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Note: This page contains sample records for the topic "total load served" from the National Library of EnergyBeta (NLEBeta).
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they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
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1

Modeling and analysis of unsymmetrical transformer banks serving unbalanced loads  

SciTech Connect

It is a common practice to serve combination three-phase and single-phase loads from an unsymmetrical three-phase transformer bank and a four-wire secondary. Depending upon the loads and company standards, nine different transformer connections can be considered along with either an open four wire or a quadraplex secondary. The selection of the proper connection and transformer ratings can be achieved by the correct modeling and analysis of a system consisting of an equivalent source, transformer bank, secondary, and loads. This paper develops the models for the nine transformer connections, the secondary, and the loads. A computer program has been written that allows the analysis of the system for loading and short-circuit studies. The paper includes several examples of different normal and abnormal operating conditions on some of the transformer connections.

Kersting, W.H. [New Mexico State Univ., Las Cruces, NM (United States). Coll. of Engineering; Phillips, W.H. [WH Power Consultants, Las Cruces, NM (United States)

1996-05-01T23:59:59.000Z

2

The analysis of an ungrounded Wye-Delta transformer bank serving an induction motor and single-phase lighting loads  

SciTech Connect

This paper documents the detailed analysis of the three-phase ungrounded wye-delta transformer bank serving a combination three-phase induction motor and single-phase lighting load. A 3 x 3 transfer function matrix is developed that makes it possible to determine the load line-to-line voltages with a knowledge of the primary voltages of the transformer bank. Included in the transfer function matrix are the transformer impedances, unsymmetrical secondary impedance matrix, and the impedances of the motor and single-phase lighting load.

Kersting, W.H.; Rathbun, J.S.

2000-02-01T23:59:59.000Z

3

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.

4

Classification of total load demand profiles for war-ships based on pattern recognition methods  

Science Conference Proceedings (OSTI)

The classification of total load demand profiles for every type of war-ships is crucial information, because it is the necessary base for a series of studies and operations, such as load estimation, load shedding and power management systems. In this ... Keywords: adequacy measures, clustering algorithms, load profiles, pattern recognition, warship

G. J. Tsekouras; I. S. Karanasiou; F. D. Kanellos

2011-07-01T23:59:59.000Z

5

Adrien Serve  

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

Adrien Serve Sustainable Energy Systems Group Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 90R2002 Berkeley CA 94720 Office Location: 90-2002A (510) 495-2862 AServe...

6

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...

7

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...

8

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...

9

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...

10

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...

11

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...

12

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...

13

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

14

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

15

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

16

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","RESIDENTIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TOTAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","COMMERCIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","INDUSTRIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TRANSPORTATIONPHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"  

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

TRANSPORTATIONPHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"

17

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.....................................................

18

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.....................................................

19

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.....................................................

20

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.....................................................

Note: This page contains sample records for the topic "total load served" 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

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.....................................................

22

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......................................

23

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

24

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.....................................................

25

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.....................................................

26

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

27

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

28

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...............................

29

Serve on a Committee  

Science Conference Proceedings (OSTI)

Consider serving on a committee and make a difference to your professional society. Serve on a Committee Volunteer Opportunities aocs Author authors. speakers awards call for papers committees fats global governance inform job listings member memb

30

Science Serving Sustainability  

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

Science Goal 8: Science Serving Sustainability Maintaining the conditions of a building improves the health of not only the surrounding ecosystems, but also the well-being of its...

31

FINAL PROJECT REPORT LOAD MODELING TRANSMISSION RESEARCH  

E-Print Network (OSTI)

composition: The total load profile obtained from  load individual load types if  load profiles of individual load composition validation: Load profiles generated by the load 

Lesieutre, Bernard

2013-01-01T23:59:59.000Z

32

Science Serving Sustainability  

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

Science Science Goal 8: Science Serving Sustainability Maintaining the conditions of a building improves the health of not only the surrounding ecosystems, but also the well-being of its occupants. Energy Conservation» Efficient Water Use & Management» High Performance Sustainable Buildings» Greening Transportation» Green Purchasing & Green Technology» Pollution Prevention» Science Serving Sustainability» ENVIRONMENTAL SUSTAINABILITY GOALS at LANL Community involvement: Andy Erickson and Duncan McBranch of LANL join John Arrowsmith of Los Alamos County to discuss the photovoltaic array collaboration with community leaders. Powered by solar: This collaboratively built model home in Los Alamos is entirely powered by a photovoltaic array field, showcasing the potential for solar-powering communities. Community involvement: A ribbon cutting ceremony marks the opening of the photovoltaic powered model home in Los Alamos County, a joint venture of LANL and the county. Engaging the surrounding communities: LANL takes opportunities to engage the surrounding communities in order to develop relationships fostering sustainable actions. Here, delegates applaud the opening of SERF which will help reduce liquid waste at LANL.

33

PUBLIC SERV COM  

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

'EXEC-2009-002897 'EXEC-2009-002897 PUBLIC SERV COM 1 2/25/2009 5:00 PM February 25,2009 The Honorable Steven Chu Secretary, U. S. Department of Energy 1000 Independence Avenue, S .W. Washingon, D.C. 20585 Re: State Energy Program Assurances Dear Secretary Chu: As a condition of receiving our State's share o f the $3.1, billion funding for the S t a t e Energy Program (SEP) under the American Recovery and Renewal Act of 2009 (H.R. l)(ARRA), I am providing the following assurances. I have written to our public u t i l i t y commission and requested that it consider additional actions to promote energy efficiency, consistent with the Federal statutory language contained in H.R. 1 and its obligations to maintain just and reasonable rates, while protecting the public. As set forth in the attached letter, the Chairperson of the Public Service

34

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","RESIDENTIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TOTAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","COMMERCIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","INDUSTRIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TRANSPORTATION PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"  

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

UTILITY FOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"

35

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","RESIDENTIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL PHOTOVOLTAIC ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","TOTAL PHOTOVOLTAIC INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","COMMERCIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","INDUSTRIAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TRANSPORTATION PHOTOVOLTAIC NET METERING CUSTOMER COUNT","TOTAL PHOTOVOLTAIC NET METERING CUSTOMER COUNT","RESIDENTIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION WIND ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL WIND ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL WIND INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL WIND INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL WIND INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION WIND INSTALLED NET METERING CAPACITY (MW)","TOTAL WIND INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL WIND NET METERING CUSTOMER COUNT","COMMERCIAL WIND NET METERING CUSTOMER COUNT","INDUSTRIAL WIND NET METERING CUSTOMER COUNT","TRANSPORTATION WIND NET METERING CUSTOMER COUNT","TOTAL WIND NET METERING CUSTOMER COUNT","RESIDENTIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","COMMERCIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION OTHER ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL OTHER ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL OTHER INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL OTHER INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL OTHER INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION OTHER INSTALLED NET METERING CAPACITY (MW)","TOTAL OTHER INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL OTHER NET METERING CUSTOMER COUNT","COMMERCIAL OTHER NET METERING CUSTOMER COUNT","INDUSTRIAL OTHER NET METERING CUSTOMER COUNT","TRANSPORTATION OTHER NET METERING CUSTOMER COUNT","TOTAL OTHER NET METERING CUSTOMER COUNT","RESIDENTIAL TOTAL ENERGY SOLD BACK TO THE UTILITY (MWh)","COMMERCIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","INDUSTRIAL TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TRANSPORTATION TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","TOTAL ELECTRIC ENERGY SOLD BACK (MWh)","RESIDENTIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","COMMERCIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","INDUSTRIAL TOTAL INSTALLED NET METERING CAPACITY (MW)","TRANSPORTATION TOTAL INSTALLED NET METERING CAPACITY (MW)","TOTAL INSTALLED NET METERING CAPACITY (MW)","RESIDENTIAL TOTAL NET METERING CUSTOMER COUNT","COMMERCIAL TOTAL NET METERING CUSTOMER COUNT","INDUSTRIAL TOTAL NET METERING CUSTOMER COUNT","TRANSPORTATION TOTAL NET METERING CUSTOMER COUNT","TOTAL NET METERING CUSTOMER COUNT","RESIDENTIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","COMMERCIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","INDUSTRIAL ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TRANSPORTATION ELECTRIC ENERGY SOLD BACK TO THE UTILITY FOR ALL STATES SERVED(MWh)","TOTAL ELECTRIC ENERGY SOLD BACK TO THE UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"  

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

UTILITYFOR ALL STATES SERVED(MWh)","RESIDENTIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","COMMERCIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INDUSTRIAL INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","TRANSPORTATION INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","INSTALLED NET METERING CAPACITY FOR ALL STATES SERVED(MW)","RESIDENTIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","COMMERCIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","INDUSTRIAL NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","TRANSPORTATION NET METERING CUSTOMER COUNT FOR ALL STATES SERVED","NET METERING CUSTOMER COUNT FOR ALL STATES SERVED"

36

Number of Marketers Serving Residential Customers, December 2002  

U.S. Energy Information Administration (EIA)

Number of Marketers Serving Residential Customers, December 2002. State/District *Total Marketers ... Gives number of marketers but no names: Georgia: 10: 10:

37

Reconsideration of EPA’s Approval of Vermont’s 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

38

Definition: Load-Serving Entity | Open Energy Information  

Open Energy Info (EERE)

Also Known As Electricity companies, electric utility Related Terms transmission service, transmission lines, energy, Interconnected Operations Service, transmission line...

39

Integration of Contracted Renewable Energy and Spot Market Supply to Serve  

E-Print Network (OSTI)

Integration of Contracted Renewable Energy and Spot Market Supply to Serve Flexible Loads Anthony-mail: oren@ieor.berkeley.edu). Abstract: We present a contract for integrating renewable energy supply and electricity spot markets for serving deferrable electric loads in order to mitigate renewable energy

Oren, Shmuel S.

40

Denton County Electric Cooperative d/b/a CoServ Electric Smart Grid Project  

Open Energy Info (EERE)

d/b/a CoServ Electric Smart Grid Project d/b/a CoServ Electric Smart Grid Project Jump to: navigation, search Project Lead Denton County Electric Cooperative d/b/a CoServ Electric Country United States Headquarters Location Corinth, Texas Recovery Act Funding $17,205,844.00 Total Project Value $40,966,296.00 Coverage Area Coverage Map: Denton County Electric Cooperative d/b/a CoServ Electric Smart Grid Project Coordinates 33.1540091°, -97.0647322° 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":[]}

Note: This page contains sample records for the topic "total load served" 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

CLEAN List Serve | Open Energy Information  

Open Energy Info (EERE)

List Serve List Serve Jump to: navigation, search Home | About | Inventory | Partnerships | Capacity Building | Webinars | Reports | Events | News | List Serve CLEAN List Serve Note: The list is too big to send in a single email, so send your message to part one AND part two CLEAN List Serve Name Organization Email Adib, Rana Renewable Energy Policy Network for the 21st Century (REN21) Agbemabiese, Lawrence United Nations Environment Programme Akbar, Sameer World Bank Alers, Marcel United Nations Development Programme Ashvie, Tim Climate and Development Knowledge Network (CDKN) Barnards, Geoff Bauer, Florian Renewable Energy and Energy Efficiency Partnership (REEEP) Bazilian, Morgan United Nations Industrial Development Organization (UNIDO) Ben Fadhl, Fatma United Nations Environment Programme

42

Minority Serving Institutions | Department of Energy  

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

Institutions Institutions Minority Serving Institutions Map by Matt Loveless, Department of Energy. Our Office of Minority Economic Impact works daily to tap into the talents of students and faculty attending our nation's Minority Serving Institutions. To accomplish the mission of the Department of Energy, we need the best and brightest individuals to work at and partner with the Department. We're proud of the work of our Minority Educational Institution partners, and we work to advance our partnerships daily. Minority Serving Institutions are institutions of higher education that serve minority populations. They are unique both in their missions and in their day-to-day operations. Some of these colleges and universities are located in remote regions of the country, whereas others serve urban

43

Minority Serving Institutions Internship Program | National Nuclear  

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

Institutions Internship Program | National Nuclear Institutions Internship Program | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Minority Serving Institutions Internship Program Home > Federal Employment > Apply for Our Jobs > How to Apply > Student Jobs > Minority Serving Institutions Internship Program Minority Serving Institutions Internship Program

44

Minority Serving Institutions Internship Program | National Nuclear  

National Nuclear Security Administration (NNSA)

Institutions Internship Program | National Nuclear Institutions Internship Program | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Minority Serving Institutions Internship Program Home > Federal Employment > Apply for Our Jobs > How to Apply > Student Jobs > Minority Serving Institutions Internship Program Minority Serving Institutions Internship Program

45

Strawberry Electric Serv Dist | Open Energy Information  

Open Energy Info (EERE)

Strawberry Electric Serv Dist Strawberry Electric Serv Dist Jump to: navigation, search Name Strawberry Electric Serv Dist Place Utah Utility Id 18206 Utility Location Yes Ownership P NERC Location WECC NERC WECC Yes Activity Transmission Yes Activity Buying Transmission Yes Activity Distribution Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] Energy Information Administration Form 826[2] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png No rate schedules available. Average Rates Residential: $0.1060/kWh Commercial: $0.1170/kWh Industrial: $0.0912/kWh The following table contains monthly sales and revenue data for Strawberry Electric Serv Dist (Utah).

46

Here to Serve | Department of Energy  

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

Here to Serve Here to Serve Here to Serve May 11, 2012 - 8:53am Addthis Dot Harris Dot Harris The Honorable Dot Harris, Director, Office of Economic Impact and Diversity It is a pleasure to join the Administration as the Director of the Office of Economic Impact and Diversity here at the Department of Energy. When President Obama nominated me for this position, I immediately recognized the vital importance of the work that this office does. From assisting small businesses to supporting minority serving institutions to enhancing the diversity and inclusion at the Department, this office has a mission-critical role for our clean energy future. I care deeply about my responsibilities in this position. As the Director at the Office of Economic Impact and Diversity, I'll be leading the

47

One Video Stream to Serve Diverse Receivers  

E-Print Network (OSTI)

The fundamental problem of wireless video multicast is to scalably serve multiple receivers which may have very different channel characteristics. Ideally, one would like to broadcast a single stream that allows each ...

Woo, Grace

2008-10-18T23:59:59.000Z

48

Load cell  

DOE Patents (OSTI)

A load cell combines the outputs of a plurality of strain gauges to measure components of an applied load. Combination of strain gauge outputs allows measurement of any of six load components without requiring complex machining or mechanical linkages to isolate load components. An example six axis load cell produces six independent analog outputs, each directly proportional to one of the six general load components. 16 figs.

Spletzer, B.L.

1998-12-15T23:59:59.000Z

49

Load cell  

DOE Patents (OSTI)

A load cell combines the outputs of a plurality of strain gauges to measure components of an applied load. Combination of strain gauge outputs allows measurement of any of six load components without requiring complex machining or mechanical linkages to isolate load components. An example six axis load cell produces six independent analog outputs which can be combined to determine any one of the six general load components.

Spletzer, Barry L. (Albuquerque, NM)

2001-01-01T23:59:59.000Z

50

Load cell  

DOE Patents (OSTI)

A load cell combines the outputs of a plurality of strain gauges to measure components of an applied load. Combination of strain gauge outputs allows measurement of any of six load components without requiring complex machining or mechanical linkages to isolate load components. An example six axis load cell produces six independent analog outputs, each directly proportional to one of the six general load components.

Spletzer, Barry L. (Albuquerque, NM)

1998-01-01T23:59:59.000Z

51

Wyandotte Municipal Serv Comm | Open Energy Information  

Open Energy Info (EERE)

Wyandotte Municipal Serv Comm Wyandotte Municipal Serv Comm Place Michigan Utility Id 21048 Utility Location Yes Ownership M NERC Location RFC NERC RFC Yes Operates Generating Plant Yes Activity Generation Yes Activity Transmission Yes Activity Buying Transmission Yes Activity Distribution Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png HEATING STEAM RATE Residential HOT WATER DISTRICT HEATING RATE Commercial LARGE GENERAL SERVICE RATE Commercial Commercial LARGE GENERAL SERVICE RATE Commercial (Time-Differentiated Meter) Commercial LARGE GENERAL SERVICE RATE Industrial (Time-Differentiated Meter)

52

CoServ Electric Cooperative - Commercial Energy Efficient Lighting...  

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

CoServ Electric Cooperative - Commercial Energy Efficient Lighting Rebate Program CoServ Electric Cooperative - Commercial Energy Efficient Lighting Rebate Program Eligibility...

53

Definition: Native Load | Open Energy Information  

Open Energy Info (EERE)

Terms Load-Serving Entity References Glossary of Terms Used in Reliability Standards An LikeLike UnlikeLike You like this.Sign Up to see what your friends like. inline...

54

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, ...

55

Load Control  

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

Visualization and Controls Peer Review Visualization and Controls Peer Review Load Control for System Reliability and Measurement-Based Stability Assessment Dan Trudnowski, PhD, PE Montana Tech Butte, MT 59701 dtrudnowski@mtech.edu 406-496-4681 October 2006 2 Presentation Outline * Introduction - Goals, Enabling technologies, Overview * Load Control - Activities, Status * Stability Assessment - Activities, Status * Wrap up - Related activities, Staff 3 Goals * Research and develop technologies to improve T&D reliability * Technologies - Real-time load control methodologies - Measurement-based stability-assessment 4 Enabling Technologies * Load control enabled by GridWise technology (e.g. PNNL's GridFriendly appliance) * Real-time stability assessment enabled by Phasor Measurement (PMU) technology 5 Project Overview * Time line: April 18, 2006 thru April 17, 2008

56

Multidimensional spectral load balancing  

DOE Patents (OSTI)

A method of and apparatus for graph partitioning involving the use of a plurality of eigenvectors of the Laplacian matrix of the graph of the problem for which load balancing is desired. The invention is particularly useful for optimizing parallel computer processing of a problem and for minimizing total pathway lengths of integrated circuits in the design stage.

Hendrickson, Bruce A. (Albuquerque, NM); Leland, Robert W. (Albuquerque, NM)

1996-12-24T23:59:59.000Z

57

Evolving non-intrusive load monitoring  

Science Conference Proceedings (OSTI)

Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off ... Keywords: NILM, evolution, evolutionary algorithm, knapsack problem, non-intrusive load monitoring

Dominik Egarter; Anita Sobe; Wilfried Elmenreich

2013-04-01T23:59:59.000Z

58

DOE Joint Genome Institute: Trillions Served: Massive, Complex...  

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

the number of burgers served has eclipsed the billion mark, while the U.S. Department of Energy (DOE) Joint Genome Institute (JGI) will now serve up trillions of nucleotides of...

59

CoServ - Solar Energy Rebate (Texas) | Department of Energy  

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

2W '''''NOTE: Available funding for fiscal year 2012 has been exhausted for the CoServ Solar PV Rebate Program.''''' CoServ Electric Cooperative provides a variety of "Think...

60

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.

Note: This page contains sample records for the topic "total load served" 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

LOADING DEVICE  

DOE Patents (OSTI)

A device is presented for loading or charging bodies of fissionable material into a reactor. This device consists of a car, mounted on tracks, into which the fissionable materials may be placed at a remote area, transported to the reactor, and inserted without danger to the operating personnel. The car has mounted on it a heavily shielded magazine for holding a number of the radioactive bodies. The magazine is of a U-shaped configuration and is inclined to the horizontal plane, with a cap covering the elevated open end, and a remotely operated plunger at the lower, closed end. After the fissionable bodies are loaded in the magazine and transported to the reactor, the plunger inserts the body at the lower end of the magazine into the reactor, then is withdrawn, thereby allowing gravity to roll the remaining bodies into position for successive loading in a similar manner.

Ohlinger, L.A.

1958-10-01T23:59:59.000Z

62

CoServ - Solar Energy Rebate (Texas) | Department of Energy  

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

CoServ - Solar Energy Rebate (Texas) CoServ - Solar Energy Rebate (Texas) CoServ - Solar Energy Rebate (Texas) < Back Eligibility Commercial Residential Savings Category Solar Buying & Making Electricity Maximum Rebate $5,000 Program Info Funding Source CoServ Start Date 01/01/2011 State Texas Program Type Utility Rebate Program Rebate Amount $2/W Provider CoServ Electric Cooperative '''''NOTE: Available funding for fiscal year 2012 has been exhausted for the CoServ Solar PV Rebate Program.''''' CoServ Electric Cooperative provides a variety of "Think Green Rebates" to its members, including a solar energy rebate. The solar photovoltaic (PV) system must be less than or equal to 50 kW, but the rebate is available only on the first 2.5 kW. Customers must sign an interconnection agreement

63

Federal Energy Management Program: FDA Construction Project Serves as a  

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

FDA Construction FDA Construction Project Serves as a Super ESPC Model to someone by E-mail Share Federal Energy Management Program: FDA Construction Project Serves as a Super ESPC Model on Facebook Tweet about Federal Energy Management Program: FDA Construction Project Serves as a Super ESPC Model on Twitter Bookmark Federal Energy Management Program: FDA Construction Project Serves as a Super ESPC Model on Google Bookmark Federal Energy Management Program: FDA Construction Project Serves as a Super ESPC Model on Delicious Rank Federal Energy Management Program: FDA Construction Project Serves as a Super ESPC Model on Digg Find More places to share Federal Energy Management Program: FDA Construction Project Serves as a Super ESPC Model on AddThis.com... Energy Savings Performance Contracts

64

Giving Back to Those Who Served | Department of Energy  

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

Giving Back to Those Who Served Giving Back to Those Who Served Giving Back to Those Who Served November 10, 2010 - 6:58pm Addthis While we officially observe Veterans Day just once a year, we remember the service of those who have worn the uniform of the United States each and every day. Tomorrow, the Department of Energy honors those who serve, nearly 2,300 of which are DOE employees. A day dedicated to love of country, willingness to serve, and willingness to sacrifice for the common good of the Nation, it is appropriate that on this Veterans Day we look for ways we can serve others. The Department, along with the rest of the Federal government, is in the midst of the 2010 Combined Federal Campaign, a fundraising drive dedicated to giving back to those in need. We know why we give. We know how fortunate we are as Federal employees, and

65

Duffy Served as EM's First Assistant Secretary | Department of Energy  

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

Duffy Served as EM's First Assistant Secretary Duffy Served as EM's First Assistant Secretary Duffy Served as EM's First Assistant Secretary June 26, 2013 - 12:00pm Addthis Leo Duffy served as head of DOE's nuclear cleanup program from 1989 to 1993. Leo Duffy served as head of DOE's nuclear cleanup program from 1989 to 1993. Editor's note: In an occasional EM Update series, we feature interviews with former EM Assistant Secretaries to reflect on their achievements and challenges in the world's largest nuclear cleanup and to discuss endeavors in life after EM. More than 24 years ago, Leo Duffy became the first Assistant Secretary of DOE's Office of Environmental Restoration and Waste Management. Confirmed during President George H.W. Bush's administration, Duffy served as the head of this new mission from 1989 to 1993. That pioneering program is now

66

Duffy Served as EM's First Assistant Secretary | Department of Energy  

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

Duffy Served as EM's First Assistant Secretary Duffy Served as EM's First Assistant Secretary Duffy Served as EM's First Assistant Secretary June 26, 2013 - 12:00pm Addthis Leo Duffy served as head of DOE's nuclear cleanup program from 1989 to 1993. Leo Duffy served as head of DOE's nuclear cleanup program from 1989 to 1993. Editor's note: In an occasional EM Update series, we feature interviews with former EM Assistant Secretaries to reflect on their achievements and challenges in the world's largest nuclear cleanup and to discuss endeavors in life after EM. More than 24 years ago, Leo Duffy became the first Assistant Secretary of DOE's Office of Environmental Restoration and Waste Management. Confirmed during President George H.W. Bush's administration, Duffy served as the head of this new mission from 1989 to 1993. That pioneering program is now

67

Giving Back to Those Who Served | Department of Energy  

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

Giving Back to Those Who Served Giving Back to Those Who Served Giving Back to Those Who Served November 10, 2010 - 6:58pm Addthis While we officially observe Veterans Day just once a year, we remember the service of those who have worn the uniform of the United States each and every day. Tomorrow, the Department of Energy honors those who serve, nearly 2,300 of which are DOE employees. A day dedicated to love of country, willingness to serve, and willingness to sacrifice for the common good of the Nation, it is appropriate that on this Veterans Day we look for ways we can serve others. The Department, along with the rest of the Federal government, is in the midst of the 2010 Combined Federal Campaign, a fundraising drive dedicated to giving back to those in need. We know why we give. We know how fortunate we are as Federal employees, and

68

CoServ Electric Cooperative - Commercial Energy Efficient Lighting...  

Open Energy Info (EERE)

icon Twitter icon CoServ Electric Cooperative - Commercial Energy Efficient Lighting Rebate Program (Texas) This is the approved revision of this page, as well as being...

69

Deadline Monday for Minority Serving Institutions Solar Science...  

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

25, 2013. The funding opportunity, which was posted February 20 by the Department's SunShot Initative, is seeking applications from Minority Serving Institutions to support...

70

Federal Energy Management Program: FDA Construction Project Serves...  

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

Program: FDA Construction Project Serves as a Super ESPC Model on AddThis.com... Energy Savings Performance Contracts Assistance & Contacts Resources Laws & Regulations...

71

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales...  

Open Energy Info (EERE)

navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for December 2008. Monthly Electric Utility Sales and Revenue Data Short...

72

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales...  

Open Energy Info (EERE)

navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for January 2009. Monthly Electric Utility Sales and Revenue Data Short...

73

LOADED WAVEGUIDES  

DOE Patents (OSTI)

>Loaded waveguides are described for the propagation of electromagnetic waves with reduced phase velocities. A rectangular waveguide is dimensioned so as to cut-off the simple H/sub 01/ mode at the operating frequency. The waveguide is capacitance loaded, so as to reduce the phase velocity of the transmitted wave, by connecting an electrical conductor between directly opposite points in the major median plane on the narrower pair of waveguide walls. This conductor may take a corrugated shape or be an aperature member, the important factor being that the electrical length of the conductor is greater than one-half wavelength at the operating frequency. Prepared for the Second U.N. International ConferThe importance of nuclear standards is duscussed. A brief review of the international callaboration in this field is given. The proposal is made to let the International Organization for Standardization (ISO) coordinate the efforts from other groups. (W.D.M.)

Mullett, L.B.; Loach, B.G.; Adams, G.L.

1958-06-24T23:59:59.000Z

74

Using Utility Load Data to Estimate Demand for Space Cooling and Potential for Shiftable Loads  

SciTech Connect

This paper describes a simple method to estimate hourly cooling demand from historical utility load data. It compares total hourly demand to demand on cool days and compares these estimates of total cooling demand to previous regional and national estimates. Load profiles generated from this method may be used to estimate the potential for aggregated demand response or load shifting via cold storage.

Denholm, P.; Ong, S.; Booten, C.

2012-05-01T23:59:59.000Z

75

CoServ Electric Cooperative - Commercial Energy Efficient Lighting Rebate  

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

CoServ Electric Cooperative - Commercial Energy Efficient Lighting CoServ Electric Cooperative - Commercial Energy Efficient Lighting Rebate Program CoServ Electric Cooperative - Commercial Energy Efficient Lighting Rebate Program < Back Eligibility Commercial Industrial Savings Category Appliances & Electronics Commercial Lighting Lighting Program Info Funding Source Via partnership with whole sale provider Brazos Electric Power, Inc. and escheat funds Start Date 09/01/2009 State Texas Program Type Utility Rebate Program Rebate Amount Custom Lighting Upgrade: 0.30/watt saved per fixture T8 Fluorescent Upgrade: 1.50 - 2.25/bulb per fixture Provider CoServ Electric Cooperative CoServ Electric Cooperative provides rebates for commercial and industrial customers who upgrade to high efficiency lighting for the workplace. A rebate of $0.30/watt saved is available on custom lighting upgrades and a

76

Deadline Monday for Minority Serving Institutions Solar Science Funding  

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

Deadline Monday for Minority Serving Institutions Solar Science Deadline Monday for Minority Serving Institutions Solar Science Funding Opportunity Application Deadline Monday for Minority Serving Institutions Solar Science Funding Opportunity Application March 21, 2013 - 4:33pm Addthis Deadline Monday for Minority Serving Institutions Solar Science Funding Opportunity Application The deadline to apply for the Diversity In Science and Technology Advances National Clean Energy in Solar (DISTANCE-SOLAR) Funding Opportunity Announcement, is this Monday, March 25, 2013. The funding opportunity, which was posted February 20 by the Department's SunShot Initative, is seeking applications from Minority Serving Institutions to support solar science and technology research to advance the development of a diverse and innovative workforce.

77

Deadline Monday for Minority Serving Institutions Solar Science Funding  

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

Deadline Monday for Minority Serving Institutions Solar Science Deadline Monday for Minority Serving Institutions Solar Science Funding Opportunity Application Deadline Monday for Minority Serving Institutions Solar Science Funding Opportunity Application March 21, 2013 - 4:33pm Addthis Deadline Monday for Minority Serving Institutions Solar Science Funding Opportunity Application The deadline to apply for the Diversity In Science and Technology Advances National Clean Energy in Solar (DISTANCE-SOLAR) Funding Opportunity Announcement, is this Monday, March 25, 2013. The funding opportunity, which was posted February 20 by the Department's SunShot Initative, is seeking applications from Minority Serving Institutions to support solar science and technology research to advance the development of a diverse and innovative workforce.

78

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - March 2008  

Open Energy Info (EERE)

March 2008 March 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for March 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-03 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-03-01 End Date 2008-04-01 Residential Revenue(Thousand $) 25715 Residential Sales (MWh) 250621 Residential Consumers 337464 Commercial Revenue(Thousand $) 15187 Commercial Sales (MWh) 156079 Commercial Consumers 52810 Industrial Revenue (Thousand $) 1664 Industrial Sales (MWh) 17211 Industrial Consumers 529 Other Revenue (Thousand $) 106 Other Sales (MWh) 880 Other Consumers 1 Total Revenue (Thousand $) 42672 Total Sales (MWh) 424791 Total Consumers 390804 Source: Energy Information Administration. Form EIA-826 Database Monthly

79

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - May 2008 |  

Open Energy Info (EERE)

Central Illinois Central Illinois Pub Serv Co for May 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-05 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-05-01 End Date 2008-06-01 Residential Revenue(Thousand $) 24553 Residential Sales (MWh) 218454 Residential Consumers 337410 Commercial Revenue(Thousand $) 19095 Commercial Sales (MWh) 187996 Commercial Consumers 55845 Industrial Revenue (Thousand $) 1116 Industrial Sales (MWh) 34382 Industrial Consumers 519 Other Revenue (Thousand $) 52 Other Sales (MWh) 702 Other Consumers 1 Total Revenue (Thousand $) 44816 Total Sales (MWh) 441534 Total Consumers 393775 Source: Energy Information Administration. Form EIA-826 Database Monthly Electric Utility Sales and Revenue Data [1]

80

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - January  

Open Energy Info (EERE)

January January 2009 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for January 2009. Monthly Electric Utility Sales and Revenue Data Short Name 2009-01 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2009-01-01 End Date 2009-02-01 Residential Revenue(Thousand $) 38208 Residential Sales (MWh) 442616 Residential Consumers 329875 Commercial Revenue(Thousand $) 18652 Commercial Sales (MWh) 197785 Commercial Consumers 47346 Industrial Revenue (Thousand $) 1173 Industrial Sales (MWh) 16509 Industrial Consumers 453 Other Revenue (Thousand $) 100 Other Sales (MWh) 1537 Other Consumers 1 Total Revenue (Thousand $) 58133 Total Sales (MWh) 658447 Total Consumers 377675

Note: This page contains sample records for the topic "total load served" 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

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - April 2008  

Open Energy Info (EERE)

Central Illinois Central Illinois Pub Serv Co for April 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-04 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-04-01 End Date 2008-05-01 Residential Revenue(Thousand $) 24400 Residential Sales (MWh) 247343 Residential Consumers 331573 Commercial Revenue(Thousand $) 14383 Commercial Sales (MWh) 152042 Commercial Consumers 52280 Industrial Revenue (Thousand $) 1241 Industrial Sales (MWh) 13081 Industrial Consumers 524 Other Revenue (Thousand $) 92 Other Sales (MWh) 1113 Other Consumers 1 Total Revenue (Thousand $) 40116 Total Sales (MWh) 413579 Total Consumers 384378 Source: Energy Information Administration. Form EIA-826 Database Monthly Electric Utility Sales and Revenue Data [1]

82

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - December  

Open Energy Info (EERE)

Central Illinois Central Illinois Pub Serv Co for December 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-12 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-12-01 End Date 2009-01-01 Residential Revenue(Thousand $) 35185 Residential Sales (MWh) 410509 Residential Consumers 327240 Commercial Revenue(Thousand $) 19393 Commercial Sales (MWh) 208884 Commercial Consumers 48125 Industrial Revenue (Thousand $) 1172 Industrial Sales (MWh) 15357 Industrial Consumers 466 Other Revenue (Thousand $) 78 Other Sales (MWh) 1202 Other Consumers 1 Total Revenue (Thousand $) 55828 Total Sales (MWh) 635952 Total Consumers 375832 Source: Energy Information Administration. Form EIA-826 Database Monthly Electric Utility Sales and Revenue Data [1]

83

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - June 2008 |  

Open Energy Info (EERE)

June 2008 June 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for June 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-06 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-06-01 End Date 2008-07-01 Residential Revenue(Thousand $) 39796 Residential Sales (MWh) 376563 Residential Consumers 348410 Commercial Revenue(Thousand $) 25354 Commercial Sales (MWh) 244206 Commercial Consumers 62285 Industrial Revenue (Thousand $) 1913 Industrial Sales (MWh) 11642 Industrial Consumers 542 Other Revenue (Thousand $) 54 Other Sales (MWh) 697 Other Consumers 1 Total Revenue (Thousand $) 67117 Total Sales (MWh) 633108 Total Consumers 411238 Source: Energy Information Administration. Form EIA-826 Database Monthly

84

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - February  

Open Energy Info (EERE)

February February 2009 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for February 2009. Monthly Electric Utility Sales and Revenue Data Short Name 2009-02 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2009-02-01 End Date 2009-03-01 Residential Revenue(Thousand $) 28078 Residential Sales (MWh) 297866 Residential Consumers 341636 Commercial Revenue(Thousand $) 15755 Commercial Sales (MWh) 165037 Commercial Consumers 49052 Industrial Revenue (Thousand $) 639 Industrial Sales (MWh) 16720 Industrial Consumers 474 Other Revenue (Thousand $) 128 Other Sales (MWh) 2187 Other Consumers 1 Total Revenue (Thousand $) 44600 Total Sales (MWh) 481810 Total Consumers 391163

85

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - January  

Open Energy Info (EERE)

January January 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for January 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-01 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-01-01 End Date 2008-02-01 Residential Revenue(Thousand $) 38361 Residential Sales (MWh) 457391 Residential Consumers 334784 Commercial Revenue(Thousand $) 20964 Commercial Sales (MWh) 244215 Commercial Consumers 52783 Industrial Revenue (Thousand $) 1321 Industrial Sales (MWh) 21368 Industrial Consumers 539 Other Revenue (Thousand $) 52 Other Sales (MWh) 707 Other Consumers 1 Total Revenue (Thousand $) 60698 Total Sales (MWh) 723681 Total Consumers 388107

86

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - October  

Open Energy Info (EERE)

Central Illinois Central Illinois Pub Serv Co for October 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-10 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-10-01 End Date 2008-11-01 Residential Revenue(Thousand $) 27599 Residential Sales (MWh) 248769 Residential Consumers 329654 Commercial Revenue(Thousand $) 19506 Commercial Sales (MWh) 193998 Commercial Consumers 48492 Industrial Revenue (Thousand $) 1811 Industrial Sales (MWh) 14741 Industrial Consumers 477 Other Revenue (Thousand $) 55 Other Sales (MWh) 713 Other Consumers 1 Total Revenue (Thousand $) 48971 Total Sales (MWh) 458221 Total Consumers 378624 Source: Energy Information Administration. Form EIA-826 Database Monthly Electric Utility Sales and Revenue Data [1]

87

Minority Serving Institution Internship Program | National Nuclear Security  

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

Institution Internship Program | National Nuclear Security Institution Internship Program | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Minority Serving Institution Internship Program Home > Federal Employment > Our Jobs > Opportunities for Students > Minority Serving Institution Internship Program Minority Serving Institution Internship Program

88

Minority Serving Institution Internship Program | National Nuclear Security  

National Nuclear Security Administration (NNSA)

Institution Internship Program | National Nuclear Security Institution Internship Program | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Minority Serving Institution Internship Program Home > Federal Employment > Our Jobs > Opportunities for Students > Minority Serving Institution Internship Program Minority Serving Institution Internship Program

89

CoServ - Solar Energy Rebate | Open Energy Information  

Open Energy Info (EERE)

Page Edit with form History Share this page on Facebook icon Twitter icon CoServ - Solar Energy Rebate This is the approved revision of this page, as well as being the most...

90

Join our Webinar for Minority Serving Institutions on Science Education  

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

Join our Webinar for Minority Serving Institutions on Science Join our Webinar for Minority Serving Institutions on Science Education Opportunities This Monday Join our Webinar for Minority Serving Institutions on Science Education Opportunities This Monday December 13, 2012 - 9:19am Addthis Faculty and Students - Check out these Office of Science opportunities before they close by listening in on our webinar: Monday, December 17, at 1pm EST. Faculty and Students - Check out these Office of Science opportunities before they close by listening in on our webinar: Monday, December 17, at 1pm EST. Annie Whatley Annie Whatley Deputy Director, Office of Minority Economic Impact You're Invited: Faculty and Students at Minority Serving Institutions (MSIs) are invited to participate in an Energy Department webinar on Monday, December 17, 2012,

91

Minority Serving Institution Technical Consortium Model | Department of  

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

Minority Serving Institution Technical Consortium Model Minority Serving Institution Technical Consortium Model Minority Serving Institution Technical Consortium Model In October 2012, the National Nuclear Security Administration (NNSA) awarded $4 million in grants to 22 Historically Black Colleges and Universities (HBCUs) in key STEM areas. This funding launched NNSA's new Minority Serving Institution Partnership Program, a consortium program organized to build a sustainable STEM pipeline between six Energy Department plants and laboratories and the HBCUs. The Program is designed to enrich the STEM capabilities of HBCUs in a sustainable manner that aligns with the broad interests of Energy Department sites and emphasizes the STEM career pipeline. The program brings together 8 teams from HBCUs that share similar interests

92

Bringing Proposal Writing Training to Faculty at Minority Serving  

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

Bringing Proposal Writing Training to Faculty at Minority Serving Bringing Proposal Writing Training to Faculty at Minority Serving Institutions Bringing Proposal Writing Training to Faculty at Minority Serving Institutions June 20, 2012 - 12:20pm Addthis Bringing Proposal Writing Training to Faculty at Minority Serving Institutions Bill Valdez Bill Valdez Principal Deputy Director Money doesn't grow on trees, but it does grow from developing your school and business to the point that you're ready to compete for a Department of Energy funding opportunity. Our office's mission is to make sure that the energy programs here, including our competitive funding opportunities, are accessible to minorities and historically disadvantaged communities. That's why we're hosting technical assistance workshops at events across the country, engaging students, faculty, and staff at Minority

93

Commercial Products Show Potential to serve as Nuclear Material and  

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

Commercial Products Show Potential to serve as Nuclear Material and Commercial Products Show Potential to serve as Nuclear Material and Activity Monitoring Technologies | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Home > NNSA Blog > Commercial Products Show Potential to serve as ... Commercial Products Show Potential to serve as Nuclear Material and

94

Commercial Products Show Potential to serve as Nuclear Material and  

National Nuclear Security Administration (NNSA)

Commercial Products Show Potential to serve as Nuclear Material and Commercial Products Show Potential to serve as Nuclear Material and Activity Monitoring Technologies | National Nuclear Security Administration Our Mission Managing the Stockpile Preventing Proliferation Powering the Nuclear Navy Emergency Response Recapitalizing Our Infrastructure Continuing Management Reform Countering Nuclear Terrorism About Us Our Programs Our History Who We Are Our Leadership Our Locations Budget Our Operations Media Room Congressional Testimony Fact Sheets Newsletters Press Releases Speeches Events Social Media Video Gallery Photo Gallery NNSA Archive Federal Employment Apply for Our Jobs Our Jobs Working at NNSA Blog Home > NNSA Blog > Commercial Products Show Potential to serve as ... Commercial Products Show Potential to serve as Nuclear Material and

95

Power Choice/Pepco Energy Serv | Open Energy Information  

Open Energy Info (EERE)

Choice/Pepco Energy Serv Choice/Pepco Energy Serv Jump to: navigation, search Name Power Choice/Pepco Energy Serv Place New Jersey Utility Id 14405 References EIA Form EIA-861 Final Data File for 2010 - File2_2010[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png No rate schedules available. Average Rates No Rates Available References ↑ "EIA Form EIA-861 Final Data File for 2010 - File2_2010" Retrieved from "http://en.openei.org/w/index.php?title=Power_Choice/Pepco_Energy_Serv&oldid=412767" Categories: EIA Utility Companies and Aliases Utility Companies Organizations Stubs What links here Related changes Special pages Printable version

96

Bringing Proposal Writing Training to Faculty at Minority Serving  

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

Bringing Proposal Writing Training to Faculty at Minority Serving Bringing Proposal Writing Training to Faculty at Minority Serving Institutions Bringing Proposal Writing Training to Faculty at Minority Serving Institutions June 20, 2012 - 12:20pm Addthis Bringing Proposal Writing Training to Faculty at Minority Serving Institutions Bill Valdez Bill Valdez Principal Deputy Director Money doesn't grow on trees, but it does grow from developing your school and business to the point that you're ready to compete for a Department of Energy funding opportunity. Our office's mission is to make sure that the energy programs here, including our competitive funding opportunities, are accessible to minorities and historically disadvantaged communities. That's why we're hosting technical assistance workshops at events across the country, engaging students, faculty, and staff at Minority

97

CoServ Electric Cooperative- Residential Energy Efficiency Rebate Program  

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

CoServ Electric Cooperative's "Think Green Rebate Program" provides a range of incentives encouraging its residential customers to upgrade to high efficiency equipment in their homes. Rebates are...

98

Spinning Reserve from Responsive Load  

SciTech Connect

As power system costs rise and capacity is strained demand response can provide a significant system reliability benefit at a potentially attractive cost. The 162 room Music Road Hotel in Pigeon Forge Tennessee agreed to host a spinning reserve test. The Tennessee Valley Authority (TVA) supplied real-time metering and monitoring expertise to record total hotel load during both normal operations and testing. Preliminary testing showed that hotel load can be curtailed by 22% to 37% depending on the outdoor temperature and the time of day. The load drop was very rapid, essentially as fast as the 2 second metering could detect.

Kueck, John D [ORNL; Kirby, Brendan J [ORNL; Laughner, T [Tennessee Valley Authority (TVA); Morris, K [Tennessee Valley Authority (TVA)

2009-01-01T23:59:59.000Z

99

Energy Department National Labs and Minority Serving Institutions...  

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

Person Solar Energy Potential Solar Energy Potential Renewable Energy Production By State Renewable Energy Production By State 2009 Total Energy Production by State 2009 Total...

100

Static identification of delinquent loads  

E-Print Network (OSTI)

The effective use of processor caches is crucial to the performance of applications. It has been shown that cache misses are not evenly distributed throughout a program. In applications running on RISC-style processors, a small number of delinquent load instructions are responsible for most of the cache misses. Identification of delinquent loads is the key to the success of many cache optimization and prefetching techniques. In this paper, we propose a method for identifying delinquent loads that can be implemented at compile time. Our experiments over eighteen benchmarks from the SPEC suite shows that our proposed scheme is stable across benchmarks, inputs, and cache structures, identifying an average of 10 % of the total number of loads in the benchmarks we tested that account for over 90 % of all data cache misses. As far as we know, this is the first time a technique for static delinquent load identification with such a level of precision and coverage has been reported. While comparable techniques can also identify load instructions that cover 90 % of all data cache misses, they do so by selecting over 50 % of all load instructions in the code, resulting in a high number of false positives. If basic block profiling is used in conjunction with our heuristic, then our results show that it is possible to pin down just 1.3 % of the load instructions that account for 82 % of all data cache misses. 1.

Vlad-mihai Panait; Amit Sasturkar Ý

2004-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

Public Serv Comm of Yazoo City | Open Energy Information  

Open Energy Info (EERE)

Serv Comm of Yazoo City Serv Comm of Yazoo City Jump to: navigation, search Name Public Serv Comm of Yazoo City Place Mississippi Utility Id 21095 Utility Location Yes Ownership P NERC Location SERC NERC SERC Yes NERC SPP Yes RTO SPP Yes Operates Generating Plant Yes Activity Generation Yes Activity Distribution Yes Activity Bundled Services Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png Electric - City Commercial Electric - PSC Commercial Electric - Schools Commercial Electric Commercial - Large Commercial Electric Commercial - Seasonal Commercial Electric Commercial - Small Commercial

102

South Carolina Pub Serv Auth | Open Energy Information  

Open Energy Info (EERE)

Pub Serv Auth Pub Serv Auth Jump to: navigation, search Name South Carolina Pub Serv Auth Place South Carolina Utility Id 17543 Utility Location Yes Ownership S NERC Location SERC NERC SERC Yes Operates Generating Plant Yes Activity Generation Yes Activity Transmission Yes Activity Buying Transmission Yes Activity Distribution Yes Activity Wholesale Marketing Yes Alt Fuel Vehicle Yes Alt Fuel Vehicle2 Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] Energy Information Administration Form 826[2] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png GA-12(General Service) Commercial GB-12 (Medium General Service) Commercial

103

Rebate Program Serves Alaskans with Disabilities | Department of Energy  

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

Serves Alaskans with Disabilities Serves Alaskans with Disabilities Rebate Program Serves Alaskans with Disabilities July 21, 2010 - 2:10pm Addthis Lorelei Laird Writer, Energy Empowers What are the key facts? Energy efficient appliances are made more affordable in rural Alaska thanks to $500 rebates. With rates of up to .50 kWh in Alaska, energy efficient appliances reduce energy bills. Alaska applies Recovery Act funding to lower administrative costs of their rebate program. According to the U.S. Census Population Finder, the estimated population of Alaska as of 2009 was 698,473. In the same year, Alaska was awarded $658,000 as part of the State Appliance Rebate Program, an American Recovery and Reinvestment Act program that helps Americans purchase ENERGY STAR appliances to replace older, inefficient models. That grant worked out

104

A Minority Serving Institution Leads the Way in Better Buildings |  

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

A Minority Serving Institution Leads the Way in Better Buildings A Minority Serving Institution Leads the Way in Better Buildings A Minority Serving Institution Leads the Way in Better Buildings July 5, 2012 - 6:06pm Addthis Secretary Chu visits Delaware State University to commemorate the school's efforts with the Better Buildings Initiative. Secretary Chu visits Delaware State University to commemorate the school's efforts with the Better Buildings Initiative. Dot Harris Dot Harris The Honorable Dot Harris, Director, Office of Economic Impact and Diversity In February 2011, President Obama announced the Better Buildings Initiative to make commercial and industrial buildings 20 percent more energy efficient by 2020 and accelerate private sector investment in energy efficiency. Delaware State University (DSU) was selected as one of the

105

Central Vermont Pub Serv Corp | Open Energy Information  

Open Energy Info (EERE)

Pub Serv Corp Pub Serv Corp Jump to: navigation, search Name Central Vermont Pub Serv Corp Place Vermont Service Territory Vermont Website www.cvps.com Green Button Reference Page www.efficiencyvermont.com Green Button Committed Yes Utility Id 3292 Utility Location Yes Ownership I NERC Location NPCC NERC NPCC Yes ISO NE Yes Operates Generating Plant Yes Activity Generation Yes Activity Transmission Yes Activity Distribution Yes Activity Wholesale Marketing Yes Alt Fuel Vehicle Yes Alt Fuel Vehicle2 Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] Energy Information Administration Form 826[2] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules

106

1995 Pacific Northwest Loads and Resources Study.  

Science Conference Proceedings (OSTI)

The study establishes the planning basis for supplying electricity to customers. The study presents projections of regional and Federal system load and resource capabilities, and serves as a benchmark for annual BPA determinations made pursuant to the 1981 regional power sales contracts.

United States. Bonneville Power Administration.

1995-12-01T23:59:59.000Z

107

Critically loaded queueing models that are throughput suboptimal  

E-Print Network (OSTI)

This paper introduces and analyzes the notion of throughput suboptimality for many-server queueing systems in heavy traffic. The queueing model under consideration has multiple customer classes, indexed by a finite set $\\mathcal{I}$, and heterogenous, exponential servers. Servers are dynamically chosen to serve customers, and buffers are available for customers waiting to be served. The arrival rates and the number of servers are scaled up in such a way that the processes representing the number of class-$i$ customers in the system, $i\\in\\mathcal{I}$, fluctuate about a static fluid model, that is assumed to be critically loaded in a standard sense. At the same time, the fluid model is assumed to be throughput suboptimal. Roughly, this means that the servers can be allocated so as to achieve a total processing rate that is greater than the total arrival rate. We show that there exists a dynamic control policy for the queueing model that is efficient in the following strong sense: Under this policy, for every f...

Atar, Rami; 10.1214/08-AAP551

2009-01-01T23:59:59.000Z

108

An Evaluation of the HVAC Load Potential for Providing Load Balancing Service  

Science Conference Proceedings (OSTI)

This paper investigates the potential of providing aggregated intra-hour load balancing services using heating, ventilating, and air-conditioning (HVAC) systems. A direct-load control algorithm is presented. A temperature-priority-list method is used to dispatch the HVAC loads optimally to maintain consumer-desired indoor temperatures and load diversity. Realistic intra-hour load balancing signals were used to evaluate the operational characteristics of the HVAC load under different outdoor temperature profiles and different indoor temperature settings. The number of HVAC units needed is also investigated. Modeling results suggest that the number of HVACs needed to provide a {+-}1-MW load balancing service 24 hours a day varies significantly with baseline settings, high and low temperature settings, and the outdoor temperatures. The results demonstrate that the intra-hour load balancing service provided by HVAC loads meet the performance requirements and can become a major source of revenue for load-serving entities where the smart grid infrastructure enables direct load control over the HAVC loads.

Lu, Ning

2012-09-30T23:59:59.000Z

109

Periodic load balancing  

Science Conference Proceedings (OSTI)

Multiprocessor load balancing aims to improve performance by moving jobs from highly loaded processors to more lightly loaded processors. Some schemes allow only migration of new jobs upon arrival, while other schemes allow migration of ... Keywords: heavy traffic diffusion approximations, load balancing, periodic load balancing, reflected Brownian motion, resource sharing, transient behavior

Gísli Hjálmtýsson; Ward Whitt

1998-06-01T23:59:59.000Z

110

New Castle Municipal Serv Comm | Open Energy Information  

Open Energy Info (EERE)

New Castle Municipal Serv Comm New Castle Municipal Serv Comm Place Delaware Utility Id 13424 Utility Location Yes Ownership M NERC Location RFC NERC RFC Yes Activity Distribution Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png Commercial Space Heating (Grandfathered) Commercial Large General Service Commercial Demand Rate (Primary) Commercial Large General Service Commercial Demand Rate (Secondary) Commercial Medium General Service Commercial Demand Rate Commercial Residential Service Residential Residential Space Heating Residential Small General Service Commercial Non-Demand Rate Commercial

111

and could possibly serve as a repository for  

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

and could possibly serve as a repository for and could possibly serve as a repository for captured CO 2 emissions. The formation is covered by layers of low permeability rock and possesses several properties that are conducive to CO 2 storage, such as the appropriate depth, thickness, porosity, and permeability. Prior to drilling the test well, MRCSP conducted a seismic survey at the site and obtained necessary permits for the injection test from the U.S. Environmental Protection Agency (EPA) and the Kentucky Division of Oil and Gas. Following the permitting process, the researchers injected clean brine in order to determine formation properties like the maximum injection rate and then injected approximately 1,000 metric tons of CO 2 in two, 500-meter-ton steps. The injection rate, pressure, temperature,

112

Crack coalescence in rock-like material under cycling loading  

E-Print Network (OSTI)

A total of 170 tests (68 tests for monotonic loading, 102 tests for cyclic loading) have been performed to investigate crack initiation, propagation and coalescence. The specimens have two pre-existing flaws which are ...

Ko, Tae Young, 1973-

2005-01-01T23:59:59.000Z

113

TotalView Parallel Debugger at NERSC  

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

Totalview Totalview Totalview Description TotalView from Rogue Wave Software is a parallel debugging tool that can be run with up to 512 processors. It provides both X Windows-based Graphical User Interface (GUI) and command line interface (CLI) environments for debugging. The performance of the GUI can be greatly improved if used in conjunction with free NX software. The TotalView documentation web page is a good resource for learning more about some of the advanced TotalView features. Accessing Totalview at NERSC To use TotalView at NERSC, first load the TotalView modulefile to set the correct environment settings with the following command: % module load totalview Compiling Code to Run with TotalView In order to use TotalView, code must be compiled with the -g option. We

114

How well do home energy audits serve the homeowner?  

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

How well do home energy audits serve the homeowner? How well do home energy audits serve the homeowner? Title How well do home energy audits serve the homeowner? Publication Type Conference Proceedings LBNL Report Number LBNL-5712E Year of Publication 2012 Authors Ingle, Aaron, Mithra M. Moezzi, Loren Lutzenhiser, and Richard C. Diamond Conference Name 2012 Summer Study on Energy Efficiency in Buildings Date Published 05/2012 Conference Location Pacific Grove, CA, USA Abstract Home energy audits administered by utilities and government typically provide homeowners with lists of technical upgrade recommendations intended to increase the technical energy efficiency of the house. Audits proceed with assessment of physical characteristics, subsequently processed with a computational model and transformed into a report, sometimes customized by the auditor. While the design of an energy audit reflects program and policy points of view - balancing program cost with expected program savings, educating people about the value of energy efficiency, etc. - it is crucial to consider the criteria for a good home energy audit and recommendations from homeowners' points of view. How well do home energy audits currently meet these criteria? How well do asset-based assessments match what homeowners seem to want? We consider these questions based on a study of 286 homeowners who participated in a Seattle City Light home energy audit program. Findings suggest that there is substantial opportunity to reorient audit programs to better fit the realities of why homeowners undertake energy audits and retrofits. In the Seattle City Light program, participating homeowners found certain elements of the audit they received - interaction with professional auditors, blower door testing, and customized, specific upgrade recommendations - to be more compelling than the standardized and quantitative elements. Rather than being engaged with increasing energy efficiency, as invited by an asset perspective and asset-based efficiency scores, homeowners wanted to build better understanding of their home's energy use and to learn how to solve specific problems, especially reflecting their household's actual energy use practices.

115

Building Energy Software Tools Directory: HAP System Design Load  

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

HAP System Design Load HAP System Design Load HAP System Design Load logo. Provides the load estimating and system design features found in its popular cousin � Carrier�s Hourly Analysis Program (HAP). By focusing on system design features, the HAP System Design Load program serves as a simpler, more efficient tool for those users only interested in system design; energy simulation features are omitted. Like the HAP program, HAP System Design Load provides the ease of use of a Windows-based graphical user interface and the computing power of modern 32-bit software. HAP System Design Load uses a system-based approach to HVAC load estimating. This approach tailors sizing procedures and results to the specific type of system being considered. A wide variety of equipment types

116

Battery loading device  

SciTech Connect

A battery loading device for loading a power source battery, built in small appliances having a battery loading chamber for selectively loading a number of cylindrical unit batteries or a one body type battery having the same voltage as a number of cylindrical unit batteries, whereby the one body type battery and the battery loading chamber are shaped similarly and asymmetrically in order to prevent the one body type battery from being inserted in the wrong direction.

Phara, T.; Suzuki, M.

1984-08-28T23:59:59.000Z

117

Validation of a Building Simulation Tool Using Field Data for Three Identical Configuration Full-Serve Restaurants Using Different HVAC Systems  

E-Print Network (OSTI)

A new building application for a pre-existing HVAC software tool which calculates the benefits of desiccant-assisted HVAC equipment versus the performance of a standard vapor-compression system is used to model the monitored results, see Yborra and Spears (2000), for three full-service restaurants. A standard vapor-compression system, an enthalpy assisted vapor-compression system, and a desiccant-assisted vapor-compression system are compared. The vapor-compression portion of each system is comprised of three rooftop units, specifications for each may be found in Yborra and Spears, "Field- Evaluation of Alternative HVAC Strategies to Meet Ventilation, Comfort, and Humidity Control Criteria at Three Full-Serve Restaurants". The software tool uses DOE 2.1E as a calculation engine which runs in the background. Previously, the software tool could model two different hotel configurations, a quickserve restaurant, a supermarket, a retail store, an ice arena, a school, a movie theater, a nursing home and a hospital. With the larger eating area, the full-serve restaurant had the capacity for sensible or enthalpy heat recovery from the exhausted air in the sit-down area. Quick-Serve Restaurants (QSR's) were precluded from these energy saving devices as the exhausted air was heavily laden with grease. Still, even with the kitchen exhausts facing away from the rooftop unit (RTU) intakes, the enthalpy wheels showed noticeable loading from grease. As the field monitoring was performed near Philadelphia, PA, National Renewable Energy Laboratory (NREL) hour-by-hour bin TMY2 meteorological data was used for Philadelphia to model the annual outdoor conditions experienced by each site. Output was provided in the form of humidity bins, monthly energy usage and cost, as well as total annual gas and electric costs. As the fill-serve restaurants were located on the North-Eastem region of the United States, patron comfort was of greater importance to management than annual energy cost savings. Once the model results were determined to properly reflect those of the case studies, the different building equipment types were "moved" around the United States by choosing different bin weather data sets corresponding to Chicago, IL, Atlanta, GA, and Houston, TX. While the default energy rates available in the program are 4 years old, the economic results provide a sound cost comparison.

Brillhart, P. L.; Worek, W. M.

2000-01-01T23:59:59.000Z

118

Variability of Load and Net Load in Case of Large Scale Distributed Wind Power  

Science Conference Proceedings (OSTI)

Large scale wind power production and its variability is one of the major inputs to wind integration studies. This paper analyses measured data from large scale wind power production. Comparisons of variability are made across several variables: time scale (10-60 minute ramp rates), number of wind farms, and simulated vs. modeled data. Ramp rates for Wind power production, Load (total system load) and Net load (load minus wind power production) demonstrate how wind power increases the net load variability. Wind power will also change the timing of daily ramps.

Holttinen, H.; Kiviluoma, J.; Estanqueiro, A.; Gomez-Lazaro, E.; Rawn, B.; Dobschinski, J.; Meibom, P.; Lannoye, E.; Aigner, T.; Wan, Y. H.; Milligan, M.

2011-01-01T23:59:59.000Z

119

Abstract--Load serving entities (LSE) and holders of default service obligations, in restructured electricity markets, provide  

E-Print Network (OSTI)

, in restructured electricity markets, provide electricity service at regulated or contracted fixed prices while standard forward contracts and commodity derivatives. Keywords: Electricity Markets, Risk Management, Volumetric hedging, I. INTRODUCTION The introduction of competitive wholesale markets in the electricity

Oren, Shmuel S.

120

Case history study of total energy system at Western Mall Shopping Center, Sioux Falls, South Dakota  

SciTech Connect

Western Mall Total Energy Plant in Sioux Falls, South Dakota, serves an enclosed mall shopping center of 462,000 ft/sup 2/. The plant provides most of the mall and tenants with electricity, space-heating, and air-conditioning services from a natural gas-fueled engine-generator plant with hot water heat recovery, supplementary gas-fueled boiler, and absorption water chiller. Heating load served by the plant is calculated to be 15,000,000 Btu at -30/sup 0/F winter design condition with 70/sup 0/F space temperature. Maximum observed cooling load at 100/sup 0/F, 75/sup 0/ W.B. outdoor conditions is about 750 tons of refrigeration. Engine heat is recovered in a water system operated at 210 to 240/sup 0/F; an auxiliary scotch marine type, firetype gas-fueled boiler provides up to 14,000,000 Btu/h or supplementary heat. Energy customers have recently begun to exercise considerable control over their uses of electricity with more careful operation of lighting and appliances and with some replacement of illumination devices with more-efficient equipment. It is concluded that central heating and air-conditioning facilities provide the owner with an assured means for serving the shopping center, regardless of which energy source is most economical or least available. The hot and chilled water can be obtained from gas fuel as at present, from fuel oil, propane, all electric, or coal firing. Adapting the conversion equipment is difficult only for coal because of the space requirement for storage and handling that fuel. The power-generating capacity in place is an asset that should be used to serve the tenants because it reduces the public utility company need for expanded capacity. (MCW)

1977-11-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

DOE Joint Genome Institute: Adaptable Button Mushroom Serves Up  

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

8, 2012 8, 2012 Adaptable Button Mushroom Serves Up Biomass-Degrading Genes Critical to Managing the Planet's Carbon Stores The button mushroom occupies a prominent place in our diet and in the grocery store where it boasts a tasty multibillion-dollar niche, while in nature, Agaricus bisporus is known to decay leaf matter on the forest floor. Now, owing to an international collaboration of two-dozen institutions led by the French National Institute for Agricultural Research (INRA) and the U.S. Department of Energy Joint Genome Institute (DOE JGI), the full repertoire of A. bisporus genes has been determined. In particular, new work shows how its genes are actually deployed not only in leaf decay but also wood decay and in the development of fruiting bodies (the above ground part of the mushroom harvested for food). The work also

122

Northern Indiana Pub Serv Co | Open Energy Information  

Open Energy Info (EERE)

Co Co Jump to: navigation, search Name Northern Indiana Pub Serv Co Place Indiana Utility Id 13756 Utility Location Yes Ownership I NERC Location RFC NERC RFC Yes ISO MISO Yes Operates Generating Plant Yes Activity Generation Yes Activity Transmission Yes Activity Buying Transmission Yes Activity Distribution Yes Activity Wholesale Marketing Yes Activity Bundled Services Yes Alt Fuel Vehicle Yes Alt Fuel Vehicle2 Yes References EIA Form EIA-861 Final Data File for 2010 - File1_a[1] Energy Information Administration Form 826[2] LinkedIn Connections CrunchBase Profile No CrunchBase profile. Create one now! This article is a stub. You can help OpenEI by expanding it. Utility Rate Schedules Grid-background.png Adjustment of Charges for Cost of Fuel Rider Adjustment of Charges for Regional Transmission Organization

123

Static load test of Arquin-designed CMU wall.  

Science Conference Proceedings (OSTI)

The Arquin Corporation has developed a new method of constructing CMU (concrete masonry unit) walls. This new method uses polymer spacers connected to steel wires that serve as reinforcing as well as means of accurately placing the spacers so that the concrete block can be dry stacked. The hollows of the concrete block used in constructing the wall are then filled with grout. As part of a New Mexico Small Business Assistance Program (NMSBAP), Sandia National Laboratories conducted a series of tests that statically loaded wall segments to compare the Arquin method to a more traditional method of constructing CMU walls. A total of 12 tests were conducted, three with the Arquin method using a W5 reinforcing wire, three with the traditional method of construction using a number 3 rebar as reinforcing, three with the Arquin method using a W2 reinforcing wire, and three with the traditional construction method but without rebar. The results of the tests showed that the walls constructed with the Arquin method and with a W5 reinforcing wire withstood more load than any of the other three types of walls that were tested.

Jensen, Richard Pearson; Cherry, Jeffery L.

2008-12-01T23:59:59.000Z

124

Dynamic load test of Arquin-designed CMU wall.  

SciTech Connect

The Arquin Corporation has developed a new method of constructing CMU (concrete masonry unit) walls. This new method uses polymer spacers connected to steel wires that serve as reinforcing as well as a means of accurately placing the spacers so that the concrete block can be dry stacked. The hollows of the concrete block are then filled with grout. As part of a New Mexico Small Business Assistance Program (NMSBA), Sandia National Laboratories conducted a series of tests that dynamically loaded wall segments to compare the performance of walls constructed using the Arquin method to a more traditional method of constructing CMU walls. A total of four walls were built, two with traditional methods and two with the Arquin method. Two of the walls, one traditional and one Arquin, had every third cell filled with grout. The remaining two walls, one traditional and one Arquin, had every cell filled with grout. The walls were dynamically loaded with explosive forces. No significant difference was noted between the performance of the walls constructed by the Arquin method when compared to the walls constructed by the traditional method.

Jensen, Richard Pearson

2010-02-01T23:59:59.000Z

125

Load sensing system  

DOE Patents (OSTI)

A load sensing system inexpensively monitors the weight and temperature of stored nuclear material for long periods of time in widely variable environments. The system can include an electrostatic load cell that encodes weight and temperature into a digital signal which is sent to a remote monitor via a coaxial cable. The same cable is used to supply the load cell with power. When multiple load cells are used, vast

Sohns, Carl W. (Oak Ridge, TN); Nodine, Robert N. (Knoxville, TN); Wallace, Steven Allen (Knoxville, TN)

1999-01-01T23:59:59.000Z

126

Discharge circuits and loads  

SciTech Connect

This will be an overview in which some of the general properties of loads are examined: their interface with the energy storage and switching devices; general problems encountered with different types of loads; how load behavior and fault modes can impact on the design of a power conditioning system (PCS).

Sarjeant, W.J.

1980-10-15T23:59:59.000Z

127

On loading rate effects in toughening processes  

SciTech Connect

Environmental crack tip reactions are a known source of premature fracture in oxides. These rate-dependent phenomena commonly are studied in strength tests where loading rate serves as the major experimental variable. A material susceptible to environmentally-assisted crack growth is stronger at fast testing rates. A topic which has received far less attention is the influence of stressing rate or loading rate on the shielding processes which occur at some distance from the crack tip, although the inverse has been studied by Deuerler et al. The authors present here the first known documentation of a loading rate effect on shielding phenomena in ceramic materials. For these experiments Coors AD 94 alumina was chosen for study.

Tandon, S.; Faber, K.T. [Northwestern Univ., Evanston, IL (United States)

1996-03-01T23:59:59.000Z

128

Load Management - A Better Way  

E-Print Network (OSTI)

Ohio Edison Company serves about 800,000 customers in Ohio and Pennsylvania, making it one of the 20 largest electric utilities in the nation. The 'cost of service' concept has been basic to rate design throughout the history of the company, and is evident today as the demand related charges have escalated in recent rate cases reflecting the higher costs of installing new generating facilities at today's high construction and financing costs. This paper will describe one of the many applications of load management techniques which has enabled the company to shift well over 100,000 kilowatts of customer load from the on-peak period to the off-peak period in the last four to five years. This is helping delay the need for new plants and allows existing plants to be more fully utilized, resulting in lower costs to customers who use their electric service wisely and possibly lower rate increases in the future than would have been required otherwise.

Easley, J. F.

1982-01-01T23:59:59.000Z

129

Water and energy conservation system for food serving establishments  

SciTech Connect

A water and energy conserving apparatus is described for supplying pre-heated water to a hot water heater and for cooling at least one refrigeration unit using a compressible medium in a food serving establishment comprising, a pre-heater tank adapted to receive water from a cold water source and having a cold water inlet line connected to the cold water source and a cold water outlet line. A heat exchanger which is associated with the refrigeration unit is connected to the cold water output line coming from the tank. A hot water output line is connected between the heat exchanger and the tank for returning water from the heat exchanger to the tank. The compressible medium which is hot is supplied from the refrigeration unit to the heat exchanger and the water flowing through the heat exchanger cools the compressible medium thus picking up heat. A circulator is connected into the hot water output line for circulating water from the tank to the heat exchanger and back. A drain line is connected to the heated water output line and includes a normally closed solenoid valve. The drain line is connected to a drain and is provided to vent water from the pre-heater tank. A thermostat is connected to the cold water output line coming from the tank to sense the temperature. The thermostat is connected to a power supply which powers the solenoid and when the temperature of water in the cold water output line rises above a selected value, which is preferably in the vicinity of 85 degrees Fahrenheit, the solenoid valve is energized to open the flow of water in the drain line and vent water from the pre-heater tank. A pre-heater water line is connected between the pre-heater tank and the hot water heater to supply pre-heated water to the hot water heater to conserve energy used in heating the otherwise cold water normally supplied to the hot water heater.

Papadakos, J.

1981-01-27T23:59:59.000Z

130

Electrical and Production Load Factors  

E-Print Network (OSTI)

Load factors are an important simplification of electrical energy use data and depend on the ratio of average demand to peak demand. Based on operating hours of a facility they serve as an important benchmarking tool for the industrial sector. The operating hours of small and medium sized manufacturing facilities are analyzed to identify the most common operating hour or shift work patterns. About 75% of manufacturing facilities fall into expected operating hour patterns with operating hours near 40, 80, 120 and 168 hours/week. Two types of load factors, electrical and production are computed for each shift classification within major industry categories in the U.S. The load factor based on monthly billing hours (ELF) increases with operating hours from about 0.4 for a nominal one shift operation, to about 0.7 for around-the-clock operation. On the other hand, the load factor based on production hours (PLF) shows an inverse trend, varying from about 1.4 for one shift operation to 0.7 for around-the-clock operation. When used as a diagnostic tool, if the PLF exceeds unity, then unnecessary energy consumption may be taking place. For plants operating at 40 hours per week, the ELF value was found to greater than the theoretical maximum, while the PLF value was greater than one, suggesting that these facilities may have significant energy usage outside production hours. The data for the PLF however, is more scattered for plants operating less than 80 hours per week, indicating that grouping PLF data based on operating hours may not be a reasonable approach to benchmarking energy use in industries. This analysis uses annual electricity consumption and demand along with operating hour data of manufacturing plants available in the U.S. Department of Energy’s Industrial Assessment Center (IAC) database. The annual values are used because more desirable monthly data are not available. Monthly data are preferred as they capture the load profile of the facility more accurately. The data there come from Industrial Assessment Centers which employ university engineering students, faculty and staff to perform energy assessments for small to medium-sized manufacturing plants. The nation-wide IAC program is sponsored by the U.S. Department of Energy.

Sen, Tapajyoti

2009-12-01T23:59:59.000Z

131

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

132

Load sensing system  

DOE Patents (OSTI)

A load sensing system inexpensively monitors the weight and temperature of stored nuclear material for long periods of time in widely variable environments. The system can include an electrostatic load cell that encodes weight and temperature into a digital signal which is sent to a remote monitor via a coaxial cable. The same cable is used to supply the load cell with power. When multiple load cells are used, vast inventories of stored nuclear material can be continuously monitored and inventoried of minimal cost. 4 figs.

Sohns, C.W.; Nodine, R.N.; Wallace, S.A.

1999-05-04T23:59:59.000Z

133

Structural load combinations  

SciTech Connect

This paper presents the latest results of the program entitled, ''Probability Based Load Combinations For Design of Category I Structures''. In FY 85, a probability-based reliability analysis method has been developed to evaluate safety of shear wall structures. The shear walls are analyzed using stick models with beam elements and may be subjected to dead load, live load and in-plane eqrthquake. Both shear and flexure limit states are defined analytically. The limit state probabilities can be evaluated on the basis of these limit states. Utilizing the reliability analysis method mentioned above, load combinations for the design of shear wall structures have been established. The proposed design criteria are in the load and resistance factor design (LRFD) format. In this study, the resistance factors for shear and flexure and load factors for dead and live loads are preassigned, while the load factor for SSE is determined for a specified target limit state probability of 1.0 x 10/sup -6/ or 1.0 x 10/sup -5/ during a lifetime of 40 years. 23 refs., 9 tabs.

Hwang, H.; Reich, M.; Ellingwood, B.; Shinozuka, M.

1985-01-01T23:59:59.000Z

134

Watershed Mercury Loading Framework  

Science Conference Proceedings (OSTI)

This report explains and illustrates a simplified stochastic framework, the Watershed Mercury Loading Framework, for organizing and framing site-specific knowledge and information on mercury loading to waterbodies. The framework permits explicit treatment of data uncertainties. This report will be useful to EPRI members, state and federal regulatory agencies, and watershed stakeholders concerned with mercury-related human and ecological health risk.

2003-05-23T23:59:59.000Z

135

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

136

load | OpenEI  

Open Energy Info (EERE)

load load Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

137

Load-management decision  

Science Conference Proceedings (OSTI)

Utilities require baseload, intermediate, and peaking plants to meet fluctuating customer demand. These can be supplemented with off-peak generation and storage and load management, which can take the form of direct utility control over interruptible and deferrable customers or customer incentives that require off-peak demand. Utilities should make a careful analysis of their load profile, their generation mix, their ability to shift loads, and customer attitudes before deciding on a load-management program that fits their individual needs. They should also be aware that load management is only a limited resource with a number of uncertainties. Research programs into customer relations, system reliability, communications devices, and special control switches and meters will help to relieve some of the uncertainties. (DCK)

Lihach, N.; Gupta, P.

1982-05-01T23:59:59.000Z

138

WHAT THE SMART GRID MEANS TO YOU AND THE PEOPLE YOU SERVE | Department...  

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

WHAT THE SMART GRID MEANS TO YOU AND THE PEOPLE YOU SERVE WHAT THE SMART GRID MEANS TO YOU AND THE PEOPLE YOU SERVE The U.S. Department of Energy (DOE) is charged under the Energy...

139

WHAT THE SMART GRID MEANS TO YOU AND THE PEOPLE YOU SERVE | Department...  

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

SERVE WHAT THE SMART GRID MEANS TO YOU AND THE PEOPLE YOU SERVE The U.S. Department of Energy (DOE) is charged under the Energy Independence and Security Act of 2007 (EISA 2007)...

140

An efficient load model for analyzing demand side management impacts  

SciTech Connect

The main objective of implementing Demand Side Management (DSM) in power systems is to change the utility's load shape--i.e. changes in the time pattern and magnitude of utility's load. Changing the load shape as a result of demand side activities could change the peak load, base load and/or energy demand. Those three variables have to be explicitly modeled into the load curve for properly representing the effects of demand side management. The impact of DSM will be manifested as higher or lower reliability levels. This paper presents an efficient technique to model the system load such that the impact of demand side management on the power system can be easily and accurately evaluated. The proposed technique to model the load duration curve will facilitate the representation of DSM impacts on loss-of-load probability, energy not served and energy consumption. This will provide an analytical method to study the impact of DSM on capacity requirements. So far iterative methods have been applied to study these impacts. The proposed analytical method results in a faster solution with higher accuracy. It takes only 18 seconds on an 80486 PC to solve each case study involving different peak and base loads, and energy use.

Rahman, S.; Rinaldy (Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States))

1993-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

building load data | OpenEI Community  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Load data Image source: NREL...

142

electric load data | OpenEI Community  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Load data Image source: NREL...

143

commercial load | OpenEI Community  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Load data Image source: NREL...

144

residential load | OpenEI Community  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Load data Image source: NREL...

145

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...

146

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*...

147

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*...

148

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...

149

HLW Glass Waste Loadings  

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

HLW HLW Glass Waste Loadings Ian L. Pegg Vitreous State Laboratory The Catholic University of America Washington, DC Overview Overview  Vitrification - general background  Joule heated ceramic melter (JHCM) technology  Factors affecting waste loadings  Waste loading requirements and projections  WTP DWPF  DWPF  Yucca Mountain License Application requirements on waste loading  Summary Vitrification  Immobilization of waste by conversion into a glass  Internationally accepted treatment for HLW  Why glass?  Amorphous material - able to incorporate a wide spectrum of elements over wide ranges of composition; resistant to radiation damage  Long-term durability - natural analogs Relatively simple process - amenable to nuclearization at large  Relatively simple process - amenable to nuclearization at large scale  There

150

OpenEI - load  

Open Energy Info (EERE)

are given by a location defined by the Typical Meteorological Year (TMY) for which the weather data was collected. Commercial load data is sorted by the (TMY) site as a...

151

Definition: Interruptible Load Or Interruptible Demand | Open Energy  

Open Energy Info (EERE)

Interruptible Load Or Interruptible Demand Interruptible Load Or Interruptible Demand Jump to: navigation, search Dictionary.png Interruptible Load Or Interruptible Demand Demand that the end-use customer makes available to its Load-Serving Entity via contract or agreement for curtailment.[1] View on Wikipedia Wikipedia Definition View on Reegle Reegle Definition No reegle definition available. Also Known As non-firm service Related Terms transmission lines, electricity generation, transmission line, firm transmission service, smart grid References ↑ Glossary of Terms Used in Reliability Standards An inli LikeLike UnlikeLike You like this.Sign Up to see what your friends like. ne Glossary Definition Retrieved from "http://en.openei.org/w/index.php?title=Definition:Interruptible_Load_Or_Interruptible_Demand&oldid=502615"

152

Analysis of industrial load management  

SciTech Connect

Industrial Load Management, ILM, has increased the possibilities of changing load profiles and raising load factors. This paper reports on load profile measurements and feasible load management applications that could be implemented in industry e.g. bivalent systems for heating of premises and processes, load priority systems, energy storage and rescheduling processes or parts of processes due to differential electricity rates. Industrial load variations on hourly, daily and seasonal basis are treated as well as the impact by load management on load curves e g peak clipping, valley filling and increased off-peak electricity usage.

Bjork, C.O.; Karlsson, B.G.

1986-04-01T23:59:59.000Z

153

Composite Load Model Evaluation  

Science Conference Proceedings (OSTI)

The WECC load modeling task force has dedicated its effort in the past few years to develop a composite load model that can represent behaviors of different end-user components. The modeling structure of the composite load model is recommended by the WECC load modeling task force. GE Energy has implemented this composite load model with a new function CMPLDW in its power system simulation software package, PSLF. For the last several years, Bonneville Power Administration (BPA) has taken the lead and collaborated with GE Energy to develop the new composite load model. Pacific Northwest National Laboratory (PNNL) and BPA joint force and conducted the evaluation of the CMPLDW and test its parameter settings to make sure that: • the model initializes properly, • all the parameter settings are functioning, and • the simulation results are as expected. The PNNL effort focused on testing the CMPLDW in a 4-bus system. An exhaustive testing on each parameter setting has been performed to guarantee each setting works. This report is a summary of the PNNL testing results and conclusions.

Lu, Ning; Qiao, Hong (Amy)

2007-09-30T23:59:59.000Z

154

Distribution substation load impacts of residential air conditioner load control  

SciTech Connect

An ongoing experiment to monitor the substation level load impacts of end-use load control is described. An overview of the data acquisition system, experimental procedures and analysis techniques are provided. Results of the 1983 and 1984 experiments demonstrate the value of aggregate load impact monitoring as a means of verifying load research results, calculating the diversity of end-use loads, and predicting the impacts of load management on the transmission and distribution systems.

Heffner, G.C.; Kaufman, D.A.

1985-07-01T23:59:59.000Z

155

Load Monitoring CEC/LMTF Load Research Program  

SciTech Connect

This white paper addresses the needs, options, current practices of load monitoring. Recommendations on load monitoring applications and future directions are also presented.

Huang, Zhenyu; Lesieutre, B.; Yang, Steve; Ellis, A.; Meklin, A.; Wong, B.; Gaikwad, A.; Brooks, D.; Hammerstrom, Donald J.; Phillips, John; Kosterev, Dmitry; Hoffman, M.; Ciniglio, O.; Hartwell, R.; Pourbeik, P.; Maitra, A.; Lu, Ning

2007-11-30T23:59:59.000Z

156

Demand or Request: Will Load Behave?  

Science Conference Proceedings (OSTI)

Power planning engineers are trained to design an electric system that satisfies predicted electrical demand under stringent conditions of availability and power quality. Like responsible custodians, we plan for the provision of electrical sustenance and shelter to those in whose care regulators have given us the responsibility to serve. Though most customers accept this nurturing gladly, a growing number are concerned with the economic costs and environmental impacts of service at a time when technology (particularly distributed generation, storage, automation, and information networks) offers alternatives for localized control and competitive service. As customers’ and their systems mature, a new relationship with the electricity provider is emerging. Demand response is perhaps the first unsteady step where the customer participates as a partner in system operations. This paper explores issues system planners need to consider as demand response matures to significant levels beyond direct load control and toward a situation where service is requested and bargains are reached with the electricity provider based on desired load behavior. On one hand, predicting load growth and behavior appears more daunting than ever. On the other, for the first time load becomes a new resource whose behavior can be influenced during system operations to balance system conditions.

Widergren, Steven E.

2009-07-30T23:59:59.000Z

157

SYSPLAN. Load Leveling Battery System Costs  

SciTech Connect

SYSPLAN evaluates capital investment in customer side of the meter load leveling battery systems. Such systems reduce the customer`s monthly electrical demand charge by reducing the maximum power load supplied by the utility during the customer`s peak demand. System equipment consists of a large array of batteries, a current converter, and balance of plant equipment and facilities required to support the battery and converter system. The system is installed on the customer`s side of the meter and controlled and operated by the customer. Its economic feasibility depends largely on the customer`s load profile. Load shape requirements, utility rate structures, and battery equipment cost and performance data serve as bases for determining whether a load leveling battery system is economically feasible for a particular installation. Life-cycle costs for system hardware include all costs associated with the purchase, installation, and operation of battery, converter, and balance of plant facilities and equipment. The SYSPLAN spreadsheet software is specifically designed to evaluate these costs and the reduced demand charge benefits; it completes a 20 year period life cycle cost analysis based on the battery system description and cost data. A built-in sensitivity analysis routine is also included for key battery cost parameters. The life cycle cost analysis spreadsheet is augmented by a system sizing routine to help users identify load leveling system size requirements for their facilities. The optional XSIZE system sizing spreadsheet which is included can be used to identify a range of battery system sizes that might be economically attractive. XSIZE output consisting of system operating requirements can then be passed by the temporary file SIZE to the main SYSPLAN spreadsheet.

Hostick, C.J. [Pacific Northwest Lab., Richland, WA (United States)

1988-03-22T23:59:59.000Z

158

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...

159

DiffServ node with join minimum cost queue policy and multiclass traffic  

Science Conference Proceedings (OSTI)

DiffServ, the vehicle for providing relative QoS in the Internet is also easily amenable to simple and effective pricing mechanisms. By pricing access to a relative QoS, we can model a DiffServ node as a 'Join Minimum Cost Queue' in which an arriving ... Keywords: diffServ, finite buffer queues, join minimum cost queue, join shortest queue, network pricing, quasi-birth--death processes, queue control

Rahul Tandra; N. Hemachandra; D. Manjunath

2004-01-01T23:59:59.000Z

160

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

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

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

Note: This page contains sample records for the topic "total load served" 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

Denton County Electric Cooperative d/b/a CoServ Electric Smart...  

Open Energy Info (EERE)

throughout CoServ Electric's service territory and explores the application of distribution automation and customer systems. The project is aimed at improving customer...

162

Mass-Loaded Flows  

E-Print Network (OSTI)

A key process within astronomy is the exchange of mass, momentum, and energy between diffuse plasmas in many types of astronomical sources (including planetary nebulae, wind-blown bubbles, supernova remnants, starburst superwinds, and the intracluster medium) and dense, embedded clouds or clumps. This transfer affects the large scale flows of the diffuse plasmas as well as the evolution of the clumps. I review our current understanding of mass-injection processes, and examine intermediate-scale structure and the global effect of mass-loading on a flow. I then discuss mass-loading in a variety of diffuse sources.

J. M. Pittard

2006-07-13T23:59:59.000Z

163

Energy Storage for Use in Load Frequency Control  

E-Print Network (OSTI)

Certain energy storage technologies are well-suited to the high-frequency, high-cycling operation which is required in provision of load frequency control (LFC). To limit the total stored energy capacity required while ...

Leitermann, Olivia

164

New method developed for LPG offshore loading  

SciTech Connect

An innovative concept for refrigerated LPG offshore loading has been developed by TOTAL and Enterprise D'Equipments Mecaniques at Hydrauliques. Known as CHAGAL, the system integrates with the catenary anchor leg mooring offshore loading system commonly used for crude oil. CHAGAL provides a suitable answer to short-term development schemes of LPG trade. It can be adapted for possible extrapolation to cryogenic temperatures of LNG and it opens a new way to the development of offshore liquefaction projects for which the offloading of production is still an unsolved key problem.

1985-10-01T23:59:59.000Z

165

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...

166

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...

167

OpenEI Community - load data  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Utility Rate OpenEI Community...

168

OpenEI Community - electric load data  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Utility Rate OpenEI Community...

169

OpenEI Community - building load  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Utility Rate OpenEI Community...

170

OpenEI Community - residential load  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Utility Rate OpenEI Community...

171

OpenEI Community - commercial load  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Utility Rate OpenEI Community...

172

OpenEI Community - building load data  

Open Energy Info (EERE)

building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Utility Rate OpenEI Community...

173

Load management strategies for electric utilities: a production cost simulation  

SciTech Connect

This paper deals with the development and application of a simulation model for analyzing strategies for managing the residential loads of electric utilities. The basic components of the model are (1) a production-cost model, which simulates daily operation of an electric power system; (2) a load model, which disaggregates system loads into appliance loads and other loads; and (3) a comparison model, which compares the production costs and energy consumption needed to meet a particular load profile to the corresponding costs and energy consumption required for another load profile. The profiles in each pair define alternative ways of meeting the same demand. A method for disaggregating load profiles into appliance components is discussed and several alternative strategies for residential load management for a typical northeastern electric utility are formulated. The method is based on an analysis of the composition of electric loads for a number of classes of residential customers in the model utility system. The effect of alternative load management strategies on the entire residential loadcurve is determined by predicting the effects of these strategies on the specific appliance components of the loadcurve. The results of using the model to analyze alternative strategies for residential load management suggest that load management strategies in the residential sector, if adopted by utilities whose operating and load characteristics are similar to those of the system modeled here, must take into account a wide variety of appliances to achieve significant changes in the total load profile. Moreover, the results also suggest that it is not easy to reduce costs significantly through new strategies for managing residential loads only and that, to be worthwhile, cost-reducing strategies will have to encompass many kinds of appliances.

Blair, P.D.

1979-03-01T23:59:59.000Z

174

Cooling load estimation methods  

DOE Green Energy (OSTI)

Ongoing research on quantifying the cooling loads in residential buildings, particularly buildings with passive solar heating systems, is described. Correlations are described that permit auxiliary cooling estimates from monthly average insolation and weather data. The objective of the research is to develop a simple analysis method, useful early in design, to estimate the annual cooling energy required of a given building.

McFarland, R.D.

1984-01-01T23:59:59.000Z

175

LOADING AND UNLOADING DEVICE  

DOE Patents (OSTI)

A device for loading and unloading fuel rods into and from a reactor tank through an access hole includes parallel links carrying a gripper. These links enable the gripper to go through the access hole and then to be moved laterally from the axis of the access hole to the various locations of the fuel rods in the reactor tank.

Treshow, M.

1960-08-16T23:59:59.000Z

176

Buildings Stock Load Control  

E-Print Network (OSTI)

Researchers and practitioners have proposed a variety of solutions to reduce electricity consumption and curtail peak demand. This research focuses on electricity demand control by applying some strategies in existing building to reduce it during the extreme climate period. The first part of this paper presents the objectives of the study: ? to restrict the startup polluting manufacturing units (power station), ? to limit the environmental impacts (greenhouse emission), ? to reduce the transport and distribution electricity infrastructures The second part presents the approach used to rise the objectives : ? To aggregat the individual loads and to analyze the impact of different strategies from load shedding to reduce peak power demand by: ? Developing models of tertiary buildings stocks (Schools, offices, Shops, hotels); ? Making simulations for different load shedding strategies to calculate potential peak power saving. The third part is dedicated to the description of the developed models: An assembly of the various blocks of the library of simbad and simulink permit to model building. Finally the last part prensents the study results: Graphs and tables to see the load shedding strategies impacts.

Joutey, H. A.; Vaezi-Nejad, H.; Clemoncon, B.; Rosenstein, F.

2006-01-01T23:59:59.000Z

177

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

178

Harry Black Mr. Harry Black currently serves as the Director of Finance for the City of  

E-Print Network (OSTI)

Harry Black Mr. Harry Black currently serves as the Director of Finance for the City of Baltimore of Risk Management. Mr. Black has served as: Executive Vice President & COO of Global Commerce Solutions. Mr. Black is the author of "Achieving Economic Development Success: Tools that work," a nuts

Noakes, David R.

179

Consistent-degradation macroblock grouping for parallel video streams over DiffServ networks  

Science Conference Proceedings (OSTI)

This paper presents a consistent-degradation macroblock grouping scheme for improving loss resilience of parallel video streams over a two-class DiffServ network. By jointly exploiting the H.264 flexible macroblock ordering (FMO) tool, a multi-stream ... Keywords: DiffServ network, Flexible macroblock ordering, Loss resilience, Parallel video streams, Transmission distortion

Hao Liu; HaiQin Xu; ShuGuang Zhao

2012-01-01T23:59:59.000Z

180

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

Note: This page contains sample records for the topic "total load served" 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

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

182

Direct methanol fuel cells at reduced catalyst loadings  

DOE Green Energy (OSTI)

We focus in this paper on the reduction of catalyst loading in direct methanol fuel cells currently under development at Los Alamos National Laboratory. Based on single-cell DMFC testing, we discuss performance vs. catalyst loading trade-offs and demonstrate optimization of the anode performance. We also show test data for a short five-cell DMFC stack with the average total platinum loading of 0.53 mg cm{sup -2} and compare performance of this stack with the performance of a single direct methanol fuel cell using similar total amount of precious metal.

Zelenay, P. (Piotr); Guyon, F. (Francois); Gottesfeld, Shimshon

2001-01-01T23:59:59.000Z

183

DIRECT METHANOL FUEL CELLS AT REDUCED CATALYST LOADINGS  

DOE Green Energy (OSTI)

We focus in this paper on the reduction of catalyst loading in direct methanol fuel cells currently under development at Los Alamos National Laboratory. Based on single-cell DMFC testing, we discuss performance vs. catalyst loading trade-offs and demonstrate optimization of the anode performance. We also show test data for a short five-cell DMFC stack with the average total platinum loading of 0.53 mg cm{sup {minus}2} and compare performance of this stack with the performance of a single direct methanol fuel cell using similar total amount of precious metal.

P. ZELENAY; F. GUYON; SM. GOTTESFELD

2001-05-01T23:59:59.000Z

184

Improved load ratio method for predicting crack length  

SciTech Connect

The elastic compliance from unloading/reloading sequences in a load-displacement record estimates well crack length in elastic-plastic fracture toughness tests of compact tension [C(T)] and bending type specimens. The need for partial unloading of the specimen makes it difficult to run the test under static loading and impossible under either dynamic loading or very high temperatures. Furthermore, fracture toughness testing in which crack length is determined from elastic compliance requires high precision testing equipment and highly skilled technicians. As a result, such tests are confined usually to research laboratories and seldom used under production settings. To eliminate these problems, an improved load ratio method of predicting crack length is proposed that utilizes only the recorded load versus load-line displacement curve (or load versus crack-mouth-opening displacement curve) without unloading/reloading sequences. As a result, the instrumentation is much simpler than in the elastic compliance or potential drop methods. If only a monotonic load-displacement record is to be measured the fracture toughness test becomes almost as simple to perform as a tension test. The method described here improves in three ways the ``original load ratio method`` proposed by Hu et al. First, a blunting term is added to the crack length before maximum load. Second, a strain hardening correction is included after maximum load. And, third, the initial crack length and the physical (final) crack length measured at the end of the test serve to anchor the predicted crack lengths, forcing agreement between predicted and measured values. The method predicts crack extension with excellent accuracy in specimens fabricated from A302, A508, and A533B piping and pressure vessel steels, A588 and A572 structural steels, and HY-80 ship steel.

Chen, X.; Albrecht, P. [Univ. of Maryland, College Park, MD (United States). Inst. for Systems Research; Wright, W. [Federal Highway Administration, McLean, VA (United States). Turner-Fairbank Highway Research Center; Joyce, J.A. [Naval Academy, Annapolis, MD (United States). Mechanical Engineering Dept.

1995-04-01T23:59:59.000Z

185

Load Capacity of Bodies  

E-Print Network (OSTI)

For the stress analysis in a plastic body $\\Omega$, we prove that there exists a maximal positive number $C$, the \\emph{load capacity ratio,} such that the body will not collapse under any external traction field $t$ bounded by $Y_{0}C$, where $Y_0$ is the elastic limit. The load capacity ratio depends only on the geometry of the body and is given by $$ \\frac{1}{C}=\\sup_{w\\in LD(\\Omega)_D} \\frac{\\int_{\\partial\\Omega}|w|dA} {\\int_{\\Omega}|\\epsilon(w)|dV}=\\left\\|\\gamma_D\\right\\|. $$ Here, $LD(\\Omega)_D$ is the space of isochoric vector fields $w$ for which the corresponding stretchings $\\epsilon(w)$ are assumed to be integrable and $\\gamma_D$ is the trace mapping assigning the boundary value $\\gamma_D(w)$ to any $w\\in LD(\\Omega)_D$.

Reuven Segev

2005-11-01T23:59:59.000Z

186

Load responsive hydrodynamic bearing  

Science Conference Proceedings (OSTI)

A load responsive hydrodynamic bearing is provided in the form of a thrust bearing or journal bearing for supporting, guiding and lubricating a relatively rotatable member to minimize wear thereof responsive to relative rotation under severe load. In the space between spaced relatively rotatable members and in the presence of a liquid or grease lubricant, one or more continuous ring shaped integral generally circular bearing bodies each define at least one dynamic surface and a plurality of support regions. Each of the support regions defines a static surface which is oriented in generally opposed relation with the dynamic surface for contact with one of the relatively rotatable members. A plurality of flexing regions are defined by the generally circular body of the bearing and are integral with and located between adjacent support regions. Each of the flexing regions has a first beam-like element being connected by an integral flexible hinge with one of the support regions and a second beam-like element having an integral flexible hinge connection with an adjacent support region. A least one local weakening geometry of the flexing region is located intermediate the first and second beam-like elements. In response to application of load from one of the relatively rotatable elements to the bearing, the beam-like elements and the local weakening geometry become flexed, causing the dynamic surface to deform and establish a hydrodynamic geometry for wedging lubricant into the dynamic interface.

Kalsi, Manmohan S. (Houston, TX); Somogyi, Dezso (Sugar Land, TX); Dietle, Lannie L. (Stafford, TX)

2002-01-01T23:59:59.000Z

187

1993 Pacific Northwest Loads and Resources Study, Technical Appendix: Volume 2, Book 2, Capacity.  

DOE Green Energy (OSTI)

Monthly totals of utility loads and capacities extrapolated as far as 2009 with a probability estimate of enough water resources for hydro power.

United States. Bonneville Power Administration.

1993-12-01T23:59:59.000Z

188

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...

189

SolarTotal | Open Energy Information  

Open Energy Info (EERE)

SolarTotal SolarTotal Jump to: navigation, search Name SolarTotal Place Bemmel, Netherlands Zip 6681 LN Sector Solar Product The company sells and installs PV solar instalations Coordinates 51.894112°, 5.89881° 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":51.894112,"lon":5.89881,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

190

EM Employee Serves Military in Afghanistan, Manages $5.8 Billion Army Task  

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

Employee Serves Military in Afghanistan, Manages $5.8 Billion Employee Serves Military in Afghanistan, Manages $5.8 Billion Army Task Order EM Employee Serves Military in Afghanistan, Manages $5.8 Billion Army Task Order February 27, 2013 - 12:00pm Addthis James Hawkins James Hawkins BAGRAM AIRFIELD, Afghanistan - EM employee James Hawkins is currently serving the U.S. military in Afghanistan, where he is administering a $5.8 billion task order for the Army. A major in the U.S. Air Force Reserves, Hawkins is an administrative contracting officer for the Defense Contract Management Agency, a component of the Defense Department that directly contributes to the military readiness of the U.S. and its allies. Hawkins is an acquisition planning manager and procurement analyst in the Office of Procurement Planning in EM's Office of Acquisition and Project

191

Four Minority Serving Institutions Selected to Compete in the 2013 Solar  

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

Four Minority Serving Institutions Selected to Compete in the 2013 Four Minority Serving Institutions Selected to Compete in the 2013 Solar Decathlon Four Minority Serving Institutions Selected to Compete in the 2013 Solar Decathlon January 27, 2012 - 3:38pm Addthis An aerial shot of Orange County Great Park, site of the 2013 Solar Decathlon. An aerial shot of Orange County Great Park, site of the 2013 Solar Decathlon. Of the elite twenty teams that have been selected from across the country and around the world to compete in the 2013 Solar Decathlon, four are Minority Serving Institutions (MSIs), showcasing the talent of our nation's MSIs in science, technology, engineering, and mathematics. Congratulations are in order to these MSIs - and their partners - for making it to the Solar Decathlon: The University of Texas at El Paso and El Paso Community College (El

192

Return of the RedwoodGrillE All sandwiches served with lettuce, tomato, red onion and  

E-Print Network (OSTI)

.. . . . . . . . . . . . . . . . . . . . .$3.29 Organic spring mix garnished with carrot, red onion, tomato and cucumber with choice. . . . . . . . . . . . . . . . . . . . . . . . . . $5.69 Choose your flavor; served over white rice and garnished with bok choy, carrot, cabbage, red

California at Santa Cruz, University of

193

Chemical & Engineering News Serving the chemical, life sciences and laboratory worlds  

E-Print Network (OSTI)

Chemical & Engineering News Serving the chemical, life sciences and laboratory worlds Science the hydroxyl oxygen and alcoholic hydrogen stabilizes the transition state. Chemical & Engineering News ISSN 0009-2347 Copyright © 2010 American Chemical Society #12;

Truhlar, Donald G

194

Variable loading roller  

DOE Patents (OSTI)

An automatic loading roller for transmitting torque in traction drive devices in manipulator arm joints includes a two-part camming device having a first cam portion rotatable in place on a shaft by an input torque and a second cam portion coaxially rotatable and translatable having a rotating drive surface thereon for engaging the driven surface of an output roller with a resultant force proportional to the torque transmitted. Complementary helical grooves in the respective cam portions interconnected through ball bearings interacting with those grooves effect the rotation and translation of the second cam portion in response to rotation of the first. 14 figs.

Williams, D.M.

1988-01-21T23:59:59.000Z

195

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

196

Engineering guidelines for total energy are even more vital during fuel shortage  

SciTech Connect

Large total-energy facilities, from 3 to 20 MW in capacity, are studied, but the guidelines are applicable to small units also. Heat-balance analysis, fuel costs, load factor, load-profile match, and control-system design are engineering parameters for total-energy systems that will improve fuel economy. (MCW)

Kauffmann, W.M.

1974-04-01T23:59:59.000Z

197

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 ........................................

198

Total Building Air Management: When Dehumidification Counts  

E-Print Network (OSTI)

Industry trends toward stringent indoor air quality codes, spearheaded by ASHRAE 62-89: Ventilation for Acceptable Indoor Air Quality, present four challenges to the building industry in hot and humid climates: 1. Infusion of large quantities of make-up air to code based on zone requirements 2. Maintenance of tight wet bulb and dry bulb temperature tolerances within zones based on use 3. Energy management and cost containment 4. Control of mold and mildew and the damage they cause Historically, total air management of sensible and latent heat, filtration and zone pressure was brought about through the implementation of non-integrated, composite systems. Composite systems typically are built up of multi-vendor equipment each of which perform specific, independent functions in the total control of the indoor air environment. Composite systems have a high up-front cost, are difficult to maintain and are costly to operate. Today, emerging technologies allow the implementation of fully integrated system for total building air management. These systems provide a single-vendor solution that is cost effective to purchase, maintain and operate. Operating saving of 23% and ROIs of 2.3 years have been shown. Equipment specification is no longer based primarily on total building load. Maximum benefits of these dynamic systems are realized when systems are designed with a total operating strategy in mind. This strategy takes into consideration every factor of building air management including: 1. Control of sensible heat 2. Balance management of heat rejection 3. Latent heat management 4. Control of process hot water 5. Indoor air quality management 6. Containment of energy consumption 7. Load shedding

Chilton, R. L.; White, C. L.

1996-01-01T23:59:59.000Z

199

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.      ...

200

MTS Table Top Load frame  

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

MTS Table Top Load frame MTS Table Top Load frame The Non-destructive Evaluation group operates an MTS Table Top Load frame for ultimate strength and life cycle testing of various ceramic, ceramic-matrix (FGI), carbon, carbon fiber, cermet (CMC) and metal alloy engineering samples. The load frame is a servo-hydraulic type designed to function in a closed loop configuration under computer control. The system can perform non-cyclic, tension, compression and flexure testing and cyclic fatigue tests. The system is comprised of two parts: * The Load Frame and * The Control System. Load Frame The Load Frame (figure 1) is a cross-head assembly which includes a single moving grip, a stationary grip and LVDT position sensor. It can generate up to 25 kN (5.5 kip) of force in the sample under test and can

Note: This page contains sample records for the topic "total load served" from the National Library of EnergyBeta (NLEBeta).
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201

Wind loading on solar collectors  

DOE Green Energy (OSTI)

The present design methodology for the determination of wind loading on the various solar collectors has been reviewed and assessed. The total force coefficients of flat plates of aspect ratios 1.0 and 3.0, respectively, at various angles of attack obtained by using the guidelines of the ANSI A58.1-1982, have been compared with those obtained by using the methodology of the ASCE Task Committee, 1961, and the experimental results of the full-scale test of heliostats by Peglow. The turbulent energy spectra, currently employed in the building code, are compared with those of Kaimal et al., Lumley, and Ponofsky for wind velocities of 20.0 m/s and 40.24 m/s at an elevation of 9.15 m. The longitudinal spectra of the building code overestimates the Kaimal spectra in the frequency range of 0.007 Hz to 0.08 Hz and underestimates beyond the frequency of 0.08 Hz. The peak angles of attack, on the heliostat, stowed in horizontal position, due to turbulent vertical and lateral components of wind velocity, have been estimated by using Daniel's methodology for three wind velocities and compared with the value suggested by the code. The experimental results of a simple test in the laboratory indicate the feasibility of decreasing the drag forces of the flat plate by reducing the solidity ratio.

Bhaduri, S.; Murphy, L.M.

1985-06-01T23:59:59.000Z

202

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

203

Low-cost load research for electric utilities  

Science Conference Proceedings (OSTI)

Golden Valley Electric Association (GVEA) developed two pragmatic approaches to meet most load-research objectives at a substantially lower cost than would be incurred with traditional techniques. GVEA serves three customer classes, with most of its load in the Fairbanks area. GVEA's new approaches simulate load curves for individual customer classes to the degree necessary to meet most load-research objectives for the utility, including applications to cost-of-service analysis, rate design, demand-side management, and load forecasting. These approaches make class load-shape information available to utilities that cannot otherwise afford to develop such data. Although the two approaches were developed for a small utility, they are likely to work at least as well for medium and large utilities. The first approach simulates class curves by combining load data from system feeders with information on customer mix and energy usage. GVEA's supervisory control and data acquisition system gives hourly data on feeder loads, and its billing database provides the number of customers and kilowatt-hour usage by customer class on each feeder. The second approach enhances load-research results by redefining target parameters. Data from several like-hours are used to calculate substitutes for the parameters traditionally defined from single-hour data points. The precision of peak responsibility estimates, for example, can be improved if several of the highest hourly demands in a given time period are used rather than the single highest hourly demand. Arguably, use of several highest hourly demands can also improve the reliability of the allocation of responsibility.

Gray, D.A.; Butcher, M.

1994-08-01T23:59:59.000Z

204

Energy Efficiency Indicators for High Electric-Load Buildings  

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

Energy Efficiency Indicators for High Electric-Load Buildings Energy Efficiency Indicators for High Electric-Load Buildings Speaker(s): Bernard Aebischer Date: February 6, 2003 - 12:00pm Location: Bldg. 90 Seminar Host/Point of Contact: Kristina LaCommare Energy per unit of floor area is not an adequate indictor for energy efficiency in high electric-load buildings. For two activities, restaurants and computer centres, alternative indicators for energy efficiency are discussed. Prerequisites in order to be able to use these indicators in energy efficiency programmes are discussed. The opportunity of an internationally coordinated research activity is also presented. Since 1999, Dr. Bernard Aebischer has served as a senior scientist at CEPE (Centre for Energy Policy and Economics) of the Swiss Federal Institutes of

205

Comparison of Wind Power and Load Forecasting Error Distributions: Preprint  

DOE Green Energy (OSTI)

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

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

2012-07-01T23:59:59.000Z

206

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 ....................

207

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 ....................

208

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - September  

Open Energy Info (EERE)

source source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Page Edit History Facebook icon Twitter icon » Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - September 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for September 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-09 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-09-01 End Date 2008-10-01 Residential Revenue(Thousand $) 21156 Residential Sales (MWh) 187445 Residential Consumers 329283 Commercial Revenue(Thousand $) 14874 Commercial Sales (MWh) 128656 Commercial Consumers 48190

209

FDA Construction Project Serves as a Super ESPC Model | Department of  

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

FDA Construction Project Serves as a Super ESPC Model FDA Construction Project Serves as a Super ESPC Model FDA Construction Project Serves as a Super ESPC Model October 7, 2013 - 1:55pm Addthis The U.S. Food and Drug Administration (FDA) has initiated an $890 million energy-saving construction project at the site of its new headquarters-a 1940s-era Navy base in White Oak, Maryland. Using a wide range of energy efficiency measures and solar energy, it has led to one of the largest Super Energy Savings Performance Contracts (ESPC). Watch the video modules below to learn more about this successful Super ESPC project. And find out how you can apply the FDA's energy management performance model to your federal agency's construction or building renovation project. Related Links Learn more about the energy-efficient and renewable energy technologies

210

Lawrence Livermore, Intel, Cray produce big data machine to serve as  

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

1 1 For immediate release: 11/04/2013 | NR-13-11-01 High Resolution Image Catalyst is a unique high performance computing (HPC) cluster that will serve research scientists and provide a proving ground for new HPC and Big Data technologies and architectures. It was recently installed at Lawrence Livermore National Laboratory. Lawrence Livermore, Intel, Cray produce big data machine to serve as catalyst for next-generation HPC clusters Donald B Johnston, LLNL, (925) 423-4902, johnston19@llnl.gov Lawrence Livermore National Laboratory in partnership with Intel and Cray, today announced a unique high performance computing (HPC) cluster that will serve research scientists at all three institutions and provide a proving ground for new HPC and Big Data technologies and architectures.

211

Family-Owned Restaurant Serves Up Huge Energy Savings | Department of  

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

Family-Owned Restaurant Serves Up Huge Energy Savings Family-Owned Restaurant Serves Up Huge Energy Savings Family-Owned Restaurant Serves Up Huge Energy Savings May 8, 2013 - 2:27pm Addthis Energy efficiency upgrades helped the Athenian Corner reduce its operating costs and improved the restaurant's bottom line. | Photo courtesy of BetterBuildings Lowell Energy Upgrade program. Energy efficiency upgrades helped the Athenian Corner reduce its operating costs and improved the restaurant's bottom line. | Photo courtesy of BetterBuildings Lowell Energy Upgrade program. Rebecca Matulka Rebecca Matulka Digital Communications Specialist, Office of Public Affairs What are the key facts? The Athenian Corner, a family-owned restaurant in Lowell, Massachusetts, made energy efficiency upgrades that are saving it more than

212

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - February  

Open Energy Info (EERE)

source source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Page Edit History Facebook icon Twitter icon » Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - February 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for February 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-02 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-02-01 End Date 2008-03-01 Residential Revenue(Thousand $) 32207 Residential Sales (MWh) 371971 Residential Consumers 331256 Commercial Revenue(Thousand $) 18469 Commercial Sales (MWh) 200148 Commercial Consumers 52121

213

Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - November  

Open Energy Info (EERE)

source source History View New Pages Recent Changes All Special Pages Semantic Search/Querying Get Involved Help Apps Datasets Community Login | Sign Up Search Page Edit History Facebook icon Twitter icon » Central Illinois Pub Serv Co (Illinois) EIA Revenue and Sales - November 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Central Illinois Pub Serv Co for November 2008. Monthly Electric Utility Sales and Revenue Data Short Name 2008-11 Utility Company Central Illinois Pub Serv Co (Illinois) Place Illinois Start Date 2008-11-01 End Date 2008-12-01 Residential Revenue(Thousand $) 36996 Residential Sales (MWh) 319196 Residential Consumers 331439 Commercial Revenue(Thousand $) 20266 Commercial Sales (MWh) 191904 Commercial Consumers 48563

214

Effects of pulsed-power loads upon an electric power grid  

DOE Green Energy (OSTI)

Certain proposed particle-accelerator and laser experiments, and other devices related to fusion research, require multi-megawatt, repetitive power pulses, often at low (subsynchronous) frequency. While some power-delivery technologies call for a certain degree of buffering of the utility demand using capacitive, inductive, or inertial energy storage, considerations have also been made for serving such loads directly from the line. In either case, such pulsed loads represent non-traditional applications from the utility's perspective which, in certain cases, can have significant design and operational implications. This paper outlines an approach to the analysis of the effects of such loads upon the electric power grid using existing analysis techniques. The impacts studied include busvoltage flicker, transient and dynamic stability, and torsional excitation. The impact of a particular pulsed load is examined and illustrated for the power network serving the Los Alamos National Laboratory. 19 refs., 13 figs.

Smolleck, H.A.; Ranade, S.J.; Prasad, N.R. (New Mexico State Univ., Las Cruces, NM (USA). Dept. of Electrical and Computer Engineering); Velasco, R.O. (Los Alamos National Lab., NM (USA))

1990-01-01T23:59:59.000Z

215

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

216

Load Management: Opportunity or Calamity?  

E-Print Network (OSTI)

After the change in the economics of generating electricity which took place in 1973, many utilities are examining options to hold down their costs. One fact which is clear is that the difference between peak and off peak generating costs is much larger now than prior to 1973. Utilities are examining two options which can be termed load management. One option is to control discretionary loads during peak periods. Cycling of residential water heaters or shutting off industrial electric furnaces during peak periods are both examples of load control which lower the costs borne by the utility. The other option is the use of seasonal surcharges or time-of-day rates to induce customers to alter their usage patterns. Both these load management options focus on reducing utility costs overall without regard to the cost to the consumers affected by the load management options. The issue, then, is whether industrial customers can find opportunities to lower their costs under load management.

Males, R.; Hassig, N.

1981-01-01T23:59:59.000Z

217

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

218

Dynamic load balancing of applications  

DOE Patents (OSTI)

An application-level method for dynamically maintaining global load balance on a parallel computer, particularly on massively parallel MIMD computers. Global load balancing is achieved by overlapping neighborhoods of processors, where each neighborhood performs local load balancing. The method supports a large class of finite element and finite difference based applications and provides an automatic element management system to which applications are easily integrated.

Wheat, Stephen R. (Albuquerque, NM)

1997-01-01T23:59:59.000Z

219

Dynamic load balancing of applications  

DOE Patents (OSTI)

An application-level method for dynamically maintaining global load balance on a parallel computer, particularly on massively parallel MIMD computers is disclosed. Global load balancing is achieved by overlapping neighborhoods of processors, where each neighborhood performs local load balancing. The method supports a large class of finite element and finite difference based applications and provides an automatic element management system to which applications are easily integrated. 13 figs.

Wheat, S.R.

1997-05-13T23:59:59.000Z

220

Alaska Village Electric Load Calculator  

DOE Green Energy (OSTI)

As part of designing a village electric power system, the present and future electric loads must be defined, including both seasonal and daily usage patterns. However, in many cases, detailed electric load information is not readily available. NREL developed the Alaska Village Electric Load Calculator to help estimate the electricity requirements in a village given basic information about the types of facilities located within the community. The purpose of this report is to explain how the load calculator was developed and to provide instructions on its use so that organizations can then use this model to calculate expected electrical energy usage.

Devine, M.; Baring-Gould, E. I.

2004-10-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

High-Power Rf Load  

DOE Patents (OSTI)

A compact high-power RF load comprises a series of very low Q resonators, or chokes [16], in a circular waveguide [10]. The sequence of chokes absorb the RF power gradually in a short distance while keeping the bandwidth relatively wide. A polarizer [12] at the input end of the load is provided to convert incoming TE.sub.10 mode signals to circularly polarized TE.sub.11 mode signals. Because the load operates in the circularly polarized mode, the energy is uniformly and efficiently absorbed and the load is more compact than a rectangular load. Using these techniques, a load having a bandwidth of 500 MHz can be produced with an average power dissipation level of 1.5 kW at X-band, and a peak power dissipation of 100 MW. The load can be made from common lossy materials, such as stainless steel, and is less than 15 cm in length. These techniques can also produce loads for use as an alternative to ordinary waveguide loads in small and medium RF accelerators, in radar systems, and in other microwave applications. The design is easily scalable to other RF frequencies and adaptable to the use of other lossy materials.

Tantawi, Sami G. (San Mateo, CA); Vlieks, Arnold E. (Livermore, CA)

1998-09-01T23:59:59.000Z

222

Copyright 2009, The Ohio State University Selecting, Storing, and Serving Ohio Strawberries--page 1  

E-Print Network (OSTI)

in the refrigerator immediately. Just before serving, wash them in gently flowing· coldwaterinacolander to the many variables, such as moisture con- tent, size, and variety, it is impossible to give specific temperature for 24 hours or until jam has set. Store uncooked jams in refrigerator or freezer. They can

223

Integrating Renewable Energy Contracts and Wholesale Dynamic Pricing to Serve Aggregate  

E-Print Network (OSTI)

1 Integrating Renewable Energy Contracts and Wholesale Dynamic Pricing to Serve Aggregate Flexible batteries, with renewable energy resources. We formulate a stochastic optimal control problem that describes and the degree to which the aggregator can respond to dynamic pricing. Index Terms--Dynamic pricing, renewable

Oren, Shmuel S.

224

I. Bread, cereal, rice, and pasta 6-11 servings (aim for 50% whole grain)  

E-Print Network (OSTI)

Cranberry juice Dinner leftovers such as Chicken and veggie stir-fry over Steamed brown rice Low fat yogurt, and cheese (2-3 servings) IV. Fats, oils, and sweets (sparingly) Breakfast Ideas Oatmeal Raisins or dried cranberries Low-fat milk Low-fat mozzarella cheese sticks Whole-grain crackers Mixed dried fruits Low-fat

de la Torre, José R.

225

Load management and the La Vereda passive solar community  

SciTech Connect

Reviewed are preliminary data available from some of the passive solar homes now operational at the La Vereda subdivision in Santa Fe, New Mexico. The major emphasis is Load Management - an electric utility term pertaining to when and how much energy is used by the customer. A customer's home is considered to be Load Managed when its major demands for electricity occur at times during the day when the utility has surplus generation capacity. For most utilities this surplus occurs during the night and is referred to as the off-peak period. Compared to conventional electric homes, the La Vereda passive solar homes are Naturally Load Managed because most of their backup heating requirements occur during the utility's off-peak period. Naturally Load Managed homes like these allow the backup heating system to operate freely whenever the space needs heat. Load data from six La Vereda homes are compared to similar data from 1) a group of nonsolar super-insulated total electric homes, and 2) the utility's winter system peak day load profile. The comparison verifies the Natural Load Management characteristics of the well-designed passive solar home. The free operation of the backup heating system, especially during cloudy or severe weather, can reduce the Natural Load Management characteristics of the La Verda homes. Is it possible to Force Load Management on a home, regardless of weather conditions and still guarantee that all space heating requirements are satisfied with off-peak energy. One home at La Vereda is discussed that has an experimental Forced Load Management backup heating system designed to use energy only during the utility's off-peak period. Load data from this home is presented and compared to other homes at La Verda.

Pyde, S.E.

1981-01-01T23:59:59.000Z

226

LOAD FORECASTING Eugene A. Feinberg  

E-Print Network (OSTI)

's electricity price forecasting model, produces forecast of gas demand consistent with electric load. #12Gas demand Council's Market Price of Electricity Forecast Natural GasDemand Electric Load Aggregating Natural between the natural gas and electricity and new uses of natural gas emerge. T natural gas forecasts

Feinberg, Eugene A.

227

building load | OpenEI  

Open Energy Info (EERE)

load load Dataset Summary Description This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also includes the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Source Commercial and Residential Reference Building Models Date Released April 18th, 2013 (9 months ago) Date Updated July 02nd, 2013 (7 months ago) Keywords building building demand building load Commercial data demand Energy Consumption energy data hourly kWh load profiles Residential Data Quality Metrics Level of Review Some Review Comment Temporal and Spatial Coverage Frequency Annually

228

Wind load reduction for heliostats  

DOE Green Energy (OSTI)

This report presents the results of wind-tunnel tests supported through the Solar Energy Research Institute (SERI) by the Office of Solar Thermal Technology of the US Department of Energy as part of the SERI research effort on innovative concentrators. As gravity loads on drive mechanisms are reduced through stretched-membrane technology, the wind-load contribution of the required drive capacity increases in percentage. Reduction of wind loads can provide economy in support structure and heliostat drive. Wind-tunnel tests have been directed at finding methods to reduce wind loads on heliostats. The tests investigated primarily the mean forces, moments, and the possibility of measuring fluctuating forces in anticipation of reducing those forces. A significant increase in ability to predict heliostat wind loads and their reduction within a heliostat field was achieved.

Peterka, J.A.; Hosoya, N.; Bienkiewicz, B.; Cermak, J.E.

1986-05-01T23:59:59.000Z

229

1998 Pacific Northwest Loads and Resources Study: The White Book.  

DOE Green Energy (OSTI)

The Pacific Northwest Loads and Resources Study (White Book) is published annually by BPA and establishes the planning basis for supplying electricity to customers. It serves a dual purpose. First, the White Book presents projections of regional and Federal system load and resource capabilities, along with relevant definitions and explanations. Second, the White Book serves as a benchmark for annual BPA determinations made pursuant to the 1981 regional power sales contracts. Specifically, BPA uses the information in the White Book for determining the notice required when customers request to increase or decrease the amount of power purchased from BPA. The White Book compiles information obtained from several formalized resource planning reports and data submittals, including those from the Northwest Power Planning Council (Council) and the Pacific Northwest Utilities Conference Committee (PNUCC). The White Book is not an operational planning guide, nor is it used for inventory planning to determine BPA revenues. Operation of the Federal Columbia River Power System (FCRPS) is based on a set of criteria different from that used for resource planning decisions. Operational planning is dependent upon real-time or near-term knowledge of system conditions, including expectations of river flows and runoff, market opportunities, availability of reservoir storage, energy exchanges, and other factors affecting the dynamics of operating a power system. The 1998 White Book is presented in two documents: (1) this summary of Federal system and Pacific Northwest region loads and resources; and (2) a technical appendix detailing the loads and resources for each major Pacific Northwest generating utility. This analysis updates the December 1997 Pacific Northwest Loads and Resources Study.

United States. Bonneville Power Administration.

1998-12-01T23:59:59.000Z

230

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

231

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

232

load data | OpenEI Community  

Open Energy Info (EERE)

51 51 Varnish cache server Home Groups Community Central Green Button Applications Developer Utility Rate FRED: FRee Energy Database More Public Groups Private Groups Features Groups Blog posts Content Stream Documents Discussions Polls Q & A Events Notices My stuff Energy blogs 429 Throttled (bot load) Error 429 Throttled (bot load) Throttled (bot load) Guru Meditation: XID: 2142234851 Varnish cache server load data Home Sfomail's picture Submitted by Sfomail(48) Member 17 May, 2013 - 12:03 Commercial and Residential Hourly Load Data Now Available on OpenEI! building load building load data commercial load data dataset datasets electric load data load data load profile OpenEI residential load TMY3 United States Load data Image source: NREL Files: application/zip icon System Advisor Model Tool for Downloading Load Data

233

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

234

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..............................................................

235

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.....................................................

236

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.......................................................

237

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

238

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.................................

239

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...........................

240

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)

Note: This page contains sample records for the topic "total load served" 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

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

242

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.................................................................

243

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)...........................

244

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.........................................................

245

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

246

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.................................

247

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.......................................................

248

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.................................

249

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..........................

250

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.................................

251

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..........................

252

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..........................................................

253

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

254

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.......................................................................

255

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.....................................................................

256

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...................................................................

257

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.........................................................

258

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.....................................................................

259

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.................................

260

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....................................................

Note: This page contains sample records for the topic "total load served" 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

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.......................................................

262

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.................................

263

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.................................

264

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.................................................................

265

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

266

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.........................................................

267

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.................................

268

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.............................................................

269

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.......................................................

270

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......................................................................

271

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..........................................

272

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.........................................................

273

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

274

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.........................................................

275

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

276

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

277

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...................................................................

278

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......................................................................

279

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...

280

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...

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


281

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...

282

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...

283

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...

284

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...

285

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...

286

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...

287

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......

288

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...

289

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......

290

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......

291

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...

292

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...

293

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...

294

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...

295

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...

296

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...

297

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......

298

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...

299

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...

300

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

Annual Energy Outlook 2012 (EIA)

at All... 2.9 1.1 0.5 Q 0.4 Battery-Operated AppliancesTools Use Battery-Operated AppliancesTools......

Note: This page contains sample records for the topic "total load served" 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

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...

302

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...

303

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...

304

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......

305

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......

306

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...

307

EQUUS Total Return Inc | Open Energy Information  

Open Energy Info (EERE)

EQUUS Total Return Inc EQUUS Total Return Inc Jump to: navigation, search Name EQUUS Total Return Inc Place Houston, Texas Product A business development company and VC investor that trades as a closed-end fund. EQUUS is managed by MCC Global NV, a Frankfurt stock exchange listed management and merchant banking group. Coordinates 29.76045°, -95.369784° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":29.76045,"lon":-95.369784,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

308

Assessing and Reducing Miscellaneous Electric Loads (MELs) in Banks  

Science Conference Proceedings (OSTI)

Miscellaneous electric loads (MELs) are loads outside of a building's core functions of heating, ventilating, air conditioning, lighting, and water heating. MELs are a large percentage of total building energy loads. This report reviews methods for reducing MELs in Banks. Reducing MELs in a bank setting requires both local and corporate action. Corporate action centers on activities to prioritize and allocate the right resources to correct procurement and central control issues. Local action includes branch assessment or audits to identify specific loads and needs. The worksheet at the end of this guide can help with cataloging needed information and estimating savings potential. The following steps provide a guide to MEL reductions in Bank Branches. The general process has been adapted from a process developed for office buildings the National Renewable Energy Laboratory (NREL, 2011).

Rauch, Emily M.

2012-09-01T23:59:59.000Z

309

Advanced nonintrusive load monitoring system  

E-Print Network (OSTI)

There is a need for flexible, inexpensive metering technologies that can be deployed in many different monitoring scenarios. Individual loads may be expected to compute information about their power consumption. Utility ...

Wichakool, Warit, 1977-

2011-01-01T23:59:59.000Z

310

OpenEI - building load  

Open Energy Info (EERE)

are given by a location defined by the Typical Meteorological Year (TMY) for which the weather data was collected. Commercial load data is sorted by the (TMY) site as a...

311

Permanent Load Shift Control Strategies  

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

of Permanent Load Shifting for HVAC and other storage assets as it relates to summer on-peak demand, how it can be dynamically and autonomously controlled, and its relationship...

312

Building load control and optimization  

E-Print Network (OSTI)

Researchers and practitioners have proposed a variety of solutions to reduce electricity consumption and curtail peak demand. This research focuses on load control by improving the operations in existing building HVAC ...

Xing, Hai-Yun Helen, 1976-

2004-01-01T23:59:59.000Z

313

Offshore loading-system design is aimed at higher efficiency  

SciTech Connect

Mobil Shipping and Transporation Co. has completed the design of an offshore loading system aimed at overcoming the limitations of existing terminals. The loading/mooring/storage system (LMS) is a semisubmersible vessel with ship mooring and loading facilities atop a box-shaped crude storage structure, which is well below the water line away from the effect of waves and clear of loading tankers' bows. The storage volume equals or exceeds that of a VLCC. There are 15 dual-purpose cargo/ballast tanks in the lower section with a control tower over the center tank. The loading system is designed to load 800,000 bbl of crude in about 12 hr. Each tank contains a diagonally suspended synthetic rubber diaphragm that will isolate crude from water ballast. Computer simulations based on North Sea weather data indicate that the LMS will allow marine loading efficiencies of at least 95Vertical Bar3< with total storage of a week's production or more. Other advantages of the LMS include the ability to moor shuttle tankers up to 200,000 dwt; self-contained repair and maintenance capabilities; and mobility. Variations of anchoring and riser systems for the LMS are discussed.

1978-05-08T23:59:59.000Z

314

Computational study of compressive loading of carbon nanotubes  

Science Conference Proceedings (OSTI)

A reduced-order general continuum method is used to examine the mechanical behavior of single-walled carbon nanotubes (CNTs) under compressive loading and unloading conditions. Quasi-static solutions are sought where the total energy of the system is ... Keywords: carbon nanotube, component, finite element method, mechanical properties

Yang Yang; William W. Liou

2010-03-01T23:59:59.000Z

315

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......................................

316

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...

317

Measured electric hot water standby and demand loads from Pacific Northwest homes. End-Use Load and Consumer Assessment Program  

SciTech Connect

The Bonneville Power Administration began the End-Use Load and Consumer Assessment Program (ELCAP) in 1983 to obtain metered hourly end-use consumption data for a large sample of new and existing residential and commercial buildings in the Pacific Northwest. Loads and load shapes from the first 3 years of data fro each of several ELCAP residential studies representing various segments of the housing population have been summarized by Pratt et al. The analysis reported here uses the ELCAP data to investigate in much greater detail the relationship of key occupant and tank characteristics to the consumption of electricity for water heating. The hourly data collected provides opportunities to understand electricity consumption for heating water and to examine assumptions about water heating that are critical to load forecasting and conservation resource assessments. Specific objectives of this analysis are to: (A) determine the current baseline for standby heat losses by determining the standby heat loss of each hot water tank in the sample, (B) examine key assumptions affecting standby heat losses such as hot water temperatures and tank sizes and locations, (C) estimate, where possible, impacts on standby heat losses by conservation measures such as insulating tank wraps, pipe wraps, anticonvection valves or traps, and insulating bottom boards, (D) estimate the EF-factors used by the federal efficiency standards and the nominal R-values of the tanks in the sample, (E) develop estimates of demand for hot water for each home in the sample by subtracting the standby load from the total hot water load, (F) examine the relationship between the ages and number of occupants and the hot water demand, (G) place the standby and demand components of water heating electricity consumption in perspective with the total hot water load and load shape.

Pratt, R.G.; Ross, B.A.

1991-11-01T23:59:59.000Z

318

Energy, Power Quality, and Customer Load Efficiency Optimization and Total Energy  

Science Conference Proceedings (OSTI)

Using this report's worksheets and procedures, utilities can evaluate a wide range of common end-user productivity and power quality concerns that lead to new customer services and sales initiatives.

2002-02-14T23:59:59.000Z

319

Load transfer coupling regression curve fitting for distribution load forecasting  

SciTech Connect

The planning of distribution facilities requires forecasts of future substation and feeder loads. Extrapolation based on a curve fit to past annual peak loads is currently the most popular manner of accomplishing this forecast. Curve fitting suffers badly from data shifts caused by switching as loads are routinely moved from one substation to another during the course of utility operations. This switching contaminates the data, reducing forecast accuracy. A new regression application reduces error due to these transfers by over an order of magnitude. A key to the usefulness of this method is that the amount of the transfer, and its direction (whether it was to or from a substation), is not a required input. The new technique, aspects of computer implementation of it, and a series of tests showing its advantage over normal multiple regression methods are given.

Willis, H.L.; Powell, R.W.

1984-05-01T23:59:59.000Z

320

Realizing load reduction functions by aperiodic switching of load groups  

SciTech Connect

This paper investigates the problem of scheduling ON/OFF switching of residential appliances under the control of a Load Management System (LMS). The scheduling process is intended to reduce the controlled appliances` power demand in accordance with a predefined load reduction profile. To solve this problem, a solution approach, based on the methodology of Pulse Width Modulation (PWM), is introduced. This approach provides a flexible mathematical basis for studying different aspects of the scheduling problem. The conventional practices in this area are shown to be special cases of the PWM technique. By applying the PWM-based technique to the scheduling problem, important classes of scheduling errors are identified and analytical expressions describing them are derived. These expressions are shown to provide sufficient information to compensate for the errors. Detailed simulations of load groups` response to switching actions are use to support conclusions of this study.

Navid-Azarbaijani, N. [McGill Univ., Montreal, Quebec (Canada). Dept. of Electrical Engineering; Banakar, M.H. [CAE Electronics Ltd., St. Laurent, Quebec (Canada)

1996-05-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

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

322

FINAL PROJECT REPORT LOAD MODELING TRANSMISSION RESEARCH  

E-Print Network (OSTI)

PSLF that incorporates motor  A?C, ZIP, and electronic load the fractions motors A?C, ZIP, and electronic loads.    Usethat incorporates motor  A?C, ZIP, and  electronic load 

Lesieutre, Bernard

2013-01-01T23:59:59.000Z

323

load profile | OpenEI Community  

Open Energy Info (EERE)

load profile Home Sfomail's picture Submitted by Sfomail(48) Member 17 May, 2013 - 13:03 Commercial and Residential Hourly Load Data Now Available on OpenEI building load building...

324

Monthly Crustal Loading Corrections for Satellite Altimetry  

Science Conference Proceedings (OSTI)

Satellite altimeter measurements of sea surface height include a small contribution from vertical motion of the seafloor caused by crustal loading. Loading by ocean tides is routinely allowed for in altimeter data processing. Here, loading by ...

R. D. Ray; S. B. Luthcke; T. van Dam

2013-05-01T23:59:59.000Z

325

Loading capacity of various filters for lithium fire generated aerosols  

Science Conference Proceedings (OSTI)

The lithium aerosol loading capacity of a prefilter, HEPA filters and a sand and gravel bed filter was determined. The test aerosol was characterized and was generated by burning lithium in an unlimited air atmosphere. Correlation to sodium aerosol loading capacities were made to relate existing data to lithium aerosol loadings under varying conditions. This work is being conducted in support of the fusion reactor safety program. The lithium aerosol was generated by burning lithium pools, up to 45 kgs, in a 340 m/sup 3/ low humidity air atmosphere to supply aerosol to recirculating filter test loops. The aerosol was sampled to determine particle size, mass concentrations and chemical species. The dew point and gas concentrations were monitored throughout the tests. Loop inlet aerosol mass concentrations ranged up to 5 gr/m/sup 3/. Chemical compounds analyzed to be present in the aerosol include Li/sub 2/O, LiOH, and Li/sub 2/CO/sub 3/. HEPA filters with and without separators and a prefilter and HEPA filter in series were loaded with 7.8 to 11.1 kg/m/sup 2/ of aerosol at a flow rate of 1.31 m/sec and 5 kPa pressure drop. The HEPA filter loading capacity was determined to be greater at a lower flow rate. The loading capacity increased from 0.4 to 2.8 kg by decreasing the flow rate from 1.31 to 0.26 m/sec for a pressure drop of 0.11 kPa due to aerosol buildup. The prefilter tested in series with a HEPA did not increase the total loading capacity significantly for the same total pressure drop. Separators in the HEPA had only minor effect on loading capacity. The sand and gravel bed filter loaded to 0.50 kg/m/sup 2/ at an aerosol flow rate of 0.069 m/sec and final pressure drop of 6.2 kPa. These loading capacities and their dependence on test variables are similar to those reported for sodium aerosols except for the lithium aerosol HEPA loading capacity dependence upon flow rate.

Jeppson, D.W.; Barreca, J.R.

1980-10-23T23:59:59.000Z

326

Peak load management: Potential options  

SciTech Connect

This report reviews options that may be alternatives to transmission construction (ATT) applicable both generally and at specific locations in the service area of the Bonneville Power Administration (BPA). Some of these options have potential as specific alternatives to the Shelton-Fairmount 230-kV Reinforcement Project, which is the focus of this study. A listing of 31 peak load management (PLM) options is included. Estimated costs and normalized hourly load shapes, corresponding to the respective base load and controlled load cases, are considered for 15 of the above options. A summary page is presented for each of these options, grouped with respect to its applicability in the residential, commercial, industrial, and agricultural sectors. The report contains comments on PLM measures for which load shape management characteristics are not yet available. These comments address the potential relevance of the options and the possible difficulty that may be encountered in characterizing their value should be of interest in this investigation. The report also identifies options that could improve the efficiency of the three customer utility distribution systems supplied by the Shelton-Fairmount Reinforcement Project. Potential cogeneration options in the Olympic Peninsula are also discussed. These discussions focus on the options that appear to be most promising on the Olympic Peninsula. Finally, a short list of options is recommended for investigation in the next phase of this study. 9 refs., 24 tabs.

Englin, J.E.; De Steese, J.G.; Schultz, R.W.; Kellogg, M.A.

1989-10-01T23:59:59.000Z

327

Building Energy Software Tools Directory: Load Express  

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

graphical interface makes Load Express a powerful engineering tool with a very short learning curve. The "rookie" or experienced user can quickly and accurately perform load...

328

Self-aligning biaxial load frame  

DOE Patents (OSTI)

An self-aligning biaxial loading apparatus for use in testing the strength of specimens while maintaining a constant specimen centroid during the loading operation. The self-aligning biaxial loading apparatus consists of a load frame and two load assemblies for imparting two independent perpendicular forces upon a test specimen. The constant test specimen centroid is maintained by providing elements for linear motion of the load frame relative to a fixed cross head, and by alignment and linear motion elements of one load assembly relative to the load frame. 3 figures.

Ward, M.B.; Epstein, J.S.; Lloyd, W.R.

1994-01-18T23:59:59.000Z

329

Decentralized customerlevel under frequency load shedding in...  

Open Energy Info (EERE)

enables the management of large groups of distributed loads under a single innovative control schemes to use the flexibility of electrical loads for power system purposes....

330

A bio-inspired multi-agent system framework for real-time load management in all-electric ship power systems  

Science Conference Proceedings (OSTI)

All-electric ship power systems have limited generation capacity and finite rotating inertia compared with large power systems. Moreover, all-electric ship power systems include large portions of nonlinear loads and dynamic loads relative to the total ...

Xianyong Feng / Karen L. Butler-Purry

2012-01-01T23:59:59.000Z

331

Method for loading resin beds  

DOE Patents (OSTI)

An improved method of preparing nuclear reactor fuel by carbonizing a uranium loaded cation exchange resin provided by contacting a H.sup.+ loaded resin with a uranyl nitrate solution deficient in nitrate, comprises providing the nitrate deficient solution by a method comprising the steps of reacting in a reaction zone maintained between about 145.degree.-200.degree. C, a first aqueous component comprising a uranyl nitrate solution having a boiling point of at least 145.degree. C with a second aqueous component to provide a gaseous phase containing HNO.sub.3 and a reaction product comprising an aqueous uranyl nitrate solution deficient in nitrate.

Notz, Karl J. (Oak Ridge, TN); Rainey, Robert H. (Knoxville, TN); Greene, Charles W. (Knoxville, TN); Shockley, William E. (Oak Ridge, TN)

1978-01-01T23:59:59.000Z

332

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...

333

Selecting a Control Strategy for Plug and Process Loads  

Science Conference Proceedings (OSTI)

Plug and Process Loads (PPLs) are building loads that are not related to general lighting, heating, ventilation, cooling, and water heating, and typically do not provide comfort to the building occupants. PPLs in commercial buildings account for almost 5% of U.S. primary energy consumption. On an individual building level, they account for approximately 25% of the total electrical load in a minimally code-compliant commercial building, and can exceed 50% in an ultra-high efficiency building such as the National Renewable Energy Laboratory's (NREL) Research Support Facility (RSF) (Lobato et al. 2010). Minimizing these loads is a primary challenge in the design and operation of an energy-efficient building. A complex array of technologies that measure and manage PPLs has emerged in the marketplace. Some fall short of manufacturer performance claims, however. NREL has been actively engaged in developing an evaluation and selection process for PPLs control, and is using this process to evaluate a range of technologies for active PPLs management that will cap RSF plug loads. Using a control strategy to match plug load use to users' required job functions is a huge untapped potential for energy savings.

Lobato, C.; Sheppy, M.; Brackney, L.; Pless, S.; Torcellini, P.

2012-09-01T23:59:59.000Z

334

Random Deployment of Data Collectors for Serving Randomly-Located Sensors  

E-Print Network (OSTI)

Recently, wireless communication industries have begun to extend their services to machine-type communication devices as well as to the user equipment. Such machine-type communication devices as meters and sensors need intermittent uplink resources to report measured or sensed data to their serving data collector. It is however hard to dedicate limited uplink resources to each of them. Thus, efficient service of a tremendous number of devices with low activities may consider simple random access as a solution. The data collectors receiving the measured data from many sensors simultaneously can successfully decode only signals with signal-to-interference-plus-noise-ratio (SINR) above a certain value. The main design issues for this environment become how many data collectors are needed, how much power sensor nodes transmit with, and how wireless channels affect the performance. This paper provides answers to those questions through a stochastic analysis based on a spatial point process and on simulations.

Kwon, Taesoo

2011-01-01T23:59:59.000Z

335

PRB rail loadings shatter record  

Science Conference Proceedings (OSTI)

Rail transport of coal in the Powder River Basin has expanded, with a record 2,197 trains loaded in a month. Arch Coal's Thunder basin mining complex has expanded by literally bridging the joint line railway. The dry fork mine has also celebrated its safety achievements. 4 photos.

Buchsbaum, L.

2008-09-15T23:59:59.000Z

336

Automatic Electric Load Identification in  

E-Print Network (OSTI)

Abstract — A microgrid is the power system of choice for the electrification of rural areas in developing countries. It should be able to adapt to changing load situations without the need for specialists to change the configuration of the microgrid controller. This paper proposes a self-configuring microgrid management system that is able to adjust both generation and demand of the system, so that also in case of growing electricity demand the grid can still be operable by disconnecting unessential loads. A crucial task for the microgrid controller is to automatically identify the connected loads on the basis of their consumption behaviors. For this, a template-matching algorithm is proposed that is based on Dynamic Time Warping, which is primarily used in speech recognition. It has been found that for load profile analysis, simple signal features such as the number of rising edges or the aggregated energy consumption in a given time window is sufficient to describe the signal. In contrast to speech recognition, frequency domain analysis is not necessary.

Self-configuring Microgrids; Friederich Kupzog; Tehseen Zia; Adeel Abbas Zaidi

2009-01-01T23:59:59.000Z

337

Total Energy Facilities Biomass Facility | Open Energy Information  

Open Energy Info (EERE)

Total Energy Facilities Biomass Facility Total Energy Facilities Biomass Facility Jump to: navigation, search Name Total Energy Facilities Biomass Facility Facility Total Energy Facilities Sector Biomass Facility Type Non-Fossil Waste Location Los Angeles County, California Coordinates 34.3871821°, -118.1122679° 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":34.3871821,"lon":-118.1122679,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

338

Total China Investment Co Ltd | Open Energy Information  

Open Energy Info (EERE)

Total China Investment Co Ltd Total China Investment Co Ltd Jump to: navigation, search Name Total (China) Investment Co. Ltd. Place Beijing, China Zip 100004 Product Total has been present in China for about 30 years through its activities of Exploration & Production, Gas & Power, Refining & Marketing, and Chemicals. Coordinates 39.90601°, 116.387909° 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":39.90601,"lon":116.387909,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

339

On-line load relief control  

SciTech Connect

This paper describes the results of an investigation concerning the on-line prediction and enhancement of load relief. The effects of voltage fluctuation, system voltage profile control and generator voltage adjustment on load relief and load shedding operations during under-frequency transients are studied. The technique promoted in the paper may be used to reduce system spinning reserve or prospective load shedding.

Jovanovic, S.; Fox, B.; Thompson, J.G. (Queen' s Univ. of Belfast (United Kingdom))

1994-11-01T23:59:59.000Z

340

Load Forecasting for Modern Distribution Systems  

Science Conference Proceedings (OSTI)

Load forecasting is a fundamental activity for numerous organizations and activities within a utility, including planning, operations, and control. Transmission and Distribution (T&D) planning and design engineers use the load forecast to determine whether any changes and additions are needed to the electric system to satisfy the anticipated load. Other load forecast users include system operations, financial ...

2013-03-08T23:59:59.000Z

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


341

Load Forecast For use in Resource Adequacy  

E-Print Network (OSTI)

Load Forecast 2019 For use in Resource Adequacy Massoud Jourabchi #12;In today's presentation d l­ Load forecast methodology ­ Drivers of the forecast f i­ Treatment of conservation ­ Incorporating impact of weather ­ Forecast for 2019 #12;Regional Loads (MWA and MW)Regional Loads (MWA and MW

342

Renewable Energy Load Matching Software  

The most common way of describing the quality of an existing or potential wind or solar power generation site is the total amount of energy expected ...

343

Load-shape development aids planning  

SciTech Connect

The concept that provides capable, load-shape development, is being adopted by several utilities and power pools. Public Service Electric and Gas Company has developed a computer simulation model that can predict a utility's load shape for up to a 30-year period. The objective of the PSE and G model, known as EICS (Electric Load-Curve Synthesis) is to provide a demand profile, to examine the impact of load mangement and other activities upon a system's load shape, and to apply appropriate forecast non-load-management and load-management impacts before finally examining the resulting revised load-shape. Other models dealing with load-shape are discussed. Specifically, the Systems Control Inc. model for EPRI (SCI/EPRI), useful in performing accurate simulations of various load-control strategies involving customer appliance control is mentioned.

Gellings, C.W.

1979-12-15T23:59:59.000Z

344

Characterizing Household Plug Loads through Self-Administered Load Research  

Science Conference Proceedings (OSTI)

Household miscellaneous loads, which include consumer electronics, are the fastest growing segment of household energy use in the United States. Although the relative energy intensity of applications such as heating and cooling is declining, the DOEAnnual Energy Outlook forecasts that the intensity of residential miscellaneous end uses will increase substantially by 2030. Studies by TIAX and Ecos Consulting reveal that miscellaneous devices8212smaller devices in terms of energy draw but growing in usage8...

2009-12-09T23:59:59.000Z

345

A new pricing mechanism for a high-priority DiffServ-based service  

E-Print Network (OSTI)

and the maximum number of hops within the TD that a customer's EF traffic can traverse. Also, the prerequisite among all EF customers as explained in section 3. In the case of a TD with D tot 05.0=a , maximum number to all customers can be distributed according to totb = k b b tot i (12) where is the total number

Bouras, Christos

346

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

347

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...

348

Remote Area Power Supply (RAPS) load and resource profiles.  

SciTech Connect

In 1997, an international team interested in the development of Remote Area Power Supply (RAPS) systems for rural electrification projects around the world was organized by the International Lead Zinc Research Organization (ILZRO) with the support of Sandia National Laboratories (SNL). The team focused on defining load and resource profiles for RAPS systems. They identified single family homes, small communities, and villages as candidates for RAPS applications, and defined several different size/power requirements for each. Based on renewable energy and resource data, the team devised a ''strawman'' series of load profiles. A RAPS system typically consists of a renewable and/or conventional generator, power conversion equipment, and a battery. The purpose of this report is to present data and information on insolation levels and load requirements for ''typical'' homes, small communities, and larger villages around the world in order to facilitate the development of robust design practices for RAPS systems, and especially for the storage battery component. These systems could have significant impact on areas of the world that would otherwise not be served by conventional electrical grids.

Giles, Lauren (Energetics, Inc., Washington, DC); Skolnik, Edward G. (Energetics, Inc., Washington, DC); Marchionini, Brian (Energetics, Inc., Washington, DC); Fall, Ndeye K. (Energetics, Inc., Washington, DC)

2007-07-01T23:59:59.000Z

349

Remote Area Power Supply (RAPS) load and resource profiles.  

SciTech Connect

In 1997, an international team interested in the development of Remote Area Power Supply (RAPS) systems for rural electrification projects around the world was organized by the International Lead Zinc Research Organization (ILZRO) with the support of Sandia National Laboratories (SNL). The team focused on defining load and resource profiles for RAPS systems. They identified single family homes, small communities, and villages as candidates for RAPS applications, and defined several different size/power requirements for each. Based on renewable energy and resource data, the team devised a ''strawman'' series of load profiles. A RAPS system typically consists of a renewable and/or conventional generator, power conversion equipment, and a battery. The purpose of this report is to present data and information on insolation levels and load requirements for ''typical'' homes, small communities, and larger villages around the world in order to facilitate the development of robust design practices for RAPS systems, and especially for the storage battery component. These systems could have significant impact on areas of the world that would otherwise not be served by conventional electrical grids.

Giles, Lauren (Energetics, Inc., Washington, DC); Skolnik, Edward G. (Energetics, Inc., Washington, DC); Marchionini, Brian (Energetics, Inc., Washington, DC); Fall, Ndeye K. (Energetics, Inc., Washington, DC)

2007-07-01T23:59:59.000Z

350

Load Scheduling with Profile Information  

E-Print Network (OSTI)

. Within the past five years, many manufactures have added hardware performance counters to their microprocessors to generate profile data cheaply. We show how to use Compaq's DCPI tool to determine load latencies which are at a fine, instruction granularity and use them as fodder for improving instruction scheduling. We validate our heuristic for using DCPI latency data to classify loads as hits and misses against simulation numbers. We map our classification into the Multiflow compiler's intermediate representation, and use a locality sensitive Balanced scheduling algorithm. Our experiments illustrate that our algorithm improves run times by 1% on average, but up to 10% on a Compaq Alpha. 1 Introduction This paper explores how to use hardware performance counters to produce fine grain latency information to improve compiler scheduling. We use this information to hide latencies with any available instruction level parallelism (ILP). (ILP for an instruction is the number of o...

Götz Lindenmaier; Kathryn S. McKinley; Olivier Temam

2000-01-01T23:59:59.000Z

351

PASSIVE DETECTION OF VEHICLE LOADING  

SciTech Connect

The Digital Imaging and Remote Sensing Laboratory (DIRS) at the Rochester Institute of Technology, along with the Savannah River National Laboratory is investigating passive methods to quantify vehicle loading. The research described in this paper investigates multiple vehicle indicators including brake temperature, tire temperature, engine temperature, acceleration and deceleration rates, engine acoustics, suspension response, tire deformation and vibrational response. Our investigation into these variables includes building and implementing a sensing system for data collection as well as multiple full-scale vehicle tests. The sensing system includes; infrared video cameras, triaxial accelerometers, microphones, video cameras and thermocouples. The full scale testing includes both a medium size dump truck and a tractor-trailer truck on closed courses with loads spanning the full range of the vehicle's capacity. Statistical analysis of the collected data is used to determine the effectiveness of each of the indicators for characterizing the weight of a vehicle. The final sensing system will monitor multiple load indicators and combine the results to achieve a more accurate measurement than any of the indicators could provide alone.

Garrett, A.

2012-01-03T23:59:59.000Z

352

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

353

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

354

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:

355

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"

356

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

357

1995 Pacific Northwest Loads and Resources Study, Technical Appendix: Volume 1.  

SciTech Connect

The Pacific Northwest Loads and Resources Study (WhiteBook), is published annually by BPA, and establishes the planning basis for supplying electricity to customers. It serves a dual purpose. First, the White Book presents projections of regional and Federal system load and resource capabilities, along with relevant definitions and explanations. Second, the White Book serves as a benchmark for annual BPA determinations made pursuant to the 1981 regional power sales contracts. Specifically, BPA uses the, information in the White Book for determining the notice required when customers request to increase or decrease the amount of power purchased from BPA. Aside from these purposes, the White Book is used for input to BPA`s resource planning process. The White Book compiles information obtained from several formalized resource planning reports and data submittals, including those from the Northwest Power Planning Council (Council) and the Pacific Northwest Utilities Conference Committee (PNUCC).

United States. Bonneville Power Administration.

1995-12-01T23:59:59.000Z

358

A Liquid-Helium-Cooled Absolute Reference Cold Load forLong-Wavelength Radiometric Calibration  

SciTech Connect

We describe a large (78-cm) diameter liquid-helium-cooled black-body absolute reference cold load for the calibration of microwave radiometers. The load provides an absolute calibration near the liquid helium (LHe) boiling point, accurate to better than 30 mK for wavelengths from 2.5 to 25 cm (12-1.2 GHz). The emission (from non-LHe temperature parts of the cold load) and reflection are small and well determined. Total corrections to the LHe boiling point temperature are {le} 50 mK over the operating range. This cold load has been used at several wavelengths at the South Pole and at the White Mountain Research Station. In operation, the average LHe loss rate was {le} 4.4 l/hr. Design considerations, radiometric and thermal performance and operational aspects are discussed. A comparison with other LHe-cooled reference loads including the predecessor of this cold load is given.

Bensadoun, M.; Witebsky, C.; Smoot, George F.; De Amici,Giovanni; Kogut, A.; Levin, S.

1990-05-01T23:59:59.000Z

359

Independent review of estimated load reductions for PJM's small customer load response pilot project  

E-Print Network (OSTI)

of Estimated Load Reductions for PJM’s Small Customer Loadof Estimated Load Reductions for PJM’s Small Customer LoadResponse Pilot Project Prepared for PJM Interconnection, LLC

Heffner, G.; Moezzi, M.; Goldman, C.

2004-01-01T23:59:59.000Z

360

Development and application of the spatially explicit load enrichment calculation tool (select) to determine potential E. coli loads in watersheds  

E-Print Network (OSTI)

According to the USEPA National Section 303(d) List Fact Sheet, bacterial pathogens are the leading cause of water quality impairments in Texas. The automated Spatially Explicit Load Enrichment Calculation Tool (SELECT) uses spatially variable factors such as land use, soil condition, and distance to streams to characterize pathogen sources across a watershed. The results support development of Total Maximum Daily Loads (TMDLs) where bacterial contamination is of concern. SELECT calculates potential E. coli loads by distributing the contributing source populations across suitable habitats, applying a fecal production rate, and then aggregating the potential load to the subwatersheds. SELECT provides a Graphical User Interface (GUI), developed in Visual Basic for Applications (VBA) within ArcGIS 9.X, where project parameters can be adjusted for various pollutant loading scenarios. A new approach for characterizing E. coli loads resulting from on-site wastewater treatment systems (OWTSs) was incorporated into the SELECT methodology. The pollutant connectivity factor (PCF) module was created to identify areas potentially contributing E. coli loads to waterbodies during runoff events by weighting the influence of potential loading, runoff potential, and travel distance. Simulation results indicate livestock and wildlife are potentially contributing large amounts of E. coli in the Lake Granbury Watershed in areas where these contributing sources are not currently monitored for E. coli. The bacterial water quality violations near Lake Granbury are most likely the result of malfunctioning OWTSs and pet waste in the runoff. The automated SELECT was verified by characterizing the potential E. coli loading in the Plum Creek Watershed and comparing to results from a prior study (Teague, 2007). The E. coli potential load for the watershed was lower than the previous study due to major differences in assumptions. Comparing the average ranked PCF estimated by physical properties of the watershed with the statistical clustering of watershed characteristics provided similar groupings. SELECT supports the need to evaluate each contributing source separately to effectively allocate site specific best management practices (BMPs). This approach can be used as a screening step for determining areas where detailed investigation is merited. SELECT in conjunction with PCF and clustering analysis can assist decision makers develop Watershed Protection Plans (WPPs) and determine TMDLs.

Riebschleager, Kendra Jean

2008-08-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

Automated fuel pin loading system  

DOE Patents (OSTI)

An automated loading system for nuclear reactor fuel elements utilizes a gravity feed conveyor which permits individual fuel pins to roll along a constrained path perpendicular to their respective lengths. The individual lengths of fuel cladding are directed onto movable transports, where they are aligned coaxially with the axes of associated handling equipment at appropriate production stations. Each fuel pin can be reciprocated axially and/or rotated about its axis as required during handling steps. The fuel pins are inserted as a batch prior to welding of end caps by one of two disclosed welding systems.

Christiansen, David W. (Kennewick, WA); Brown, William F. (West Richland, WA); Steffen, Jim M. (Richland, WA)

1985-01-01T23:59:59.000Z

362

High loading uranium fuel plate  

DOE Patents (OSTI)

Two embodiments of a high uranium fuel plate are disclosed which contain a meat comprising structured uranium compound confined between a pair of diffusion bonded ductile metal cladding plates uniformly covering the meat, the meat having a uniform high fuel loading comprising a content of uranium compound greater than about 45 Vol. % at a porosity not greater than about 10 Vol. %. In a first embodiment, the meat is a plurality of parallel wires of uranium compound. In a second embodiment, the meat is a dispersion compact containing uranium compound. The fuel plates are fabricated by a hot isostatic pressing process.

Wiencek, Thomas C. (Bolingbrook, IL); Domagala, Robert F. (Indian Head Park, IL); Thresh, Henry R. (Palos Heights, IL)

1990-01-01T23:59:59.000Z

363

Interruptible load control for Taiwan Power Company  

SciTech Connect

Load management is the planning and implementation of those utility activities designed to influence customer use of electricity in ways that will produce desired changes in the utility's load shape. Interruptible load program is an option of load management which provides incentive rate to customers to interrupt or reduce the power demand during the system peak period or emergency condition. Therefore, how to design a proper incentive rate is the most important issue in implementing this program. This paper describes three alternatives designed for the interruptible load program, one of which was activated by Taiwan Power Company (Taipower) and some preliminary results were obtained. The effect of the interruptible load to the system peak demand reduction and the change of daily load curve for large industrial customers were analyzed. This paper estimates the avoided cost and design more appropriate incentive rate structure for interruptible load program.

Chen, C.S.; Leu, J.T. (Dept. of Electrical Engineering, National Sun Yat-Sen Univ., Kaohsiung (TW))

1990-05-01T23:59:59.000Z

364

Loads on drillpipe during jarring operations  

Science Conference Proceedings (OSTI)

Jarring implies heavy loads on the drillstring. The highest load on the drillpipe before jarring is at the rig floor. This paper discusses loads on drillpipe before, under, and after jarring. The authors show that for most situations, the shock wave from the jar impact does not imply additional load on the drillpipe compared with static load. The theoretical results are confirmed by measurements of a jarring operation with stuck point at [approx] 1,200 m measured depth. Loads on the drillpipe can be a limited factor in jarring operations because fear of possible additional loads from jarring dynamics may restrict the trip force (overpull) on the jar. The main conclusion is that dynamic jar forces do not give additional loads on drillpipe. This information can be used to set an optimal trip force on the jar.

Aarrestad, T.V.; Kyllingstad, A.

1994-12-01T23:59:59.000Z

365

1994 Pacific Northwest Loads and Resources Study.  

Science Conference Proceedings (OSTI)

The 1994 Pacific Northwest Loads and Resources Study presented herein establishes a picture of how the agency is positioned today in its loads and resources balance. It is a snapshot of expected resource operation, contractual obligations, and rights. This study does not attempt to present or analyze future conservation or generation resource scenarios. What it does provide are base case assumptions from which scenarios encompassing a wide range of uncertainties about BPA`s future may be evaluated. The Loads and Resources Study is presented in two documents: (1) this summary of Federal system and Pacific Northwest region loads and resources and (2) a technical appendix detailing the loads and resources for each major Pacific Northwest generating utility. This analysis updates the 1993 Pacific Northwest Loads and Resources Study, published in December 1993. In this loads and resources study, resource availability is compared with a range of forecasted electricity consumption. The Federal system and regional analyses for medium load forecast are presented.

United States. Bonneville Power Administration.

1994-12-01T23:59:59.000Z

366

Plug Load Behavioral Change Demonstration Project  

SciTech Connect

This report documents the methods and results of a plug load study of the Environmental Protection Agency's Region 8 Headquarters in Denver, Colorado, conducted by the National Renewable Energy Laboratory. The study quantified the effect of mechanical and behavioral change approaches on plug load energy reduction and identified effective ways to reduce plug load energy. Load reduction approaches included automated energy management systems and behavioral change strategies.

Metzger, I.; Kandt, A.; VanGeet, O.

2011-08-01T23:59:59.000Z

367

Spinning reserve from hotel load response  

SciTech Connect

Even though preliminary tests were not conducted during times of highest system or hotel loading during the summer, they showed that hotel load can be curtailed by 22 to 37 percent depending on the outdoor temperature and time of day. Full response occurred in 12 to 60 seconds from when the system operator's command to shed load was issued and the load drop was very rapid. (author)

Kirby, Brendan; Kueck, John; Laughner, Theo; Morris, Keith

2008-12-15T23:59:59.000Z

368

Load Shape Library Version 1.0  

Science Conference Proceedings (OSTI)

The downloadable report details EPRI's efforts to develop a framework of a load database and web-accessible repository of end-use and whole-premise data for application to energy efficiency assessments. The tool provides access to the best available end-use load data and whole-premise data by sector, region and building type. Improved end-use load research data will benefit load forecasters, system planners, energy efficiency program managers and rate design analysts by facilitating integration ...

2013-02-12T23:59:59.000Z

369

Glossary of terms related to load management  

Science Conference Proceedings (OSTI)

Part I of the Glossary of Terms related to Load Management has been prepared by the Terminology Task Force of the Load Management Subcommittee. The glossary contains many definitions of terms used by the electric utility industry concerning the subject of Load Management. The terms are listed in alphabetical order and cross-referenced where necessary.

Gellings, C.W.

1985-09-01T23:59:59.000Z

370

Properly Evaluating load-following products  

SciTech Connect

The authors briefly survey the jurisdictions where load-following products have been successfully used, examine the characteristics of the load-following products, and explain the shortcomings and inaccurate conclusions of previous analyses. A more thorough analysis reveals that the load-following products fulfill the public policy objectives for which they have been designed and do not adversely impact wholesale electricity markets.

Cavicchi, Joseph; Lemon, Andrew

2009-01-15T23:59:59.000Z

371

Adaptive load sharing for network processors  

Science Conference Proceedings (OSTI)

A novel scheme for processing packets in a router is presented that provides load sharing among multiple network processors distributed within the router. It is complemented by a feedback control mechanism designed to prevent processor overload. Incoming ... Keywords: computer networks, feedback control, load balancing, load sharing, packet processing, router architecture

Lukas Kencl; Jean-Yves Le Boudec

2008-04-01T23:59:59.000Z

372

Efficient real-time divisible load scheduling  

Science Conference Proceedings (OSTI)

Providing QoS and performance guarantees to arbitrarily divisible loads has become a significant problem for many cluster-based research computing facilities. While progress is being made in scheduling arbitrarily divisible loads, current approaches ... Keywords: Arbitrarily divisible loads, Cluster computing, Real-time computing, Scheduling efficiency

Anwar Mamat; Ying Lu; Jitender Deogun; Steve Goddard

2012-12-01T23:59:59.000Z

373

Domestic load scheduling using genetic algorithms  

Science Conference Proceedings (OSTI)

An approach using a genetic algorithm to optimize the scheduling of domestic electric loads, according to technical and user-defined constraints and input signals, is presented and illustrative results are shown. The aim is minimizing the end-user's ... Keywords: automated energy management, domestic load scheduling, electric loads, genetic algorithms

Ana Soares; Állvaro Gomes; Carlos Henggeler Antunes; Hugo Cardoso

2013-04-01T23:59:59.000Z

374

Neural-wavelet Methodology for Load Forecasting  

Science Conference Proceedings (OSTI)

Intelligent demand-side management represents a future trend of power system regulation. A key issue in intelligent demand-side management is accurate prediction of load within a local area grid (LAG), which is defined as a set of customers with an appropriate ... Keywords: load forecasting, load identification, neural-wavelet

Rong Gao; Lefteri H. Tsoukalas

2001-05-01T23:59:59.000Z

375

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

376

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

377

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

378

Load Scheduling with Profile Information  

E-Print Network (OSTI)

Abstract Within the past five years, many manufactureshave added hardware performance counters to their microprocessors to generate profile data cheaply.Translating aggregate data such as basic block execution frequencies from the executable to the com-piler intermediate representation is fairly straightforward. In this paper, we show how to use Com-paq's DCPI tool to determine load latencies which are at a fine, instruction granularity and then usethem to provide fodder for improving instruction scheduling. We validate our heuristic for usingDCPI latency data to classify loads as hits and misses against simulation numbers, demonstratingthat we can gather correct latencies cheaply at runtime. We map our classification into the Multiflowcompiler's intermediate representation, and use a locality sensitive Balanced scheduling algorithm. Ourexperiments illustrate that our algorithm has the potential to improve run times by up to 10 % on a Com-paq Alpha when compared to Balanced scheduling, but that a variety of pitfalls make consistent im-provements difficult to attain. 1 Introduction In this paper, we explore how to use hardware per-formance counters to produce fine grain latency information to improve compiler scheduling. We usethis information to hide latencies with any avail\\Lambda The authors

unknown authors

1999-01-01T23:59:59.000Z

379

FY 93 Thermal Loading Systems Study Final Report  

Science Conference Proceedings (OSTI)

The objective of the Mined Geologic Disposal System (MGDS) Thermal Loading Systems Study being conducted by the is to identify a thermal strategy that will meet the performance requirements for waste isolation and will be safe and licensable. Specifically, both postclosure and preclosure performance standards must be met by the thermal loading strategy ultimately selected. In addition cost and schedule constraints must be considered. The Systems Engineering approach requires structured, detailed analyses that will ultimately provide the technical basis for the development, integration, and evaluation of the overall system, not just a subelement of that system. It is also necessary that the systems study construct options from within the range that are allowed within the current legislative and programmatic framework. For example the total amount of fuel that can legally be emplaced is no more than 70,000 metric tons of uranium (MTU) which is composed of 63,000 MTU spent fuel and 7,000 MTU of defense high level waste. It is the intent of this study to begin the structured development of the basis for a thermal loading decision. However, it is recognized that to be able to make a final decision on thermal loading will require underground data on the effects of heating as well as a suite of ''validated'' models. It will be some time before these data and models are available to the program. Developing a final, thermal loading decision will, therefore, be an iterative process. In the interim, the objective of the thermal loading systems study has been to utilize the information available to assess the impact of thermal loading. Where technical justification exists, recommendations to narrow the range of thermal loading options can be made. Additionally, recommendations as to the type of testing and accuracy of the testing needed to establish the requisite information will be made. A constraint on the ability of the study to select an option stems from the lack of primary hard data, uncertainties in derived data, unsubstantiated models, and the inability to fully consider simultaneously coupled processes. As such, the study must rely on idealized models and available data to compare the thermal loading options. This report presents the findings of the FY 1993 MGDS Thermal Loading Systems Study. The objectives of the study were to: (1) if justified, place bounds on the thermal loading which would establish the loading that is ''too hot''; (2) ''grade'' or evaluate the performance as a function of thermal loading of the potential repository to contain high level spent nuclear fuel against performance criteria; (3) evaluate the performance of the various options with respect to cost, safety, and operability; and (4) recommend the additional types of tests and/or analyses to be conducted to provide the necessary information for a thermal loading selection.

S.F. Saterlie

1994-08-29T23:59:59.000Z

380

ELECTRICAL LOAD ANTICIPATOR AND RECORDER  

DOE Patents (OSTI)

A system is described in which an indication of the prevailing energy consumption in an electrical power metering system and a projected power demand for one demand in terval is provided at selected increments of time within the demand interval. Each watt-hour meter in the system is provided with an impulse generator that generates two impulses for each revolution of the meter disc. In each demand interval, for example, one half-hour, of the metering system, the total impulses received from all of the meters are continuously totaled for each 5-minute interval and multiplied by a number from 6 to 1 depending upon which 5- minute interval the impulses were received. This value is added to the total pulses received in the intervals preceding the current 5-minute interval within the half-hour demand interval tc thereby provide an indication of the projected power demand every 5 minutes in the demand interval.

Werme, J.E.

1961-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

1993 Pacific Northwest Loads and Resources Study.  

SciTech Connect

The Loads and Resources Study is presented in three documents: (1) this summary of Federal system and Pacific Northwest region loads and resources; (2) a technical appendix detailing forecasted Pacific Northwest economic trends and loads, and (3) a technical appendix detailing the loads and resources for each major Pacific Northwest generating utility. In this loads and resources study, resource availability is compared with a range of forecasted electricity consumption. The forecasted future electricity demands -- firm loads -- are subtracted from the projected capability of existing and {open_quotes}contracted for{close_quotes} resources to determine whether Bonneville Power Administration (BPA) and the region will be surplus or deficit. If resources are greater than loads in any particular year or month, there is a surplus of energy and/or capacity, which BPA can sell to increase revenues. Conversely, if firm loads exceed available resources, there is a deficit of energy and/or capacity, and additional conservation, contract purchases, or generating resources will be needed to meet load growth. The Pacific Northwest Loads and Resources Study analyzes the Pacific Northwest`s projected loads and available generating resources in two parts: (1) the loads and resources of the Federal system, for which BPA is the marketing agency; and (2) the larger Pacific Northwest regional power system, which includes loads and resource in addition to the Federal system. The loads and resources analysis in this study simulates the operation of the power system under the Pacific Northwest Coordination Agreement (PNCA) produced by the Pacific Northwest Coordinating Group. This study presents the Federal system and regional analyses for five load forecasts: high, medium-high, medium, medium-low, and low. This analysis projects the yearly average energy consumption and resource availability for Operating Years (OY) 1994--95 through 2003--04.

United States. Bonneville Power Administration.

1993-12-01T23:59:59.000Z

382

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:

383

High payload six-axis load sensor  

DOE Patents (OSTI)

A repairable high-payload six-axis load sensor includes a table, a base, and at least three shear-pin load transducers removably mounted between the table and the base. Removable mounting permits easy replacement of damaged shear pins. Preferably, the shear-pin load transducers are responsive to shear forces imparted along the two axes perpendicular to an axis of minimum sensitivity characteristic of the transducer. Responsive to an applied shear force, each shear-pin load transducer can produce an electrical signal proportional to the reaction force. The load sensor can further include a structure for receiving the proportional electrical signals and computing the applied load corresponding to the proportional electrical signals. The computed load can be expressed in terms of a three-dimensional XYZ Cartesian coordinate system.

Jansen, John F. (Knoxville, TN); Lind, Randall F. (Knoxville, TN)

2003-01-01T23:59:59.000Z

384

Analysis Methodology for Industrial Load Profiles  

E-Print Network (OSTI)

A methodology is provided for evaluating the impact of various demand-side management (DSM) options on industrial customers. The basic approach uses customer metered load profile data as a basis for the customer load shape. DSM technologies are represented as load shapes and are used as a basis for altering the customers existing measured load shape. The impact of load shape changes on the customer is evaluated in terms of a change in the electric bill by using a software analytical tool called LOADEXPERT™. The software calculates the customer's bill for a particular rate structure and a given load shape. The output data from LOADEXPERT™ are used to calculate the rate of return on the DSM technology investment. Other uses of load profile data are provided.

Reddoch, T. W.

1991-06-01T23:59:59.000Z

385

A Novel Approach to Determining Motor Load  

E-Print Network (OSTI)

Properly sized electric motors are essential if industrial plant efficiency is to be optimized and energy costs minimized. Because of the difficulty in making power measurements on three phase motors, loading is rarely, if ever, checked. A simple indication of motor load can be achieved by measuring operating speed because speed and load are almost linearly related. The decrease in motor speed from no load conditions, referred to as slip, can be determined with a non-contact, optical tachometer. Field measurements of motor slip were conducted at a textile plant to quantify motor load conditions. To verify the relationship between operating speed and load, measurements of motor power consumption on a representative number of motors were also conducted. The results of the motor survey, including number of motors, size, and load, are summarized in this paper along with an estimate of the savings from replacing oversized motors.

Brown, M.

1992-04-01T23:59:59.000Z

386

Low cutter load raise head  

SciTech Connect

A raise head having a multiplicity of cutters for enlarging a pilot hole into a larger diameter hole by disintegrating the earth formations that surround the pilot hole is provided that will require lower cutter loads to penetrate the formations being bored by directing the rock fracture planes toward the pilot hole forcing the rock to yield with less input energy. The cutters are positioned on the raise head to provide an earth formation contact profile with a major portion of said earth formation contact profile extending outward and upward from said pilot hole. The included angle between the major portion of the earth formation contact profile and the axis of the pilot hole is less than 90/sup 0/.

Saxman, W.C.

1981-03-31T23:59:59.000Z

387

Kinesin's backsteps under mechanical load  

E-Print Network (OSTI)

Kinesins move processively toward the plus end of microtubules by hydrolyzing ATP for each step. From an enzymatic perspective, the mechanism of mechanical motion coupled to the nucleotide chemistry is often well explained using a single-loop cyclic reaction. However, several difficulties arise in interpreting kinesin's backstepping within this framework, especially when external forces oppose the motion of kinesin. We review evidence, such as an ATP-independent stall force and a slower cycle time for backsteps, that has emerged to challenge the idea that kinesin backstepping is due to ATP synthesis, i.e., the reverse cycle of kinesin's forward-stepping chemomechanics. Supplementing the conventional single-loop chemomechanics with routes for ATP-hydrolyzing backward steps and nucleotide-free steps, especially under load, gives a better physical interpretation of the experimental data on backsteps.

Changbong Hyeon; Stefan Klumpp; José N. Onuchic

2009-04-18T23:59:59.000Z

388

Estimation of heat load in waste tanks using average vapor space temperatures  

SciTech Connect

This report describes a method for estimating the total heat load in a high-level waste tank with passive ventilation. This method relates the total heat load in the tank to the vapor space temperature and the depth of waste in the tank. Q{sub total} = C{sub f} (T{sub vapor space {minus}} T{sub air}) where: C{sub f} = Conversion factor = (R{sub o}k{sub soil}{sup *}area)/(z{sub tank} {minus} z{sub surface}); R{sub o} = Ratio of total heat load to heat out the top of the tank (function of waste height); Area = cross sectional area of the tank; k{sub soil} = thermal conductivity of soil; (z{sub tank} {minus} z{sub surface}) = effective depth of soil covering the top of tank; and (T{sub vapor space} {minus} T{sub air}) = mean temperature difference between vapor space and the ambient air at the surface. Three terms -- depth, area and ratio -- can be developed from geometrical considerations. The temperature difference is measured for each individual tank. The remaining term, the thermal conductivity, is estimated from the time-dependent component of the temperature signals coming from the periodic oscillations in the vapor space temperatures. Finally, using this equation, the total heat load for each of the ferrocyanide Watch List tanks is estimated. This provides a consistent way to rank ferrocyanide tanks according to heat load.

Crowe, R.D.; Kummerer, M.; Postma, A.K.

1993-12-01T23:59:59.000Z

389

Total energy cycle emissions and energy use of electric vehicles  

DOE Green Energy (OSTI)

The purpose of this project is to provide estimates of changes in life cycle energy use and emissions that would occur with the introduction of EVs. The topics covered include a synopsis of the methodology used in the project, stages in the EV and conventional vehicle energy cycles, characterization of EVs by type and driving cycle, load analysis and capacity of the electric utility, analysis of the materials used for vehicle and battery, description of the total energy cycle analysis model, energy cycle primary energy resource consumption, greenhouse gas emissions, energy cycle emissions, and conclusions.

Singh, M.

1997-12-31T23:59:59.000Z

390

Load Reduction, Demand Response and Energy Efficient Technologies and Strategies  

SciTech Connect

The Department of Energy’s (DOE’s) Pacific Northwest National Laboratory (PNNL) was tasked by the DOE Office of Electricity (OE) to recommend load reduction and grid integration strategies, and identify additional demand response (energy efficiency/conservation opportunities) and strategies at the Forest City Housing (FCH) redevelopment at Pearl Harbor and the Marine Corps Base Hawaii (MCBH) at Kaneohe Bay. The goal was to provide FCH staff a path forward to manage their electricity load and thus reduce costs at these FCH family housing developments. The initial focus of the work was at the MCBH given the MCBH has a demand-ratchet tariff, relatively high demand (~18 MW) and a commensurate high blended electricity rate (26 cents/kWh). The peak demand for MCBH occurs in July-August. And, on average, family housing at MCBH contributes ~36% to the MCBH total energy consumption. Thus, a significant load reduction in family housing can have a considerable impact on the overall site load. Based on a site visit to the MCBH and meetings with MCBH installation, FCH, and Hawaiian Electric Company (HECO) staff, recommended actions (including a "smart grid" recommendation) that can be undertaken by FCH to manage and reduce peak-demand in family housing are made. Recommendations are also made to reduce overall energy consumption, and thus reduce demand in FCH family housing.

Boyd, Paul A.; Parker, Graham B.; Hatley, Darrel D.

2008-11-19T23:59:59.000Z

391

A Dual Supply Buck Converter with Improved Light Load Efficiency  

E-Print Network (OSTI)

Power consumption is the primary concern in battery-operated portable applications. Buck converters have gained popularity in powering portable devices due to their compact size, good current delivery capability and high efficiency. However, portable devices are operating under light load condition for the most of the time. Conventional buck converters suffer from low light-load efficiency which severely limits battery lifetime. In this project, a novel technique for buck converter is proposed to reduce the switching loss by reducing the effective input supply voltage at light load. This is achieved by switching between two different input voltages (3.3V and 1.65V) depending on the output current value. Experimental results show that this technique improves the efficiency at light loads by 18.07%. The buck voltage possesses an output voltage of 0.9V and provides a maximum output current of 400mA. The buck converter operates at a switching frequency of 1MHz. The prototype was fabricated using 0.18µm CMOS technology, and occupies a total active area of 0.6039mm^2.

Chen, Hui

2013-05-01T23:59:59.000Z

392

Optimizing Process Loads in Industrial Cogeneration Energy Systems  

E-Print Network (OSTI)

Optimum dispatch of energy supply systems can result in large savings in industrial facilities. Identifying the configuration of available equipment, and its loading to minimize total energy consumption to satisfy given load demands, has very high payback potential. This paper discusses an approach to determine integrated energy supply and end use optimum equipment dispatch to simultaneously satisfy given power, process steam and additional "end energy" product needs such as compressed fluids, chemical unit production, etc. Techniques applied to power generation and industrial cogeneration are extended to solving this trigeneration problem where the optimum dispatch of the final load devices (i.e. compressors, fans, pumps, etc.) are an integral part of the total energy system optimization. An example industrial trigeneration system is discussed to illustrate the application and procedures. The methods of considering alternate energy sources, for end use optimization with export power and steam generation will be illustrated. The savings associated with operations optimization readily justify the hardware and software costs required for implementation of Optimization Energy Management Systems (OEMS). An OEMS capability for this application is briefly discussed.

Ahner, D. J.; Babson, P. E.

1995-04-01T23:59:59.000Z

393

Wind loads on flat plate photovoltaic array fields. Phase II. Final report  

SciTech Connect

This report describes a theoretical study of the aerodynamic forces resulting from winds acting on flat plate photovoltaic arrays. Local pressure distributions and total aerodynamic forces on the arrays are shown. Design loads are presented to cover the conditions of array angles relative to the ground from 20/sup 0/ to 60/sup 0/, variable array spacings, a ground clearance gap up to 1.2 m (4 ft) and array slant heights of 2.4 m (8 ft) and 4.8 m (16 ft). Several means of alleviating the wind loads on the arrays are detailed. The expected reduction of the steady state wind velocity with the use of fences as a load alleviation device are indicated to be in excess of a factor of three for some conditions. This yields steady state wind load reductions as much as a factor of ten compared to the load incurred if no fence is used to protect the arrays. This steady state wind load reduction is offset by the increase in turbulence due to the fence but still an overall load reduction of 2.5 can be realized. Other load alleviation devices suggested are the installation of air gaps in the arrays, blocking the flow under the arrays and rounding the edges of the array. Included is an outline of a wind tunnel test plan to supplement the theoretical study and to evaluate the load alleviation devices.

Miller, R.; Zimmerman, D.

1979-09-01T23:59:59.000Z

394

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

395

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

396

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

397

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...

398

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...

399

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...

400

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...

Note: This page contains sample records for the topic "total load served" 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 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...

402

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...

403

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...

404

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...

405

Investigation of residential central air conditioning load shapes in NEMS  

E-Print Network (OSTI)

of Residential Central Air Conditioning Load Shapes in NEMSof Residential Central Air Conditioning Load Shapes in NEMSof Residential Central Air Conditioning Load Shapes in NEMS

Hamachi LaCommare, Kristina; Marnay, Chris; Gumerman, Etan; Chan, Peter; Rosenquist, Greg; Osborn, Julie

2002-01-01T23:59:59.000Z

406

37th Telecommunications Policy Research Conference, Sept. 2009 The Business Case of a Nationwide Wireless Network that Serves both Public  

E-Print Network (OSTI)

of public safety. In 2007, the U.S. Federal Communications Commission tried to establish a public-private again, and if so, how. Thus, this paper considers the viability of a public- private partnership penetration each year. We apply this model to both a public-private partnership that serves commercial

Peha, Jon M.

407

Penn State Faculty Handbook The Faculty Handbook is designed to serve as an orientation and reference guide for all faculty,  

E-Print Network (OSTI)

Penn State Faculty Handbook Welcome The Faculty Handbook is designed to serve as an orientation to locate and use a wide range of University resources. The Handbook contains information about the overall of the University #12;2 About the Faculty Handbook This handbook is intended for use as a general reference rather

Maroncelli, Mark

408

Relationships between the Irrigation-Pumping Electrical Loads and the Local Climate in Climate Division 9, Idaho  

Science Conference Proceedings (OSTI)

The electrical load from irrigation pumps is an important part of the overall electricity demand in many agricultural areas of the U.S. west. The date the pumps turn on and the total electrical load they present over the summer varies from year ...

Eric J. Alfaro; David W. Pierce; Anne C. Steinemann; Alexander Gershunov

2005-12-01T23:59:59.000Z

409

NREL: Vehicles and Fuels Research - Vehicle Ancillary Loads Reduction  

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

Research Research Search More Search Options Site Map Photo of Advanced Automotive Manikin Reducing fuel consumption by air conditioning systems is the focus of Vehicle Ancillary Loads Reduction (VALR) activities at NREL. About 7 billion gallons of fuel-about 5.5% of total national light-duty vehicle fuel use-are used annually just to cool light-duty vehicles in the United States. That's why our VALR team works with industry to help increase fuel economy and reduce tailpipe emissions by reducing the ancillary loads requirements in vehicles while maintaining the thermal comfort of the passengers. Approaches include improved cabin insulation, advanced window systems, advanced cooling and venting systems, and heat generated cooling. Another focus of the VALR project is ADAM, the ADvanced Automotive Manikin

410

On Load Balancing in a Dense Wireless  

E-Print Network (OSTI)

We study the load balancing problem in a dense wireless multihop network, where a typical path consists of large number of hops, i.e., the spatial scales of a typical distance between source and destination, and mean distance between the neighbouring nodes are strongly separated. In this limit, we present a general framework for analysing the traffic load resulting from a given set of paths and traffic demands. We formulate the load balancing problem as a minmax problem and give two lower bounds for the achievable minimal maximum traffic load. The framework is illustrated by an example of uniformly distributed traffic demands in a unit disk with a few families of paths given in advance. With these paths we are able to decrease the maximum traffic load by factor of 33 40% depending on the assumptions. The obtained traffic load level also comes quite near the tightest lower bound.

Multihop Network Esa; Esa Hyytiä

2006-01-01T23:59:59.000Z

411

Profile Guided Load Marking for Memory Renaming  

E-Print Network (OSTI)

Memory operations remain a significant bottleneck in dynamically scheduled pipelined processors, due in part to the inability to statically determine the existence of memory address dependencies. Hardware memory renaming techniques have been proposed which predict which stores a load might be dependent upon. These prediction techniques can be used to speculatively forward a value from a predicted store dependency to a load through a value prediction table; however, these techniques require large and time-consuming hardware tables. In this paper we propose a software-guided approach for identifying dependencies between store and load instructions and the Load Marking (LM) architecture to communicate these dependencies to the hardware. Compiler analysis and profiles are used to find important store/load relationships, and these relationships are identified during execution via hints or an n-bit tag. For those loads that are not marked for renaming, we then use additional profiling inform...

Glenn Reinman; Brad Calder; Dean Tullsen; Gary Tyson; Todd Austin

1998-01-01T23:59:59.000Z

412

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

413

Assessment of Industrial-Sector Load Shapes  

Science Conference Proceedings (OSTI)

The load shapes of industrial-sector customers are becoming increasingly important for utility forecasting, marketing, and demand-side management planning and evaluation activities. This report analyzes load shapes for various industry segments and investigates the transfer of these load shapes across service territories. This report is available only to funders of Program 101A or 101.001. Funders may download this report at http://my.primen.com/Applications/DE/Community/index.asp .

1993-02-18T23:59:59.000Z

414

Trends in electric utility load duration curves  

SciTech Connect

This report documents the development and analysis of annual and quarterly load duration curves for each of the 10 Federal regions. The report describes analyses performed to test for changes in load duration curve shapes over time. These analyses are intended to aid the electric utility analyses and modeling activities of the Energy Information Administration (EIA) by expanding the understanding of current and expected load duration curve shapes. 7 figs., 13 tabs.

1984-12-01T23:59:59.000Z

415

Loads, capacity, and failure rate modeling  

SciTech Connect

Both failure rate and load capacity (stress-strength) interferenece methodologies are employed in the reliability analysis at nuclear facilities. Both of the above have been utilized in a heuristic failure rate model in terms of load capacity inference. Analytical solutions are used to demonstrate that infant mortality and random aging failures may be expressed implicity in terms of capacity variability, load variability, and capacity deterioration, and that mode interactions play a role in the formation of the bathtub curve for failure rates.

Lewis, E.E.; Chen, Hsin-Chieh

1994-12-31T23:59:59.000Z

416

1991 Pacific Northwest Loads and Resources Study.  

SciTech Connect

This study establishes the Bonneville Power Administration's (BPA) planning basis for supplying electricity to BPA customers. The Loads and Resources Study is presented in three documents: (1) this summary of federal system and Pacific Northwest region loads and resources; (2) a technical appendix detailing forecasted Pacific Northwest economic trends and loads, and (3) a technical appendix detailing the loads and resources for each major Pacific Northwest generating utility. This analysis updates our 1990 study. BPS's long-range planning incorporates resource availability with a range of forecasted electrical consumption. The forecasted future electrical demands-firm loads--are subtracted from the projected capability of existing resources to determine whether BPA and the region will be surplus or deficit. If resources are greater than loads in any particular year or month, there is a surplus of energy and/or capacity, which BPA can sell to increase revenues. Conversely, if firm loads exceed available resources, there is a deficit of energy and/or capacity, then additional conservation, contract purchases, or generating resources will be needed to meet load growth. This study analyzes the Pacific Northwest's projected loads and available generating resources in two parts: (1) the loads and resources of the federal system, for which BPA is the marketing agency; and (2) the larger Pacific Northwest regional profile, which includes loads and resources in addition to the federal system. This study presents the federal system and regional analyses for five load forecasts: high, medium-high, medium, medium-low, and low. This analysis projects the yearly average energy consumption and resource availability for 1992- 2012.

United States. Bonneville Power Administration.

1991-12-01T23:59:59.000Z

417

Reservoir compaction loads on casings and liners  

Science Conference Proceedings (OSTI)

Pressure drawdown due to production from a reservoir causes compaction of the reservoir formation which induces axial and radial loads on the wellbore. Reservoir compaction loads increase during the production life of a well, and are greater for deviated wells. Presented here are casing and liner loads at initial and final pressure drawdowns for a particular reservoir and at well deviation angles of 0 to 45 degrees.

Wooley, G.R.; Prachner, W.

1984-09-01T23:59:59.000Z

418

NORMAL LOAD BEARING BY SITE SPECIFIC CANISTER  

Science Conference Proceedings (OSTI)

The overall purpose of this calculation is to perform a preliminary analysis of the Site Specific Canister/Basket, subject to static gravity loads that include the self weight of the Canister Shell, the Basket, the Spent Nuclear Fuel, the Shield Plug and the related hardware, so that the loads are approximately known for sizing purposes. Based on these loads the stress levels in various components of the Site Specific Canister/Basket are evaluated.

NA

2005-03-23T23:59:59.000Z

419

Measured Peak Equipment Loads in Laboratories  

SciTech Connect

This technical bulletin documents measured peak equipment load data from 39 laboratory spaces in nine buildings across five institutions. The purpose of these measurements was to obtain data on the actual peak loads in laboratories, which can be used to rightsize the design of HVAC systems in new laboratories. While any given laboratory may have unique loads and other design considerations, these results may be used as a 'sanity check' for design assumptions.

Mathew, Paul A.

2007-09-12T23:59:59.000Z

420

PLUTONIUM LOADING CAPACITY OF REILLEX HPQ ANION EXCHANGE COLUMN - AFS-2 PLUTONIUM FLOWSHEET FOR MOX  

SciTech Connect

Radioactive plutonium (Pu) anion exchange column experiments using scaled HB-Line designs were performed to investigate the dependence of column loading performance on the feed composition in the H-Canyon dissolution process for plutonium oxide (PuO{sub 2}) product shipped to the Mixed Oxide (MOX) Fuel Fabrication Facility (MFFF). These loading experiments show that a representative feed solution containing {approx}5 g Pu/L can be loaded onto Reillex{trademark} HPQ resin from solutions containing 8 M total nitrate and 0.1 M KF provided that the F is complexed with Al to an [Al]/[F] molar ratio range of 1.5-2.0. Lower concentrations of total nitrate and [Al]/[F] molar ratios may still have acceptable performance but were not tested in this study. Loading and washing Pu losses should be relatively low (<1%) for resin loading of up to 60 g Pu/L. Loading above 60 g Pu/L resin is possible, but Pu wash losses will increase such that 10-20% of the additional Pu fed may not be retained by the resin as the resin loading approaches 80 g Pu/L resin.

Kyser, E.; King, W.; O'Rourke, P.

2012-07-26T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

Load relaxation studies of zircaloy-4  

SciTech Connect

The results of the present work have shown (1) the load relaxation data of Zircaloy-4 in the temperature range of 200/sup 0/C to 385/sup 0/C can be represented by the phenomenological model based on Hart's plastic equation of state; (2) the room temperature load relaxation data reflect the effects of deformation twinning; (3) at higher temperatures the load relaxation data suggest the contribution of grain boundary sliding; and (4) the effects of strain aging can be identified based on the load relaxation data.

Huang, F.H.; Sabol, G.P.; McDonald, S.G.; Li, C.Y.

1976-01-01T23:59:59.000Z

422

OpenEI Community - load profile  

Open Energy Info (EERE)

/0 en Commercial and /0 en Commercial and Residential Hourly Load Data Now Available on OpenEI! http://en.openei.org/community/blog/commercial-and-residential-hourly-load-data-now-available-openei <span class=Load data" src="http://en.openei.org/community/files/load_data_figure_small.jpg" style="width:527px; height:285px" title="" />Image source: NREL 

Files: 
application/zip icon

423

NREL: Vehicle Ancillary Loads Reduction - Laboratory Capabilities  

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

VALR Lab. NREL's Vehicle Ancillary Loads Reduction Laboratory houses ADAM, our advanced thermal manikin, as well as a passenger compartment climate simulator, testing equipment...

424

Building Energy Software Tools Directory: Load Express  

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

Create project files with Load Express in just 4 easy steps. Select a weather profile, enter simulation parameters, define the zonesrooms in the building and create air handler...

425

Effect of Nonproportional Loadings on Ductile Fracture  

Science Conference Proceedings (OSTI)

Abstract Scope, This study aims at understanding the effects of load path ... D-2: Forging of Magnesium Alloy by Impulsive Energy at Room Temperature.

426

Thermoplastic Flour Containing Eugenol-loaded Chitosan ...  

Science Conference Proceedings (OSTI)

Presentation Title, Thermoplastic Flour Containing Eugenol-loaded Chitosan ... Properties of AA 7075 Reinforced with Nanometric ZrO2 Obtained by Ball-milling.

427

Building Energy Software Tools Directory : HVAC Residential Load...  

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

HVAC Residential Load Calcs HD for the iPad Back to Tool HVAC Residential Load Calcs HD screenshot HVAC Residential Load Calcs HD screenshot HVAC Residential Load Calcs HD...

428

Methods for Analyzing Electric Load Shape and its Variability  

E-Print Network (OSTI)

15 Figure 12: Load profile by day of week, averaged over thebetween the average load profile and the profile of a givenfrom the average load profile. Figure 12: Load profile by

Price, Philip

2010-01-01T23:59:59.000Z

429

ELECTRICAL LOAD ANTICIPATOR AND RECORDER  

DOE Patents (OSTI)

A system is descrbied in which an indication of the prevailing energy consumption in an electrical power metering system and a projected Power demand for one demand interval is provided at selected increments of time withm the demand interval. Each watthour meter in the system is provided with an impulse generator that generates two impulses for each revolution of the meter disc. The total pulses received frorn all the meters are continuously totaled and are fed to a plurality of parallel connected gated counters. Each counter has its gate opened at different sub-time intervals during the demand interval. A multiplier is connected to each of the gated counters except the last one and each multiplier is provided with a different multiplier constant so as to provide an estimate of the power to be drawn over the entire demand interval at the end of each of the different sub-time intervals. Means are provided for recording the ontputs from the different circuits in synchronism with the actuation oi each gate circuit.

Russell, J.B.; Thomas, R.J.

1961-07-25T23:59:59.000Z

430

Spinning Reserve From Hotel Load Response: Initial Progress  

SciTech Connect

This project was motivated by the fundamental match between hotel space conditioning load response capability and power system contingency response needs. As power system costs rise and capacity is strained demand response can provide a significant system reliability benefit at a potentially attractive cost. At ORNL s suggestion, Digital Solutions Inc. adapted its hotel air conditioning control technology to supply power system spinning reserve. This energy saving technology is primarily designed to provide the hotel operator with the ability to control individual room temperature set-points based upon occupancy (25% to 50% energy savings based on an earlier study [Kirby and Ally, 2002]). DSI added instantaneous local load shedding capability in response to power system frequency and centrally dispatched load shedding capability in response to power system operator command. The 162 room Music Road Hotel in Pigeon Forge Tennessee agreed to host the spinning reserve test. The Tennessee Valley Authority supplied real-time metering equipment in the form of an internet connected Dranetz-BMI power quality meter and monitoring expertise to record total hotel load during both normal operations and test results. The Sevier County Electric System installed the metering. Preliminary testing showed that hotel load can be curtailed by 22% to 37% depending on the outdoor temperature and the time of day. These results are prior to implementing control over the common area air conditioning loads. Testing was also not at times of highest system or hotel loading. Full response occurred in 12 to 60 seconds from when the system operator s command to shed load was issued. The load drop was very rapid, essentially as fast as the 2 second metering could detect, with all units responding essentially simultaneously. Load restoration was ramped back in over several minutes. The restoration ramp can be adjusted to the power system needs. Frequency response testing was not completed. Initial testing showed that the units respond very quickly. Problems with local power quality generated false low frequency signals which required testing to be stopped. This should not be a problem in actual operation since the frequency trip points will be staggered to generate a droop curve which mimics generator governor response. The actual trip frequencies will also be low enough to avoid power quality problems. The actual trip frequencies are too low to generate test events with sufficient regularity to complete testing in a reasonable amount of time. Frequency response testing will resume once the local power quality problem is fully understood and reasonable test frequency settings can be determined. Overall the preliminary testing was extremely successful. The hotel response capability matches the power system reliability need, being faster than generation response and inherently available when the power system is under the most stress (times of high system and hotel load). Periodic testing is scheduled throughout the winter and spring to characterize hotel response capability under a full range of conditions. More extensive testing will resume when summer outdoor temperatures are again high enough to fully test hotel response.

Kueck, John D [ORNL; Kirby, Brendan J [ORNL

2008-11-01T23:59:59.000Z

431

Exploring the inherent benefits of RFID and automated self-serve checkouts in a B2C environment  

Science Conference Proceedings (OSTI)

Automated identification services such as RFID and self-serve checkouts that require many different technological components in order to successfully operate and be accepted in a B2C (Business-to-Customer) environment. In theory, ... Keywords: B2C, CRM, RFID, SCM, autoID, automatic identification, business information systems, business-to-, customer, customer relationship management, radio frequency identification, retail grocery, retailing, self service checkouts, self-checkout, strategy, supply chain management, technology adoption

Alan D. Smith

2005-07-01T23:59:59.000Z

432

The Promise of Load Balancing the Parameterization of Moist Convection Using a Model Data Load Index  

Science Conference Proceedings (OSTI)

The parameterization of physical processes in atmospheric general circulation models contributes to load imbalances among individual processors of message-passing distributed-multiprocessor systems. Load imbalances increase the overall time to ...

S. P. Muszala; D. A. Connors; J. J. Hack; G. Alaghband

2006-04-01T23:59:59.000Z

433

Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-  

E-Print Network (OSTI)

, and the Office of Electricity Delivery and Energy Reliability, Permitting, Siting and Analysis of the ULBNL-63728 Estimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non .............................................................................................................. 9 4. Baseline Profile (BLP) Models

434

Independent review of estimated load reductions for PJM's small customer load response pilot project  

E-Print Network (OSTI)

Abbreviations ADDF ALM AMR CSP DOE DR ELRP ETS EWH FERC HVa Curtailment Service Provider (CSP) at PJM’s request. LBNLof load control tests. The CSP collected hourly load data

Heffner, G.; Moezzi, M.; Goldman, C.

2004-01-01T23:59:59.000Z

435

Decentralized agent-based underfrequency load shedding  

Science Conference Proceedings (OSTI)

As part of the transition to a smart grid efforts are being made to decentralize control of electric power systems and modernize protection schemes that are currently in use. One specific application of distributed control is underfrequency load shedding ... Keywords: UFLS, Underfrequency load shedding, intelligent agents

Sara Mullen; Getiria Onsongo

2010-12-01T23:59:59.000Z

436

Design Ground Snow Loads for Ohio  

Science Conference Proceedings (OSTI)

The weight of snow with a mean recurrence interval of 50 years, called the design ground snow load, is used by engineers and planners to estimate the weight of snow that roofs must be designed to support National maps of ground snow load have ...

Thomas W. Schmidlin; Dennis J. Edgell; Molly A. Delaney

1992-06-01T23:59:59.000Z

437

Apparatus for loading a band saw blade  

DOE Patents (OSTI)

A band saw blade is loaded between pairs of guide wheels upon tensioning the blade by guiding the blade between pairs of spaced guide plates which define converging slots that converge toward the guide wheels. The approach is particularly useful in loading blades on underwater band saw machines used to cut radioactive materials. 2 figs.

Reeves, S.R.

1990-03-20T23:59:59.000Z

438

Protecting consumer privacy from electric load monitoring  

Science Conference Proceedings (OSTI)

The smart grid introduces concerns for the loss of consumer privacy; recently deployed smart meters retain and distribute highly accurate profiles of home energy use. These profiles can be mined by Non Intrusive Load Monitors (NILMs) to expose much of ... Keywords: load monitor, privacy, smart meter

Stephen McLaughlin; Patrick McDaniel; William Aiello

2011-10-01T23:59:59.000Z

439

Quantifying the effectiveness of load balance algorithms  

Science Conference Proceedings (OSTI)

Load balance is critical for performance in large parallel applications. An imbalance on today's fastest supercomputers can force hundreds of thousands of cores to idle, and on future exascale machines this cost will increase by over a factor of a thousand. ... Keywords: framework, load balance, modeling, performance, simulation

Olga Pearce; Todd Gamblin; Bronis R. de Supinski; Martin Schulz; Nancy M. Amato

2012-06-01T23:59:59.000Z

440

Household type load's effects on photovoltaic systems  

Science Conference Proceedings (OSTI)

The solar energy is one of the most important energy sources available because, besides the fact that it is not polluting the environment and it helps to the reduction of green house effect, it is free of charge and it can be easily converted to other ... Keywords: A.C. loads, D.C. loads, compact fluorescent lamp, photovoltaic system, power LED

Nazmi Ekren; Nevzat Onat; Safak Saglam

2008-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

Load Modeling Using a Measurement Based Approach  

Science Conference Proceedings (OSTI)

This report summarizes the work performed in the second phase of a multi-year collaborative load modeling research program that was initiated in 2004. The measurement based approach described in this report will help utilities to develop representative load models using suitable measurement data.

2007-12-17T23:59:59.000Z

442

Apparatus for loading a band saw blade  

DOE Patents (OSTI)

A band saw blade is loaded between pairs of guide wheels upon tensioning the blade by guiding the blade between pairs of spaced guide plates which define converging slots that converge toward the guide wheels. The approach is particularly useful in loading blades on underwater band saw machines used to cut radioactive materials.

Reeves, Steven R. (49 Williams Ave., West Valley, NY 14171)

1990-01-01T23:59:59.000Z

443

Plug Load Energy Analysis: The Role of Plug Loads in LEED Certification  

E-Print Network (OSTI)

benchmark and is bound to change as the proportion of plug load energy use grows in commercial buildings.

Fuertes, Gwen; Schiavon, Stefano

2013-01-01T23:59:59.000Z

444

Combustion Impacts of Flexible Operation: Low Load, Load Following, and Increased Staging Impact on Boiler Tubes  

Science Conference Proceedings (OSTI)

Over the past few years, coal-fired generating units have changed from stable base load operation to flexible operation, including periods of prolonged low-load operation. These changes in operation can have various adverse effects on all plant equipment, particularly in older units and may impact their ability to operate without tube failures due to elevated levels of fireside corrosion and circumferential cracking. This report discusses the combustion-related impacts of low-load, load-following, ...

2013-12-23T23:59:59.000Z

445

Combi Systems for Low Load homes  

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

text styles text styles Combi Systems for Low Load Homes Center for Energy and Environment, NorthernSTAR, Ben Schoenbauer * Low load homes are more common than ever. * Typical space heating and DHW equipment have capacities larger than necessary * A single heating plant could provide high efficiency heat at lower costs, increased durability and improved combustion safety Context Technical Approach * A condensing water heater and hydronic air handler will used to provide space and water heating loads in almost 300 weatherized homes. * System specifications, sizing, and installation optimization guidelines were all developed. * Contractor capability was developed in MN market, but may not be developed in all local. 4 Recommended Guidance * Determine peak load on system: - Space heating design load (ie 40,000 Btu/hr)

446

An Analysis of Load Balancing Technology - Comparing LSF with other Load Balancing Software Packages  

E-Print Network (OSTI)

This paper examines what the load balancing needs of organizations are today, the solution LSF (Load Sharing Facility) provides for them, and how other currently available load balancing products compare to LSF. Jean Suplick (suplick@cxsoft.convex.com) CXSOFT Richardson, Texas January 1994 Why clusters?

Jean Suplick

1994-01-01T23:59:59.000Z

447

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","NUMBER OF RESIDENTIAL AMR METERS","NUMBER OF COMMERCIAL AMR METERS","NUMBER OF INDUSTRIAL AMR METERS","NUMBER OF TRANSPORTATION AMR METERS","TOTAL NUMBER OF AMR METERS","NUMBER OF RESIDENTIAL AMI METERS","NUMBER OF COMMERCIAL AMI METERS","NUMBER OF INDUSTRIAL AMI METERS","NUMBER OF TRANSPORTATION AMI METERS","TOTAL NUMBER OF AMI METERS","RESIDENTIAL ENERGY SERVED THRU AMI METERS (MWh)","COMMERCIAL ENERGY SERVED THRU AMI METERS (MWh)","INDUSTRIAL ENERGY SERVED THRU AMI METERS (MWh)","TRANSPORTATION ENERGY SERVED THRU AMI METERS (MWh)","TOTAL ENERGY SERVED THRU AMI METERS (MWh)"  

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

3,1,"AK",213,"Alaska Electric Light&Power Co",10789,1063,76,0,11928,0,0,0,0,0,0,0,0,0,0 3,1,"AK",213,"Alaska Electric Light&Power Co",10789,1063,76,0,11928,0,0,0,0,0,0,0,0,0,0 2013,1,"AK",3522,"Chugach Electric Assn Inc",69377,8707,,,78084,,,,,0,,,,,0 2013,1,"AK",7353,"Golden Valley Elec Assn Inc",38017,6318,503,,44838,,,,,0,,,,,0 2013,1,"AK",10210,"Ketchikan Public Utilities",0,0,0,0,0,3437,350,0,0,3787,5208.03,789.27,0,0,5997.31 2013,1,"AK",10433,"Kodiak Electric Assn Inc",4585,1038,105,0,5728,,,,,0,,,,,0 2013,1,"AK",10451,"Kotzebue Electric Assn Inc",915,6,0,0,921,,,,,0,,,,,0 2013,1,"AK",11824,"Matanuska Electric Assn Inc",47829,3616,0,0,51445,0,0,0,0,0,0,0,0,0,0 2013,1,"AK",19558,"Homer Electric Assn Inc",25421,2737,,,28158,46,6,,,52,2.37,0.87,,,3.24

448

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","NUMBER OF RESIDENTIAL AMR METERS","NUMBER OF COMMERCIAL AMR METERS","NUMBER OF INDUSTRIAL AMR METERS","NUMBER OF TRANSPORTATION AMR METERS","TOTAL NUMBER OF AMR METERS","NUMBER OF RESIDENTIAL AMI METERS","NUMBER OF COMMERCIAL AMI METERS","NUMBER OF INDUSTRIAL AMI METERS","NUMBER OF TRANSPORTATION AMI METERS","TOTAL NUMBER OF AMI METERS","RESIDENTIAL ENERGY SERVED THRU AMI METERS (MWh)","COMMERCIAL ENERGY SERVED THRU AMI METERS (MWh)","INDUSTRIAL ENERGY SERVED THRU AMI METERS (MWh)","TRANSPORTATION ENERGY SERVED THRU AMI METERS (MWh)","TOTAL ENERGY SERVED THRU AMI METERS (MWh)"  

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

1,1,"AK",213,"Alaska Electric Light&Power Co",9111,782,58,0,9951,0,0,0,0,0,0,0,0,0,0 1,1,"AK",213,"Alaska Electric Light&Power Co",9111,782,58,0,9951,0,0,0,0,0,0,0,0,0,0 2011,1,"AK",1651,"Bethel Utilities Corp",0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 2011,1,"AK",10210,"Ketchikan Public Utilities",0,0,0,0,0,2974,264,2,0,3240,4461,786,114,0,5361 2011,1,"AK",10433,"Kodiak Electric Assn Inc",4574,976,101,0,5651,,,,,0,,,,,0 2011,1,"AK",11824,"Matanuska Electric Assn Inc",47365,3590,,,50955,,,,,0,,,,,0 2011,1,"AK",19558,"Homer Electric Assn Inc",24337,2482,0,0,26819,31,4,0,0,35,1.29,0.01,0,0,1.31 2011,1,"AL",195,"Alabama Power Co",,,,,,1227639,172463,5845,0,1405947,1548871.17,1047712.67,1805530.5,0,4402114.33

449

"YEAR","MONTH","STATE","UTILITY CODE","UTILITY NAME","NUMBER OF RESIDENTIAL AMR METERS","NUMBER OF COMMERCIAL AMR METERS","NUMBER OF INDUSTRIAL AMR METERS","NUMBER OF TRANSPORTATION AMR METERS","TOTAL NUMBER OF AMR METERS","NUMBER OF RESIDENTIAL AMI METERS","NUMBER OF COMMERCIAL AMI METERS","NUMBER OF INDUSTRIAL AMI METERS","NUMBER OF TRANSPORTATION AMI METERS","TOTAL NUMBER OF AMI METERS","RESIDENTIAL ENERGY SERVED THRU AMI METERS (MWh)","COMMERCIAL ENERGY SERVED THRU AMI METERS (MWh)","INDUSTRIAL ENERGY SERVED THRU AMI METERS (MWh)","TRANSPORTATION ENERGY SERVED THRU AMI METERS (MWh)","TOTAL ENERGY SERVED THRU AMI METERS (MWh)"  

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

2,1,"AK",213,"Alaska Electric Light&Power Co",10105,925,62,0,11092,0,0,0,0,0,0,0,0,0,0 2,1,"AK",213,"Alaska Electric Light&Power Co",10105,925,62,0,11092,0,0,0,0,0,0,0,0,0,0 2012,1,"AK",3522,"Chugach Electric Assn Inc",77639,,,,77639,,,,,0,,,,,0 2012,1,"AK",7353,"Golden Valley Elec Assn Inc",37816,6372,488,,44676,,,,,0,,,,,0 2012,1,"AK",10210,"Ketchikan Public Utilities",0,0,0,0,0,3262,312,0,0,3574,5074.17,742.17,0,0,5816.34 2012,1,"AK",10433,"Kodiak Electric Assn Inc",4574,1018,100,,5692,,,,,0,,,,,0 2012,1,"AK",10451,"Kotzebue Electric Assn Inc",915,6,,,921,,,,,0,,,,,0 2012,1,"AK",11824,"Matanuska Electric Assn Inc",47769,3513,0,0,51282,,,,,0,,,,,0 2012,1,"AK",19558,"Homer Electric Assn Inc",24988,2579,,,27567,41,5,,,46,2.05,0.06,,,2.1

450

Energy-Aware Load Balancing in Content Delivery Networks  

E-Print Network (OSTI)

Internet-scale distributed systems such as content delivery networks (CDNs) operate hundreds of thousands of servers deployed in thousands of data center locations around the globe. Since the energy costs of operating such a large IT infrastructure are a significant fraction of the total operating costs, we argue for redesigning CDNs to incorporate energy optimizations as a first-order principle. We propose techniques to turn off CDN servers during periods of low load while seeking to balance three key design goals: maximize energy reduction, minimize the impact on client-perceived service availability (SLAs), and limit the frequency of on-off server transitions to reduce wear-and-tear and its impact on hardware reliability. We propose an optimal offline algorithm and an online algorithm to extract energy savings both at the level of local load balancing within a data center and global load balancing across data centers. We evaluate our algorithms using real production workload traces from a large commercial ...

Mathew, Vimal; Shenoy, Prashant

2011-01-01T23:59:59.000Z

451

Total Cost of Motor-Vehicle Use  

E-Print Network (OSTI)

Grand total social cost of highway transportation Subtotal:of alternative transportation investments. A social-costtransportation option that has These costs will be inefficiently incurred if people do not fully lower total social costs.

Delucchi, Mark A.

1996-01-01T23:59:59.000Z

452

Total cost model for making sourcing decisions  

E-Print Network (OSTI)

This thesis develops a total cost model based on the work done during a six month internship with ABB. In order to help ABB better focus on low cost country sourcing, a total cost model was developed for sourcing decisions. ...

Morita, Mark, M.B.A. Massachusetts Institute of Technology

2007-01-01T23:59:59.000Z

453

Contractor: Contract Number: Contract Type: Total Estimated  

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

Number: Contract Type: Total Estimated Contract Cost: Performance Period Total Fee Earned FY2008 2,550,203 FY2009 39,646,446 FY2010 64,874,187 FY2011 66,253,207 FY2012...

454

Fractionally total colouring Gn,p  

Science Conference Proceedings (OSTI)

We study the fractional total chromatic number of G"n","p as p varies from 0 to 1. We also present an algorithm that computes the fractional total chromatic number of a random graph in polynomial expected time. Keywords: Fractional total colouring, Graph colouring, Random graphs

Conor Meagher; Bruce Reed

2008-04-01T23:59:59.000Z

455

Dehumidification and cooling loads from ventilation air  

SciTech Connect

The importance of controlling humidity in buildings is cause for concern, in part, because of indoor air quality problems associated with excess moisture in air-conditioning systems. But more universally, the need for ventilation air has forced HVAC equipment (originally optimized for high efficiency in removing sensible heat loads) to remove high moisture loads. To assist cooling equipment and meet the challenge of larger ventilation loads, several technologies have succeeded in commercial buildings. Newer technologies such as subcool/reheat and heat pipe reheat show promise. These increase latent capacity of cooling-based systems by reducing their sensible capacity. Also, desiccant wheels have traditionally provided deeper-drying capacity by using thermal energy in place of electrical power to remove the latent load. Regardless of what mix of technologies is best for a particular application, there is a need for a more effective way of thinking about the cooling loads created by ventilation air. It is clear from the literature that all-too-frequently, HVAC systems do not perform well unless the ventilation air loads have been effectively addressed at the original design stage. This article proposes an engineering shorthand, an annual load index for ventilation air. This index will aid in the complex process of improving the ability of HVAC systems to deal efficiently with the amount of fresh air the industry has deemed useful for maintaining comfort in buildings. Examination of typical behavior of weather shows that latent loads usually exceed sensible loads in ventilation air by at least 3:1 and often as much as 8:1. A designer can use the engineering shorthand indexes presented to quickly assess the importance of this fact for a given system design. To size those components after they are selected, the designer can refer to Chapter 24 of the 1997 ASHRAE Handbook--Fundamentals, which includes separate values for peak moisture and peak temperature.

Harriman, L.G. III [Mason-Grant, Portsmouth, NH (United States); Plager, D. [Quantitative Decision Support, Portsmouth, NH (United States); Kosar, D. [Gas Research Inst., Chicago, IL (United States)

1997-11-01T23:59:59.000Z

456

Automatic Detection of Unsafe Component Loadings  

E-Print Network (OSTI)

Dynamic loading of software components (e.g., libraries or modules) is a widely used mechanism for improved system modularity and flexibility. Correct component resolution is critical for reliable and secure software execution, however, programming mistakes may lead to unintended or even malicious components to be resolved and loaded. In particular, dynamic loading can be hijacked by placing an arbitrary file with the specified name in a directory searched before resolving the target component. Although this issue has been known for quite some time, it was not considered serious because exploiting it requires access to the local file system on the vulnerable host. Recently such vulnerabilities started to receive considerable attention as their remote exploitation became realistic; it is now important to detect and fix these vulnerabilities. In this paper, we present the first automated technique to detect vulnerable and unsafe dynamic component loadings. Our analysis has two phases: 1) apply dynamic binary instrumentation to collect runtime information on component loading (online phase); and 2) analyze the collected information to detect vulnerable component loadings (offline phase). For evaluation, we implemented our technique to detect vulnerable and unsafe DLL loadings in popular Microsoft Windows software. Our results show that unsafe DLL loading is prevalent and can lead to serious security threats. Our tool detected more than 1,700 unsafe DLL loadings in 28 widely used software and discovered serious attack vectors for remote code execution. Microsoft has opened a Microsoft Security Response Center (MSRC) case on our reported issues and is working with us and other affected software vendors to develop necessary patches.

Taeho Kwon; Zhendong Su

2010-01-01T23:59:59.000Z

457

300 Billion Served  

Science Conference Proceedings (OSTI)

Understanding the public's sources, perceptions, uses, and values of weather forecasts is integral to providing those forecasts in the most societally beneficial manner. To begin developing this knowledge, we conducted a nationwide survey with ...

Jeffrey K. Lazo; Rebecca E. Morss; Julie L. Demuth

2009-06-01T23:59:59.000Z

458

Testimonials: Why Serve?  

Science Conference Proceedings (OSTI)

... So I've picked up so much in leadership management skills just by ... better leadership skills, a major level of excitement and energy, and more ...

2013-06-27T23:59:59.000Z

459

Load Data Analysis and PowerShape Training: Strategic Load Research and Advanced Topics in Load Profiling for Settlements  

Science Conference Proceedings (OSTI)

Load shapes, representing usage patterns in the electric and gas industry, are a key factor in energy company operations and management. In the emerging restructured energy market, retail energy suppliers market energy to final customers and must arrange for electricity generation or gas delivery to meet their customers' needs. EPRI and Primen sponsored a workshop in September 2000 that addressed a range of issues associated with load shapes, including modeling, profiling for retail market settlement, re...

2000-12-20T23:59:59.000Z

460

Space cooling demands from office plug loads  

Science Conference Proceedings (OSTI)

Undersizing space cooling systems for office buildings can result in uncomfortable and angry tenants on peak cooling days. However, oversizing wastes money because more capacity is installed than is needed, and oversized systems have a lower energy efficiency which makes operating costs higher than necessary. Oversizing can adversely affect comfort as well, because oversized systems may provide poor humidity control and large temperature variations. Correct system sizing requires estimating building heat loads accurately. This paper discusses the heat load generated by the plug load, which includes any electrical equipment that is plugged into outlets.

Komor, P.

1997-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "total load served" 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

Million Cu. Feet Percent of National Total  

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

8 8 North Carolina - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S35. Summary statistics for natural gas - North Carolina, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

462

Million Cu. Feet Percent of National Total  

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

2 2 New Jersey - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S32. Summary statistics for natural gas - New Jersey, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

463

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Maryland - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S22. Summary statistics for natural gas - Maryland, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 7 7 7 7 8 Production (million cubic feet) Gross Withdrawals From Gas Wells 35 28 43 43 34 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 35

464

Million Cu. Feet Percent of National Total  

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

0 0 New Hampshire - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S31. Summary statistics for natural gas - New Hampshire, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

465

Million Cu. Feet Percent of National Total  

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

2 2 Maryland - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S22. Summary statistics for natural gas - Maryland, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 7 7 7 8 9 Production (million cubic feet) Gross Withdrawals From Gas Wells 28 43 43 34 44 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 28

466

Million Cu. Feet Percent of National Total  

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

2 2 Missouri - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S27. Summary statistics for natural gas - Missouri, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 53 100 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

467

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

2 2 Massachusetts - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S23. Summary statistics for natural gas - Massachusetts, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

468

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 South Carolina - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S42. Summary statistics for natural gas - South Carolina, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

469

Million Cu. Feet Percent of National Total  

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

0 0 Rhode Island - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S41. Summary statistics for natural gas - Rhode Island, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

470

Million Cu. Feet Percent of National Total  

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

38 38 Nevada - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S30. Summary statistics for natural gas - Nevada, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 4 4 4 3 4 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 4 4 4 3 4

471

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Idaho - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S14. Summary statistics for natural gas - Idaho, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

472

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

4 4 Washington - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S49. Summary statistics for natural gas - Washington, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

473

Million Cu. Feet Percent of National Total  

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

0 0 Maine - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S21. Summary statistics for natural gas - Maine, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0

474

Million Cu. Feet Percent of National Total  

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

8 8 Minnesota - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S25. Summary statistics for natural gas - Minnesota, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

475

Million Cu. Feet Percent of National Total  

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

2 2 South Carolina - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S42. Summary statistics for natural gas - South Carolina, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

476

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 North Carolina - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S35. Summary statistics for natural gas - North Carolina, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

477

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Iowa - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S17. Summary statistics for natural gas - Iowa, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0

478

Million Cu. Feet Percent of National Total  

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

4 4 Massachusetts - Natural Gas 2012 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S23. Summary statistics for natural gas - Massachusetts, 2008-2012 2008 2009 2010 2011 2012 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

479

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

6 6 Minnesota - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S25. Summary statistics for natural gas - Minnesota, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0 0 0

480

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 New Jersey - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S32. Summary statistics for natural gas - New Jersey, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0 0 0 From Coalbed Wells 0 0 0 0 0 From Shale Gas Wells 0 0 0 0 0 Total 0

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481

Million Cu. Feet Percent of National Total  

Gasoline and Diesel Fuel Update (EIA)

0 0 Vermont - Natural Gas 2011 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S47. Summary statistics for natural gas - Vermont, 2007-2011 2007 2008 2009 2010 2011 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oi