Geothermal Exploration Using 3D Gravity Inversion In Walker Valley, Nevada Jump to: navigation, search OpenEI Reference...

  13. Measurement Of Gas Electron Multiplier (GEM) Detector Characteristics...

    Office of Scientific and Technical Information (OSTI)

    Measurement Of Gas Electron Multiplier (GEM) Detector Characteristics Citation Details In-Document Search Title: Measurement Of Gas Electron Multiplier (GEM) Detector ...

  14. Thermal Hydraulic Characteristics of Fuel Defects in Plate Type...

    Office of Scientific and Technical Information (OSTI)

    in Plate Type Nuclear Research Reactors Citation Details In-Document Search Title: Thermal Hydraulic Characteristics of Fuel Defects in Plate Type Nuclear Research Reactors ...

  15. Effects of Ion Beam on Nanoindentation Characteristics of Glassy...

    Office of Scientific and Technical Information (OSTI)

    Effects of Ion Beam on Nanoindentation Characteristics of Glassy Polymeric Carbon Surface Citation Details In-Document Search Title: Effects of Ion Beam on Nanoindentation ...

  16. Receiver Operating Characteristic (ROC) Curves: An Analysis Tool...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Receiver Operating Characteristic (ROC) Curves: An Analysis Tool for Detection Performance Citation Details In-Document Search Title: Receiver Operating ...

  17. The Nevada railroad system: Physical, operational, and accident characteristics

    SciTech Connect (OSTI)

    1991-09-01

    This report provides a description of the operational and physical characteristics of the Nevada railroad system. To understand the dynamics of the rail system, one must consider the system`s physical characteristics, routing, uses, interactions with other systems, and unique operational characteristics, if any. This report is presented in two parts. The first part is a narrative description of all mainlines and major branchlines of the Nevada railroad system. Each Nevada rail route is described, including the route`s physical characteristics, traffic type and volume, track conditions, and history. The second part of this study provides a more detailed analysis of Nevada railroad accident characteristics than was presented in the Preliminary Nevada Transportation Accident Characterization Study (DOE, 1990).

  18. Transport characteristics across drum filter vents and polymer bags

    SciTech Connect (OSTI)

    Liekhus, K.J.

    1994-08-01

    The rate at which hydrogen (H {sub 2}) or a volatile organic compound (VOC) exits a layer of confinement in a vented waste drum is proportional to the concentration difference across the layer. The proportionality constant is the gas transport characteristic. A series of transport experiments were conducted to determine H{sub 2} and VOC transport characteristics across different drum filter vents and polymer bags. This report reviews the methods and results of past investigators in defining transport characteristics across filter vents and polymer bags, describes the apparatus and procedures used in these experiments, compares the reported and estimated transport characteristics with earlier results, and discusses the impact of changing the transport characteristic values used in model calculations.

  19. Table A28. Components of Onsite Electricity Generation by Census Region, Cens

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

    Components of Onsite Electricity Generation by Census Region, Census Division, and" " Economic Characteristics of the Establishment, 1994" " (Estimates in Million Kilowatthours)" ,,,"Renewables" ,,,"(excluding Wood",,"RSE" " "," "," ","and"," ","Row" "Economic Characteristics(a)","Total","Cogeneration(b)","Other

  20. Table A31. Quantity of Electricity Sold to Utility and Nonutility Purchasers

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

    Quantity of Electricity Sold to Utility and Nonutility Purchasers by Census Region," " Census Division, and Economic Characteristics of the Establishment, 1994" " (Estimates in Million Kilowatthours)" ,,,,"RSE" " "," ","Utility ","Nonutility","Row" "Economic Characteristics(a)","Total Sold","Purchaser(b)","Purchaser(c)","Factors" ,"Total United

  1. Level: National and Regional Data; Row: Selected NAICS Codes...

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

    ... for any table cell, multiply the cel corresponding RSE column and RSE row factors. ... Selected Wood and Wood-Related Products in Fuel Consumption, 2006 Level: National and ...

  2. Level: National Data; Row: NAICS Codes; Column: Usage within...

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

    ... Oxy - Fuel Firing Computer Control of Building Wide Evironment(c Computer Control of ... for any table cell, multiply the cell's corresponding RSE column and RSE row factors. ...

  3. Table N1.1. First Use of Energy for All Purposes (Fuel and...

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

    ... oil converted to residual and distillate" "fuel oils) are excluded." " NFNo applicable ... for any table cell, multiply the cell's" "corresponding RSE column and RSE row factors. ...

  4. " Row: NAICS Codes;" " ...

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

    ,,"Total United States" ,"RSE Column ... 324110," Petroleum Refineries",46,152,42,126,78,35,14... ,,"Total United States" ,"RSE Column ...

  5. RSE Table E6.1 and E6.2. Relative Standard Errors for Tables E6.1 and E6.2

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

    E6.1 and E6.2. Relative Standard Errors for Tables E6.1 and E6.2;" " Unit: Percents." " "," ",," ","Distillate"," "," ",," " " ",,,,"Fuel Oil",,,"Coal" " "," ","Net","Residual","and",,"LPG and","(excluding Coal"," " "End Use","Total","Electricity(a)","Fuel

  6. RSE Table N1.1 and N1.2. Relative Standard Errors for Tables N1.1 and N1.2

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

    1 and N1.2. Relative Standard Errors for Tables N1.1 and N1.2;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," "," ",," "," ",," "," ",," ","Shipments" "NAICS"," ",,"Net","Residual","Distillate",,"LPG

  7. RSE Table N2.1 and N2.2. Relative Standard Errors for Tables N2.1 and N2.2

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

    N2.1 and N2.2. Relative Standard Errors for Tables N2.1 and N2.2;" " Unit: Percents." " "," " "NAICS"," "," ","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Subsector and Industry","Total","Fuel Oil","Fuel Oil(b)","Natural Gas(c)","NGL(d)","Coal","and Breeze","Other(e)"

  8. RSE Table N3.1 and N3.2. Relative Standard Errors for Tables N3.1 and N3.2

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

    N3.1 and N3.2. Relative Standard Errors for Tables N3.1 and N3.2;" " Unit: Percents." " "," "," ",," "," "," "," "," "," "," ",," " "NAICS"," "," ","Net","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Subsector and

  9. RSE Table N4.1 and N4.2. Relative Standard Errors for Tables N4.1 and N4.2

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

    N4.1 and N4.2. Relative Standard Errors for Tables N4.1 and N4.2;" " Unit: Percents." " "," "," ",," "," "," "," "," "," "," ",," " "NAICS"," "," ",,"Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Subsector and

  10. RSE Table N6.1 and N6.2. Relative Standard Errors for Tables N6.1 and N6.2

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

    1 and N6.2. Relative Standard Errors for Tables N6.1 and N6.2;" " Unit: Percents." " "," "," ",," ","Distillate"," "," ",," " " "," ",,,,"Fuel Oil",,,"Coal" "NAICS"," "," ","Net","Residual","and",,"LPG and","(excluding Coal"," " "Code(a)","End

  11. RSE Table N6.3 and N6.4. Relative Standard Errors for Tables N6.3 and N6.4

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

    3 and N6.4. Relative Standard Errors for Tables N6.3 and N6.4;" " Unit: Percents." " "," ",," ","Distillate"," "," " " "," ",,,"Fuel Oil",,,"Coal" "NAICS"," ","Net Demand","Residual","and",,"LPG and","(excluding Coal" "Code(a)","End Use","for Electricity(b)","Fuel

  12. RSE Table N8.1 and N8.2. Relative Standard Errors for Tables N8.1 and N8.2

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

    1 and N8.2. Relative Standard Errors for Tables N8.1 and N8.2;" " Unit: Percents." ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Selected","Wood and Other","Biomass","Components" ,,,,,,,"Coal Components",,,"Coke",,"Electricity","Components",,,,,,,,,,,,,"Natural Gas","Components",,"Steam","Components" ,,,,,,,,,,,,,,"Total",,,,,,,,,,,,,,,,,,,,,,,"Wood

  13. RSE Table S1.1 and S1.2. Relative Standard Errors for Tables S1.1 and S1.2

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

    S1.1 and S1.2. Relative Standard Errors for Tables S1.1 and S1.2;" " Unit: Percents." " "," "," "," "," "," "," "," "," "," "," " " "," "," ",," "," ",," "," ",," ","Shipments" "SIC"," ",,"Net","Residual","Distillate",,"LPG

  14. RSE Table S2.1 and S2.2. Relative Standard Errors for Tables S2.1 and S2.2

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

    S2.1 and S2.2. Relative Standard Errors for Tables S2.1 and S2.2;" " Unit: Percents." " "," "," ",," "," "," "," "," "," ",," " "SIC"," "," ","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Major Group and Industry","Total","Fuel Oil","Fuel

  15. RSE Table S3.1 and S3.2. Relative Standard Errors for Tables S3.1 and S3.2

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

    S3.1 and S3.2. Relative Standard Errors for Tables S3.1 and S3.2;" " Unit: Percents." " "," " "SIC"," "," ","Net","Residual","Distillate",,"LPG and",,"Coke"," " "Code(a)","Major Group and Industry","Total","Electricity(b)","Fuel Oil","Fuel Oil(c)","Natural

  16. A molecular dynamics investigation of the diffusion characteristics...

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

    137, 83-91 (2011) DOI: 10.1016j.micromeso.2010.08.026 Full-size image (36 K) Abstract: Molecular dynamics (MD) simulations are used to investigate the diffusion characteristics...

  17. Chemical and light-stable isotope characteristics of waters from...

    Open Energy Info (EERE)

    light-stable isotope characteristics of waters from the raft river geothermal area and environs, Cassia County, Idaho, Box Elder county, Utah Jump to: navigation, search OpenEI...

  18. Characteristics and Effects of Lubricant Additive Chemistry and Exhaust

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

    Conditions on Diesel Particulate Filter Service Life and Vehicle Fuel Economy | Department of Energy Characteristics and Effects of Lubricant Additive Chemistry and Exhaust Conditions on Diesel Particulate Filter Service Life and Vehicle Fuel Economy Characteristics and Effects of Lubricant Additive Chemistry and Exhaust Conditions on Diesel Particulate Filter Service Life and Vehicle Fuel Economy qAsh accumulation is a dynamic process … Ash first primarily accumulates along channel walls

  19. System Design - Lessons Learned, Generic Concepts, Characteristics &

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

    Impacts | Department of Energy System Design - Lessons Learned, Generic Concepts, Characteristics & Impacts System Design - Lessons Learned, Generic Concepts, Characteristics & Impacts Presented at the DOE-DOD Shipboard APU Workshop on March 29, 2011. apu2011_11_hoffman.pdf (1.75 MB) More Documents & Publications Fuel Cell 101 DOE-DOD Shipboard APU Workshop Agenda Manufacturing Fuel Cell Manhattan

  20. Wafer Characteristics via Reflectometry and Wafer Processing Apparatus and

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

    Method - Energy Innovation Portal Solar Photovoltaic Solar Photovoltaic Find More Like This Return to Search Wafer Characteristics via Reflectometry and Wafer Processing Apparatus and Method National Renewable Energy Laboratory Contact NREL About This Technology Technology Marketing Summary Wafers find a variety of uses in the semiconductor, solar energy and other industries. Wafer quality often depends on variables such as thickness and surface characteristics. Depending on end use, poor

  1. NQA-1 Commercial Grade Dedication Critical Characteristics | Department

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

    of Energy NQA-1 Commercial Grade Dedication Critical Characteristics NQA-1 Commercial Grade Dedication Critical Characteristics May 5, 2015 Presenter: Randy P. Lanham, PE, CSP, Fire Protection Chief Engineer Consolidated Nuclear Solutions - Pantex, LLC Topics Covered: CGD Definition Safety Function / DSA Requirements Example of CGD for items Example form Questions Commercial-Grade Dedication (CGD) for acceptance of commercial grade items procured under an ASME NQA-1 Quality Program. NQA-1

  2. Particulate Matter Characteristics for Highly Dilute Stoichiometric GDI

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

    Engine Operations | Department of Energy Matter Characteristics for Highly Dilute Stoichiometric GDI Engine Operations Particulate Matter Characteristics for Highly Dilute Stoichiometric GDI Engine Operations The overall goal of this study is to help identify which conditions and potential mechanisms impede soot formation in GDI operations. p-24_storey.pdf (602.58 KB) More Documents & Publications Ethanol Effects on Lean-Burn and Stoichiometric GDI Emissions Effects of Advanced

  3. Distributed Generation System Characteristics and Costs in the Buildings Sector

    Gasoline and Diesel Fuel Update (EIA)

    Distributed Generation System Characteristics and Costs in the Buildings Sector August 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Distributed Generation System Characteristics and Costs in the Buildings Sector i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and

  4. An Experimental Study of PM Emission Characteristics of Commercial Diesel

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

    Engine with Urea-SCR System | Department of Energy Study of PM Emission Characteristics of Commercial Diesel Engine with Urea-SCR System An Experimental Study of PM Emission Characteristics of Commercial Diesel Engine with Urea-SCR System Poster presentation at the 2007 Diesel Engine-Efficiency & Emissions Research Conference (DEER 2007). 13-16 August, 2007, Detroit, Michigan. Sponsored by the U.S. Department of Energy's (DOE) Office of FreedomCAR and Vehicle Technologies (OFCVT).

  5. Flame and flow characteristics of double concentric jets

    SciTech Connect (OSTI)

    Huang, R.F.; Yang, J.T.; Lee, P.C.

    1997-01-01

    The characteristic flame and flow modes of a double concentric type of combustor possessing a central air jet and an annular propane gas are experimentally studied. Subject to the effects of the gravitational, inertial, and pressure forces, the cold flow is classified into three primary patterns: annular fountain, unstable fountain, and recirculation bubble flows. Using direct and schlieren photography techniques, the flames in the velocity domain of annulus and central jets are systematically classified into several characteristic modes. At low central jet velocity, a central flame enclosed in a annular diffusion flame might exist. At high central jet velocity, only the annular flames exist. The existence of the central flame dominates the flame and flow behaviors at low central jet velocity. The interaction between the central jet and the recirculation bubble in the near wake region dominates the flame characteristics at high central jet velocity. The interaction between the flame behavior and the flow patterns in each characteristic mode is comprehensively discussed. The temperature profiles are probed by a fine-wire thermocouple. The radial temperature profiles for each characteristic flame mode at various levels are presented to show the thermal structures.

  6. Measurement of physical characteristics of materials by ultrasonic methods

    DOE Patents [OSTI]

    Lu, W.Y.; Min, S.

    1998-09-08

    A method is described for determining and evaluating physical characteristics of a material. In particular, the present invention provides for determining and evaluating the anisotropic characteristics of materials, especially those resulting from such manufacturing processes as rolling, forming, extruding, drawing, forging, etc. In operation, a complex ultrasonic wave is created in the material of interest by any method. The wave form may be any combination of wave types and modes and is not limited to fundamental plate modes. The velocity of propagation of selected components which make up the complex ultrasonic wave are measured and evaluated to determine the physical characteristics of the material including, texture, strain/stress, grain size, crystal structure, etc. 14 figs.

  7. Optimization of distribution transformer efficiency characteristics. Final report, March 1979

    SciTech Connect (OSTI)

    Not Available

    1980-06-01

    A method for distribution transformer loss evaluation was derived. The total levalized annual cost method was used and was extended to account properly for conditions of energy cost inflation, peak load growth, and transformer changeout during the evaluation period. The loss costs included were the no-load and load power losses, no-load and load reactive losses, and the energy cost of regulation. The demand and energy components of loss costs were treated separately to account correctly for the diversity of load losses and energy cost inflation. The complete distribution transformer loss evaluation equation is shown, with the nomenclature and definitions for the parameters provided. Tasks described are entitled: Establish Loss Evaluation Techniques; Compile System Cost Parameters; Compile Load Parameters and Loading Policies; Develop Transformer Cost/Performance Relationship; Define Characteristics of Multiple Efficiency Transformer Package; Minimize Life Cycle Cost Based on Single Efficiency Characteristic Transformer Design; Minimize Life Cycle Cost Based on Multiple Efficiency Characteristic Transformer Design; and Interpretation.

  8. High pressure injection and atomization characteristics of methanol

    SciTech Connect (OSTI)

    Aigal, A.K.; Pundir, B.P.; Khatchian, A.S.

    1986-01-01

    Research on conversion of diesel engines for operation on methanol is, currently, of worldwide interest. Due to requirements of higher cyclic delivery of methanol and changes in fuel properties e.g. compressibility, wave propagation velocity, viscosity, surface tension, density etc., injection and atomization characteristics of methanol are expected to be different from diesel. From the equation of continuity and forces acting on the injection system elements and applying the principles of similarity, modifications required in the injection system were identified. Methanol injection and atomization characteristics were studied with a modified injection system and compared with those observed with diesel fuel. Methanol gave more favourable cyclic delivery characteristics than diesel. Laser diffraction technique was used to study time and space resolved drop size distribution in methanol and diesel sprays. With methanol, drop size distribution were, generally, much narrower and droplets were smaller than diesel. Spatial distribution of drop size in methanol spray showed somewhat different trends than for diesel.

  9. Measurement of physical characteristics of materials by ultrasonic methods

    DOE Patents [OSTI]

    Lu, Wei-yang (Pleasanton, CA); Min, Shermann (Livermore, CA)

    1998-01-01

    A method is described for determining and evaluating physical characteristics of a material. In particular, the present invention provides for determining and evaluating the anisotropic characteristics of materials, especially those resulting from such manufacturing processes as rolling, forming, extruding, drawing, forging, etc. In operation, a complex ultrasonic wave is created in the material of interest by any method. The wave form may be any combination of wave types and modes and is not limited to fundamental plate modes. The velocity of propagation of selected components which make up the complex ultrasonic wave are measured and evaluated to determine the physical characteristics of the material including, texture, strain/stress, grain size, crystal structure, etc.

  10. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update (EIA)

    questionnaires 2001 Average LPG Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 9.4 9.2 19.6 41 19 40.2 16 607 0.29 598 231 Census Region and

  11. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update (EIA)

    questionnaires 0 Average Natural Gas Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 57.7 44.8 106.3 109 46 84.2 32 609 0.26 472 181 Census Region

  12. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update (EIA)

    questionnaires 3 Average Natural Gas Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 58.7 46.0 111.9 115 47 89.9 34 696 0.29 546 206 Census Region

  13. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update (EIA)

    questionnaires Natural Gas, 1997 Average Natural Gas Residential Buildings Consumption Expenditures Total per Floor- per Square per per per Total Total space (1) Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 61.9 51.3 106.1 103 50 85.3 32 698 0.34

  14. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update (EIA)

    questionnaires 2001 Average Natural Gas Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 66.9 53.8 137.2 90 35 72.4 27 873 0.34 702 265 Census Region

  15. Table HC1.1.1 Housing Unit Characteristics by

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

    1 Housing Unit Characteristics by" " Total, Heated, and Cooled Floorspace, 2005" ,,,"Total Square Footage" ,"Housing Units",,"Total",,"Heated",,"Cooled" "Housing Unit Characteristics","Millions","Percent","Billions","Percent","Billions","Percent","Billions","Percent" "Total",111.1,100,256.5,100,179.8,100,114.5,100 "Census Region

  16. how-to-speed-up-traffic

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

    Housing Unit Tables (Million U.S. Households; 49 pages, 210 kb) Contents Pages HC1-1a. Housing Unit Characteristics by Climate Zone, Million U.S. Households, 2001 5 HC1-2a. Housing Unit Characteristics by Year of Construction, Million U.S. Households, 2001 4 HC1-3a. Housing Unit Characteristics by Household Income, Million U.S. Households, 2001 4 HC1-4a. Housing Unit Characteristics by Type of Housing Unit, Million U.S. Households, 2001 4 HC1-5a. Housing Unit Characteristics by Type of

  17. "Table A10. Total Consumption of LPG, Distillate Fuel Oil, and Residual Fuel"

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

    0. Total Consumption of LPG, Distillate Fuel Oil, and Residual Fuel" " Oil for Selected Purposes by Census Region and Economic Characteristics of the" " Establishment, 1991" " (Estimates in Barrels per Day)" ,,,," Inputs for Heat",,," Primary Consumption" " "," Primary Consumption for all Purposes",,," Power, and Generation of Electricity",,," for Nonfuel Purposes",,,"RSE" ,"

  18. "Table A33. Total Quantity of Purchased Energy Sources by Census Region, Census Division,"

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

    Quantity of Purchased Energy Sources by Census Region, Census Division," " and Economic Characteristics of the Establishment, 1994" " (Estimates in Btu or Physical Units)" ,,,,,"Natural",,,"Coke" " ","Total","Electricity","Residual","Distillate","Gas(c)"," ","Coal","and Breeze","Other(d)","RSE" "

  19. "Table A37. Total Expenditures for Purchased Energy Sources by Census Region,"

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

    7. Total Expenditures for Purchased Energy Sources by Census Region," " Census Division, and Economic Characteristics of the Establishment, 1994" " (Estimates in Million Dollars)" " "," "," "," ",," "," "," "," "," ","RSE" " "," "," ","Residual","Distillate","Natural"," ","

  20. Table 11.6 Electricity: Sales to Utility and Nonutility Purchasers, 2002

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

    Electricity: Sales to Utility and Nonutility Purchasers, 2002;" " Level: National and Regional Data; " " Row: Values of Shipments and Employment Sizes;" " Column: Utility and Nonutility Purchasers;" " Unit: Million Kilowatthours." ,"Total of",,,"RSE" "Economic","Sales and","Utility","Nonutility","Row" "Characteristic(a)","Transfers