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


1

Handbook of solar energy data for south-facing surfaces in the United States. Volume II. Average hourly and total daily insolation data for 235 localities (Alaska - Montana)  

DOE Green Energy (OSTI)

Average hourly and daily total insolaion estimates are given for 235 US sites at a variety of array tilt angles. (MHR)

Smith, J.H.

1980-01-15T23:59:59.000Z

2

Handbook of solar energy data for south-facing surfaces in the United States. Volume III. Average hourly and total daily insolation data for 235 localities (North Carolina - Wyoming)  

DOE Green Energy (OSTI)

Average hourly and daily total insolation estimates are given for 235 US sites at a variety of array tilt angles. (MHR)

Smith, J.H.

1980-01-15T23:59:59.000Z

3

Comparison of Daily Averaged Reflection, Transmission, and Absorption for Selected Radiative Flux Transfer Approximations  

Science Conference Proceedings (OSTI)

This paper compares accuracy for the daily averaged reflection, transmission, and absorption of solar flux derived from the delta-four-stream approximation and a few selected two-stream approximations. In the chosen variety of two-stream ...

Xun Zhu; Albert Arking

1994-12-01T23:59:59.000Z

4

IEP - Water-Energy Interface: Total Maximum Daily Load Page  

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

Total Maximum Daily Loads (TMDLs) Total Maximum Daily Loads (TMDLs) The overall goal of the Clean Water Act is to "restore and maintain the chemical, physical, and biological integrity of the Nation’s waters." In 1999, EPA proposed changes to Section 303(d), to establish Total Maximum Daily Loads (TMDLs) for watersheds that do not meet this goal. The TMDL is the highest amount of a given pollutant that is permissible in that body of water over a given period of time. TMDLs include both waste load allocation (WLA) for point sources and load allocations for non-point sources. In Appalachia, acid mine drainage (AMD) is the single most damaging non-point source. There is also particular concern of the atmospheric deposition of airborne sulfur, nitrogen, and mercury compounds. States are currently in the process of developing comprehensive lists of impaired waters and establishing TMDLs for those waters. EPA has recently proposed a final rule that will require states to develop TMDLs and implement plans for improving water quality within the next 10 years. Under the new rule, TMDL credits could be traded within a watershed.

5

Why should I recycle? The average American generates 4.5 pounds of waste daily.  

E-Print Network (OSTI)

Why should I recycle? The average American generates 4.5 pounds of waste daily. Instead of throwing throughout campus.These guidelines will help you recycle more and waste less. What's recyclable? · Mixed and plastic-coated papers · Tissue and paper towels · Paper or containers soiled by food or organic waste

Tsien, Roger Y.

6

Combined Total Amount of Oil and Gas Recovered Daily from the...  

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

Combined Total Amount of Oil and Gas Recovered Daily from the Top Hat and Choke Line oil recovery systems - XLS Combined Total Amount of Oil and Gas Recovered Daily from the Top...

7

Combined Total Amount of Oil and Gas Recovered Daily from the...  

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

Combined Total Amount of Oil and Gas Recovered Daily from the Top Hat and Choke Line oil recovery systems - ODS format Combined Total Amount of Oil and Gas Recovered Daily from the...

8

Predicted daily and yearly average radiative performance of hyperbolic spiral solar concentrators  

SciTech Connect

Some possible applications of solar energy, such as absorption cooling and air conditioning, process heating and preheating unconventional power production systems, require heat at temperatures higher than those associated with flat plate collectors, but below those associated with focussing collectors. Such a level of collection temperatures is economically obtained using non-imaging solar collectors. They are non-focussing, moderate concentrating ratio and trough-like collectors, which are usually arranged east-west, facing south or north. One of these concentrators is the hyperbolic spiral collector, which may be a semi- or compound one. It has been shown that the optical characteristics of semi- and compound hyperbolic spiral concentrators (SHSC and CHSC) are better than those of the compound parabolic one. In this work, the instantaneous radiative performance of both semi- and compound hyperbolic spiral concentrators are extended to average daily and yearly performance. Concentrators of various angles of acceptance are used in the analysis. Its effect upon the daily and yearly performance of the concentrator is discussed. The performance is also studied for various tilt adjustment routines. The results show that the number of tilt adjustments per year is an important factor affecting the daily and yearly performance of both SHSC and CHSC. It has been found that the SHSC is more affected by tilt adjustments than the compound one. The results also indicate that concentrators of small angle of acceptance are much affected by the number of adjustments. The results also show that there is not much difference between weekly and monthly adjustments.

Rabie, L.H.

1983-12-01T23:59:59.000Z

9

Trends in Total Precipitation and Frequency of Daily Precipitation Extremes over China  

Science Conference Proceedings (OSTI)

Based on a newly developed daily precipitation dataset of 740 stations in China and more robust trend detection techniques, trends in annual and seasonal total precipitation and in extreme daily precipitation, defined as those larger than its ...

Panmao Zhai; Xuebin Zhang; Hui Wan; Xiaohua Pan

2005-04-01T23:59:59.000Z

10

Time Series of Daily Averaged Cloud Fractions over Landfast First-Year Sea Ice from Multiple Data Sources  

Science Conference Proceedings (OSTI)

The time series of daily averaged cloud fractions (CFs) collected from different platformstwo Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on Terra and Aqua satellites, the National Centers for Environmental Prediction (NCEP)...

Xin Jin; John M. Hanesiak; David G. Barber

2007-11-01T23:59:59.000Z

11

Combined Total Amount of Oil and Gas Recovered Daily from the...  

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

XLS Combined Total Amount of Oil and Gas Recovered Daily from the Top Hat and Choke Line oil recovery systems - XLS Updated through 12:00 AM on July 16, 2010. 52Item84Recovery...

12

Combined Total Amount of Oil and Gas Recovered Daily from the...  

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

ODS format Combined Total Amount of Oil and Gas Recovered Daily from the Top Hat and Choke Line oil recovery systems - ODS format Updated through 12:00 AM on July 16, 2010....

13

Real-Time Forcast Model Analysis of Daily Average Building Load for a Thermal Storage System Control  

E-Print Network (OSTI)

Thermal storage systems were originally designed to shift the on-peak cooling production to off-peak cooling production to reduce the on-peak demand. Based on the current electricity charging structure, the reduction of both on-peak and off-peak demands is becoming an exceedingly important issue. Reduction of both on-peak and off-peak demands can also extend the life span and defer or eliminate the replacement of power transformers due to potential shortage of building power capacity with anticipated equipment load increases. The next day daily average electricity demand is a critical set point to operate chillers and associated pumps at the appropriate time. For this paper, a mathematic analysis was conducted for annual daily average cooling of a building and three real-time building load forecasting models were developed. They are first-order autogressive model, random walk model and linear regression model. Finally, the comparison of results show the random walk model provides the best forecast.

Song, L.; Joo, I. S.; Guwana, S.

2009-11-01T23:59:59.000Z

14

Stock mechanics: theory of conservation of total energy and predictions of coming short-term fluctuations of Dow Jones Industrials Average (DJIA)  

E-Print Network (OSTI)

Predicting absolute magnitude of fluctuations of price, even if their sign remains unknown, is important for risk analysis and for option prices. In the present work, we display our predictions about absolute magnitude of daily fluctuations of the Dow Jones Industrials Average (DJIA), utilizing the original theory of conservation of total energy, for the coming 500 days.

Tuncay, C

2006-01-01T23:59:59.000Z

15

"2012 Total Electric Industry- Average Retail Price (cents/kWh)"  

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

Average Retail Price (cents/kWh)" Average Retail Price (cents/kWh)" "(Data from forms EIA-861- schedules 4A-D, EIA-861S and EIA-861U)" "State","Residential","Commercial","Industrial","Transportation","Total" "New England",15.713593,13.679941,11.83487,6.6759453,14.017926 "Connecticut",17.343298,14.652335,12.672933,9.6930118,15.54464 "Maine",14.658797,11.52742,7.9819499,".",11.812709 "Massachusetts",14.912724,13.841518,12.566635,4.9056852,13.78825 "New Hampshire",16.070168,13.36121,11.83228,".",14.192854 "Rhode Island",14.404061,11.867247,10.676724,8.2796427,12.740867 "Vermont",17.006075,14.316157,9.9796777,".",14.220244

16

Relative Accuracy of 1-Minute and Daily Total Solar Radiation Data for 12 Global and 4 Direct Beam Solar Radiometers  

Science Conference Proceedings (OSTI)

We evaluated the relative performance of 12 global and four direct beam solar radiometers deployed at a single site over a 12-month period. Test radiometer irradiances were compared with a reference irradiance consisting of either an absolute cavity radiometer (during calibrations) or a low uncertainty thermopile pyrheliometer (during the evaluation period) for pyrheliometers; and for pyranometers a reference global irradiance computed from the reference pyrheliometer and diffuse irradiance from a shaded pyranometer. One minute averages of 3-second data for 12 months from the test instrument measurements were compared with the computed reference data set. Combined uncertainty in the computed reference irradiance is 1.8% {+-} 0.5%. Total uncertainty in the pyranometer comparisons is {+-}2.5%. We show mean percent difference between reference global irradiance and test pyranometer 1 minute data as a function of zenith angle, and percent differences between daily totals for the reference and test irradiances as a function of day number. We offer no explicit conclusion about the performance of instrument models, as a general array of applications with a wide range of instrumentation and accuracy requirements could be addressed with any of the radiometers.

Myers, D.; Wilcox, S. M.

2009-01-01T23:59:59.000Z

17

Solar: monthly and annual average global horizontal (GHI) GIS...  

Open Energy Info (EERE)

on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and...

18

Solar: monthly and annual average direct normal (DNI), global...  

Open Energy Info (EERE)

on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and...

19

Solar: monthly and annual average direct normal (DNI) GIS data...  

Open Energy Info (EERE)

on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and...

20

Solar: annual average direct normal (DNI) GIS data at 10km resolution...  

Open Energy Info (EERE)

visible irradiance, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total of the normal or beam...

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


21

Solar: monthly and annual average latitude tilt GIS data at 40km...  

Open Energy Info (EERE)

on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and...

22

Total..........................................................  

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

Housing Units (millions) Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Census Division Total South...

23

Total..........................................................  

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

Division Total West Mountain Pacific Energy Information Administration: 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing...

24

Total..........................................................  

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

(millions) Census Division Total South Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC13.7...

25

Total..........................................................  

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

Census Division Total Midwest Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC12.7...

26

Total..........................................................  

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

Census Division Total Northeast Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC11.7...

27

Total..........................................................  

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

Census Division Total South Energy Information Administration: 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing...

28

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

(millions) Census Division Total West Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC14.7...

29

Total  

Gasoline and Diesel Fuel Update (EIA)

Total Total .............. 16,164,874 5,967,376 22,132,249 2,972,552 280,370 167,519 18,711,808 1993 Total .............. 16,691,139 6,034,504 22,725,642 3,103,014 413,971 226,743 18,981,915 1994 Total .............. 17,351,060 6,229,645 23,580,706 3,230,667 412,178 228,336 19,709,525 1995 Total .............. 17,282,032 6,461,596 23,743,628 3,565,023 388,392 283,739 19,506,474 1996 Total .............. 17,680,777 6,370,888 24,051,665 3,510,330 518,425 272,117 19,750,793 Alabama Total......... 570,907 11,394 582,301 22,601 27,006 1,853 530,841 Onshore ................ 209,839 11,394 221,233 22,601 16,762 1,593 180,277 State Offshore....... 209,013 0 209,013 0 10,244 260 198,509 Federal Offshore... 152,055 0 152,055 0 0 0 152,055 Alaska Total ............ 183,747 3,189,837 3,373,584 2,885,686 0 7,070 480,828 Onshore ................ 64,751 3,182,782

30

Total............................................................  

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

Total................................................................... Total................................................................... 111.1 2,033 1,618 1,031 791 630 401 Total Floorspace (Square Feet) Fewer than 500............................................... 3.2 357 336 113 188 177 59 500 to 999....................................................... 23.8 733 667 308 343 312 144 1,000 to 1,499................................................. 20.8 1,157 1,086 625 435 409 235 1,500 to 1,999................................................. 15.4 1,592 1,441 906 595 539 339 2,000 to 2,499................................................. 12.2 2,052 1,733 1,072 765 646 400 2,500 to 2,999................................................. 10.3 2,523 2,010 1,346 939 748 501 3,000 to 3,499................................................. 6.7 3,020 2,185 1,401 1,177 851 546

31

Total...................  

Gasoline and Diesel Fuel Update (EIA)

4,690,065 52,331,397 2,802,751 4,409,699 7,526,898 209,616 1993 Total................... 4,956,445 52,535,411 2,861,569 4,464,906 7,981,433 209,666 1994 Total................... 4,847,702 53,392,557 2,895,013 4,533,905 8,167,033 202,940 1995 Total................... 4,850,318 54,322,179 3,031,077 4,636,500 8,579,585 209,398 1996 Total................... 5,241,414 55,263,673 3,158,244 4,720,227 8,870,422 206,049 Alabama ...................... 56,522 766,322 29,000 62,064 201,414 2,512 Alaska.......................... 16,179 81,348 27,315 12,732 75,616 202 Arizona ........................ 27,709 689,597 28,987 49,693 26,979 534 Arkansas ..................... 46,289 539,952 31,006 67,293 141,300 1,488 California ..................... 473,310 8,969,308 235,068 408,294 693,539 36,613 Colorado...................... 110,924 1,147,743

32

Reconsideration of EPAs Approval of Vermonts 2002 Lake Champlain Phosphorus Total Maximum Daily Load (TMDL) and Determination to Disapprove the TMDL  

E-Print Network (OSTI)

Section 303(d) of the Clean Water Act (Act) requires states to identify waters that do not or are not expected to meet applicable water quality standards after imposition of technology-based controls alone. In that event, the waters are considered impaired, and must be identified or listed under Section 303(d) of the Act. Once such waters are identified, states are to develop TMDLs for any pollutant that is causing the impairment, at a level necessary to attain and maintain the applicable state water quality standards with seasonal variations and a margin of safety that accounts for any lack of knowledge concerning the relationship between effluent limitations and water quality. The total maximum daily load that applies to a water segment is the sum of the load allocations (LA) of pollutants from nonpoint sources, the wasteload allocations (WLA) of pollutants from point sources, and a margin of safety. 1 See 40 C.F.R. 130.2(g)-(i), 130.2(c)(1). Once the public has had the opportunity to review and comment on such TMDLs, states are required to submit the TMDLs to EPA for review and approval. If EPA disapproves a TMDL, it must then establish the TMDL at the level necessary to implement the applicable water quality standards and the state must incorporate the TMDL into its continuing planning process.

A. Statutory; Regulatory Background

2011-01-01T23:59:59.000Z

33

Total..........................................................................  

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

25.6 25.6 40.7 24.2 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.5 0.9 1.0 500 to 999........................................................... 23.8 4.6 3.9 9.0 6.3 1,000 to 1,499..................................................... 20.8 2.8 4.4 8.6 5.0 1,500 to 1,999..................................................... 15.4 1.9 3.5 6.0 4.0 2,000 to 2,499..................................................... 12.2 2.3 3.2 4.1 2.6 2,500 to 2,999..................................................... 10.3 2.2 2.7 3.0 2.4 3,000 to 3,499..................................................... 6.7 1.6 2.1 2.1 0.9 3,500 to 3,999..................................................... 5.2 1.1 1.7 1.5 0.9 4,000 or More.....................................................

34

Total..........................................................................  

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

4.2 4.2 7.6 16.6 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 1.0 0.2 0.8 500 to 999........................................................... 23.8 6.3 1.4 4.9 1,000 to 1,499..................................................... 20.8 5.0 1.6 3.4 1,500 to 1,999..................................................... 15.4 4.0 1.4 2.6 2,000 to 2,499..................................................... 12.2 2.6 0.9 1.7 2,500 to 2,999..................................................... 10.3 2.4 0.9 1.4 3,000 to 3,499..................................................... 6.7 0.9 0.3 0.6 3,500 to 3,999..................................................... 5.2 0.9 0.4 0.5 4,000 or More.....................................................

35

Total.........................................................................  

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

Floorspace (Square Feet) Floorspace (Square Feet) Total Floorspace 2 Fewer than 500.................................................. 3.2 Q 0.8 0.9 0.8 0.5 500 to 999.......................................................... 23.8 1.5 5.4 5.5 6.1 5.3 1,000 to 1,499.................................................... 20.8 1.4 4.0 5.2 5.0 5.2 1,500 to 1,999.................................................... 15.4 1.4 3.1 3.5 3.6 3.8 2,000 to 2,499.................................................... 12.2 1.4 3.2 3.0 2.3 2.3 2,500 to 2,999.................................................... 10.3 1.5 2.3 2.7 2.1 1.7 3,000 to 3,499.................................................... 6.7 1.0 2.0 1.7 1.0 1.0 3,500 to 3,999.................................................... 5.2 0.8 1.5 1.5 0.7 0.7 4,000 or More.....................................................

36

Total..........................................................................  

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

. . 111.1 20.6 15.1 5.5 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.5 0.4 500 to 999........................................................... 23.8 4.6 3.6 1.1 1,000 to 1,499..................................................... 20.8 2.8 2.2 0.6 1,500 to 1,999..................................................... 15.4 1.9 1.4 0.5 2,000 to 2,499..................................................... 12.2 2.3 1.7 0.5 2,500 to 2,999..................................................... 10.3 2.2 1.7 0.6 3,000 to 3,499..................................................... 6.7 1.6 1.0 0.6 3,500 to 3,999..................................................... 5.2 1.1 0.9 0.3 4,000 or More.....................................................

37

Total..........................................................................  

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

7.1 7.1 7.0 8.0 12.1 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.4 Q Q 0.5 500 to 999........................................................... 23.8 2.5 1.5 2.1 3.7 1,000 to 1,499..................................................... 20.8 1.1 2.0 1.5 2.5 1,500 to 1,999..................................................... 15.4 0.5 1.2 1.2 1.9 2,000 to 2,499..................................................... 12.2 0.7 0.5 0.8 1.4 2,500 to 2,999..................................................... 10.3 0.5 0.5 0.4 1.1 3,000 to 3,499..................................................... 6.7 0.3 Q 0.4 0.3 3,500 to 3,999..................................................... 5.2 Q Q Q Q 4,000 or More.....................................................

38

Total..........................................................................  

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

7.1 7.1 19.0 22.7 22.3 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 2.1 0.6 Q 0.4 500 to 999........................................................... 23.8 13.6 3.7 3.2 3.2 1,000 to 1,499..................................................... 20.8 9.5 3.7 3.4 4.2 1,500 to 1,999..................................................... 15.4 6.6 2.7 2.5 3.6 2,000 to 2,499..................................................... 12.2 5.0 2.1 2.8 2.4 2,500 to 2,999..................................................... 10.3 3.7 1.8 2.8 2.1 3,000 to 3,499..................................................... 6.7 2.0 1.4 1.7 1.6 3,500 to 3,999..................................................... 5.2 1.6 0.8 1.5 1.4 4,000 or More.....................................................

39

Total..........................................................................  

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

0.7 0.7 21.7 6.9 12.1 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.6 Q Q 500 to 999........................................................... 23.8 9.0 4.2 1.5 3.2 1,000 to 1,499..................................................... 20.8 8.6 4.7 1.5 2.5 1,500 to 1,999..................................................... 15.4 6.0 2.9 1.2 1.9 2,000 to 2,499..................................................... 12.2 4.1 2.1 0.7 1.3 2,500 to 2,999..................................................... 10.3 3.0 1.8 0.5 0.7 3,000 to 3,499..................................................... 6.7 2.1 1.2 0.5 0.4 3,500 to 3,999..................................................... 5.2 1.5 0.8 0.3 0.4 4,000 or More.....................................................

40

Total..........................................................  

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

.. .. 111.1 24.5 1,090 902 341 872 780 441 Total Floorspace (Square Feet) Fewer than 500...................................... 3.1 2.3 403 360 165 366 348 93 500 to 999.............................................. 22.2 14.4 763 660 277 730 646 303 1,000 to 1,499........................................ 19.1 5.8 1,223 1,130 496 1,187 1,086 696 1,500 to 1,999........................................ 14.4 1.0 1,700 1,422 412 1,698 1,544 1,348 2,000 to 2,499........................................ 12.7 0.4 2,139 1,598 Q Q Q Q 2,500 to 2,999........................................ 10.1 Q Q Q Q Q Q Q 3,000 or More......................................... 29.6 0.3 Q Q Q Q Q Q Heated Floorspace (Square Feet) None...................................................... 3.6 1.8 1,048 0 Q 827 0 407 Fewer than 500......................................

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


41

Total...................................................................  

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

2,033 2,033 1,618 1,031 791 630 401 Total Floorspace (Square Feet) Fewer than 500............................................... 3.2 357 336 113 188 177 59 500 to 999....................................................... 23.8 733 667 308 343 312 144 1,000 to 1,499................................................. 20.8 1,157 1,086 625 435 409 235 1,500 to 1,999................................................. 15.4 1,592 1,441 906 595 539 339 2,000 to 2,499................................................. 12.2 2,052 1,733 1,072 765 646 400 2,500 to 2,999................................................. 10.3 2,523 2,010 1,346 939 748 501 3,000 to 3,499................................................. 6.7 3,020 2,185 1,401 1,177 851 546 3,500 to 3,999................................................. 5.2 3,549 2,509 1,508

42

Total...........................................................  

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

26.7 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................... 3.2 1.9 0.9 Q Q Q 1.3 2.3 500 to 999........................................... 23.8 10.5 7.3 3.3 1.4 1.2 6.6 12.9 1,000 to 1,499..................................... 20.8 5.8 7.0 3.8 2.2 2.0 3.9 8.9 1,500 to 1,999..................................... 15.4 3.1 4.2 3.4 2.0 2.7 1.9 5.0 2,000 to 2,499..................................... 12.2 1.7 2.7 2.9 1.8 3.2 1.1 2.8 2,500 to 2,999..................................... 10.3 1.2 2.2 2.3 1.7 2.9 0.6 2.0 3,000 to 3,499..................................... 6.7 0.9 1.4 1.5 1.0 1.9 0.4 1.4 3,500 to 3,999..................................... 5.2 0.8 1.2 1.0 0.8 1.5 0.4 1.3 4,000 or More...................................... 13.3 0.9 1.9 2.2 2.0 6.4 0.6 1.9 Heated Floorspace

43

Total...........................................................  

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

14.7 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500.................................... 3.2 0.7 Q 0.3 0.3 0.7 0.6 0.3 Q 500 to 999........................................... 23.8 2.7 1.4 2.2 2.8 5.5 5.1 3.0 1.1 1,000 to 1,499..................................... 20.8 2.3 1.4 2.4 2.5 3.5 3.5 3.6 1.6 1,500 to 1,999..................................... 15.4 1.8 1.4 2.2 2.0 2.4 2.4 2.1 1.2 2,000 to 2,499..................................... 12.2 1.4 0.9 1.8 1.4 2.2 2.1 1.6 0.8 2,500 to 2,999..................................... 10.3 1.6 0.9 1.1 1.1 1.5 1.5 1.7 0.8 3,000 to 3,499..................................... 6.7 1.0 0.5 0.8 0.8 1.2 0.8 0.9 0.8 3,500 to 3,999..................................... 5.2 1.1 0.3 0.7 0.7 0.4 0.5 1.0 0.5 4,000 or More...................................... 13.3

44

Total................................................  

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

.. .. 111.1 86.6 2,522 1,970 1,310 1,812 1,475 821 1,055 944 554 Total Floorspace (Square Feet) Fewer than 500............................. 3.2 0.9 261 336 162 Q Q Q 334 260 Q 500 to 999.................................... 23.8 9.4 670 683 320 705 666 274 811 721 363 1,000 to 1,499.............................. 20.8 15.0 1,121 1,083 622 1,129 1,052 535 1,228 1,090 676 1,500 to 1,999.............................. 15.4 14.4 1,574 1,450 945 1,628 1,327 629 1,712 1,489 808 2,000 to 2,499.............................. 12.2 11.9 2,039 1,731 1,055 2,143 1,813 1,152 Q Q Q 2,500 to 2,999.............................. 10.3 10.1 2,519 2,004 1,357 2,492 2,103 1,096 Q Q Q 3,000 or 3,499.............................. 6.7 6.6 3,014 2,175 1,438 3,047 2,079 1,108 N N N 3,500 to 3,999.............................. 5.2 5.1 3,549 2,505 1,518 Q Q Q N N N 4,000 or More...............................

45

Solar: monthly and annual average direct normal (DNI), global horizontal  

Open Energy Info (EERE)

South America from NREL South America from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to solar collectors. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

46

Solar: monthly and annual average direct normal (DNI), global horizontal  

Open Energy Info (EERE)

Central America and the Carribean from NREL Central America and the Carribean from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to solar collectors. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

47

Solar: monthly and annual average direct normal (DNI), global horizontal  

Open Energy Info (EERE)

East Asia from NREL East Asia from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to solar collectors. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

48

Solar: monthly and annual average direct normal (DNI), global horizontal  

Open Energy Info (EERE)

Africa from NREL Africa from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal and tilted flat-plates, and 2-axis tracking concentrating collectors. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to solar collectors. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

49

DOE Average Results  

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

DOE DOE Average Results FY 12 DOE Target FY 12 Customer Perspective: Customer Satisfaction: -Timeliness 92 88 -Quality 94 92 Effective Service Partnership: -Extent of Customer Satisfaction with the responsiveness, etc. 90 92 Internal Business Perspective: Acquisition Excellence: -Extent to which internal quality control systems are effective 90 88 Most Effective Use of Contracting Approaches to Maximize Efficiency and Cost Effectiveness: Use of Competition: -% of total $'s obligated on competitive acquisitions >$3000 (Agency Level Only) 94 85 -% of acquisition actions competed for actions > $3000 (Agency Level Only) 65 68 Performance Based Acquisition: - % PBA actions relative to total eligible new acquisition actions (applicable to new actions > $25K) 82

50

Temperature Effects on the Winter Daily Electric Load  

Science Conference Proceedings (OSTI)

Here we describe the relationship between average daily temperature and winter-daily electric load, as ascertained on the largest electric district in Italy. In particular, it is shown that a sudden 6C temperature decrease (not a rare event) ...

Paolo Bolzern; Giorgio Fronza; Giuseppe Brusasca

1982-02-01T23:59:59.000Z

51

Daily Occurrence Reports  

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

Occurrence Reporting and Processing System Home ORPS Database Access Daily Occurrence Reports Weekly Summary of Significant Occurrences Occurrence Reporting Quality ORPS Training...

52

Background: Long-Term Daily and Monthly Climate Records from Stations  

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

Background: Long-Term Daily and Monthly Climate Records from Stations Background: Long-Term Daily and Monthly Climate Records from Stations Across the Contiguous United States The United States Historical Climatology Network (USHCN) is a high-quality data set of daily and monthly records of basic meteorological variables from 1218 observing stations across the 48 contiguous United States. Daily data include observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth; monthly data consist of monthly-averaged maximum, minimum, and mean temperature and total monthly precipitation. Most of these stations are U.S. Cooperative Observing Network stations located generally in rural locations, while some are National Weather Service First-Order stations that are often located in more urbanized environments. The USHCN has been developed over the years at

53

Solar: monthly and annual average direct normal (DNI) GIS data at 40km  

Open Energy Info (EERE)

22 22 Varnish cache server Solar: monthly and annual average direct normal (DNI) GIS data at 40km resolution for Mexico, Central America, and the Caribbean Islands from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Mexico, Central America, and the Caribbean Islands. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is

54

Solar: monthly and annual average direct normal (DNI) GIS data at 40km  

Open Energy Info (EERE)

49031 49031 Varnish cache server Solar: monthly and annual average direct normal (DNI) GIS data at 40km resolution for China from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for China. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to

55

Average Commercial Price  

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

Residential Price Average Commercial Price Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes...

56

AVERAGE SHIFTED HISTOGRAM  

Science Conference Proceedings (OSTI)

... LET YPPF = XCDF LET XPPF = YCDF. Default: None Synonyms: ASH is a synonym for the AVERAGE SHIFTED HISTOGRAM command. ...

2010-12-06T23:59:59.000Z

57

Climate Reference Network Daily01 Product | Data.gov  

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

Daily01 Product Daily01 Product Agriculture Community Menu DATA APPS EVENTS DEVELOPER STATISTICS COLLABORATE ABOUT Agriculture You are here Data.gov » Communities » Agriculture » Data Climate Reference Network Daily01 Product Dataset Summary Description The U.S. Climate Reference Network is designed specifically to monitor national climate change with best scientific practice and adherence to the accepted principles of climate observations. USCRN daily temperature mean, maximum, and minimum, daily precipitation, daily global solar radiation, and daily average surface infrared temperature data are available in the Daily01 file set for all stations in the network. Daily mean, maximum, and minimum relative humidity are available for most stations. Tags {"Climate Reference Network",USCRN,CRN,"air temperature",temperature,precipitation,"global solar radiation"," surface temperature","surface infrared temperature","relative humidity","natural resources",water,air,"soil "}

58

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

Mexico, Central America, and the Caribbean Islands from NREL Mexico, Central America, and the Caribbean Islands from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal flat-plate collectors, for Mexico, Central America, and the Caribbean Islands. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate

59

Solar: annual average global horizontal (GHI) GIS data at 10km resolution  

Open Energy Info (EERE)

global horizontal (GHI) GIS data at 10km resolution global horizontal (GHI) GIS data at 10km resolution for Cuba from SUNY Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal flat-plate solar collectors for Cuba (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a horizontal flat-plate solar collector, such as a Photovoltaic (PV) solar panel. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 10 km by 10 km in size. The solar resource value is represented as kilowatt-hours per square meter per day for each month. The data were developed from the State University of New York's (SUNY) GOES satellite solar model. This model uses information on hourly satellite observed visible irradiance, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total of the normal or beam insolation falling on a tracking concentrator pointed

60

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

Ethiopia from NREL Ethiopia from NREL Dataset Summary Description (Abstract): Monthly average solar resource for horizontal flat-plate collectors for Ethiopia. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the

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


61

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

China from NREL China from NREL Dataset Summary Description (Abstract): Monthly average solar resource for horizontal flat-plate collectors for China. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations are used to validate the data where possible. The modeled values are accurate to approximately 10% of a true measured value within the grid cell due to the uncertainties associated with meteorological input to the model. The local cloud cover can vary significantly even within a single grid cell as a result of terrain effects and other microclimate influences. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

62

Solar: monthly and annual average latitude tilt GIS data at 40km resolution  

Open Energy Info (EERE)

China from NREL China from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for flat-plate collectors tilted at latitude for China. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. Existing ground measurement stations

63

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

Brazil from NREL Brazil from NREL Dataset Summary Description (Abstract): Monthly average solar resource for horizontal flat-plate collectors for Brazil. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

64

Solar: monthly and annual average direct normal (DNI) GIS data at 40km for  

Open Energy Info (EERE)

km for km for Sri Lanka from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Sri Lanka (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

65

Solar: monthly and annual average direct normal (DNI) GIS data at 40km  

Open Energy Info (EERE)

Kenya from NREL Kenya from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Kenya. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

66

Solar: monthly and annual average direct normal (DNI) GIS data at 40km  

Open Energy Info (EERE)

Nepal from NREL Nepal from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Nepal. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

67

Solar: monthly and annual average latitude tilt GIS data at 40km resolution  

Open Energy Info (EERE)

Ghana from NREL Ghana from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for flat-plate collectors tilted at latitude for Ghana. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water vapor and trace gases, and the amount of aerosols in the atmosphere to

68

Solar: monthly and annual average direct normal (DNI) GIS data at 40km  

Open Energy Info (EERE)

Ethiopia from NREL Ethiopia from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Ethiopia. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

69

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

Nepal from NREL Nepal from NREL Dataset Summary Description (Abstract): Monthly average solar resource for horizontal flat-plate collectors for Nepal. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

70

Solar: monthly and annual average direct normal (DNI) GIS data at 40km  

Open Energy Info (EERE)

Ghana from NREL Ghana from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Ghana. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

71

Solar: monthly and annual average latitude tilt GIS data at 40km resolution  

Open Energy Info (EERE)

Mexico, Central America, and the Caribbean Islands from NREL Mexico, Central America, and the Caribbean Islands from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for flat-plate collectors tilted at latitude, for Mexico, Central America, and the Caribbean Islands. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The

72

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

Ghana from NREL Ghana from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal flat-plate collectors for Ghana. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

73

Solar: monthly and annual average direct normal (DNI) GIS data at 40km  

Open Energy Info (EERE)

Brazil from NREL Brazil from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Brazil. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

74

Solar: annual average direct normal (DNI) GIS data at 10km resolution for  

Open Energy Info (EERE)

GIS data at 10km resolution for GIS data at 10km resolution for Cuba from SUNY Dataset Summary Description (Abstract): Monthly Average Solar Resource for 2-axis tracking concentrating collectors for Cuba (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a concentrating collector, such as a dish collector, which tracks the sun continuously. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 10 km by 10 km in size. The solar resource value is represented as kilowatt-hours per square meter per day for each month. The data were developed from the State University of New York's (SUNY) GOES satellite solar model. This

75

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

Kenya from NREL Kenya from NREL Dataset Summary Description (Abstract): Monthly average solar resource for horizontal flat-plate collectors for Kenya. (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

76

Solar: monthly and annual average global horizontal (GHI) GIS data at 40km  

Open Energy Info (EERE)

Sri Lanka from NREL Sri Lanka from NREL Dataset Summary Description (Abstract): Monthly Average Solar Resource for horizontal flat-plate collectors, for Sri Lanka (Purpose): Provide information on the solar resource potential for the data domain. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented horizontally. (Supplemental Information): These data provide monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. The solar resource value is represented as watt-hours per square meter per day for each month. The data were developed from NREL's Climatological Solar Radiation (CSR) Model. This model uses information on cloud cover, atmospheric water

77

Core Measure Average KTR Results  

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

Measure Measure Average KTR Results FY 12 Target FY 12 DOE M&O CONTRACTOR (KTR) BSC RESULTS FY 2012 Customer Perspective and level of communication provided by the procurement office 95 92 Internal Business Perspective: Assessment (%) of the degree to which the purchasing system is in compliance with stakeholder requirements 97 Local Goals % Delivery on-time (includes JIT, excludes Purchase Cards) 88 84 % of total dollars obligated, on actions > $150K , that were awarded using effective competition 73 Local Goals Rapid Purchasing Techniques: -% of transactions placed by users 77 Local Goals -% of transactions placed through electronic commerce 62 Local Goals Average Cycle Time: -Average cycle time for <= $150K 8 6 to 9 days

78

Daily Temperature Lag  

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

Daily Temperature Lag Daily Temperature Lag Name: Shyammayi Status: teacher Grade: K-2 Country: Mauritius Date: Summer 2011 Question: At what time of the day is the temperature hottest? At what time of the day is the temperature coldest? Replies: In general, the hottest part of the day is late afternoon. The sun has passed its peak in the sky but still heats the Earth up until very late in the afternoon. The lowest temperatures are around dawn. Earth has had all night to get rid of the day's heat by radiating it into space. After sunrise, temperatures begin to climb. This can be changed by local storms, sea breezes or mountain breezes and even monsoon winds. Hope this helps. R. W. "Bob" Avakian Instructor Arts and Sciences/CRC Oklahoma State Univ. Inst. of Technology Shyammayi

79

average | OpenEI  

Open Energy Info (EERE)

average average Dataset Summary Description This dataset is part of a larger internal dataset at the National Renewable Energy Laboratory (NREL) that explores various characteristics of large solar electric (both PV and CSP) facilities around the United States. This dataset focuses on the land use characteristics for solar facilities that are either under construction or currently in operation. Source Land-Use Requirements for Solar Power Plants in the United States Date Released June 25th, 2013 (7 months ago) Date Updated Unknown Keywords acres area average concentrating solar power csp Density electric hectares km2 land land requirements land use land-use mean photovoltaic photovoltaics PV solar statistics Data application/vnd.openxmlformats-officedocument.spreadsheetml.sheet icon Master Solar Land Use Spreadsheet (xlsx, 1.5 MiB)

80

Variable Average Absolute Percent Differences  

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

Variable Variable Average Absolute Percent Differences Percent of Projections Over- Estimated Gross Domestic Product Real Gross Domestic Product (Average Cumulative Growth)* (Table 2) 1.0 42.6 Petroleum Imported Refiner Acquisition Cost of Crude Oil (Constant $) (Table 3a) 35.2 18.6 Imported Refiner Acquisition Cost of Crude Oil (Nominal $) (Table 3b) 34.7 19.7 Total Petroleum Consumption (Table 4) 6.2 66.5 Crude Oil Production (Table 5) 6.0 59.6 Petroleum Net Imports (Table 6) 13.3 67.0 Natural Gas Natural Gas Wellhead Prices (Constant $) (Table 7a) 30.7 26.1 Natural Gas Wellhead Prices (Nominal $) (Table 7b) 30.0 27.1 Total Natural Gas Consumption (Table 8) 7.8 70.2 Natural Gas Production (Table 9) 7.1 66.0 Natural Gas Net Imports (Table 10) 29.3 69.7 Coal Coal Prices to Electric Generating Plants (Constant $)** (Table 11a)

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


81

A Probabilistic Forecast Approach for Daily Precipitation Totals  

Science Conference Proceedings (OSTI)

Commonly, postprocessing techniques are employed to calibrate a model forecast. Here, a probabilistic postprocessor is presented that provides calibrated probability and quantile forecasts of precipitation on the local scale. The forecasts are ...

Petra Friederichs; Andreas Hense

2008-08-01T23:59:59.000Z

82

Average Residential Price  

Gasoline and Diesel Fuel Update (EIA)

Citygate Price Residential Price Commercial Price Industrial Price Electric Power Price Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Gas in Underground Storage Base Gas in Underground Storage Working Gas in Underground Storage Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

83

Average Commercial Price  

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

Citygate Price Residential Price Commercial Price Industrial Price Electric Power Price Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Gas in Underground Storage Base Gas in Underground Storage Working Gas in Underground Storage Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

84

Average Commercial Price  

Gasoline and Diesel Fuel Update (EIA)

Citygate Price Residential Price Commercial Price Industrial Price Electric Power Price Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Gas in Underground Storage Base Gas in Underground Storage Working Gas in Underground Storage Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

85

Average Residential Price  

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

Citygate Price Residential Price Commercial Price Industrial Price Electric Power Price Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Gas in Underground Storage Base Gas in Underground Storage Working Gas in Underground Storage Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

86

Census Division Number of Average Monthly Average Retail Price...  

Gasoline and Diesel Fuel Update (EIA)

Average Monthly Average Retail Price Average Monthly Bill State Consumers Consumption (kWh) (Cents per Kilowatthour) (Dollar and cents) New England 34,271 67,907 12.55 8,520.25...

87

Variability in daily, zonal mean lower-stratospheric temperatures  

Science Conference Proceedings (OSTI)

Satellite data from the microwave sounding unit (MSU) channel 4, when carefully merged, provide daily zonal anomalies of lower-stratosphere temperature with a level of precision between 0.01[degrees] and 0.08[degrees]C per 2.5[degrees] latitude band. Global averages of these daily zonal anomalies reveal the prominent warming events due to volcanic aerosol in 1982 (El Chichon) and 1991 (Mt. Pinatubo), which are on the order of 1[degrees]C. The quasibiennel oscillation (QBO) may be extracted from these zonal data by applying a spatial filter between 15[degrees]N and 15[degrees]S latitude, which resembles the meridional curvature. Previously published relationships between the QBO and the north polar stratospheric temperatures during northern winter are examined but were not found to be reproduced in the MSU4 data. Sudden stratospheric warmings in the north polar region are represented in the MSU4 data for latitudes poleward of 70[degrees]N. In the Southern Hemisphere, there appears to be a moderate relationship between total ozone concentration and MSU4 temperatures, though it has been less apparent in 1991 and 1992. In terms of empirical modes of variability revealed significant power in the 15-20 day period band.

Christy, J.R. (Univ. of Alabama, Huntsville, AL (United States)); Drouilhet, S.J. Jr. (Moorhead State Univ., MN (United States))

1994-01-01T23:59:59.000Z

88

Average Residential Price  

Gasoline and Diesel Fuel Update (EIA)

Pipeline and Distribution Use Price Citygate Price Residential Price Commercial Price Industrial Price Vehicle Fuel Price Electric Power Price Proved Reserves as of 12/31 Reserves Adjustments Reserves Revision Increases Reserves Revision Decreases Reserves Sales Reserves Acquisitions Reserves Extensions Reserves New Field Discoveries New Reservoir Discoveries in Old Fields Estimated Production Number of Producing Gas Wells Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production Natural Gas Processed NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals LNG Storage Additions LNG Storage Withdrawals LNG Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Lease Fuel Plant Fuel Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

89

Average Commercial Price  

Gasoline and Diesel Fuel Update (EIA)

Pipeline and Distribution Use Price Citygate Price Residential Price Commercial Price Industrial Price Vehicle Fuel Price Electric Power Price Proved Reserves as of 12/31 Reserves Adjustments Reserves Revision Increases Reserves Revision Decreases Reserves Sales Reserves Acquisitions Reserves Extensions Reserves New Field Discoveries New Reservoir Discoveries in Old Fields Estimated Production Number of Producing Gas Wells Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production Natural Gas Processed NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals LNG Storage Additions LNG Storage Withdrawals LNG Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Lease Fuel Plant Fuel Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

90

Average Residential Price  

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

Pipeline and Distribution Use Price Citygate Price Residential Price Commercial Price Industrial Price Vehicle Fuel Price Electric Power Price Proved Reserves as of 12/31 Reserves Adjustments Reserves Revision Increases Reserves Revision Decreases Reserves Sales Reserves Acquisitions Reserves Extensions Reserves New Field Discoveries New Reservoir Discoveries in Old Fields Estimated Production Number of Producing Gas Wells Gross Withdrawals Gross Withdrawals From Gas Wells Gross Withdrawals From Oil Wells Gross Withdrawals From Shale Gas Wells Gross Withdrawals From Coalbed Wells Repressuring Nonhydrocarbon Gases Removed Vented and Flared Marketed Production Natural Gas Processed NGPL Production, Gaseous Equivalent Dry Production Imports By Pipeline LNG Imports Exports Exports By Pipeline LNG Exports Underground Storage Capacity Underground Storage Injections Underground Storage Withdrawals Underground Storage Net Withdrawals LNG Storage Additions LNG Storage Withdrawals LNG Storage Net Withdrawals Total Consumption Lease and Plant Fuel Consumption Lease Fuel Plant Fuel Pipeline & Distribution Use Delivered to Consumers Residential Commercial Industrial Vehicle Fuel Electric Power Period: Monthly Annual

91

2010 Expert and Consultant Daily Wages  

Science Conference Proceedings (OSTI)

2010 Expert and Consultant Daily Wages. Based on the OPM Salary Table 2010-GS. ... Daily Daily. Per Diem Salary Scale Minimum Maximum. ...

2012-04-27T23:59:59.000Z

92

Validation in an Arid Area of an Algorithm for the Estimation of Daily Solar Radiation  

Science Conference Proceedings (OSTI)

The ThorntonRunning algorithm to estimate daily global radiation was tested at a site in a coastal desert of the eastern Mediterranean. In this algorithm three factors are multiplied in order to compute the daily global radiation: the total ...

P. R. Berliner; K. Droppelmann

2003-04-01T23:59:59.000Z

93

Seasonal Predictability of Daily Rainfall Characteristics in Central Northern Chile for Dry-Land Management  

Science Conference Proceedings (OSTI)

The seasonal predictability of daily winter rainfall characteristics relevant to dry-land management was investigated in the Coquimbo region of central northern Chile, with focus on the seasonal rainfall total, daily rainfall frequency, and mean ...

Koen Verbist; Andrew W. Robertson; Wim M. Cornelis; Donald Gabriels

2010-09-01T23:59:59.000Z

94

EIA Average Energy Consumption 2005  

U.S. Energy Information Administration (EIA)

Table US8. Average Consumption by Fuels Used, 2005 Physical Units per Household Fuels Used (physical units of consumption per household using the fuel)

95

Average Interest Rate for Treasury Securities | Data.gov  

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

Average Interest Rate for Treasury Securities Average Interest Rate for Treasury Securities Consumer Data Apps Challenges Resources About Blogs Let's Talk Feedback Consumer You are here Data.gov » Communities » Consumer » Data Average Interest Rate for Treasury Securities Dataset Summary Description This dataset shows the average interest rates for U.S Treasury securities for the most recent month compared with the same month of the previous year. The data is broken down by the various marketable and non-marketable securities. The summary page for the data provides links for monthly reports from 2001 through the current year. Average Interest Rates are calculated on the total unmatured interest-bearing debt. The average interest rates for total marketable, total non-marketable and total interest-bearing debt do not include the U.S. Treasury Inflation-Protected Securities.

96

A simple method to downscale daily wind statistics to hourly wind data  

E-Print Network (OSTI)

Wind is the principal driver in the wind erosion models. The hourly wind speed data were generally required for precisely wind erosion modeling. In this study, a simple method to generate hourly wind speed data from daily wind statistics (daily average and maximum wind speeds together or daily average wind speed only) was established. A typical windy location with 3285 days (9 years) measured hourly wind speed data were used to validate the downscaling method. The results showed that the overall agreement between observed and simulated cumulative wind speed probability distributions appears excellent, especially for the wind speeds greater than 5 m s-1 range (erosive wind speed). The results further revealed that the values of daily average erosive wind power density (AWPD) calculated from generated wind speeds fit the counterparts computed from measured wind speeds well with high models' efficiency (Nash-Sutcliffe coefficient). So that the hourly wind speed data can be predicted from daily average and maximu...

Guo, Zhongling

2013-01-01T23:59:59.000Z

97

Variability in Daily, Zonal Mean Lower-Stratospheric Temperatures  

Science Conference Proceedings (OSTI)

Satellite data from the microwave sounding unit (MSU) channel 4, when carefully merged, provide daily zonal anomalies of lower-stratosphere temperature with a level of precision between 0.01 and 0.08C per 2.5 latitude band. Global averages of ...

John R. Christy; S. James Drouilhet Jr.

1994-01-01T23:59:59.000Z

98

DOE Solar Decathlon: 2007 Daily Journals  

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

Decathlon Director, Richard King, and his wife, Melissa. Richard King, Solar Decathlon organizer, keeps a daily journal during the 2007 Solar Decathlon. Solar Decathlon 2007 Daily...

99

Grid-Averaged Surface Fluxes  

Science Conference Proceedings (OSTI)

This study examines the inadequacies of formulations for surface fluxes for use in numerical models of atmospheric flow. The difficulty is that numerical models imply spatial averaging over each grid area. Existing formulations am based on the ...

L. Mahrt

1987-08-01T23:59:59.000Z

100

High average power pockels cell  

DOE Patents (OSTI)

A high average power pockels cell is disclosed which reduces the effect of thermally induced strains in high average power laser technology. The pockels cell includes an elongated, substantially rectangular crystalline structure formed from a KDP-type material to eliminate shear strains. The X- and Y-axes are oriented substantially perpendicular to the edges of the crystal cross-section and to the C-axis direction of propagation to eliminate shear strains.

Daly, Thomas P. (Pleasanton, CA)

1991-01-01T23:59:59.000Z

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


101

Energy Assurance Daily | Department of Energy  

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

Energy Assurance Daily Energy Assurance Daily Energy Assurance Daily Energy Assurance Daily provides a summary of public information concerning current energy issues. Published Monday through Friday to inform stakeholders of developments affecting energy systems, flows, and markets, it provides highlights of energy issues rather than a comprehensive coverage. Energy Assurance Daily covers: Major energy developments Electricity, petroleum, and natural gas industries Other relevant news Energy prices The Infrastructure Security and Energy Restoration (ISER) Division cannot guarantee the accuracy of the material in the Energy Assurance Daily. Any further use is subject to the copyright restrictions of the source document. The Energy Assurance Daily has workable hypertext links to the

102

national total  

U.S. Energy Information Administration (EIA)

AC Argentina AR Aruba AA Bahamas, The BF Barbados BB Belize BH Bolivia BL Brazil BR Cayman Islands CJ ... World Total ww NA--Table Posted: December 8, ...

103

West Texas Intermediate Spot Average ............................  

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

Crude Oil (dollars per barrel) Crude Oil (dollars per barrel) West Texas Intermediate Spot Average ............................ 102.88 93.42 92.24 87.96 94.34 94.10 105.84 96.30 95.67 95.33 95.67 93.33 94.12 97.64 95.00 Brent Spot Average ........................................................... 118.49 108.42 109.61 110.09 112.49 102.58 110.27 108.29 106.33 105.00 103.00 102.00 111.65 108.41 104.08 Imported Average .............................................................. 108.14 101.18 97.18 97.64 98.71 97.39 103.07 100.03 99.64 99.33 99.69 97.35 101.09 99.85 99.04 Refiner Average Acquisition Cost ...................................... 107.61 101.44 97.38 97.27 101.14 99.45 105.24 100.44 100.15 99.82 100.18 97.83 100.83 101.61 99.50 Liquid Fuels (cents per gallon) Refiner Prices for Resale Gasoline .........................................................................

104

Ecological Modelling 143 (2001) 227243 A globally applicable model of daily solar irradiance  

E-Print Network (OSTI)

. At Luquillo, Puerto Rico, the daily atmospheric transmittance for solar radiation was approximately equal incoming radiation. This sensi- tivity depends upon the local partitioning of solar energy that varies, nearly linear relationship between ~ and daily average relative humidity (rhave) at Luquillo, Puerto Rico

Hunt Jr., E. Raymond

105

Generating Multiyear Gridded Daily Rainfall over New Zealand  

Science Conference Proceedings (OSTI)

Daily rainfall totals are a key input for hydrological models that are designed to simulate water and pollutant flow through both soil and waterways. Within New Zealand there are large areas and many river catchments where no long-term rainfall ...

Andrew Tait; Richard Turner

2005-09-01T23:59:59.000Z

106

national average for heating oil  

U.S. Energy Information Administration (EIA)

Propane Missouri North Dakota X South Dakota TOTAL List of States included on Winter Heating Fuels Survey (SHOPP) Release date: January 2012 22.00 24.00. Author: MRO

107

Solar: annual average direct normal (DNI) map at 40km resolution for  

Open Energy Info (EERE)

map at 40km resolution for map at 40km resolution for Central America from NREL Dataset Summary Description (Abstract): A map depicting model estimates of monthly average daily total radiation using inputs derived from satellite and surface observations of cloud cover, aerosol optical depth, precipitable water vapor, albedo, atmospheric pressure and ozone sampled at a 40km resolution. (Purpose): A visual depiction of solar energy resource for concentrating solar power systems. Source NREL Date Released December 11th, 2003 (11 years ago) Date Updated October 30th, 2007 (7 years ago) Keywords Central America direct normal DNI map NREL solar SWERA UNEP Data application/pdf icon Download Map (pdf, 67.1 KiB) Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency

108

annual average heating degree days | OpenEI  

Open Energy Info (EERE)

average heating degree days average heating degree days Dataset Summary Description (Abstract): Heating Degree Days below 18° C (degree days)The monthly accumulation of degrees when the daily mean temperature is below 18° C.NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly Average & Annual Sum (July 1983 - June 2005)Parameter: Heating Degree Days Below 18 degrees C (degree days)Internet: http://eosweb.larc.nasa.gov/sse/ Source U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE) Date Released March 31st, 2009 (5 years ago) Date Updated April 01st, 2009 (5 years ago) Keywords annual average heating degree days climate GIS NASA SWERA UNEP Data application/zip icon Download Shapefile (zip, 2.7 MiB)

109

Variation in Nimbus-7 Cloud Estimates. Part I: Zonal Averages  

Science Conference Proceedings (OSTI)

Zonal averages of low, middle and total cloud amount estimates derived from measurements from Nimbus-7 have been analyzed for the six-year period April 1979 through March 1985. The globally and zonally averaged values of six-year annual means and ...

Bryan C. Weare

1992-12-01T23:59:59.000Z

110

Total Imports  

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

Data Series: Imports - Total Imports - Crude Oil Imports - Crude Oil, Commercial Imports - by SPR Imports - into SPR by Others Imports - Total Products Imports - Total Motor Gasoline Imports - Finished Motor Gasoline Imports - Reformulated Gasoline Imports - Reformulated Gasoline Blended w/ Fuel Ethanol Imports - Other Reformulated Gasoline Imports - Conventional Gasoline Imports - Conv. Gasoline Blended w/ Fuel Ethanol Imports - Conv. Gasoline Blended w/ Fuel Ethanol, Ed55 & Ed55 Imports - Other Conventional Gasoline Imports - Motor Gasoline Blend. Components Imports - Motor Gasoline Blend. Components, RBOB Imports - Motor Gasoline Blend. Components, RBOB w/ Ether Imports - Motor Gasoline Blend. Components, RBOB w/ Alcohol Imports - Motor Gasoline Blend. Components, CBOB Imports - Motor Gasoline Blend. Components, GTAB Imports - Motor Gasoline Blend. Components, Other Imports - Fuel Ethanol Imports - Kerosene-Type Jet Fuel Imports - Distillate Fuel Oil Imports - Distillate F.O., 15 ppm Sulfur and Under Imports - Distillate F.O., > 15 ppm to 500 ppm Sulfur Imports - Distillate F.O., > 500 ppm to 2000 ppm Sulfur Imports - Distillate F.O., > 2000 ppm Sulfur Imports - Residual Fuel Oil Imports - Propane/Propylene Imports - Other Other Oils Imports - Kerosene Imports - NGPLs/LRGs (Excluding Propane/Propylene) Exports - Total Crude Oil and Products Exports - Crude Oil Exports - Products Exports - Finished Motor Gasoline Exports - Kerosene-Type Jet Fuel Exports - Distillate Fuel Oil Exports - Residual Fuel Oil Exports - Propane/Propylene Exports - Other Oils Net Imports - Total Crude Oil and Products Net Imports - Crude Oil Net Imports - Petroleum Products Period: Weekly 4-Week Avg.

111

Estimation of Daily Degree-hours  

Science Conference Proceedings (OSTI)

Degree-hours have many applications in fields such as agriculture, architecture, and power generation. Since daily mean temperatures are more readily available than hourly temperatures, the difference between mean daily degree-hours computed from ...

Nathaniel B. Guttman; Richard L. Lehman

1992-07-01T23:59:59.000Z

112

Homogenization of Daily Temperatures over Canada  

Science Conference Proceedings (OSTI)

A method to homogenize daily maximum and minimum temperatures over Canada is presented. The procedure is based on previously defined monthly adjustments derived from step changes identified in annual Canadian temperature series. Daily ...

Lucie A. Vincent; X. Zhang; B. R. Bonsal; W. D. Hogg

2002-06-01T23:59:59.000Z

113

Global distributions of total ozone during January and February 1979 as determined from DMSP multichannel filter radiometer measurements  

Science Conference Proceedings (OSTI)

The multichannel filter radiometer instrument (MFR) was first flown on a Defense Meteorological Satellite Program (DMSP) Block 5D series satellite in 1977. Daily analyses of the global distribution of retrieved total ozone are presented for January and February 1979. The temporal and spatial averages and variability of ozone during this period are discussed. Retrieved total column ozone data derived from the MFR measurements for January 1979 are compared with preliminary SBUV measurements and with distributions of total ozone measured between 1958 and 1967.

Luther, F.M.; Ellis, J.S.; Lovill, J.E.; Sullivan, T.J.; Weichel, R.L.

1980-09-01T23:59:59.000Z

114

Developing hourly weather data for locations having only daily weather data  

Science Conference Proceedings (OSTI)

A methodology was developed to modify an hourly TMY weather tape to be representative of a location for which only average daily weather parameters were avilable. Typical hourly and daily variations in solar flux, and other parameters, were needed to properly exercise a computer model to predict the transient performance of a solar controlled greenhouse being designed for Riyadh, Saudi Arabia. The starting point was a TMY tape for Yuma, Arizona, since the design temperatures for summer and winter are nearly identical for Yuma and Riyadh. After comparing six of the most important weather variables, the hourly values on the Yuma tape were individually adjusted to give the same overall daily average conditions as existed in the long-term Riyadh data. Finally, a statistical analysis was used to confirm quantitatively that the daily variations between the long term average values for Riyadh and the modified TMY weather tape for Yuma matched satisfactorily.

Talbert, S.G.; Herold, K.E.; Jakob, F.E.; Lundstrom, D.K.

1983-06-01T23:59:59.000Z

115

Achronal averaged null energy condition  

Science Conference Proceedings (OSTI)

The averaged null energy condition (ANEC) requires that the integral over a complete null geodesic of the stress-energy tensor projected onto the geodesic tangent vector is never negative. This condition is sufficient to prove many important theorems in general relativity, but it is violated by quantum fields in curved spacetime. However there is a weaker condition, which is free of known violations, requiring only that there is no self-consistent spacetime in semiclassical gravity in which ANEC is violated on a complete, achronal null geodesic. We indicate why such a condition might be expected to hold and show that it is sufficient to rule out closed timelike curves and wormholes connecting different asymptotically flat regions.

Graham, Noah; Olum, Ken D. [Department of Physics, Middlebury College, Middlebury, Vermont 05753 (United States) and Center for Theoretical Physics, Laboratory for Nuclear Science, and Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Institute of Cosmology, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts 02155 (United States)

2007-09-15T23:59:59.000Z

116

Achronal averaged null energy condition  

E-Print Network (OSTI)

The averaged null energy condition (ANEC) requires that the integral over a complete null geodesic of the stress-energy tensor projected onto the geodesic tangent vector is never negative. This condition is sufficient to prove many important theorems in general relativity, but it is violated by quantum fields in curved spacetime. However there is a weaker condition, which is free of known violations, requiring only that there is no self-consistent space-time in semiclassical gravity in which ANEC is violated on a complete, {\\em achronal} null geodesic. We indicate why such a condition might be expected to hold and show that it is sufficient to rule out wormholes and closed timelike curves.

Noah Graham; Ken D. Olum

2007-05-22T23:59:59.000Z

117

Energy Assurance Daily (EAD): May 2012  

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

Energy Assurance Daily provides a summary of public information concerning current energy issues. Published Monday through Friday to inform stakeholders of developments affecting energy systems,...

118

Energy Assurance Daily (EAD): July 2012  

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

Energy Assurance Daily provides a summary of public information concerning current energy issues. Published Monday through Friday to inform stakeholders of developments affecting energy systems,...

119

Energy Assurance Daily (EAD): January - March 2012  

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

Energy Assurance Daily provides a summary of public information concerning current energy issues. Published Monday through Friday to inform stakeholders of developments affecting energy systems,...

120

Energy Assurance Daily (EAD): April 2012  

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

Energy Assurance Daily provides a summary of public information concerning current energy issues. Published Monday through Friday to inform stakeholders of developments affecting energy systems,...

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


121

Energy Assurance Daily (EAD): June 2012  

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

Energy Assurance Daily provides a summary of public information concerning current energy issues. Published Monday through Friday to inform stakeholders of developments affecting energy systems,...

122

Spectral and Parametric Averaging for Integrable Systems  

E-Print Network (OSTI)

We analyze two theoretical approaches to ensemble averaging for integrable systems in quantum chaos - spectral averaging and parametric averaging. For spectral averaging, we introduce a new procedure - rescaled spectral averaging. Unlike traditional spectral averaging, it can describe the correlation function of spectral staircase and produce persistent oscillations of the interval level number variance. Parametric averaging, while not as accurate as rescaled spectral averaging for the correlation function of spectral staircase and interval level number variance, can also produce persistent oscillations of the global level number variance and better describes saturation level rigidity as a function of the running energy. Overall, it is the most reliable method for a wide range of statistics.

Tao Ma; R. A. Serota

2013-06-03T23:59:59.000Z

123

Rainfall Intensity, the Weibull Distribution, and Estimation of Daily Surface Runoff  

Science Conference Proceedings (OSTI)

A new method for estimating absorption and runoff at a point on the basis of total daily precipitation and the absorption capacity of the soil is proposed. The method is based on a statistical characterization of the variation of precipitation ...

Daniel S. Wilks

1989-01-01T23:59:59.000Z

124

2011 Expert and Consultant Daily Wages Based on the OPM ...  

Science Conference Proceedings (OSTI)

Page 1. 2011 Expert and Consultant Daily Wages Based on the OPM Salary Table 2011-GS ... Daily Daily Per Diem Salary Scale Minimum Maximum ...

2011-02-25T23:59:59.000Z

125

Troposphere-Stratosphere (Surface-55 km) Monthly Winter General Circulation Statistics for the Northern Hemisphere-Four Year Averages  

Science Conference Proceedings (OSTI)

Monthly mean Northern Hemisphere general circulation statistics are presented for the four-year average December, January and February months of the winters 197879 through 198182. These calculations start with daily maps for eighteen pressure ...

Marvin A. Geller; Mao-Fou Wu; Melvyn E. Gelman

1983-05-01T23:59:59.000Z

126

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

1 Average Square Footage of Midwest Homes, by Housing Characteristics, 2009" 1 Average Square Footage of Midwest Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Midwest",25.9,2272,1898,1372,912,762,551 "Midwest Divisions and States" "East North Central",17.9,2251,1869,1281,892,741,508 "Illinois",4.8,2186,1911,1451,860,752,571 "Michigan",3.8,1954,1559,962,729,582,359 "Wisconsin",2.3,2605,2091,1258,1105,887,534

127

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

3 Average Square Footage of West Homes, by Housing Characteristics, 2009" 3 Average Square Footage of West Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total West",24.8,1708,1374,800,628,506,294 "West Divisions and States" "Mountain",7.9,1928,1695,1105,723,635,415 "Mountain North",3.9,2107,1858,912,776,684,336 "Colorado",1.9,2082,1832,722,896,788,311 "Idaho, Montana, Utah, Wyoming",2,2130,1883,1093,691,610,354

128

The Relationship between Meteorological Parameters and Daily Summer Rainfall Amount and Coverage in West-Central Florida  

Science Conference Proceedings (OSTI)

Considerable daily variations of summer convective rainfall average areal coverage and rainfall amount were identified in west-central Florida for the period MaySeptember 19972000 using a 29-site rainfall network. Pearson correlation ...

Ira S. Brenner

2004-04-01T23:59:59.000Z

129

Table AP6. Average Consumption for Home Appliances and Lighting by ...  

U.S. Energy Information Administration (EIA)

Natural Gas LPG Total Refrigerators Other Appliances and Lighting Table AP6. Average Consumption for Home Appliances and Lighting by Fuels Used, 2005

130

"Table HC1.2.3 Living Space Characteristics by Average Floorspace--"  

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

3 Living Space Characteristics by Average Floorspace--" 3 Living Space Characteristics by Average Floorspace--" " Single-Family Housing Units and Mobile Homes, 2005" ,,"Single- Family and Mobile Homes (millions)","Average Square Feet per Housing Unit" ," Housing Units (millions)" ,,,"Single-Family Detached",,,"Single-Family Attached",,,"Mobile Homes" "Housing Unit Characteristics",,,"Total1","Heated","Cooled","Total1","Heated","Cooled","Total1","Heated","Cooled" "Total",111.1,86.6,2522,1970,1310,1812,1475,821,1055,944,554 "Total Floorspace (Square Feet)" "Fewer than 500",3.2,0.9,261,336,162,"Q","Q","Q",334,260,"Q"

131

Optimization Online - String-Averaging Projected Subgradient ...  

E-Print Network (OSTI)

Aug 29, 2013 ... Optimization Online. String-Averaging Projected Subgradient Methods for Constrained Minimization. Yair Censor(yair ***at*** math.haifa.ac.il)

132

Average Stock Levels: Crude Market & Propane  

U.S. Energy Information Administration (EIA)

This graph shows that propane was not alone in experiencing excess supply in 1998 and extraordinary stock builds. Note that the graph shows average stock levels ...

133

Dynamic Multiscale Averaging (DMA) of Turbulent Flow  

SciTech Connect

A new approach called dynamic multiscale averaging (DMA) for computing the effects of turbulent flow is described. The new method encompasses multiple applications of temporal and spatial averaging, that is, multiscale operations. Initially, a direct numerical simulation (DNS) is performed for a relatively short time; it is envisioned that this short time should be long enough to capture several fluctuating time periods of the smallest scales. The flow field variables are subject to running time averaging during the DNS. After the relatively short time, the time-averaged variables are volume averaged onto a coarser grid. Both time and volume averaging of the describing equations generate correlations in the averaged equations. These correlations are computed from the flow field and added as source terms to the computation on the next coarser mesh. They represent coupling between the two adjacent scales. Since they are computed directly from first principles, there is no modeling involved. However, there is approximation involved in the coupling correlations as the flow field has been computed for only a relatively short time. After the time and spatial averaging operations are applied at a given stage, new computations are performed on the next coarser mesh using a larger time step. The process continues until the coarsest scale needed is reached. New correlations are created for each averaging procedure. The number of averaging operations needed is expected to be problem dependent. The new DMA approach is applied to a relatively low Reynolds number flow in a square duct segment. Time-averaged stream-wise velocity and vorticity contours from the DMA approach appear to be very similar to a full DNS for a similar flow reported in the literature. Expected symmetry for the final results is produced for the DMA method. The results obtained indicate that DMA holds significant potential in being able to accurately compute turbulent flow without modeling for practical engineering applications.

Richard W. Johnson

2012-09-01T23:59:59.000Z

134

Bayesian curve estimation by model averaging  

Science Conference Proceedings (OSTI)

A Bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian model averaging (BMA), second, the ... Keywords: BIC criterion, Bayesian model averaging, Local polynomial regression, Nonparametric curve fitting, Robustness

Daniel Pea; Dolores Redondas

2006-02-01T23:59:59.000Z

135

Property:SalinityAverage | Open Energy Information  

Open Energy Info (EERE)

SalinityAverage SalinityAverage Jump to: navigation, search Property Name SalinityAverage Property Type Number Description Mean average of the low and high end measurements of the salinity [ppm] of the fluid. This is a property of type Page. Subproperties This property has the following 1 subproperty: C Coso Geothermal Area Pages using the property "SalinityAverage" Showing 19 pages using this property. A Amedee Geothermal Area + 975 + B Beowawe Hot Springs Geothermal Area + 700 + Blue Mountain Geothermal Area + 4300 + Brady Hot Springs Geothermal Area + 3500 + C Chena Geothermal Area + 325 + D Desert Peak Geothermal Area + 6700 + Dixie Valley Geothermal Area + 2295 + E East Mesa Geothermal Area + 3750 + G Geysers Geothermal Area + 217 + K Kilauea East Rift Geothermal Area + 18750 +

136

Seasonal Variation in Daily Temperature Ranges  

Science Conference Proceedings (OSTI)

Abrupt spring and autumnal changes in the daily temperature range, from low winter values to higher nonwinter values, were noted in the Minneapolis-St. Paul temperature record. Since this feature was even more evident in five rural and small town ...

David L. Ruschy; Donald G. Baker; Richard H. Skaggs

1991-12-01T23:59:59.000Z

137

Table 17. Average Price of U.S. Coke Exports  

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

Average Price of U.S. Coke Exports Average Price of U.S. Coke Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Table 17. Average Price of U.S. Coke Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Year to Date Continent and Country of Destination April - June 2013 January - March 2013 April - June 2012 2013 2012 Percent Change North America Total 240.59 241.38 218.40 240.85 225.80 6.7 Canada* 147.49 330.47 243.04 183.08 286.56 -36.1 Mexico 316.57 211.63 189.12 273.97 171.71 59.6 Other** 612.42 485.63 134.48 525.92 135.04 289.5 South America Total 140.65 156.15 322.70 148.29 250.36 -40.8 Other** 140.65 156.15 322.70 148.29 250.36 -40.8 Europe Total 259.26 255.24 - 257.06 427.83 -39.9 Other**

138

Table 22. Average Price of U.S. Coke Imports  

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

Average Price of U.S. Coke Imports Average Price of U.S. Coke Imports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Table 22. Average Price of U.S. Coke Imports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Year to Date Continent and Country of Origin April - June 2013 January - March 2013 April - June 2012 2013 2012 Percent Change North America Total 263.21 252.66 353.05 261.29 356.01 -26.6 Canada 263.51 252.66 353.05 258.82 356.01 -27.3 Panama 263.09 - - 263.09 - - South America Total 196.86 194.14 175.88 195.94 181.01 8.2 Brazil - - 157.60 - 157.60 - Colombia 196.86 194.14 322.06 195.94 246.68 -20.6 Europe Total 181.55 232.13 385.65 225.53 384.96 -41.4 Czech Republic - 475.91 - 475.91 - - Spain 360.51

139

Lagged Average Predictions in a Predictability Experiment  

Science Conference Proceedings (OSTI)

Lagged average predictions are examined here within the context of an idealized predictability experiment. Lagged predictions contribute to making better forecasts than the forecasts obtained from using only the latest initial state. Analytic ...

John O. Roads

1988-01-01T23:59:59.000Z

140

Probabilistic Visibility Forecasting Using Bayesian Model Averaging  

Science Conference Proceedings (OSTI)

Bayesian model averaging (BMA) is a statistical postprocessing technique that has been used in probabilistic weather forecasting to calibrate forecast ensembles and generate predictive probability density functions (PDFs) for weather quantities. ...

Richard M. Chmielecki; Adrian E. Raftery

2011-05-01T23:59:59.000Z

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


141

average air temperature | OpenEI  

Open Energy Info (EERE)

average air temperature average air temperature Dataset Summary Description (Abstract): Air Temperature at 10 m Above The Surface Of The Earth (deg C)NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly & Annual Average (July 1983 - June 2005)Parameter: Air Temperature at 10 m Above The Surface Of The Earth (deg C)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; Source U.S. National Aeronautics and Space Administration (NASA), Surface meteorology and Solar Energy (SSE) Date Released March 31st, 2009 (5 years ago) Date Updated April 01st, 2009 (5 years ago) Keywords average air temperature

142

The Shape of Averaged Drop Size Distributions  

Science Conference Proceedings (OSTI)

The shape of averaged drop size distributions (DSD) is studied from a large sample of data (892 h) collected at several sites of various latitudes. The results show that neither the hypothesis of an exponential distribution to represent rainfall ...

Henri Sauvageot; Jean-Pierre Lacaux

1995-04-01T23:59:59.000Z

143

Average Data for Each Choke Setting (before 24-May 2010 06:00), 6-hour average (  

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

Average Data for Each Choke Setting (before 24-May 2010 06:00), 6-hour average (after 24-May 2010 06:00):" Average Data for Each Choke Setting (before 24-May 2010 06:00), 6-hour average (after 24-May 2010 06:00):" ,,"Choke","Average","Average","Fluid","Methanol","Water","Oil","Gas","Hyd. Eq.","Gas" ,"Choke","Setting","Upstream","Upstream","Recovery","Recovery","Recovery","Recovery","Recovery","Recovery","Recovery" "Date and Time","Setting","Duration","Pressure","Temp.","Rate","Rate","Rate","Rate","Rate","Rate","Portion" "dd-mmm-yy","(64ths)","(hours)","(psia)","(degF)","(bfpd)","(bfpd)","(bwpd)","(bopd)","(mmcfpd)","(boepd)","(%)"

144

A high average power pockels cell  

DOE Patents (OSTI)

A high average power pockels cell is disclosed which reduced the effect of thermally induced strains in high average power laser technology. The pockels cell includes an elongated, substantially rectangular crystalline structure formed from a KDP-type material to eliminate shear strains. The X- and Y-axes are oriented substantially perpendicular to the edges of the crystal cross-section and to the C-axis direction of propagation to eliminate shear strains.

Daly, T.P.

1986-02-10T23:59:59.000Z

145

Average transmission probability of a random stack  

E-Print Network (OSTI)

The transmission through a stack of identical slabs that are separated by gaps with random widths is usually treated by calculating the average of the logarithm of the transmission probability. We show how to calculate the average of the transmission probability itself with the aid of a recurrence relation and derive analytical upper and lower bounds. The upper bound, when used as an approximation for the transmission probability, is unreasonably good and we conjecture that it is asymptotically exact.

Yin Lu; Christian Miniatura; Berthold-Georg Englert

2009-07-31T23:59:59.000Z

146

DOE Solar Decathlon: 2009 Daily Journals  

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

Richard King next to a deck and planter boxes. Decathlete Way and the U.S. Capitol are in the background. Richard King next to a deck and planter boxes. Decathlete Way and the U.S. Capitol are in the background. Solar Decathlon Director Richard King takes a break from the competition along Decathlete Way. Solar Decathlon 2009 Daily Journals The daily journals highlighted the events of the U.S. Department of Energy Solar Decathlon 2009. Each day, Richard King, Solar Decathlon director, covered the latest on the teams, their standings, and the events going on in the solar village. October 19, 2009 I personally believe one of the greatest discoveries in the field of energy from the 20th century is our ability to generate electricity from sunlight using photovoltaic solar cells. Read more. October 17, 2009 Solar Decathlon 2009 was intriguing and suspenseful to the very end. None

147

Estimating Averaging Times for Point and Path-Averaged Measurements of Turbulence Spectra  

Science Conference Proceedings (OSTI)

Uncertainty over how long to average turbulence variables to achieve some desired level of statistical stability is a common concern in boundary-layer meteorology. Several models exist that predict averaging times for measurements of variances ...

Edgar L. Andreas

1988-03-01T23:59:59.000Z

148

Average Residential Price - Energy Information Administration  

U.S. Energy Information Administration (EIA)

... electric power price data are for regulated electric ... Gas volumes delivered for vehicle fuel are included in the State monthly totals from January 2011 ...

149

Trends in Total and Extreme South American Rainfall in 19602000 and Links with Sea Surface Temperature  

Science Conference Proceedings (OSTI)

A weeklong workshop in Brazil in August 2004 provided the opportunity for 28 scientists from southern South America to examine daily rainfall observations to determine changes in both total and extreme rainfall. Twelve annual indices of daily ...

M. R. Haylock; T. C. Peterson; L. M. Alves; T. Ambrizzi; Y. M. T. Anunciao; J. Baez; V. R. Barros; M. A. Berlato; M. Bidegain; G. Coronel; V. Corradi; V. J. Garcia; A. M. Grimm; D. Karoly; J. A. Marengo; M. B. Marino; D. F. Moncunill; D. Nechet; J. Quintana; E. Rebello; M. Rusticucci; J. L. Santos; I. Trebejo; L. A. Vincent

2006-04-01T23:59:59.000Z

150

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

6 Average Square Footage of Mobile Homes, by Housing Characteristics, 2009" 6 Average Square Footage of Mobile Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Mobile Homes",6.9,1087,985,746,413,375,283 "Census Region" "Northeast",0.5,1030,968,711,524,492,362 "Midwest",1.1,1090,1069,595,400,392,218 "South",3.9,1128,1008,894,423,378,335 "West",1.4,995,867,466,369,322,173 "Urban and Rural3" "Urban",3.5,1002,919,684,396,364,271

151

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

2 Average Square Footage of South Homes, by Housing Characteristics, 2009" 2 Average Square Footage of South Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total South",42.1,1867,1637,1549,732,642,607 "South Divisions and States" "South Atlantic",22.2,1944,1687,1596,771,668,633 "Virginia",3,2227,1977,1802,855,759,692 "Georgia",3.5,2304,1983,1906,855,736,707 "Florida",7,1668,1432,1509,690,593,625 "DC, DE, MD, WV",3.4,2218,1831,1440,864,713,561

152

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

4 Average Square Footage of Single-Family Homes, by Housing Characteristics, 2009" 4 Average Square Footage of Single-Family Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Single-Family",78.6,2422,2002,1522,880,727,553 "Census Region" "Northeast",12.7,2843,2150,1237,1009,763,439 "Midwest",19.2,2721,2249,1664,1019,842,624 "South",29.7,2232,1945,1843,828,722,684 "West",16.9,2100,1712,1009,725,591,348 "Urban and Rural3"

153

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

0 Average Square Footage of Northeast Homes, by Housing Characteristics, 2009" 0 Average Square Footage of Northeast Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Northeast",20.8,2121,1663,921,836,656,363 "Northeast Divisions and States" "New England",5.5,2232,1680,625,903,680,253 "Massachusetts",2.5,2076,1556,676,850,637,277 "CT, ME, NH, RI, VT",3,2360,1781,583,946,714,234 "Mid-Atlantic",15.3,2080,1657,1028,813,647,402

154

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

5 Average Square Footage of Multi-Family Homes, by Housing Characteristics, 2009" 5 Average Square Footage of Multi-Family Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Multi-Family",28.1,930,807,535,453,393,261 "Census Region" "Northeast",7.6,991,897,408,471,426,194 "Midwest",5.6,957,857,518,521,466,282 "South",8.4,924,846,819,462,423,410 "West",6.5,843,606,329,374,269,146 "Urban and Rural3" "Urban",26.9,927,803,531,450,390,258

155

,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"  

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

9 Average Square Footage of U.S. Homes, by Housing Characteristics, 2009" 9 Average Square Footage of U.S. Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total",113.6,1971,1644,1230,766,639,478 "Census Region" "Northeast",20.8,2121,1663,921,836,656,363 "Midwest",25.9,2272,1898,1372,912,762,551 "South",42.1,1867,1637,1549,732,642,607 "West",24.8,1708,1374,800,628,506,294 "Urban and Rural3" "Urban",88.1,1857,1546,1148,728,607,450

156

Table 8. Average Price of U.S. Coal Exports  

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

Average Price of U.S. Coal Exports Average Price of U.S. Coal Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Table 8. Average Price of U.S. Coal Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Year to Date Continent and Country of Destination April - June 2013 January - March 2013 April - June 2012 2013 2012 Percent Change North America Total 78.29 77.25 102.62 77.88 105.14 -25.9 Canada* 81.61 80.70 110.67 81.30 112.16 -27.5 Dominican Republic 78.54 75.09 73.89 75.77 76.61 -1.1 Honduras - 54.58 54.43 54.58 54.43 0.3 Jamaica 480.00 54.43 - 54.72 55.42 -1.3 Mexico 73.45 75.81 94.36 74.35 100.95 -26.3 Other** 80.33 389.30 70.37 82.45 76.10 8.3 South America Total 107.72 108.02 149.99 107.88

157

Comparison of the prediction accuracy of daily and monthly regression models for energy consumption in commercial buildings  

E-Print Network (OSTI)

The measured energy savings from retrofits in commercial buildings are generally determined as the difference between the energy consumption predicted using a baseline model and the measured energy consumption during the post retrofit period. Most baseline models are developed by regressing the daily energy consumption versus the daily average temperature (daily models) or by regressing the monthly energy consumption versus the monthly average temperature (monthly models). Since the post-retrofit weather is generally different from the weather used for model development, the prediction error of the baseline model may be different from the fitting error. Daily and monthly baseline models were developed for a midsize commercial building with (i) dual-duct CAV and VAV systems, (ii) office and university occupancy schedules, and (iii) different operating practices using the weather of a mild weather year. The prediction errors were identified as the difference between the energy use predicted by the regression models and the values simulated by a calibrated simulation program when both models use weather from a year very different from the weather year used to develop the regression model. The major results are summarized below: 1. When the AHUs operate 24 hours per day, annual energy prediction errors of daily regression models were found to be less than 1.4%. The errors of monthly regression models were found to be in the same range as the error of the daily models. 2. When the AHUs were shut down during unoccupied periods, annual prediction errors for both daily and monthly regression models were as high as 15%. However, the prediction error of daily regression models can be decreased to a range of 2% to 3% if the daily average energy consumption is regressed versus the average temperature during the operation period. Based on these findings, we suggest use of daily or monthly regression models when the AHUs are operated 24 hours per day. When shut-down is performed during unoccupied hours, daily energy consumption should be regressed versus the average ambient temperature during operating hours to develop the baseline model.

Wang, Jinrong

1996-01-01T23:59:59.000Z

158

World average top-quark mass  

SciTech Connect

This paper summarizes a talk given at the Top2008 Workshop at La Biodola, Isola d Elba, Italy. The status of the world average top-quark mass is discussed. Some comments about the challanges facing the experiments in order to further improve the precision are offered.

Glenzinski, D.; /Fermilab

2008-01-01T23:59:59.000Z

159

STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES  

E-Print Network (OSTI)

CALIFORNIA ENERGY COMMISSION STAFF FORECAST: AVERAGE RETAIL ELECTRICITY PRICES 2005 TO 2018 report, Staff Forecast: Retail Electricity Prices, 2005 to 2018, was prepared with contributions from the technical assistance provided by Greg Broeking of R.W. Beck, Inc. in preparing retail price forecasts

160

Exact bounds for average pairwise network reliability  

Science Conference Proceedings (OSTI)

Several methods for finding exact bounds of average pairwise network connectivity (APNC) are proposed. These methods allows faster decision making about if a network is reliable for its purpose. Previous results on cumulitive updating of all-terminal ... Keywords: algorithm, network reliability, pairwise connectivity

Alexey Rodionov; Olga Rodionova

2013-01-01T23:59:59.000Z

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


161

Early Clinical Outcomes Demonstrate Preserved Cognitive Function in Children With Average-Risk Medulloblastoma When Treated With Hyperfractionated Radiation Therapy  

SciTech Connect

Purpose: To report on acute toxicity, longitudinal cognitive function, and early clinical outcomes in children with average-risk medulloblastoma. Methods and Materials: Twenty children {>=}5 years of age classified as having average-risk medulloblastoma were accrued on a prospective protocol of hyperfractionated radiation therapy (HFRT) alone. Radiotherapy was delivered with two daily fractions (1 Gy/fraction, 6 to 8 hours apart, 5 days/week), initially to the neuraxis (36 Gy/36 fractions), followed by conformal tumor bed boost (32 Gy/32 fractions) for a total tumor bed dose of 68 Gy/68 fractions over 6 to 7 weeks. Cognitive function was prospectively assessed longitudinally (pretreatment and at specified posttreatment follow-up visits) with the Wechsler Intelligence Scale for Children to give verbal quotient, performance quotient, and full-scale intelligence quotient (FSIQ). Results: The median age of the study cohort was 8 years (range, 5-14 years), representing a slightly older cohort. Acute hematologic toxicity was mild and self-limiting. Eight (40%) children had subnormal intelligence (FSIQ <85), including 3 (15%) with mild mental retardation (FSIQ 56-70) even before radiotherapy. Cognitive functioning for all tested domains was preserved in children evaluable at 3 months, 1 year, and 2 years after completion of HFRT, with no significant decline over time. Age at diagnosis or baseline FSIQ did not have a significant impact on longitudinal cognitive function. At a median follow-up time of 33 months (range, 16-58 months), 3 patients had died (2 of relapse and 1 of accidental burns), resulting in 3-year relapse-free survival and overall survival of 83.5% and 83.2%, respectively. Conclusion: HFRT without upfront chemotherapy has an acceptable acute toxicity profile, without an unduly increased risk of relapse, with preserved cognitive functioning in children with average-risk medulloblastoma.

Gupta, Tejpal, E-mail: tejpalgupta@rediffmail.com [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)] [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India); Jalali, Rakesh [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)] [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India); Goswami, Savita [Department of Clinical Psychology and Psychiatry Unit, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)] [Department of Clinical Psychology and Psychiatry Unit, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India); Nair, Vimoj [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)] [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India); Moiyadi, Aliasgar [Division of Neuro-Surgery, Department of Surgical Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)] [Division of Neuro-Surgery, Department of Surgical Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India); Epari, Sridhar [Department of Pathology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)] [Department of Pathology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India); Sarin, Rajiv [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)] [Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer and Tata Memorial Hospital, Mumbai (India)

2012-08-01T23:59:59.000Z

162

The Effect of Spatial and Temporal Averaging on Sampling Strategies for Cloud Amount Data  

Science Conference Proceedings (OSTI)

Sampling and averaging strategies are as significant an influence upon the resulting cloud climatologies as the resolution of the original cloud archives. An investigation of total cloud amount data, as represented by the U.S. Air Force 3-...

N. A. Hughes; A. Henderson-Sellers

1983-03-01T23:59:59.000Z

163

Average U.S. residential summer 2013 electric bill expected to be ...  

U.S. Energy Information Administration (EIA)

The average U.S. household electric bill for June through August is expected to total $395, down 2.5% from last summer and the cheapest in four years.

164

Sources Of Average Individual Radiation Exposure  

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

Of Average Individual Radiation Exposure Of Average Individual Radiation Exposure Natural background Medical Consumer products Industrial, security, educational and research Occupational 0.311 rem 0.300 rem 0.013 rem 0.0003 rem 0.0005 rem Savannah River Nuclear Solutions, LLC, provides radiological protection services and oversight at the Savannah River Site (SRS). These services include radiation dose measurements for persons who enter areas where they may be exposed to radiation or radioactive material. The results are periodically reported to monitored individuals. The results listed are based on a radiation dose system developed by the International Commission on Radiation Protection. The system uses the terms "effective dose," "equivalent dose" and units of rem. You may be more familiar with the term "millirem" (mrem), which is 1/1000 of a rem.

165

Fat turnover in obese slower than average  

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

9-04 9-04 For immediate release: 09/23/2011 | NR-11-09-04 Fat turnover in obese slower than average Anne M Stark, LLNL, (925) 422-9799, stark8@llnl.gov Printer-friendly This scanning electron micrograph image shows part of a lobule of adipose tissue (body fat). Adipose tissue is specialized connective tissue that functions as the major storage site for fat. Photo courtesy of David Gregory & Debbie Marshall/Wellcome Images LIVERMORE, Calif. -- It may be more difficult for obese people to lose fat because the "turnover" rate is much slower for those overweight than average weight individuals. New research in the Sept. 25 online edition of the journal Nature shows that the turnover (storage and loss rate) of fat in the human body is about 1 1/2 years compared to fat cells, which turnover about every 10 years,

166

Natural Gas Prices: Well Above Recent Averages  

Gasoline and Diesel Fuel Update (EIA)

5 5 Notes: The recent surge in spot prices at the Henry Hub are well above a typical range for 1998-1999 (in this context, defined as the average, +/- 2 standard deviations). Past price surges have been of short duration. The possibility of a downward price adjustment before the end of next winter is a source of considerable risk for storage operators who acquire gas at recent elevated prices. Storage levels in the Lower 48 States were 7.5 percent below the 5-year average (1995-1999) by mid-August (August 11), although the differential is only 6.4 percent in the East, which depends most heavily on storage to meet peak demand. Low storage levels are attributable, at least in part, to poor price incentives: high current prices combined with only small price

167

HIGH AVERAGE POWER OPTICAL FEL AMPLIFIERS.  

SciTech Connect

Historically, the first demonstration of the optical FEL was in an amplifier configuration at Stanford University [l]. There were other notable instances of amplifying a seed laser, such as the LLNL PALADIN amplifier [2] and the BNL ATF High-Gain Harmonic Generation FEL [3]. However, for the most part FELs are operated as oscillators or self amplified spontaneous emission devices. Yet, in wavelength regimes where a conventional laser seed can be used, the FEL can be used as an amplifier. One promising application is for very high average power generation, for instance FEL's with average power of 100 kW or more. The high electron beam power, high brightness and high efficiency that can be achieved with photoinjectors and superconducting Energy Recovery Linacs (ERL) combine well with the high-gain FEL amplifier to produce unprecedented average power FELs. This combination has a number of advantages. In particular, we show that for a given FEL power, an FEL amplifier can introduce lower energy spread in the beam as compared to a traditional oscillator. This properly gives the ERL based FEL amplifier a great wall-plug to optical power efficiency advantage. The optics for an amplifier is simple and compact. In addition to the general features of the high average power FEL amplifier, we will look at a 100 kW class FEL amplifier is being designed to operate on the 0.5 ampere Energy Recovery Linac which is under construction at Brookhaven National Laboratory's Collider-Accelerator Department.

BEN-ZVI, ILAN, DAYRAN, D.; LITVINENKO, V.

2005-08-21T23:59:59.000Z

168

Simulation of Daily Weather Data Using Theoretical Probability Distributions  

Science Conference Proceedings (OSTI)

A computer simulation model was constructed to supply daily weather data to a plant disease management model for potato late blight. In the weather model Monte Carlo techniques were employed to generate daily values of precipitation, maximum ...

J. A. Bruhn; W. E. Fry; G. W. Fick

1980-09-01T23:59:59.000Z

169

SPLIDHOM: A Method for Homogenization of Daily Temperature Observations  

Science Conference Proceedings (OSTI)

One major concern of climate change is the possible rise of temperature extreme events, in terms of occurrence and intensity. To study this phenomenon, reliable daily series are required, for instance to compute daily-based indices: high-order ...

Olivier Mestre; Christine Gruber; Clmentine Prieur; Henri Caussinus; Sylvie Jourdain

2011-11-01T23:59:59.000Z

170

Impact Ionization Model Using Average Energy and Average Square Energy of Distribution Function  

E-Print Network (OSTI)

Impact Ionization Model Using Average Energy and Average Square Energy of Distribution Function Ken relaxation length, v sat ø h''i (¸ 0:05¯m), the energy distribution function is not well described calculation of impact ionization coefficient requires the use of a high energy distribution function because

Dunham, Scott

171

Solar Decathlon 2005 Daily Event Schedule  

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

DAILY EVENT SCHEDULE DAILY EVENT SCHEDULE Last updated on September 30, 2005 Note: This schedule is not part of the official Rules and Regulations and is subject to change at any time. Weds, Sept 28 12:00 AM 12:30 AM 1:00 AM 1:30 AM 2:00 AM 2:30 AM 3:00 AM 3:30 AM 4:00 AM 4:30 AM 5:00 AM 5:30 AM 6:00 AM 6:30 AM 7:00 AM 7:30 AM 8:00 AM 8:30 AM 9:00 AM 9:30 AM 10:00 AM 10:30 AM 11:00 AM 11:30 AM 12:00 PM 12:30 PM 1:00 PM 1:30 PM 2:00 PM 2:30 PM 3:00 PM 3:30 PM 4:00 PM 4:30 PM 5:00 PM 5:30 PM 6:00 PM 6:30 PM 7:00 PM 7:30 PM 8:00 PM 8:30 PM 9:00 PM 9:30 PM 10:00 PM 10:30 PM 11:00 PM 11:30 PM Registration Forrestal Bldg (DOE) Cafeteria National Mall Team/Organizer meeting and Safety Orientation Thurs, Sept 29 12:00 AM 12:30 AM 1:00 AM 1:30 AM 2:00 AM 2:30 AM 3:00 AM 3:30 AM 4:00 AM 4:30 AM 5:00 AM 5:30 AM 6:00 AM 6:30 AM 7:00

172

Average Price of Natural Gas Production  

Gasoline and Diesel Fuel Update (EIA)

. . Quantity and Average Price of Natural Gas Production in the United States, 1930-1996 (Volumes in Million Cubic Feet, Prices in Dollars per Thousand Cubic Feet) Table Year Gross Withdrawals Used for Repressuring Nonhydro- carbon Gases Removed Vented and Flared Marketed Production Extraction Loss Dry Production Average Wellhead Price of Marketed Production 1930 ....................... NA NA NA NA 1,978,911 75,140 1,903,771 0.08 1931 ....................... NA NA NA NA 1,721,902 62,288 1,659,614 0.07 1932 ....................... NA NA NA NA 1,593,798 51,816 1,541,982 0.06 1933 ....................... NA NA NA NA 1,596,673 48,280 1,548,393 0.06 1934 ....................... NA NA NA NA 1,815,796 52,190 1,763,606 0.06 1935 ....................... NA NA NA NA 1,968,963 55,488 1,913,475 0.06 1936 ....................... 2,691,512 73,507 NA 392,528 2,225,477

173

Average values and dispersion (in parentheses)  

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

Average values and dispersion (in parentheses) Average values and dispersion (in parentheses) Base-pair Parameters --------------------------------------------------------------------------------------- Shear Stretch Stagger Buckle Propeller Opening 3DNA A 0.01(0.23) -0.18(0.10) 0.02(0.25) -0.13(7.77) -11.79(4.14) 0.57(2.80) B 0.00(0.21) -0.15(0.12) 0.09(0.19) 0.53(6.74) -11.35(5.26) 0.63(3.05) CEHS A 0.01(0.23) -0.18(0.10) 0.02(0.25) -0.13(7.75) -11.82(4.14) 0.56(2.78) B 0.00(0.21) -0.14(0.12) 0.09(0.19) 0.53(6.73) -11.37(5.27) 0.62(3.03) CompDNA A 0.01(0.23) -0.18(0.10) 0.02(0.25) -0.12(7.70) -11.81(4.14) 0.56(2.79) B 0.00(0.21) -0.15(0.12) 0.09(0.19) 0.53(6.70) -11.37(5.26) 0.62(3.03) Curves A 0.01(0.23) -0.18(0.10) 0.02(0.25) -0.13(7.85) -11.76(4.12) 0.57(2.80)

174

Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging  

Science Conference Proceedings (OSTI)

The authors introduce two ways to produce locally calibrated grid-based probabilistic forecasts of temperature. Both start from the Global Bayesian model averaging (Global BMA) statistical postprocessing method, which has constant predictive bias ...

William Kleiber; Adrian E. Raftery; Jeffrey Baars; Tilmann Gneiting; Clifford F. Mass; Eric Grimit

2011-08-01T23:59:59.000Z

175

Insolation data manual: long-term monthly averages of solar radiation, temperature, degree-days and global anti K/sub T/ for 248 national weather service stations  

DOE Green Energy (OSTI)

Monthly averaged data is presented which describes the availability of solar radiation at 248 National Weather Service stations. Monthly and annual average daily insolation and temperature values have been computed from a base of 24 to 25 years of data. Average daily maximum, minimum, and monthly temperatures are provided for most locations in both Celsius and Fahrenheit. Heating and cooling degree-days were computed relative to a base of 18.3/sup 0/C (65/sup 0/F). For each station, global anti K/sub T/ (cloudiness index) were calculated on a monthly and annual basis. (MHR)

Knapp, C L; Stoffel, T L; Whitaker, S D

1980-10-01T23:59:59.000Z

176

Geographic Gossip: Efficient Averaging for Sensor Networks  

E-Print Network (OSTI)

Gossip algorithms for distributed computation are attractive due to their simplicity, distributed nature, and robustness in noisy and uncertain environments. However, using standard gossip algorithms can lead to a significant waste in energy by repeatedly recirculating redundant information. For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is related to the slow mixing times of random walks on the communication graph. We propose and analyze an alternative gossiping scheme that exploits geographic information. By utilizing geographic routing combined with a simple resampling method, we demonstrate substantial gains over previously proposed gossip protocols. For regular graphs such as the ring or grid, our algorithm improves standard gossip by factors of $n$ and $\\sqrt{n}$ respectively. For the more challenging case of random geometric graphs, our algorithm computes the true average to accuracy $\\epsilon$ using $O(\\frac{n^{1.5}}{\\sqrt{\\log ...

Dimakis, Alexandros G; Wainwright, Martin J

2007-01-01T23:59:59.000Z

177

Total Crude by Pipeline  

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

Product: Total Crude by All Transport Methods Domestic Crude by All Transport Methods Foreign Crude by All Transport Methods Total Crude by Pipeline Domestic Crude by Pipeline Foreign Crude by Pipeline Total Crude by Tanker Domestic Crude by Tanker Foreign Crude by Tanker Total Crude by Barge Domestic Crude by Barge Foreign Crude by Barge Total Crude by Tank Cars (Rail) Domestic Crude by Tank Cars (Rail) Foreign Crude by Tank Cars (Rail) Total Crude by Trucks Domestic Crude by Trucks Foreign Crude by Trucks Period: Product: Total Crude by All Transport Methods Domestic Crude by All Transport Methods Foreign Crude by All Transport Methods Total Crude by Pipeline Domestic Crude by Pipeline Foreign Crude by Pipeline Total Crude by Tanker Domestic Crude by Tanker Foreign Crude by Tanker Total Crude by Barge Domestic Crude by Barge Foreign Crude by Barge Total Crude by Tank Cars (Rail) Domestic Crude by Tank Cars (Rail) Foreign Crude by Tank Cars (Rail) Total Crude by Trucks Domestic Crude by Trucks Foreign Crude by Trucks Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Product Area 2007 2008 2009 2010 2011 2012 View

178

Compare All CBECS Activities: Total Energy Use  

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

Total Energy Use Total Energy Use Compare Activities by ... Total Energy Use Total Major Fuel Consumption by Building Type Commercial buildings in the U.S. used a total of approximately 5.7 quadrillion Btu of all major fuels (electricity, natural gas, fuel oil, and district steam or hot water) in 1999. Office buildings used the most total energy of all the building types, which was not a surprise since they were the most common commercial building type and had an above average energy intensity. Figure showing total major fuel consumption by building type. If you need assistance viewing this page, please call 202-586-8800. Major Fuel Consumption per Building by Building Type Because there were relatively few inpatient health care buildings and they tend to be large, energy intensive buildings, their energy consumption per building was far above that of any other building type.

179

Average Price (Cents/kilowatthour) by State by Provider, 1990-2012  

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

Average Price (Cents/kilowatthour) by State by Provider, 1990-2012" Average Price (Cents/kilowatthour) by State by Provider, 1990-2012" "Year","State","Industry Sector Category","Residential","Commercial","Industrial","Transportation","Other","Total" 2012,"AK","Total Electric Industry",17.88,14.93,16.82,0,"NA",16.33 2012,"AL","Total Electric Industry",11.4,10.63,6.22,0,"NA",9.18 2012,"AR","Total Electric Industry",9.3,7.71,5.77,11.23,"NA",7.62 2012,"AZ","Total Electric Industry",11.29,9.53,6.53,0,"NA",9.81 2012,"CA","Total Electric Industry",15.34,13.41,10.49,7.17,"NA",13.53

180

Table 19. Average Price of U.S. Coal Imports  

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

Price of U.S. Coal Imports Price of U.S. Coal Imports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Table 19. Average Price of U.S. Coal Imports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Year to Date Continent and Country of Origin April - June 2013 January - March 2013 April - June 2012 2013 2012 Percent Change North America Total 147.86 138.39 191.01 144.86 197.96 -26.8 Canada 147.86 138.39 191.00 144.86 197.95 -26.8 Mexico - - 286.23 - 286.23 - South America Total 75.29 80.74 86.52 77.20 87.17 -11.4 Argentina - - 504.70 - 504.70 - Colombia 74.87 80.74 83.03 76.96 85.25 -9.7 Peru 87.09 - - 87.09 - - Venezuela 91.81 - 122.01 91.81 112.61 -18.5 Europe Total - 136.50 137.33 136.50 146.31 -6.7

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


181

Table HC1.2.2 Living Space Characteristics by Average Floorspace  

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

2 Living Space Characteristics by Average Floorspace, " 2 Living Space Characteristics by Average Floorspace, " " Per Housing Unit and Per Household Member, 2005" ,,"Average Square Feet" ," Housing Units (millions)" ,,"Per Housing Unit",,,"Per Household Member" "Living Space Characteristics",,"Total1","Heated","Cooled","Total1","Heated","Cooled" "Total",111.1,2033,1618,1031,791,630,401 "Total Floorspace (Square Feet)" "Fewer than 500",3.2,357,336,113,188,177,59 "500 to 999",23.8,733,667,308,343,312,144 "1,000 to 1,499",20.8,1157,1086,625,435,409,235 "1,500 to 1,999",15.4,1592,1441,906,595,539,339 "2,000 to 2,499",12.2,2052,1733,1072,765,646,400

182

Table HC1.2.4 Living Space Characteristics by Average Floorspace--Apartments, 2  

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

2.4 Living Space Characteristics by Average Floorspace--Apartments, 2005" 2.4 Living Space Characteristics by Average Floorspace--Apartments, 2005" ,,,"Average Square Feet per Apartment in a --" ," Housing Units (millions)" ,,,"2 to 4 Unit Building",,,"5 or More Unit Building" ,,"Apartments (millions)" "Living Space Characteristics",,,"Total","Heated","Cooled","Total","Heated","Cooled" "Total",111.1,24.5,1090,902,341,872,780,441 "Total Floorspace (Square Feet)" "Fewer than 500",3.1,2.3,403,360,165,366,348,93 "500 to 999",22.2,14.4,763,660,277,730,646,303 "1,000 to 1,499",19.1,5.8,1223,1130,496,1187,1086,696 "1,500 to 1,999",14.4,1,1700,1422,412,1698,1544,1348

183

Total OECD Oil Stocks  

Gasoline and Diesel Fuel Update (EIA)

5 Notes: OECD oil inventory levels are not expected to rise sufficiently during the rest of the year to match the average levels seen prior to the wide swings since 1995. This...

184

Daily Thermal Predictions of the AGR-1 Experiment with Gas Gaps Varying with Time  

SciTech Connect

A new daily as-run thermal analysis was performed at the Idaho National Laboratory on the Advanced Gas Reactor (AGR) test experiment number one at the Advanced Test Reactor (ATR). This thermal analysis incorporates gas gaps changing with time during the irradiation experiment. The purpose of this analysis was to calculate the daily average temperatures of each compact to compare with experimental results. Post irradiation examination (PIE) measurements of the graphite holder and fuel compacts showed the gas gaps varying from the beginning of life. The control temperature gas gap and the fuel compact graphite holder gas gaps were linearly changed from the original fabrication dimensions, to the end of irradiation measurements. A steady-state thermal analysis was performed for each daily calculation. These new thermal predictions more closely match the experimental data taken during the experiment than previous analyses. Results are presented comparing normalized compact average temperatures to normalized log(R/B) Kr-85m. The R/B term is the measured release rate divided by the predicted birth rate for the isotope Kr-85m. Correlations between these two normalized values are presented.

Grant Hawkes; James Sterbentz; John Maki; Binh Pham

2012-06-01T23:59:59.000Z

185

Yearly average performance of the principal solar collector types  

DOE Green Energy (OSTI)

The results of hour-by-hour simulations for 26 meteorological stations are used to derive universal correlations for the yearly total energy that can be delivered by the principal solar collector types: flat plate, evacuated tubes, CPC, single- and dual-axis tracking collectors, and central receiver. The correlations are first- and second-order polynomials in yearly average insolation, latitude, and threshold (= heat loss/optical efficiency). With these correlations, the yearly collectible energy can be found by multiplying the coordinates of a single graph by the collector parameters, which reproduces the results of hour-by-hour simulations with an accuracy (rms error) of 2% for flat plates and 2% to 4% for concentrators. This method can be applied to collectors that operate year-around in such a way that no collected energy is discarded, including photovoltaic systems, solar-augmented industrial process heat systems, and solar thermal power systems. The method is also recommended for rating collectors of different type or manufacturer by yearly average performance, evaluating the effects of collector degradation, the benefits of collector cleaning, and the gains from collector improvements (due to enhanced optical efficiency or decreased heat loss per absorber surface). For most of these applications, the method is accurate enough to replace a system simulation.

Rabl, A.

1981-01-01T23:59:59.000Z

186

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings...

187

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings*...

188

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings*...

189

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings...

190

Table 5B. Commercial Average Monthly Bill by Census Division ...  

U.S. Energy Information Administration (EIA)

Home > Electricity > Electric Sales, Revenue, and Price > Commercial Average Monthly Bill by Census Division, and State: Table 5B. Commercial Average Monthly Bill by ...

191

On the String Averaging Method for Sparse Common Fixed Points ...  

E-Print Network (OSTI)

Jul 10, 2008 ... gate a string-averaging algorithmic scheme that favorably handles the ... are special cases of the string-averaging and of the BIP algorithmic...

192

Table 5A. Residential Average Monthly Bill by Census Division ...  

U.S. Energy Information Administration (EIA)

Table 5A. Residential Average Monthly Bill by Census Division, and State, 2009: Census Division State Number of Consumers Average Monthly Consumption ...

193

Average summer gasoline prices expected to be slightly lower ...  

U.S. Energy Information Administration (EIA)

The retail price for regular gasoline is expected to average $3.63 per gallon during this summer driving season, slightly below average prices over ...

194

Table 5B. Commercial average monthly bill by census division...  

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

" Census Division " " State ","Number of Consumers "," Average Monthly Consumption (kWh)","Price (Cents per Kilowatthour)","Average Monthly Bill (Dollar and cents)" "New...

195

Table HC1.1.4 Housing Unit Characteristics by Average Floorspace--Apartments, 2  

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

4 Housing Unit Characteristics by Average Floorspace--Apartments, 2005" 4 Housing Unit Characteristics by Average Floorspace--Apartments, 2005" ,,,"Average Square Feet per Apartment in a --" ," Housing Units (millions)" ,,,"2 to 4 Unit Building",,,"5 or More Unit Building" ,,"Apartments (millions)" "Housing Unit Characteristics",,,"Total","Heated","Cooled","Total","Heated","Cooled" "Total",111.1,24.5,1090,902,341,872,780,441 "Census Region and Division" "Northeast",20.6,6.7,1247,1032,"Q",811,788,147 "New England",5.5,1.9,1365,1127,"Q",814,748,107 "Middle Atlantic",15.1,4.8,1182,978,"Q",810,800,159 "Midwest",25.6,4.6,1349,1133,506,895,810,346

196

Table 7.2 Average Prices of Purchased Energy Sources, 2002  

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

2 Average Prices of Purchased Energy Sources, 2002;" 2 Average Prices of Purchased Energy Sources, 2002;" " Level: National and Regional Data; " " Row: NAICS Codes; " " Column: All Energy Sources Collected;" " Unit: U.S. Dollars per Million Btu." ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Selected Wood and Other Biomass Components" ,,,,,,"Coal Components",,,"Coke",,,"Electricity Components",,,,,,,,,,,,,,"Natural Gas Components",,,"Steam Components" ,,,,,,,,,,,,,,"Total",,,,,,,,,,,,,,,,,,,,,,,"Wood Residues" " "," "," ",,,,,"Bituminous",,,,,,"Electricity","Diesel Fuel",,,,,,"Motor",,,,,,,"Natural Gas",,,"Steam",,,," ",,,"and","Wood-Related",," ",," "

197

Table 7.5 Average Prices of Selected Purchased Energy Sources, 2002  

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

5 Average Prices of Selected Purchased Energy Sources, 2002;" 5 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 Million Btu." " ",," "," ",," "," ","RSE" "Economic",,"Residual","Distillate","Natural ","LPG and",,"Row" "Characteristic(a)","Electricity","Fuel Oil","Fuel Oil(b)","Gas(c)","NGL(d)","Coal","Factors" ,"Total United States"

198

Table N8.2. Average Prices of Purchased Energy Sources, 1998  

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

2. Average Prices of Purchased Energy Sources, 1998;" 2. Average Prices of Purchased Energy Sources, 1998;" " Level: National and Regional Data; " " Row: NAICS Codes; Column: All Energy Sources Collected;" " Unit: U.S. Dollars per Million Btu." ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Selected","Wood and Other","Biomass","Components" ,,,,,,,"Coal Components",,,"Coke",,"Electricity","Components",,,,,,,,,,,,,"Natural Gas","Components",,"Steam","Components" ,,,,,,,,,,,,,,"Total",,,,,,,,,,,,,,,,,,,,,,,"Wood Residues" " "," "," ",,,,,"Bituminous",,,,,,"Electricity","Diesel Fuel",,,,,,"Motor",,,,,,,"Natural Gas",,,"Steam",,,," ",,,"and","Wood-Related",," ",," "

199

Table 7.1 Average Prices of Purchased Energy Sources, 2002  

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

Average Prices of Purchased Energy Sources, 2002;" Average Prices of Purchased Energy Sources, 2002;" " Level: National and Regional Data; " " Row: NAICS Codes;" " Column: All Energy Sources Collected;" " Unit: U.S. Dollars per Physical Units." ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Selected Wood and Other Biomass Components" ,,,,,,"Coal Components",,,"Coke",,,"Electricity Components",,,,,,,,,,,,,,"Natural Gas Components",,,"Steam Components" ,,,,,,,,,,,,,,"Total",,,,,,,,,,,,,,,,,,,,,,,"Wood Residues" " "," "," ",,,,,"Bituminous",,,,,,"Electricity","Diesel Fuel",,,,,,"Motor",,,,,,,"Natural Gas",,,"Steam",,,," ",,,"and","Wood-Related",," ",," "

200

"Table E8.2. Average Prices of Selected Purchased Energy Sources, 1998;"  

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

2. Average Prices of Selected Purchased Energy Sources, 1998;" 2. Average Prices of Selected Purchased Energy Sources, 1998;" " Level: National and Regional Data; " " Row: Values of Shipments and Employment Sizes;" " Column: Energy Sources;" " Unit: U.S. Dollars per Million Btu." " ",," "," ",," "," ","RSE" "Economic",,"Residual","Distillate",,"LPG and",,"Row" "Characteristic(a)","Electricity","Fuel Oil","Fuel Oil(b)","Natural Gas(c)","NGL(d)","Coal","Factors" ,"Total United States"

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


201

Texas Gulf Coast Refinery District API Gravity (Weighted Average ...  

U.S. Energy Information Administration (EIA)

Texas Gulf Coast Refinery District API Gravity (Weighted Average) of Crude Oil Input to Refineries (Degree)

202

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Survey: Energy End-Use Consumption Tables Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other...

203

Daily snow depth measurements from 195 stations in the United States  

SciTech Connect

This document describes a database containing daily measurements of snow depth at 195 National Weather Service (NWS) first-order climatological stations in the United States. The data have been assembled and made available by the National Climatic Data Center (NCDC) in Asheville, North Carolina. The 195 stations encompass 388 unique sampling locations in 48 of the 50 states; no observations from Delaware or Hawaii are included in the database. Station selection criteria emphasized the quality and length of station records while seeking to provide a network with good geographic coverage. Snow depth at the 388 locations was measured once per day on ground open to the sky. The daily snow depth is the total depth of the snow on the ground at measurement time. The time period covered by the database is 1893--1992; however, not all station records encompass the complete period. While a station record ideally should contain daily data for at least the seven winter months (January through April and October through December), not all stations have complete records. Each logical record in the snow depth database contains one station`s daily data values for a period of one month, including data source, measurement, and quality flags.

Allison, L.J. [ed.] [Oak Ridge National Lab., TN (United States). Carbon Dioxide Information Analysis Center; Easterling, D.R.; Jamason, P.; Bowman, D.P.; Hughes, P.Y.; Mason, E.H. [National Oceanic and Atmospheric Administration, Asheville, NC (United States). National Climatic Data Center

1997-02-01T23:59:59.000Z

204

"Table A42. Average Prices of Purchased Energy Sources by...  

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

Nonutility(c)","Total","from Utility(b)","from Nonutility(c)","Total","Total","Anthracite","Coal","Lignite","Coal Coke","Breeze","Petroleum Coke","Waste","from...

205

U.S. Total Exports  

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

TX Roma, TX Total to Portugal Sabine Pass, LA Total to Russia Kenai, AK Total to South Korea Freeport, TX Sabine Pass, LA Total to Spain Cameron, LA Sabine Pass, LA Total to...

206

U.S. Total Exports  

Gasoline and Diesel Fuel Update (EIA)

Rio Bravo, TX Roma, TX Total to Portugal Sabine Pass, LA Total to Russia Total to South Korea Freeport, TX Sabine Pass, LA Total to Spain Cameron, LA Sabine Pass, LA Total to...

207

21 briefing pages total  

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

briefing pages total p. 1 briefing pages total p. 1 Reservist Differential Briefing U.S. Office of Personnel Management December 11, 2009 p. 2 Agenda - Introduction of Speakers - Background - References/Tools - Overview of Reservist Differential Authority - Qualifying Active Duty Service and Military Orders - Understanding Military Leave and Earnings Statements p. 3 Background 5 U.S.C. 5538 (Section 751 of the Omnibus Appropriations Act, 2009, March 11, 2009) (Public Law 111-8) Law requires OPM to consult with DOD Law effective first day of first pay period on or after March 11, 2009 (March 15 for most executive branch employees) Number of affected employees unclear p. 4 Next Steps

208

Barge Truck Total  

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

Barge Barge Truck Total delivered cost per short ton Shipments with transportation rates over total shipments Total delivered cost per short ton Shipments with transportation rates over total shipments Year (nominal) (real) (real) (percent) (nominal) (real) (real) (percent) 2008 $6.26 $5.77 $36.50 15.8% 42.3% $6.12 $5.64 $36.36 15.5% 22.2% 2009 $6.23 $5.67 $52.71 10.8% 94.8% $4.90 $4.46 $33.18 13.5% 25.1% 2010 $6.41 $5.77 $50.83 11.4% 96.8% $6.20 $5.59 $36.26 15.4% 38.9% Annual Percent Change First to Last Year 1.2% 0.0% 18.0% - - 0.7% -0.4% -0.1% - - Latest 2 Years 2.9% 1.7% -3.6% - - 26.6% 25.2% 9.3% - - - = No data reported or value not applicable STB Data Source: The Surface Transportation Board's 900-Byte Carload Waybill Sample EIA Data Source: Form EIA-923 Power Plant Operations Report

209

Summary Max Total Units  

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

Max Total Units Max Total Units *If All Splits, No Rack Units **If Only FW, AC Splits 1000 52 28 28 2000 87 59 35 3000 61 33 15 4000 61 33 15 Totals 261 153 93 ***Costs $1,957,500.00 $1,147,500.00 $697,500.00 Notes: added several refrigerants removed bins from analysis removed R-22 from list 1000lb, no Glycol, CO2 or ammonia Seawater R-404A only * includes seawater units ** no seawater units included *** Costs = (total units) X (estimate of $7500 per unit) 1000lb, air cooled split systems, fresh water Refrig Voltage Cond Unit IF-CU Combos 2 4 5 28 References Refrig Voltage C-U type Compressor HP R-404A 208/1/60 Hermetic SA 2.5 R-507 230/1/60 Hermetic MA 2.5 208/3/60 SemiHerm SA 1.5 230/3/60 SemiHerm MA 1.5 SemiHerm HA 1.5 1000lb, remote rack systems, fresh water Refrig/system Voltage Combos 12 2 24 References Refrig/system Voltage IF only

210

Backstage at the Daily Show | Department of Energy  

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

Backstage at the Daily Show Backstage at the Daily Show Backstage at the Daily Show Addthis Description Backstage footage from Secretary Chu's appearance on the Daily Show where he discuses the green room candy dish and possible lighting considerations. Speakers Secretary Steven Chu Duration 1:32 Topic Energy Efficiency Credit Energy Department Video SECRETARY STEVEN CHU: I'm in the famous green room of the Jon Stewart show. If you look around, I have all these games: Monopoly, a Rubik's Cube, Pictureka! Now if - this is to amuse myself, but in actual fact, the most famous part of this room is this. There's enough chocolate here to put you on a high that - (chuckles) - will really get you going. This is my wife, Jean. JEAN CHU: (Chuckles.) I'm - (chuckles) - edit this out! (Begin recorded segment.)

211

On the Conditional Distribution of Daily Precipitation Amounts  

Science Conference Proceedings (OSTI)

Possible conditional dependence of the distribution of daily precipitation amounts on the occurrence of precipitation on the preceding day is examined. Test results based on 25 years of data at 30 stations in the conterminous United States show ...

Edwin H. Chin; John F. Miller

1980-09-01T23:59:59.000Z

212

Comprehensive Automated Quality Assurance of Daily Surface Observations  

Science Conference Proceedings (OSTI)

This paper describes a comprehensive set of fully automated quality assurance (QA) procedures for observations of daily surface temperature, precipitation, snowfall, and snow depth. The QA procedures are being applied operationally to the Global ...

Imke Durre; Matthew J. Menne; Byron E. Gleason; Tamara G. Houston; Russell S. Vose

2010-08-01T23:59:59.000Z

213

Thailand Daily Rainfall and Comparison with TRMM Products  

Science Conference Proceedings (OSTI)

Daily rainfall data collected from more than 100 gauges over Thailand for the period 19932002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data ...

Roongroj Chokngamwong; Long S. Chiu

2008-04-01T23:59:59.000Z

214

Climatologically Aided Mapping of Daily Precipitation and Temperature  

Science Conference Proceedings (OSTI)

Accurately mapped meteorological data are an essential component for hydrologic and ecological research conducted at broad scales. A simple yet effective method for mapping daily weather conditions across heterogeneous landscapes is described and ...

Richard D. Hunter; Ross K. Meentemeyer

2005-10-01T23:59:59.000Z

215

Realizations of Daily Weather in Forecast Seasonal Climate  

Science Conference Proceedings (OSTI)

Stochastic daily weather time series models (?weather generators?) are parameterized consistent with both local climate and probabilistic seasonal forecasts. Both single-station weather generators, and spatial networks of coherently operating ...

D. S. Wilks

2002-04-01T23:59:59.000Z

216

Regional, Very Heavy Daily Precipitation in NARCCAP Simulations  

Science Conference Proceedings (OSTI)

The authors analyze the ability of the North American Regional Climate Change Assessment Program's ensemble of climate models to simulate very heavy daily precipitation and its supporting processes, comparing simulations that used observation-...

Sho Kawazoe; William J. Gutowski Jr.

2013-08-01T23:59:59.000Z

217

Regional, Very Heavy Daily Precipitation in CMIP5 Simulations  

Science Conference Proceedings (OSTI)

The authors analyze the ability of global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble to simulate very heavy daily precipitation and its supporting processes, comparing them with ...

Sho Kawazoe; William J. Gutowski Jr.

2013-08-01T23:59:59.000Z

218

Uncertainty of Daily Isolation Estimates from a Mesoscale Pyranometer Network  

Science Conference Proceedings (OSTI)

Daily insulation values at the earth's surface are required for modeling of biophysical processes and solar energy engineering design. Ground-based pyranometer networks have proliferated in recent years, offering improved spatial coverage for ...

William L. Bland

1996-02-01T23:59:59.000Z

219

Vehicle Technologies Office: Fact #328: July 12, 2004 Expected Average  

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

8: July 12, 2004 8: July 12, 2004 Expected Average Annual Miles to someone by E-mail Share Vehicle Technologies Office: Fact #328: July 12, 2004 Expected Average Annual Miles on Facebook Tweet about Vehicle Technologies Office: Fact #328: July 12, 2004 Expected Average Annual Miles on Twitter Bookmark Vehicle Technologies Office: Fact #328: July 12, 2004 Expected Average Annual Miles on Google Bookmark Vehicle Technologies Office: Fact #328: July 12, 2004 Expected Average Annual Miles on Delicious Rank Vehicle Technologies Office: Fact #328: July 12, 2004 Expected Average Annual Miles on Digg Find More places to share Vehicle Technologies Office: Fact #328: July 12, 2004 Expected Average Annual Miles on AddThis.com... Fact #328: July 12, 2004 Expected Average Annual Miles Twenty-five percent of the respondents to a nationwide survey said that

220

Vehicle Technologies Office: Fact #536: September 15, 2008 Average...  

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

6: September 15, 2008 Average Used Car Prices Up and Used Light Truck Prices Down to someone by E-mail Share Vehicle Technologies Office: Fact 536: September 15, 2008 Average Used...

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


221

Vehicle Technologies Office: Fact #517: May 5, 2008 State Average...  

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

7: May 5, 2008 State Average Gasoline Prices, April 18, 2008 to someone by E-mail Share Vehicle Technologies Office: Fact 517: May 5, 2008 State Average Gasoline Prices, April 18,...

222

Vehicle Technologies Office: Fact #87: May 4, 1999 Average Annual...  

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

7: May 4, 1999 Average Annual Miles per Vehicle by Vehicle Type and Age to someone by E-mail Share Vehicle Technologies Office: Fact 87: May 4, 1999 Average Annual Miles per...

223

Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging  

Science Conference Proceedings (OSTI)

Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on ...

J. Mc Lean Sloughter; Adrian E. Raftery; Tilmann Gneiting; Chris Fraley

2007-09-01T23:59:59.000Z

224

Vehicle Technologies Office: Fact #744: September 10, 2012 Average...  

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

4: September 10, 2012 Average New Light Vehicle Price Grows Faster than Average Used Light Vehicle Price to someone by E-mail Share Vehicle Technologies Office: Fact 744:...

225

Improving Wind ProfilerMeasured Winds Using Coplanar Spectral Averaging  

Science Conference Proceedings (OSTI)

A method is presented that increases the detectability of weak clear-air signals by averaging Doppler spectra from coplanar wind profiler beams. The method, called coplanar spectral averaging (CSA), is applied to both simulated wind profiler ...

Robert Schafer; Susan K. Avery; Kenneth S. Gage; Paul E. Johnston; D. A. Carter

2004-11-01T23:59:59.000Z

226

On Lateral Dispersion Coefficients as Functions of Averaging Time  

Science Conference Proceedings (OSTI)

Plume dispersion coefficients are discussed in terms of single-particle and relative diffusion, and are investigated as functions of averaging time. To demonstrate the effects of averaging time on the relative importance of various dispersion ...

C. M. Sheih

1980-05-01T23:59:59.000Z

227

Vorticity Dynamics and Zonally Averaged Ocean Circulation Models  

Science Conference Proceedings (OSTI)

Diagnostic equations relating the zonally averaged overturning circulation to northsouth density variations are derived and used to determine a new closure scheme for use in zonally averaged ocean models. The presentation clarifies the dynamical ...

Daniel G. Wright; Cornelis B. Vreugdenhil; Tertia M. C. Hughes

1995-09-01T23:59:59.000Z

228

A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts  

Science Conference Proceedings (OSTI)

Using a 20-yr ECMWF ensemble reforecast dataset of total precipitation and a 20-yr dataset of a dense precipitation observation network in the Netherlands, a comparison is made between the raw ensemble output, Bayesian model averaging (BMA), and ...

Maurice J. Schmeits; Kees J. Kok

2010-11-01T23:59:59.000Z

229

Table 5C. Industrial Average Monthly Bill by Census Division ...  

U.S. Energy Information Administration (EIA)

Home > Electricity > Electric Sales, Revenue, and Price > Industrial Average Monthly Bill by Census Division, and State: Table 5C. Industrial ...

230

Maryland Average Price of Natural Gas Delivered to Residential and ...  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States

231

Optimization Online - "Block-Iterative and String-Averaging ...  

E-Print Network (OSTI)

Jul 19, 2009 ... Optimization Online. "Block-Iterative and String-Averaging Projection Algorithms in Proton Computed Tomography Image Reconstruction".

232

Table 4. Average retail price for bundled and unbundled consumers ...  

U.S. Energy Information Administration (EIA)

Table 4. Average retail price for bundled and unbundled consumers by sector, Census Division, and State 2011

233

Assessing Energy Impact of Plug-In Hybrid Electric Vehicles: Significance of Daily Distance Variation over Time and Among Drivers  

Science Conference Proceedings (OSTI)

Accurate assessment of the impact of plug-in hybrid electric vehicles (PHEVs) on petroleum and electricity consumption is a necessary step toward effective policies. Variations in daily vehicle miles traveled (VMT) over time and among drivers affect PHEV energy impact, but the significance is not well understood. This paper uses a graphical illustration, a mathematical derivation, and an empirical study to examine the cause and significance of such an effect. The first two methods reveal that ignoring daily variation in VMT always causes underestimation of petroleum consumption and overestimation of electricity consumption by PHEVs; both biases increase as the assumed PHEV charge-depleting (CD) range moves closer to the average daily VMT. The empirical analysis based on national travel survey data shows that the assumption of uniform daily VMT over time and among drivers causes nearly 68% underestimation of expected petroleum use and nearly 48% overestimation of expected electricity use by PHEVs with a 40-mi CD range (PHEV40s). Also for PHEV40s, consideration of daily variation in VMT over time but not among drivers similar to the way the utility factor curve is derived in SAE Standard SAE J2841 causes underestimation of expected petroleum use by more than 24% and overestimation of expected electricity use by about 17%. Underestimation of petroleum use and overestimation of electricity use increase with larger-battery PHEVs.

Lin, Zhenhong [ORNL; Greene, David L [ORNL

2012-01-01T23:59:59.000Z

234

Experimental analysis of thermal performance of flat plate and evacuated tube solar collectors in stationary standard and daily conditions  

SciTech Connect

New comparative tests on two different types of solar collectors are presented in this paper. A standard glazed flat plate collector and an evacuated tube collector are installed in parallel and tested at the same working conditions; the evacuated collector is a direct flow through type with external compound parabolic concentrator (CPC) reflectors. Efficiency in steady-state and quasi-dynamic conditions is measured following the standard and it is compared with the input/output curves measured for the whole day. The first purpose of the present work is the comparison of results in steady-state and quasi-dynamic test methods both for flat plate and evacuated tube collectors. Besides this, the objective is to characterize and to compare the daily energy performance of these two types of collectors. An effective mean for describing and analyzing the daily performance is the so called input/output diagram, in which the collected solar energy is plotted against the daily incident solar radiation. Test runs have been performed in several conditions to reproduce different conventional uses (hot water, space heating, solar cooling). Results are also presented in terms of daily efficiency versus daily average reduced temperature difference: this allows to represent the comparative characteristics of the two collectors when operating under variable conditions, especially with wide range of incidence angles. (author)

Zambolin, E.; Del Col, D. [Dipartimento di Fisica Tecnica, Universita degli Studi di Padova, Via Venezia 1, 35131 Padova (Italy)

2010-08-15T23:59:59.000Z

235

U.S. Total Exports  

Annual Energy Outlook 2012 (EIA)

NY Waddington, NY Sumas, WA Sweetgrass, MT Total to Chile Sabine Pass, LA Total to China Kenai, AK Sabine Pass, LA Total to India Freeport, TX Sabine Pass, LA Total to Japan...

236

Total atmospheric emissivities for a tropical climate  

SciTech Connect

The total atmospheric flux emissivities as a function of water vapor optical depth are reported for meteorological condtions in Thailand. The water vapor optical depth was first calculated as a function of height up to 12 km from the annual average upper air pressures, temperature, and dew points at Bangkok. The flux emissivity was then computed using tabulated data for the flux emissivities of water vapor, carbon dioxide, and ozone at 20/sup 0/C. (SPH)

Exell, R.H.B.

1978-01-01T23:59:59.000Z

237

Table 7.3 Average Prices of Purchased Electricity, Natural Gas, and Steam, 2010;  

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

3 Average Prices of Purchased Electricity, Natural Gas, and Steam, 2010; 3 Average Prices of Purchased Electricity, Natural Gas, and Steam, 2010; Level: National and Regional Data; Row: NAICS Codes; Column: Supplier Sources of Purchased Electricity, Natural Gas, and Steam; Unit: U.S. Dollars per Physical Units. Electricity Components Natural Gas Components Steam Components Electricity Natural Gas Steam Electricity from Sources Natural Gas from Sources Steam from Sources Electricity from Local Other than Natural Gas from Local Other than Steam from Local Other than NAICS Total Utility(b) Local Utility(c) Total Utility(b) Local Utility(c) Total Utility(b) Local Utility(c) Code(a) Subsector and Industry (kWh) (kWh) (kWh) (1000 cu ft) (1000 cu ft) (1000 cu ft) (million Btu)

238

Table 16. Recoverable Coal Reserves and Average Recovery Percentage at Producing Underground Coal Mines by State and Mining Method,  

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

Recoverable Coal Reserves and Average Recovery Percentage at Producing Underground Coal Mines by State and Mining Method, Recoverable Coal Reserves and Average Recovery Percentage at Producing Underground Coal Mines by State and Mining Method, 2012 (million short tons) U.S. Energy Information Administration | Annual Coal Report 2012 Table 16. Recoverable Coal Reserves and Average Recovery Percentage at Producing Underground Coal Mines by State and Mining Method, 2012 (million short tons) U.S. Energy Information Administration | Annual Coal Report 2012 Continuous 1 Conventional and Other 2 Longwall 3 Total Coal-Producing State Recoverable Coal Reserves at Producing Mines Average Recovery Percentage Recoverable Coal Reserves at Producing Mines Average Recovery Percentage Recoverable Coal Reserves at Producing Mines Average Recovery Percentage Recoverable Coal Reserves at Producing Mines Average Recovery Percentage

239

Total Sales of Kerosene  

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

End Use: Total Residential Commercial Industrial Farm All Other Period: End Use: Total Residential Commercial Industrial Farm All Other Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: End Use Area 2007 2008 2009 2010 2011 2012 View History U.S. 492,702 218,736 269,010 305,508 187,656 81,102 1984-2012 East Coast (PADD 1) 353,765 159,323 198,762 237,397 142,189 63,075 1984-2012 New England (PADD 1A) 94,635 42,570 56,661 53,363 38,448 15,983 1984-2012 Connecticut 13,006 6,710 8,800 7,437 7,087 2,143 1984-2012 Maine 46,431 19,923 25,158 24,281 17,396 7,394 1984-2012 Massachusetts 7,913 3,510 5,332 6,300 2,866 1,291 1984-2012 New Hampshire 14,454 6,675 8,353 7,435 5,472 1,977 1984-2012

240

Enhancement of Watershed Analysis Risk Management Framework (WARMF) for Mercury Watershed Management and Total Maximum Daily Loads (TMDLs)  

Science Conference Proceedings (OSTI)

This report documents the enhancement of EPRI's Watershed Analysis Risk Management Framework (WARMF) to enable it to simulate the biogeochemical cycling and fish accumulation of mercury in the environment. This report should be of value to the power sector, industry, environmental organizations, government, and public agencies concerned about environmental mercury.

2006-03-13T23:59:59.000Z

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


241

Vehicle Technologies Office: Fact #310: March 8, 2004 Average Material  

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

0: March 8, 2004 0: March 8, 2004 Average Material Consumption for a Domestic Automobile to someone by E-mail Share Vehicle Technologies Office: Fact #310: March 8, 2004 Average Material Consumption for a Domestic Automobile on Facebook Tweet about Vehicle Technologies Office: Fact #310: March 8, 2004 Average Material Consumption for a Domestic Automobile on Twitter Bookmark Vehicle Technologies Office: Fact #310: March 8, 2004 Average Material Consumption for a Domestic Automobile on Google Bookmark Vehicle Technologies Office: Fact #310: March 8, 2004 Average Material Consumption for a Domestic Automobile on Delicious Rank Vehicle Technologies Office: Fact #310: March 8, 2004 Average Material Consumption for a Domestic Automobile on Digg Find More places to share Vehicle Technologies Office: Fact #310:

242

Orbit-averaged guiding-center Fokker-Planck operator  

Science Conference Proceedings (OSTI)

A general orbit-averaged guiding-center Fokker-Planck operator suitable for the numerical analysis of transport processes in axisymmetric magnetized plasmas is presented. The orbit-averaged guiding-center operator describes transport processes in a three-dimensional guiding-center invariant space: the orbit-averaged magnetic-flux invariant {psi}, the minimum-B pitch-angle coordinate {xi}{sub 0}, and the momentum magnitude p.

Brizard, A. J. [Department of Chemistry and Physics, Saint Michael's College, Colchester, Vermont 05439 (United States); Decker, J.; Peysson, Y.; Duthoit, F.-X. [CEA, IRFM, Saint-Paul-lez-Durance F-13108 (France)

2009-10-15T23:59:59.000Z

243

Warm Weather and the Daily Commute | Department of Energy  

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

Warm Weather and the Daily Commute Warm Weather and the Daily Commute Warm Weather and the Daily Commute May 7, 2013 - 12:02pm Addthis Biking to work helps you get some exercise while reducing your carbon footprint. | Photo courtesy of iStockphoto.com/olaser Biking to work helps you get some exercise while reducing your carbon footprint. | Photo courtesy of iStockphoto.com/olaser Elizabeth Spencer Communicator, National Renewable Energy Laboratory How can I participate? Check out options for busing or carpooling in your area or, if you live close, try walking or biking to work. You know the weather is starting to warm up when you start hearing about those "bike, bus, or walk to work" challenges. And while my local news just started drumming up publicity for theirs, I've seen these events pop up in

244

Invisible Science: Lab Breakthroughs in Our Daily Lives | Department of  

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

Invisible Science: Lab Breakthroughs in Our Daily Lives Invisible Science: Lab Breakthroughs in Our Daily Lives Invisible Science: Lab Breakthroughs in Our Daily Lives April 24, 2012 - 2:30pm Addthis The Lab Breakthroughs video series focuses on the array of technological advancements and discoveries that stem from research performed in the National Labs, including improvements in industrial processes, discoveries in fundamental scientific research, and innovative medicines. See the Lab Breakthroughs topic page for the most recent videos and Q&As with researchers. The Lab Breakthroughs video series focuses on the array of technological advancements and discoveries that stem from research performed in the National Labs, including improvements in industrial processes, discoveries

245

Daylighter Daily Solar Roof Light | Open Energy Information  

Open Energy Info (EERE)

Daylighter Daily Solar Roof Light Daylighter Daily Solar Roof Light Jump to: navigation, search Name Daylighter Daily Solar Roof Light Address 1991 Crocker Road, Suite 600 Place Cleveland, Ohio Zip 44145 Sector Solar Product Installation; Manufacturing Phone number 440-892-3312 Website http://www.SolarLightisFree.co Coordinates 41.4648875°, -81.9506519° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":41.4648875,"lon":-81.9506519,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

246

"Table A25 Average Prices of Selected Purchased Energy Sources by Census"  

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

Average Prices of Selected Purchased Energy Sources by Census" Average Prices of Selected Purchased Energy Sources by Census" " Region, Industry Group, and Selected Industries, 1991: Part 2" " (Estimates in Dollars per Million Btu)" ,,,,,,,,"RSE" "SIC"," "," ","Residual","Distillate"," "," "," ","Row" "Code(a)","Industry Groups and Industry","Electricity","Fuel Oil","Fuel Oil(b)","Natural Gas(c)","LPG","Coal","Factors" ,,"Total United States" ,"RSE Column Factors:",0.7,0.8,1,2.8,1,0.7 20,"Food and Kindred Products",15.789,2.854,6.064,2.697,7.596,1.433,4.5

247

Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach  

Science Conference Proceedings (OSTI)

The present study endeavors to generate autoregressive neural network (AR-NN) models to forecast the monthly total ozone concentration over Kolkata (22^o34', 88^o22'), India. The issues associated with the applicability of neural network to geophysical ... Keywords: Autoregressive moving average, Autoregressive neural network, Monthly total ozone, Predictive model

Goutami Chattopadhyay; Surajit Chattopadhyay

2009-09-01T23:59:59.000Z

248

Average Depth of Crude Oil and Natural Gas Wells  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Average depth may ...

249

Table AP7. Average Expenditures for Home Appliances and Lighting ...  

U.S. Energy Information Administration (EIA)

A household is assigned to a climate zone according to the 30-year average annual degree-days for an appropriate nearby weather station.

250

Electric Sales, Revenue, and Average Price 2011 - Energy Information...  

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

All Electricity Reports Electric Sales, Revenue, and Average Price With Data for 2011 | Release Date: September 27, 2012 | Next Release Date: September, 2013 Previous editions...

251

Chapter 5. Retail Sales, Revenue, and Average Retail Price of ...  

U.S. Energy Information Administration (EIA)

106 U.S. Energy Information Administration/Electric Power Monthly June 2012 Chapter 5. Retail Sales, Revenue, and Average Retail Price of Electricity

252

Table 14a. Average Electricity Prices, Projected vs. Actual  

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

a. Average Electricity Prices, Projected vs. Actual" "Projected Price in Constant Dollars" " (constant dollars, cents per kilowatt-hour in ""dollar year"" specific to each AEO)"...

253

Average wholesale electric power prices rose in 2010 - Today in ...  

U.S. Energy Information Administration (EIA)

Average wholesale electric power prices rose in 2010, due to higher national natural gas prices and increased demand for electricity, particularly in the Eastern ...

254

Electric Sales, Revenue, and Average Price 2011 - Energy ...  

U.S. Energy Information Administration (EIA)

Class of Ownership, Number of Consumers, Sales, Revenue, and Average Retail Price for Power Marketers and Energy Service Providers by State: T12:

255

Today in Energy - Average wholesale natural gas prices mostly ...  

U.S. Energy Information Administration (EIA)

Average spot natural gas prices, which reflect the wholesale price of natural gas at major trading points, generally declined in most U.S. regional markets about 7% ...

256

Ohio Average Price of Natural Gas Delivered to Residential and ...  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States (Dollars per Thousand Cubic Feet ...

257

Average prices for spot sulfur dioxide emissions allowances at ...  

U.S. Energy Information Administration (EIA)

The weighted average spot price for sulfur dioxide (SO 2) emissions allowances awarded to winning bidders at Environmental Protection Agency's (EPA) annual auction on ...

258

Climate: monthly and annual average relative humidity GIS data...  

Open Energy Info (EERE)

Climate: monthly and annual average relative humidity GIS data at one-degree resolution of the World from NASASSE

(Abstract):
Relative Humidity at 10 m...

259

Pennsylvania Average Price of Natural Gas Delivered to Residential ...  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States (Dollars per Thousand Cubic Feet ...

260

District of Columbia Average Price of Natural Gas Delivered to ...  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States (Dollars per Thousand Cubic Feet ...

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


261

Michigan Average Price of Natural Gas Delivered to Residential and ...  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States (Dollars per Thousand Cubic Feet ...

262

,"U.S. Natural Gas Average Annual Consumption per Commercial...  

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

,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","U.S. Natural Gas Average Annual Consumption per Commercial Consumer (Mcf)",1,"Annual",2011...

263

,"U.S. Natural Gas Average Annual Consumption per Industrial...  

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

,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","U.S. Natural Gas Average Annual Consumption per Industrial Consumer (Mcf)",1,"Annual",2011...

264

2012 Brief: Average wholesale electricity prices down compared ...  

U.S. Energy Information Administration (EIA)

2012 Brief: Average wholesale electricity prices down compared to last year. ... wholesale electric power prices often trend together with natural gas prices.

265

Average wholesale spot natural gas prices rose across the country ...  

U.S. Energy Information Administration (EIA)

Wholesale spot natural gas prices rose across the country in 2010. Average spot natural gas prices at the Henry Huba key benchmark location for pricing throughout ...

266

Figure 34. Ratio of average per megawatthour fuel costs ...  

U.S. Energy Information Administration (EIA)

Title: Figure 34. Ratio of average per megawatthour fuel costs for natural gas combined-cycle plants to coal-fired steam turbines in the RFC west ...

267

EIA Renewable Energy- Average Energy Conversion Efficiency of ...  

U.S. Energy Information Administration (EIA)

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

268

,"Selected National Average Natural Gas Prices"  

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

Selected National Average Natural Gas Prices" Selected National Average Natural Gas Prices" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Average Natural Gas Prices",11,"Monthly","11/2013","1/15/1973" ,"Data 2","Annual Average Natural Gas Prices",11,"Annual",2012,"6/30/1922" ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","ngm03vmall.xls" ,"Available from Web Page:","http://www.eia.gov/oil_gas/natural_gas/data_publications/natural_gas_monthly/ngm.html"

269

New York Average Price of Natural Gas Delivered to Residential ...  

U.S. Energy Information Administration (EIA)

Average Price of Natural Gas Delivered to Residential and Commercial Consumers by Local Distribution and Marketers in Selected States (Dollars per ...

270

Total Marketed Production ..............  

Gasoline and Diesel Fuel Update (EIA)

billion cubic feet per day) billion cubic feet per day) Total Marketed Production .............. 68.95 69.77 70.45 71.64 71.91 71.70 71.46 71.57 72.61 72.68 72.41 72.62 70.21 71.66 72.58 Alaska ......................................... 1.04 0.91 0.79 0.96 1.00 0.85 0.77 0.93 0.97 0.83 0.75 0.91 0.93 0.88 0.87 Federal GOM (a) ......................... 3.93 3.64 3.44 3.82 3.83 3.77 3.73 3.50 3.71 3.67 3.63 3.46 3.71 3.70 3.62 Lower 48 States (excl GOM) ...... 63.97 65.21 66.21 66.86 67.08 67.08 66.96 67.14 67.92 68.18 68.02 68.24 65.58 67.07 68.09 Total Dry Gas Production .............. 65.46 66.21 66.69 67.79 68.03 67.83 67.61 67.71 68.69 68.76 68.50 68.70 66.55 67.79 68.66 Gross Imports ................................ 8.48 7.60 7.80 7.95 8.27 7.59 7.96 7.91 7.89 7.17 7.61 7.73 7.96 7.93 7.60 Pipeline ........................................

271

Total Biofuels Consumption (2005 - 2009) Total annual biofuels...  

Open Energy Info (EERE)

Total Biofuels Consumption (2005 - 2009) Total annual biofuels consumption (Thousand Barrels Per Day) for 2005 - 2009 for over 230 countries and regions. ...

272

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Released: September, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings* ........................... 3,037 115 397 384 52 1,143 22 354 64 148 357 Building Floorspace (Square Feet) 1,001 to 5,000 ........................... 386 19 43 18 11 93 7 137 8 12 38 5,001 to 10,000 .......................... 262 12 35 17 5 83 4 56 6 9 35 10,001 to 25,000 ........................ 407 20 46 44 8 151 3 53 9 19 54 25,001 to 50,000 ........................ 350 15 55 50 9 121 2 34 7 16 42 50,001 to 100,000 ...................... 405 16 57 65 7 158 2 29 6 18 45 100,001 to 200,000 .................... 483 16 62 80 5 195 1 24 Q 31 56 200,001 to 500,000 .................... 361 8 51 54 5 162 1 9 8 19 43 Over 500,000 ............................. 383 8 47 56 3 181 2 12 8 23 43 Principal Building Activity

273

On an Additive Model of Daily Temperature Climates  

Science Conference Proceedings (OSTI)

Guttman and Plantico reported on an additive model to describe daily temperature climates. This note reports on spectral analyses of the nonrandom residuals from the model. We concluded that quasi-periodic features are not present in the 195180 ...

Nathaniel B. Guttman; Marc S. Plantico

1989-10-01T23:59:59.000Z

274

Global Increasing Trends in Annual Maximum Daily Precipitation  

Science Conference Proceedings (OSTI)

This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. ...

Seth Westra; Lisa V. Alexander; Francis W. Zwiers

2013-06-01T23:59:59.000Z

275

Daily rainfall disaggregation using HYETOS model for Peninsular Malaysia  

Science Conference Proceedings (OSTI)

In this paper, we have examined the applicability of single site disaggregation model (HYETOS) based on the Poisson cluster model to disaggregate daily rainfall to hourly data using proportional adjusting procedure. In this study, the modified Bartlett ... Keywords: disaggregation, hyetos, poisson cluster processes

Ibrahim Suliman Hanaish; Kamarulzaman Ibrahim; Abdul Aziz Jemain

2011-07-01T23:59:59.000Z

276

Description: Lithium batteries are used daily in our work  

E-Print Network (OSTI)

with batteries from the same package or with the same expiration date. Avoid at all costs batteries that haveDescription: Lithium batteries are used daily in our work activities from flashlights, cell phones containing one SureFire 3-volt non-rechargeable 123 lithium battery and one Interstate 3-volt non

277

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Revised: December, 2008 Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings ............................. 91.0 33.0 7.2 6.1 7.0 18.7 2.7 5.3 1.0 2.2 7.9 Building Floorspace (Square Feet) 1,001 to 5,000 ........................... 99.0 30.7 6.7 2.7 7.1 13.9 7.1 19.9 1.1 1.7 8.2 5,001 to 10,000 .......................... 80.0 30.1 5.5 2.6 6.1 13.6 5.2 8.2 0.8 1.4 6.6 10,001 to 25,000 ........................ 71.0 28.2 4.5 4.1 4.1 14.5 2.3 4.5 0.8 1.6 6.5 25,001 to 50,000 ........................ 79.0 29.9 6.8 5.9 6.3 14.9 1.7 3.9 0.8 1.8 7.1 50,001 to 100,000 ...................... 88.7 31.6 7.6 7.6 6.5 19.6 1.7 3.4 0.7 2.0 8.1 100,001 to 200,000 .................... 104.2 39.1 8.2 8.9 7.9 22.9 1.1 2.9 Q 3.2 8.7 200,001 to 500,000 ....................

278

Total Space Heat-  

Gasoline and Diesel Fuel Update (EIA)

Revised: December, 2008 Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings ............................. 91.0 33.0 7.2 6.1 7.0 18.7 2.7 5.3 1.0 2.2 7.9 Building Floorspace (Square Feet) 1,001 to 5,000 ........................... 99.0 30.7 6.7 2.7 7.1 13.9 7.1 19.9 1.1 1.7 8.2 5,001 to 10,000 .......................... 80.0 30.1 5.5 2.6 6.1 13.6 5.2 8.2 0.8 1.4 6.6 10,001 to 25,000 ........................ 71.0 28.2 4.5 4.1 4.1 14.5 2.3 4.5 0.8 1.6 6.5 25,001 to 50,000 ........................ 79.0 29.9 6.8 5.9 6.3 14.9 1.7 3.9 0.8 1.8 7.1 50,001 to 100,000 ...................... 88.7 31.6 7.6 7.6 6.5 19.6 1.7 3.4 0.7 2.0 8.1 100,001 to 200,000 .................... 104.2 39.1 8.2 8.9 7.9 22.9 1.1 2.9 Q 3.2 8.7 200,001 to 500,000 ....................

279

A Kalman-filter bias correction of ozone deterministic, ensemble-averaged, and probabilistic forecasts  

SciTech Connect

Kalman filtering (KF) is used to postprocess numerical-model output to estimate systematic errors in surface ozone forecasts. It is implemented with a recursive algorithm that updates its estimate of future ozone-concentration bias by using past forecasts and observations. KF performance is tested for three types of ozone forecasts: deterministic, ensemble-averaged, and probabilistic forecasts. Eight photochemical models were run for 56 days during summer 2004 over northeastern USA and southern Canada as part of the International Consortium for Atmospheric Research on Transport and Transformation New England Air Quality (AQ) Study. The raw and KF-corrected predictions are compared with ozone measurements from the Aerometric Information Retrieval Now data set, which includes roughly 360 surface stations. The completeness of the data set allowed a thorough sensitivity test of key KF parameters. It is found that the KF improves forecasts of ozone-concentration magnitude and the ability to predict rare events, both for deterministic and ensemble-averaged forecasts. It also improves the ability to predict the daily maximum ozone concentration, and reduces the time lag between the forecast and observed maxima. For this case study, KF considerably improves the predictive skill of probabilistic forecasts of ozone concentration greater than thresholds of 10 to 50 ppbv, but it degrades it for thresholds of 70 to 90 ppbv. Moreover, KF considerably reduces probabilistic forecast bias. The significance of KF postprocessing and ensemble-averaging is that they are both effective for real-time AQ forecasting. KF reduces systematic errors, whereas ensemble-averaging reduces random errors. When combined they produce the best overall forecast.

Monache, L D; Grell, G A; McKeen, S; Wilczak, J; Pagowski, M O; Peckham, S; Stull, R; McHenry, J; McQueen, J

2006-03-20T23:59:59.000Z

280

Long-run marginal costs lower than average costs  

SciTech Connect

The thesis of this article is that the long-run marginal costs of electricity are not always greater than the present average costs, as is often assumed. As long as short-run costs decrease with new plant additions, the long-run marginal cost is less than long-run average cost. When average costs increase with new additions, long-run marginal costs are greater than long-run average costs. The long-run marginal costs of a particular utility may be less than, equal to, or greater than its long-run average costs - even with inflation present. The way to determine which condition holds for a given utility is to estimate costs under various combinations of assumptions: probable load growth, zero load growth, and load growth greater than expected; and changes in load factor with attendant costs. Utilities that can demonstrate long-run marginal costs lower than long-run average costs should be encouraged to build plant and increase load, for the resulting productivity gains and slowing of inflation. Utilities that face long-run marginal costs greater than long-run average costs should discourage growth in sales through any available means.

Hunter, S.R.

1980-01-03T23:59:59.000Z

Note: This page contains sample records for the topic "average daily total" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
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We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Determination of Total Petroleum Hydrocarbons (TPH) Using Total Carbon Analysis  

SciTech Connect

Several methods have been proposed to replace the Freon(TM)-extraction method to determine total petroleum hydrocarbon (TPH) content. For reasons of cost, sensitivity, precision, or simplicity, none of the replacement methods are feasible for analysis of radioactive samples at our facility. We have developed a method to measure total petroleum hydrocarbon content in aqueous sample matrixes using total organic carbon (total carbon) determination. The total carbon content (TC1) of the sample is measured using a total organic carbon analyzer. The sample is then contacted with a small volume of non-pokar solvent to extract the total petroleum hydrocarbons. The total carbon content of the resultant aqueous phase of the extracted sample (TC2) is measured. Total petroleum hydrocarbon content is calculated (TPH = TC1-TC2). The resultant data are consistent with results obtained using Freon(TM) extraction followed by infrared absorbance.

Ekechukwu, A.A.

2002-05-10T23:59:59.000Z

282

U.S. Total Exports  

Gasoline and Diesel Fuel Update (EIA)

Babb, MT Havre, MT Port of Morgan, MT Pittsburg, NH Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Sweetgrass, MT Total to Chile Sabine Pass, LA Total to China Kenai, AK Sabine Pass, LA Total to India Freeport, TX Sabine Pass, LA Total to Japan Cameron, LA Kenai, AK Sabine Pass, LA Total to Mexico Douglas, AZ Nogales, AZ Calexico, CA Ogilby Mesa, CA Otay Mesa, CA Alamo, TX Clint, TX Del Rio, TX Eagle Pass, TX El Paso, TX Hidalgo, TX McAllen, TX Penitas, TX Rio Bravo, TX Roma, TX Total to Portugal Sabine Pass, LA Total to Russia Total to South Korea Freeport, TX Sabine Pass, LA Total to Spain Cameron, LA Sabine Pass, LA Total to United Kingdom Sabine Pass, LA Period: Monthly Annual

283

Figure 33. Ratio of average per megawatthour fuel costs for ...  

U.S. Energy Information Administration (EIA)

Sheet3 Sheet2 Sheet1 Figure 33. Ratio of average per megawatthour fuel costs for natural gas combined-cycle plants to coal-fired steam turbines in the SERC southeast ...

284

Figure 27. Ratio of average per megawatthour fuel costs for ...  

U.S. Energy Information Administration (EIA)

Sheet3 Sheet2 Sheet1 Figure 27. Ratio of average per megawatthour fuel costs for natural gas combined-cycle plants to coal-fired steam turbines in five cases, 2008-2040

285

ARM - Evaluation Product - Average of Cloud Condensation Nuclei...  

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

are averaged together. The %ss in the CCN data stream is calculated using a heat transfer and fluid dynamics model flow model (Lance et al., 2006). The model uses the...

286

Virginia Average Price of Natural Gas Delivered to Residential...  

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

10.34 9.79 11.62 14.97 NA 20.70 1989-2013 Commercial Average Price 8.35 8.21 9.11 9.52 9.96 10.36...

287

New York Average Price of Natural Gas Delivered to Residential...  

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

94 11.57 12.82 15.94 18.40 18.73 1989-2013 Commercial Average Price 8.38 8.32 8.27 8.38 7.91 6.66...

288

Maryland Average Price of Natural Gas Delivered to Residential...  

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

0.35 10.04 12.02 15.43 17.45 16.48 1989-2013 Commercial Average Price 9.03 9.30 10.67 11.84 12.79 NA...

289

Average Diurnal Variation of Summer Lightning over the Floirida Peninsula  

Science Conference Proceedings (OSTI)

Data derived from a large network of electric field mills have been used to determine the average diurnal variation of lightning in a Florida seacoast environment. These data were obtained at the NASA Kennedy Space Center (KSC) and the Cape ...

Launa M. Maier; E. Philip Krider; Michael W. Maier

1984-06-01T23:59:59.000Z

290

Table 14b. Average Electricity Prices, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

b. Average Electricity Prices, Projected vs. Actual Projected Price in Nominal Dollars (nominal dollars, cents per kilowatt-hour) 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002...

291

2012 Brief: Average wholesale electricity prices down compared to ...  

U.S. Energy Information Administration (EIA)

Average, on-peak (weekdays from 7:00 a.m. to 11:00 p.m.) day-ahead electricity prices were lower across the entire United States in 2012 compared to 2011.

292

Table 14b. Average Electricity Prices, Projected vs. Actual  

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

b. Average Electricity Prices, Projected vs. Actual" "Projected Price in Nominal Dollars" " (nominal dollars, cents per kilowatt-hour)" ,1993,1994,1995,1996,1997,1998,1999,2000,200...

293

Does anyone have access to 2012 average residential rates by...  

Open Energy Info (EERE)

Does anyone have access to 2012 average residential rates by utility company? I'm seeing an inconsistency between the OpenEI website and EIA 861 data set. Home > Groups > Utility...

294

Exact Averaging of Atmospheric State and Flow Variables  

Science Conference Proceedings (OSTI)

A new set of averaging rules is put forward that exactly determines the means of air temperature, mixing ratio, and velocity by incorporating weighting factors in accordance with physical conservation laws. For the temperature and velocity, ...

Andrew S. Kowalski

2012-05-01T23:59:59.000Z

295

Figure 88. Annual average Henry Hub spot prices for natural ...  

U.S. Energy Information Administration (EIA)

Sheet3 Sheet2 Sheet1 Figure 88. Annual average Henry Hub spot prices for natural gas in five cases, 1990-2040 (2011 dollars per million Btu) Reference

296

Figure 86. Annual average Henry Hub spot natural gas prices ...  

U.S. Energy Information Administration (EIA)

Sheet3 Sheet2 Sheet1 Figure 86. Annual average Henry Hub spot natural gas prices, 1990-2040 (2011 dollars per million Btu) Henry Hub Spot Price 1990.00

297

Using Bayesian Model Averaging to Calibrate Forecast Ensembles  

Science Conference Proceedings (OSTI)

Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing ensembles based on Bayesian model averaging (BMA), ...

Adrian E. Raftery; Tilmann Gneiting; Fadoua Balabdaoui; Michael Polakowski

2005-05-01T23:59:59.000Z

298

Pennsylvania Average Price of Natural Gas Delivered to Residential...  

Gasoline and Diesel Fuel Update (EIA)

7 10.55 11.24 13.80 16.87 19.85 1989-2013 Commercial Average Price 9.51 9.87 10.27 11.46 12.38 12.89...

299

Semi-supervised training for the averaged perceptron POS tagger  

Science Conference Proceedings (OSTI)

This paper describes POS tagging experiments with semi-supervised training as an extension to the (supervised) averaged perceptron algorithm, first introduced for this task by (Collins, 2002). Experiments with an iterative training on standard-sized ...

Drahomra "johanka" Spoustov; Jan Haji?; Jan Raab; Miroslav Spousta

2009-03-01T23:59:59.000Z

300

A Statistical Averaging Method for Wind Profiler Doppler Spectra  

Science Conference Proceedings (OSTI)

A new method is presented for Doppler spectral averaging that more reliably identifies the profiler radar return from clear air in the presence of contaminationfor example, from migrating bird echoes. These very sensitive radars profile the wind ...

David A. Merritt

1995-10-01T23:59:59.000Z

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


301

The averaged control system of fast oscillating control systems  

E-Print Network (OSTI)

For control systems that either have an explicit periodic dependence on time or have periodic solutions and small controls, we define an average control system that takes into account all possible variations of the control, and prove that its solutions approximate all solutions of the oscillating systems as oscillations go faster. The dimension of its velocity set is characterised geomtrically. When it is maximum the average system defines a Finsler metric, unfortunately not twice differntiable in general. Under particular assumptions, valid for the control two body system, this Finsler metric generates a Hamiltonian flow on the cotangent bundle. For minimum time control, this average system proves that averaging the Hamiltonian given by the maximum principle is a valid approximation.

Bombrun, Alex

2011-01-01T23:59:59.000Z

302

Implication of Spatial Averaging in Complex-Terrain Wind Studies  

Science Conference Proceedings (OSTI)

Studies of wind over complex terrain have been conducted at three times and two locations in Northern California. Instrumentation included conventional cup-vane anemometers and optical anemometers with spatial averaging over path lengths of 0.6-1 ...

W. M. Porch

1982-09-01T23:59:59.000Z

303

Vehicle Technologies Office: Fact #638: August 30, 2010 Average...  

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

8: August 30, 2010 Average Expenditure for a New Car Declines in Relation to Family Earnings to someone by E-mail Share Vehicle Technologies Office: Fact 638: August 30, 2010...

304

Electric Sales, Revenue, and Average Price 2011 - Energy ...  

U.S. Energy Information Administration (EIA)

2001-2010 are Excel zipped files & 1994-2000 are PDF files ... and Average Retail Price for Power Marketers and ... U.S. Department of Energy USA.gov FedStats.

305

U.S. Natural Gas Average Consumption per Commercial Consumer...  

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

Commercial Consumer (Thousand Cubic Feet) U.S. Natural Gas Average Consumption per Commercial Consumer (Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

306

Solar: monthly and annual average global horizontal irradiance...  

Open Energy Info (EERE)

b>
Global Horizontal Irradiance
NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Jan 2008)
22-year Monthly & Annual Average...

307

Solar: monthly and annual average direct normal irradiance GIS...  

Open Energy Info (EERE)

>
Direct Normal Irradiance (kWhm2day)
NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Jan 2008)
22-year Monthly & Annual Average...

308

Benthic Boundary-Layer Velocity Profiles: Dependence on Averaging Period  

Science Conference Proceedings (OSTI)

The relationship between benthic boundary-layer velocity profiles and current meter averaging time is investigated using detailed (0.61 Hz) current measurements recorded within 1 m of the bottom on the inner continental shelf. The percentage of ...

Barry M. Lesht

1980-06-01T23:59:59.000Z

309

Average utilization of the nation's natural gas combined-cycle ...  

U.S. Energy Information Administration (EIA)

... (purple line) and 2010 (red line) average capacity factors for natural gas plant operations between 10 p.m. and 6 a.m. rose from 26% to 32%.

310

STATE OF CALIFORNIA AREA WEIGHTED AVERAGE CALCULATION WORKSHEET: RESIDENTIAL  

E-Print Network (OSTI)

there is more than one level of floor, wall, or ceiling insulation in a building, or more than one type of a building feature, material, or construction assembly occur in a building, a weighted average

311

Solar: monthly and annual average latitude tilt irradiance GIS...  

Open Energy Info (EERE)

& Annual Average (July 1983 - June 2005)
Parameter: Latitude Tilt Radiation (kWhm2day)
Internet: http:eosweb.larc.nasa.govsse
Note 1:...

312

Some Considerations Relevant to Computing Average Hemispheric Temperature Anomalies  

Science Conference Proceedings (OSTI)

Three data bases of gridded surface temperature anomalies were used to assess the sensitivity of the average estimated Northern Hemisphere (NH) temperature anomaly to: 1) extreme gridpoint values and 2) zonal band contributions. Over the last 100 ...

S. L. Grotch

1987-07-01T23:59:59.000Z

313

New Mexico Natural Gas Average Consumption per Industrial Consumer...  

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

Industrial Consumer (Thousand Cubic Feet) New Mexico Natural Gas Average Consumption per Industrial Consumer (Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

314

New Mexico Natural Gas Average Consumption per Commercial Consumer...  

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

Commercial Consumer (Thousand Cubic Feet) New Mexico Natural Gas Average Consumption per Commercial Consumer (Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

315

Table A44. Average Prices of Purchased Electricity and Steam  

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

4. Average Prices of Purchased Electricity and Steam" 4. Average Prices of Purchased Electricity and Steam" " by Type of Supplier, Census Region, Census Division, and" " Economic Characteristics of the Establishment, 1994" " (Estimates in Dollars per Physical Units)" ," Electricity",," Steam" ," (kWh)",," (million Btu)" ,,,,,"RSE" ,"Utility","Nonutility","Utility","Nonutility","Row" "Economic Characteristics(a)","Supplier(b)","Supplier(c)","Supplier(b)","Supplier(c)","Factors"

316

U.S. Refiner Sales to End Users (Average) Prices  

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

Sales Type: Sales to End Users, Average Through Retail Outlets Sales for Resale, Average DTW Rack Bulk Sales Type: Sales to End Users, Average Through Retail Outlets Sales for Resale, Average DTW Rack Bulk Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Formulation/ Grade Sales Type Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History Conventional, Average 3.030 3.137 3.122 3.063 3.042 2.972 1994-2013 Conventional Regular 3.005 3.116 3.102 3.040 3.017 2.948 1994-2013 Conventional Midgrade 3.167 3.256 3.239 3.200 3.193 3.121 1994-2013 Conventional Premium 3.269 3.354 3.327 3.291 3.274 3.203 1994-2013 Oxygenated, Average - - - - - - 1994-2013 Oxygenated Regular - - - - - - 1994-2013 Oxygenated Midgrade - - - - - - 1994-2013

317

Bayesian Model Averagings Problematic Treatment of Extreme Weather and a Paradigm Shift That Fixes It  

Science Conference Proceedings (OSTI)

Methods of ensemble postprocessing in which continuous probability density functions are constructed from ensemble forecasts by centering functions around each of the ensemble members have come to be called Bayesian model averaging (BMA) or ...

Craig H. Bishop; Kevin T. Shanley

2008-12-01T23:59:59.000Z

318

Average Soil Water Retention Curves Measured by Neutron Radiography  

SciTech Connect

Water retention curves are essential for understanding the hydrologic behavior of partially-saturated porous media and modeling flow transport processes within the vadose zone. In this paper we report direct measurements of the main drying and wetting branches of the average water retention function obtained using 2-dimensional neutron radiography. Flint sand columns were saturated with water and then drained under quasi-equilibrium conditions using a hanging water column setup. Digital images (2048 x 2048 pixels) of the transmitted flux of neutrons were acquired at each imposed matric potential (~10-15 matric potential values per experiment) at the NCNR BT-2 neutron imaging beam line. Volumetric water contents were calculated on a pixel by pixel basis using Beer-Lambert s law after taking into account beam hardening and geometric corrections. To remove scattering effects at high water contents the volumetric water contents were normalized (to give relative saturations) by dividing the drying and wetting sequences of images by the images obtained at saturation and satiation, respectively. The resulting pixel values were then averaged and combined with information on the imposed basal matric potentials to give average water retention curves. The average relative saturations obtained by neutron radiography showed an approximate one-to-one relationship with the average values measured volumetrically using the hanging water column setup. There were no significant differences (at p < 0.05) between the parameters of the van Genuchten equation fitted to the average neutron radiography data and those estimated from replicated hanging water column data. Our results indicate that neutron imaging is a very effective tool for quantifying the average water retention curve.

Cheng, Chu-Lin [ORNL; Perfect, Edmund [University of Tennessee, Knoxville (UTK); Kang, Misun [ORNL; Voisin, Sophie [ORNL; Bilheux, Hassina Z [ORNL; Horita, Juske [Texas Tech University (TTU); Hussey, Dan [NIST Center for Neutron Research (NCRN), Gaithersburg, MD

2011-01-01T23:59:59.000Z

319

Cointegration of the Daily Electric Power System Load and the Weather  

E-Print Network (OSTI)

The paper examines the cointegration of the daily electric power system load and the weather by a field intelligent system. The daily load has been modelled by dynamic regressions. A "Daily Artificial Dispather" thermal intelligent system has been costructed. Time and energy tests have been obtained for this intelligent system. The improvement in the daily load forecast, achieved by this intelligent system, has been obtained. The predicted daily electricity price has been found.

Stefanov, Stefan Z

2007-01-01T23:59:59.000Z

320

Combinatorial aspects of total positivity  

E-Print Network (OSTI)

In this thesis I study combinatorial aspects of an emerging field known as total positivity. The classical theory of total positivity concerns matrices in which all minors are nonnegative. While this theory was pioneered ...

Williams, Lauren Kiyomi

2005-01-01T23:59:59.000Z

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


321

API Gravity, Weighted Average Refinery Crude Oil Input ...  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Totals may not equal ...

322

Sulfur Content, Weighted Average Refinery Crude Oil Input ...  

U.S. Energy Information Administration (EIA)

-No Data Reported; --= Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Notes: Totals may not equal ...

323

Total correlations and mutual information  

E-Print Network (OSTI)

In quantum information theory it is generally accepted that quantum mutual information is an information-theoretic measure of total correlations of a bipartite quantum state. We argue that there exist quantum states for which quantum mutual information cannot be considered as a measure of total correlations. Moreover, for these states we propose a different way of quantifying total correlations.

Zbigniew Walczak

2008-06-30T23:59:59.000Z

324

"2012 Average Monthly Bill- Industrial"  

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

Industrial" Industrial" "(Data from forms EIA-861- schedules 4A-D, EIA-861S and EIA-861U)" "State","Number of Customers","Average Monthly Consumption (kWh)","Average Price (cents/kWh)","Average Monthly Bill (Dollar and cents)" "New England",34164,67854.037,11.83487,8030.4373 "Connecticut",4647,63947.063,12.672933,8103.9685 "Maine",2780,90741.457,7.9819499,7242.9376 "Massachusetts",21145,66710.826,12.566635,8383.3057 "New Hampshire",3444,47247.217,11.83228,5590.423 "Rhode Island",1927,39935.911,10.676724,4263.8471 "Vermont",221,536044.12,9.9796777,53495.475 "Middle Atlantic",45836,126368.14,7.4903534,9465.42 "New Jersey",12729,50817.89,10.516509,5344.2677

325

"2012 Average Monthly Bill- Residential"  

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

Residential" Residential" "(Data from forms EIA-861- schedules 4A-D, EIA-861S and EIA-861U)" "State","Number of Customers","Average Monthly Consumption (kWh)","Average Price (cents/kWh)","Average Monthly Bill (Dollar and cents)" "New England",6203726,634.13095,15.713593,99.644755 "Connecticut",1454651,730.85302,17.343298,126.75402 "Maine",703770,530.56349,14.658797,77.774225 "Massachusetts",2699141,627.15845,14.912724,93.52641 "New Hampshire",601697,614.81776,16.070168,98.802249 "Rhode Island",435448,597.34783,14.404061,86.042344 "Vermont",309019,565.03618,17.006075,96.090478 "Middle Atlantic",15727423,700.63673,15.272654,107.00582

326

INVERSIONS FOR AVERAGE SUPERGRANULAR FLOWS USING FINITE-FREQUENCY KERNELS  

Science Conference Proceedings (OSTI)

I analyze the maps recording the travel-time shifts caused by averaged plasma anomalies under an 'average supergranule', constructed by means of statistical averaging over 5582 individual supergranules with large divergence signals detected in two months of Helioseismic and Magnetic Imager Dopplergrams. By utilizing a three-dimensional validated time-distance inversion code, I measure a peak vertical velocity of 117 {+-} 2 m s{sup -1} at depths around 1.2 Mm in the center of the supergranule and a root-mean-square vertical velocity of 21 m s{sup -1} over the area of the supergranule. A discrepancy between this measurement and the measured surface vertical velocity (a few m s{sup -1}) can be explained by the existence of the large-amplitude vertical flow under the surface of supergranules with large divergence signals, recently suggested by Duvall and Hanasoge.

Svanda, Michal, E-mail: michal@astronomie.cz [Astronomical Institute, Academy of Sciences of the Czech Republic (v.v.i.), Fricova 298, CZ-25165 Ondrejov (Czech Republic)

2012-11-10T23:59:59.000Z

327

Globally Averaged Atmospheric CFC-11 Concentrations: Monthly and Annual  

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

Chlorofluorocarbons » Chlorofluorocarbons » Atmospheric CFC-11 Concentrations Globally Averaged Atmospheric CFC-11 Concentrations: Monthly and Annual Data for the Period 1975-1992 DOI: 10.3334/CDIAC/atg.db1010 data Data (DB1010) Investigator M. A. K. Khalil and R. A. Rasmussen Description This data set presents globally averaged atmospheric concentrations of chlorofluorocarbon 11, known also as CFC-11 or F-11 (chemical name: trichlorofluoromethane; formula: CCl3F). The monthly global average data are derived from flask air samples collected at eight sites in six locations over the period August 1980-July 1992. The sites are Barrow (Alaska), Cape Meares (Oregon), Cape Kumukahi and Mauna Loa (Hawaii), Cape Matatula (American Samoa), Cape Grim (Tasmania), Palmer Station, and the

328

Comparison of Average Transport and Dispersion Among a Gaussian, a  

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

Comparison of Average Transport and Dispersion Among a Gaussian, a Comparison of Average Transport and Dispersion Among a Gaussian, a Two-Dimensional, and a Three-Dimensional Model Comparison of Average Transport and Dispersion Among a Gaussian, a Two-Dimensional, and a Three-Dimensional Model The Nuclear Regulatory Commission's (NRC's) code for predicting off-site consequences, MACCS2 (Chanin, et al. 1998) (MELCOR Accident Consequence Code System, Version 2), uses a simplified model for atmospheric transport and d ispersion (ATD), that is, a straight-line Gaussian model. The MACCS2 calculations are used by the NRC for planning purposes, for cost-benefit analyses, and in level-3 probabilistic risk analyses (PRAs). The MACCS2 ATD model has been criticized as being overly simplistic, even for its purposes. The justification for its use has been

329

Bounce-averaged Fokker-Planck code for stellarator transport  

Science Conference Proceedings (OSTI)

A computer code for solving the bounce-averaged Fokker-Planck equation appropriate to stellarator transport has been developed, and its first applications made. The code is much faster than the bounce-averaged Monte-Carlo codes, which up to now have provided the most efficient numerical means for studying stellarator transport. Moreover, because the connection to analytic kinetic theory of the Fokker-Planck approach is more direct than for the Monte-Carlo approach, a comparison of theory and numerical experiment is now possible at a considerably more detailed level than previously.

Mynick, H.E.; Hitchon, W.N.G.

1985-07-01T23:59:59.000Z

330

Flavor Physics Data from the Heavy Flavor Averaging Group (HFAG)  

DOE Data Explorer (OSTI)

The Heavy Flavor Averaging Group (HFAG) was established at the May 2002 Flavor Physics and CP Violation Conference in Philadelphia, and continues the LEP Heavy Flavor Steering Group's tradition of providing regular updates to the world averages of heavy flavor quantities. Data are provided by six subgroups that each focus on a different set of heavy flavor measurements: B lifetimes and oscillation parameters, Semi-leptonic B decays, Rare B decays, Unitarity triangle parameters, B decays to charm final states, and Charm Physics.

Heavy Flavor Averaging Group (HFAG)

331

AMPERE AVERAGE CURRENT PHOTOINJECTOR AND ENERGY RECOVERY LINAC.  

SciTech Connect

High-power Free-Electron Lasers were made possible by advances in superconducting linac operated in an energy-recovery mode. In order to get to much higher power levels, say a fraction of a megawatt average power, many technological barriers are yet to be broken. We describe work on CW, high-current and high-brightness electron beams. This will include a description of a superconducting, laser-photocathode RF gun employing a new secondary-emission multiplying cathode, an accelerator cavity, both capable of producing of the order of one ampere average current and plans for an ERL based on these units.

BEN-ZVI,I.; BURRILL,A.; CALAGA,R.; ET AL.

2004-08-17T23:59:59.000Z

332

Table 10. Average Price of U.S. Steam Coal Exports  

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

Average Price of U.S. Steam Coal Exports Average Price of U.S. Steam Coal Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Table 10. Average Price of U.S. Steam Coal Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Year to Date Continent and Country of Destination April - June 2013 January - March 2013 April - June 2012 2013 2012 Percent Change North America Total 65.10 63.67 73.81 64.48 78.90 -18.3 Canada* 59.34 55.22 63.02 57.57 73.63 -21.8 Dominican Republic 78.47 74.41 73.89 75.40 76.61 -1.6 Honduras - 54.58 54.43 54.58 54.43 0.3 Jamaica 480.00 54.43 - 54.72 55.42 -1.3 Mexico 69.42 73.33 82.64 70.83 86.44 -18.1 Other** 80.33 389.30 70.37 82.45 76.10 8.3 South America Total 79.44 77.85 70.55

333

Table 12. Average Price of U.S. Metallurgical Coal Exports  

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

Average Price of U.S. Metallurgical Coal Exports Average Price of U.S. Metallurgical Coal Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Table 12. Average Price of U.S. Metallurgical Coal Exports (dollars per short ton) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2013 Year to Date Continent and Country of Destination April - June 2013 January - March 2013 April - June 2012 2013 2012 Percent Change North America Total 92.50 99.40 146.56 94.82 140.70 -32.6 Canada* 99.83 125.20 142.46 106.43 138.19 -23.0 Dominican Republic 114.60 77.21 - 77.27 - - Mexico 78.93 78.54 180.76 78.77 153.65 -48.7 South America Total 119.26 117.51 167.05 118.30 168.12 -29.6 Argentina 146.70 131.08 182.47 137.36 196.37 -30.1 Brazil 119.21 117.38 165.61 118.20

334

Total....................................................................................  

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

5.6 5.6 17.7 7.9 Personal Computers Do Not Use a Personal Computer.................................. 35.5 8.1 5.6 2.5 Use a Personal Computer.............................................. 75.6 17.5 12.1 5.4 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 14.1 10.0 4.0 Laptop Model............................................................. 16.9 3.4 2.1 1.3 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 3.4 2.5 0.9 2 to 15 Hours............................................................. 29.1 7.0 4.8 2.3 16 to 40 Hours........................................................... 13.5 2.8 2.1 0.7 41 to 167 Hours......................................................... 6.3

335

Total...................................................................  

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

15.2 15.2 7.8 1.0 1.2 3.3 1.9 For Two Housing Units............................. 0.9 Q N Q 0.6 N Heat Pump.................................................. 9.2 7.4 0.3 Q 0.7 0.5 Portable Electric Heater............................... 1.6 0.8 Q Q Q 0.3 Other Equipment......................................... 1.9 0.7 Q Q 0.7 Q Fuel Oil........................................................... 7.7 5.5 0.4 0.8 0.9 0.2 Steam or Hot Water System........................ 4.7 2.9 Q 0.7 0.8 N For One Housing Unit.............................. 3.3 2.9 Q Q Q N For Two Housing Units............................. 1.4 Q Q 0.5 0.8 N Central Warm-Air Furnace........................... 2.8 2.4 Q Q Q 0.2 Other Equipment......................................... 0.3 0.2 Q N Q N Wood..............................................................

336

Total...............................................................  

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

Do Not Have Cooling Equipment................. Do Not Have Cooling Equipment................. 17.8 5.3 4.7 2.8 1.9 3.1 3.6 7.5 Have Cooling Equipment.............................. 93.3 21.5 24.1 17.8 11.2 18.8 13.0 31.1 Use Cooling Equipment............................... 91.4 21.0 23.5 17.4 11.0 18.6 12.6 30.3 Have Equipment But Do Not Use it............. 1.9 0.5 0.6 0.4 Q Q 0.5 0.8 Air-Conditioning Equipment 1, 2 Central System............................................ 65.9 11.0 16.5 13.5 8.7 16.1 6.4 17.2 Without a Heat Pump.............................. 53.5 9.4 13.6 10.7 7.1 12.7 5.4 14.5 With a Heat Pump................................... 12.3 1.7 2.8 2.8 1.6 3.4 1.0 2.7 Window/Wall Units...................................... 28.9 10.5 8.1 4.5 2.7 3.1 6.7 14.1 1 Unit....................................................... 14.5 5.8 4.3 2.0 1.1 1.3 3.4 7.4 2 Units.....................................................

337

Total.............................................................................  

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

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 1.4 1.0 0.4 2 Times A Day...................................................... 24.6 5.8 3.5 2.3 Once a Day........................................................... 42.3 10.7 7.8 2.9 A Few Times Each Week...................................... 27.2 5.6 4.0 1.6 About Once a Week.............................................. 3.9 0.9 0.6 0.3 Less Than Once a Week....................................... 4.1 1.1 0.7 0.4 No Hot Meals Cooked........................................... 0.9 Q Q N Conventional Oven Use an Oven......................................................... 109.6 25.3 17.6 7.7 More Than Once a Day..................................... 8.9 1.3 0.8 0.5 Once a Day.......................................................

338

Total...............................................................  

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

26.7 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Personal Computers Do Not Use a Personal Computer ........... 35.5 17.1 10.8 4.2 1.8 1.6 10.3 20.6 Use a Personal Computer......................... 75.6 9.6 18.0 16.4 11.3 20.3 6.4 17.9 Number of Desktop PCs 1.......................................................... 50.3 8.3 14.2 11.4 7.2 9.2 5.3 14.2 2.......................................................... 16.2 0.9 2.6 3.7 2.9 6.2 0.8 2.6 3 or More............................................. 9.0 0.4 1.2 1.3 1.2 5.0 0.3 1.1 Number of Laptop PCs 1.......................................................... 22.5 2.2 4.6 4.5 2.9 8.3 1.4 4.0 2.......................................................... 4.0 Q 0.4 0.6 0.4 2.4 Q 0.5 3 or More............................................. 0.7 Q Q Q Q 0.4 Q Q Type of Monitor Used on Most-Used PC Desk-top

339

Total...............................................................  

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

20.6 20.6 25.6 40.7 24.2 Personal Computers Do Not Use a Personal Computer ........... 35.5 6.9 8.1 14.2 6.4 Use a Personal Computer......................... 75.6 13.7 17.5 26.6 17.8 Number of Desktop PCs 1.......................................................... 50.3 9.3 11.9 18.2 11.0 2.......................................................... 16.2 2.9 3.5 5.5 4.4 3 or More............................................. 9.0 1.5 2.1 2.9 2.5 Number of Laptop PCs 1.......................................................... 22.5 4.7 4.6 7.7 5.4 2.......................................................... 4.0 0.6 0.9 1.5 1.1 3 or More............................................. 0.7 Q Q Q 0.3 Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)................... 45.0 7.9 11.4 15.4 10.2 Flat-panel LCD.................................

340

Total................................................................  

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

111.1 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Do Not Have Space Heating Equipment....... 1.2 0.5 0.3 0.2 Q 0.2 0.3 0.6 Have Main Space Heating Equipment.......... 109.8 26.2 28.5 20.4 13.0 21.8 16.3 37.9 Use Main Space Heating Equipment............ 109.1 25.9 28.1 20.3 12.9 21.8 16.0 37.3 Have Equipment But Do Not Use It.............. 0.8 0.3 0.3 Q Q N 0.4 0.6 Main Heating Fuel and Equipment Natural Gas.................................................. 58.2 12.2 14.4 11.3 7.1 13.2 7.6 18.3 Central Warm-Air Furnace........................ 44.7 7.5 10.8 9.3 5.6 11.4 4.6 12.0 For One Housing Unit........................... 42.9 6.9 10.3 9.1 5.4 11.3 4.1 11.0 For Two Housing Units......................... 1.8 0.6 0.6 Q Q Q 0.4 0.9 Steam or Hot Water System..................... 8.2 2.4 2.5 1.0 1.0 1.3 1.5 3.6 For One Housing Unit...........................

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341

Total...........................................................  

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

Q Q Table HC3.2 Living Space Characteristics by Owner-Occupied Housing Units, 2005 2 to 4 Units 5 or More Units Mobile Homes Million U.S. Housing Units Owner- Occupied Housing Units (millions) Type of Owner-Occupied Housing Unit Housing Units (millions) Single-Family Units Apartments in Buildings With-- Living Space Characteristics Detached Attached Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC3.2 Living Space Characteristics by Owner-Occupied Housing Units, 2005 2 to 4 Units 5 or More Units Mobile Homes Million U.S. Housing Units Owner- Occupied Housing Units (millions) Type of Owner-Occupied Housing Unit Housing Units (millions)

342

Total........................................................................  

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

25.6 25.6 40.7 24.2 Do Not Have Space Heating Equipment............... 1.2 Q Q Q 0.7 Have Main Space Heating Equipment.................. 109.8 20.5 25.6 40.3 23.4 Use Main Space Heating Equipment.................... 109.1 20.5 25.6 40.1 22.9 Have Equipment But Do Not Use It...................... 0.8 N N Q 0.6 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 11.4 18.4 13.6 14.7 Central Warm-Air Furnace................................ 44.7 6.1 16.2 11.0 11.4 For One Housing Unit................................... 42.9 5.6 15.5 10.7 11.1 For Two Housing Units................................. 1.8 0.5 0.7 Q 0.3 Steam or Hot Water System............................. 8.2 4.9 1.6 1.0 0.6 For One Housing Unit................................... 5.1 3.2 1.1 0.4

343

Total...........................................................................  

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

0.6 0.6 15.1 5.5 Do Not Have Cooling Equipment............................. 17.8 4.0 2.4 1.7 Have Cooling Equipment.......................................... 93.3 16.5 12.8 3.8 Use Cooling Equipment........................................... 91.4 16.3 12.6 3.7 Have Equipment But Do Not Use it.......................... 1.9 0.3 Q Q Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 6.0 5.2 0.8 Without a Heat Pump........................................... 53.5 5.5 4.8 0.7 With a Heat Pump............................................... 12.3 0.5 0.4 Q Window/Wall Units.................................................. 28.9 10.7 7.6 3.1 1 Unit................................................................... 14.5 4.3 2.9 1.4 2 Units.................................................................

344

Total.......................................................................  

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

4.2 4.2 7.6 16.6 Personal Computers Do Not Use a Personal Computer ................... 35.5 6.4 2.2 4.2 Use a Personal Computer................................ 75.6 17.8 5.3 12.5 Number of Desktop PCs 1.................................................................. 50.3 11.0 3.4 7.6 2.................................................................. 16.2 4.4 1.3 3.1 3 or More..................................................... 9.0 2.5 0.7 1.8 Number of Laptop PCs 1.................................................................. 22.5 5.4 1.5 3.9 2.................................................................. 4.0 1.1 0.3 0.8 3 or More..................................................... 0.7 0.3 Q Q Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)...........................

345

Total....................................................................................  

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

111.1 47.1 19.0 22.7 22.3 Personal Computers Do Not Use a Personal Computer.................................. 35.5 16.9 6.5 4.6 7.6 Use a Personal Computer.............................................. 75.6 30.3 12.5 18.1 14.7 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 22.9 9.8 14.1 11.9 Laptop Model............................................................. 16.9 7.4 2.7 4.0 2.9 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 5.7 1.8 2.9 3.2 2 to 15 Hours............................................................. 29.1 11.9 5.1 6.5 5.7 16 to 40 Hours........................................................... 13.5 5.5 2.5 3.3 2.2 41 to 167 Hours.........................................................

346

Total........................................................................  

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

7.1 7.1 19.0 22.7 22.3 Do Not Have Space Heating Equipment............... 1.2 0.7 Q 0.2 Q Have Main Space Heating Equipment.................. 109.8 46.3 18.9 22.5 22.1 Use Main Space Heating Equipment.................... 109.1 45.6 18.8 22.5 22.1 Have Equipment But Do Not Use It...................... 0.8 0.7 Q N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 27.0 11.9 14.9 4.3 Central Warm-Air Furnace................................ 44.7 19.8 8.6 12.8 3.6 For One Housing Unit................................... 42.9 18.8 8.3 12.3 3.5 For Two Housing Units................................. 1.8 1.0 0.3 0.4 Q Steam or Hot Water System............................. 8.2 4.4 2.1 1.4 0.3 For One Housing Unit................................... 5.1 2.1 1.6 1.0

347

Total........................................................................  

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

15.1 15.1 5.5 Do Not Have Space Heating Equipment............... 1.2 Q Q Q Have Main Space Heating Equipment.................. 109.8 20.5 15.1 5.4 Use Main Space Heating Equipment.................... 109.1 20.5 15.1 5.4 Have Equipment But Do Not Use It...................... 0.8 N N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 11.4 9.1 2.3 Central Warm-Air Furnace................................ 44.7 6.1 5.3 0.8 For One Housing Unit................................... 42.9 5.6 4.9 0.7 For Two Housing Units................................. 1.8 0.5 0.4 Q Steam or Hot Water System............................. 8.2 4.9 3.6 1.3 For One Housing Unit................................... 5.1 3.2 2.2 1.0 For Two Housing Units.................................

348

Total.................................................................  

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

49.2 49.2 15.1 15.6 11.1 7.0 5.2 8.0 Have Cooling Equipment............................... 93.3 31.3 15.1 15.6 11.1 7.0 5.2 8.0 Use Cooling Equipment................................ 91.4 30.4 14.6 15.4 11.1 6.9 5.2 7.9 Have Equipment But Do Not Use it............... 1.9 1.0 0.5 Q Q Q Q Q Do Not Have Cooling Equipment................... 17.8 17.8 N N N N N N Air-Conditioning Equipment 1, 2 Central System............................................. 65.9 3.9 15.1 15.6 11.1 7.0 5.2 8.0 Without a Heat Pump................................ 53.5 3.5 12.9 12.7 8.6 5.5 4.2 6.2 With a Heat Pump..................................... 12.3 0.4 2.2 2.9 2.5 1.5 1.0 1.8 Window/Wall Units........................................ 28.9 27.5 0.5 Q 0.3 Q Q Q 1 Unit......................................................... 14.5 13.5 0.3 Q Q Q N Q 2 Units.......................................................

349

Total........................................................................  

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

7.1 7.1 7.0 8.0 12.1 Do Not Have Space Heating Equipment............... 1.2 Q Q Q 0.2 Have Main Space Heating Equipment.................. 109.8 7.1 6.8 7.9 11.9 Use Main Space Heating Equipment.................... 109.1 7.1 6.6 7.9 11.4 Have Equipment But Do Not Use It...................... 0.8 N Q N 0.5 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 3.8 0.4 3.8 8.4 Central Warm-Air Furnace................................ 44.7 1.8 Q 3.1 6.0 For One Housing Unit................................... 42.9 1.5 Q 3.1 6.0 For Two Housing Units................................. 1.8 Q N Q Q Steam or Hot Water System............................. 8.2 1.9 Q Q 0.2 For One Housing Unit................................... 5.1 0.8 Q N Q For Two Housing Units.................................

350

Total........................................................................  

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

5.6 5.6 17.7 7.9 Do Not Have Space Heating Equipment............... 1.2 Q Q N Have Main Space Heating Equipment.................. 109.8 25.6 17.7 7.9 Use Main Space Heating Equipment.................... 109.1 25.6 17.7 7.9 Have Equipment But Do Not Use It...................... 0.8 N N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 18.4 13.1 5.3 Central Warm-Air Furnace................................ 44.7 16.2 11.6 4.7 For One Housing Unit................................... 42.9 15.5 11.0 4.5 For Two Housing Units................................. 1.8 0.7 0.6 Q Steam or Hot Water System............................. 8.2 1.6 1.2 0.4 For One Housing Unit................................... 5.1 1.1 0.9 Q For Two Housing Units.................................

351

Total...........................................................................  

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

4.2 4.2 7.6 16.6 Do Not Have Cooling Equipment............................. 17.8 10.3 3.1 7.3 Have Cooling Equipment.......................................... 93.3 13.9 4.5 9.4 Use Cooling Equipment........................................... 91.4 12.9 4.3 8.5 Have Equipment But Do Not Use it.......................... 1.9 1.0 Q 0.8 Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 10.5 3.9 6.5 Without a Heat Pump........................................... 53.5 8.7 3.2 5.5 With a Heat Pump............................................... 12.3 1.7 0.7 1.0 Window/Wall Units.................................................. 28.9 3.6 0.6 3.0 1 Unit................................................................... 14.5 2.9 0.5 2.4 2 Units.................................................................

352

Total...........................................................  

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

Q Q Million U.S. Housing Units Renter- Occupied Housing Units (millions) Type of Renter-Occupied Housing Unit U.S. Housing Units (millions Single-Family Units Apartments in Buildings With-- Living Space Characteristics Detached Attached Table HC4.2 Living Space Characteristics by Renter-Occupied Housing Units, 2005 2 to 4 Units 5 or More Units Mobile Homes Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing Units Renter- Occupied Housing Units (millions) Type of Renter-Occupied Housing Unit U.S. Housing Units (millions Single-Family Units Apartments in Buildings With-- Living Space Characteristics Detached Attached Table HC4.2 Living Space Characteristics by Renter-Occupied Housing Units, 2005

353

Total....................................................................................  

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

Personal Computers Personal Computers Do Not Use a Personal Computer.................................. 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer.............................................. 75.6 26.6 14.5 4.1 7.9 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 20.5 11.0 3.4 6.1 Laptop Model............................................................. 16.9 6.1 3.5 0.7 1.9 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 5.0 2.6 1.0 1.3 2 to 15 Hours............................................................. 29.1 10.3 5.9 1.6 2.9 16 to 40 Hours........................................................... 13.5 4.1 2.3 0.6 1.2 41 to 167 Hours.........................................................

354

Total..............................................................  

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

,171 ,171 1,618 1,031 845 630 401 Census Region and Division Northeast................................................... 20.6 2,334 1,664 562 911 649 220 New England.......................................... 5.5 2,472 1,680 265 1,057 719 113 Middle Atlantic........................................ 15.1 2,284 1,658 670 864 627 254 Midwest...................................................... 25.6 2,421 1,927 1,360 981 781 551 East North Central.................................. 17.7 2,483 1,926 1,269 999 775 510 West North Central................................. 7.9 2,281 1,930 1,566 940 796 646 South.......................................................... 40.7 2,161 1,551 1,295 856 615 513 South Atlantic......................................... 21.7 2,243 1,607 1,359 896 642 543 East South Central.................................

355

Total.........................................................................................  

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

..... ..... 111.1 7.1 7.0 8.0 12.1 Personal Computers Do Not Use a Personal Computer...................................... 35.5 3.0 2.0 2.7 3.1 Use a Personal Computer.................................................. 75.6 4.2 5.0 5.3 9.0 Most-Used Personal Computer Type of PC Desk-top Model............................................................. 58.6 3.2 3.9 4.0 6.7 Laptop Model................................................................. 16.9 1.0 1.1 1.3 2.4 Hours Turned on Per Week Less than 2 Hours......................................................... 13.6 0.7 0.9 0.9 1.4 2 to 15 Hours................................................................. 29.1 1.7 2.1 1.9 3.4 16 to 40 Hours............................................................... 13.5 0.9 0.9 0.9 1.8 41 to 167 Hours.............................................................

356

Total.............................................................................  

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

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 2.6 0.7 1.9 2 Times A Day...................................................... 24.6 6.6 2.0 4.6 Once a Day........................................................... 42.3 8.8 2.9 5.8 A Few Times Each Week...................................... 27.2 4.7 1.5 3.1 About Once a Week.............................................. 3.9 0.7 Q 0.6 Less Than Once a Week....................................... 4.1 0.7 0.3 0.4 No Hot Meals Cooked........................................... 0.9 0.2 Q Q Conventional Oven Use an Oven......................................................... 109.6 23.7 7.5 16.2 More Than Once a Day..................................... 8.9 1.7 0.4 1.3 Once a Day.......................................................

357

Total..............................................................................  

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

0.7 0.7 21.7 6.9 12.1 Do Not Have Cooling Equipment................................ 17.8 1.4 0.8 0.2 0.3 Have Cooling Equipment............................................. 93.3 39.3 20.9 6.7 11.8 Use Cooling Equipment.............................................. 91.4 38.9 20.7 6.6 11.7 Have Equipment But Do Not Use it............................. 1.9 0.5 Q Q Q Air-Conditioning Equipment 1, 2 Central System........................................................... 65.9 32.1 17.6 5.2 9.3 Without a Heat Pump.............................................. 53.5 23.2 10.9 3.8 8.4 With a Heat Pump................................................... 12.3 9.0 6.7 1.4 0.9 Window/Wall Units..................................................... 28.9 8.0 3.4 1.7 2.9 1 Unit......................................................................

358

Total....................................................................  

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

14.7 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Household Size 1 Person.......................................................... 30.0 4.6 2.5 3.7 3.2 5.4 5.5 3.7 1.6 2 Persons......................................................... 34.8 4.3 1.9 4.4 4.1 5.9 5.3 5.5 3.4 3 Persons......................................................... 18.4 2.5 1.3 1.7 1.9 2.9 3.5 2.8 1.6 4 Persons......................................................... 15.9 1.9 0.8 1.5 1.6 3.0 2.5 3.1 1.4 5 Persons......................................................... 7.9 0.8 0.4 1.0 1.1 1.2 1.1 1.5 0.9 6 or More Persons........................................... 4.1 0.5 0.3 0.3 0.6 0.5 0.7 0.8 0.4 2005 Annual Household Income Category Less than $9,999............................................. 9.9 1.9 1.1 1.3 0.9 1.7 1.3 1.1 0.5 $10,000 to $14,999..........................................

359

Total....................................................................................  

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

25.6 25.6 40.7 24.2 Personal Computers Do Not Use a Personal Computer.................................. 35.5 6.9 8.1 14.2 6.4 Use a Personal Computer.............................................. 75.6 13.7 17.5 26.6 17.8 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 10.4 14.1 20.5 13.7 Laptop Model............................................................. 16.9 3.3 3.4 6.1 4.1 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 2.4 3.4 5.0 2.9 2 to 15 Hours............................................................. 29.1 5.2 7.0 10.3 6.6 16 to 40 Hours........................................................... 13.5 3.1 2.8 4.1 3.4 41 to 167 Hours.........................................................

360

Total....................................................................................  

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

4.2 4.2 7.6 16.6 Personal Computers Do Not Use a Personal Computer.................................. 35.5 6.4 2.2 4.2 Use a Personal Computer.............................................. 75.6 17.8 5.3 12.5 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 13.7 4.2 9.5 Laptop Model............................................................. 16.9 4.1 1.1 3.0 Hours Turned on Per Week Less than 2 Hours..................................................... 13.6 2.9 0.9 2.0 2 to 15 Hours............................................................. 29.1 6.6 2.0 4.6 16 to 40 Hours........................................................... 13.5 3.4 0.9 2.5 41 to 167 Hours......................................................... 6.3

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


361

Total..................................................................  

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

33.0 33.0 8.0 3.4 5.9 14.4 1.2 Do Not Have Cooling Equipment..................... 17.8 6.5 1.6 0.9 1.3 2.4 0.2 Have Cooling Equipment................................. 93.3 26.5 6.5 2.5 4.6 12.0 1.0 Use Cooling Equipment.................................. 91.4 25.7 6.3 2.5 4.4 11.7 0.8 Have Equipment But Do Not Use it................. 1.9 0.8 Q Q 0.2 0.3 Q Type of Air-Conditioning Equipment 1, 2 Central System.............................................. 65.9 14.1 3.6 1.5 2.1 6.4 0.6 Without a Heat Pump.................................. 53.5 12.4 3.1 1.3 1.8 5.7 0.6 With a Heat Pump....................................... 12.3 1.7 0.6 Q 0.3 0.6 Q Window/Wall Units....................................... 28.9 12.4 2.9 1.0 2.5 5.6 0.4 1 Unit.......................................................... 14.5 7.3 1.2 0.5 1.4 3.9 0.2 2 Units.........................................................

362

Total....................................................................................  

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

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day................................................. 8.2 3.7 1.6 1.4 1.5 2 Times A Day.............................................................. 24.6 10.8 4.1 4.3 5.5 Once a Day................................................................... 42.3 17.0 7.2 8.7 9.3 A Few Times Each Week............................................. 27.2 11.4 4.7 6.4 4.8 About Once a Week..................................................... 3.9 1.7 0.6 0.9 0.8 Less Than Once a Week.............................................. 4.1 2.2 0.6 0.8 0.5 No Hot Meals Cooked................................................... 0.9 0.4 Q Q Q Conventional Oven Use an Oven................................................................. 109.6 46.2 18.8

363

Total...................................................................  

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

Single-Family Units Single-Family Units Detached Type of Housing Unit Table HC2.7 Air Conditioning Usage Indicators by Type of Housing Unit, 2005 Million U.S. Housing Units Air Conditioning Usage Indicators Attached 2 to 4 Units 5 or More Units Mobile Homes Apartments in Buildings With-- Housing Units (millions) Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Single-Family Units Detached Type of Housing Unit Table HC2.7 Air Conditioning Usage Indicators by Type of Housing Unit, 2005 Million U.S. Housing Units Air Conditioning Usage Indicators Attached 2 to 4 Units 5 or More Units Mobile Homes Apartments in Buildings With-- Housing Units (millions) At Home Behavior Home Used for Business

364

Total.............................................................................  

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

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 2.1 1.8 0.3 Have Cooling Equipment............................................ 93.3 23.5 16.0 7.5 Use Cooling Equipment............................................. 91.4 23.4 15.9 7.5 Have Equipment But Do Not Use it............................ 1.9 Q Q Q Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 17.3 11.3 6.0 Without a Heat Pump............................................. 53.5 16.2 10.6 5.6 With a Heat Pump................................................. 12.3 1.1 0.8 0.4 Window/Wall Units.................................................. 28.9 6.6 4.9 1.7 1 Unit..................................................................... 14.5 4.1 2.9 1.2 2 Units...................................................................

365

Total..............................................................................  

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

20.6 20.6 25.6 40.7 24.2 Do Not Have Cooling Equipment................................ 17.8 4.0 2.1 1.4 10.3 Have Cooling Equipment............................................. 93.3 16.5 23.5 39.3 13.9 Use Cooling Equipment.............................................. 91.4 16.3 23.4 38.9 12.9 Have Equipment But Do Not Use it............................. 1.9 0.3 Q 0.5 1.0 Air-Conditioning Equipment 1, 2 Central System........................................................... 65.9 6.0 17.3 32.1 10.5 Without a Heat Pump.............................................. 53.5 5.5 16.2 23.2 8.7 With a Heat Pump................................................... 12.3 0.5 1.1 9.0 1.7 Window/Wall Units..................................................... 28.9 10.7 6.6 8.0 3.6 1 Unit......................................................................

366

Total..........................................................  

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

60,000 to 79,999 80,000 or More Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing...

367

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

Usage Indicators by U.S. Census Region, 2005 Million U.S. Housing Units Air Conditioning Usage Indicators U.S. Census Region Northeast Midwest South West Energy Information...

368

Total..........................................................  

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

Homes Million U.S. Housing Units Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC3.7...

369

Total..........................................................  

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

Homes Million U.S. Housing Units Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC4.7...

370

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

Self-Reported) City Town Suburbs Rural Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC8.7...

371

Total..........................................................  

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

East North Central West North Central Energy Information Administration: 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Million U.S. Housing...

372

Total..........................................................  

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

U.S. Housing Units Home Electronics Usage Indicators Table HC10.12 Home Electronics Usage Indicators by U.S. Census Region, 2005 Housing Units (millions) Energy Information...

373

Total..........................................................  

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

U.S. Housing Units Home Electronics Usage Indicators Table HC8.12 Home Electronics Usage Indicators by UrbanRural Location, 2005 Housing Units (millions) Energy Information...

374

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

7.0 7.7 6.6 Have Equipment But Do Not Use it... 1.9 Q N Q 0.6 Air-Conditioning Equipment 1, 2 Central System......

375

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

Air-Conditioning Equipment 1, 2 Central System... 65.9 47.5 4.0 2.8 7.9 3.7 Without a Heat Pump... 53.5...

376

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

91.4 23.4 15.9 7.5 Have Equipment But Do Not Use it... 1.9 Q Q Q Air-Conditioning Equipment 1, 2 Central System......

377

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

18.0 Have Equipment But Do Not Use it... 1.9 0.9 0.3 0.3 0.4 Air-Conditioning Equipment 1, 2 Central System......

378

Total..........................................................  

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

m... 3.2 0.2 Q 0.1 Telephone and Office Equipment CellMobile Telephone... 84.8 14.9 11.1 3.9 Cordless...

379

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

m... 3.2 0.9 0.7 Q Telephone and Office Equipment CellMobile Telephone... 84.8 19.3 13.2 6.1 Cordless...

380

Total..........................................................  

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

Q 0.5 Q Q Monitor is Turned Off... 0.5 N Q Q Q Q N Q Use of Internet Have Access to Internet Yes... 66.9...

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


381

Total..........................................................  

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

Four Most Populated States New York Florida Texas California Million U.S. Housing Units Home Electronics Usage Indicators Table HC15.12 Home Electronics Usage Indicators by Four...

382

Total  

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

Normal ButaneButylene Other Liquids Oxygenates Fuel Ethanol MTBE Other Oxygenates Biomass-based Diesel Other Renewable Diesel Fuel Other Renewable Fuels Gasoline Blending...

383

Total  

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

Normal ButaneButylene Other Liquids Oxygenates Fuel Ethanol MTBE Other Oxygenates Biomass-based Diesel Fuel Other Renewable Diesel Fuel Other Renewable Fuels Gasoline Blending...

384

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

111.1 7.1 7.0 8.0 12.1 Personal Computers Do Not Use a Personal Computer ... 35.5 3.0 2.0 2.7 3.1 Use a Personal Computer......

385

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

... 25.8 2.8 5.8 5.5 3.8 7.9 1.4 5.1 Use of Most-Used Ceiling Fan Used All Summer... 18.7 4.2 4.9 4.1 2.1 3.4 2.4 6.3...

386

Total..........................................................  

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

Heating Characteristics Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC5.4 Space Heating...

387

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

at All... 2.9 1.1 0.5 Q 0.4 Battery-Operated AppliancesTools Use Battery-Operated AppliancesTools......

388

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

3.3 Not Used at All... 2.9 0.7 0.5 Q Battery-Operated AppliancesTools Use Battery-Operated AppliancesTools... 54.9...

389

Total..........................................................  

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

3.6 Not Used at All... 2.9 0.8 0.3 0.4 Battery-Operated AppliancesTools Use Battery-Operated AppliancesTools... 54.9...

390

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

1.1 Not Used at All... 2.9 0.4 Q 0.2 Battery-Operated AppliancesTools Use Battery-Operated AppliancesTools... 54.9...

391

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

at All... 2.9 1.4 0.4 0.4 0.7 Battery-Operated AppliancesTools Use Battery-Operated AppliancesTools......

392

Total..........................................................  

Gasoline and Diesel Fuel Update (EIA)

5 or More Units Mobile Homes Apartments in Buildings With-- Housing Units (millions) At Home Behavior Home Used for Business Yes......

393

Total..........................................................  

Annual Energy Outlook 2012 (EIA)

... 34.3 1.2 0.9 2.2 2.9 5.4 7.0 8.2 6.6 Adequacy of Insulation Well Insulated... 29.5 1.5 0.9 2.3 2.7 4.1...

394

Total.............................................................................  

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

Cooking Appliances Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 1.2 1.0 0.2 2 Times A Day...................................................... 24.6 4.0 2.7 1.2 Once a Day........................................................... 42.3 7.9 5.4 2.5 A Few Times Each Week...................................... 27.2 6.0 4.8 1.2 About Once a Week.............................................. 3.9 0.6 0.5 Q Less Than Once a Week....................................... 4.1 0.6 0.4 Q No Hot Meals Cooked........................................... 0.9 0.3 Q Q Conventional Oven Use an Oven......................................................... 109.6 20.3 14.9 5.4 More Than Once a Day..................................... 8.9 1.4 1.2 0.3 Once a Day.......................................................

395

Total...............................................................  

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

47.1 47.1 19.0 22.7 22.3 Personal Computers Do Not Use a Personal Computer ........... 35.5 16.9 6.5 4.6 7.6 Use a Personal Computer......................... 75.6 30.3 12.5 18.1 14.7 Number of Desktop PCs 1.......................................................... 50.3 21.1 8.3 10.7 10.1 2.......................................................... 16.2 6.2 2.8 4.1 3.0 3 or More............................................. 9.0 2.9 1.4 3.2 1.6 Number of Laptop PCs 1.......................................................... 22.5 9.1 3.6 6.0 3.8 2.......................................................... 4.0 1.5 0.6 1.3 0.7 3 or More............................................. 0.7 0.3 Q Q Q Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)................... 45.0 17.7 7.5 10.2 9.6 Flat-panel LCD.................................

396

Total........................................................  

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

111.1 24.5 1,090 902 341 872 780 441 Census Region and Division Northeast............................................. 20.6 6.7 1,247 1,032 Q 811 788 147 New England.................................... 5.5 1.9 1,365 1,127 Q 814 748 107 Middle Atlantic.................................. 15.1 4.8 1,182 978 Q 810 800 159 Midwest................................................ 25.6 4.6 1,349 1,133 506 895 810 346 East North Central............................ 17.7 3.2 1,483 1,239 560 968 842 351 West North Central........................... 7.9 1.4 913 789 329 751 745 337 South................................................... 40.7 7.8 881 752 572 942 873 797 South Atlantic................................... 21.7 4.9 875 707 522 1,035 934 926 East South Central........................... 6.9 0.7 Q Q Q 852 826 432 West South Central..........................

397

Total...............................................................  

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

0.7 0.7 21.7 6.9 12.1 Personal Computers Do Not Use a Personal Computer ........... 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer......................... 75.6 26.6 14.5 4.1 7.9 Number of Desktop PCs 1.......................................................... 50.3 18.2 10.0 2.9 5.3 2.......................................................... 16.2 5.5 3.0 0.7 1.8 3 or More............................................. 9.0 2.9 1.5 0.5 0.8 Number of Laptop PCs 1.......................................................... 22.5 7.7 4.3 1.1 2.4 2.......................................................... 4.0 1.5 0.9 Q 0.4 3 or More............................................. 0.7 Q Q Q Q Type of Monitor Used on Most-Used PC Desk-top CRT (Standard Monitor)................... 45.0 15.4 7.9 2.8 4.8 Flat-panel LCD.................................

398

Total.................................................................  

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

26.7 26.7 28.8 20.6 13.1 22.0 16.6 38.6 Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day.............................. 8.2 2.9 2.5 1.3 0.5 1.0 2.4 4.6 2 Times A Day........................................... 24.6 6.5 7.0 4.3 3.2 3.6 4.8 10.3 Once a Day................................................ 42.3 8.8 9.8 8.7 5.1 10.0 5.0 12.9 A Few Times Each Week........................... 27.2 5.6 7.2 4.7 3.3 6.3 3.2 7.5 About Once a Week................................... 3.9 1.1 1.1 0.6 0.5 0.6 0.4 1.4 Less Than Once a Week............................ 4.1 1.3 1.0 0.9 0.5 0.4 0.7 1.4 No Hot Meals Cooked................................ 0.9 0.5 Q Q Q Q 0.2 0.5 Conventional Oven Use an Oven.............................................. 109.6 26.1 28.5 20.2 12.9 21.8 16.3 37.8 More Than Once a Day..........................

399

Total..................................................................  

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

. . 111.1 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Do Not Have Cooling Equipment..................... 17.8 3.9 1.8 2.2 2.1 3.1 2.6 1.7 0.4 Have Cooling Equipment................................. 93.3 10.8 5.6 10.3 10.4 15.8 16.0 15.6 8.8 Use Cooling Equipment.................................. 91.4 10.6 5.5 10.3 10.3 15.3 15.7 15.3 8.6 Have Equipment But Do Not Use it................. 1.9 Q Q Q Q 0.6 0.4 0.3 Q Type of Air-Conditioning Equipment 1, 2 Central System.............................................. 65.9 3.7 2.6 6.1 6.8 11.2 13.2 13.9 8.2 Without a Heat Pump.................................. 53.5 3.6 2.3 5.5 5.8 9.5 10.1 10.3 6.4 With a Heat Pump....................................... 12.3 Q 0.3 0.6 1.0 1.7 3.1 3.6 1.7 Window/Wall Units....................................... 28.9 7.3 3.2 4.5 3.7 4.8 3.0 1.9 0.7 1 Unit..........................................................

400

Total..............................................  

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

111.1 86.6 2,720 1,970 1,310 1,941 1,475 821 1,059 944 554 Census Region and Division Northeast.................................... 20.6 13.9 3,224 2,173 836 2,219 1,619 583 903 830 Q New England.......................... 5.5 3.6 3,365 2,154 313 2,634 1,826 Q 951 940 Q Middle Atlantic........................ 15.1 10.3 3,167 2,181 1,049 2,188 1,603 582 Q Q Q Midwest...................................... 25.6 21.0 2,823 2,239 1,624 2,356 1,669 1,336 1,081 961 778 East North Central.................. 17.7 14.5 2,864 2,217 1,490 2,514 1,715 1,408 907 839 553 West North Central................. 7.9 6.4 2,729 2,289 1,924 1,806 1,510 1,085 1,299 1,113 1,059 South.......................................... 40.7 33.0 2,707 1,849 1,563 1,605 1,350 954 1,064 970 685 South Atlantic......................... 21.7 16.8 2,945 1,996 1,695 1,573 1,359 909 1,044 955

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


401

Total.................................................................................  

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

... ... 111.1 20.6 15.1 5.5 Do Not Have Cooling Equipment................................. 17.8 4.0 2.4 1.7 Have Cooling Equipment............................................. 93.3 16.5 12.8 3.8 Use Cooling Equipment............................................... 91.4 16.3 12.6 3.7 Have Equipment But Do Not Use it............................. 1.9 0.3 Q Q Type of Air-Conditioning Equipment 1, 2 Central System.......................................................... 65.9 6.0 5.2 0.8 Without a Heat Pump.............................................. 53.5 5.5 4.8 0.7 With a Heat Pump................................................... 12.3 0.5 0.4 Q Window/Wall Units.................................................... 28.9 10.7 7.6 3.1 1 Unit.......................................................................

402

Total.............................................................................  

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

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 8.5 2.7 2.6 4.0 Have Cooling Equipment............................................ 93.3 38.6 16.2 20.1 18.4 Use Cooling Equipment............................................. 91.4 37.8 15.9 19.8 18.0 Have Equipment But Do Not Use it............................ 1.9 0.9 0.3 0.3 0.4 Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 25.8 10.9 16.6 12.5 Without a Heat Pump............................................. 53.5 21.2 9.7 13.7 8.9 With a Heat Pump................................................. 12.3 4.6 1.2 2.8 3.6 Window/Wall Units.................................................. 28.9 13.4 5.6 3.9 6.1 1 Unit.....................................................................

403

Total.............................................................................  

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

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 10.3 3.1 7.3 Have Cooling Equipment............................................ 93.3 13.9 4.5 9.4 Use Cooling Equipment............................................. 91.4 12.9 4.3 8.5 Have Equipment But Do Not Use it............................ 1.9 1.0 Q 0.8 Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 10.5 3.9 6.5 Without a Heat Pump............................................. 53.5 8.7 3.2 5.5 With a Heat Pump................................................. 12.3 1.7 0.7 1.0 Window/Wall Units.................................................. 28.9 3.6 0.6 3.0 1 Unit..................................................................... 14.5 2.9 0.5 2.4 2 Units...................................................................

404

Total..................................................................  

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

78.1 78.1 64.1 4.2 1.8 2.3 5.7 Do Not Have Cooling Equipment..................... 17.8 11.3 9.3 0.6 Q 0.4 0.9 Have Cooling Equipment................................. 93.3 66.8 54.7 3.6 1.7 1.9 4.8 Use Cooling Equipment.................................. 91.4 65.8 54.0 3.6 1.7 1.9 4.7 Have Equipment But Do Not Use it................. 1.9 1.1 0.8 Q N Q Q Type of Air-Conditioning Equipment 1, 2 Central System.............................................. 65.9 51.7 43.9 2.5 0.7 1.6 3.1 Without a Heat Pump.................................. 53.5 41.1 34.8 2.1 0.5 1.2 2.6 With a Heat Pump....................................... 12.3 10.6 9.1 0.4 Q 0.3 0.6 Window/Wall Units....................................... 28.9 16.5 12.0 1.3 1.0 0.4 1.7 1 Unit.......................................................... 14.5 7.2 5.4 0.5 0.2 Q 0.9 2 Units.........................................................

405

Total.............................................................................  

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

Do Not Have Cooling Equipment............................... Do Not Have Cooling Equipment............................... 17.8 1.4 0.8 0.2 0.3 Have Cooling Equipment............................................ 93.3 39.3 20.9 6.7 11.8 Use Cooling Equipment............................................. 91.4 38.9 20.7 6.6 11.7 Have Equipment But Do Not Use it............................ 1.9 0.5 Q Q Q Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 32.1 17.6 5.2 9.3 Without a Heat Pump............................................. 53.5 23.2 10.9 3.8 8.4 With a Heat Pump................................................. 12.3 9.0 6.7 1.4 0.9 Window/Wall Units.................................................. 28.9 8.0 3.4 1.7 2.9 1 Unit.....................................................................

406

Total........................................................................  

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

4.2 4.2 7.6 16.6 Do Not Have Space Heating Equipment............... 1.2 0.7 Q 0.7 Have Main Space Heating Equipment.................. 109.8 23.4 7.5 16.0 Use Main Space Heating Equipment.................... 109.1 22.9 7.4 15.4 Have Equipment But Do Not Use It...................... 0.8 0.6 Q 0.5 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 14.7 4.6 10.1 Central Warm-Air Furnace................................ 44.7 11.4 4.0 7.4 For One Housing Unit................................... 42.9 11.1 3.8 7.3 For Two Housing Units................................. 1.8 0.3 Q Q Steam or Hot Water System............................. 8.2 0.6 0.3 0.3 For One Housing Unit................................... 5.1 0.4 0.2 0.1 For Two Housing Units.................................

407

Total..............................................................  

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

Do Not Have Cooling Equipment................ Do Not Have Cooling Equipment................ 17.8 5.3 4.7 2.8 1.9 3.1 3.6 7.5 Have Cooling Equipment............................. 93.3 21.5 24.1 17.8 11.2 18.8 13.0 31.1 Use Cooling Equipment.............................. 91.4 21.0 23.5 17.4 11.0 18.6 12.6 30.3 Have Equipment But Do Not Use it............. 1.9 0.5 0.6 0.4 Q Q 0.5 0.8 Type of Air-Conditioning Equipment 1, 2 Central System.......................................... 65.9 11.0 16.5 13.5 8.7 16.1 6.4 17.2 Without a Heat Pump.............................. 53.5 9.4 13.6 10.7 7.1 12.7 5.4 14.5 With a Heat Pump................................... 12.3 1.7 2.8 2.8 1.6 3.4 1.0 2.7 Window/Wall Units................................... 28.9 10.5 8.1 4.5 2.7 3.1 6.7 14.1 1 Unit...................................................... 14.5 5.8 4.3 2.0 1.1 1.3 3.4 7.4 2 Units....................................................

408

Residual Fuel Oil Prices, Average - Sales to End Users  

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

Product/Sales Type: Residual Fuel, Average - Sales to End Users Residual Fuel, Average - Sales for Resale Sulfur Less Than or Equal to 1% - Sales to End Users Sulfur Less Than or Equal to 1% - Sales for Resale Sulfur Greater Than 1% - Sales to End Users Sulfur Greater Than 1% - Sales for Resale Period: Monthly Annual Product/Sales Type: Residual Fuel, Average - Sales to End Users Residual Fuel, Average - Sales for Resale Sulfur Less Than or Equal to 1% - Sales to End Users Sulfur Less Than or Equal to 1% - Sales for Resale Sulfur Greater Than 1% - Sales to End Users Sulfur Greater Than 1% - Sales for Resale Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Product/Sales Type Area Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History U.S. - - - - - - 1983-2013 East Coast (PADD 1) - - - - - - 1983-2013 New England (PADD 1A) - - - - - - 1983-2013 Connecticut - - - - - - 1983-2013 Maine - - - - - - 1983-2013 Massachusetts - - - - - - 1983-2013

409

Pennsylvania Average Price of Natural Gas Delivered to Residential...  

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

Percent Sold by Local Distribution Companies 91.2 91.2 2006-2011 Commercial Average Price 12.77 14.29 11.83 10.47 10.42 10.21 1967-2012 Local Distribution Companies 11.29 NA...

410

Michigan Average Price of Natural Gas Delivered to Residential...  

Annual Energy Outlook 2012 (EIA)

8.75 8.56 9.26 11.63 12.68 13.68 1989-2013 Commercial Average Price 7.67 7.51 7.84 8.78 9.69 10.30...

411

HIGH AVERAGE POWER UV FREE ELECTRON LASER EXPERIMENTS AT JLAB  

Science Conference Proceedings (OSTI)

Having produced 14 kW of average power at {approx}2 microns, JLAB has shifted its focus to the ultraviolet portion of the spectrum. This presentation will describe the JLab UV Demo FEL, present specifics of its driver ERL, and discuss the latest experimental results from FEL experiments and machine operations.

Douglas, David; Evtushenko, Pavel; Gubeli, Joseph; Hernandez-Garcia, Carlos; Legg, Robert; Neil, George; Powers, Thomas; Shinn, Michelle D; Tennant, Christopher

2012-07-01T23:59:59.000Z

412

Table WF01. Average Consumer Prices and Expenditures for ...  

U.S. Energy Information Administration (EIA)

Heating Oil U.S. Average Consumption (gallons) 522.7 531.7 572.5 538.2 574.1 465.3 539.9 546.9 1.3 ... Wood 2,094 2,179 2,353 2,424 2,454 2,520 2,582 ...

413

Using Bayesian Model Averaging to Calibrate Forecast Ensembles 1  

E-Print Network (OSTI)

Using Bayesian Model Averaging to Calibrate Forecast Ensembles 1 Adrian E. Raftery, Fadoua forecasting often exhibit a spread-skill relationship, but they tend to be underdispersive. This paper of PDFs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal

Washington at Seattle, University of

414

Information Leakage via Electromagnetic Emanation and Effectiveness of Averaging Technique  

Science Conference Proceedings (OSTI)

It is well known that there is relationship between electromagnetic emanation and processing information in IT devices such as personal computers and smart cards. In this paper, we show how to estimate amount of information that is leaked as electromagnetic ... Keywords: Tempest, Channel capacity, electromagnetic emanation, averaging technique

Hidema Tanaka

2008-04-01T23:59:59.000Z

415

Spatial analysis based on variance of moving window averages  

E-Print Network (OSTI)

R02 R04 R06 R08 R16 I n Window size B R02 R06 R10 R14 R20R04 R08 R12 R16 R24 R36 I n Window size Wu et al. , Fig. 2Based on Variance of Moving Window Averages B. M. Wu, K. V.

Wu, B M; Subbarao, K V; Ferrandino, F J; Hao, J J

2006-01-01T23:59:59.000Z

416

A Vertically Averaged Circulation Model Using Boundary-Fitted Coordinates  

Science Conference Proceedings (OSTI)

A two-dimensional vertically averaged circulation model using boundary-fitted coordinates has been developed for predicting sea level and currants in estuarine and shelf waters. The basic idea of the approach is to use a set of coupled quasi-...

Malcolm L. Spaulding

1984-05-01T23:59:59.000Z

417

High average power diode pumped solid state lasers for CALIOPE  

Science Conference Proceedings (OSTI)

Diode pumping of solid state media offers the opportunity for very low maintenance, high efficiency, and compact laser systems. For remote sensing, such lasers may be used to pump tunable non-linear sources, or if tunable themselves, act directly or through harmonic crystals as the probe. The needs of long range remote sensing missions require laser performance in the several watts to kilowatts range. At these power performance levels, more advanced thermal management technologies are required for the diode pumps. The solid state laser design must now address a variety of issues arising from the thermal loads, including fracture limits, induced lensing and aberrations, induced birefringence, and laser cavity optical component performance degradation with average power loading. In order to highlight the design trade-offs involved in addressing the above issues, a variety of existing average power laser systems are briefly described. Included are two systems based on Spectra Diode Laboratory`s water impingement cooled diode packages: a two times diffraction limited, 200 watt average power, 200 Hz multi-rod laser/amplifier by Fibertek, and TRW`s 100 watt, 100 Hz, phase conjugated amplifier. The authors also present two laser systems built at Lawrence Livermore National Laboratory (LLNL) based on their more aggressive diode bar cooling package, which uses microchannel cooler technology capable of 100% duty factor operation. They then present the design of LLNL`s first generation OPO pump laser for remote sensing. This system is specified to run at 100 Hz, 20 nsec pulses each with 300 mJ, less than two times diffraction limited, and with a stable single longitudinal mode. The performance of the first testbed version will be presented. The authors conclude with directions their group is pursuing to advance average power lasers. This includes average power electro-optics, low heat load lasing media, and heat capacity lasers.

Comaskey, B.; Halpin, J.; Moran, B.

1994-07-01T23:59:59.000Z

418

Table 7.1 Average Prices of Purchased Energy Sources, 2010  

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

Average Prices of Purchased Energy Sources, 2010; Average Prices of Purchased Energy Sources, 2010; Level: National and Regional Data; Row: NAICS Codes; Column: All Energy Sources Collected; Unit: U.S. Dollars per Physical Units. Coal NAICS TOTAL Acetylene Breeze Total Anthracite Code(a) Subsector and Industry (million Btu) (cu ft) (short tons) (short tons) (short tons) Total United States 311 Food 9.12 0.26 0.00 53.43 90.85 3112 Grain and Oilseed Milling 6.30 0.29 0.00 51.34 50.47 311221 Wet Corn Milling 4.87 0.48 0.00 47.74 50.47 31131 Sugar Manufacturing 5.02 0.31 0.00 53.34 236.66 3114 Fruit and Vegetable Preserving and Specialty Foods 9.78 0.27 0.00 90.59 0.00 3115 Dairy Products 11.21 0.10 0.00 103.12 0.00 3116 Animal Slaughtering and Processing

419

Idle Operating Total Stream Day  

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

3 3 Idle Operating Total Stream Day Barrels per Idle Operating Total Calendar Day Barrels per Atmospheric Crude Oil Distillation Capacity Idle Operating Total Operable Refineries Number of State and PAD District a b b 11 10 1 1,293,200 1,265,200 28,000 1,361,700 1,329,700 32,000 ............................................................................................................................................... PAD District I 1 1 0 182,200 182,200 0 190,200 190,200 0 ................................................................................................................................................................................................................................................................................................ Delaware......................................

420

China Total Cloud Amount Trends  

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

Trends in Total Cloud Amount Over China DOI: 10.3334CDIACcli.008 data Data image Graphics Investigator Dale P. Kaiser Carbon Dioxide Information Analysis Center, Environmental...

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


421

Addition of Bevacizumab to Standard Radiation Therapy and Daily Temozolomide Is Associated With Minimal Toxicity in Newly Diagnosed Glioblastoma Multiforme  

SciTech Connect

Purpose: To determine the safety of the addition of bevacizumab to standard radiation therapy and daily temozolomide for newly diagnosed glioblastoma multiforme (GBM). Methods and Materials: A total of 125 patients with newly diagnosed GBM were enrolled in the study, and received standard radiation therapy and daily temozolomide. All patients underwent a craniotomy and were at least 2 weeks postoperative. Radiation therapy was administered in 1.8-Gy fractions, with the clinical target volume for the primary course treated to a dose of 45 to 50.4 Gy, followed by a boost of 9 to 14.4 Gy, to a total dose of 59.4 Gy. Patients received temozolomide at 75 mg/m{sup 2} daily throughout the course of radiation therapy. Bevacizumab was given at 10 mg/kg intravenously every 14 days, beginning a minimum of 4 weeks postoperatively. Results: Of the 125 patients, 120 (96%) completed the protocol-specified radiation therapy. Five patients had to stop the protocol therapy, 2 patients with pulmonary emboli, and 1 patient each with a Grade 2 central nervous system hemorrhage, Grade 4 pancytopenia, and wound dehiscence requiring surgical intervention. All 5 patients ultimately finished the radiation therapy. After radiation therapy, 3 patients had progressive disease, 2 had severe fatigue and decreased performance status, 1 patient had a colonic perforation, and 1 had a rectal fissure; these 7 patients therefore did not proceed with the protocol-specified adjuvant temozolomide, bevacizumab, and irinotecan. However, 113 patients (90%) were able to continue on study. Conclusions: The addition of bevacizumab to standard radiation therapy and daily temozolomide was found to be associated with minimal toxicity in patients newly diagnosed with GBM.

Vredenburgh, James J., E-mail: vrede001@mc.duke.edu [Department of Medicine, Duke University Medical Center, Durham, NC (United States); Desjardins, Annick [Department of Neurology, Duke University Medical Center, Durham, NC (United States); Kirkpatrick, John P. [Department of Radiation Oncology, Duke University Medical Center, Durham, NC (United States); Reardon, David A. [Department of Surgery, Duke University Medical Center, Durham, NC (United States); Department of Pediatrics, Duke University Medical Center, Durham, NC (United States); Peters, Katherine B. [Department of Neurology, Duke University Medical Center, Durham, NC (United States); Herndon, James E.; Marcello, Jennifer [Department of Cancer Center Biostatistics, Duke University Medical Center, Durham, NC (United States); Bailey, Leighann; Threatt, Stevie; Sampson, John; Friedman, Allan [Department of Surgery, Duke University Medical Center, Durham, NC (United States); Friedman, Henry S. [Department of Surgery, Duke University Medical Center, Durham, NC (United States); Department of Pediatrics, Duke University Medical Center, Durham, NC (United States)

2012-01-01T23:59:59.000Z

422

Table 7.2 Average Prices of Purchased Energy Sources, 2010;  

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

Table 7.2 Average Prices of Purchased Energy Sources, 2010; Level: National and Regional Data; Row: NAICS Codes; Column: All Energy Sources Collected; Unit: U.S. Dollars per Million Btu. Selected Wood and Other Biomass Components Coal Components Coke Electricity Components Natural Gas Components Steam Components Total Wood Residues Bituminous Electricity Diesel Fuel Motor Natural Gas Steam and Wood-Related and Electricity from Sources and Gasoline Pulping Liquor Natural Gas from Sources Steam from Sources Waste Gases Waste Oils Industrial Wood Byproducts and NAICS Coal Subbituminous Coal Petroleum Electricity from Local Other than Distillate Diesel Distillate Residual Blast Coke Oven (excluding or LPG and Natural Gas from Local

423

Nonlocal imaging by conditional averaging of random reference measurements  

E-Print Network (OSTI)

We report the nonlocal imaging of an object by conditional averaging of the random exposure frames of a reference detector, which only sees the freely propagating field from a thermal light source. A bucket detector, synchronized with the reference detector, records the intensity fluctuations of an identical beam passing through the object mask. These fluctuations are sorted according to their values relative to the mean, then the reference data in the corresponding time-bins for a given fluctuation range are averaged, to produce either positive or negative images. Since no correlation calculations are involved, this correspondence imaging technique challenges our former interpretations of "ghost" imaging. Compared with conventional correlation imaging or compressed sensing schemes, both the number of exposures and computation time are greatly reduced, while the visibility is much improved. A simple statistical model is presented to explain the phenomenon.

Kai-Hong Luo; Boqiang Huang; Wei-Mou Zheng; Ling-An Wu

2013-03-22T23:59:59.000Z

424

Database of average-power damage thresholds at 1064 nm  

Science Conference Proceedings (OSTI)

We have completed a database of average-power, laser-induced, damage thresholds at 1064 nm on a variety of materials. Measurements were made with a newly constructed laser to provide design input for moderate and high average-power laser projects. The measurements were conducted with 16-ns pulses at pulse-repetition frequencies ranging from 6 to 120 Hz. Samples were typically irradiated for time ranging from a fraction of a second up to 5 minutes (36,000 shots). We tested seven categories of samples which included antireflective coatings, high reflectors, polarizers, single and multiple layers of the same material, bare and overcoated metal surfaces, bare polished surfaces, and bulk materials. The measured damage threshold ranged from 46 J/cm/sup 2/ for a bare polished glass substrate. 4 refs., 7 figs., 1 tab.

Rainer, F.; Hildum, E.A.; Milam, D.

1987-12-14T23:59:59.000Z

425

Probability density function transformation using seeded localized averaging  

SciTech Connect

Seeded Localized Averaging (SLA) is a spectrum acquisition method that averages pulse-heights in dynamic windows. SLA sharpens peaks in the acquired spectra. This work investigates the transformation of the original probability density function (PDF) in the process of applying SLA procedure. We derive an analytical expression for the resulting probability density function after an application of SLA. In addition, we prove the following properties: 1) for symmetric distributions, SLA preserves both the mean and symmetry. 2) for uni-modal symmetric distributions, SLA reduces variance, sharpening the distributions peak. Our results are the first to prove these properties, reinforcing past experimental observations. Specifically, our results imply that in the typical case of a spectral peak with Gaussian PDF the full width at half maximum (FWHM) of the transformed peak becomes narrower even with averaging of only two pulse-heights. While the Gaussian shape is no longer preserved, our results include an analytical expression for the resulting distribution. Examples of the transformation of other PDFs are presented. (authors)

Dimitrov, N. B. [Operations Research Dept., Naval Postgraduate School, Monterey, CA 93943 (United States); Jordanov, V. T. [Yantel, LLC, Santa Fe, NM 87508 (United States)

2011-07-01T23:59:59.000Z

426

total energy | OpenEI  

Open Energy Info (EERE)

total energy total energy Dataset Summary Description This dataset comes from the Energy Information Administration (EIA), and is part of the 2011 Annual Energy Outlook Report (AEO2011). This dataset is table 1, and contains only the reference case. The dataset uses quadrillion BTUs, and quantifies the energy prices using U.S. dollars. The data is broken down into total production, imports, exports, consumption, and prices for energy types. Source EIA Date Released April 26th, 2011 (3 years ago) Date Updated Unknown Keywords 2011 AEO consumption EIA export import production reference case total energy Data application/vnd.ms-excel icon AEO2011: Total Energy Supply, Disposition, and Price Summary - Reference Case (xls, 112.8 KiB) Quality Metrics Level of Review Peer Reviewed

427

Statistical Tests for Comparison of Daily Variability in Observed and Simulated Climates  

Science Conference Proceedings (OSTI)

Tests for differences in daily variability based on the jackknife are presented. These tests properly account for the effect of autocorrelation in the data and are reasonably robust against departures from normality. Three measures for the daily ...

T. Adri Buishand; Jules J. Beersma

1996-10-01T23:59:59.000Z

428

An Overview of the Global Historical Climatology Network-Daily Database  

Science Conference Proceedings (OSTI)

A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, ...

Matthew J. Menne; Imke Durre; Russell S. Vose; Byron E. Gleason; Tamara G. Houston

2012-07-01T23:59:59.000Z

429

Evaluation of ASCAT-Based Daily Gridded Winds in the Tropical Indian Ocean  

Science Conference Proceedings (OSTI)

The quality of daily gridded Advanced Scatterometer (ASCAT; DASCAT) blended winds is examined in the tropical Indian Ocean using 3-day running mean gridded Quick Scatterometer (QuikSCAT; QSCAT) winds and in situ daily winds from the Research ...

S. Sivareddy; M. Ravichandran; M. S. Girishkumar

2013-07-01T23:59:59.000Z

430

Local-Scale Variability of Daily Solar RadiationSan Diego County, California  

Science Conference Proceedings (OSTI)

The spatial variability of daily solar radiation values over a region of several hundred square kilometers was examined. Coefficients of variability were obtained as the standard deviations of between-station daily radiation difference divided by ...

Edward Aguado

1986-05-01T23:59:59.000Z

431

Trends in Daily Solar Radiation and Precipitation Coefficients of Variation since 1984  

Science Conference Proceedings (OSTI)

This study investigates the possibility of changes in daily scale solar radiation and precipitation variability. Coefficients of variation (CVs) were computed for the daily downward surface solar radiation product from the International Satellite ...

David Medvigy; Claudie Beaulieu

2012-02-01T23:59:59.000Z

432

Observational Evidence for Reduction of Daily Maximum Temperature by Croplands in the Midwest United States  

Science Conference Proceedings (OSTI)

Climate model simulations have shown that conversion of natural forest vegetation to croplands in the United States cooled climate. The cooling was greater for daily maximum temperature than for daily minimum temperature, resulting in a reduced ...

Gordon B. Bonan

2001-06-01T23:59:59.000Z

433

A Method to Estimate Missing Daily Maximum and Minimum Temperature Observations  

Science Conference Proceedings (OSTI)

A method to estimate missing daily maximum and minimum temperatures is presented. Temperature estimates are based on departures from daily temperature normals at the three closest stations with similar observation times. Although applied to ...

Arthur T. DeGaetano; Keith L. Eggleston; Warren W. Knapp

1995-02-01T23:59:59.000Z

434

Characteristics of Daily and Extreme Temperatures over Canada  

Science Conference Proceedings (OSTI)

Recent studies have shown that, since 1900, mean annual temperature over southern Canada has increased by an average of 0.9C, with the largest warming during winter and early spring. Every season was associated with greater increases in minimum ...

B. R. Bonsal; X. Zhang; L. A. Vincent; W. D. Hogg

2001-05-01T23:59:59.000Z

435

Form EIA-930 HOURLY AND DAILY BALANCING AUTHORITY OPERATIONS REPORT  

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

930 930 HOURLY AND DAILY BALANCING AUTHORITY OPERATIONS REPORT INSTRUCTIONS Due Date: mm/dd/yyyy Approved: OMB No. 1905-0129 Approval Expires: 10/31/2016 Burden: 0.19 hours Page 1 Draft for Discussion only PURPOSE Form EIA-930 requires Internet posting of hourly balancing authority operating data. The posted data are used to monitor the current status and trends of the electric power industry, and to support enhancement of electric system operations. REQUIRED RESPONDENTS For the contiguous United States: all entities that are listed in NERC's Compliance Registry as a balancing authority must post balancing authority operating information required by this survey. Other than the Midwest ISO (MISO), registered balancing authorities that are parties

436

Climate: monthly and annual average cooling degree days above 10° C GIS  

Open Energy Info (EERE)

cooling degree days above 10° C GIS cooling degree days above 10° C GIS data at one-degree resolution of the World from NASA/SSE Dataset Summary Description (Abstract): Cooling Degree Days above 10° C (degree days)The monthly accumulation of degrees when the daily mean temperature is above 10° C.NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly Average & Annual Sum (July 1983 - June 2005)Parameter: Cooling Degree Days Above 10 degrees C (degree days)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180,

437

Climate: monthly and annual average heating degree days below 18° C GIS  

Open Energy Info (EERE)

heating degree days below 18° C GIS heating degree days below 18° C GIS data at one-degree resolution of the World from NASA/SSE Dataset Summary Description (Abstract): Heating Degree Days below 18° C (degree days)The monthly accumulation of degrees when the daily mean temperature is below 18° C.NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly Average & Annual Sum (July 1983 - June 2005)Parameter: Heating Degree Days Below 18 degrees C (degree days)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180,

438

Average waiting time profiles of uniform DQDB model  

SciTech Connect

The Distributed Queue Dual Bus (DQDB) system consists of a linear arrangement of N nodes that communicate with each other using two contra-flowing buses; the nodes use an extremely simple protocol to send messages on these buses. This simple, but elegant, system has been found to be very challenging to analyze. We consider a simple and uniform abstraction of this model to highlight the fairness issues in terms of average waiting time. We introduce a new approximation method to analyze the performance of DQDB system in terms of the average waiting time of a node expressed as a function of its position. Our approach abstracts the intimate relationship between the load of the system and its fairness characteristics, and explains all basic behavior profiles of DQDB observed in previous simulation. For the uniform DQDB with equal distance between adjacent nodes, we show that the system operates under three basic behavior profiles and a finite number of their combinations that depend on the load of the network. Consequently, the system is not fair at any load in terms of the average waiting times. In the vicinity of a critical load of 1 {minus} 4/N, the uniform network runs into a state akin to chaos, where its behavior fluctuates from one extreme to the other with a load variation of 2/N. Our analysis is supported by simulation results. We also show that the main theme of the analysis carries over to the general (non-uniform) DQDB; by suitably choosing the inter-node distances, the DQDB can be made fair around some loads, but such system will become unfair as the load changes.

Rao, N.S.V. [Oak Ridge National Lab., TN (United States); Maly, K.; Olariu, S.; Dharanikota, S.; Zhang, L.; Game, D. [Old Dominion Univ., Norfolk, VA (United States). Dept. of Computer Science

1993-09-07T23:59:59.000Z

439

Status of Average-x from Lattice QCD  

Science Conference Proceedings (OSTI)

As algorithms and computing power have advanced, lattice QCD has become a precision technique for many QCD observables. However, the calculation of nucleon matrix elements remains an open challenge. I summarize the status of the lattice effort by examining one observable that has come to represent this challenge, average-x: the fraction of the nucleon's momentum carried by its quark constituents. Recent results confirm a long standing tendency to overshoot the experimentally measured value. Understanding this puzzle is essential to not only the lattice calculation of nucleon properties but also the broader effort to determine hadron structure from QCD.

Dru Renner

2011-09-01T23:59:59.000Z

440

Long-term average performance benefits of parabolic trough improvements  

DOE Green Energy (OSTI)

Improved parabolic trough concentrating collectors will result from better design, improved fabrication techniques, and the development and utilization of improved materials. This analysis quantifies the relative merit of various technological advancements in improving the long-term average performance of parabolic trough concentrating collectors and presents them graphically as a function of operating temperature for north-south, east-west, and polar mounted parabolic troughs. Substantial annual energy gains (exceeding 50% at 350/sup 0/C) are shown to be attainable with improved parabolic troughs.

Gee, R.; Gaul, H.; Kearney, D.; Rabl, A.

1979-10-01T23:59:59.000Z

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


441

U.S. Total Exports  

Annual Energy Outlook 2012 (EIA)

Springs, VT U.S. Pipeline Total from Mexico Ogilby, CA Otay Mesa, CA Galvan Ranch, TX LNG Imports from Algeria LNG Imports from Australia LNG Imports from Brunei LNG Imports...

442

Table 7.4 Average Prices of Selected Purchased Energy Sources, 2010;  

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

4 Average Prices of Selected Purchased Energy Sources, 2010; 4 Average Prices of Selected Purchased Energy Sources, 2010; Level: National and Regional Data; Row: Values of Shipments and Employment Sizes; Column: Energy Sources; Unit: U.S. Dollars per Physical Units. Residual Distillate LPG and Economic Electricity Fuel Oil Fuel Oil(b) Natural Gas(c) NGL(d) Coal Characteristic(a) (kWh) (gallons) (gallons) (1000 cu ft) (gallons) (short tons) Total United States Value of Shipments and Receipts (million dollars) Under 20 0.093 1.55 2.58 6.64 1.80 78.29 20-49 0.075 1.66 2.45 6.44 1.80 80.13 50-99 0.070 1.64 1.79 6.04 2.19 68.10 100-249 0.061 1.62 2.38 5.51 1.69 100.69 250-499 0.056 1.69 2.41 5.54 1.59 92.51 500 and Over 0.054 1.54 2.35 5.08 1.15 96.25 Total

443

An Improved Quality Control for AIRS Total Column Ozone Observations within and around Hurricanes  

Science Conference Proceedings (OSTI)

Atmospheric Infrared Sounder (AIRS) provides twice-daily global observations from which total column ozone data can be retrieved. However, 20% ~ 30% of AIRS ozone data are flagged to be of bad quality. Most of the flagged data were identified to ...

H. Wang; X. Zou; G. Li

2012-03-01T23:59:59.000Z

444

Table 14a. Average Electricity Prices, Projected vs. Actual  

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

a. Average Electricity Prices, Projected vs. Actual a. Average Electricity Prices, Projected vs. Actual Projected Price in Constant Dollars (constant dollars, cents per kilowatt-hour in "dollar year" specific to each AEO) AEO Dollar Year 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 AEO 1995 1993 6.80 6.80 6.70 6.70 6.70 6.70 6.70 6.80 6.80 6.90 6.90 6.90 7.00 7.00 7.10 7.10 7.20 AEO 1996 1994 7.09 6.99 6.94 6.93 6.96 6.96 6.96 6.97 6.98 6.97 6.98 6.95 6.95 6.94 6.96 6.95 6.91 AEO 1997 1995 6.94 6.89 6.90 6.91 6.86 6.84 6.78 6.73 6.66 6.60 6.58 6.54 6.49 6.48 6.45 6.36

445

A high average power electro-optic switch using KTP  

SciTech Connect

High damage threshold, high thermal conductivity, and small thermo-optic coefficients make KTiOPO{sub 4} (KTP) an attractive material for use in a high average power Q-switch. However, electro-chromic damage and refractive index homogeneity have prevented the utilization of KTP in such a device in the past. This work shows that electro-chromic damage is effectively suppressed using capacitive coupling, and a KTP crystal can be Q-switched for 1.5 {times} 10{sup 9} shots without any detectable electro-chromic damage. In addition, KTP with the high uniformity and large aperture size needed for a KTP electro-optic Q-switch can be obtained from flux crystals grown at constant temperature. A thermally compensated, dual crystal KTP Q-switch, which successfully produced 50 mJ pulses with a pulse width of 8 ns (FWHM), has been constructed. In addition, in off-line testing the Q-switch showed less than 7% depolarization at an average power loading of 3.2 kW/cm{sup 2}.

Ebbers, C.A.; Cook, W.M.; Velsko, S.P.

1994-04-01T23:59:59.000Z

446

[Global warming and the running average sunspot number  

SciTech Connect

It has been reported in your pages that the Bush administration`s views and actions regarding how or whether to react to possible global warming due to greenhouse gases have been influenced by the so-called Marshall report. This unrefereed report, released by the George C. Marshall Institute, had as its principal conclusion the finding that the 0.5{degree} C global warming of the last century was mostly due to solar variability and, thus, the greenhouse warming of the 21st century can be expected to be a relatively small l{degree} C or so. The authors support this finding by comparing the 33-year running average sunspot number with the trend in annual average global temperature and noting the parallel between the two, especially during the 1940s--1960s when the temperature trend was downward. Subsequent letters to Science debated the merits of this and other conclusions contained in the report. I now present additional technical evidence which shows that, quite aside from the question of whether the data presented in the report support its conclusions, the actual figure on which the above conclusion is based is in error.

Fernau, M.E.

1994-05-01T23:59:59.000Z

447

Calculations of nonspherically averaged charge densities for subtitutionally disordered alloys  

Science Conference Proceedings (OSTI)

Based on screening transformations of muffin-tin orbitals introduced by Andersen et al. [Phys. Rev. Lett. 53, 2571 (1984)], we have developed a formalism for calculating the non-spherically averaged charge densities of substitutionally disordered alloys using the Korringa-Kohn-Rostoker coherent potential approximation (KKR CPA) method in the atomic-sphere approximation (ASA). We have validated our method by calculating charge densities for ordered structures, where we find that our approach yields charge densities that are essentially indistinguishable from the results of full-potential methods. For substitutionally disordered alloys, where full-potential methods have not been implemented so far, our approach can be used to calculate reliable non-spherically averaged charge densities from spherically symmetric one-electron potentials obtained from the KKR-ASA CPA. We report on our study of differences in charge denisty between ordered AlLi in L1{sub o} phase and substitutionally disordered Al{sub 0.5}Li{sub 0.5} on face-centered cubic lattice.

Singh, P.P.; Gonis, A.

1994-02-01T23:59:59.000Z

448

Plasma dynamics and a significant error of macroscopic averaging  

E-Print Network (OSTI)

The methods of macroscopic averaging used to derive the macroscopic Maxwell equations from electron theory are methodologically incorrect and lead in some cases to a substantial error. For instance, these methods do not take into account the existence of a macroscopic electromagnetic field EB, HB generated by carriers of electric charge moving in a thin layer adjacent to the boundary of the physical region containing these carriers. If this boundary is impenetrable for charged particles, then in its immediate vicinity all carriers are accelerated towards the inside of the region. The existence of the privileged direction of acceleration results in the generation of the macroscopic field EB, HB. The contributions to this field from individual accelerated particles are described with a sufficient accuracy by the Lienard-Wiechert formulas. In some cases the intensity of the field EB, HB is significant not only for deuteron plasma prepared for a controlled thermonuclear fusion reaction but also for electron plasma in conductors at room temperatures. The corrected procedures of macroscopic averaging will induce some changes in the present form of plasma dynamics equations. The modified equations will help to design improved systems of plasma confinement.

Marek A. Szalek

2005-05-22T23:59:59.000Z

449

A Study to Quantify the Effectiveness of Daily Endorectal Balloon for Prostate Intrafraction Motion Management  

Science Conference Proceedings (OSTI)

Purpose: To quantify intrafraction prostate motion between patient groups treated with and without daily endorectal balloon (ERB) employed during prostate radiotherapy and establish the effectiveness of the ERB. Methods: Real-time intrafraction prostate motion from 29 non-ERB (1,061 sessions) and 30 ERB (1,008 sessions) patients was evaluated based on three-dimensional (3D), left, right, cranial, caudal, anterior, and posterior displacements. The average percentage of time with 3D and unidirectional prostate displacements >2, 3, 4, 5, 6, 7, 8, 9, and 10 mm in 1-min intervals was calculated for up to 6 min of treatment time. The Kolmogorov-Smirnov method was used to evaluate the intrafraction prostate motion pattern between both groups. Results: Large 3D motion (up to 1 cm or more) was only observed in the non-ERB group. The motion increased as a function of elapsed time for displacements >2-8 mm for the non-ERB group and >2-4 mm for the ERB group (p percentage time distributions between the two groups were significantly different for motion >5 mm (p percentage of time that the prostate was displaced in any direction was less in the ERB group for almost all magnitudes of motion considered. The directional analysis shows that the ERB reduced IMs in almost all directions, especially the anterior-posterior direction.

Wang, Ken Kang-Hsin, E-mail: wangken@uphs.upenn.edu [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, PA (United States); Vapiwala, Neha; Deville, Curtiland; Plastaras, John P.; Scheuermann, Ryan; Lin Haibo; Bar Ad, Voika; Tochner, Zelig; Both, Stefan [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, PA (United States)

2012-07-01T23:59:59.000Z

450

Network-based consensus averaging with general noisy channels  

E-Print Network (OSTI)

This paper focuses on the consensus averaging problem on graphs under general noisy channels. We study a particular class of distributed consensus algorithms based on damped updates, and using the ordinary differential equation method, we prove that the updates converge almost surely to exact consensus for finite variance noise. Our analysis applies to various types of stochastic disturbances, including errors in parameters, transmission noise, and quantization noise. Under a suitable stability condition, we prove that the error is asymptotically Gaussian, and we show how the asymptotic covariance is specified by the graph Laplacian. For additive parameter noise, we show how the scaling of the asymptotic MSE is controlled by the spectral gap of the Laplacian.

Rajagopal, Ram

2008-01-01T23:59:59.000Z

451

Measurement strategies for estimating long-term average wind speeds  

DOE Green Energy (OSTI)

The uncertainty and bias in estimates of long-term average wind speeds inherent in continuous and intermittent measurement strategies are examined by simulating the application of the strategies to 40 data sets. Continuous strategies have smaller uncertainties for fixed duration measurement programs, but intermittent strategies make more efficient use of instruments and have smaller uncertainties for a fixed amount of instrument use. Continuous strategies tend to give biased estimates of the long-term annual mean speed unless an integral number of years' data is collected or the measurement program exceeds 3 years in duration. Intermittent strategies with three or more month-long measurement periods per year do not show any tendency toward bias.

Ramsdell, J.V.; Houston, S.; Wegley, H.L.

1980-10-01T23:59:59.000Z

452

Climate: monthly and annual average relative humidity GIS data at  

Open Energy Info (EERE)

relative humidity GIS data at relative humidity GIS data at one-degree resolution of the World from NASA/SSE Dataset Summary Description (Abstract): Relative Humidity at 10 m Above The Surface Of The Earth (%)NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly & Annual Average (July 1983 - June 2005)Parameter: Relative Humidity at 10 m Above The Surface Of The Earth (%)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of

453

Dependence of Extreme Daily Maximum Temperatures on Antecedent Soil Moisture in the Contiguous United States during Summer  

Science Conference Proceedings (OSTI)

The paper presents an analysis of the dependence of summertime daily maximum temperature on antecedent soil moisture using daily surface observations from a selection of stations in the contiguous United States and daily time series of soil ...

Imke Durre; John M. Wallace; Dennis P. Lettenmaier

2000-07-01T23:59:59.000Z

454

Compact Totally Disconnected Moufang Buildings  

E-Print Network (OSTI)

Let $\\Delta$ be a spherical building each of whose irreducible components is infinite, has rank at least 2 and satisfies the Moufang condition. We show that $\\Delta$ can be given the structure of a topological building that is compact and totally disconnected precisely when $\\Delta$ is the building at infinity of a locally finite affine building.

Grundhofer, T; Van Maldeghem, H; Weiss, R M

2010-01-01T23:59:59.000Z

455

Total Imports of Residual Fuel  

Annual Energy Outlook 2012 (EIA)

2007 2008 2009 2010 2011 2012 View History U.S. Total 135,676 127,682 120,936 133,646 119,888 93,672 1936-2012 PAD District 1 78,197 73,348 69,886 88,999 79,188 59,594 1981-2012...

456

Table 15. Average Electricity Prices, Projected vs. Actual  

Gasoline and Diesel Fuel Update (EIA)

Average Electricity Prices, Projected vs. Actual Average Electricity Prices, Projected vs. Actual (nominal cents per kilowatt-hour) 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 AEO 1982 6.38 6.96 7.63 8.23 8.83 9.49 AEO 1983 6.85 7.28 7.74 8.22 8.68 9.18 13.12 AEO 1984 6.67 7.05 7.48 7.89 8.25 8.65 11.53 AEO 1985 6.62 6.94 7.32 7.63 7.89 8.15 8.46 8.85 9.20 9.61 10.04 AEO 1986 6.67 6.88 7.05 7.18 7.35 7.52 7.65 7.87 8.31 8.83 9.41 10.01 10.61 11.33 12.02 AEO 1987 6.63 6.65 6.92 7.12 7.38 7.62 7.94 8.36 8.86 11.99 AEO 1989* 6.50 6.75 7.14 7.48 7.82 8.11 8.50 8.91 9.39 9.91 10.49 11.05 11.61 AEO 1990 6.49 6.72 8.40 10.99 14.5 AEO 1991 6.94 7.31 7.59 7.82 8.18 8.38 8.54 8.73 8.99 9.38 9.83 10.29 10.83 11.36 11.94 12.58 13.21 13.88 14.58 15.21 AEO 1992 6.97 7.16 7.32 7.56 7.78 8.04 8.29 8.57 8.93 9.38 9.82 10.26 10.73 11.25 11.83 12.37 12.96 13.58 14.23 AEO 1993

457

Plate with a hole obeys the averaged null energy condition  

SciTech Connect

The negative energy density of Casimir systems appears to violate general relativity energy conditions. However, one cannot test the averaged null energy condition (ANEC) using standard calculations for perfectly reflecting plates, because the null geodesic would have to pass through the plates, where the calculation breaks down. To avoid this problem, we compute the contribution to ANEC for a geodesic that passes through a hole in a single plate. We consider both Dirichlet and Neumann boundary conditions in two and three space dimensions. We use a Babinet's principle argument to reduce the problem to a complementary finite disk correction to the perfect mirror result, which we then compute using scattering theory in elliptical and spheroidal coordinates. In the Dirichlet case, we find that the positive correction due to the hole overwhelms the negative contribution of the infinite plate. In the Neumann case, where the infinite plate gives a positive contribution, the hole contribution is smaller in magnitude, so again ANEC is obeyed. These results can be extended to the case of two plates in the limits of large and small hole radii. This system thus provides another example of a situation where ANEC turns out to be obeyed when one might expect it to be violated.

Graham, Noah; Olum, Ken D. [Department of Physics, Middlebury College, Middlebury, Vermont 05753 (United States); Institute of Cosmology, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts 02155 (United States)

2005-07-15T23:59:59.000Z

458

Climate: monthly and annual average atmospheric pressure GIS data at  

Open Energy Info (EERE)

atmospheric pressure GIS data at atmospheric pressure GIS data at one-degree resolution of the World from NASA/SSE Dataset Summary Description (Abstract):Atmospheric Pressure (kPa)NASA Surface meteorology and Solar Energy (SSE) Release 6.0 Data Set (Nov 2007)22-year Monthly & Annual Average (July 1983 - June 2005)Parameter: Atmospheric Pressure (kPa)Internet: http://eosweb.larc.nasa.gov/sse/Note 1: SSE Methodology & Accuracy sections onlineNote 2: Lat/Lon values indicate the lower left corner of a 1x1 degree region. Negative values are south and west; positive values are north and east. Boundaries of the -90/-180 region are -90 to -89 (south) and -180 to -179 (west). The last region, 89/180, is bounded by 89 to 90 (north) and 179 to 180 (east). The mid-point of the region is +0.5 added to the the Lat/Lon value. These data are

459

Long-term average performance benefits of parabolic trough improvements  

DOE Green Energy (OSTI)

Improved parabolic trough concentrating collectors will result from better design, improved fabrication techniques, and the development and utilization of improved materials. The difficulty of achieving these improvements varies as does their potential for increasing parabolic trough performance. The purpose of this analysis is to quantify the relative merit of various technology advancements in improving the long-term average performance of parabolic trough concentrating collectors. The performance benefits of improvements are determined as a function of operating temperature for north-south, east-west, and polar mounted parabolic troughs. The results are presented graphically to allow a quick determination of the performance merits of particular improvements. Substantial annual energy gains are shown to be attainable. Of the improvements evaluated, the development of stable back-silvered glass reflective surfaces offers the largest performance gain for operating temperatures below 150/sup 0/C. Above 150/sup 0/C, the development of trough receivers that can maintain a vacuum is the most significant potential improvement. The reduction of concentrator slope errors also has a substantial performance benefit at high operating temperatures.

Gee, R.; Gaul, H.W.; Kearney, D.; Rabl, A.

1980-03-01T23:59:59.000Z

460

Buildings","Total  

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

L2. Floorspace Lit by Lighting Types (Non-Mall Buildings), 1999" L2. Floorspace Lit by Lighting Types (Non-Mall Buildings), 1999" ,"Floorspace (million square feet)" ,"Total (Lit or Unlit) in All Buildings","Total (Lit or Unlit) in Buildings With Any Lighting","Lighted Area Only","Area Lit by Each Type of Light" ,,,,"Incan- descent","Standard Fluor-escent","Compact Fluor- escent","High Intensity Discharge","Halogen" "All Buildings* ...............",61707,58693,49779,6496,37150,3058,5343,1913 "Building Floorspace" "(Square Feet)" "1,001 to 5,000 ...............",6750,5836,4878,757,3838,231,109,162 "5,001 to 10,000 ..............",7940,7166,5369,1044,4073,288,160,109 "10,001 to 25,000 .............",10534,9773,7783,1312,5712,358,633,232

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


461

Performance Period Total Fee Paid  

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

Period Period Total Fee Paid 4/29/2012 - 9/30/2012 $418,348 10/1/2012 - 9/30/2013 $0 10/1/2013 - 9/30/2014 $0 10/1/2014 - 9/30/2015 $0 10/1/2015 - 9/30/2016 $0 Cumulative Fee Paid $418,348 Contract Type: Cost Plus Award Fee Contract Period: $116,769,139 November 2011 - September 2016 $475,395 $0 Fee Information Total Estimated Contract Cost $1,141,623 $1,140,948 $1,140,948 $5,039,862 $1,140,948 Maximum Fee $5,039,862 Minimum Fee Fee Available Portage, Inc. DE-DT0002936 EM Contractor Fee Site: MOAB Uranium Mill Tailings - MOAB, UT Contract Name: MOAB Uranium Mill Tailings Remedial Action Contract September 2013 Contractor: Contract Number:

462

Buildings","Total  

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

L1. Floorspace Lit by Lighting Type for Non-Mall Buildings, 1995" L1. Floorspace Lit by Lighting Type for Non-Mall Buildings, 1995" ,"Floorspace (million square feet)" ,"Total (Lit or Unlit) in All Buildings","Total (Lit or Unlit) in Buildings With Any Lighting","Lighted Area Only","Area Lit by Each Type of Light" ,,,,"Incan- descent","Standard Fluor-escent","Compact Fluor- escent","High Intensity Discharge","Halogen" "All Buildings*",54068,51570,45773,6746,34910,1161,3725,779 "Building Floorspace" "(Square Feet)" "1,001 to 5,000",6272,5718,4824,986,3767,50,22,54 "5,001 to 10,000",7299,6667,5728,1240,4341,61,169,45 "10,001 to 25,000",10829,10350,8544,1495,6442,154,553,"Q"

463

ARM - Measurement - Total cloud water  

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

cloud water cloud water 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 : Total cloud water The total concentration (mass/vol) of ice and liquid water particles in a cloud; this includes condensed water content (CWC). Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available measurements, including those recorded for diagnostic or quality assurance purposes. External Instruments NCEPGFS : National Centers for Environment Prediction Global Forecast System Field Campaign Instruments CSI : Cloud Spectrometer and Impactor PDI : Phase Doppler Interferometer

464

Buildings","Total  

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

L3. Floorspace Lit by Lighting Type (Non-Mall Buildings), 2003" L3. Floorspace Lit by Lighting Type (Non-Mall Buildings), 2003" ,"Floorspace (million square feet)" ,"Total (Lit or Unlit) in All Buildings","Total (Lit or Unlit) in Buildings With Any Lighting","Lighted Area Only","Area Lit by Each Type of Light" ,,,,"Incan- descent","Standard Fluor-escent","Compact Fluor- escent","High Intensity Discharge","Halogen" "All Buildings* ...............",64783,62060,51342,5556,37918,4004,4950,2403 "Building Floorspace" "(Square Feet)" "1,001 to 5,000 ...............",6789,6038,4826,678,3932,206,76,124 "5,001 to 10,000 ..............",6585,6090,4974,739,3829,192,238,248 "10,001 to 25,000 .............",11535,11229,8618,1197,6525,454,506,289

465

"2012 Retail Power Marketers Sales- Total"  

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

Total" Total" "(Data from form EIA-861 schedule 4B)" "Entity","State","Ownership","Customers (Count)","Sales (Megawatthours)","Revenues (Thousands Dollars)","Average Price (cents/kWh)" "3 Phases Renewables","CA","Power Marketer",354,148820,7268.5,4.8840882 "Calpine Power America LLC","CA","Power Marketer",1,1072508,54458,5.0776311 "City of Corona - (CA)","CA","Municipal",859,65933,5749.5,8.720216 "Commerce Energy, Inc.","CA","Power Marketer",23386,596604,37753,6.3279831 "Constellation NewEnergy, Inc","CA","Power Marketer",362,4777373,250287.4,5.2390173

466

"2012 Utility Bundled Retail Sales- Total"  

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

Total" Total" "(Data from forms EIA-861- schedules 4A & 4D and EIA-861S)" "Entity","State","Ownership","Customers (Count)","Sales (Megawatthours)","Revenues (Thousands Dollars)","Average Price (cents/kWh)" "Alaska Electric Light&Power Co","AK","Investor Owned",16180,399144,41820,10.477422 "Alaska Power and Telephone Co","AK","Investor Owned",6976,64788,18175,28.053035 "Alaska Village Elec Coop, Inc","AK","Cooperative",7923,73956,42708,57.74785 "Anchorage Municipal Light and Power","AK","Municipal",30747,1100665,100959.2,9.1725639 "Barrow Utils & Elec Coop, Inc","AK","Cooperative",1871,49580,5293,10.675676

467

FY 2008 CONTRACTOR PURCHASING BALANCED SCORECARD RESULTS DEPARTMENTAL AVERAGES BY FISCAL YEAR  

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

8 CONTRACTOR PURCHASING BALANCED SCORECARD RESULTS 8 CONTRACTOR PURCHASING BALANCED SCORECARD RESULTS DEPARTMENTAL AVERAGES BY FISCAL YEAR FY 2008 FY 2004 FY 2005 FY2006 FY2007 FY2008 National Taraets Customer Perspective Objective: Customer Satisfaction 93 9 3 Core Measure: Customer Satisfaction Rating Internal Business Perspective Objective: Effective Internal Controls 95 93 Core Measure: assessment of degree to which purchasing systems are in compliance Objective: Effective Supplier Management 8 3 8 1 Core Measure: % Delivery on-time, including Just-in-Time Objective: Use of Effective Competition 7 1 74 Core Measure: % of total dollars obligated on actions over $100,000 that were competed N/A - locally set 84 N/A - locally set 2 FY 2008 FY 2004 FY 2005 FY2006 FY 2007 FY2008 National Tarpets

468

FY 2009 CONTRACTOR PURCHASING BALANCED SCORECARD RESULTS DEPARTMENTAL AVERAGES BY FISCAL YEAR  

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

9 CONTRACTOR PURCHASING BALANCED SCORECARD RESULTS 9 CONTRACTOR PURCHASING BALANCED SCORECARD RESULTS DEPARTMENTAL AVERAGES BY FISCAL YEAR FY 2009 FY2005 FY2006 FY 2007 FY 2008 FY 2009 National Tarpets Customer Perspective Objective: Customer Satisfaction Core Measure: Customer Satisfaction Rating Internal Business Perspective Objective: Effective Internal Controls 93 94 Core Measure: assessment of degree to which purchasing systems are in compliance Objective: Effective Supplier Management 8 1 83 Core Measure: % Delivery on-time, including Just-in-Time Objective: Use of Effective Competition 74 69 Core Measure: % of total dollars obligated on actions over $100,000 that were competed 97 97 N/A - locally set 84 8 6 84 67 74 N/A - locally set 2 FY 2009 FY 2005 FY 2006 FY 2007 FY 2008 FY2009 National Targets

469

ARECIBO MULTI-FREQUENCY TIME-ALIGNED PULSAR AVERAGE-PROFILE AND POLARIZATION DATABASE  

Science Conference Proceedings (OSTI)

We present Arecibo time-aligned, total intensity profiles for 46 pulsars over an unusually wide range of radio frequencies and multi-frequency, polarization-angle density diagrams, and/or polarization profiles for 57 pulsars at some or all of the frequencies 50, 111/130, 430, and 1400 MHz. The frequency-dependent dispersion delay has been removed in order to align the profiles for study of their spectral evolution, and wherever possible the profiles of each pulsar are displayed on the same longitude scale. Most of the pulsars within Arecibo's declination range that are sufficiently bright for such spectral or single pulse analysis are included in this survey. The calibrated single pulse sequences and average profiles are available by web download for further study.

Hankins, Timothy H. [Physics Department, New Mexico Tech, Socorro, NM 87801 (United States); Rankin, Joanna M. [Physics Department, University of Vermont, Burlington, VT 05401 (United States)], E-mail: thankins@nrao.edu, E-mail: Joanna.Rankin@uvm.edu

2010-01-15T23:59:59.000Z

470

Daily intake of antioxidants in relation to survival among adult patients diagnosed with malignant glioma  

E-Print Network (OSTI)

II patients, moderate intake of water-soluble folate wasfor tertiles of daily intake of water-soluble antioxidants4: Associations between intake of water-soluble antioxidants

2010-01-01T23:59:59.000Z

471

Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications  

E-Print Network (OSTI)

estimating daily net photosynthesis. Ecol. Model. 58, 209 Canopy reflectance, photosynthesis, and transpira- tion.model of forest photosynthesis compared with measurements by

Chen, J.M; Liu, J; Cihlar, J; Goulden, M.L

1999-01-01T23:59:59.000Z

472

2012 Brief: Average 2012 crude oil prices remain near 2011 levels ...  

U.S. Energy Information Administration (EIA)

Brent crude oil averaged $111.67 per barrel, slightly above the 2011 average of $111.26. West Texas Intermediate oil averaged $94.05 per barrel in 2012, ...

473

Total Adjusted Sales of Kerosene  

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

End Use: Total Residential Commercial Industrial Farm All Other Period: End Use: Total Residential Commercial Industrial Farm All Other Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: End Use Area 2007 2008 2009 2010 2011 2012 View History U.S. 492,702 218,736 269,010 305,508 187,656 81,102 1984-2012 East Coast (PADD 1) 353,765 159,323 198,762 237,397 142,189 63,075 1984-2012 New England (PADD 1A) 94,635 42,570 56,661 53,363 38,448 15,983 1984-2012 Connecticut 13,006 6,710 8,800 7,437 7,087 2,143 1984-2012 Maine 46,431 19,923 25,158 24,281 17,396 7,394 1984-2012 Massachusetts 7,913 3,510 5,332 6,300 2,866 1,291 1984-2012 New Hampshire 14,454 6,675 8,353 7,435 5,472 1,977 1984-2012

474

Solar total energy project Shenandoah  

DOE Green Energy (OSTI)

This document presents the description of the final design for the Solar Total Energy System (STES) to be installed at the Shenandoah, Georgia, site for utilization by the Bleyle knitwear plant. The system is a fully cascaded total energy system design featuring high temperature paraboloidal dish solar collectors with a 235 concentration ratio, a steam Rankine cycle power conversion system capable of supplying 100 to 400 kW(e) output with an intermediate process steam take-off point, and a back pressure condenser for heating and cooling. The design also includes an integrated control system employing the supervisory control concept to allow maximum experimental flexibility. The system design criteria and requirements are presented including the performance criteria and operating requirements, environmental conditions of operation; interface requirements with the Bleyle plant and the Georgia Power Company lines; maintenance, reliability, and testing requirements; health and safety requirements; and other applicable ordinances and codes. The major subsystems of the STES are described including the Solar Collection Subysystem (SCS), the Power Conversion Subsystem (PCS), the Thermal Utilization Subsystem (TUS), the Control and Instrumentation Subsystem (CAIS), and the Electrical Subsystem (ES). Each of these sections include design criteria and operational requirements specific to the subsystem, including interface requirements with the other subsystems, maintenance and reliability requirements, and testing and acceptance criteria. (WHK)

None

1980-01-10T23:59:59.000Z

475

Grantee Total Number of Homes  

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

Grantee Grantee Total Number of Homes Weatherized through November 2011 [Recovery Act] Total Number of Homes Weatherized through November 2011 (Calendar Year 2009 - November 2011) [Recovery Act + Annual Program Funding] Alabama 6,704 7,867 1 Alaska 443 2,363 American Samoa 304 410 Arizona 6,354 7,518 Arkansas 5,231 6,949 California 41,649 50,002 Colorado 12,782 19,210 Connecticut 8,940 10,009 2 Delaware** 54 54 District of Columbia 962 1,399 Florida 18,953 20,075 Georgia 13,449 14,739 Guam 574 589 Hawaii 604 1,083 Idaho** 4,470 6,614 Illinois 35,530 44,493 Indiana** 18,768 21,689 Iowa 8,794 10,202 Kansas 6,339 7,638 Kentucky 7,639 10,902 Louisiana 4,698 6,946 Maine 5,130 6,664 Maryland 8,108 9,015 Massachusetts 17,687 21,645 Michigan 29,293 37,137 Minnesota 18,224 22,711 Mississippi 5,937 6,888 Missouri 17,334 20,319 Montana 3,310 6,860 Navajo Nation

476

Determination of total ozone from DMSP multichannel filter radiometer measurements  

Science Conference Proceedings (OSTI)

The multichannel filter radiometer (MFR) infrared sensor was first flown in 1977 on a Defense Meteorological Satellite Program (DMSP) Block 5D series satellite operated by the US Air Force. The first four satellites in this series carried MFR sensors from which total atmospheric column ozone amounts may be derived. The last MFR sensor ceased operating on February 16, 1980. The series of four sensors spans a data period of nearly three years. The MFR sensor measures infrared radiances for 16 channels. Total ozone amounts are determined from sets of radiance measurements using an empirical relationship that is developed using linear regression analysis. Total ozone is modeled as a linear combination of terms involving functions of the MFR radiances for four channels (1, 3, 7 and 16) and the secant of the zenith angle. The ozone retrieval methodology is described schematically. The ozone retrieval model is developed through regression analysis using sets of simulated MFR radiances derived from detailed radiative transfer calculations. The MFR total ozone data are compared with independent ground-based Dobson measurements in order to evaluate the ozone retrieval methodology. Many Dobson observatories have been providing their daily measurements of total ozone which are taken close in time to DMSP overpass times. MFR total ozone data are compared with Dobson measurements taken between January 1 and February 15, 1979, and the results are summarized. Comparisons were made where the MFR and Dobson measurements are within 300 km and 300 minutes of each other. Percentages are computed with respect to the Dobson values. The MFR data were processed using a preliminary methodology, and the data will be reprocessed in the near future.

Luther, F.M.; Weichel, R.L.

1980-09-01T23:59:59.000Z

477

Taking China's Temperature: Daily Range, Warming Trends, and Regional Variations, 19552000  

Science Conference Proceedings (OSTI)

In analyzing daily climate data from 305 weather stations in China for the period from 1955 to 2000, the authors found that surface air temperatures are increasing with an accelerating trend after 1990. They also found that the daily maximum (T...

Binhui Liu; Ming Xu; Mark Henderson; Ye Qi; Yiqing Li

2004-11-01T23:59:59.000Z

478

Daily Precipitation Forecasting in Dakar Using the NCEPNCAR Reanalyses  

Science Conference Proceedings (OSTI)

In order to predict the daily rain amount at Dakar at 15-day lead times, 65 thermodynamical and dynamical indices are computed at each grid point for the area 15S30N, 30W30E. The data used are NCEPNCAR reanalyses and daily rainfall ...

Abdoulaye Deme; Alain Viltard; Pierre de Flice

2003-02-01T23:59:59.000Z

479

Southeastern United States Daily Temperature Ranges Associated with the El NioSouthern Oscillation  

Science Conference Proceedings (OSTI)

The daily temperature range (DTR), daily maximum minus minimum temperature, at 290 Southeast United States stations is examined with respect to the warm and cold phases of the El NioSouthern Oscillation (ENSO) for the period of 19482009. A ...

Daniel M. Gilford; Shawn R. Smith; Melissa L. Griffin; Anthony Arguez

480

Total Number of Operable Refineries  

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

Data Series: Total Number of Operable Refineries Number of Operating Refineries Number of Idle Refineries Atmospheric Crude Oil Distillation Operable Capacity (B/CD) Atmospheric Crude Oil Distillation Operating Capacity (B/CD) Atmospheric Crude Oil Distillation Idle Capacity (B/CD) Atmospheric Crude Oil Distillation Operable Capacity (B/SD) Atmospheric Crude Oil Distillation Operating Capacity (B/SD) Atmospheric Crude Oil Distillation Idle Capacity (B/SD) Vacuum Distillation Downstream Charge Capacity (B/SD) Thermal Cracking Downstream Charge Capacity (B/SD) Thermal Cracking Total Coking Downstream Charge Capacity (B/SD) Thermal Cracking Delayed Coking Downstream Charge Capacity (B/SD Thermal Cracking Fluid Coking Downstream Charge Capacity (B/SD) Thermal Cracking Visbreaking Downstream Charge Capacity (B/SD) Thermal Cracking Other/Gas Oil Charge Capacity (B/SD) Catalytic Cracking Fresh Feed Charge Capacity (B/SD) Catalytic Cracking Recycle Charge Capacity (B/SD) Catalytic Hydro-Cracking Charge Capacity (B/SD) Catalytic Hydro-Cracking Distillate Charge Capacity (B/SD) Catalytic Hydro-Cracking Gas Oil Charge Capacity (B/SD) Catalytic Hydro-Cracking Residual Charge Capacity (B/SD) Catalytic Reforming Charge Capacity (B/SD) Catalytic Reforming Low Pressure Charge Capacity (B/SD) Catalytic Reforming High Pressure Charge Capacity (B/SD) Catalytic Hydrotreating/Desulfurization Charge Capacity (B/SD) Catalytic Hydrotreating Naphtha/Reformer Feed Charge Cap (B/SD) Catalytic Hydrotreating Gasoline Charge Capacity (B/SD) Catalytic Hydrotreating Heavy Gas Oil Charge Capacity (B/SD) Catalytic Hydrotreating Distillate Charge Capacity (B/SD) Catalytic Hydrotreating Kerosene/Jet Fuel Charge Capacity (B/SD) Catalytic Hydrotreating Diesel Fuel Charge Capacity (B/SD) Catalytic Hydrotreating Other Distillate Charge Capacity (B/SD) Catalytic Hydrotreating Residual/Other Charge Capacity (B/SD) Catalytic Hydrotreating Residual Charge Capacity (B/SD) Catalytic Hydrotreating Other Oils Charge Capacity (B/SD) Fuels Solvent Deasphalting Charge Capacity (B/SD) Catalytic Reforming Downstream Charge Capacity (B/CD) Total Coking Downstream Charge Capacity (B/CD) Catalytic Cracking Fresh Feed Downstream Charge Capacity (B/CD) Catalytic Hydro-Cracking Downstream Charge Capacity (B/CD) Period:

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


481

Principal Investigators: Long-Term Daily and Monthly Climate Records from  

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

Principal Investigators: Long-Term Daily and Monthly Climate Records from Principal Investigators: Long-Term Daily and Monthly Climate Records from Stations Across the Contiguous United States M.J. Menne, C.N. Williams, Jr., and R.S. Vose National Climatic Data Center National Oceanic and Atmospheric Administration CDIAC and the USHCN PIs encourage users to make this site their main source for obtaining USHCN data, where you can take advantage of data plotting, and, for daily data, user-friendly station-specific downloading. This site will update both daily and monthly data near the beginning of each year, so as to include data through December of the previous year. However, if you need real-time data updates, you should visit the NCDC website. For NCDC-updated daily data please use the Global Historical Climatology Network (GHCN) website where you will find a directory of USHCN stations.

482

Today in Energy - Daily Prices - Prices - U.S. Energy Information  

Gasoline and Diesel Fuel Update (EIA)

December 20, 2013Daily Prices December 20, 2013Daily Prices Daily wholesale and retail prices for various energy products are shown below, including spot prices and select futures prices at national or regional levels. Prices are updated each weekday (excluding federal holidays), typically between 7:30 and 8:30 a.m. This page is meant to provide a snapshot of selected daily prices only. Prices are republished by EIA with permission as follows: Wholesale Spot Petroleum Prices from Thomson Reuters, Retail Petroleum Prices from AAA Fuel Gauge Report, Prompt-Month Energy Futures from CME Group, and Select Spot Prices from SNL Energy. Daily Prices Wholesale Spot Petroleum Prices, 12/19/13 Close Product Area Price Percent Change* Crude Oil ($/barrel) WTI 98.40 +0.8 Brent 110.78 +1.1 Louisiana Light 108.27 +4.9

483

Seasonal Differences in the Trend of Total Ozone and Contributions from Tropospheric and Stratospheric Layers  

Science Conference Proceedings (OSTI)

Based on an average of the total-ozone changes determined by means of linear regression at individual Dobson stations within climatic zones, trends of total ozone for each of the four seasons have been evaluated for five climatic zones, and the ...

J. K. Angell

1987-04-01T23:59:59.000Z

484

Short-term relationship of total electron content with geomagnetic activity in equatorial regions  

E-Print Network (OSTI)

Short-term relationship of total electron content with geomagnetic activity in equatorial regions X equatorial ionosphere and geomagnetic activity is examined. Hourly averages of the total electron content for equatorial geomagnetic activity, at three local times (0700­0800, 1200­1300, and 1600­1700 LT) from March

Qiyu, Sun

485

Total quality management implementation guidelines  

SciTech Connect

These Guidelines were designed by the Energy Quality Council to help managers and supervisors in the Department of Energy Complex bring Total Quality Management to their organizations. Because the Department is composed of a rich mixture of diverse organizations, each with its own distinctive culture and quality history, these Guidelines are intended to be adapted by users to meet the particular needs of their organizations. For example, for organizations that are well along on their quality journeys and may already have achieved quality results, these Guidelines will provide a consistent methodology and terminology reference to foster their alignment with the overall Energy quality initiative. For organizations that are just beginning their quality journeys, these Guidelines will serve as a startup manual on quality principles applied in the Energy context.

Not Available

1993-12-01T23:59:59.000Z

486

Total Ore Processing Integration and Management  

SciTech Connect

This report outlines the technical progress achieved for project DE-FC26-03NT41785 (Total Ore Processing Integration and Management) during the period 01 January through 31 March of 2006. (1) Work in Progress: Minntac Mine--Graphical analysis of drill monitor data moved from two-dimensional horizontal patterns to vertical variations in measured and calculated parameters. The rock quality index and the two dimensionless ({pi}) indices developed by Kewen Yin of the University of Minnesota are used by Minntac Mine to design their blasts, but the drill monitor data from any given pattern is obviously not available for the design of that shot. Therefore, the blast results--which are difficult to quantify in a short time--must be back-analyzed for comparison with the drill monitor data to be useful for subsequent blast designs. {pi}{sub 1} indicates the performance of the drill, while {pi}{sub 2} is a measure of the rock resistance to drilling. As would be expected, since a drill tends to perform better in rock that offers little resistance, {pi}{sub 1} and {pi}{sub 2} are strongly inversely correlated; the relationship is a power function rather than simply linear. Low values of each Pi index tend to be quantized, indicating that these two parameters may be most useful above certain minimum magnitudes. (2) Work in Progress: Hibtac Mine--Statistical examination of a data set from Hibtac Mine (Table 1) shows that incorporating information on the size distribution of material feeding from the crusher to the autogenous mills improves the predictive capability of the model somewhat (43% vs. 44% correlation coefficient), but a more important component is production data from preceding days (26% vs. 44% correlation coefficient), determined using exponentially weighted moving average predictive variables. This lag effect likely reflects the long and varied residence times of the different size fragments in the grinding mills. The rock sizes are also correlated with the geologic layers from which they originate. Additional predictive parameters include electric power drawn by the crusher and the inverse of the average grind index of the ore being milled.

Leslie Gertsch

2006-05-15T23:59:59.000Z

487

Map Data: Total Production | Department of Energy  

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

Total Production Map Data: Total Production totalprod2009final.csv More Documents & Publications Map Data: Renewable Production Map Data: State Consumption...

488

Total Space Heating Water Heating Cook-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings* ... 222 194 17...

489

Total Space Heating Water Heating Cook-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings ... 2,100...

490

Total Space Heating Water Heating Cook-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings* ... 1,928 1,316...

491

Total Space Heating Water Heating Cook-  

Gasoline and Diesel Fuel Update (EIA)

Energy Consumption Survey: Energy End-Use Consumption Tables Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All...

492

Total Space Heating Water Heating Cook-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings* ... 1,870 1,276...

493

Total Space Heating Water Heating Cook-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings* ... 1,602 1,397...

494

Total Space Heating Water Heating Cook-  

Gasoline and Diesel Fuel Update (EIA)

Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings ... 2,037...

495

Total Imports of Residual Fuel  

Gasoline and Diesel Fuel Update (EIA)

May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 View May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 View History U.S. Total 5,752 5,180 7,707 9,056 6,880 6,008 1936-2013 PAD District 1 1,677 1,689 2,008 3,074 2,135 2,814 1981-2013 Connecticut 1995-2009 Delaware 1995-2012 Florida 359 410 439 392 704 824 1995-2013 Georgia 324 354 434 364 298 391 1995-2013 Maine 65 1995-2013 Maryland 1995-2013 Massachusetts 1995-2012 New Hampshire 1995-2010 New Jersey 903 756 948 1,148 1,008 1,206 1995-2013 New York 21 15 14 771 8 180 1995-2013 North Carolina 1995-2011 Pennsylvania 1995-2013 Rhode Island 1995-2013 South Carolina 150 137 194 209 1995-2013 Vermont 5 4 4 5 4 4 1995-2013 Virginia 32 200 113 1995-2013 PAD District 2 217 183 235 207 247 179 1981-2013 Illinois 1995-2013

496

U.S. Total Exports  

Gasoline and Diesel Fuel Update (EIA)

Noyes, MN Warroad, MN Babb, MT Port of Del Bonita, MT Port of Morgan, MT Sweetgrass, MT Whitlash, MT Portal, ND Sherwood, ND Pittsburg, NH Champlain, NY Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Highgate Springs, VT U.S. Pipeline Total from Mexico Ogilby, CA Otay Mesa, CA Galvan Ranch, TX LNG Imports from Algeria LNG Imports from Australia LNG Imports from Brunei LNG Imports from Canada Highgate Springs, VT LNG Imports from Egypt Cameron, LA Elba Island, GA Freeport, TX Gulf LNG, MS LNG Imports from Equatorial Guinea LNG Imports from Indonesia LNG Imports from Malaysia LNG Imports from Nigeria Cove Point, MD LNG Imports from Norway Cove Point, MD Freeport, TX Sabine Pass, LA LNG Imports from Oman LNG Imports from Peru Cameron, LA Freeport, TX LNG Imports from Qatar Elba Island, GA Golden Pass, TX Sabine Pass, LA LNG Imports from Trinidad/Tobago Cameron, LA Cove Point, MD Elba Island, GA Everett, MA Freeport, TX Gulf LNG, MS Lake Charles, LA Sabine Pass, LA LNG Imports from United Arab Emirates LNG Imports from Yemen Everett, MA Freeport, TX Sabine Pass, LA LNG Imports from Other Countries Period: Monthly Annual

497

Natural Gas Total Liquids Extracted  

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

Thousand Barrels) Thousand Barrels) Data Series: Natural Gas Processed Total Liquids Extracted NGPL Production, Gaseous Equivalent Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Data Series Area 2007 2008 2009 2010 2011 2012 View History U.S. 658,291 673,677 720,612 749,095 792,481 873,563 1983-2012 Alabama 13,381 11,753 11,667 13,065 1983-2010 Alaska 22,419 20,779 19,542 17,798 18,314 18,339 1983-2012 Arkansas 126 103 125 160 212 336 1983-2012 California 11,388 11,179 11,042 10,400 9,831 9,923 1983-2012 Colorado 27,447 37,804 47,705 57,924 1983-2010 Florida 103 16 1983-2008 Illinois 38 33 24 231 705 0 1983-2012

498

Using a Soil Hydrology Model to Obtain Regionally Averaged Soil Moisture Values  

Science Conference Proceedings (OSTI)

The Soil Hydrology Model (SHM) was modified, and daily simulations of soil volumetric water content were made at 38 Oklahoma Mesonet sites for July 1997. These model results were compared with soil moisture observations made at the mesonet sites ...

Todd M. Crawford; David J. Stensrud; Toby N. Carlson; William J. Capehart

2000-08-01T23:59:59.000Z

499

Vehicle Technologies Office: Fact #615: March 22, 2010 Average Vehicle Trip  

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

5: March 22, 5: March 22, 2010 Average Vehicle Trip Length to someone by E-mail Share Vehicle Technologies Office: Fact #615: March 22, 2010 Average Vehicle Trip Length on Facebook Tweet about Vehicle Technologies Office: Fact #615: March 22, 2010 Average Vehicle Trip Length on Twitter Bookmark Vehicle Technologies Office: Fact #615: March 22, 2010 Average Vehicle Trip Length on Google Bookmark Vehicle Technologies Office: Fact #615: March 22, 2010 Average Vehicle Trip Length on Delicious Rank Vehicle Technologies Office: Fact #615: March 22, 2010 Average Vehicle Trip Length on Digg Find More places to share Vehicle Technologies Office: Fact #615: March 22, 2010 Average Vehicle Trip Length on AddThis.com... Fact #615: March 22, 2010 Average Vehicle Trip Length According to the latest National Household Travel Survey, the average trip

500

U.S. Total Exports  

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

International Falls, MN Noyes, MN Warroad, MN Babb, MT Havre, MT Port of Del Bonita, MT Port of Morgan, MT Sweetgrass, MT Whitlash, MT Portal, ND Sherwood, ND Pittsburg, NH Champlain, NY Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Highgate Springs, VT North Troy, VT LNG Imports into Cameron, LA LNG Imports into Cove Point, MD LNG Imports into Elba Island, GA LNG Imports into Everett, MA LNG Imports into Freeport, TX LNG Imports into Golden Pass, TX LNG Imports into Gulf Gateway, LA LNG Imports into Gulf LNG, MS LNG Imports into Lake Charles, LA LNG Imports into Neptune Deepwater Port LNG Imports into Northeast Gateway LNG Imports into Sabine Pass, LA U.S. Pipeline Total from Mexico Ogilby, CA Otay Mesa, CA Alamo, TX El Paso, TX Galvan Ranch, TX Hidalgo, TX McAllen, TX Penitas, TX LNG Imports from Algeria Cove Point, MD Everett, MA Lake Charles, LA LNG Imports from Australia Everett, MA Lake Charles, LA LNG Imports from Brunei Lake Charles, LA LNG Imports from Canada Highgate Springs, VT LNG Imports from Egypt Cameron, LA Cove Point, MD Elba Island, GA Everett, MA Freeport, TX Gulf LNG, MS Lake Charles, LA Northeast Gateway Sabine Pass, LA LNG Imports from Equatorial Guinea Elba Island, GA Lake Charles, LA LNG Imports from Indonesia Lake Charles, LA LNG Imports from Malaysia Gulf Gateway, LA Lake Charles, LA LNG Imports from Nigeria Cove Point, MD Elba Island, GA Freeport, TX Gulf Gateway, LA Lake Charles, LA Sabine Pass, LA LNG Imports from Norway Cove Point, MD Sabine Pass, LA LNG Imports from Oman Lake Charles, LA LNG Imports from Peru Cameron, LA Freeport, TX Sabine Pass, LA LNG Imports from Qatar Cameron, LA Elba Island, GA Golden Pass, TX Gulf Gateway, LA Lake Charles, LA Northeast Gateway Sabine Pass, LA LNG Imports from Trinidad/Tobago Cameron, LA Cove Point, MD Elba Island, GA Everett, MA Freeport, TX Gulf Gateway, LA Gulf LNG, MS Lake Charles, LA Neptune Deepwater Port Northeast Gateway Sabine Pass, LA LNG Imports from United Arab Emirates Lake Charles, LA LNG Imports from Yemen Everett, MA Freeport, TX Neptune Deepwater Port Sabine Pass, LA LNG Imports from Other Countries Lake Charles, LA Period: Monthly Annual