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


1

AVG Koeln GmbH | Open Energy Information  

Open Energy Info (EERE)

search Name AVG Koeln GmbH Place Kln, Germany Zip 50735 Product Operating a Waste-to-Energy facility in Kln, Germany. References AVG Koeln GmbH1 LinkedIn...

2

Property:AvgAnnlGrossOpCpcty | Open Energy Information  

Open Energy Info (EERE)

AvgAnnlGrossOpCpcty AvgAnnlGrossOpCpcty Jump to: navigation, search Property Name AvgAnnlGrossOpCpcty Property Type Number Description Avg. Annual Gross Operating Capacity(MW). Pages using the property "AvgAnnlGrossOpCpcty" Showing 6 pages using this property. F Faulkner I Energy Generation Facility + 49.5 + N Navy I Geothermal Facility + 81.7 + Navy II Geothermal Facility + 86 + Neal Hot Springs Geothermal Power Plant + 22 + North Brawley Geothermal Power Plant + 50 + R Raft River Geothermal Facility + 11.5 + Retrieved from "http://en.openei.org/w/index.php?title=Property:AvgAnnlGrossOpCpcty&oldid=400186#SMWResults" Categories: Properties Geothermal Energy Generation Facilities properties What links here Related changes Special pages Printable version

3

max kwh | OpenEI Community  

Open Energy Info (EERE)

max kwh max kwh Home Ewilson's picture Submitted by Ewilson(53) Contributor 4 January, 2013 - 08:42 Rates with tier problems max kwh tiers I've detected that the following rates all have the improper number of "Max kWh" values (should be one less than the number of charges, since the highest tier is always "all remaining"). This is likely due to users not understanding the meaning of "Max kWh"--often I see things like: "300, 700, 1000" (derived from "first 300, next 700, greater than 1000") which should be entered as "300, 1000". This is why we need checks on input that prevent users from entering this incorrectly. Here is the list (my script only checked residential rates): Syndicate content 429 Throttled (bot load)

4

kWh | OpenEI  

Open Energy Info (EERE)

kWh kWh 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

5

Property:AvgGeoFluidTemp | Open Energy Information  

Open Energy Info (EERE)

AvgGeoFluidTemp AvgGeoFluidTemp Jump to: navigation, search Property Name AvgGeoFluidTemp Property Type Temperature Description Average temperature of geofluid in a geothermal area. Subproperties This property has the following 15 subproperties: B Beowawe Hot Springs Geothermal Area Brady Hot Springs Geothermal Area C Chena Geothermal Area D Desert Peak Geothermal Area E East Mesa Geothermal Area G Geysers Geothermal Area H Heber Geothermal Area L Lightning Dock Geothermal Area R Roosevelt Hot Springs Geothermal Area S Salton Sea Geothermal Area San Emidio Desert Geothermal Area S cont. Soda Lake Geothermal Area Steamboat Springs Geothermal Area Stillwater Geothermal Area W Wabuska Hot Springs Geothermal Area Pages using the property "AvgGeoFluidTemp" Showing 10 pages using this property.

6

Property:AvgReservoirDepth | Open Energy Information  

Open Energy Info (EERE)

AvgReservoirDepth AvgReservoirDepth Jump to: navigation, search Property Name AvgReservoirDepth Property Type Quantity Description Average depth to reservoir Use this type to express a quantity of length. The default unit is the meter (m). Acceptable units (and their conversions) are: Meters - 1 m, meter, meters Meter, Meters, METER, METERS Kilometers - 0.001 km, kilometer, kilometers, Kilometer, Kilometers, KILOMETERS, KILOMETERS Miles - 0.000621371 mi, mile, miles, Mile, Miles, MILE, MILES Feet - 3.28084 ft, foot, feet, Foot, Feet, FOOT, FEET Yards - 1.09361 yd, yard, yards, Yard, Yards, YARD, YARDS Pages using the property "AvgReservoirDepth" Showing 24 pages using this property. A Amedee Geothermal Area + 213 m0.213 km 0.132 mi 698.819 ft 232.939 yd + B Beowawe Hot Springs Geothermal Area + 850 m0.85 km

7

Property:IndustrialAvgRate | Open Energy Information  

Open Energy Info (EERE)

IndustrialAvgRate IndustrialAvgRate Jump to: navigation, search Property Name IndustrialAvgRate Property Type Number Description Industrial Average Rate Subproperties This property has the following 279 subproperties: A AEP Generating Company AEP Texas Central Company AEP Texas North Company AES Eastern Energy LP APN Starfirst, L.P. Accent Energy Holdings, LLC Alabama Municipal Elec Authority Alaska Electric & Energy Coop Alaska Energy Authority Alaska Power and Telephone Co Allegheny Electric Coop Inc Alliant Energy Ameren Energy Marketing Ameren Illinois Company American Electric Power Co., Inc. American Mun Power-Ohio, Inc American Samoa Power Authority American Transmission Systems Inc Anoka Electric Coop Appalachian Power Co Aquila Inc Aquila Inc (Missouri) Arizona Electric Pwr Coop Inc

8

Property:CommercialAvgRate | Open Energy Information  

Open Energy Info (EERE)

CommercialAvgRate CommercialAvgRate Jump to: navigation, search Property Name CommercialAvgRate Property Type Number Description Commercial Average Rate Subproperties This property has the following 279 subproperties: A AEP Generating Company AEP Texas Central Company AEP Texas North Company AES Eastern Energy LP APN Starfirst, L.P. Accent Energy Holdings, LLC Alabama Municipal Elec Authority Alaska Electric & Energy Coop Alaska Energy Authority Alaska Power and Telephone Co Allegheny Electric Coop Inc Alliant Energy Ameren Energy Marketing Ameren Illinois Company American Electric Power Co., Inc. American Mun Power-Ohio, Inc American Samoa Power Authority American Transmission Systems Inc Anoka Electric Coop Appalachian Power Co Aquila Inc Aquila Inc (Missouri) Arizona Electric Pwr Coop Inc

9

Property:Incentive/PVComFitDolKWh | Open Energy Information  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:Incentive/PVComFitDolKWh Jump to: navigation, search Property Name Incentive/PVComFitDolKWh Property Type String Description Feed-in tariff for commercial systems. The $ amount per kWh generated such that the incentive is disbursed over time based on metered production. 100% of energy generated is exported; none is used on-site. Ex: TVA Green Power Switch $0.15/kWh; We Energies $0.225/kWh Format: $0.225 [1] References ↑ DSIRE Pages using the property "Incentive/PVComFitDolKWh" Showing 25 pages using this property. (previous 25) (next 25) A Alliant Energy (Wisconsin Power and Light) - Advanced Renewables Tariff (Wisconsin) + $0.25 + C CPS Energy - Solartricity Producer Program (Texas) + $0.27 +

10

Property:Incentive/PVResFitDolKWh | Open Energy Information  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:Incentive/PVResFitDolKWh Jump to: navigation, search Property Name Incentive/PVResFitDolKWh Property Type String Description Feed-in Tariff (FIT): The $ amount per kWh generated such that the incentive is disbursed over time based on metered production. 100% of energy generated is exported; none is used on-site. Ex: TVA Green Power Switch $0.15/kWh; We Energies $0.225/kWh Format: $0.225 [1] References ↑ DSIRE Pages using the property "Incentive/PVResFitDolKWh" Showing 25 pages using this property. (previous 25) (next 25) A Alliant Energy (Wisconsin Power and Light) - Advanced Renewables Tariff (Wisconsin) + $0.25 + C CPS Energy - Solartricity Producer Program (Texas) + $0.27 +

11

Property:Incentive/PVNPFitDolKWh | Open Energy Information  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:Incentive/PVNPFitDolKWh Jump to: navigation, search Property Name Incentive/PVNPFitDolKWh Property Type String Description Feed-in tariff for non-profit and/or government systems. The $ amount per kWh generated such that the incentive is disbursed over time based on metered production. 100% of energy generated is exported; none is used on-site. Ex: TVA Green Power Switch $0.15/kWh; We Energies $0.225/kWh Format: $0.225 [1] References ↑ DSIRE Pages using the property "Incentive/PVNPFitDolKWh" Showing 25 pages using this property. (previous 25) (next 25) A Alliant Energy (Wisconsin Power and Light) - Advanced Renewables Tariff (Wisconsin) + $0.25 + C CPS Energy - Solartricity Producer Program (Texas) + $0.27 +

12

Property:CoolingTowerWaterUseAnnlAvgConsumed | Open Energy Information  

Open Energy Info (EERE)

Name CoolingTowerWaterUseAnnlAvgConsumed Property Type Number Description Cooling Tower Water use (annual average) (afday) Consumed. Retrieved from "http:en.openei.orgw...

13

Applying Psychology to Economic Policy Design: Using Incentive Preserving Rebates to Increase Acceptance of Critical Peak Electricity Pricing  

E-Print Network (OSTI)

type model avg. daily use Summer 2002, kWh climate zone2 climate zone3 climate zone 4 apartment intercept N R 2 iv. I tested a

Letzler, Robert

2007-01-01T23:59:59.000Z

14

Property:CoolingTowerWaterUseAnnlAvgGross | Open Energy Information  

Open Energy Info (EERE)

Property Name CoolingTowerWaterUseAnnlAvgGross Property Type Number Description Cooling Tower Water use (annual average) (afday) Gross. Retrieved from "http:en.openei.orgw...

15

Beyond kWh and kW demand: Understanding the new real-time electric...  

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

Beyond kWh and kW demand: Understanding the new real-time electric power measurement system in LBNL Building 90 Speaker(s): Alex McEachern Date: January 14, 2010 - 12:00pm...

16

Cost analysis of 50 kWh zinc--chlorine batteries for mobile applications  

DOE Green Energy (OSTI)

The costs comprising the projected selling price of a 50-kWh zinc--chlorine battery for mobile applications were analyzed. This analysis is predicated on a battery whose engineering and design specifications are well crystallized. Such a design has been proposed and a process plan conceived. This, in turn, led to a simulated manufacturing plan. This analysis showed that no critical resources or complex manufacturing operations are required. The projected cost presumes a production level of 25,000 batteries per year. In that context, a selling price was estimated, in mid-1977 dollars, to be $1645 per battery or $33/kWh. This price excludes the battery charger, for which an added $400 ($8/kWh) is considered reasonable. 8 figures, 19 tables.

Catherino, H.; Henriksen, G.L.; Whittlesey, C.C.; Warde, C.J.; Carr, P.; Symons, P.C.

1978-01-01T23:59:59.000Z

17

Development of 5kWh Flywheel Energy Storage System Using MATLAB/xPC Target  

Science Conference Proceedings (OSTI)

A 5kWh class FESS(Flywheel Energy Storage System) with the operating speed range of 9,000~15,000rpm has been developed. The system consists of a composite flywheel rotor, active magnetic bearings, a motor/generator and its controller. Because Active ... Keywords: FESS, Magnetic bearing, rotor dynamics, Imbalace Response, xPC Target

Cheol Hoon Park; Sang-Kyu Choi; Young Su Son; Young Hee Han

2009-03-01T23:59:59.000Z

18

7 - Appendix B - Electricity Data.xls  

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

USE AND COST Costa Mesa Main PO Utility:SCE Vehicles: 20 Read Date Days Total kWh Daily Avg kWh Bill Amount Average Cost (kWh) kWhday Vehicle 22-Mar-03 29 5075 204...

19

Property:Building/SPBreakdownOfElctrcityUseKwhM2Total | Open Energy  

Open Energy Info (EERE)

SPBreakdownOfElctrcityUseKwhM2Total" SPBreakdownOfElctrcityUseKwhM2Total" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 71.4577086539 + Sweden Building 05K0002 + 110.926946534 + Sweden Building 05K0003 + 72.9096074806 + Sweden Building 05K0004 + 66.0248923654 + Sweden Building 05K0005 + 54.8654809632 + Sweden Building 05K0006 + 65.291976787 + Sweden Building 05K0007 + 65.5403331042 + Sweden Building 05K0008 + 41.6418235453 + Sweden Building 05K0009 + 56.5413268466 + Sweden Building 05K0010 + 150.269021739 + Sweden Building 05K0011 + 27.5018481341 + Sweden Building 05K0012 + 37.9937990385 + Sweden Building 05K0013 + 68.8990371973 + Sweden Building 05K0014 + 166.794253904 + Sweden Building 05K0015 + 71.0813662687 + Sweden Building 05K0016 + 38.5267410327 +

20

Property:Building/SPPurchasedEngyPerAreaKwhM2Total | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyPerAreaKwhM2Total" SPPurchasedEngyPerAreaKwhM2Total" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 221.549575215 + Sweden Building 05K0002 + 213.701117318 + Sweden Building 05K0003 + 195.801526718 + Sweden Building 05K0004 + 174.148148148 + Sweden Building 05K0005 + 340.088495575 + Sweden Building 05K0006 + 211.255924171 + Sweden Building 05K0007 + 144.028151521 + Sweden Building 05K0008 + 171.282051282 + Sweden Building 05K0009 + 140.296360236 + Sweden Building 05K0010 + 300.961098398 + Sweden Building 05K0011 + 98.1045751634 + Sweden Building 05K0012 + 106.609793929 + Sweden Building 05K0013 + 175.776187637 + Sweden Building 05K0014 + 291.160427408 + Sweden Building 05K0015 + 174.193548387 + Sweden Building 05K0016 + 145.793794187 +

Note: This page contains sample records for the topic "kwh daily avg" 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

Property:Building/SPPurchasedEngyPerAreaKwhM2DstrtHeating | Open Energy  

Open Energy Info (EERE)

Property Property Edit with form History Facebook icon Twitter icon » Property:Building/SPPurchasedEngyPerAreaKwhM2DstrtHeating Jump to: navigation, search This is a property of type String. District heating Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2DstrtHeating" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 111.56331078 + Sweden Building 05K0002 + 72.7932960894 + Sweden Building 05K0003 + 111.899416255 + Sweden Building 05K0004 + 72.865497076 + Sweden Building 05K0005 + 285.840707965 + Sweden Building 05K0006 + 128.449958182 + Sweden Building 05K0007 + 63.8377147588 + Sweden Building 05K0008 + 115.128205128 + Sweden Building 05K0009 + 66.5515753129 + Sweden Building 05K0010 + 148.741418764 +

22

Property:Building/SPBreakdownOfElctrcityUseKwhM2Misc | Open Energy  

Open Energy Info (EERE)

SPBreakdownOfElctrcityUseKwhM2Misc SPBreakdownOfElctrcityUseKwhM2Misc Jump to: navigation, search This is a property of type String. Miscellaneous Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Misc" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 9.09953195331 + Sweden Building 05K0003 + 8.78442379242 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 12.9530389597 + Sweden Building 05K0008 + 6.03377747253 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 10.9950724049 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 14.2856105095 + Sweden Building 05K0014 + 27.8718727739 +

23

Property:Building/SPBreakdownOfElctrcityUseKwhM2HeatPumps | Open Energy  

Open Energy Info (EERE)

SPBreakdownOfElctrcityUseKwhM2HeatPumps SPBreakdownOfElctrcityUseKwhM2HeatPumps Jump to: navigation, search This is a property of type String. Heat pumps Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2HeatPumps" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

24

Property:Building/SPPurchasedEngyPerAreaKwhM2Oil-FiredBoiler | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyPerAreaKwhM2Oil-FiredBoiler SPPurchasedEngyPerAreaKwhM2Oil-FiredBoiler Jump to: navigation, search This is a property of type String. Oil-fired boiler Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2Oil-FiredBoiler" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

25

Property:Building/SPPurchasedEngyPerAreaKwhM2ElctrcHeating | Open Energy  

Open Energy Info (EERE)

SPPurchasedEngyPerAreaKwhM2ElctrcHeating" SPPurchasedEngyPerAreaKwhM2ElctrcHeating" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.915704329247 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.745132743363 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 25.8064516129 + Sweden Building 05K0016 + 5.89159465829 + Sweden Building 05K0017 + 0.0 + Sweden Building 05K0018 + 0.0 + Sweden Building 05K0019 + 0.0 +

26

Property:Building/SPBreakdownOfElctrcityUseKwhM2ElctrcHeating | Open Energy  

Open Energy Info (EERE)

SPBreakdownOfElctrcityUseKwhM2ElctrcHeating" SPBreakdownOfElctrcityUseKwhM2ElctrcHeating" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.915704329247 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.745132743363 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 25.8064516129 + Sweden Building 05K0016 + 5.89159465829 + Sweden Building 05K0017 + 0.0 + Sweden Building 05K0018 + 0.0 + Sweden Building 05K0019 + 0.0 +

27

Property:Building/SPPurchasedEngyPerAreaKwhM2OtherElctrty | Open Energy  

Open Energy Info (EERE)

OtherElctrty OtherElctrty Jump to: navigation, search This is a property of type String. Other electricity Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2OtherElctrty" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 70.305743501 + Sweden Building 05K0002 + 95.9357541899 + Sweden Building 05K0003 + 72.2496632241 + Sweden Building 05K0004 + 65.8830409357 + Sweden Building 05K0005 + 53.5026548673 + Sweden Building 05K0006 + 58.7608028994 + Sweden Building 05K0007 + 61.5607534672 + Sweden Building 05K0008 + 40.3846153846 + Sweden Building 05K0009 + 56.4810818587 + Sweden Building 05K0010 + 152.219679634 + Sweden Building 05K0011 + 25.5555555556 + Sweden Building 05K0012 + 35.8807888323 + Sweden Building 05K0013 + 61.3267863536 +

28

Property:Building/SPBreakdownOfElctrcityUseKwhM2Pumps | Open Energy  

Open Energy Info (EERE)

Pumps Pumps Jump to: navigation, search This is a property of type String. Pumps Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Pumps" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 6.37190900733 + Sweden Building 05K0002 + 6.03888185355 + Sweden Building 05K0003 + 3.38991548528 + Sweden Building 05K0004 + 4.33303636174 + Sweden Building 05K0005 + 2.75390897598 + Sweden Building 05K0006 + 7.77750996655 + Sweden Building 05K0007 + 1.66724551261 + Sweden Building 05K0008 + 3.32543498168 + Sweden Building 05K0009 + 3.08636405861 + Sweden Building 05K0010 + 14.8373684211 + Sweden Building 05K0011 + 1.47492819795 + Sweden Building 05K0012 + 3.32673206926 + Sweden Building 05K0013 + 2.63132906976 +

29

Property:Building/SPBreakdownOfElctrcityUseKwhM2LargeComputersServers |  

Open Energy Info (EERE)

LargeComputersServers LargeComputersServers Jump to: navigation, search This is a property of type String. Large computers / servers Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2LargeComputersServers" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 2.88701226026 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 3.90838206628 + Sweden Building 05K0005 + 0.697674418605 + Sweden Building 05K0006 + 1.18332311465 + Sweden Building 05K0007 + 11.4098804421 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.556088941246 + Sweden Building 05K0010 + 10.0228832952 + Sweden Building 05K0011 + 0.471022727273 + Sweden Building 05K0012 + 0.774049003718 + Sweden Building 05K0013 + 0.0 +

30

Property:Building/SPBreakdownOfElctrcityUseKwhM2Elevators | Open Energy  

Open Energy Info (EERE)

Elevators Elevators Jump to: navigation, search This is a property of type String. Elevators Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Elevators" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.139664804469 + Sweden Building 05K0003 + 5.78356533453 + Sweden Building 05K0004 + 0.0116959064327 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.699648105982 + Sweden Building 05K0008 + 0.192307692308 + Sweden Building 05K0009 + 0.0661775284132 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.163674492353 + Sweden Building 05K0014 + 2.7497571546 +

31

Property:Building/SPBreakdownOfElctrcityUseKwhM2Fans | Open Energy  

Open Energy Info (EERE)

Fans Fans Jump to: navigation, search This is a property of type String. Fans Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Fans" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 5.21311928139 + Sweden Building 05K0002 + 18.5995610535 + Sweden Building 05K0003 + 20.3514016294 + Sweden Building 05K0004 + 8.08671679198 + Sweden Building 05K0005 + 16.0166245259 + Sweden Building 05K0006 + 10.358795651 + Sweden Building 05K0007 + 8.3953561818 + Sweden Building 05K0008 + 9.28527472527 + Sweden Building 05K0009 + 12.8398873749 + Sweden Building 05K0010 + 20.0966982674 + Sweden Building 05K0011 + 6.90408963585 + Sweden Building 05K0012 + 8.60719192175 + Sweden Building 05K0013 + 16.7539365907 +

32

Property:Building/SPBreakdownOfElctrcityUseKwhM2Copiers | Open Energy  

Open Energy Info (EERE)

Copiers Copiers Jump to: navigation, search This is a property of type String. Copiers Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Copiers" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.85593220339 + Sweden Building 05K0003 + 0.447247706422 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.897811865554 + Sweden Building 05K0008 + 0.9 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 7.78032036613 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 1.24104401228 + Sweden Building 05K0014 + 2.91414481058 + Sweden Building 05K0015 + 0.41935483871 +

33

Property:Building/SPPurchasedEngyPerAreaKwhM2ElctrtyTotal | Open Energy  

Open Energy Info (EERE)

ElctrtyTotal ElctrtyTotal Jump to: navigation, search This is a property of type String. Electricity, total Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2ElctrtyTotal" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 71.2214478303 + Sweden Building 05K0002 + 95.9357541899 + Sweden Building 05K0003 + 72.2496632241 + Sweden Building 05K0004 + 65.8830409357 + Sweden Building 05K0005 + 54.2477876106 + Sweden Building 05K0006 + 58.7608028994 + Sweden Building 05K0007 + 61.5607534672 + Sweden Building 05K0008 + 40.3846153846 + Sweden Building 05K0009 + 56.4810818587 + Sweden Building 05K0010 + 152.219679634 + Sweden Building 05K0011 + 25.5555555556 + Sweden Building 05K0012 + 35.8807888323 + Sweden Building 05K0013 + 61.3267863536 +

34

Property:Building/SPBreakdownOfElctrcityUseKwhM2CirculationFans | Open  

Open Energy Info (EERE)

CirculationFans CirculationFans Jump to: navigation, search This is a property of type String. Circulation fans Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2CirculationFans" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 13.3422495258 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 2.80646609789 + Sweden Building 05K0004 + 8.95823904901 + Sweden Building 05K0005 + 5.55016340076 + Sweden Building 05K0006 + 6.81308969891 + Sweden Building 05K0007 + 2.02541916787 + Sweden Building 05K0008 + 0.625641025641 + Sweden Building 05K0009 + 7.59721281624 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.757191316527 + Sweden Building 05K0012 + 6.04077487892 + Sweden Building 05K0013 + 0.767224182906 +

35

Property:Building/SPPurchasedEngyPerAreaKwhM2DstrtColg | Open Energy  

Open Energy Info (EERE)

DstrtColg DstrtColg Jump to: navigation, search This is a property of type String. District cooling Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2DstrtColg" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 38.7648166048 + Sweden Building 05K0002 + 44.9720670391 + Sweden Building 05K0003 + 11.6524472384 + Sweden Building 05K0004 + 35.3996101365 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 24.0451630889 + Sweden Building 05K0007 + 18.6296832954 + Sweden Building 05K0008 + 15.7692307692 + Sweden Building 05K0009 + 17.2637030643 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 5.09803921569 + Sweden Building 05K0012 + 15.0675825393 + Sweden Building 05K0013 + 21.4822771214 +

36

Property:Building/SPBreakdownOfElctrcityUseKwhM2AirCompressors | Open  

Open Energy Info (EERE)

AirCompressors AirCompressors Jump to: navigation, search This is a property of type String. Air compressors Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2AirCompressors" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 1.33591087145 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 1.86549707602 + Sweden Building 05K0005 + 2.04651162791 + Sweden Building 05K0006 + 1.92596566524 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.970107495214 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 1.30894886364 + Sweden Building 05K0012 + 2.01978262942 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 +

37

Property:Building/SPBreakdownOfElctrcityUseKwhM2Lighting | Open Energy  

Open Energy Info (EERE)

This is a property of type String. This is a property of type String. Lighting Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Lighting" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 13.6004313481 + Sweden Building 05K0002 + 51.2740526316 + Sweden Building 05K0003 + 25.3519773429 + Sweden Building 05K0004 + 14.5539566929 + Sweden Building 05K0005 + 17.1088606195 + Sweden Building 05K0006 + 11.7758321884 + Sweden Building 05K0007 + 16.0796522459 + Sweden Building 05K0008 + 15.7053876478 + Sweden Building 05K0009 + 19.44639866 + Sweden Building 05K0010 + 37.0625 + Sweden Building 05K0011 + 12.9336787565 + Sweden Building 05K0012 + 12.985779547 + Sweden Building 05K0013 + 21.6361810339 + Sweden Building 05K0014 + 29.853732347 +

38

Property:Building/SPBreakdownOfElctrcityUseKwhM2LargeKitchens | Open Energy  

Open Energy Info (EERE)

LargeKitchens LargeKitchens Jump to: navigation, search This is a property of type String. Large kitchens Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2LargeKitchens" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.763086941039 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.409356725146 + Sweden Building 05K0005 + 2.13953488372 + Sweden Building 05K0006 + 0.383200490497 + Sweden Building 05K0007 + 3.38701556508 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.294507436313 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.177556818182 + Sweden Building 05K0012 + 0.0953379731147 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 +

39

Property:Building/SPBreakdownOfElctrcityUseKwhM2Pcs | Open Energy  

Open Energy Info (EERE)

Pcs Pcs Jump to: navigation, search This is a property of type String. PCs Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Pcs" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 26.0998626444 + Sweden Building 05K0002 + 22.2888135593 + Sweden Building 05K0003 + 4.12075688073 + Sweden Building 05K0004 + 22.9175048733 + Sweden Building 05K0005 + 6.03962790698 + Sweden Building 05K0006 + 15.790619252 + Sweden Building 05K0007 + 5.8172794947 + Sweden Building 05K0008 + 4.66333333333 + Sweden Building 05K0009 + 8.50154616404 + Sweden Building 05K0010 + 8.05491990847 + Sweden Building 05K0011 + 2.70028409091 + Sweden Building 05K0012 + 2.19353608542 + Sweden Building 05K0013 + 8.43270214944 +

40

Property:Building/SPBreakdownOfElctrcityUseKwhM2Printers | Open Energy  

Open Energy Info (EERE)

Printers Printers Jump to: navigation, search This is a property of type String. Printers Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Printers" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.928422444931 + Sweden Building 05K0002 + 1.42372881356 + Sweden Building 05K0003 + 0.412844036697 + Sweden Building 05K0004 + 0.980506822612 + Sweden Building 05K0005 + 1.76744186047 + Sweden Building 05K0006 + 1.27988963826 + Sweden Building 05K0007 + 1.12158808933 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.765719334413 + Sweden Building 05K0010 + 1.01601830664 + Sweden Building 05K0011 + 0.774147727273 + Sweden Building 05K0012 + 1.11545428544 + Sweden Building 05K0013 + 0.549891248721 +

Note: This page contains sample records for the topic "kwh daily avg" 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

Property:Building/SPBreakdownOfElctrcityUseKwhM2SmallKitchensCoffeeRms |  

Open Energy Info (EERE)

SmallKitchensCoffeeRms SmallKitchensCoffeeRms Jump to: navigation, search This is a property of type String. Small kitchens / coffee rooms Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2SmallKitchensCoffeeRms" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 1.20677966102 + Sweden Building 05K0003 + 1.46100917431 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 2.53105456775 + Sweden Building 05K0007 + 1.08639747349 + Sweden Building 05K0008 + 0.910666666667 + Sweden Building 05K0009 + 2.06390811368 + Sweden Building 05K0010 + 3.29519450801 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 1.54234902764 +

42

Property:Building/SPBreakdownOfElctrcityUseKwhM2Refrigeration | Open Energy  

Open Energy Info (EERE)

Refrigeration Refrigeration Jump to: navigation, search This is a property of type String. Refrigeration Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Refrigeration" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 2.77390577084 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 37.1080462614 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.895094880057 + Sweden Building 05K0014 + 12.4536103016 + Sweden Building 05K0015 + 0.0 +

43

Cogenerator to quit Con Ed by selling kWh to neighbor  

SciTech Connect

Selling 125 kilowatts of electricity around the clock to a nearby supermarket will make cogeneration feasible for the Flagship Restaurant in White Plains, NY, allowing it to drop off Consolidated Edison's grid and pay for a necessary backup generator, according to John Prayias, the restaurant's owner. The ambitious $536,000 project, which will be financed conventionally with a commercial bank loan, will eliminate the Flagship's $70,000 electricity costs and the $7240 spent of heating and domestic hot water, Prayias said. By selling the power to the supermarket at 9 cents per kilowatt hour - 3 cents less than Con Ed's rate of 12 cents per kWh - the restaurant will collect $120,000 a year in revenues - just about enough to cover the cost of diesel fuel for the 350-kW system and pay for monitoring and maintenance.

Springer, N.

1986-02-10T23:59:59.000Z

44

DOE: T-O-D rates shift kWh and kW  

SciTech Connect

Technical report:In 1975, FEA, in cooperation with state and local utility authorities, initiated a series of field projects that collected electricity usage data under new electric utility rate designs and load management techniques. Individual projects included a wide range of load-management and electricity rate design alternatives, with emphasis on several forms of time-of-day rates. The program is now funded by the U.S. Dept. of Energy. Objectives of the program, kWh usage effects, kw demand effects, and sources of the load changes are discussed. Initial results indicate actual or effective shifts in electricity consumption from peak to off-peak periods, and reductions in diversified demand coincident with system peaks. (10 graphs)

Johnson, C.R.; Mintz, S.

1978-11-15T23:59:59.000Z

45

Property:Building/SPPurchasedEngyPerAreaKwhM2DigesterLandfillGas | Open  

Open Energy Info (EERE)

DigesterLandfillGas DigesterLandfillGas Jump to: navigation, search This is a property of type String. Digester / landfill gas Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2DigesterLandfillGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 +

46

Property:Building/SPBreakdownOfElctrcityUseKwhM2Laundry | Open Energy  

Open Energy Info (EERE)

Laundry Laundry Jump to: navigation, search This is a property of type String. Laundry Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2Laundry" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

47

Property:Building/SPPurchasedEngyPerAreaKwhM2WoodChips | Open Energy  

Open Energy Info (EERE)

WoodChips WoodChips Jump to: navigation, search This is a property of type String. Wood chips Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2WoodChips" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

48

Property:Building/SPPurchasedEngyPerAreaKwhM2Pellets | Open Energy  

Open Energy Info (EERE)

Pellets Pellets Jump to: navigation, search This is a property of type String. Pellets Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2Pellets" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

49

Property:Building/SPPurchasedEngyPerAreaKwhM2Other | Open Energy  

Open Energy Info (EERE)

This is a property of type String. This is a property of type String. Other Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2Other" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 + Sweden Building 05K0018 + 0.0 +

50

Property:Building/SPPurchasedEngyPerAreaKwhM2Logs | Open Energy Information  

Open Energy Info (EERE)

Logs Logs Jump to: navigation, search This is a property of type String. Logs Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2Logs" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

51

Property:Building/SPBreakdownOfElctrcityUseKwhM2ElctrcEngineHeaters | Open  

Open Energy Info (EERE)

ElctrcEngineHeaters ElctrcEngineHeaters Jump to: navigation, search This is a property of type String. Electric engine heaters Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2ElctrcEngineHeaters" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 2.44788473329 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.353408923575 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.835160644485 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 +

52

Property:Building/SPPurchasedEngyPerAreaKwhM2TownGas | Open Energy  

Open Energy Info (EERE)

TownGas TownGas Jump to: navigation, search This is a property of type String. Town gas Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2TownGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

53

Property:Building/SPPurchasedEngyPerAreaKwhM2NaturalGas | Open Energy  

Open Energy Info (EERE)

NaturalGas NaturalGas Jump to: navigation, search This is a property of type String. Natural gas Pages using the property "Building/SPPurchasedEngyPerAreaKwhM2NaturalGas" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.0 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 + Sweden Building 05K0016 + 0.0 + Sweden Building 05K0017 + 0.0 +

54

Utility Cycle Testing of a 500-kWh Zinc Chloride Battery at the Battery Energy Storage Test (BEST) Facility  

Science Conference Proceedings (OSTI)

A 500-kWh zinc chloride battery test system completed an entire schedule of 80 simulated utility and customer application cycles--the most diverse and severe known to be successfully performed by any advanced battery system. Encouraged by these results, researchers plan to have a 2-MW demonstration battery system ready for testing in 1986.

1985-10-09T23:59:59.000Z

55

CEMEX: Cement Manufacturer Saves 2.1 Million kWh Annually with a Motor Retrofit Project  

Science Conference Proceedings (OSTI)

This DOE Industrial Technologies Program spotlight describes how the CEMEX cement manufacturing plant in Davenport, California, saves 2 million kWh and $168,000 in energy costs annually by replacing 13 worn-out motors with new energy-efficient ones.

Not Available

2005-11-01T23:59:59.000Z

56

Beyond kWh and kW demand: Understanding the new real-time electric power  

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

Beyond kWh and kW demand: Understanding the new real-time electric power Beyond kWh and kW demand: Understanding the new real-time electric power measurement system in LBNL Building 90 Speaker(s): Alex McEachern Date: January 14, 2010 - 12:00pm Location: 90-3122 In the Summer of 2009, LBNL researchers installed end-use sub-metering equipment and associated Energy Information System (EIS) tools to characterize energy use and comfort in Building 90. Seven of 40 key electric loads were measured using advanced meters that make sophisticated real-time measurements of dozens of power flow parameters, power disturbances, and harmonics. The talk will review some electrical engineering fundamentals, how use and interpret data measured in building 90 in real-time. The real-time data available includes power, volt-amps, VAR's, unbalance voltage and current, voltage and current distortion,

57

Daily Occurrence Reports  

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

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

58

Development of zinc-bromine batteries for utility energy storage. First annual report, 1 September 1978-31 August 1979. [8-kWh submodule  

SciTech Connect

Development work on the Zn/Br battery is reported. A major improvement was the use of a bipolar cell design; this design is superior with respect to cost, performance, and simplicity. A cost and design study for an 80-kWh module resulted in a cost estimate of $54/kWh(1979$) for purchased materials and components, on the basis of 2500 MWh of annual production. A cell submodule (nominal 2 kWh) of full-sized electrodes (1 ft/sup 2/) accrued over 200 continuous cycles in a hands-off, automatic routine with efficiencies in the range of 53 to 56%. Initial testing of a full-sized 8-kWh submodule demonstrated energy efficiencies of 65 to 67%. 23 figures, 10 tables. (RWR)

Putt, R.; Attia, A.J.; Lu, P.Y.; Heyland, J.H.

1980-05-01T23:59:59.000Z

59

Daily Temperature Lag  

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

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

60

Property:Building/SPBreakdownOfElctrcityUseKwhM2HeatPumpsUsedForColg | Open  

Open Energy Info (EERE)

HeatPumpsUsedForColg HeatPumpsUsedForColg Jump to: navigation, search This is a property of type String. Heat pumps used for cooling Pages using the property "Building/SPBreakdownOfElctrcityUseKwhM2HeatPumpsUsedForColg" Showing 25 pages using this property. (previous 25) (next 25) S Sweden Building 05K0001 + 0.0 + Sweden Building 05K0002 + 0.0 + Sweden Building 05K0003 + 0.0 + Sweden Building 05K0004 + 0.0 + Sweden Building 05K0005 + 0.0 + Sweden Building 05K0006 + 0.250906049624 + Sweden Building 05K0007 + 0.0 + Sweden Building 05K0008 + 0.0 + Sweden Building 05K0009 + 0.0 + Sweden Building 05K0010 + 0.0 + Sweden Building 05K0011 + 0.0 + Sweden Building 05K0012 + 0.0 + Sweden Building 05K0013 + 0.0 + Sweden Building 05K0014 + 0.0 + Sweden Building 05K0015 + 0.0 +

Note: This page contains sample records for the topic "kwh daily avg" 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

2010 Expert and Consultant Daily Wages  

Science Conference Proceedings (OSTI)

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

2012-04-27T23:59:59.000Z

62

Design and cost study of nickel--zinc batteries for electric vehicle. Final report. [24 kWh battery of 48 325-Ah cells, 35 Wh/lb  

DOE Green Energy (OSTI)

A battery module configuration consisting of four 325-Ah cells was selected. Twelve such modules would make up a 24-kWh battery. The key design parameter is operation current density. An energy density of 2.1 Wh/in./sup 3/ and 35 Wh/lb was obtained. A flow diagram was drawn for the manufacturing process. An eight-month period would be required to set up a pilot plant. The material requirements for 100,000 batteries per year would not have a significant impact on current U.S. consumption. 29 figures, 28 tables (RWR)

Klein, M; Dube, D

1976-10-01T23:59:59.000Z

63

DOE Solar Decathlon: 2007 Daily Journals  

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

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

64

Energy Assurance Daily | Department of Energy  

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

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

65

Estimation of Daily Degree-hours  

Science Conference Proceedings (OSTI)

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

Nathaniel B. Guttman; Richard L. Lehman

1992-07-01T23:59:59.000Z

66

Homogenization of Daily Temperatures over Canada  

Science Conference Proceedings (OSTI)

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

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

2002-06-01T23:59:59.000Z

67

Energy Assurance Daily (EAD): July 2012  

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

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

68

Energy Assurance Daily (EAD): January - March 2012  

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

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

69

Energy Assurance Daily (EAD): April 2012  

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

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

70

Energy Assurance Daily (EAD): June 2012  

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

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

71

Energy Assurance Daily (EAD): May 2012  

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

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

72

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

Science Conference Proceedings (OSTI)

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

2011-02-25T23:59:59.000Z

73

Electric vehicle propulsion batteries: design and cost study for nickel/zinc battery manufacture. Task A. [25 kWh, 700 pounds, 245 Ah at 100+ V, 4. 77 ft/sup 3/  

DOE Green Energy (OSTI)

For satisfying the 25-kWh energy requirement necessary for vehicle propulsion, a 700-pound nickel--zinc battery was configured. Containing 64 individual cells, the unit was selected for minimum weight from computed packaging possibilities. Unit volume was projected to be 4.77 cubic feet. Capacity of the cells delivering 100+ volts was set at 245 ampere-hours. Selection was made primarily because of the compatibility with expressed vehicle requirements of a lower-current system. Manufacturing costs were computed for a unit using sintered positive electrodes at $86/kWh, pilot plant rate, and $78/kWh, production plant rate. Based on a lower than anticipated cost differential between sintered and nonsintered positive electrodes and certain other performance differences, the sintered electrode was chosen for the battery design. Capital expenditures for a production rate of 10,000 batteries per year are estimated to be $2,316,500. Capital expenditure for demonstrating production rates in a pilot plant facility is approximately $280,000, with the use of some shared available equipment. 29 figures, 9 tables.

None

1977-01-01T23:59:59.000Z

74

Seasonal Variation in Daily Temperature Ranges  

Science Conference Proceedings (OSTI)

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

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

1991-12-01T23:59:59.000Z

75

DOE Solar Decathlon: 2009 Daily Journals  

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

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

76

OpenEI - kWh  

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...

77

Climate Reference Network Daily01 Product | Data.gov  

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

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

78

Simulation of Daily Weather Data Using Theoretical Probability Distributions  

Science Conference Proceedings (OSTI)

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

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

1980-09-01T23:59:59.000Z

79

SPLIDHOM: A Method for Homogenization of Daily Temperature Observations  

Science Conference Proceedings (OSTI)

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

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

2011-11-01T23:59:59.000Z

80

Temperature Effects on the Winter Daily Electric Load  

Science Conference Proceedings (OSTI)

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

Paolo Bolzern; Giorgio Fronza; Giuseppe Brusasca

1982-02-01T23:59:59.000Z

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81

Solar Decathlon 2005 Daily Event Schedule  

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

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

82

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

6893,36894,36894,83,83,83,800,1,2 6893,36894,36894,83,83,83,800,1,2 "Entergy",36894,36895,36895,93,93,93,800,1,2 "Entergy",36895,36896,36896,83,78.5,80.83,7200,9,4 "Entergy",36896,36899,36899,78,67,74.25,3200,4,5 "Entergy",36899,36900,36900,57,54,55.5,1600,2,4 "Entergy",36900,36901,36901,53,53,53,1600,1,2 "Entergy",36902,36903,36903,67.5,65,66.5,4000,5,3 "Entergy",36903,36906,36906,52.5,48,50.25,1600,2,3 "Entergy",36907,36908,36908,52,45,48.86,8800,11,4 "Entergy",36908,36909,36909,56,51,51.95,16800,21,6 "Entergy",36909,36910,36910,50,48.5,49.33,24000,30,7 "Entergy",36910,36913,36913,56.5,54,55.25,11200,13,7 "Entergy",36913,36914,36914,63,57,58.38,6400,8,3 "Entergy",36914,36915,36915,61.5,42,55.75,15200,19,9

83

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

449,39450,39450,180,158,161.65,26400,33,22 449,39450,39450,180,158,161.65,26400,33,22 "NEPOOL MH DA LMP",39450,39451,39451,123,108,114.27,36800,46,28 "NEPOOL MH DA LMP",39451,39454,39454,77,75.5,76.31,21600,26,17 "NEPOOL MH DA LMP",39454,39455,39455,68.25,66,67.1,41600,51,26 "NEPOOL MH DA LMP",39455,39456,39456,69.5,68,68.71,21600,27,18 "NEPOOL MH DA LMP",39456,39457,39457,81,74,75.75,30400,35,17 "NEPOOL MH DA LMP",39457,39458,39458,75,69.75,71.18,24800,31,19 "NEPOOL MH DA LMP",39458,39461,39461,80.5,77,79.38,17600,19,17 "NEPOOL MH DA LMP",39461,39462,39462,102,95,98.76,52000,64,24 "NEPOOL MH DA LMP",39462,39463,39463,90.5,87.5,88.59,34400,43,25 "NEPOOL MH DA LMP",39463,39464,39464,85,83.5,84.21,20800,26,14

84

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

815,39818,39818,43,42.5,42.75,5.17,1600,2,4 815,39818,39818,43,42.5,42.75,5.17,1600,2,4 "ERCOT-South",39818,39819,39819,40,39.5,39.88,-2.87,3200,4,3," " "ERCOT-South",39819,39820,39820,39,38,38.73,-1.15,8800,9,9 "ERCOT-South",39820,39821,39821,41.5,39,39.82,1.09,8800,11,9 "ERCOT-South",39821,39822,39822,38.75,37.5,38.03,-1.79,6400,8,10 "ERCOT-South",39822,39825,39825,43.5,43.5,43.5,5.47,800,1,2 "ERCOT-South",39825,39826,39826,55,50.5,52.95,9.45,8800,11,12,,," " "ERCOT-South",39826,39827,39827,45.5,43.5,44.44,-8.51,14400,18,18 "ERCOT-South",39827,39828,39828,45,44.25,44.68,0.24,12000,14,12 "ERCOT-South",39828,39829,39829,44,42.75,43.18,-1.5,8000,10,10 "ERCOT-South",39833,39834,39834,33,32.5,32.75,-10.43,9600,12,8

85

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

54.5,53.4,53.98,5.44,3200,4,7 54.5,53.4,53.98,5.44,3200,4,7 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",49,47.25,48.27,-5.71,8000,10,12 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56,53.5,54.75,6.48,4800,6,10 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",97,87,89.96,35.21,20800,18,16 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56.25,51,53.71,-36.25,16800,19,15 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.75,46,46.33,-7.38,17600,22,17

86

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

Nepool MH Da Lmp ",39815,39818,39818,65.55,65,65.44,-5.89,12000,15,9 Nepool MH Da Lmp ",39815,39818,39818,65.55,65,65.44,-5.89,12000,15,9 "Nepool MH Da Lmp",39818,39819,39819,67,65,66.22,0.78,39200,46,22 "Nepool MH Da Lmp ",39819,39820,39820,65,63.25,63.83,-2.39,20000,24,18 "Nepool MH Da Lmp ",39820,39821,39821,67.5,65.75,66.47,2.64,28000,33,16 "Nepool MH Da Lmp ",39821,39822,39822,78.5,76,77.31,10.84,21600,27,16 "Nepool MH Da Lmp ",39822,39825,39825,100,90,94.19,16.88,28800,35,19 "Nepool MH Da Lmp ",39825,39826,39826,81,72.75,74.76,-19.43,36000,44,24 "Nepool MH Da Lmp ",39826,39827,39827,101,98,99.83,25.07,16000,20,18 "Nepool MH Da Lmp",39827,39828,39828,130,117,120.32,20.49,40000,50,27 "Nepool MH Da Lmp ",39828,39829,39829,120,106,109.76,-10.56,72800,91,35

87

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

SP 15",39449,39450,39450,74.6,69.25,73.56,97200,234,36 SP 15",39449,39450,39450,74.6,69.25,73.56,97200,234,36 "SP 15",39450,39451,39452,70,63,68.49,291200,275,37 "SP 15",39451,39454,39454,75,68,69.2,140000,326,39 "SP 15",39454,39455,39455,73.25,69,71.52,144800,329,37 "SP 15",39455,39456,39456,72.25,70.25,71.32,198000,425,35 "SP 15",39456,39457,39457,73.75,70.75,72.79,157600,351,37 "SP 15",39457,39458,39459,70.25,67.25,68.46,226400,268,33 "SP 15",39458,39461,39461,75,73.25,73.77,184000,366,38 "SP 15",39461,39462,39462,78.25,75,75.77,110800,235,34 "SP 15",39462,39463,39464,88,77.5,79.42,323200,351,36 "SP 15",39463,39465,39466,79,74.25,77.52,259200,302,36 "SP 15",39464,39468,39468,84.45,77,82.35,126400,287,36

88

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

54.55,54.05,54.37,1.9,8800,20,11 54.55,54.05,54.37,1.9,8800,20,11 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",53.25,52.75,53.09,-1.28,35200,64,16 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52,51.25,51.51,-1.58,13600,28,17 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56.5,53.25,54.08,2.57,65600,71,17 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",51.15,50.8,51.01,-3.07,27600,53,19 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",50.75,50,50.18,-0.83,23200,39,11

89

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

084,39085,39085,62,55,55.98,10400,13,10 084,39085,39085,62,55,55.98,10400,13,10 "NEPOOL MH DA LMP",39085,39086,39086,54.75,52.75,53.53,30400,38,20 "NEPOOL MH DA LMP",39086,39087,39087,56,55,55.35,24800,31,19 "NEPOOL MH DA LMP",39087,39090,39090,58,56.5,57.08,8000,10,12 "NEPOOL MH DA LMP",39090,39091,39091,58.75,57.25,57.86,34400,41,19 "NEPOOL MH DA LMP",39091,39092,39092,60.5,59,59.8,20800,25,19 "NEPOOL MH DA LMP",39092,39093,39093,65,63.5,64.04,13600,16,15 "NEPOOL MH DA LMP",39093,39094,39094,61.25,59.75,60.82,15200,19,14 "NEPOOL MH DA LMP",39094,39097,39097,62,59,60.95,16800,21,16 "NEPOOL MH DA LMP",39097,39098,39098,69.25,67,68.25,22400,28,15 "NEPOOL MH DA LMP",39098,39099,39099,89,84.5,86.33,34400,43,26

90

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

53.5,48,50.93,,13600,17,11 53.5,48,50.93,,13600,17,11 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",57.5,52.75,55,4.07,31200,39,15 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",51.5,49.5,50.38,-4.62,3200,4,4 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52,49.5,51.25,0.87,19200,24,12 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.75,45.25,45.8,-5.45,21600,27,14 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",43,39.5,41.3,-4.5,10400,13,8

91

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

1246,41247,41247,28.5,26.5,27.76,-0.16,63200,141,25 1246,41247,41247,28.5,26.5,27.76,-0.16,63200,141,25 "Mid Columbia Peak",41247,41248,41248,28.5,27,27.86,0.1,79200,187,26 "Mid Columbia Peak",41248,41249,41249,28,23.5,27.02,-0.84,76000,170,25 "Mid Columbia Peak",41249,41250,41251,23.25,21.25,22.44,-4.58,159200,191,23 "Mid Columbia Peak",41250,41253,41253,25.25,21.25,23.45,1.01,74800,176,25 "Mid Columbia Peak",41253,41254,41254,23.75,20.75,22.51,-0.94,92800,209,26 "Mid Columbia Peak",41254,41255,41255,24.5,23,23.84,1.33,100800,222,27 "Mid Columbia Peak",41255,41256,41256,28,25.5,26.88,3.04,80800,182,26 "Mid Columbia Peak",41256,41257,41258,27.75,26.5,27.13,0.25,152000,171,25 "Mid Columbia Peak",41257,41260,41260,25.75,23.25,24.43,-2.7,76000,180,25

92

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

38.75,37.25,37.95,-2.02,13600,17,14 38.75,37.25,37.95,-2.02,13600,17,14 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",43.5,40,42.39,4.44,10000,25,20 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39.5,37.75,38.26,-4.13,9200,23,15 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",40.25,37.25,38.46,0.2,7600,19,14 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",41,38,38.93,0.47,9200,23,15 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",38.25,36.5,37.29,-1.64,13600,17,17

93

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

623,37624,37624,37.45,33.75,35.69,28800,36,19 623,37624,37624,37.45,33.75,35.69,28800,36,19 "PJM West",37624,37627,37627,48,47,47.58,28800,32,20 "PJM West",37627,37628,37628,50.5,48,49.53,33600,42,19 "PJM West",37628,37629,37629,47,44.25,45.39,35200,44,20 "PJM West",37629,37630,37630,39,37,37.73,27200,33,19 "PJM West",37630,37631,37631,43.5,41.75,42.44,25600,27,17 "PJM West",37631,37634,37634,64,56.5,58.31,20800,26,19 "PJM West",37634,37635,37635,56,54.8,55.52,19200,24,19 "PJM West",37635,37636,37636,56.5,54.9,55.51,28000,33,19 "PJM West",37636,37637,37637,53,50.25,51.89,32000,40,22 "PJM West",37637,37638,37638,54,52,52.63,30400,38,23 "PJM West",37638,37641,37641,48.25,47,47.48,26400,33,17

94

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

258,37259,37259,33.75,32.5,33.23,10400,13,12 258,37259,37259,33.75,32.5,33.23,10400,13,12 "NEPOOL",37259,37260,37260,36.25,35,35.98,24800,31,18 "NEPOOL",37260,37263,37263,34,33.25,33.66,8800,11,12 "NEPOOL",37263,37264,37264,34,33.5,33.67,10400,13,11 "NEPOOL",37264,37265,37265,32.6,31,32.04,9600,11,13 "NEPOOL",37265,37266,37266,29.5,28.7,29.1,10400,13,11 "NEPOOL",37266,37267,37267,29.25,28.25,28.75,12000,15,12 "NEPOOL",37267,37270,37270,31,30,30.24,16800,17,13 "NEPOOL",37270,37271,37271,30.5,29.75,30.09,30400,36,15 "NEPOOL",37271,37272,37272,29.5,28.65,28.98,23200,28,15 "NEPOOL",37272,37273,37273,30.4,29.8,30.02,32800,39,16 "NEPOOL",37273,37274,37274,30,29.1,29.37,11200,14,15 "NEPOOL",37274,37277,37277,30,29.25,29.72,6400,8,9

95

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

182,40183,40183,89,82.75,86.08,20.49,214400,242,55 182,40183,40183,89,82.75,86.08,20.49,214400,242,55 "PJM Wh Real Time Peak",40183,40184,40184,80.65,74.5,77.16,-8.92,270400,295,56 "PJM Wh Real Time Peak",40184,40185,40185,80.5,77.5,78.92,1.76,93600,111,47 "PJM Wh Real Time Peak",40185,40186,40186,86,78.25,80.64,1.72,278400,316,62 "PJM Wh Real Time Peak",40186,40189,40189,82.75,72,80.64,0,81600,98,36 "PJM Wh Real Time Peak",40189,40190,40190,73,65.75,67.86,-12.78,178400,205,50 "PJM Wh Real Time Peak",40190,40191,40191,55.25,53,53.89,-13.97,162400,180,50 "PJM Wh Real Time Peak",40191,40192,40192,49.75,48,48.84,-5.05,97600,109,45 "PJM Wh Real Time Peak",40192,40193,40193,46.25,43.5,44.65,-4.19,99200,117,46 "PJM Wh Real Time Peak",40193,40196,40196,46,44.95,45.38,0.73,59200,71,35

96

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

8721,38722,38722,57.5,57.5,57.5,-22.5,800,1,2 8721,38722,38722,57.5,57.5,57.5,-22.5,800,1,2 "ERCOT-South",38748,38749,38749,57,57,57,-0.5,800,1,2 "ERCOT-South",38751,38754,38754,59,59,59,2,1600,2,3 "ERCOT-South",38786,38789,38789,48,48,48,-11,800,1,2 "ERCOT-South",38803,38804,38804,52.5,50.5,51.06,3.06,6400,8,7 "ERCOT-South",38804,38805,38805,54.75,54.75,54.75,3.69,3200,2,3 "ERCOT-South",38805,38806,38806,55.25,53.5,54.21,-0.54,4800,6,5 "ERCOT-South",38806,38807,38807,58,58,58,3.79,800,1,2,,,,," " "ERCOT-South",38810,38811,38811,60,60,60,2,800,1,2 "ERCOT-South",38811,38812,38812,64,64,64,4,800,1,2 "ERCOT-South",38812,38813,38813,63,62.5,62.63,-1.37,3200,4,6 "ERCOT-South",38813,38814,38814,62,62,62,-0.63,800,1,2

97

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

546,40547,40547,51,47.5,48.71,-0.32,96800,116,39 546,40547,40547,51,47.5,48.71,-0.32,96800,116,39 "PJM Wh Real Time Peak",40547,40548,40548,49.25,47.45,48.14,-0.57,64000,67,40 "PJM Wh Real Time Peak",40548,40549,40549,53.5,51.5,52.27,4.13,55200,66,37 "PJM Wh Real Time Peak",40549,40550,40550,60.5,57,58.43,6.16,80000,93,39 "PJM Wh Real Time Peak",40550,40553,40553,63.5,57,60.43,2,105600,124,41 "PJM Wh Real Time Peak",40553,40554,40554,69.5,64.25,66.98,6.55,128800,145,44 "PJM Wh Real Time Peak",40554,40555,40555,72.25,62,67.54,0.56,158400,194,51 "PJM Wh Real Time Peak",40555,40556,40556,84,75,80.13,12.59,92800,116,46 "PJM Wh Real Time Peak",40556,40557,40557,89.5,80.5,84.09,3.96,108800,133,42 "PJM Wh Real Time Peak",40557,40560,40560,57.55,55,56.11,-27.98,88800,105,40

98

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

40182,40183,40183,52.5,51.5,51.85,0.9,67600,116,25 40182,40183,40183,52.5,51.5,51.85,0.9,67600,116,25 "SP-15 Gen DA LMP Peak",40183,40184,40184,51.75,50.5,51.01,-0.84,61600,115,25 "SP-15 Gen DA LMP Peak",40184,40185,40185,53,50.5,51.39,0.38,59600,115,24 "SP-15 Gen DA LMP Peak",40185,40186,40187,58.5,55,56.79,5.4,394400,381,29 "SP-15 Gen DA LMP Peak",40186,40189,40189,51.25,50.75,51,-5.79,59200,116,26 "SP-15 Gen DA LMP Peak",40189,40190,40190,50.25,49,49.8,-1.2,53600,102,25 "SP-15 Gen DA LMP Peak",40190,40191,40192,51.5,50.75,51.12,1.32,59200,61,19 "SP-15 Gen DA LMP Peak",40191,40193,40194,49,48.25,48.35,-2.77,77600,71,20 "SP-15 Gen DA LMP Peak",40192,40196,40196,50.5,50,50.3,1.95,38800,71,18 "SP-15 Gen DA LMP Peak",40193,40197,40197,51.35,50,50.93,0.63,66800,84,19

99

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

084,39085,39085,43,43,43,4800,6,6 084,39085,39085,43,43,43,4800,6,6 "Entergy",39085,39086,39086,40,34,38.3,4000,5,6 "Entergy",39086,39087,39087,38,37,37.5,1600,2,2 "Entergy",39087,39090,39090,41,41,41,800,1,2 "Entergy",39090,39091,39091,49,46,48.14,5600,6,6 "Entergy",39091,39092,39092,48,48,48,2400,3,4 "Entergy",39092,39093,39093,49,47,48,1600,2,3 "Entergy",39093,39094,39094,45,44,44.5,1600,2,4 "Entergy",39094,39097,39097,51,47,49.33,2400,3,5 "Entergy",39097,39098,39098,58.5,53.5,56.06,6400,8,8 "Entergy",39098,39099,39099,62,56,58.97,7200,9,9 "Entergy",39099,39100,39100,54.5,53,53.6,4000,5,5 "Entergy",39100,39101,39101,50.75,50,50.15,4000,5,9 "Entergy",39101,39104,39104,55,53,54,2400,3,3

100

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

546,40547,40547,55.25,54,54.67,7.01,27200,29,18 546,40547,40547,55.25,54,54.67,7.01,27200,29,18 "Nepool MH DA LMP",40547,40548,40548,50,48.75,49.39,-5.28,14400,16,14 "Nepool MH DA LMP",40548,40549,40549,54.25,53,53.44,4.05,24800,31,23 "Nepool MH DA LMP",40549,40550,40550,55.5,53.25,54.05,0.61,84800,80,24 "Nepool MH DA LMP",40550,40553,40553,65.5,64.75,65.01,10.96,21600,25,18 "Nepool MH DA LMP",40553,40554,40554,71,68.5,69.33,4.32,15200,18,17 "Nepool MH DA LMP",40554,40555,40555,79,72,77.51,8.18,68800,85,29 "Nepool MH DA LMP",40555,40556,40556,100.5,88,94.96,17.45,40000,49,23 "Nepool MH DA LMP",40556,40557,40557,92.25,87,87.7,-7.26,25600,31,23 "Nepool MH DA LMP",40557,40560,40560,66,63.5,65.03,-22.67,28000,30,17

Note: This page contains sample records for the topic "kwh daily avg" 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

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

355,38356,38356,41,39,40.13,6.73,12000,14,13 355,38356,38356,41,39,40.13,6.73,12000,14,13 "PJM Wh Real Time Peak",38356,38357,38357,41,40,40.57,0.44,13600,15,15 "PJM Wh Real Time Peak",38357,38358,38358,44,42,43.23,2.66,30400,35,16 "PJM Wh Real Time Peak",38358,38359,38359,46.25,44,45.07,1.84,17600,22,12 "PJM Wh Real Time Peak",38359,38362,38362,39.5,38.75,39.17,-5.9,9600,12,11 "PJM Wh Real Time Peak",38362,38363,38363,45,41.5,43.31,4.14,26400,32,17 "PJM Wh Real Time Peak",38363,38364,38364,44,41.25,41.8,-1.51,16000,19,15 "PJM Wh Real Time Peak",38364,38365,38365,39.5,38.5,39.1,-2.7,10400,13,13 "PJM Wh Real Time Peak",38365,38366,38366,51.5,47,48.26,9.16,57600,58,17 "PJM Wh Real Time Peak",38366,38369,38369,65,63,63.48,15.22,23200,21,14

102

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

182,40183,40183,100.5,95,97,19.88,33600,42,27 182,40183,40183,100.5,95,97,19.88,33600,42,27 "Nepool MH DA LMP",40183,40184,40184,95,90,92.96,-4.04,39200,49,25 "Nepool MH DA LMP",40184,40185,40185,94,83,86.45,-6.51,33600,42,30 "Nepool MH DA LMP",40185,40186,40186,90,81.5,83.19,-3.26,47200,53,27 "Nepool MH DA LMP",40186,40189,40189,91,88.75,89.88,6.69,42400,53,30 "Nepool MH DA LMP",40189,40190,40190,71,67.75,68.95,-20.93,78400,95,30 "Nepool MH DA LMP",40190,40191,40191,61.25,58.75,59.99,-8.96,52800,64,31 "Nepool MH DA LMP",40191,40192,40192,56.25,54.75,55.33,-4.66,71200,82,32 "Nepool MH DA LMP",40192,40193,40193,53.75,53,53.36,-1.97,44000,55,25 "Nepool MH DA LMP",40193,40196,40196,55.75,54.75,55.64,2.28,21600,25,12

103

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

Indiana Rt Peak",41246,41247,41247,31.5,31.5,31.5,-1.5,1600,2,3 Indiana Rt Peak",41246,41247,41247,31.5,31.5,31.5,-1.5,1600,2,3 "Indiana Rt Peak",41247,41248,41248,34,33.5,33.75,2.25,1600,2,3 "Indiana Rt Peak",41248,41249,41249,37.25,37,37.13,3.38,8000,10,9 "Indiana Rt Peak",41249,41250,41250,34.25,33.25,33.67,-3.46,2400,3,6 "Indiana Rt Peak",41250,41253,41253,38.25,37,37.5,3.83,12800,16,13 "Indiana Rt Peak",41253,41254,41254,37.75,37.5,37.63,0.13,1600,2,4 "Indiana Rt Peak",41254,41255,41255,34,34,34,-3.63,2400,3,4 "Indiana Rt Peak",41255,41256,41256,32.25,32,32.19,-1.81,3200,4,6 "Indiana Rt Peak",41256,41257,41257,31,31,31,-1.19,1600,2,3 "Indiana Rt Peak",41257,41260,41260,33,32,32.5,1.5,1600,2,4 "Indiana Rt Peak",41260,41261,41261,33.9,33.5,33.66,1.16,3200,4,7

104

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

40911,40912,40912,35.25,34,34.38,-13.52,6400,8,9 40911,40912,40912,35.25,34,34.38,-13.52,6400,8,9 "Indiana",40912,40913,40913,31,30.45,30.73,-3.65,4800,6,7 "Indiana",40913,40914,40914,31,28.75,30.27,-0.46,20000,25,14 "Indiana",40917,40918,40918,29.05,29,29.03,-1.24,1600,2,4 "Indiana",40918,40919,40919,29.5,28.5,29.02,-0.01,5600,7,8 "Indiana",40919,40920,40920,32.25,30.75,31.59,2.57,6400,8,7 "Indiana",40920,40921,40921,35,33.25,33.92,2.33,30400,37,19 "Indiana",40921,40924,40924,29.5,29,29.25,-4.67,1600,2,4 "Indiana",40924,40925,40925,31.5,29.75,30.52,1.27,7200,9,8 "Indiana",40925,40926,40926,30.25,29.5,30,-0.52,3200,4,6 "Indiana",40926,40927,40927,33.75,32,32.61,2.61,13600,17,16 "Indiana",40927,40928,40928,33.5,32.5,33,0.39,9600,12,12

105

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

37.25,35.5,36.16,3.13,27200,25,16 37.25,35.5,36.16,3.13,27200,25,16 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",32,31,31.63,-4.53,12800,15,14 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",26.25,25.5,25.86,-5.77,7200,7,10 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39.5,38.5,39.21,13.35,20000,24,13 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",47.75,45,46.51,7.3,27200,32,19 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",43.5,42,42.79,-3.72,39200,46,20

106

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

26,25.25,25.71,-1.15,6800,16,15 26,25.25,25.71,-1.15,6800,16,15 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",24,23.25,23.63,-2.08,14400,17,14 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",23.85,22,23.36,-0.27,8800,22,16 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",21.85,19.25,20.77,-2.59,10000,25,15 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",21.75,20,21.32,0.55,9600,23,14 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",21.25,19,20.42,-0.9,7200,16,14

107

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

40.5,40.35,40.43,2.67,3200,8,3 40.5,40.35,40.43,2.67,3200,8,3 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",41,40.85,40.97,0.54,2000,2,3 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",36.25,36.25,36.25,-4.72,3200,1,2 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39.05,39,39.02,2.77,1200,2,2 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",36.25,36.25,36.25,-2.77,3200,2,3 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",36.75,36.5,36.63,0.38,1600,4,3

108

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

258,37259,37259,31,27.5,29.51,108000,101,28 258,37259,37259,31,27.5,29.51,108000,101,28 "PJM West",37259,37260,37260,28.25,26.95,27.38,107200,96,32 "PJM West",37260,37263,37263,26.7,26.25,26.45,102400,106,29 "PJM West",37263,37264,37264,26.25,25.45,25.75,87200,81,27 "PJM West",37264,37265,37265,24.85,24.2,24.45,53600,58,27 "PJM West",37265,37266,37266,23.6,22.5,23.05,88000,87,25 "PJM West",37266,37267,37267,23.05,22.75,22.91,72000,79,24 "PJM West",37267,37270,37270,25.1,24.55,24.88,75200,82,29 "PJM West",37270,37271,37271,23.65,22.6,23.44,47200,44,22 "PJM West",37271,37272,37272,23.05,22.85,22.95,42400,47,21 "PJM West",37272,37273,37273,23.6,23.1,23.33,68000,76,27 "PJM West",37273,37274,37274,23.8,23.3,23.47,72800,73,28

109

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

50.25,49,49.68,2.51,19200,46,20 50.25,49,49.68,2.51,19200,46,20 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",49.5,48.5,49.1,-0.58,18000,43,18 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",49.25,47,48.32,-0.78,27200,63,23 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",55,50.5,52.65,4.33,23200,29,20 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",47.75,46.5,47.18,-5.47,13600,34,19 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",47.75,44.75,45.82,-1.36,13600,28,18

110

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

8,50.33,2.26,87200,193,30 8,50.33,2.26,87200,193,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45.5,48.4,-1.93,70400,154,29 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45,46.48,-1.92,62000,146,28 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",49,51.48,5,90400,108,29 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44.5,45.53,-5.95,38800,94,28

111

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

1246,41247,41247,44.25,43.5,43.87,2.68,16400,29,14 1246,41247,41247,44.25,43.5,43.87,2.68,16400,29,14 "SP-15 Gen DA LMP Peak",41247,41248,41248,43,42,42.36,-1.51,36800,59,23 "SP-15 Gen DA LMP Peak",41248,41249,41249,40.25,39.75,40,-2.36,17200,24,11 "SP-15 Gen DA LMP Peak",41249,41250,41251,37,36.5,36.56,-3.44,31200,28,13 "SP-15 Gen DA LMP Peak",41250,41253,41253,41.25,40,40.84,4.28,12000,26,16 "SP-15 Gen DA LMP Peak",41253,41254,41254,39.5,38.5,39.08,-1.76,12400,26,15 "SP-15 Gen DA LMP Peak",41254,41255,41255,39.45,39,39.11,0.03,15600,26,13 "SP-15 Gen DA LMP Peak",41255,41256,41256,43.75,42,43.02,3.91,16000,32,20 "SP-15 Gen DA LMP Peak",41256,41257,41258,43,40.5,42.17,-0.85,38400,32,18 "SP-15 Gen DA LMP Peak",41257,41260,41260,42,41.5,41.62,-0.55,6400,10,11

112

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

7,49.6,0.49,22400,56,24 7,49.6,0.49,22400,56,24 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",54,56.09,6.49,29200,73,27 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",57.5,60.07,3.98,28400,71,26 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",50,55.19,-4.88,32800,41,20 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52.5,56.14,0.95,20800,52,22

113

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

084,39085,39085,43.25,43.25,43.25,-1.79,800,1,2 084,39085,39085,43.25,43.25,43.25,-1.79,800,1,2 "ERCOT-South",39086,39087,39087,42.5,42.25,42.38,-0.87,1600,2,4 "ERCOT-South",39087,39090,39090,43.25,43.25,43.25,0.87,800,1,2 "ERCOT-South",39090,39091,39091,45,45,45,1.75,800,1,2 "ERCOT-South",39091,39092,39092,44.5,44.5,44.5,-0.5,800,1,2,,,," " "ERCOT-South",39099,39100,39100,62,62,62,17.5,3200,4,6 "ERCOT-South",39100,39101,39101,56.5,56,56.17,-5.83,2400,3,5 "ERCOT-South",39101,39104,39104,55,55,55,-1.17,800,1,2 "ERCOT-South",39104,39105,39105,57.25,57,57.08,2.08,2400,3,4 "ERCOT-South",39105,39106,39106,59,58,58.54,1.46,4800,6,5 "ERCOT-South",39106,39107,39107,58,57.75,57.81,-0.73,3200,4,5 "ERCOT-South",39107,39108,39108,54.5,54.5,54.5,-3.31,800,1,2

114

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

720,38721,38721,69,68,68.6,1.54,74400,63,23 720,38721,38721,69,68,68.6,1.54,74400,63,23 "PJM Wh Real Time Peak",38721,38722,38722,74.25,69,70.77,2.17,68000,68,33 "PJM Wh Real Time Peak",38722,38723,38723,77.75,73.5,76.91,6.14,61600,70,35 "PJM Wh Real Time Peak",38723,38726,38726,74,69,70.06,-6.85,55200,57,22 "PJM Wh Real Time Peak",38726,38727,38727,63,61.75,62.52,-7.54,60800,72,29 "PJM Wh Real Time Peak",38727,38728,38728,55,51,53.51,-9.01,68800,55,30 "PJM Wh Real Time Peak",38728,38729,38729,50.5,49,49.37,-4.14,56000,55,25 "PJM Wh Real Time Peak",38729,38730,38730,50.6,49.5,50.17,0.8,54400,55,25 "PJM Wh Real Time Peak",38730,38733,38733,63.5,59,60.85,10.68,36800,37,23 "PJM Wh Real Time Peak",38733,38734,38734,65,64,64.63,3.78,12000,10,13

115

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

449,39450,39450,74,72,73,1600,2,4 449,39450,39450,74,72,73,1600,2,4 "Entergy",39450,39451,39451,64,64,64,800,1,2 "Entergy",39451,39454,39454,47.5,46.5,47,2400,3,3 "Entergy",39454,39455,39455,41.5,41,41.17,2400,3,3 "Entergy",39455,39456,39456,43,43,43,800,1,2 "Entergy",39456,39457,39457,52,49,50.33,2400,3,5 "Entergy",39457,39458,39458,49,49,49,800,1,2 "Entergy",39458,39461,39461,67,67,67,800,1,2 "Entergy",39461,39462,39462,73,73,73,800,1,2 "Entergy",39462,39463,39463,69,68,68.33,2400,3,5 "Entergy",39463,39464,39464,70,64,68,2400,3,3 "Entergy",39464,39465,39465,65,65,65,1600,2,2 "Entergy",39465,39468,39468,79,75,76.67,2400,3,5 "Entergy",39468,39469,39469,74,73,73.7,4000,5,8

116

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

8720,38721,38721,51,50,50.625,3200,4,4 8720,38721,38721,51,50,50.625,3200,4,4 "Entergy",38721,38722,38722,56.5,53.5,55.3,4000,5,7 "Entergy",38722,38723,38723,60,60,60,5600,6,5 "Entergy",38723,38726,38726,59,58,58.5,1600,2,3 "Entergy",38726,38727,38727,55.5,53,54.1,4000,5,5 "Entergy",38727,38728,38728,53.5,52,53.0938,6400,8,9 "Entergy",38728,38729,38729,49,46,47.6667,9600,11,8 "Entergy",38729,38730,38730,49,47.5,48.0417,4800,6,7 "Entergy",38730,38733,38733,54.25,54.25,54.25,800,1,2 "Entergy",38733,38734,38734,53.75,53.75,53.75,800,1,2 "Entergy",38734,38735,38735,62,58,60.1,4000,5,6 "Entergy",38735,38736,38736,60,58,58.875,4800,4,5 "Entergy",38736,38737,38737,55,50,53.1944,7200,9,8

117

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

623,37624,37624,32.5,29,30.16,20800,26,20 623,37624,37624,32.5,29,30.16,20800,26,20 "Entergy",37624,37627,37627,36.75,34.75,35.54,28800,27,18 "Entergy",37627,37628,37628,38,35.5,36.31,45600,53,26 "Entergy",37628,37629,37629,35,31.25,33.69,26400,33,21 "Entergy",37629,37630,37630,33.55,32.75,33.19,22400,26,20 "Entergy",37630,37631,37631,37.75,34.5,35.51,36000,45,24 "Entergy",37631,37634,37634,43.75,38.25,41.62,36800,46,20 "Entergy",37634,37635,37635,42.5,38,40.72,17600,22,18 "Entergy",37635,37636,37636,43,42,42.61,16800,21,17 "Entergy",37636,37637,37637,43,41.25,42.02,12000,15,15 "Entergy",37637,37638,37638,50,44.15,45.85,8800,10,13 "Entergy",37638,37641,37641,41,39.25,40.1,31200,29,16 "Entergy",37641,37642,37642,41.75,38,40.09,25600,27,15

118

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

43.75,40,42.24,2.81,10000,25,19 43.75,40,42.24,2.81,10000,25,19 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",40,38.75,39.35,-2.89,12400,31,16 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45,41.5,43.54,4.19,16000,38,20 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44,42.25,43.09,-0.45,13600,34,19 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",41.5,40,40.64,-2.45,20000,25,16 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",42.25,41,41.35,0.71,14000,34,17

119

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

6894,36895,36895,74.5,74,74.25,1600,2,3 6894,36895,36895,74.5,74,74.25,1600,2,3 "NEPOOL",36899,36900,36900,83,81,82,1600,2,3 "NEPOOL",36900,36901,36901,89,88,88.67,2400,3,3 "NEPOOL",36901,36902,36902,77.5,73,75.25,1600,2,3 "NEPOOL",36902,36903,36903,75.75,75.75,75.75,800,1,2 "NEPOOL",36903,36906,36906,75,74,74.5,2400,3,3 "NEPOOL",36906,36907,36907,80,76.5,77.75,3200,4,3 "NEPOOL",36907,36908,36908,79.5,76,78.38,3200,4,4 "NEPOOL",36908,36909,36909,75.5,74.5,75,3200,3,4 "NEPOOL",36909,36910,36910,71.75,70.75,71.25,1600,2,3 "NEPOOL",36910,36913,36913,74.75,74,74.4,4000,5,3 "NEPOOL",36914,36915,36915,67.5,66.5,67,2400,3,3 "NEPOOL",36915,36916,36916,67,65.75,66.33,2400,3,2 "NEPOOL",36916,36917,36917,65,61.25,63.38,3200,4,3

120

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

1,47,48.2,3.37,9600,24,17 1,47,48.2,3.37,9600,24,17 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56,53,55.36,7.17,9600,24,17 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",58.2,55,57.22,1.85,9200,23,17 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52.25,49,50.04,-7.18,8400,21,19 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45,43.5,44.24,-5.8,26400,28,22 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52.5,50,51.46,7.22,7600,19,15

Note: This page contains sample records for the topic "kwh daily avg" 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

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

48,45.75,46.49,-0.96,30000,63,25 48,45.75,46.49,-0.96,30000,63,25 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",47.5,45,46.75,0.26,31600,79,22 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",51,45,45.83,-0.92,40000,50,24 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",51.25,47.75,48.43,2.6,26000,51,22 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52.75,49.25,50.5,2.07,27200,68,23 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",52.5,51.5,52.02,1.52,46400,55,20

122

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

62,66.21,-0.74,44400,109,30 62,66.21,-0.74,44400,109,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",60,64.12,-2.09,45200,113,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",59,60.9,-3.22,99200,123,29 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",62,63.2,2.3,50400,114,31 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",61.75,62.98,-0.22,48800,122,31

123

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

43.25,42,42.63,4.13,1600,2,4 43.25,42,42.63,4.13,1600,2,4 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",42.65,42.65,42.65,0.02,800,1,2 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45.25,44,44.86,2.21,5600,7,8 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.5,45.75,46.08,1.22,2400,3,6 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45,45,45,-1.08,4000,4,4 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44.75,44.75,44.75,-0.25,1600,2,4

124

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

355,38356,38356,56.85,56.25,56.7,6400,7,7 355,38356,38356,56.85,56.25,56.7,6400,7,7 "NEPOOL MH DA LMP",38356,38357,38357,55.25,55,55.0833,2400,3,3 "NEPOOL MH DA LMP",38357,38358,38358,59,59,59,800,1,2 "NEPOOL MH DA LMP",38358,38359,38359,57.5,57,57.25,2400,3,5 "NEPOOL MH DA LMP",38359,38362,38362,55.5,55.5,55.5,3200,4,6 "NEPOOL MH DA LMP",38362,38363,38363,58.75,58,58.575,9600,11,10 "NEPOOL MH DA LMP",38363,38364,38364,57.75,57.5,57.625,1600,2,4 "NEPOOL MH DA LMP",38364,38365,38365,55.75,55.25,55.4688,12800,15,11 "NEPOOL MH DA LMP",38365,38366,38366,58.5,58.25,58.4583,4800,5,6 "NEPOOL MH DA LMP",38366,38369,38369,92,85,88.7143,5600,7,8 "NEPOOL MH DA LMP",38369,38370,38370,97.5,97,97.1667,2400,3,5

125

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

22.6,23.25,-1.53,6400,14,16 22.6,23.25,-1.53,6400,14,16 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",18.25,18.97,-4.28,6400,8,9 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",18,19.32,0.35,5600,14,10 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",17,17.24,-2.08,7200,12,10 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",18,18.61,1.38,7200,17,17

126

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

988,37991,37991,38.5,38,38.29,10400,13,11 988,37991,37991,38.5,38,38.29,10400,13,11 "Entergy",37991,37992,37992,56,50.5,51.79,15200,19,13 "Entergy",37992,37993,37993,60,56,58.95,12000,15,9 "Entergy",37993,37994,37994,55,51,52.44,16800,21,14 "Entergy",37994,37995,37995,43,40.5,41.28,7200,9,9 "Entergy",37995,37998,37998,45,39,40.86,5600,7,8 "Entergy",37998,37999,37999,39.5,38,38.42,8000,10,7 "Entergy",37999,38000,38000,39,36,37.48,10400,12,9 "Entergy",38000,38001,38001,40.25,38,38.66,14400,17,10 "Entergy",38001,38002,38002,39,36.25,36.98,10400,12,9 "Entergy",38002,38005,38005,39,37,37.44,13600,12,9 "Entergy",38005,38006,38006,55,48,52.64,5600,7,10 "Entergy",38006,38007,38007,54,47,50.58,12000,15,11

127

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

65.25,63,64.48,0.53,9600,12,15 65.25,63,64.48,0.53,9600,12,15 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",59,57,57.68,-6.8,20000,23,13 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",58,57,57.45,-0.23,8800,9,9 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",57,55.75,56.53,-0.92,8000,10,12 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",57.5,56,56.46,-0.07,10400,13,10 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",59.25,56.75,58.09,1.63,20000,25,17

128

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

7988,37991,37991,62,62,62,800,1,2 7988,37991,37991,62,62,62,800,1,2 "NEPOOL MH DA LMP",37991,37992,37992,70,69,69.5,1600,2,2 "NEPOOL MH DA LMP",37992,37993,37993,75.25,72,73.81,3200,4,6 "NEPOOL MH DA LMP",37993,37994,37994,81,76,78.3,8000,10,11 "NEPOOL MH DA LMP",37994,37995,37995,85.75,81.5,84.24,12800,16,12 "NEPOOL MH DA LMP",37998,37999,37999,77,72.5,74.12,6400,8,9 "NEPOOL MH DA LMP",37999,38000,38000,120,92,104.81,16800,21,11 "NEPOOL MH DA LMP",38000,38001,38001,375,270,311.75,6400,8,8 "NEPOOL MH DA LMP",38001,38002,38002,175,170,171,4000,5,5 "NEPOOL MH DA LMP",38005,38006,38006,90,84,86.78,7200,9,7 "NEPOOL MH DA LMP",38006,38007,38007,94,81.5,87.42,10400,13,13 "NEPOOL MH DA LMP",38007,38008,38008,76,72,74.69,6400,8,8

129

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

31.9,30.75,31.02,,14000,34,10 31.9,30.75,31.02,,14000,34,10 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",28.85,28,28.3,-2.72,52000,59,13 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",31.5,31,31.22,2.92,20000,41,13 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",34.25,33.4,33.8,2.58,22000,47,13 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",30,29.75,29.9,-3.9,52800,54,16 "NP15","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",28.25,27.85,27.95,-1.95,48000,57,11

130

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

59.05,59,59.03,2.03,1600,2,3 59.05,59,59.03,2.03,1600,2,3 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",63,63,63,3.97,800,1,2 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",62.5,60,61,-2,2400,3,6 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",63.75,63,63.32,2.32,5600,7,8 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",56,55,55.5,-7.82,3200,4,5 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",55.5,55.5,55.5,0,800,1,2

131

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

815,39818,39818,58.5,55.25,56.28,5.13,40000,45,27 815,39818,39818,58.5,55.25,56.28,5.13,40000,45,27 "PJM Wh Real Time Peak",39818,39819,39819,60.25,57.75,58.92,2.64,109600,119,41 "PJM Wh Real Time Peak",39819,39820,39820,58,55,56.66,-2.26,49600,60,29 "PJM Wh Real Time Peak",39820,39821,39821,55.55,55,55.21,-1.45,48000,56,34 "PJM Wh Real Time Peak",39821,39822,39822,63,60.75,61.9,6.69,38400,46,28 "PJM Wh Real Time Peak",39822,39825,39825,69,66,67.63,5.73,62400,74,37 "PJM Wh Real Time Peak",39825,39826,39826,66.5,61,64.03,-3.6,91200,107,40 "PJM Wh Real Time Peak",39826,39827,39827,85.5,80,82.91,18.88,103200,124,50 "PJM Wh Real Time Peak",39827,39828,39828,100,88,93.22,10.31,110400,135,51 "PJM Wh Real Time Peak",39828,39829,39829,110,93,98.58,5.36,77600,93,37

132

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

988,37991,37991,43.25,36,38.11,35200,40,16 988,37991,37991,43.25,36,38.11,35200,40,16 "PJM West",37991,37992,37992,53.5,50,51.99,33600,41,24 "PJM West",37992,37993,37993,70,66.25,67.48,34400,40,25 "PJM West",37993,37994,37994,62,59.65,60.58,36000,41,19 "PJM West",37994,37995,37995,56.75,53,54.66,32800,39,23 "PJM West",37995,37998,37998,53.75,51.25,52.44,40000,47,25 "PJM West",37998,37999,37999,54,52.55,53.14,37600,47,24 "PJM West",37999,38000,38000,65.25,61.5,63.18,30400,37,20 "PJM West",38000,38001,38001,88,77,82.58,50400,57,28 "PJM West",38001,38002,38002,90,77,80.76,31200,37,20 "PJM West",38002,38005,38005,53.25,52.75,53.03,30400,38,18 "PJM West",38005,38006,38006,70,67,68.64,36000,45,24

133

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

150,150,,400,1,2 150,150,,400,1,2 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",180,180,30,2400,3,4 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",310,310,130,400,1,2 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",350,350,40,400,1,2 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",165,165,-185,800,1,2

134

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

1246,41247,41247,27.5,27.5,27.5,0.17,800,1,2 1246,41247,41247,27.5,27.5,27.5,0.17,800,1,2 "Entergy Peak",41247,41248,41248,28.5,28.5,28.5,1,800,1,2 "Entergy Peak",41248,41249,41249,30,30,30,1.5,800,1,2 "Entergy Peak",41250,41253,41253,30,29,29.5,-0.5,1600,2,3 "Entergy Peak",41253,41254,41254,30,29.75,29.88,0.38,1600,2,2 "Entergy Peak",41254,41255,41255,29.75,29.75,29.75,-0.13,800,1,2 "Entergy Peak",41269,41270,41270,32,32,32,2.25,1600,2,2 "Entergy Peak",41355,41358,41358,38.5,38.5,38.5,6.5,800,1,2 "Entergy Peak",41367,41368,41368,35,35,35,-3.5,800,1,2 "Entergy Peak",41425,41428,41428,37,37,37,2,800,1,2 "Entergy Peak",41436,41437,41437,42,42,42,5,800,1,2 "Entergy Peak",41446,41449,41449,41,41,41,-1,800,1,2

135

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

32.5,33.04,-3.33,15200,19,19 32.5,33.04,-3.33,15200,19,19 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",37,37.32,4.28,7600,19,18 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",35,35.46,-1.86,9600,24,22 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",37,38.66,3.2,14800,36,27 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39.75,40.34,1.69,9200,23,22

136

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

0911,40912,40912,27,26.5,26.83,-2.17,8800,11,6 0911,40912,40912,27,26.5,26.83,-2.17,8800,11,6 "ERCOT Houston",40912,40913,40913,28.3,28,28.18,1.35,4800,6,7 "ERCOT Houston",40913,40914,40914,26.35,26.2,26.29,-1.89,3200,4,6 "ERCOT Houston",40914,40917,40917,27.25,27,27.13,0.84,8000,10,5 "ERCOT Houston",40917,40918,40918,27.75,27.5,27.58,0.45,2400,3,3 "ERCOT Houston",40918,40919,40919,27.5,27.5,27.5,-0.08,1600,2,2 "ERCOT Houston",40919,40920,40920,31.5,31,31.33,3.83,2400,3,4 "ERCOT Houston",40920,40921,40921,31,30.25,30.5,-0.83,2400,2,4 "ERCOT Houston",40925,40926,40926,26,25.75,25.96,-4.54,5600,7,4 "ERCOT Houston",40926,40927,40927,23.75,23.75,23.75,-2.21,2400,3,5 "ERCOT Houston",40928,40931,40931,22.15,22.15,22.15,-1.6,800,1,2

137

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

258,37259,37259,26,22.95,24.08,51200,64,19 258,37259,37259,26,22.95,24.08,51200,64,19 "Entergy",37259,37260,37260,28.25,24.5,26.09,38400,47,17 "Entergy",37260,37263,37263,22.5,17,20.72,34400,43,16 "Entergy",37263,37264,37264,25,19,20.17,19200,24,15 "Entergy",37264,37265,37265,20,19,19.55,44000,54,19 "Entergy",37265,37266,37266,23,18.75,19.31,50400,62,18 "Entergy",37266,37267,37267,19,15,18.21,45600,56,18 "Entergy",37267,37270,37270,18.85,17.4,18.21,65600,81,17 "Entergy",37270,37271,37271,21.75,18.2,19.01,24800,28,18 "Entergy",37271,37272,37272,22.35,18.95,20.98,31200,38,16 "Entergy",37272,37273,37273,22,19,21.2,49600,62,22 "Entergy",37273,37274,37274,22.5,19.5,20.93,46400,55,20 "Entergy",37274,37277,37277,19.75,18.75,19.26,36000,45,18

138

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

SP-15 Gen DA LMP Peak",39904,39905,39905,30.85,30,30.44,"na",69200,129,16 SP-15 Gen DA LMP Peak",39904,39905,39905,30.85,30,30.44,"na",69200,129,16 "SP-15 Gen DA LMP Peak",39905,39906,39907,28.7,27.5,28.03,-2.41,119200,103,17 "SP-15 Gen DA LMP Peak",39906,39909,39909,31.5,30.25,30.5,2.47,43200,89,17 "SP-15 Gen DA LMP Peak",39909,39910,39910,33.3,32.45,32.83,2.33,40800,80,20 "SP-15 Gen DA LMP Peak",39910,39911,39912,29,28,28.69,-4.14,116000,117,22 "SP-15 Gen DA LMP Peak",39911,39913,39914,27.25,26.55,26.88,-1.81,96800,110,21 "SP-15 Gen DA LMP Peak",39912,39916,39916,28.5,27.5,28.01,1.13,58000,119,19 "SP-15 Gen DA LMP Peak",39916,39917,39917,26.65,25,26.27,-1.74,26400,51,17 "SP-15 Gen DA LMP Peak",39917,39918,39918,28.25,27.7,27.97,1.7,55600,101,20

139

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

43,39.05,41.9,4.15,5600,7,8 43,39.05,41.9,4.15,5600,7,8 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",40.5,38.5,39.53,-2.37,3200,4,7 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39.25,38.25,38.9,-0.63,13600,17,15 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",41.5,39,40,1.1,10400,13,11 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39,37.75,38.3,-1.7,12000,14,15 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44.5,43,43.4,5.1,4000,5,5

140

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

62.5,65.15,3.64,62800,150,34 62.5,65.15,3.64,62800,150,34 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",54.25,61.54,-3.61,153600,172,34 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",60.5,62.02,0.48,81200,188,36 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",61.75,62.73,0.71,69600,168,34 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",62.75,63.47,0.74,74400,170,34

Note: This page contains sample records for the topic "kwh daily avg" 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

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

1,45.5,-0.2,22800,57,25 1,45.5,-0.2,22800,57,25 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",43.5,45.44,-0.06,96000,198,32 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",42.25,43.27,-2.17,89600,210,33 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",39,42.7,-0.57,118400,261,35 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",42.5,43.86,1.16,169600,196,33

142

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

40182,40183,40183,60.5,60.5,60.5,7.5,800,1,2 40182,40183,40183,60.5,60.5,60.5,7.5,800,1,2 "Entergy Peak",40183,40184,40184,62.25,62.25,62.25,1.75,800,1,2 "Entergy Peak",40189,40190,40190,63.5,60.75,62.42,0.17,2400,3,3 "Entergy Peak",40190,40191,40191,46,45,45.5,-16.92,1600,2,2 "Entergy Peak",40196,40197,40197,40,40,40,-5.5,800,1,2 "Entergy Peak",40197,40198,40198,40,40,40,0,800,1,2 "Entergy Peak",40198,40199,40199,38,38,38,-2,800,1,2 "Entergy Peak",40199,40200,40200,38,38,38,0,800,1,2 "Entergy Peak",40204,40205,40205,47,47,47,9,800,1,2 "Entergy Peak",40205,40206,40206,45,45,45,-2,800,1,2 "Entergy Peak",40206,40207,40207,48,48,48,3,800,1,2 "Entergy Peak",40210,40211,40211,43,43,43,-5,800,1,2

143

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

449,39450,39450,131,114,125.81,37.67,95200,116,49 449,39450,39450,131,114,125.81,37.67,95200,116,49 "PJM Wh Real Time Peak",39450,39451,39451,106,99,102.43,-23.38,78400,96,39 "PJM Wh Real Time Peak",39451,39454,39454,54,52.5,53.44,-48.99,65600,74,34 "PJM Wh Real Time Peak",39454,39455,39455,45,41,42.69,-10.75,87200,98,48 "PJM Wh Real Time Peak",39455,39456,39456,47.5,45,46.31,3.62,47200,57,36 "PJM Wh Real Time Peak",39456,39457,39457,59.5,54.25,57.53,11.22,35200,44,34 "PJM Wh Real Time Peak",39457,39458,39458,51,46.25,48.3,-9.23,72800,88,51 "PJM Wh Real Time Peak",39458,39461,39461,76.5,70,74.88,26.58,103200,121,42 "PJM Wh Real Time Peak",39461,39462,39462,80,75.5,77.94,3.06,109600,127,40 "PJM Wh Real Time Peak",39462,39463,39463,72,68,70.47,-7.47,78400,95,35

144

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

911,40912,40912,56,52,53.84,-11.87,161600,191,55 911,40912,40912,56,52,53.84,-11.87,161600,191,55 "PJM Wh Real Time Peak",40912,40913,40913,39,38,38.7,-15.14,45600,54,30 "PJM Wh Real Time Peak",40913,40914,40914,33.25,33,33.05,-5.65,42400,53,33 "PJM Wh Real Time Peak",40914,40917,40917,37.25,36.5,36.8,3.75,43200,51,34 "PJM Wh Real Time Peak",40917,40918,40918,36,35.25,35.53,-1.27,48000,57,31 "PJM Wh Real Time Peak",40918,40919,40919,35,34.2,34.6,-0.93,32000,40,28 "PJM Wh Real Time Peak",40919,40920,40920,35.5,35,35.14,0.54,43200,48,27 "PJM Wh Real Time Peak",40920,40921,40921,40.75,38.6,39.44,4.3,108000,111,39 "PJM Wh Real Time Peak",40921,40924,40924,43.5,41.6,42.69,3.25,61600,74,39 "PJM Wh Real Time Peak",40924,40925,40925,35.25,34.5,34.68,-8.01,36000,44,23

145

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

Da LMP Peak",41246,41247,41247,48,45.75,47.16,-7.85,40000,48,21 Da LMP Peak",41246,41247,41247,48,45.75,47.16,-7.85,40000,48,21 "Nepool MH Da LMP Peak",41247,41248,41248,58.5,55,57.81,10.65,26400,32,21 "Nepool MH Da LMP Peak",41248,41249,41249,79.75,75,76.49,18.68,32800,39,18 "Nepool MH Da LMP Peak",41249,41250,41250,65,50.5,51.47,-25.02,35200,42,23 "Nepool MH Da LMP Peak",41250,41253,41253,47,45.5,46.48,-4.99,12800,16,14 "Nepool MH Da LMP Peak",41253,41254,41254,50,46,47.3,0.82,38400,44,22 "Nepool MH Da LMP Peak",41254,41255,41255,70,57,59.54,12.24,39200,49,19 "Nepool MH Da LMP Peak",41255,41256,41256,50,48.25,48.97,-10.57,53600,59,29 "Nepool MH Da LMP Peak",41256,41257,41257,39.25,38.5,38.98,-9.99,11200,14,10 "Nepool MH Da LMP Peak",41257,41260,41260,45,45,45,6.02,3200,4,6

146

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

82,75,79.66,6.43,30400,38,26 82,75,79.66,6.43,30400,38,26 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",62,58,60.11,-19.55,24000,30,22 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45.05,43.75,44.81,-15.3,24000,28,17 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",38,36,36.89,-7.92,35200,39,17 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44,41.5,42.84,5.95,32000,39,23 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",48,44,46.44,3.6,22400,28,20

147

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

46,48.6,-4.22,46000,115,33 46,48.6,-4.22,46000,115,33 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.5,49.21,0.61,51600,120,30 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",45.75,46.71,-2.5,123200,150,36 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",46.5,49.35,2.64,63600,151,36 "Mid Columbia Peak","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",47.3,49.44,0.09,65600,163,34

148

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

34.5,34.5,34.5,3.21,1600,2,3 34.5,34.5,34.5,3.21,1600,2,3 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",35.75,35.5,35.58,1.08,2400,3,4 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",36.5,36,36.25,0.67,4000,5,7 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",36.25,36,36.13,-0.12,3200,4,4 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44,43.5,43.75,7.62,3200,4,6 "ERCOT Houston","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",44.25,43.75,44.04,0.29,5600,7,8

149

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

0911,40912,40912,27,26.5,26.63,-2.76,6400,8,6 0911,40912,40912,27,26.5,26.63,-2.76,6400,8,6 "ERCOT-South",40912,40913,40913,28,27.25,27.72,1.09,8000,9,7 "ERCOT-South",40913,40914,40914,25.75,25.75,25.75,-1.97,2400,3,4 "ERCOT-South",40914,40917,40917,27,27,27,1.25,1600,2,4 "ERCOT-South",40919,40920,40920,31,31,31,4,800,1,2 "ERCOT-South",40920,40921,40921,30.25,30.25,30.25,-0.75,800,1,2 "ERCOT-South",40925,40926,40926,25.5,25.5,25.5,-4.75,800,1,2 "ERCOT-South",40926,40927,40927,23.25,23.25,23.25,-2.25,800,1,2 "ERCOT-South",40931,40932,40932,24.5,24.5,24.5,1.25,800,1,2 "ERCOT-South",40932,40933,40933,26,25.75,25.96,1.46,4800,6,4 "ERCOT-South",40933,40934,40934,28,27,27.5,1.54,1600,2,4 "ERCOT-South",40934,40935,40935,29,28.75,28.88,1.38,1600,2,4

150

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

68.5,66,67.29,5.05,28400,71,21 68.5,66,67.29,5.05,28400,71,21 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",65,62.5,63.85,-3.44,27200,66,25 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",65.25,61.75,63.39,-0.46,80800,99,26 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",65.75,63.5,64.58,1.19,49200,107,25 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",65.75,64,64.98,0.4,32400,81,24 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",65.25,62.25,63.26,-1.72,78400,96,25

151

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

65.75,63,64.97,4.97,29600,55,25 65.75,63,64.97,4.97,29600,55,25 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",62.25,59,61.4,-3.57,106400,109,24 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",63,59.25,60.22,-1.18,45600,102,26 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",63.5,61.75,62.26,2.04,40400,86,26 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",64.2,62,62.52,0.26,38400,75,25 "Palo Verde","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",66.45,62,63.19,0.67,45200,87,27

152

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

PJM-West Real Time Peak",41276,41277,41277,44,41.75,42.64,-6.4,60000,72,34 PJM-West Real Time Peak",41276,41277,41277,44,41.75,42.64,-6.4,60000,72,34 "PJM-West Real Time Peak",41277,41278,41278,37,36,36.53,-6.11,19200,23,23 "PJM-West Real Time Peak",41278,41281,41281,36.5,36,36.17,-0.36,41600,48,32 "PJM-West Real Time Peak",41281,41282,41282,33.05,32.5,32.61,-3.56,20800,26,18 "PJM-West Real Time Peak",41282,41283,41283,33.75,32.5,32.91,0.3,37600,43,30 "PJM-West Real Time Peak",41283,41284,41284,31,30.25,30.64,-2.27,26400,31,26 "PJM-West Real Time Peak",41284,41285,41285,29.9,29.25,29.66,-0.98,38400,26,23 "PJM-West Real Time Peak",41285,41288,41288,32.5,31.5,32.14,2.48,40000,50,28 "PJM-West Real Time Peak",41288,41289,41289,37.5,34.5,36.5,4.36,64800,74,35

153

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

39815,39818,39818,42,39,41,4.5,2400,3,4 39815,39818,39818,42,39,41,4.5,2400,3,4 "Entergy Peak",39818,39819,39819,44.5,44.5,44.5,3.5,800,1,2 "Entergy Peak",39819,39820,39820,44.5,44,44.25,-0.25,1600,2,4 "Entergy Peak",39820,39821,39821,46,45,45.5,1.25,2400,3,6 "Entergy Peak",39821,39822,39822,45,45,45,-0.5,800,1,2 "Entergy Peak",39822,39825,39825,45,40,42.5,-2.5,1600,2,3 "Entergy Peak",39825,39826,39826,48,48,48,5.5,1600,2,3 "Entergy Peak",39827,39828,39828,55,53,54,6,1600,2,4 "Entergy Peak",39828,39829,39829,56,53,54.33,0.33,2400,3,5 "Entergy Peak",39832,39833,39833,42.5,42.5,42.5,-11.83,800,1,2 "Entergy Peak",39833,39834,39834,43,42,42.5,0,1600,2,4 "Entergy Peak",39836,39839,39839,40,38,39,-3.5,1600,2,3

154

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

1246,41247,41247,30,30,30,-2.63,1600,2,2 1246,41247,41247,30,30,30,-2.63,1600,2,2 "ERCOT Houston",41250,41253,41253,33,33,33,3,800,1,2 "ERCOT Houston",41260,41261,41261,27,26.9,26.98,-6.02,4000,5,4 "ERCOT Houston",41263,41264,41264,28.5,28.25,28.33,1.35,2400,3,4 "ERCOT Houston",41270,41271,41271,26.5,26.5,26.5,-1.83,800,1,2 "ERCOT Houston",41288,41289,41289,34.25,34,34.13,7.63,1600,2,3 "ERCOT Houston",41289,41290,41290,33.85,33.75,33.78,-0.35,2400,3,4 "ERCOT Houston",41338,41339,41339,34.75,34.25,34.58,0.8,2400,3,3 "ERCOT Houston",41372,41373,41373,42.75,42.75,42.75,8.17,800,1,2 "ERCOT Houston",41381,41382,41382,35.55,35.55,35.55,-7.2,800,1,2 "ERCOT Houston",41386,41387,41387,37.5,37.5,37.5,1.95,800,1,2

155

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

60.75,57.5,59.33,7.47,34400,42,23 60.75,57.5,59.33,7.47,34400,42,23 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",58.5,55,56.62,-2.71,36800,45,25 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",65,62.25,63.61,6.99,76000,86,34 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",66.5,60,63.84,0.23,43200,52,26 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",58.5,55,57.1,-6.74,36000,41,21 "Indiana","application/vnd.ms-excel","application/vnd.ms-excel","application/vnd.ms-excel",48,44,46.02,-11.08,33600,42,27

156

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Companies"  

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

546,40547,40547,37,37,37,0,800,1,2 546,40547,40547,37,37,37,0,800,1,2 "Entergy Peak",40547,40548,40548,36,36,36,-1,800,1,2 "Entergy Peak",40548,40549,40549,33.75,33.75,33.75,-2.25,1600,2,2 "Entergy Peak",40550,40553,40553,42,42,42,8.25,800,1,2 "Entergy Peak",40555,40556,40556,52.75,49,50.88,8.88,1600,2,3 "Entergy Peak",40562,40563,40563,38.5,38,38.1,-12.78,4000,5,4 "Entergy Peak",40563,40564,40564,39,39,39,0.9,800,1,2 "Entergy Peak",40567,40568,40568,39,39,39,0,800,1,2 "Entergy Peak",40568,40569,40569,38,38,38,-1,800,1,2 "Entergy Peak",40571,40574,40574,36,36,36,-2,800,1,2 "Entergy Peak",40574,40575,40575,39.5,39.5,39.5,3.5,800,1,2 "Entergy Peak",40575,40576,40576,37,36.5,36.75,-2.75,1600,2,2

157

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Change","Daily Volume MWh","Number of Trades","Number of Counterparties"  

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

DA LMP",40911,40912,40912,92,84.75,87.16,-14.07,46400,56,29 DA LMP",40911,40912,40912,92,84.75,87.16,-14.07,46400,56,29 "Nepool MH DA LMP",40912,40913,40913,49,46,47.55,-39.61,78400,77,24 "Nepool MH DA LMP",40913,40914,40914,39.75,39.25,39.57,-7.98,12000,15,10 "Nepool MH DA LMP",40914,40917,40917,39,38,38.39,-1.18,8800,11,9 "Nepool MH DA LMP",40917,40918,40918,38.25,38,38.14,-0.25,8000,9,11 "Nepool MH DA LMP",40918,40919,40919,41.5,39.9,40.88,2.74,70400,83,25 "Nepool MH DA LMP",40919,40920,40920,37.25,36.75,36.83,-4.05,20000,23,16 "Nepool MH DA LMP",40920,40921,40921,44,43.5,43.73,6.9,11200,11,12 "Nepool MH DA LMP",40921,40924,40924,67,65.5,66.35,22.62,16800,21,15 "Nepool MH DA LMP",40924,40925,40925,50.75,50,50.24,-16.11,11200,14,12

158

"Price Hub","Trade Date","Delivery Start Date","Delivery End Date","High Price $/MWh","Low Price $/MWh","Wtd Avg Price $/MWh","Daily Volume MWh","Number of Trades","Number of Companies"  

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

6893,36894,36894,65.5,64.5,65,1600,2,2 6893,36894,36894,65.5,64.5,65,1600,2,2 "PJM West",36894,36895,36895,63,59.5,61.25,3200,4,2 "PJM West",36895,36896,36896,60,58.5,59.12,4800,6,4 "PJM West",36899,36900,36900,59.5,59.5,59.5,800,1,2 "PJM West",36900,36901,36901,58,55.5,56.61,5600,7,6 "PJM West",36901,36902,36902,50.5,49,49.75,3200,4,4 "PJM West",36902,36903,36903,47,46,46.33,4800,6,3 "PJM West",36903,36906,36906,45.5,45,45.12,3200,4,6 "PJM West",36906,36907,36907,46,42,44.21,5600,7,6 "PJM West",36907,36908,36908,42.5,42,42.4,4000,4,7 "PJM West",36908,36909,36909,41,39,39.56,7200,7,6 "PJM West",36909,36910,36910,39.5,39,39.25,2400,3,5 "PJM West",36910,36913,36913,51,50,50.43,5600,5,6

159

Backstage at the Daily Show | Department of Energy  

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

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

160

On the Conditional Distribution of Daily Precipitation Amounts  

Science Conference Proceedings (OSTI)

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

Edwin H. Chin; John F. Miller

1980-09-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

Thailand Daily Rainfall and Comparison with TRMM Products  

Science Conference Proceedings (OSTI)

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

Roongroj Chokngamwong; Long S. Chiu

2008-04-01T23:59:59.000Z

162

Comprehensive Automated Quality Assurance of Daily Surface Observations  

Science Conference Proceedings (OSTI)

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

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

2010-08-01T23:59:59.000Z

163

Climatologically Aided Mapping of Daily Precipitation and Temperature  

Science Conference Proceedings (OSTI)

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

Richard D. Hunter; Ross K. Meentemeyer

2005-10-01T23:59:59.000Z

164

Realizations of Daily Weather in Forecast Seasonal Climate  

Science Conference Proceedings (OSTI)

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

D. S. Wilks

2002-04-01T23:59:59.000Z

165

Regional, Very Heavy Daily Precipitation in NARCCAP Simulations  

Science Conference Proceedings (OSTI)

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

Sho Kawazoe; William J. Gutowski Jr.

2013-08-01T23:59:59.000Z

166

Regional, Very Heavy Daily Precipitation in CMIP5 Simulations  

Science Conference Proceedings (OSTI)

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

Sho Kawazoe; William J. Gutowski Jr.

2013-08-01T23:59:59.000Z

167

Uncertainty of Daily Isolation Estimates from a Mesoscale Pyranometer Network  

Science Conference Proceedings (OSTI)

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

William L. Bland

1996-02-01T23:59:59.000Z

168

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

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

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

169

Daylighter Daily Solar Roof Light | Open Energy Information  

Open Energy Info (EERE)

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

170

Warm Weather and the Daily Commute | Department of Energy  

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

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

171

Global Increasing Trends in Annual Maximum Daily Precipitation  

Science Conference Proceedings (OSTI)

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

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

2013-06-01T23:59:59.000Z

172

Daily rainfall disaggregation using HYETOS model for Peninsular Malaysia  

Science Conference Proceedings (OSTI)

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

Ibrahim Suliman Hanaish; Kamarulzaman Ibrahim; Abdul Aziz Jemain

2011-07-01T23:59:59.000Z

173

Generating Multiyear Gridded Daily Rainfall over New Zealand  

Science Conference Proceedings (OSTI)

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

Andrew Tait; Richard Turner

2005-09-01T23:59:59.000Z

174

On an Additive Model of Daily Temperature Climates  

Science Conference Proceedings (OSTI)

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

Nathaniel B. Guttman; Marc S. Plantico

1989-10-01T23:59:59.000Z

175

Description: Lithium batteries are used daily in our work  

E-Print Network (OSTI)

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

176

Variability in Daily, Zonal Mean Lower-Stratospheric Temperatures  

Science Conference Proceedings (OSTI)

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

John R. Christy; S. James Drouilhet Jr.

1994-01-01T23:59:59.000Z

177

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.

178

Cointegration of the Daily Electric Power System Load and the Weather  

E-Print Network (OSTI)

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

Stefanov, Stefan Z

2007-01-01T23:59:59.000Z

179

GenForecast(26yr)(avg).PDF  

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

SLCAIP Historical & Forecast Generation at Plant Total Range of Hydrology 0 2,000,000,000 4,000,000,000 6,000,000,000 8,000,000,000 10,000,000,000 12,000,000,000 1 9 7 0 1 9 7 2 1...

180

Variability in daily, zonal mean lower-stratospheric temperatures  

Science Conference Proceedings (OSTI)

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

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

1994-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

KWH_APS_DPP07_1Page.ppt  

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

Ion-Temperature and Rotation-Velocity Profiles from a Spatially Resolving X-Ray Crystal Spectrometer on Alcator C-Mod Ion-Temperature and Rotation-Velocity Profiles from a Spatially Resolving X-Ray Crystal Spectrometer on Alcator C-Mod K. W. Hill, 1 M. Bitter, 1 P. Beiersdorfer, 3 Ch. Broennimann, 4 E. F. Eikenberry, 4 A. Ince-Cushman, 2 Ming-Feng Gu, 3 S. G. Lee, 5 M. Reinke, 2 J. E. Rice, 2 S. D. Scott, 1 and B. Stratton 1 1 Princeton Plasma Physics Laboratory, Princeton, NJ 2 MIT Plasma Science and Fusion Center, Cambridge, MA 3 LLNL, Livermore, CA 4 DECTRIS Ltd., 5232 Villigen-PSI, Switzerland 5 NFRC, Korea Basic Science Institute, Daejeon, Korea Abstract A new x-ray crystal spectrometer capable of providing spatially (~1.5 cm) and temporally (~10 ms) resolved, high resolution spectra of He-like Ar Kα lines has been installed on Alcator C-Mod. The imaging spectrometer consists of a

182

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

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

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

183

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

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

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

184

Local-Scale Variability of Daily Solar RadiationSan Diego County, California  

Science Conference Proceedings (OSTI)

The spatial variability of daily solar radiation values over a region of several hundred square kilometers was examined. Coefficients of variability were obtained as the standard deviations of between-station daily radiation difference divided by ...

Edward Aguado

1986-05-01T23:59:59.000Z

185

Trends in Daily Solar Radiation and Precipitation Coefficients of Variation since 1984  

Science Conference Proceedings (OSTI)

This study investigates the possibility of changes in daily scale solar radiation and precipitation variability. Coefficients of variation (CVs) were computed for the daily downward surface solar radiation product from the International Satellite ...

David Medvigy; Claudie Beaulieu

2012-02-01T23:59:59.000Z

186

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

Science Conference Proceedings (OSTI)

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

P. R. Berliner; K. Droppelmann

2003-04-01T23:59:59.000Z

187

Observational Evidence for Reduction of Daily Maximum Temperature by Croplands in the Midwest United States  

Science Conference Proceedings (OSTI)

Climate model simulations have shown that conversion of natural forest vegetation to croplands in the United States cooled climate. The cooling was greater for daily maximum temperature than for daily minimum temperature, resulting in a reduced ...

Gordon B. Bonan

2001-06-01T23:59:59.000Z

188

Statistical Tests for Comparison of Daily Variability in Observed and Simulated Climates  

Science Conference Proceedings (OSTI)

Tests for differences in daily variability based on the jackknife are presented. These tests properly account for the effect of autocorrelation in the data and are reasonably robust against departures from normality. Three measures for the daily ...

T. Adri Buishand; Jules J. Beersma

1996-10-01T23:59:59.000Z

189

An Overview of the Global Historical Climatology Network-Daily Database  

Science Conference Proceedings (OSTI)

A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, ...

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

2012-07-01T23:59:59.000Z

190

Evaluation of ASCAT-Based Daily Gridded Winds in the Tropical Indian Ocean  

Science Conference Proceedings (OSTI)

The quality of daily gridded Advanced Scatterometer (ASCAT; DASCAT) blended winds is examined in the tropical Indian Ocean using 3-day running mean gridded Quick Scatterometer (QuikSCAT; QSCAT) winds and in situ daily winds from the Research ...

S. Sivareddy; M. Ravichandran; M. S. Girishkumar

2013-07-01T23:59:59.000Z

191

A Method to Estimate Missing Daily Maximum and Minimum Temperature Observations  

Science Conference Proceedings (OSTI)

A method to estimate missing daily maximum and minimum temperatures is presented. Temperature estimates are based on departures from daily temperature normals at the three closest stations with similar observation times. Although applied to ...

Arthur T. DeGaetano; Keith L. Eggleston; Warren W. Knapp

1995-02-01T23:59:59.000Z

192

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

Science Conference Proceedings (OSTI)

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

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

2010-09-01T23:59:59.000Z

193

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

Science Conference Proceedings (OSTI)

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

Panmao Zhai; Xuebin Zhang; Hui Wan; Xiaohua Pan

2005-04-01T23:59:59.000Z

194

Form EIA-930 HOURLY AND DAILY BALANCING AUTHORITY OPERATIONS REPORT  

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

930 930 HOURLY AND DAILY BALANCING AUTHORITY OPERATIONS REPORT INSTRUCTIONS Due Date: mm/dd/yyyy Approved: OMB No. 1905-0129 Approval Expires: 10/31/2016 Burden: 0.19 hours Page 1 Draft for Discussion only PURPOSE Form EIA-930 requires Internet posting of hourly balancing authority operating data. The posted data are used to monitor the current status and trends of the electric power industry, and to support enhancement of electric system operations. REQUIRED RESPONDENTS For the contiguous United States: all entities that are listed in NERC's Compliance Registry as a balancing authority must post balancing authority operating information required by this survey. Other than the Midwest ISO (MISO), registered balancing authorities that are parties

195

Dependence of Extreme Daily Maximum Temperatures on Antecedent Soil Moisture in the Contiguous United States during Summer  

Science Conference Proceedings (OSTI)

The paper presents an analysis of the dependence of summertime daily maximum temperature on antecedent soil moisture using daily surface observations from a selection of stations in the contiguous United States and daily time series of soil ...

Imke Durre; John M. Wallace; Dennis P. Lettenmaier

2000-07-01T23:59:59.000Z

196

Daily intake of antioxidants in relation to survival among adult patients diagnosed with malignant glioma  

E-Print Network (OSTI)

II patients, moderate intake of water-soluble folate wasfor tertiles of daily intake of water-soluble antioxidants4: Associations between intake of water-soluble antioxidants

2010-01-01T23:59:59.000Z

197

Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications  

E-Print Network (OSTI)

estimating daily net photosynthesis. Ecol. Model. 58, 209 Canopy reflectance, photosynthesis, and transpira- tion.model of forest photosynthesis compared with measurements by

Chen, J.M; Liu, J; Cihlar, J; Goulden, M.L

1999-01-01T23:59:59.000Z

198

Daily Precipitation Forecasting in Dakar Using the NCEPNCAR Reanalyses  

Science Conference Proceedings (OSTI)

In order to predict the daily rain amount at Dakar at 15-day lead times, 65 thermodynamical and dynamical indices are computed at each grid point for the area 15S30N, 30W30E. The data used are NCEPNCAR reanalyses and daily rainfall ...

Abdoulaye Deme; Alain Viltard; Pierre de Flice

2003-02-01T23:59:59.000Z

199

Taking China's Temperature: Daily Range, Warming Trends, and Regional Variations, 19552000  

Science Conference Proceedings (OSTI)

In analyzing daily climate data from 305 weather stations in China for the period from 1955 to 2000, the authors found that surface air temperatures are increasing with an accelerating trend after 1990. They also found that the daily maximum (T...

Binhui Liu; Ming Xu; Mark Henderson; Ye Qi; Yiqing Li

2004-11-01T23:59:59.000Z

200

Southeastern United States Daily Temperature Ranges Associated with the El NioSouthern Oscillation  

Science Conference Proceedings (OSTI)

The daily temperature range (DTR), daily maximum minus minimum temperature, at 290 Southeast United States stations is examined with respect to the warm and cold phases of the El NioSouthern Oscillation (ENSO) for the period of 19482009. A ...

Daniel M. Gilford; Shawn R. Smith; Melissa L. Griffin; Anthony Arguez

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


201

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

E-Print Network (OSTI)

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

Hunt Jr., E. Raymond

202

Principal Investigators: Long-Term Daily and Monthly Climate Records from  

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

Principal Investigators: Long-Term Daily and Monthly Climate Records from Principal Investigators: Long-Term Daily and Monthly Climate Records from Stations Across the Contiguous United States M.J. Menne, C.N. Williams, Jr., and R.S. Vose National Climatic Data Center National Oceanic and Atmospheric Administration CDIAC and the USHCN PIs encourage users to make this site their main source for obtaining USHCN data, where you can take advantage of data plotting, and, for daily data, user-friendly station-specific downloading. This site will update both daily and monthly data near the beginning of each year, so as to include data through December of the previous year. However, if you need real-time data updates, you should visit the NCDC website. For NCDC-updated daily data please use the Global Historical Climatology Network (GHCN) website where you will find a directory of USHCN stations.

203

Today in Energy - Daily Prices - Prices - U.S. Energy Information  

Gasoline and Diesel Fuel Update (EIA)

December 20, 2013Daily Prices December 20, 2013Daily Prices Daily wholesale and retail prices for various energy products are shown below, including spot prices and select futures prices at national or regional levels. Prices are updated each weekday (excluding federal holidays), typically between 7:30 and 8:30 a.m. This page is meant to provide a snapshot of selected daily prices only. Prices are republished by EIA with permission as follows: Wholesale Spot Petroleum Prices from Thomson Reuters, Retail Petroleum Prices from AAA Fuel Gauge Report, Prompt-Month Energy Futures from CME Group, and Select Spot Prices from SNL Energy. Daily Prices Wholesale Spot Petroleum Prices, 12/19/13 Close Product Area Price Percent Change* Crude Oil ($/barrel) WTI 98.40 +0.8 Brent 110.78 +1.1 Louisiana Light 108.27 +4.9

204

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

E-Print Network (OSTI)

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

Guo, Zhongling

2013-01-01T23:59:59.000Z

205

Get Daily Energy Analysis Delivered to Your Website | Department of Energy  

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

Get Daily Energy Analysis Delivered to Your Website Get Daily Energy Analysis Delivered to Your Website Get Daily Energy Analysis Delivered to Your Website August 8, 2011 - 3:39pm Addthis Get Daily Energy Analysis Delivered to Your Website Matthew Loveless Matthew Loveless Data Integration Specialist, Office of Public Affairs How can I participate? Go to EIA's outreach page for Today in Energy widgets, badges and banners. Now everyone can feature the U.S. Energy Information Administration's (EIA) Today in Energy content on their website and favorite social networking sites. Today in Energy, the agency's education product published every weekday, highlights current energy issues, topics, and data trends in short articles written in plain language. EIA has banners and widgets in different colors and sizes to fit many different websites.

206

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

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

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

207

Temporal Disaggregation of Daily Temperature and Precipitation Grid Data for Norway  

Science Conference Proceedings (OSTI)

This paper presents a simple approach for the temporal disaggregation from daily to 3-hourly observed gridded temperature and precipitation (1 1 km2) on the national scale. The intended use of the disaggregated 3-hourly data is to recalibrate ...

Klaus Vormoor; Thomas Skaugen

2013-06-01T23:59:59.000Z

208

Analysis of the Impact of Snow on Daily Weather Variability in Mountainous Regions Using MM5  

Science Conference Proceedings (OSTI)

The impacts of snow on daily weather variability, as well as the mechanisms of snowmelt over the Sierra Nevada, CaliforniaNevada, mountainous region, were studied using the fifth-generation Pennsylvania State UniversityNational Center for ...

Jiming Jin; Norman L. Miller

2007-04-01T23:59:59.000Z

209

Use of a Principal Components Analysis for the Generation of Daily Time Series  

Science Conference Proceedings (OSTI)

A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then ...

Christine Dreveton; Yann Guillou

2004-07-01T23:59:59.000Z

210

Determination of Semivariogram Models to Krige Hourly and Daily Solar Irradiance in Western Nebraska  

Science Conference Proceedings (OSTI)

In this paper, linear and spherical semivariogram models were determined for use in kriging hourly and daily solar irradiation for every season of the year. The data used to generate the models were from 18 weather stations in western Nebraska. ...

G. G. Merino; D. Jones; D. E. Stooksbury; K. G. Hubbard

2001-06-01T23:59:59.000Z

211

Grid History: A Geostationary Satellite Technique for Estimating Daily Rainfall in the Tropics  

Science Conference Proceedings (OSTI)

A new technique is described for estimating daily rainfall by means of visible and infrared geostationary satellite imagery. It is designed for the tropics and warm-season midlatitudes. Because it operates on a grid of points and measures time ...

David W. Martin; Michael R. Howland

1986-02-01T23:59:59.000Z

212

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

Science Conference Proceedings (OSTI)

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

Xun Zhu; Albert Arking

1994-12-01T23:59:59.000Z

213

Cakewalking into representation : Gabriele Mnter's America travels (1898-1900) and art of dailiness  

E-Print Network (OSTI)

This study explores the fashioning of Gabriele Mnter as a German modernist with a focus on the eclipse of her struggles in coming to representation, the rich complexity of her processes, and the importance of dailiness ...

Bible, Ann Vollmann

2008-01-01T23:59:59.000Z

214

Developing hourly weather data for locations having only daily weather data  

Science Conference Proceedings (OSTI)

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

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

1983-06-01T23:59:59.000Z

215

Satellite solar insolation-based daily evapotranspiration estimation in Puerto Rico  

Science Conference Proceedings (OSTI)

A technique is presented in which satellite solar insolation estimates are used to predict daily reference evapotranspiration (ETo) using the Penman-Monteith (PM), Preistly-Taylor (PT) and Hargreaves-Samini (HS) methods for Puerto Rico. For ...

Eric W. Harmsen; John Mecikalski; Melvin J. Cardona-Soto; Alejandra Rojas Gonzalez; Ramn Vasquez

2009-05-01T23:59:59.000Z

216

Modulation of Daily Precipitation over Southwest Asia by the MaddenJulian Oscillation  

Science Conference Proceedings (OSTI)

Analysis of daily observations shows that wintertime (NovemberApril) precipitation over Southwest Asia is modulated by MaddenJulian oscillation (MJO) activity in the eastern Indian Ocean, with strength comparable to the interannual variability. ...

Mathew Barlow; Matthew Wheeler; Bradfield Lyon; Heidi Cullen

2005-12-01T23:59:59.000Z

217

Impact of Daily Arctic Sea Ice Variability in CAM3.0 during Fall and Winter  

Science Conference Proceedings (OSTI)

Climate projections suggest that an ice-free summer Arctic Ocean is possible within several decades and with this comes the prospect of increased ship traffic and safety concerns. The daily sea ice concentration tendency in five Coupled Model ...

Dyre O. Dammann; Uma S. Bhatt; Peter L. Langen; Jeremy R. Krieger; Xiangdong Zhang

2013-03-01T23:59:59.000Z

218

Downscaling and Projection of Winter Extreme Daily Precipitation over North America  

Science Conference Proceedings (OSTI)

Large-scale atmospheric variables have been statistically downscaled to derive winter (DecemberMarch) maximum daily precipitation at stations over North America using the generalized extreme value distribution (GEV). Here, the leading principal ...

Jiafeng Wang; Xuebin Zhang

2008-03-01T23:59:59.000Z

219

Observed Trends in Indices of Daily Temperature Extremes in South America 19602000  

Science Conference Proceedings (OSTI)

A workshop on enhancing climate change indices in South America was held in Macei, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality ...

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

2005-12-01T23:59:59.000Z

220

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

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

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

Note: This page contains sample records for the topic "kwh daily avg" 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

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

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

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

222

Improving Estimates of Heavy and Extreme Precipitation Using Daily Records from European Rain Gauges  

Science Conference Proceedings (OSTI)

The long-term variability in heavy precipitation characteristics over Europe for the period 19502000 is analyzed using high-quality daily records of rain gauge measurements from the European Climate Assessment (ECA) dataset. To improve the ...

Olga Zolina; Clemens Simmer; Konstantin Belyaev; Alice Kapala; Sergey Gulev

2009-06-01T23:59:59.000Z

223

Australian Experimental Model Output Statistics Forecasts of Daily Maximum and Minimum Temperature  

Science Conference Proceedings (OSTI)

Model output statistics (MOS) forecasts of daily temperature maxima and minima are developed for seven Australian cities. The developmental data and method of derivation of the MOS equations are described and the equations briefly compared to ...

F. Woodcock

1984-10-01T23:59:59.000Z

224

A Harmonic Approach for Calculating Daily Temperature Normals Constrained by Homogenized Monthly Temperature Normals  

Science Conference Proceedings (OSTI)

NOAA released the new 19812010 climate normals in July 2011. These included monthly and daily normals of minimum and maximum temperature. Monthly normals were computed from monthly temperature values that were corrected for biases (i.e., ...

Anthony Arguez; Scott Applequist

2013-07-01T23:59:59.000Z

225

Frequency Distribution of Daily ITCZ Patterns over the WesternCentral Pacific  

Science Conference Proceedings (OSTI)

This study attempts to explore a comprehensive and compact approach for delineating the multiscale and multivariate characteristics of the ITCZ over the westerncentral Pacific based on daily satellite observations of precipitation, SSTs, and ...

Baode Chen; Xin Lin; Julio T. Bacmeister

2008-09-01T23:59:59.000Z

226

A Hybrid Orographic plus Statistical Model for Downscaling Daily Precipitation in Northern California  

Science Conference Proceedings (OSTI)

A hybrid (physicalstatistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely ...

Ganesh R. Pandey; Daniel R. Cayan; Michael D. Dettinger; Konstantine P. Georgakakos

2000-12-01T23:59:59.000Z

227

EIA - Daily Report 9/14/05 - Hurricane Katrina's Impact on U...  

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

gas production was shut in, equivalent to 35.18 percent of daily Gulf of Mexico natural gas production (which had been 10 billion cubic feet per day). EIA released its monthly...

228

Systematic Biases in Manual Observations of Daily Maximum and Minimum Temperature  

Science Conference Proceedings (OSTI)

The authors demonstrate that manual observations of daily maximum and minimum temperature are strongly biased toward temperatures ending in certain digits. The nature and severity of these biases are quantified using standard statistical methods. ...

Jon M. Nese

1994-05-01T23:59:59.000Z

229

Sensitivity of Local Daily Temperature Change Estimates to the Selection of Downscaling Models and Predictors  

Science Conference Proceedings (OSTI)

A number of statistical downscaling models are applied to the Canadian Climate Centre general circulation model (CCCM) outputs to provide climate change estimates for local daily surface temperature at a network of 39 stations in central and ...

Radan Huth

2004-02-01T23:59:59.000Z

230

A Detailed Evaluation of GPCP 1 Daily Rainfall Estimates over the Mississippi River Basin  

Science Conference Proceedings (OSTI)

This study provides an intensive evaluation of the Global Precipitation Climatology Project (GPCP) 1 daily (1DD) rainfall products over the Mississippi River basin, which covers 435 1 latitude 1 longitude grids for the period of January 1997...

Mekonnen Gebremichael; Witold F. Krajewski; Mark L. Morrissey; George J. Huffman; Robert F. Adler

2005-05-01T23:59:59.000Z

231

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

Science Conference Proceedings (OSTI)

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

Daniel S. Wilks

1989-01-01T23:59:59.000Z

232

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America  

Science Conference Proceedings (OSTI)

This paper describes a comprehensive assessment of a new high-resolution, gaugesatellite-based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative ...

Daniel A. Vila; Luis Gustavo G. de Goncalves; David L. Toll; Jose Roberto Rozante

2009-04-01T23:59:59.000Z

233

Estimates of Tropical Analysis Differences in Daily Values Produced by Two Operational Centers  

Science Conference Proceedings (OSTI)

In order to asses the uncertainty of daily synoptic analyses for the atmospheric state, the intercomparison of three First GARP Global Experiment (FGGE) level IIIb datasets is conducted. The original analyses and reanalyses produced by the ...

Akira Kasahara; Arthur P. Mizzi

1992-02-01T23:59:59.000Z

234

Automation: A Step toward Improving the Quality of Daily Temperature Data Produced by Climate Observing Networks  

Science Conference Proceedings (OSTI)

The research documented in this manuscript demonstrates that undeniable differences exist between values of daily temperature recorded by the National Weather Service Cooperative Observer Program network and data recorded by the Oklahoma Mesonet. ...

Christopher A. Fiebrich; Kenneth C. Crawford

2009-07-01T23:59:59.000Z

235

Understanding the Characteristics of Daily Precipitation over the United States Using the North American Regional Reanalysis  

Science Conference Proceedings (OSTI)

This study examines the seasonal characteristics of daily precipitation over the United States using the North American Regional Reanalysis (NARR). To help understand the physical mechanisms that contribute to changes in the characteristics of ...

Emily J. Becker; Ernesto Hugo Berbery; R. Wayne Higgins

2009-12-01T23:59:59.000Z

236

Generalized Linear Models for Site-Specific Density Forecasting of U.K. Daily Rainfall  

Science Conference Proceedings (OSTI)

Site-specific probability density rainfall forecasts are needed to price insurance premiums, contracts, and other financial products based on precipitation. The spatiotemporal correlations in U.K. daily rainfall amounts over the Thames Valley are ...

Max A. Little; Patrick E. McSharry; James W. Taylor

2009-03-01T23:59:59.000Z

237

Intercomparison of Daily Precipitation Statistics over the United States in Observations and in NCEP Reanalysis Products  

Science Conference Proceedings (OSTI)

A comparison of the statistics of daily precipitation over the conterminous United States is carried out using gridded station data and three generations of reanalysis products in use at the National Centers for Environmental Prediction (NCEP). ...

R. W. Higgins; V. E. Kousky; V. B. S. Silva; E. Becker; P. Xie

2010-09-01T23:59:59.000Z

238

Influence of Daily Rainfall Characteristics on Regional Summertime Precipitation over the Southwestern United States  

Science Conference Proceedings (OSTI)

The regional variability in the summertime precipitation over the southwestern United States is studied using stochastic chain-dependent models generated from 70 yr of station-based daily precipitation observations. To begin, the spatiotemporal ...

Bruce T. Anderson; Jingyun Wang; Suchi Gopal; Guido Salvucci

2009-10-01T23:59:59.000Z

239

The Influence of Large-Scale Climate Variability on Winter Maximum Daily Precipitation over North America  

Science Conference Proceedings (OSTI)

The generalized extreme value (GEV) distribution is fitted to winter season daily maximum precipitation over North America, with indices representing El NioSouthern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the North ...

Xuebin Zhang; Jiafeng Wang; Francis W. Zwiers; Pavel Ya Groisman

2010-06-01T23:59:59.000Z

240

An analysis of the potential for shifting electric power demand within daily load requirement  

SciTech Connect

This report analyzes the potential for shifting the electric power demand within the daily load requirements for large industrial and commercial customers of the Philadelphia Electric Company. This shifting of electric power demand would tend to flatten the daily load curve of electricity demand, benefitting both the power industry and the consumer. Data on estimated summer load curves of large commercial and industrial customers are analyzed for load flattening potential. Cost savings to the customers are determined. (GRA)

Lamb, P.G.

1974-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

Daily radiation model for use in the simulation of passive solar buildings  

DOE Green Energy (OSTI)

A model is presented to characterize solar radiation with just three input parameters for each day. This compressed daily radiation data may be used in place of hourly data in simulations of passive solar buildings. This method is tested with the SUNCAT passive simulation. Global horizontal and direct normal radiation data are input using the compressed daily form instead of by hour. Simulation results are found to be comparable to results based on hourly radiation data.

Sillman, S.; Wortman, D.

1981-04-01T23:59:59.000Z

242

Estimating the Time Dependence of Air Temperature Using Daily Maxima and Minima: A Comparison of Three Methods  

Science Conference Proceedings (OSTI)

Many models in a variety of disciplines require air temperature throughout the day as an input, yet often the only data available are daily extrema. Several methods for estimating the diurnal change in temperature from daily extrema have been ...

J. M. Baker; D. C. Reicosky; D. G. Baker

1988-12-01T23:59:59.000Z

243

A Novel Method for the Homogenization of Daily Temperature Series and Its Relevance for Climate Change Analysis  

Science Conference Proceedings (OSTI)

Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order ...

Andrea Toreti; Franz G. Kuglitsch; Elena Xoplaki; Jrg Luterbacher; Heinz Wanner

2010-10-01T23:59:59.000Z

244

Spatial Interpolation of Daily Maximum and Minimum Air Temperature Based on Meteorological Model Analyses and Independent Observations  

Science Conference Proceedings (OSTI)

Hourly meteorological forecast model initializations are used to guide the spatial interpolation of daily cooperative network station data in the northeastern United States. The hourly model data are transformed to daily maximum and minimum ...

Arthur T. DeGaetano; Brian N. Belcher

2007-11-01T23:59:59.000Z

245

A Southeastern South American Daily Gridded Dataset of Observed Surface Minimum and Maximum Temperature for 19612000  

Science Conference Proceedings (OSTI)

This study presents a southeastern South American gridded dataset of daily minimum and maximum surface temperatures for 19612000. The data used for the gridding are observed daily data from meteorological stations in Argentina, Brazil, Paraguay, and ...

Brbara Tencer; Matilde Rusticucci; Phil Jones; David Lister

2011-10-01T23:59:59.000Z

246

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data  

Science Conference Proceedings (OSTI)

The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output. (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)

Behrang, M.A.; Assareh, E. [Department of Mechanical Engineering, Young Researchers Club, Islamic Azad University, Dezful Branch (Iran); Ghanbarzadeh, A.; Noghrehabadi, A.R. [Department of Mechanical Engineering, Engineering Faculty, Shahid Chamran University, Ahvaz (Iran)

2010-08-15T23:59:59.000Z

247

Development and Testing of Canada-Wide Interpolated Spatial Models of Daily MinimumMaximum Temperature and Precipitation for 19612003  

Science Conference Proceedings (OSTI)

The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of ...

Michael F. Hutchinson; Dan W. McKenney; Kevin Lawrence; John H. Pedlar; Ron F. Hopkinson; Ewa Milewska; Pia Papadopol

2009-04-01T23:59:59.000Z

248

Sheet.PDF  

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

(lb.) 150 150 1190 1190 AC kWh Recharge 31.99 35.17 36.85 33.89 AC kWhmi. 0.409 0.456 0.480 0.490 Range (mi.) 76.5 71.4 74.8 68.8 Avg. Ambient Temp. 62 F 66 F 74 F...

249

Daily snow depth measurements from 195 stations in the United States  

SciTech Connect

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

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

1997-02-01T23:59:59.000Z

250

A Gauge-Based Analysis of Daily Precipitation over East Asia  

Science Conference Proceedings (OSTI)

A new gauge-based analysis of daily precipitation has been constructed on a 0.5 latitudelongitude grid over East Asia (560N, 65155E) for a 26-yr period from 1978 to 2003 using gauge observations at over 2200 stations collected from ...

Pingping Xie; Mingyue Chen; Song Yang; Akiyo Yatagai; Tadahiro Hayasaka; Yoshihiro Fukushima; Changming Liu

2007-06-01T23:59:59.000Z

251

A Homogeneous Record (18962006) of Daily Weather and Climate at Mohonk Lake, New York  

Science Conference Proceedings (OSTI)

Reliable, long-term records of daily weather and climate are relatively rare but are crucial for understanding long-term trends and variability in extreme events and other climate metrics that are not resolvable at the monthly time scale. Here, ...

Benjamin I. Cook; Edward R. Cook; Kevin J. Anchukaitis; Paul C. Huth; John E. Thompson; Shanan F. Smiley

2010-03-01T23:59:59.000Z

252

An environmental control HW/SW framework for daily living of elderly and disabled people  

Science Conference Proceedings (OSTI)

A home control system is presented, based on standard IP LAN techniques. The system features large flexibility, dynamic reconfigurability, fault-tolerance and low costs. An integrated hardware/software framework has been conceived, which support device ... Keywords: electronic aids for daily living, remote services, smart homes

Guido Matrella; Ferdinando Grossi; Valentina Bianchi; Ilaria De Munari; Paolo Ciampolini

2008-04-01T23:59:59.000Z

253

The Daily Princetonian -International fusion project will use Princeton physics lab Summer Program  

E-Print Network (OSTI)

to determine the viability of exploiting cold fusion as an energy source around the world. Much of the researchThe Daily Princetonian - International fusion project will use Princeton physics lab Summer Program | Previous | Next | Calendar International fusion project will use Princeton physics lab By ABBY WILLIAMS

254

SpaceTime Spectral Analysis of the Southern Hemisphere Daily 500-hPa Geopotential Height  

Science Conference Proceedings (OSTI)

In this paper the authors use the NCEPDepartment of Energy (DOE) Reanalysis 2 (NCEP2) data from 1979 to 2004 to expand the daily 500-hPa geopotential height in the Southern Hemisphere (SH, 9020S) into a double Fourier series, and analyze the ...

Cheng Sun; Jianping Li

2012-12-01T23:59:59.000Z

255

Daily Simulation of Ozone and Fine Particulates over New York State: Findings and Challenges  

Science Conference Proceedings (OSTI)

This study investigates the potential utility of the application of a photochemical modeling system in providing simultaneous forecasts of ozone (O3) and fine particulate matter (PM2.5) over New York State. To this end, daily simulations from the ...

C. Hogrefe; W. Hao; K. Civerolo; J.-Y. Ku; G. Sistla; R. S. Gaza; L. Sedefian; K. Schere; A. Gilliland; R. Mathur

2007-07-01T23:59:59.000Z

256

Daily Reporting Rainfall Station DON & PROSERPINE RIVERS Manual Heavy Rainfall Station  

E-Print Network (OSTI)

Daily Reporting Rainfall Station DON & PROSERPINE RIVERS Manual Heavy Rainfall Station Manual River Station Telemetry Rainfall Station Telemetry River Station Revised: Nov 2009 MAP 121.1 FLOOD WARNING Bowen Tide TM Bowen P/S AL GretaCk Peter Faust Dam Crystal Brook Andromache R GoorgangaCk Jocheims TM

Greenslade, Diana

257

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

E-Print Network (OSTI)

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

Tsien, Roger Y.

258

Biomass burning emission inventory with daily resolution: Application to aircraft observations of Asian outflow  

E-Print Network (OSTI)

Biomass burning emission inventory with daily resolution: Application to aircraft observations for biomass burning using AVHRR satellite observations of fire activity corrected for data gaps and scan angle biomass burning in SE Asia was a major contributor to the outflow of Asian pollution observed in TRACE

Palmer, Paul

259

The development of a regional geomagnetic daily variation model using neural networks  

E-Print Network (OSTI)

The development of a regional geomagnetic daily variation model using neural networks P. R. Sutclie: 28 June 1999 / Accepted: 20 July 1999 Abstract. Global and regional geomagnetic ®eld models give the components of the geomagnetic ®eld as func- tions of position and epoch; most utilise a polynomial or Fourier

Paris-Sud XI, Université de

260

A computational intelligence scheme for the prediction of the daily peak load  

Science Conference Proceedings (OSTI)

Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important ... Keywords: Computational intelligence, Daily peak load, Mid-term load forecasting, Self-organizing map, Support vector machine

Jawad Nagi; Keem Siah Yap; Farrukh Nagi; Sieh Kiong Tiong; Syed Khaleel Ahmed

2011-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

A cyclic time-dependent Markov process to model daily patterns in wind turbine power production  

E-Print Network (OSTI)

Wind energy is becoming a top contributor to the renewable energy mix, which raises potential reliability issues for the grid due to the fluctuating nature of its source. To achieve adequate reserve commitment and to promote market participation, it is necessary to provide models that can capture daily patterns in wind power production. This paper presents a cyclic inhomogeneous Markov process, which is based on a three-dimensional state-space (wind power, speed and direction). Each time-dependent transition probability is expressed as a Bernstein polynomial. The model parameters are estimated by solving a constrained optimization problem: The objective function combines two maximum likelihood estimators, one to ensure that the Markov process long-term behavior reproduces the data accurately and another to capture daily fluctuations. A convex formulation for the overall optimization problem is presented and its applicability demonstrated through the analysis of a case-study. The proposed model is capable of r...

Scholz, Teresa; Estanqueiro, Ana

2013-01-01T23:59:59.000Z

262

Prediction of clock time hourly global radiation from daily values over  

Open Energy Info (EERE)

Prediction of clock time hourly global radiation from daily values over Prediction of clock time hourly global radiation from daily values over Bangladesh Dataset Summary Description (Abstract): A need for predicting hourly global radiation exists for many locations particularly in Bangladesh for which measured values are not available and daily values have to be estimated from sunshine data. The CPRG model has been used to predict values of hourly Gh for Dhaka (23.770N, 90.380E), Chittagong (22.270N, 91.820E) and Bogra (24.850N, 89.370E) for = ±7.50, ±22.50, ±37.50, ±52.50, ±67.50, ±82.50 and ±97.50 i.e., for ±1/2, ±3/2, ±5/2, ±7/2, ±9/2, ±11/2, ±13/2 hours before and after solar noon and the computed values for different months are symmetrical about solar noon whereas for many months experimental data show a clear asymmetry. To obtain improved

263

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

SciTech Connect

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

Grant Hawkes; James Sterbentz; John Maki; Binh Pham

2012-06-01T23:59:59.000Z

264

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping  

Science Conference Proceedings (OSTI)

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties.

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin [Department of Radiation Oncology, Virginia Commonwealth University, Richmond Virginia 23298 (United States)

2012-02-15T23:59:59.000Z

265

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

SciTech Connect

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

Rabie, L.H.

1983-12-01T23:59:59.000Z

266

EIA - Daily Report 9/13/05 - Hurricane Katrina's Impact on U.S. Oil &  

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

Tuesday, September 13, 4:00 pm Tuesday, September 13, 4:00 pm According to the Minerals Management Service (MMS), as of 11:30 September 12, Gulf of Mexico oil production was reduced by 846,720 barrels per day as a result of Hurricane Katrina, equivalent to 56.45 percent of daily Gulf of Mexico oil production (which had been1.5 million barrels per day). The MMS also reported that 3.720 billion cubic feet per day of natural gas production was shut in, equivalent to 37.20 percent of daily Gulf of Mexico natural gas production (which had been 10 billion cubic feet per day). EIA released its monthly Short-Term Energy Outlook on Wednesday, September 7. Because considerable uncertainty remains regarding the extent of Katrina's damage, EIA established three basic recovery scenarios to represent a range of plausible outcomes for oil and natural gas supply over the next several months and through 2006: (1) Fast Recovery, which assumes a very favorable set of circumstances for getting supplies back to normal; (2) Slow Recovery, which assumes that significant outages in oil and natural gas production and delivery from the Gulf area continue at least into November; and (3) Medium Recovery, which assumes a path in between Slow and Fast Recovery.

267

EIA - Daily Report 9/15/05 - Hurricane Katrina's Impact on U.S. Oil &  

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

Thursday, September 15, 3:00 pm Thursday, September 15, 3:00 pm According to the Minerals Management Service (MMS), as of 11:30 September 15, Gulf of Mexico oil production was reduced by 842,091 barrels per day as a result of Hurricane Katrina, equivalent to 56.14 percent of daily Gulf of Mexico oil production (which had been 1.5 million barrels per day). The MMS also reported that 3.411 billion cubic feet per day of natural gas production was shut in, equivalent to 34.11 percent of daily Gulf of Mexico natural gas production (which had been 10 billion cubic feet per day). EIA released its monthly Short-Term Energy Outlook on Wednesday, September 7, taking into consideration three Hurricane Katrina recovery scenarios. Petroleum As of the close of trading on Thursday, September 15, crude oil and petroleum product prices were lower, compared to the closing prices from Wednesday, September 14. The gasoline near-month futures price was down by 3.9 cents per gallon from Wednesday, settling at 189.9 cents per gallon, while the heating oil near-month futures price was down 1.3 cents per gallon, settling at 191.2 cents per gallon. The NYMEX West Texas Intermediate (WTI) crude oil futures price was down $0.34 per barrel from Wednesday, settling at $64.75.

268

The correlation of 27 day period solar activity and daily maximum temperature in continental Australia  

E-Print Network (OSTI)

We report the first observation of a 27 day period component in daily maximum temperature recorded at widely spaced locations in Australia. The 27 day component, extracted by band pass filtering, is correlated with the variation of daily solar radio flux during years close to solar minimum. We demonstrate that the correlation is related to the emergence of regions of solar activity on the Sun separated, temporally, from the emergence of other active regions. In this situation, which occurs only near solar minimum, the observed 27 day variation of temperature can be in phase or out of phase with the 27 day variation of solar activity. During solar maximum correlation of temperature and solar activity is much less defined. The amplitude of the 27 day temperature response to solar activity is large, at times as high as 6 degrees C, and much larger than the well documented temperature response to the 11 year cycle of solar activity. We demonstrate that the 27 day temperature response is localised to the Australia...

Edmonds, Ian

2013-01-01T23:59:59.000Z

269

Real-Time Study of Prostate Intrafraction Motion During External Beam Radiotherapy With Daily Endorectal Balloon  

Science Conference Proceedings (OSTI)

Purpose: To prospectively investigate intrafraction prostate motion during radiofrequency-guided prostate radiotherapy with implanted electromagnetic transponders when daily endorectal balloon (ERB) is used. Methods and Materials: Intrafraction prostate motion from 24 patients in 787 treatment sessions was evaluated based on three-dimensional (3D), lateral, cranial-caudal (CC), and anterior-posterior (AP) displacements. The mean percentage of time with 3D, lateral, CC, and AP prostate displacements >2, 3, 4, 5, 6, 7, 8, 9, and 10 mm in 1 minute intervals was calculated for up to 6 minutes of treatment time. Correlation between the mean percentage time with 3D prostate displacement >3 mm vs. treatment week was investigated. Results: The percentage of time with 3D prostate movement >2, 3, and 4 mm increased with elapsed treatment time (p 5 mm was independent of elapsed treatment time (p = 0.11). The overall mean time with prostate excursions >3 mm was 5%. Directional analysis showed negligible lateral prostate motion; AP and CC motion were comparable. The fraction of time with 3D prostate movement >3 mm did not depend on treatment week of (p > 0.05) over a 4-minute mean treatment time. Conclusions: Daily endorectal balloon consistently stabilizes the prostate, preventing clinically significant displacement (>5 mm). A 3-mm internal margin may sufficiently account for 95% of intrafraction prostate movement for up to 6 minutes of treatment time. Directional analysis suggests that the lateral internal margin could be further reduced to 2 mm.

Both, Stefan, E-mail: Stefan.Both@uphs.upenn.edu [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, PA (United States); Wang, Ken Kang-Hsin; Plastaras, John P.; Deville, Curtiland; Bar Ad, Voika; Tochner, Zelig; Vapiwala, Neha [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, PA (United States)

2011-12-01T23:59:59.000Z

270

EIA - Daily Report 9/12/05 - Hurricane Katrina's Impact on U.S. Oil &  

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

2, 5:00 pm 2, 5:00 pm According to the Minerals Management Service (MMS), as of 11:30 September 12, Gulf of Mexico oil production was reduced by 860,636 barrels per day as a result of Hurricane Katrina, equivalent to 57.38 percent of daily Gulf of Mexico oil production (which is 1.5 million barrels per day). The MMS also reported that 3.784 billion cubic feet per day of natural gas production was shut in, equivalent to 37.84 percent of daily Gulf of Mexico natural gas production (which is 10 billion cubic feet per day). EIA released its monthly Short-Term Energy Outlook on Wednesday, September 7. Because considerable uncertainty remains regarding the extent of Katrina's damage, EIA established three basic recovery scenarios to represent a range of plausible outcomes for oil and natural gas supply over the next several months and through 2006: (1) Fast Recovery, which assumes a very favorable set of circumstances for getting supplies back to normal; (2) Slow Recovery, which assumes that significant outages in oil and natural gas production and delivery from the Gulf area continue at least into November; and (3) Medium Recovery, which assumes a path in between Slow and Fast Recovery.

271

Climate Variability and the Shape of Daily Precipitation: A Case Study of ENSO and the American West  

Science Conference Proceedings (OSTI)

Characterizing the relationship between large-scale atmospheric circulation patterns and the shape of the daily precipitation distribution is fundamental to understanding how dynamical changes are manifest in the hydrological cycle, and it is also ...

Nicole Feldl; Gerard H. Roe

2011-05-01T23:59:59.000Z

272

The Simulation of Daily Temperature Time Series from GCM Output. Part I: Comparison of Model Data with Observations  

Science Conference Proceedings (OSTI)

For climate change impact analyses, local scenarios of surface variables at the daily scales are frequently required. Empirical transfer functions are a widely used technique to generate scenarios from GCM data at these scales. For successful ...

J. P. Palutikof; J. A. Winkler; C. M. Goodess; J. A. Andresen

1997-10-01T23:59:59.000Z

273

Daily Mean Sea Level Pressure Reconstructions for the EuropeanNorth Atlantic Region for the Period 18502003  

Science Conference Proceedings (OSTI)

The development of a daily historical EuropeanNorth Atlantic mean sea level pressure dataset (EMSLP) for 18502003 on a 5 latitude by longitude grid is described. This product was produced using 86 continental and island stations distributed ...

T. J. Ansell; P. D. Jones; R. J. Allan; D. Lister; D. E. Parker; M. Brunet; A. Moberg; J. Jacobeit; P. Brohan; N. A. Rayner; E. Aguilar; H. Alexandersson; M. Barriendos; T. Brandsma; N. J. Cox; P. M. Della-Marta; A. Drebs; D. Founda; F. Gerstengarbe; K. Hickey; T. Jnsson; J. Luterbacher; . Nordli; H. Oesterle; M. Petrakis; A. Philipp; M. J. Rodwell; O. Saladie; J. Sigro; V. Slonosky; L. Srnec; V. Swail; A. M. Garca-Surez; H. Tuomenvirta; X. Wang; H. Wanner; P. Werner; D. Wheeler; E. Xoplaki

2006-06-01T23:59:59.000Z

274

Combined Effects of the Southern Oscillation Index and the Pacific Decadal Oscillation on a Stochastic Daily Precipitation Model  

Science Conference Proceedings (OSTI)

The combined effects of the Southern Oscillation index (SOI) and the Pacific decadal oscillation (PDO) on a second-order Markov chain mixed exponential daily precipitation model were determined for 15 stations in Nevada, Arizona, New Mexico, and ...

David A. Woolhiser

2008-03-01T23:59:59.000Z

275

APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges  

Science Conference Proceedings (OSTI)

A daily gridded precipitation dataset covering a period of more than 57 yr was created by collecting and analyzing rain gauge observation data across Asia through the activities of the Asian PrecipitationHighly Resolved Observational Data Integration ...

Akiyo Yatagai; Kenji Kamiguchi; Osamu Arakawa; Atsushi Hamada; Natsuko Yasutomi; Akio Kitoh

2012-09-01T23:59:59.000Z

276

Development of Hourly Meteorological Values From Daily Data and Significance to Hydrological Modeling at H. J. Andrews Experimental Forest  

Science Conference Proceedings (OSTI)

Hydrologic modeling depends on having quality meteorological input available at the simulation time step. Often two needs arise: disaggregation from daily to subdaily and extend an available subdaily record. Simple techniques were tested for ...

Scott R. Waichler; Mark S. Wigmosta

2003-04-01T23:59:59.000Z

277

High-Resolution Spatial Modeling of Daily Weather Elements for a Catchment in the Oregon Cascade Mountains, United States  

Science Conference Proceedings (OSTI)

High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the ...

Christopher Daly; Jonathan W. Smith; Joseph I. Smith; Robert B. McKane

2007-10-01T23:59:59.000Z

278

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

Science Conference Proceedings (OSTI)

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

Ira S. Brenner

2004-04-01T23:59:59.000Z

279

Monte Carlo Simulation of Daily Regional Sulfur Distribution: Comparison with SURE Sulfate Data and Visual Range Observations during August 1977  

Science Conference Proceedings (OSTI)

The daily distribution of sulfate concentration over the eastern United States during August 1977 is simulated by a Monte Carlo model using quantized emissions, positioned in accordance with the 1973 EPA SO2 emission inventory. Horizontal ...

D. E. Patterson; R. B. Husar; W. E. Wilson; L. F. Smith

1981-04-01T23:59:59.000Z

280

Use of WindSat to Extend a Microwave-Based Daily Optimum Interpolation Sea Surface Temperature Time Series  

Science Conference Proceedings (OSTI)

The NOAA daily optimum interpolation sea surface temperature analysis (DOISST) is available either as a 31-yr (from 1981 onward) time series based on Advanced Very High Resolution Radiometer (AVHRR) observations or as a 9-yr (200211) time ...

Viva F. Banzon; Richard W. Reynolds

2013-04-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

Statistical Predictability and Parametric Models of Daily Ambient Temperature and Solar Irradiance: An Analysis in the Italian Climate  

Science Conference Proceedings (OSTI)

Stochasticdynamic models are discussed for both air temperature and solar irradiance daily time series in the Italian climate. Most of the methodologies discussed in this paper are well known and established for processes having a Gaussian ...

U. Amato; V. Cuomo; F. Fontana; C. Serio

1989-08-01T23:59:59.000Z

282

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

Science Conference Proceedings (OSTI)

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

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

2007-11-01T23:59:59.000Z

283

Interpolation of 196197 Daily Temperature and Precipitation Data onto Alberta Polygons of Ecodistrict and Soil Landscapes of Canada  

Science Conference Proceedings (OSTI)

Soil quality models developed for ecodistrict polygons (EDP) and the polygons of the soil landscapes of Canada (SLC) to monitor the concentration of soil organic matter require daily climate data as an important input. The objectives of this ...

Samuel S. P. Shen; Peter Dzikowski; Guilong Li; Darren Griffith

2001-12-01T23:59:59.000Z

284

A Synoptic Weather-Typing Approach to Project Future Daily Rainfall and Extremes at Local Scale in Ontario, Canada  

Science Conference Proceedings (OSTI)

This paper attempts to project possible changes in the frequency of daily rainfall events late in this century for four selected river basins (i.e., Grand, Humber, Rideau, and Upper Thames) in Ontario, Canada. To achieve this goal, automated ...

Chad Shouquan Cheng; Guilong Li; Qian Li; Heather Auld

2011-07-01T23:59:59.000Z

285

A Synoptic Weather Typing Approach to Simulate Daily Rainfall and Extremes in Ontario, Canada: Potential for Climate Change Projections  

Science Conference Proceedings (OSTI)

An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component ...

Chad Shouquan Cheng; Guilong Li; Qian Li; Heather Auld

2010-05-01T23:59:59.000Z

286

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

E-Print Network (OSTI)

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

Wang, Jinrong

1996-01-01T23:59:59.000Z

287

Daily digestible protein and energy requirements for growth and maintenance of sub-adult Pacific white shrimp (Litopenaeus vannamei)  

E-Print Network (OSTI)

This study utilized two diets (25 and 35% crude protein) fed at 10 different rates to produce differences in shrimp specific growth rate which were regressed against daily digestible protein (DP) and digestible energy (DE) intake to estimate daily DP and DE requirements for sub-adult L. vannamei. Apparent DP and DE requirement for maximum growth decreased throughout the 7-week trial as shrimp size increased. Mean apparent daily DP requirement for 7.69 to 13.08-g L. vannamei fed the 25% protein diet was 6.31 g DP kg-1 BW d-1 while the 35% protein diet produced a mean apparent daily DP requirement of 8.00 g DP kg-1 BW d-1 for 8.11- to 13.79-g L. vannamei. Maintenance requirements were estimated by regressing DP feed allowances back to zero weight-gain and were 1.03 g DP kg-1 BW d-1 for shrimp fed the 25% protein diet and 1.87 g DP kg-1 BW d-1 for shrimp fed the 35% protein diet. Mean apparent daily DE requirement for shrimp fed the 25% protein diet was 402.62 kJ DE kg-1 BW d-1 while the 35% protein diet produced an apparent daily DE requirement of 334.72 kJ DE kg-1 BW d-1. Mean apparent daily DE maintenance requirements for shrimp fed the 25% protein diet was 66.23 kJ DE kg-1 BW d-1 while the requirement was 78.82 kJ DE kg-1 BW d-1 for shrimp fed the 35% protein diet. Daily DP and DE requirements were also determined by regressing whole-body protein or energy change against daily DP and DE intake and were similar to those values obtained by regressing change in body weight against daily DP and DE intake. Another component of this project involved evaluating 32 different feedstuffs for dry matter, protein and energy digestibility coefficients. Fish meal apparent crude protein digestibility coefficients as a group were higher than all other ingredient classifications except purified ingredients. Protein in 48% soybean meal and 90% isolated soybean protein were significantly more digestible than protein found in fish, animal and marine meals tested. This data will improve the quality and reduce the cost of commercial shrimp feeds.

Siccardi, Anthony Joseph, III

2006-08-01T23:59:59.000Z

288

Daily Temperature and Precipitation Data from 223 Former-USSR Stations  

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

Climate Variables » Temperature » 223 Former-USSR Stations Climate Variables » Temperature » 223 Former-USSR Stations Daily Temperature and Precipitation Data for 223 Former-USSR Stations graphics Graphics data Data Investigators V. N. Razuvaev, E. B. Apasova, R. A. Martuganov All-Russian Research Institute of Hydrometeorological Information-World Data Centre Obninsk, Russia DOI 10.3334/CDIAC/cli.ndp040 Period of Record 1881 - 2001 (depending on station) Background Over the past several decades, many climate datasets have been exchanged directly between the principal climate data centers of the United States (NOAA's National Climatic Data Center (NCDC)) and the former-USSR/Russia (All-Russian Research Institute for Hydrometeorological Information (RIHMI)). This data exchange has its roots in a bilateral initiative known

289

EIA - Daily Report 9/19/05 - Hurricane Katrina's Impact on U.S. Oil &  

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

Monday, September 19, 5:00 pm Monday, September 19, 5:00 pm Hurricane Katrina in Perspective (see figures below). While the peak crude oil production loss from Hurricane Katrina was similar to 2004's Hurricane Ivan and even less than Hurricane Dennis earlier this year, the pace of restoration is expected to be much more similar to Hurricane Ivan than any of the other recent hurricanes. For example, while the peak daily loss in crude oil production during Hurricane Dennis was slightly more than suffered following Hurricane Katrina, within a week of the peak loss, crude oil production following Hurricane Dennis was back to normal while it will likely be months before crude oil production is back to normal following Hurricane Katrina. New York Mercantile Exchange (NYMEX) prices increased initially following the hurricane. Since early September, product prices generally have declined (increasing on September 19 with news of Tropical Storm Rita approaching the Gulf of Mexico).

290

Self-Organized Criticality in Daily Incidence of Acute Myocardial Infarction  

E-Print Network (OSTI)

Continuous periodogram power spectral analysis of daily incidence of acute myocardial infarction (AMI) reported at a leading hospital for cardiology in Pune, India for the two-year period June 1992 to May 1994 show that the power spectra follow the universal and unique inverse power law form of the statistical normal distribution. Inverse power law form for power spectra of space-time fluctuations are ubiquitous to dynamical systems in nature and have been identified as signatures of self-organized criticality. The unique quantification for self-organized criticality presented in this paper is shown to be intrinsic to quantumlike mechanics governing fractal space-time fluctuation patterns in dynamical systems. The results are consistent with El Naschie's concept of cantorian fractal spacetime characteristics for quantum systems.

A. M. Selvam; D. Sen; S. M. S. Mody

1998-10-14T23:59:59.000Z

291

EIA - Daily Report 9/16/05 - Hurricane Katrina's Impact on U.S. Oil &  

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

16, 4:00 pm 16, 4:00 pm Hurricane Katrina in Perspective (see figures below) While the peak crude oil production loss from Hurricane Katrina was similar to Hurricane Ivan last year and even less than Hurricane Dennis earlier this year, the pace of restoration is expected to be much more similar to Hurricane Ivan than any of the other recent hurricanes. For example, while the peak daily loss in crude oil production during Hurricane Dennis was slightly more than suffered following Hurricane Katrina, within a week of the peak loss, crude oil production following Hurricane Dennis was back to normal while it will likely be months before crude oil production is back to normal following Hurricane Katrina. Graph of Gulf of Mexico Shut-In Oil & Natural Gas Production due to hurricanes in 2004 & 2005

292

EIA - Daily Report 9/7/05 - Hurricane Katrina's Impact on U.S. Oil &  

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

7, 3:00 pm 7, 3:00 pm According to the Minerals Management Service (MMS), as of 11:30 September 7, Gulf of Mexico oil production was reduced by 861,000 barrels per day as a result of Hurricane Katrina, equivalent to 57.37 percent of daily Gulf of Mexico oil production (which is 1.5 million barrels per day). The MMS also reported that 4.0360 billion cubic feet per day of natural gas production was shut in, equivalent to 40.36 percent of daily Gulf of Mexico natural gas production (which is 10 billion cubic feet per day). EIA released its monthly Short-Term Energy Outlook on Wednesday, September 7. Because considerable uncertainty remains regarding the specific extent of Katrina's damage, it is difficult to provide a single forecast for the upcoming winter and subsequent months as is typical in Outlook. More detailed damage assessments should be forthcoming over the next several weeks, which should clarify our forecast. For the September Outlook, EIA established three basic scenarios to represent a range of plausible outcomes for oil and natural gas supply over the next several months and through 2006: (1) Fast Recovery, which assumes a very favorable set of circumstances for getting supplies back to normal; (2) Slow Recovery, which assumes that significant outages in oil and natural gas production and delivery from the Gulf area continue at least into November; and (3) Medium Recovery, which assumes a path in between Slow and Fast Recovery. In all cases, return to normal operations, in terms of oil and natural gas production and distribution, is achieved or nearly achieved by December. By the end of September all but about 0.9 million barrels per day of crude oil refining capacity is expected to be back at full rates under the Medium Recovery case.

293

Forecasting of preprocessed daily solar radiation time series using neural networks  

SciTech Connect

In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad hoc time series pre-processing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE {proportional_to} 21% and RMSE {proportional_to} 3.59 MJ/m{sup 2}. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data pre-processing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayesian inference. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41 55'N, 8 44'E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination..) allow to predict the best daily DC PV power production at horizon d + 1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data (R{sup 2} > 0.99 and nRMSE < 2%). (author)

Paoli, Christophe; Muselli, Marc; Nivet, Marie-Laure [University of Corsica, CNRS UMR SPE, Corte (France); Voyant, Cyril [University of Corsica, CNRS UMR SPE, Corte (France); Hospital of Castelluccio, Radiotherapy Unit, Ajaccio (France)

2010-12-15T23:59:59.000Z

294

4-Week Avg U.S. Net Imports of Crude Oil and Petroleum ...  

U.S. Energy Information Administration (EIA)

08/16 : 7,007 : 08/23 : 7,292 : 08/30 : 7,385 : 1991-Sep: 09/06 : 7,516 : 09/13 : 7,124 : 09/20 : 7,343 : 09/27 : 7,354 : 1991-Oct: 10/04 : 6,929 : ...

295

4-Week Avg U.S. Product Supplied of Finished Motor Gasoline ...  

U.S. Energy Information Administration (EIA)

Year-Month Week 1 Week 2 Week 3 Week 4 Week 5; End Date Value End Date Value End Date Value End Date Value End Date Value; 1991-Mar : 03/08 : 6,779 : 03/15 : 6,907

296

4-Week Avg U.S. Net Imports of Crude Oil (Thousand Barrels per Day)  

U.S. Energy Information Administration (EIA)

9,977 : 2010-Aug: 08/06 : 10,017 : 08/13 : 9,913 : 08/20 : 9,596 : 08/27 : 9,608 : 2010-Sep: 09/03 : 9,470 : 09/10 : 9,336 : 09/17 : 9,196 : 09/24 : 9,027 : 2010-Oct:

297

4-Week Avg U.S. Imports of Total Gasoline (Thousand Barrels per Day)  

U.S. Energy Information Administration (EIA)

413 : 1995-Jun: 06/02 : 425 : 06/09 : 399 : 06/16 : 367 : 06/23 : 409 : 06/30 : 409 : 1995-Jul: 07/07 : 408 : 07/14 : 366 : 07/21 : 343 : 07/28 : 384 : 1995-Aug: 08/04 :

298

4-Week Avg U.S. Commercial Crude Oil Imports Excluding SPR ...  

U.S. Energy Information Administration (EIA)

4,982 : 06/24 : 4,810 : 1988-Jul: 07/01 : 4,939 : 07/08 : 4,880 : 07/15 : 4,911 : 07/22 : 5,172 : 07/29 : 5,060 : 1988-Aug: 08/05 : 5,154 : 08/12 : 5,126 : 08/19 : 4,941

299

4-Week Avg Gulf Coast (PADD 3) Commercial Crude Oil Imports ...  

U.S. Energy Information Administration (EIA)

4,982 : 08/16 : 5,162 : 08/23 : 5,259 : 08/30 : 5,195 : 1996-Sep: 09/06 : 5,111 : 09/13 : 4,922 : 09/20 : 4,838 : 09/27 : 4,655 : 1996-Oct: 10/04 : 4,793 : 10/11 : 4,768

300

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

Science Conference Proceedings (OSTI)

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

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

2012-01-01T23:59:59.000Z

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301

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

SciTech Connect

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

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

2010-08-15T23:59:59.000Z

302

Table E6. Electricity Consumption (kWh) Intensities by End Use ...  

U.S. Energy Information Administration (EIA)

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* ..... ...

303

Table E5A. Electricity Consumption (kWh) by End Use for All ...  

U.S. Energy Information Administration (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

304

Table E6A. Electricity Consumption (kWh) Intensities by End Use ...  

U.S. Energy Information Administration (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

305

Reducing Open Cell Landfill Methane Emissions with a Bioactive Alternative Daily  

Science Conference Proceedings (OSTI)

Methane and carbon dioxide are formed in landfills as wastes degrade. Molecule-for-molecule, methane is about 20 times more potent than carbon dioxide at trapping heat in the earth's atmosphere, and thus, it is the methane emissions from landfills that are scrutinized. For example, if emissions composed of 60% methane and 40% carbon dioxide were changed to a mix that was 40% methane and 60% carbon dioxide, a 30% reduction in the landfill's global warming potential would result. A 10% methane, 90% carbon dioxide ratio will result in a 75% reduction in global warming potential compared to the baseline. Gas collection from a closed landfill can reduce emissions, and it is sometimes combined with a biocover, an engineered system where methane oxidizing bacteria living in a medium such as compost, convert landfill methane to carbon dioxide and water. Although methane oxidizing bacteria merely convert one greenhouse gas (methane) to another (carbon dioxide), this conversion can offer significant reductions in the overall greenhouse gas contribution, or global warming potential, associated with the landfill. What has not been addressed to date is the fact that methane can also escape from a landfill when the active cell is being filled with waste. Federal regulations require that newly deposited solid waste to be covered daily with a 6 in layer of soil or an alternative daily cover (ADC), such as a canvas tarp. The aim of this study was to assess the feasibility of immobilizing methane oxidizing bacteria into a tarp-like matrix that could be used for alternative daily cover at open landfill cells to prevent methane emissions. A unique method of isolating methanotrophs from landfill cover soil was used to create a liquid culture of mixed methanotrophs. A variety of prospective immobilization techniques were used to affix the bacteria in a tarp-like matrix. Both gel encapsulation of methanotrophs and gels with liquid cores containing methanotrophs were readily made but prone to rapid desiccation. Bacterial adsorption onto foam padding, natural sponge, and geotextile was successful. The most important factor for success appeared to be water holding capacity. Prototype biotarps made with geotextiles plus adsorbed methane oxidizing bacteria were tested for their responses to temperature, intermittent starvation, and washing (to simulate rainfall). The prototypes were mesophilic, and methane oxidation activity remained strong after one cycle of starvation but then declined with repeated cycles. Many of the cells detached with vigorous washing, but at least 30% appeared resistant to sloughing. While laboratory landfill simulations showed that four-layer composite biotarps made with two different types of geotextile could remove up to 50% of influent methane introduced at a flux rate of 22 g m{sup -2} d{sup -1}, field experiments did not yield high activity levels. Tests revealed that there were high hour-to-hour flux variations in the field, which, together with frequent rainfall events, confounded the field testing. Overall, the findings suggest that a methanotroph embedded biotarp appears to be a feasible strategy to mitigate methane emission from landfill cells, although the performance of field-tested biotarps was not robust here. Tarps will likely be best suited for spring and summer use, although the methane oxidizer population may be able to shift and adapt to lower temperatures. The starvation cycling of the tarp may require the capacity for intermittent reinoculation of the cells, although it is also possible that a subpopulation will adapt to the cycling and become dominant. Rainfall is not expected to be a major factor, because a baseline biofilm will be present to repopulate the tarp. If strong performance can be achieved and documented, the biotarp concept could be extended to include interception of other compounds beyond methane, such as volatile aromatic hydrocarbons and chlorinated solvents.

Helene Hilger; James Oliver; Jean Bogner; David Jones

2009-03-31T23:59:59.000Z

306

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

SciTech Connect

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

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

2012-01-01T23:59:59.000Z

307

A Study to Quantify the Effectiveness of Daily Endorectal Balloon for Prostate Intrafraction Motion Management  

Science Conference Proceedings (OSTI)

Purpose: To quantify intrafraction prostate motion between patient groups treated with and without daily endorectal balloon (ERB) employed during prostate radiotherapy and establish the effectiveness of the ERB. Methods: Real-time intrafraction prostate motion from 29 non-ERB (1,061 sessions) and 30 ERB (1,008 sessions) patients was evaluated based on three-dimensional (3D), left, right, cranial, caudal, anterior, and posterior displacements. The average percentage of time with 3D and unidirectional prostate displacements >2, 3, 4, 5, 6, 7, 8, 9, and 10 mm in 1-min intervals was calculated for up to 6 min of treatment time. The Kolmogorov-Smirnov method was used to evaluate the intrafraction prostate motion pattern between both groups. Results: Large 3D motion (up to 1 cm or more) was only observed in the non-ERB group. The motion increased as a function of elapsed time for displacements >2-8 mm for the non-ERB group and >2-4 mm for the ERB group (p percentage time distributions between the two groups were significantly different for motion >5 mm (p percentage of time that the prostate was displaced in any direction was less in the ERB group for almost all magnitudes of motion considered. The directional analysis shows that the ERB reduced IMs in almost all directions, especially the anterior-posterior direction.

Wang, Ken Kang-Hsin, E-mail: wangken@uphs.upenn.edu [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, PA (United States); Vapiwala, Neha; Deville, Curtiland; Plastaras, John P.; Scheuermann, Ryan; Lin Haibo; Bar Ad, Voika; Tochner, Zelig; Both, Stefan [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, PA (United States)

2012-07-01T23:59:59.000Z

308

Register Closing Effects on Forced Air Heating System Performance  

E-Print Network (OSTI)

Air Handler Power - Climate Zone 16 48 hr avg 24 hr avg FarAverage Gas Power - Climate Zone 16 48 hr avg 24 hr avg NearAverage Gas Power - Climate Zone 16 48 hr avg Near Registers

Walker, Iain S.

2003-01-01T23:59:59.000Z

309

Changes in Climate at High Southern Latitudes: A Unique Daily Record at Orcadas Spanning 19032008  

Science Conference Proceedings (OSTI)

The climate observations at Orcadas represent the only southern high-latitude site where data span more than a century, and its daily measurements are presented for the first time in this paper. Although limited to a single station, the observed ...

Natalia Zazulie; Matilde Rusticucci; Susan Solomon

2010-01-01T23:59:59.000Z

310

Long-Term Trend and Decadal Variability of Persistence of Daily 500-mb Geopotential Height Anomalies during Boreal Winter  

Science Conference Proceedings (OSTI)

An analysis has been made of the trend and decadal variability of persistence of daily 500-mb (hPa) geopotential height anomalies for the winter season. The persistence is measured based on autocorrelations at 1- and 5-day lags (denoted r1 and r5,...

Ruiqiang Ding; Jianping Li

2009-10-01T23:59:59.000Z

311

Daily combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using self organizing hierarchical particle swarm optimization technique  

Science Conference Proceedings (OSTI)

Daily optimum economic emission scheduling of hydrothermal systems is an important task in the operation of power systems. Many heuristic techniques such as differential evolution, and particle swarm optimization have been applied to solve this problem ... Keywords: Cascaded reservoirs, Combined economic emission scheduling (CEES), Hydrothermal systems, Self-organizing particle swarm optimization with time-varying acceleration coefficients (SOHPSO_TVAC)

K. K. Mandal; N. Chakraborty

2012-02-01T23:59:59.000Z

312

Nonlinear model predictive control for dosing daily anticancer agents using a novel saturating-rate cell-cycle model  

Science Conference Proceedings (OSTI)

A nonlinear model predictive control (NMPC) algorithm was developed to dose the chemotherapeutic agent tamoxifen based on a novel saturating-rate, cell-cycle model (SCM). Using daily tumor measurements, the algorithm decreased tumor volume along a specified ... Keywords: Biomedical systems, Cancer, Nonlinear model, Nonlinear model predictive control, Pharmacodynamics, Pharmacokinetics

Jeffry A. Florian, Jr.; Julie L. Eiseman; Robert S. Parker

2008-03-01T23:59:59.000Z

313

Long-Term Variability of Daily North AtlanticEuropean Pressure Patterns since 1850 Classified by Simulated Annealing Clustering  

Science Conference Proceedings (OSTI)

Reconstructed daily mean sea level pressure patterns of the North AtlanticEuropean region are classified for the period 1850 to 2003 to explore long-term changes of the atmospheric circulation and its impact on long-term temperature variability ...

A. Philipp; P. M. Della-Marta; J. Jacobeit; D. R. Fereday; P. D. Jones; A. Moberg; H. Wanner

2007-08-01T23:59:59.000Z

314

Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market  

Science Conference Proceedings (OSTI)

The problem of the identification of dependencies between time series of equity returns is analyzed. Marginal distribution functions are assumed to be known, and a bivariate chi-square test of fit is applied in a fully parametric copula approach. Several ... Keywords: Bivariate chi-square statistic, Copulas, Daily equity returns, Risk management

Oriol Roch; Antonio Alegre

2006-11-01T23:59:59.000Z

315

A Deterministic Approach to the Validation of Historical Daily Temperature and Precipitation Data from the Cooperative Network  

Science Conference Proceedings (OSTI)

It is widely known that the TD3200 (Summary of the Day Cooperative Network) database held by the National Climatic Data Center contains tens of thousands of erroneous daily values resulting from data-entry, data-recording, and data-reformatting ...

Thomas Reek; Stephen R. Doty; Timothy W. Owen

1992-06-01T23:59:59.000Z

316

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

DOE Green Energy (OSTI)

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

Smith, J.H.

1980-01-15T23:59:59.000Z

317

A Bivariate Mixed Distribution with a Heavy-tailed Component and its Application to Single-site Daily Rainfall Simulation  

Science Conference Proceedings (OSTI)

This paper presents an improved brivariate mixed distribution, which is capable of modeling the dependence of daily rainfall from two distinct sources (e.g., rainfall from two stations, two consecutive days, or two instruments such as satellite and rain gauge). The distribution couples an existing framework for building a bivariate mixed distribution, the theory of copulae and a hybrid marginal distribution. Contributions of the improved distribution are twofold. One is the appropriate selection of the bivariate dependence structure from a wider admissible choice (10 candidate copula families). The other is the introduction of a marginal distribution capable of better representing low to moderate values as well as extremes of daily rainfall. Among several applications of the improved distribution, particularly presented here is its utility for single-site daily rainfall simulation. Rather than simulating rainfall occurrences and amounts separately, the developed generator unifies the two processes by generalizing daily rainfall as a Markov process with autocorrelation described by the improved bivariate mixed distribution. The generator is first tested on a sample station in Texas. Results reveal that the simulated and observed sequences are in good agreement with respect to essential characteristics. Then, extensive simulation experiments are carried out to compare the developed generator with three other alternative models: the conventional two-state Markov chain generator, the transition probability matrix model and the semi-parametric Markov chain model with kernel density estimation for rainfall amounts. Analyses establish that overall the developed generator is capable of reproducing characteristics of historical extreme rainfall events and is apt at extrapolating rare values beyond the upper range of available observed data. Moreover, it automatically captures the persistence of rainfall amounts on consecutive wet days in a relatively natural and easy way. Another interesting observation is that the recognized overdispersion problem in daily rainfall simulation ascribes more to the loss of rainfall extremes than the under-representation of first-order persistence. The developed generator appears to be a sound option for daily rainfall simulation, especially in particular hydrologic planning situations when rare rainfall events are of great importance.

Li, Chao .; Singh, Vijay P.; Mishra, Ashok K.

2013-02-06T23:59:59.000Z

318

Addition of Bevacizumab to Standard Radiation Therapy and Daily Temozolomide Is Associated With Minimal Toxicity in Newly Diagnosed Glioblastoma Multiforme  

SciTech Connect

Purpose: To determine the safety of the addition of bevacizumab to standard radiation therapy and daily temozolomide for newly diagnosed glioblastoma multiforme (GBM). Methods and Materials: A total of 125 patients with newly diagnosed GBM were enrolled in the study, and received standard radiation therapy and daily temozolomide. All patients underwent a craniotomy and were at least 2 weeks postoperative. Radiation therapy was administered in 1.8-Gy fractions, with the clinical target volume for the primary course treated to a dose of 45 to 50.4 Gy, followed by a boost of 9 to 14.4 Gy, to a total dose of 59.4 Gy. Patients received temozolomide at 75 mg/m{sup 2} daily throughout the course of radiation therapy. Bevacizumab was given at 10 mg/kg intravenously every 14 days, beginning a minimum of 4 weeks postoperatively. Results: Of the 125 patients, 120 (96%) completed the protocol-specified radiation therapy. Five patients had to stop the protocol therapy, 2 patients with pulmonary emboli, and 1 patient each with a Grade 2 central nervous system hemorrhage, Grade 4 pancytopenia, and wound dehiscence requiring surgical intervention. All 5 patients ultimately finished the radiation therapy. After radiation therapy, 3 patients had progressive disease, 2 had severe fatigue and decreased performance status, 1 patient had a colonic perforation, and 1 had a rectal fissure; these 7 patients therefore did not proceed with the protocol-specified adjuvant temozolomide, bevacizumab, and irinotecan. However, 113 patients (90%) were able to continue on study. Conclusions: The addition of bevacizumab to standard radiation therapy and daily temozolomide was found to be associated with minimal toxicity in patients newly diagnosed with GBM.

Vredenburgh, James J., E-mail: vrede001@mc.duke.edu [Department of Medicine, Duke University Medical Center, Durham, NC (United States); Desjardins, Annick [Department of Neurology, Duke University Medical Center, Durham, NC (United States); Kirkpatrick, John P. [Department of Radiation Oncology, Duke University Medical Center, Durham, NC (United States); Reardon, David A. [Department of Surgery, Duke University Medical Center, Durham, NC (United States); Department of Pediatrics, Duke University Medical Center, Durham, NC (United States); Peters, Katherine B. [Department of Neurology, Duke University Medical Center, Durham, NC (United States); Herndon, James E.; Marcello, Jennifer [Department of Cancer Center Biostatistics, Duke University Medical Center, Durham, NC (United States); Bailey, Leighann; Threatt, Stevie; Sampson, John; Friedman, Allan [Department of Surgery, Duke University Medical Center, Durham, NC (United States); Friedman, Henry S. [Department of Surgery, Duke University Medical Center, Durham, NC (United States); Department of Pediatrics, Duke University Medical Center, Durham, NC (United States)

2012-01-01T23:59:59.000Z

319

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

E-Print Network (OSTI)

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

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

2009-11-01T23:59:59.000Z

320

Demonstration of Energy Savings of Cool Roofs  

E-Print Network (OSTI)

description building ft daily a/c savings insulation Davislocation building type daily a/c savings 1000ft insulationbuilding type location daily a/c savings 1000ft kWh/1000ft insulation

Konopacki, S.

2010-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

A Comparison of CMIP3 Simulations of Precipitation over North America with Observations: Daily Statistics and Circulation Features Accompanying Extreme Events  

Science Conference Proceedings (OSTI)

Climate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 197999. The evaluation ...

Anthony M. DeAngelis; Anthony J. Broccoli; Steven G. Decker

2013-05-01T23:59:59.000Z

322

A Comparison among Strategies for Interpolating Maximum and Minimum Daily Air Temperatures. Part II: The Interaction between Number of Guiding Variables and the Type of Interpolation Method  

Science Conference Proceedings (OSTI)

In a comparative experiment, the sequence of daily maximum and minimum temperatures for 1976 was interpolated over England and Wales to a resolution of 1 km using partial thin plate splines, ordinary kriging, trend surface, and an automatic ...

Claire H. Jarvis; Neil Stuart

2001-06-01T23:59:59.000Z

323

Evaluation of the AR4 Climate Models Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions  

Science Conference Proceedings (OSTI)

The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, ...

S. E. Perkins; A. J. Pitman; N. J. Holbrook; J. McAneney

2007-09-01T23:59:59.000Z

324

A Comparison among Strategies for Interpolating Maximum and Minimum Daily Air Temperatures. Part I: The Selection of Guiding Topographic and Land Cover Variables  

Science Conference Proceedings (OSTI)

This paper explores the derivation and selection of a comprehensive set of continuous topographic and land coverrelated variables to guide the interpolation of daily maximum and minimum temperatures over England and Wales, for an entire annual ...

Claire H. Jarvis; Neil Stuart

2001-06-01T23:59:59.000Z

325

Assessment of Reanalysis Daily Extreme Temperatures with Chinas Homogenized Historical Dataset during 19792001 Using Probability Density Functions  

Science Conference Proceedings (OSTI)

Using a recently homogenized observational daily maximum (TMAX) and minimum temperature (TMIN) dataset for China, the extreme temperatures from the 40-yr ECMWF Re-Analysis (ERA-40), the Japanese 25-year Reanalysis (JRA-25), the NCEP/Department of ...

Jiafu Mao; Xiaoying Shi; Lijuan Ma; Dale P. Kaiser; Qingxiang Li; Peter E. Thornton

2010-12-01T23:59:59.000Z

326

Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China  

Science Conference Proceedings (OSTI)

Daily actual evapotranspiration over the upper Chao river basin in North China on 23 June 2005 was estimated based on the Surface Energy Balance Algorithm for Land (SEBAL), in which the parameterization schemes for calculating the instantaneous solar ...

Yanchun Gao; Di Long; Zhao-Liang Li

2008-06-01T23:59:59.000Z

327

Mean and Variability of the WHOI Daily Latent and Sensible Heat Fluxes at In Situ Flux Measurement Sites in the Atlantic Ocean  

Science Conference Proceedings (OSTI)

Daily latent and sensible heat fluxes for the Atlantic Ocean from 1988 to 1999 with 1 1 resolution have been recently developed at Woods Hole Oceanographic Institution (WHOI) by using a variational object analysis approach. The present study ...

Lisan Yu; Robert A. Weller; Bomin Sun

2004-06-01T23:59:59.000Z

328

NIAMEY DAILY RAINFALL  

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

October for the years 2004 (blue), 2005 (pink), and 2006 (green) indicate a dry monsoon season for 2006. NIAMEY MONTHLY RAINFALL April-October, 2006 vs. 1941-2000 Long-term...

329

Ichetucknee Daily Activities  

E-Print Network (OSTI)

the beautiful, crystal clear water of the Ichetucknee River while you relax in an inner tube. Enjoy the Florida

Príncipe, José Carlos

330

Integrated Energy System Dispatch Optimization  

E-Print Network (OSTI)

characterized by their maximum demand reduction magnitude (maximum number of curtailment timesteps allowed per month daily total space and water heating (kWh) requirement demand

Firestone, Ryan; Stadler, Michael; Marnay, Chris

2006-01-01T23:59:59.000Z

331

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

Science Conference Proceedings (OSTI)

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

Myers, D.; Wilcox, S. M.

2009-01-01T23:59:59.000Z

332

Providing better indoor environmental quality brings economic benefits  

E-Print Network (OSTI)

to operate fans cost 0.10 per kWh, the daily energy costdata, and energy costs of 0.04 per kWh for heat and 0.1 0.05 and 0.15 per kWh, the benefit-cost ratios are 80 and

Fisk, William; Seppanen, Olli

2007-01-01T23:59:59.000Z

333

Hydropower Upgrades to Yield Added Generation at Average Costs Less Than 4 cents per kWh - Without New Dams  

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

$30.6 million Recovery Act investment by the Department of Energy highlights the additional potential of hydro power

334

The Daily Behavior of Pressure and Its Influence on the Wind Regime in East Antarctica during the Winters of 1993 and 1994  

Science Conference Proceedings (OSTI)

The analysis of surface pressure data obtained in 199394 at the Dumont d'Urville meteorological station and at Automatic Weather Stations Dome C, D-47, and D-80 (East Antarctica) shows the existence of a daily variation. Power spectra are ...

Igor V. Petenko; Stefania Argentini

2001-07-01T23:59:59.000Z

335

Validation of Satellite-Derived Daily Latent-Heat Flux over the South China Sea, Compared with Observations and Five Products  

Science Conference Proceedings (OSTI)

We have developed the South China Sea (SCS) daily satellite-derived latent-heat flux (SCSSLH) for the period of 1998 to 2011 at 0.250.25 resolution using data mainly from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI). Flux-...

Dongxiao Wang; Lili Zeng; Xixi Li; Ping Shi

336

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

DOE Green Energy (OSTI)

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

Smith, J.H.

1980-01-15T23:59:59.000Z

337

Evaluation of rule effectiveness and positive predictive value of clinical rules in a Dutch clinical decision support system in daily hospital pharmacy practice  

Science Conference Proceedings (OSTI)

Introduction: Our advanced clinical decision support (CDS) system, entitled 'adverse drug event alerting system' (ADEAS), is in daily use in our hospital pharmacy. It is used by hospital pharmacists to select patients at risk of possible adverse drug ... Keywords: Adverse drug events, Clinical decision support systems, Clinical pharmacy services, Clinical rules, Evaluation studies, Hospital pharmacy services, Medication safety, Positive predictive value, Rule effectiveness, University hospitals

Mirjam K. Rommers, Julitte Zwaveling, Henk-Jan Guchelaar, Irene M. Teepe-Twiss

2013-09-01T23:59:59.000Z

338

Assessment of Planning Target Volume Margins for Intensity-Modulated Radiotherapy of the Prostate Gland: Role of Daily Inter- and Intrafraction Motion  

Science Conference Proceedings (OSTI)

Purpose: To determine planning target volume margins for prostate intensity-modulated radiotherapy based on inter- and intrafraction motion using four daily localization techniques: three-point skin mark alignment, volumetric imaging with bony landmark registration, volumetric imaging with implanted fiducial marker registration, and implanted electromagnetic transponders (beacons) detection. Methods and Materials: Fourteen patients who underwent definitive intensity-modulated radiotherapy for prostate cancer formed the basis of this study. Each patient was implanted with three electromagnetic transponders and underwent a course of 39 treatment fractions. Daily localization was based on three-point skin mark alignment followed by transponder detection and patient repositioning. Transponder positioning was verified by volumetric imaging with cone-beam computed tomography of the pelvis. Relative motion between the prostate gland and bony anatomy was quantified by offline analyses of daily cone-beam computed tomography. Intratreatment organ motion was monitored continuously by the Calypso (registered) System for quantification of intrafraction setup error. Results: As expected, setup error (that is, inter- plus intrafraction motion, unless otherwise stated) was largest with skin mark alignment, requiring margins of 7.5 mm, 11.4 mm, and 16.3 mm, in the lateral (LR), longitudinal (SI), and vertical (AP) directions, respectively. Margin requirements accounting for intrafraction motion were smallest for transponder detection localization techniques, requiring margins of 1.4 mm (LR), 2.6 mm (SI), and 2.3 mm (AP). Bony anatomy alignment required 2.1 mm (LR), 9.4 mm (SI), and 10.5 mm (AP), whereas image-guided marker alignment required 2.8 mm (LR), 3.7 mm (SI), and 3.2 mm (AP). No marker migration was observed in the cohort. Conclusion: Clinically feasible, rapid, and reliable tools such as the electromagnetic transponder detection system for pretreatment target localization and, subsequently, intratreatment target location monitoring allow clinicians to reduce irradiated volumes and facilitate safe dose escalation, where appropriate.

Tanyi, James A., E-mail: tanyij@ohsu.ed [Department of Radiation Medicine, Oregon Health and Science University, Portland, OR (United States); Department of Nuclear Engineering and Radiation Health Physics, Oregon State University, Corvallis, OR (United States); He, Tongming [Department of Radiation Medicine, Oregon Health and Science University, Portland, OR (United States); Department of Nuclear Engineering and Radiation Health Physics, Oregon State University, Corvallis, OR (United States); Summers, Paige A. [Department of Medical Physics, University of Texas Graduate School of Biomedical Sciences, Houston, TX (United States); Mburu, Ruth G. [Department of Chemistry, Portland State University, Portland, OR (United States); Kato, Catherine M.; Rhodes, Stephen M.; Hung, Arthur Y.; Fuss, Martin [Department of Radiation Medicine, Oregon Health and Science University, Portland, OR (United States)

2010-12-01T23:59:59.000Z

339

1000 -1300 CALORIES DAILY 60 grams protein daily  

E-Print Network (OSTI)

fat cheese stick and peeled apples slices DINNER 2 - 3 ounces chicken with ½ cup broccoli, ¼ cup meal size o Sip fluids slowly o Avoid high fat and high sugar foods o Avoid drinking fluids Meal Plan BREAKFAST ½ - 1 cup cereal, ½ banana and 4- 8 oz low fat milk SNACK: 1 light low fat yogurt

Goldman, Steven A.

340

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

E-Print Network (OSTI)

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

A. Statutory; Regulatory Background

2011-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

_MainReport  

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

Vehicle Usage Number of trips 773,602 Total distance traveled (mi) 5,558,155 Avg trip distance (mi) 7.2 Avg distance traveled per day when the vehicle was driven (mi) 30.2 Avg...

342

Calendar Year 2007 Program Benefits for U.S. EPA Energy Star Labeled Products: Expanded Methodology  

E-Print Network (OSTI)

Preheat Energy Preheat Time Total Energy Usage Avg number ofPreheat Energy Preheat Time Total Energy Usage Avg number ofPreheat Energy Preheat Time Total Energy Usage Avg number of

Sanchez, Marla

2010-01-01T23:59:59.000Z

343

03/01/2006 09:51 AMLoading "People's Daily Online --Chinese experimental thermonuclear reactor on discharge test in July" Page 1 of 1http://english.people.com.cn/200603/01/print20060301_247035.html  

E-Print Network (OSTI)

the commercialized nuclear reactors in the world were designed for fission, a process contrary to the ITER's fusion03/01/2006 09:51 AMLoading "People's Daily Online -- Chinese experimental thermonuclear reactor experimental thermonuclear reactor on discharge test in July China's new generation experimental Tokamak fusion

344

Design, Fabrication, and Test of a 5-kWh/100-kW Flywheel Energy Storage Utilizing a High-Temperature Superconducting Bearing  

DOE Green Energy (OSTI)

The summaries of this project are: (1) Program goal is to design, develop, and demonstrate a 100 kW UPS flywheel electricity system; (2) flywheel system spin tested up to 15,000 RPM in a sensorless, closed loop mode; (3) testing identified a manufacturing deficiency in the motor stator--overheats at high speed, limiting maximum power capability; (4) successfully spin tested direct cooled HTS bearing up to 14,500 RPM (limited by Eddy current clutch set-up); (5) Testing confirmed commercial feasibility of this bearing design--Eddy Current losses are within acceptable limits; and (6) Boeing's investment in flywheel test facilities increased the spin-test capabilities to one of the highest in the nation.

Dr. Michael Strasik, Philip E Johnson; A. C. Day; J. Mittleider; M. D. Higgins; J. Edwards; J. R. Schindler; K. E. McCrary; C.R. McIver; D.; J. F. Gonder; J. R. Hull

2007-10-29T23:59:59.000Z

345

Next-Generation Flywheel Energy Storage: Development of a 100 kWh/100 kW Flywheel Energy Storage Module  

SciTech Connect

GRIDS Project: Beacon Power is developing a flywheel energy storage system that costs substantially less than existing flywheel technologies. Flywheels store the energy created by turning an internal rotor at high speedsslowing the rotor releases the energy back to the grid when needed. Beacon Power is redesigning the heart of the flywheel, eliminating the cumbersome hub and shaft typically found at its center. The improved design resembles a flying ring that relies on new magnetic bearings to levitate, freeing it to rotate faster and deliver 400% as much energy as todays flywheels. Beacon Powers flywheels can be linked together to provide storage capacity for balancing the approximately 10% of U.S. electricity that comes from renewable sources each year.

None

2010-09-22T23:59:59.000Z

346

Initial test results from the RedFlow 5 kW, 10 kWh zinc-bromide module, phase 1.  

DOE Green Energy (OSTI)

In this paper the performance results of the RedFlow zinc-bromide module (ZBM) Gen 2.0 are reported for Phase 1 of testing, which includes initial characterization of the module. This included physical measurement, efficiency as a function of charge and discharge rates, efficiency as a function of maximum charge capacity, duration of maximum power supplied, and limited cycling with skipped strip cycles. The goal of this first phase of testing was to verify manufacturer specifications of the zinc-bromide flow battery. Initial characterization tests have shown that the ZBM meets the manufacturer's specifications. Further testing, including testing as a function of temperature and life cycle testing, will be carried out during Phase 2 of the testing, and these results will be issued in the final report, after Phase 2 testing has concluded.

Ferreira, Summer Rhodes; Rose, David Martin

2012-02-01T23:59:59.000Z

347

Changes in the Economic Value of Variable Generation at High Penetration Levels: A Pilot Case Study of California  

E-Print Network (OSTI)

TES Avg. DA Wholesale Price PV Penetration (% Annual Load) (Generation Sold at Low Prices PV Penetration (% AnnualTES Avg. DA Wholesale Price PV Penetration (% Annual Load) (

Mills, Andrew

2013-01-01T23:59:59.000Z

348

http://baltoferretclub.com/travel.html http://baltoferretclub.com/evacuation.html  

E-Print Network (OSTI)

Whole eggs Liver Fish Fish oil Fat Chicken Fat Turkey Fat Poultry Fat Other Beet Pulp Brewer's Yeast;Nutritional Requirements Protein: 32-38% avg 35% Fat: 18-25% avg. 20% Fiber: Less than 3% avg. 2% Carbs than 12% avg 10% Accepted Good Ingredients Meats Chicken Turkey Lamb Chicken Meal Turkey Meal Lamb Meal

Selmic, Sandra

349

Welcome to FUPWG Fall 2012  

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

WELCOME TO MOBILE WELCOME TO MOBILE Presented by: Greg Reardon Energy Services Manager Alabama Power Company  Alabama Power Georgia Power  Gulf Power  Mississippi Power  Southern Power  Southern Company Services  Southern Telecom  Southern Linc 2 Operating Revenue - $17,657 Million Net Income - $ 2,203 Million EPS - 2.40 Shares Outstanding - 857 Million Ret on Avg Com Eqty - 13.04% Total Assets - $57,267 Million *257 CONSECUTIVE QUARTERS OF DIVIDENDS DATING TO 1948 *11 CONSECUTIVE YEARS OF DIVIDEND INCREASES 3 Customers Number of Customers Millions of kWh Sales Residential 3,809,000 53,341 Commercial 579,000 55,855

350

ARRA521 Recovery Act - Project Daily Report  

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

Spend Plan Spend Plan Obligated to Contracts Payments to Date Unpaid Balance Argonne National Laboratory $79,000,000 $79,000,000 $78,471,376 $528,624 Brookhaven National Laboratory $70,810,000 $70,809,616 $70,809,616 $0 Energy Technology Engineering Center $51,675,000 $51,675,000 $51,675,000 $0 Hanford (Office of River Protection) $326,035,000 $325,999,617 $325,999,617 $0 Hanford (Richland) $1,634,500,000 $1,633,901,799 $1,633,277,979 $623,821 Idaho $467,875,000 $467,840,907 $467,840,907 $0 Los Alamos National Laboratory $211,975,000 $211,954,781 $211,954,781 $0 Moab $108,350,000 $108,350,000 $108,204,128 $145,872 Mound $17,900,000 $17,525,587 $17,525,587 $0 Nevada Nuclear Security Site $44,325,000 $44,300,871 $44,300,869 $2 Oak Ridge $755,110,000 $754,990,004 $739,849,558 $15,140,447 Paducah $80,400,000 $80,400,000

351

Asymetric change of daily temperature range: Proceedings  

SciTech Connect

This report is a compilation of abstracts of papers presented at the MINIMAX workshop. Topics include; temperature fluxes, influence of clouds on temperature, anthropogenic influences on temperature flux, and carbon dioxide and aerosol induced greenhouse effect.

Kukla, G. [ed.] [Columbia Univ., Palisades, NY (United States). Lamont-Doherty Earth Observatory; Karl, T.R. [ed.] [National Climatic Data Center, Asheville, NC (United States); Riches, M.R. [ed.] [USDOE, Washington, DC (United States)

1994-04-01T23:59:59.000Z

352

DOE Solar Decathlon: 2005 Daily Journals  

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

sky, allowing the first rays of sunshine we have seen in more than a week to dapple solar panels and brighten facades along Decathlete Way. Read more. Friday October 14, 2005...

353

Daily Scheduling of Nurses in Operating Suites  

E-Print Network (OSTI)

This, in turn, leads to reduced patient safety and substandard treatment ... to improve the nurse scheduling process (Ernst et al., 2004). Recent surveys include...

354

PRICM-3: Daily Tour Information - TMS  

Science Conference Proceedings (OSTI)

The conference will be held July 12-16, 1998, at the Hilton Hawaiian Village Hotel, in Honolulu, ... Arizona Memorial, City Tour & Hilo Hattie .... See tranquil coves, rolling surf, huge breakers crashing against rocky cliffs and lush tropical forests.

355

Modeling and Forecasting Electric Daily Peak Loads  

E-Print Network (OSTI)

Forecast Update As part of the Mid Term Assessment, staff is preparing a long term wholesale electricity 29, 2012 Preliminary Results of the Electricity Price Forecast Update As part of the Mid Term Assessment, staff is preparing a long term wholesale electricity market price forecast. A summary of the work

Abdel-Aal, Radwan E.

356

DAILY RESEARCH NEWS | Data.gov  

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

avermissick@ars.usda.gov Unique Identifier USDA-1141 Public Access Level public Data Dictionary Data Download URL http:ars.usda.govnewsrssrss.htm Format rss License Spatial...

357

A persistent daily rhythm in photosynthesis  

E-Print Network (OSTI)

A ~ STR ACT The luminescent marine dinoflagellate, Gonyaulax polyedra, exhibits a diurnal rhythm in the rate of photosynthesis and photosynthetic capacity measured by incorporation of C140 ~ at different times of day. With cultures grown on alternating light and dark periods of 12 hours each, the maximum rate is at the 8th hour of the light period. Cultures transferred from day-night conditions to continuous dim light continue to show the rhythm of photosynthetic capacity (activity measured in bright light) but not of photosynthesis (activity measured in existing dim light). Cultures transferred to continuous bright light, however, do not show any rhythm. Several other properties of the photosynthetic rhythm are similar to those of previously reported rhythms of luminescence and cell division. This similarity suggests that a single mechanism regulates the various rhythms.

J. Woodland Hastings; Lazarus Astrachan; Beatrice M. Sweeney

1961-01-01T23:59:59.000Z

358

EIA initiates daily gasoline availability survey for ...  

U.S. Energy Information Administration (EIA)

To develop the emergency survey, EIA used the representative sample of retail stations selling gasoline used in EIA's Form EIA-878, "Motor Gasoline ...

359

Biofuels development in Maine: Using trees to oil the wheels of sustainability -Maine news, sports, obituaries, weather -Bangor Daily News http://bangordailynews.com/2013/03/12/opinion/biofuels-development-in-maine-using-trees-to-oil-the-wheels-of-sustain  

E-Print Network (OSTI)

Biofuels development in Maine: Using trees to oil the wheels of sustainability - Maine news, sports, obituaries, weather - Bangor Daily News http://bangordailynews.com/2013/03/12/opinion/biofuels-development-in-maine-using-trees-to-oil-the-wheels-of-sustainability/print/[3/13/2013 1:54:43 PM] Biofuels development

Thomas, Andrew

360

Copyright 2005 Investor's Business Daily Inc. INVESTOR'S BUSINESS DAILY MONDAY, JUNE 6, 2005 A9  

E-Print Network (OSTI)

Zucker the flexibility to work whenever she wants, a plus that appeals to many home-based entrepreneurs's no of- ficerentorutilitybilltopay,soreve- nue can be used to keep expanding the business. A home-based- allyatadinerortheclient'soffice. Zbar pointed out another option for the home-based entrepreneur whosehousecan

Kuzmanovic, Aleksandar

Note: This page contains sample records for the topic "kwh daily avg" 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

Pack.PDF  

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

February 1999 February 1999 Revised: 05/05/99 CHEVROLET S-10 ELECTRIC (NIMH BATTERIES) PERFORMANCE CHARACTERIZATION SUMMARY ELECTRIC TRANSPORTATION DIVISION Urban Range (On Urban Pomona Loop - see other side for map) Test UR1 UR2 UR3 UR4 Payload (lb.) 180 180 920 920 AC kWh Recharge 54.93 57.09 54.98 51.34 AC kWh/mi. 0.78 0.91 0.87 0.85 Range (mi.) 70.4 63.0 63.0 60.4 Avg. Ambient Temp. 63°F 66°F 63°F 50°F UR1 Urban Range Test, Min Payload, No Auxiliary Loads UR2 Urban Range Test, Min Payload, A/C on High, Headlights on Low, Radio On UR3 Urban Range Test, Max Payload, No Auxiliary Loads UR4 Urban Range Test, Max Payload, A/C on High, Headlights on Low, Radio On State of Charge Meter (UR1) Freeway Range (On Freeway Pomona Loop - see other side for map) Test FW1 FW2 FW3 FW4 Payload (lb.) 180 180 920 920 AC kWh Recharge

362

High-performance batteries for off-peak energy storage and electric-vehicle propulsion. Progress report, January--June 1975. [Li--Al/KCl--LiCl/Fe sulfide, 42 kWh  

DOE Green Energy (OSTI)

This report describes the research and management efforts, for the period January--June 1975, of Argonne National Laboratory's program on high-performance lithium/metal sulfide batteries. The batteries are being developed for two applications, off-peak energy storage in electric utility networks and electric-vehicle propulsion. The battery design for the two applications differ, particularly in cell configuration and electrode design, because of the differing performance requirements. The present cells are vertically oriented, prismatic cells with two negative electrodes of a solid lithium--aluminium alloy, a central positive electrode of iron sulfide (FeS/sub 2/ or FeS), and an electrolyte of LiCl--KCl eutectic (mp, 352/sup 0/C). The operating temperature of the cells is about 400--450/sup 0/C. Recent effort in the development of engineering-scale cells was focused on designing and fabricating vertically oriented, prismatic cells and on improving the lifetime capabilities of cells. Work on electrode development was directed toward the evaluation of the factors that influence the performance of the negative electrode and the development of new designs of vertical, prismatic iron sulfide electrodes. Materials studies included work on improving feedthroughs and separators, corrosion tests of candidate materials of construction, and postoperative examinations of cells. Cell chemistry studies included continuing investigations of cell reactions and the identification of advanced cell systems. Battery development work included the design of a battery for an electric automobile and the development of battery components. The transfer of Li--Al/FeS/sub x/ battery technology to industry is being implemented through contracts with industrial firms for the manufacture of components, electrodes, and cells.

Not Available

1976-03-01T23:59:59.000Z

363

Seagate Crystal Reports - RADCM  

Office of Environmental Management (EM)

Sludges & Treatment Residues - 1 Sludges & Treatment Residues - 1 WASTE STREAM CODE: 01582 STREAM NAME:Sludges & Treatment Residues - 1 MPC NAME:Inorganic Sludges TOTAL CURIES: 26.000 Approved Volume : Future Volume Avg: Future Volume Lower Limit: Future Volume Upper Limit: 100.000 Sludges & Treatment Residues - 1 Isotopes Scandium-46 Avg Concentration: Low Limit Concent: Upper Limit Concent: Tin-113 Avg Concentration: Low Limit Concent: Upper Limit Concent: Strontium-85 Avg Concentration: Low Limit Concent: Upper Limit Concent: Tantalum-182 Avg Concentration: Low Limit Concent: Upper Limit Concent: Californium-250 Avg Concentration: Low Limit Concent: Upper Limit Concent: Lead-212 Avg Concentration: Low Limit Concent: Upper Limit Concent: Americium-244 Avg Concentration: Low Limit Concent: Upper Limit Concent:

364

Application of multirate flowing fluid electric conductivity ...  

P i P avg P avg P 1 wb ... H. H., and C. E. Jacob (1946), A generalized graphical method for evaluating formation constants and summarizing well field history, Eos

365

Tracking the Sun III; The Installed Cost of Photovoltaics in the United States from 1998-2009  

E-Print Network (OSTI)

VT Renewable Energy Incentive 10-100 kW Avg. Cost Programlevel cost data from Wisconsins Focus on Energy RenewableRenewable WI Energy Cash-Back Rewards 10-100 kW Avg. Cost

Barbose, Galen

2011-01-01T23:59:59.000Z

366

Tracking the Sun: The Installed Cost of Photovoltaics in the U.S. from 1998-2007  

E-Print Network (OSTI)

Avg. Cost Avg. Incentive NY State Energy Research andState Energy Research and Development Authority: PV IncentiveState Energy Research and Development Authority: PV No. Systems Incentive

Wiser, Ryan

2009-01-01T23:59:59.000Z

367

Behavior of two capstone 30kW microturbines operating in parallel with impedance between them  

E-Print Network (OSTI)

Average of 96 Samples: Va = 3.52% THD, 270.0 Volts Vb =3.49% THD, 270.2 Volts Vc =3.51% THD, 271.6 Volts Percent of Fundamental Va Avg Vb Avg

Yinger, Robert J.

2004-01-01T23:59:59.000Z

368

Dr. Tracie Sempier Coastal Storms Outreach Coordinator  

E-Print Network (OSTI)

and Gas -54% of US total Crude Oil Production (based on avg. 2008-2010) -52% of US total Natural Gas Production (based on avg. 2007-2009) -47% of US total Crude Oil Refinery Capacity (based on avg. 2008 In 2009, the Gulf of Mexico accounted for over 44% of the US marine recreational fishing catch Oil

369

_MainReport  

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

Usage Usage Overall fuel economy (mpg) 139 Overall electrical energy consumption (AC Wh/mi) 293 Number of trips¹ 76,425 Total distance traveled (mi) 609,737 Avg trip distance (mi) 8.0 Avg distance traveled per day when the vehicle was driven (mi) 36.4 Avg number of trips between charging events 3.0 Avg distance traveled between charging events (mi) 24.1 Avg number of charging events per day when the vehicle was driven 1.5

370

Seagate Crystal Reports - RADCM  

Office of Environmental Management (EM)

Sludge, Salt, Liquid Sludge, Salt, Liquid WASTE STREAM CODE: 02113 STREAM NAME:Sludge, Salt, Liquid MPC NAME:Aqueous Liquids/Slurries TOTAL CURIES: Approved Volume : Future Volume Avg: Future Volume Lower Limit: Future Volume Upper Limit: 100.000 Sludge, Salt, Liquid Isotopes Americium-241 Avg Concentration: 3.4967E-001 Ci/m3 Low Limit Concent: Upper Limit Concent: Cadmium-113m Avg Concentration: 8.4542E-002 Ci/m3 Low Limit Concent: Upper Limit Concent: Niobium-93m Avg Concentration: 1.8159E-002 Ci/m3 Low Limit Concent: Upper Limit Concent: Protactinium-231 Avg Concentration: 7.8039E-004 Ci/m3 Low Limit Concent: Upper Limit Concent: Europium-152 Avg Concentration: 7.4037E-003 Ci/m3 Low Limit Concent: Upper Limit Concent: Plutonium-240 Avg Concentration: 4.4672E-002 Ci/m3 Low Limit Concent: Upper Limit Concent:

371

Backstage at the Daily Show | Department of Energy  

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

Energy 101: Solar PV Sec. Chu Online Town Hall Energy 101: Cool Roofs Energy 101: Geothermal Heat Pumps Why Cool Roofs? Chu at COP-16: Building a Sustainable Energy Future...

372

An Improved Gridded Historical Daily Precipitation Analysis for Brazil  

Science Conference Proceedings (OSTI)

A gauge-only precipitation data quality control and analysis system has been developed for monitoring precipitation at NOAAs Climate Prediction Center (CPC). Over the past 10 yr the system has been used to develop and deliver many different ...

Viviane B. S. Silva; Vernon E. Kousky; Wei Shi; R. Wayne Higgins

2007-08-01T23:59:59.000Z

373

Architecture and Daily Life: The Revitalization of a French Neighborhood  

E-Print Network (OSTI)

Glass Covered Court. Places/Volume 2, Number perimeter housing blockglass-covered entry to the pedestrian street, which recalls the sky-lit courtyards of the familistre housing block

Schuman, Tony

1985-01-01T23:59:59.000Z

374

A Probabilistic Forecast Approach for Daily Precipitation Totals  

Science Conference Proceedings (OSTI)

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

Petra Friederichs; Andreas Hense

2008-08-01T23:59:59.000Z

375

Electricity storage can take advantage of daily price ...  

U.S. Energy Information Administration (EIA)

Electricity storage technologies that can operate on timescales such as hours or days are often deployed at specific times of day to take advantage of ...

376

EIA begins posting daily energy prices on Today in Energy ...  

U.S. Energy Information Administration (EIA)

Home; Browse by Tag; Most Popular Tags. electricity; oil/petroleum; liquid fuels; ... Privacy/Security Copyright & Reuse Accessibility. Related Sites U.S. Department ...

377

Automatic Classification of Daily Fluid Intake Jonathan Lester, 2  

E-Print Network (OSTI)

beverag- es with 93.8% accuracy. Sádecká et al. [18] used fluorescent spectroscopy to differentiate 13 intensity LEDs or laser diodes. We also found that all of the features were important in classifying drinks illumination sources. (right ­ top) Avago ADJD- S371 color sensor (right ­ bottom) LED color array Table 4

Hunt, Galen

378

Electric generators' roles vary due to daily and seasonal ...  

U.S. Energy Information Administration (EIA)

Greenhouse gas data, ... The 98 MW SOWEGA Power gas turbine peaking unit rarely runs, and, when it does, it is only between 10 a.m. and 8 p.m. (blue line).

379

Backstage at the Daily Show | Department of Energy  

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

The Kill-a-Watt Competition at University of Central Florida Faces of the Recovery Act: Sun Catalytix Investing in Clean, Safe Nuclear Energy Secretary Chu Speaks at the 2010...

380

Empirical Estimation of Daily Clear Sky Solar Radiation  

Science Conference Proceedings (OSTI)

The suitability of two simple empirical equations for the estimation of clear sky radiation was investigated. Results indicated that latitude and altitude were sufficient to estimate the empirical equation coefficients and that the estimates of ...

D. F. Heermann; G. J. Harrington; K. M. Stahl

1985-03-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

Electricity storage can take advantage of daily price variations ...  

U.S. Energy Information Administration (EIA)

Electricity storage technologies that can operate on timescales such as hours or days are often deployed at specific times of day to take advantage of variations in ...

382

Estimating Winter Design Temperatures from Daily Minimum Temperatures  

Science Conference Proceedings (OSTI)

A methodology has been developed to estimate winter design temperatures (temperatures exceeded a specific number of hours during the December through February winter season-an important design parameter in building construction) from synthetic ...

Nolan J. Doesken; Thomas B. McKee

1983-10-01T23:59:59.000Z

383

A daily weather generator for use in climate change studies  

Science Conference Proceedings (OSTI)

This paper describes the development of a weather generator for use in climate impact assessments of agricultural and water system management. The generator produces internally consistent series of meteorological variables including: rainfall, temperature, humidity, ... Keywords: Climate change, Climate scenario, Rainfall model, Stochastic, Weather generator

C. G. Kilsby; P. D. Jones; A. Burton; A. C. Ford; H. J. Fowler; C. Harpham; P. James; A. Smith; R. L. Wilby

2007-12-01T23:59:59.000Z

384

Daily Temperature and Precipitation Data for 518 Russian Meteorologica...  

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

and precipitation values are contained in 518 station files in the Russiastations directory with names of the form "XXXXX.txt", with "XXXXX" representing World...

385

A Physically Based Daily Hydrometeorological Model for Complex Mountain Terrain  

Science Conference Proceedings (OSTI)

This paper describes the continued development of the physically based hydrometeorological model Generate Earth Systems Science input (GENESYS) and its application in simulating snowpack in the St. Mary (STM) River watershed, Montana. GENESYS is ...

Ryan J. MacDonald; James M. Byrne; Stefan W. Kienzle

2009-12-01T23:59:59.000Z

386

Backstage at the Daily Show | Department of Energy  

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

Pledge? Conversation on the Future of the Wind Industry Science Lecture: Talking the Higgs Boson with Dr. Joseph Incandela Bill Gates and Deputy Secretary Poneman Discuss the...

387

Stochastic Multisite Generation of Daily Precipitation Data Using Spatial Autocorrelation  

Science Conference Proceedings (OSTI)

There are a number of stochastic models that simulate weather data required for various water resources applications in hydrology, agriculture, ecosystem, and climate change studies. However, many of them ignore the dependence between station ...

Malika Khalili; Robert Leconte; Franois Brissette

2007-06-01T23:59:59.000Z

388

The Preuss School UCSD Daily Bulletin "A" Day  

E-Print Network (OSTI)

.m. ** Saturday, 2/9 -San Diego NOBCChE Science Bowl, Selected students will participate. San Diego State.m. Transportation is available to attend SEA. See the schedule below: 7:35- Keiler Middle School (Lisbon St:00 HTH CV Preuss Tues. 2/05 6:00 King Chavez Community Memorial Middle School Thurs. 2/07 4:00 Gompers

Blanco, Philip R.

389

Autocorrelation Functions Computed from Daily 500 mb Geopotential Height Analyses  

Science Conference Proceedings (OSTI)

A method of computing autocorrelation fields with the aid of empirical orthogonal functions (EOF) is applied. The isotropic parts of the fields are separated, and a one-parameter model of the isotropic autocorrelation field is constructed. The ...

Juhani Rinne; Simo Jrvenoja

1985-10-01T23:59:59.000Z

390

Climate Reference Network Daily01 Product | Data.gov  

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

Michael.Palecki@noaa.gov Unique Identifier DOC-2662 Public Access Level public Data Dictionary ftp:ftp.ncdc.noaa.govpubdatauscrnproductsdaily01README.txt Data Download URL...

391

Backstage at the Daily Show | Department of Energy  

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

A New Biofuels Technology Blooms in Iowa Faces of the Recovery Act: 1366 Technologies Home Energy Assessments The Kill-a-Watt Competition at University of Central Florida Faces of...

392

EIA initiates daily gasoline availability survey for metropolitan ...  

U.S. Energy Information Administration (EIA)

Biofuels: Ethanol & Biodiesel ... As a result of the subsequent disruptions to the supply chain for gasoline, EIA implemented an emergency survey starting on Friday ...

393

A New Approach to Homogenize Daily Radiosonde Humidity Data  

Science Conference Proceedings (OSTI)

Radiosonde humidity records represent the only in situ observations of tropospheric water vapor content with multidecadal length and quasi-global coverage. However, their use has been hampered by ubiquitous and large discontinuities resulting ...

Aiguo Dai; Junhong Wang; Peter W. Thorne; David E. Parker; Leopold Haimberger; Xiaolan L. Wang

2011-02-01T23:59:59.000Z

394

A Feasibility Study: Mining Daily Traces for Home Heating Control  

E-Print Network (OSTI)

home time and dynamically controls the HVAC system [8]. In general, automated home heating control Department of Computer Science University of Virginia {hong, whitehouse}@virginia.edu ABSTRACT HVAC systems nationwide. Recent work has been focused on auto- mated control based on occupancy prediction, where some

Whitehouse, Kamin

395

Daily Air Temperature and Electricity Load in Spain  

Science Conference Proceedings (OSTI)

Weather has a significant impact on different sectors of the economy. One of the most sensitive is the electricity market, because power demand is linked to several weather variables, mainly the air temperature. This work analyzes the ...

Enric Valor; Vicente Meneu; Vicente Caselles

2001-08-01T23:59:59.000Z

396

Characteristics of Daily and Extreme Temperatures over Canada  

Science Conference Proceedings (OSTI)

Recent studies have shown that, since 1900, mean annual temperature over southern Canada has increased by an average of 0.9C, with the largest warming during winter and early spring. Every season was associated with greater increases in minimum ...

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

2001-05-01T23:59:59.000Z

397

Daily Microwave-Derived Surface Temperature over Canada/Alaska  

Science Conference Proceedings (OSTI)

The land surface temperature variation over northern high latitudes in response to the increase in greenhouse gases is challenging because of the lack of meteorological stations. A new method to derive the surface temperature from satellite ...

A. Mialon; A. Royer; M. Fily; G. Picard

2007-05-01T23:59:59.000Z

398

United States Record-Maximum/Minimum Daily Temperatures  

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

North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Map...

399

Backstage at the Daily Show | Department of Energy  

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

Security & Safety -Emergency Response & Procedures or Search Energy.gov Search Clear Filters All Videos ARPA-E 2011 Keynote: Dr. Arun Majumdar ARPA-E 2011 Keynote: Ray Mabus,...

400

Data:Afc8896e-daa3-4c62-9903-5e233f616134 | Open Energy Information  

Open Energy Info (EERE)

Afc8896e-daa3-4c62-9903-5e233f616134 Afc8896e-daa3-4c62-9903-5e233f616134 No revision has been approved for this page. It is currently under review by our subject matter experts. Jump to: navigation, search Loading... 1. Basic Information 2. Demand 3. Energy << Previous 1 2 3 Next >> Basic Information Utility name: Lincoln Electric System Effective date: End date if known: Rate name: Large Light and Power Off-Peak Seasonal Daily Secondary Sector: Description: Excess kVA Source or reference: http://www.les.com/your_business/rate_schedules_llp.aspx Source Parent: Comments Applicability Demand (kW) Minimum (kW): Maximum (kW): History (months): Energy (kWh) Minimum (kWh): Maximum (kWh): History (months): Service Voltage Minimum (V): Maximum (V): Character of Service Voltage Category: Phase Wiring: << Previous

Note: This page contains sample records for the topic "kwh daily avg" 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

Data:B0f8cf1f-9675-4c3c-85ba-a97573938074 | Open Energy Information  

Open Energy Info (EERE)

f-9675-4c3c-85ba-a97573938074 f-9675-4c3c-85ba-a97573938074 No revision has been approved for this page. It is currently under review by our subject matter experts. Jump to: navigation, search Loading... 1. Basic Information 2. Demand 3. Energy << Previous 1 2 3 Next >> Basic Information Utility name: Lincoln Electric System Effective date: End date if known: Rate name: Large Power Contract Off-Peak Daily Primary Sector: Description: Excess kVA Source or reference: http://www.les.com/your_business/rate_schedules_lpc.aspx Source Parent: Comments Applicability Demand (kW) Minimum (kW): Maximum (kW): History (months): Energy (kWh) Minimum (kWh): Maximum (kWh): History (months): Service Voltage Minimum (V): Maximum (V): Character of Service Voltage Category: Phase Wiring: << Previous 1 2 3 Next >>

402

Data:Dcc5815a-06d5-4472-b92f-a6f4cf31491f | Open Energy Information  

Open Energy Info (EERE)

a-06d5-4472-b92f-a6f4cf31491f a-06d5-4472-b92f-a6f4cf31491f No revision has been approved for this page. It is currently under review by our subject matter experts. Jump to: navigation, search Loading... 1. Basic Information 2. Demand 3. Energy << Previous 1 2 3 Next >> Basic Information Utility name: Lincoln Electric System Effective date: End date if known: Rate name: Large Power Contract Off-Peak Daily Secondary Sector: Description: Excess kVA Source or reference: http://www.les.com/your_business/rate_schedules_lpc.aspx Source Parent: Comments Applicability Demand (kW) Minimum (kW): Maximum (kW): History (months): Energy (kWh) Minimum (kWh): Maximum (kWh): History (months): Service Voltage Minimum (V): Maximum (V): Character of Service Voltage Category: Phase Wiring: << Previous 1 2 3 Next >>

403

Data:650daf17-bfc0-4b67-8a38-44eeb0c29323 | Open Energy Information  

Open Energy Info (EERE)

daf17-bfc0-4b67-8a38-44eeb0c29323 daf17-bfc0-4b67-8a38-44eeb0c29323 No revision has been approved for this page. It is currently under review by our subject matter experts. Jump to: navigation, search Loading... 1. Basic Information 2. Demand 3. Energy << Previous 1 2 3 Next >> Basic Information Utility name: Lincoln Electric System Effective date: End date if known: Rate name: Large Power Contract Off-Peak Daily Dual Primary Sector: Description: Excess kVA Source or reference: http://www.les.com/your_business/rate_schedules_lpc.aspx Source Parent: Comments Applicability Demand (kW) Minimum (kW): Maximum (kW): History (months): Energy (kWh) Minimum (kWh): Maximum (kWh): History (months): Service Voltage Minimum (V): Maximum (V): Character of Service Voltage Category: Phase Wiring: << Previous

404

honda.PDF  

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

HONDA EV PLUS HONDA EV PLUS NIMH BATTERIES SEPTEMBER 1997 Urban Range (On Urban Pomona Loop - see other side for map) Range (mi.) Without Aux. Loads With Aux. Loads 81.7 97.7 105.3 Payload (lb.) Maximum 860 Minimum 140 UR1 UR2 UR 3 UR4 86.9 Test UR1 UR2 UR3 UR4 Payload (lb.) 140 140 860 860 AC kWh Recharge 40 43 40 45 AC kWh/mi. 0.38 0.49 0.41 0.55 Range (mi.) 105.3 86.9 97.7 81.7 Avg. Ambient Temp. 79° F 83° F 84° F 89° F State of Charge Meter (Urban Range Test) 0 20 40 60 80 100 120 0 1 2 3 4 5 6 7 8 9 State of Charge Miles Driven 0 20 40 60 80 100 120 140 Miles Remaining Miles Driven Miles Remaining Start End * * Initial " Miles Remaining" depend on driving economy before recharge Freeway Range (On Freeway Pomona Loop - see other side for map) Range (mi.) Without Aux. Loads With Aux. Loads 90.6 89.1 Maximum 860 Minimum

405

CHEVROLET S-10 ELECTRIC  

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

January - February 2000 January - February 2000 Date Prepared: 3/24/2000 1997 GM EV1 (PANASONIC PB-A BATTERIES) PERFORMANCE CHARACTERIZATION SUMMARY ELECTRIC TRANSPORTATION DIVISION Urban Range (On Urban Pomona Loop - see other side for map) Payload (lb) 90.3 88.9 (mi.) Range Without Aux. loads With Aux. loads Maximum 447 Minimum 185 72.6 79.7 UR1 UR2 UR3 UR4 Test UR1 UR2 UR3 UR4 Payload (lb.) 185 185 447 447 AC kWh Recharge 26.91 26.61 27.69 22.80 AC kWh/mi. 0.296 0.331 0.311 0.312 Range (mi.) 90.3 79.7 88.9 72.6 Avg. Ambient Temp. 65°F 72°F 70°F 71°F UR1 Urban Range Test, Min Payload, No Auxiliary Loads UR2 Urban Range Test, Min Payload, A/C on High, Headlights on Low, Radio On UR3 Urban Range Test, Max Payload, No Auxiliary Loads UR4 Urban Range Test, Max Payload, A/C on High, Headlights on Low, Radio On State of Charge Meter (UR1)

406

Sheet.PDF  

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

CHRYSLER EPIC (NIMH BATTERIES) PERFORMANCE CHARACTERIZATION SUMMARY CHRYSLER EPIC (NIMH BATTERIES) PERFORMANCE CHARACTERIZATION SUMMARY ELECTRIC TRANSPORTATION DIVISION Urban Range (On Urban Pomona Loop - see other side for map) Range (mi.) Weight (lb.) 160 930 Max. Payload Min. Payload 63.6 82.0 without aux. loads with aux. loads 77.6 67.8 Test UR1 UR2 UR3 UR4 Payload (lb.) 160 160 930 930 AC kWh Recharge 53.91 50.03 53.02 52.61 AC kWh/mi. 0.663 0.734 06.75 0.823 Range (mi.) 82.0 67.8 77.6 63.6 Avg. Ambient Temp. 75º F 80º F 79º F 85º F UR1 Urban Range Test, Min Payload, No Auxiliary Loads UR2 Urban Range Test, Min Payload, A/C on High, Headlights on Low, Radio On UR3 Urban Range Test, Max Payload, No Auxiliary Loads UR4 Urban Range Test, Max Payload, A/C on High, Headlights on Low, Radio On State of Charge Meter (UR1) SOC Meter Reading vs Miles Driven 0 10

407

Can Solar PV Rebates Be Funded with Utility Cost Savings?  

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

Jan Aceti Jan Aceti Concord Light February 19, 2013 Concord Municipal Light Plant Photo Credit: K.M. Peterson  7,600 Customers ◦ 6,000 Residential ◦ 1,600 Commercial/Institutional/Governmental  Retail Sales: 180,000,000 kWh per Year  Peak Electrical Demand: 40 MW  Power Purchased from Facilities in Northeast Year # of Installations kW DC kW AC 1999 1 5 5 2008 3 4.2 4.0 2009 5 75.0 74.6 2010 3 158 151 2011 7 36 35 2012 19 143 137 2013 2 8.2 7.7 Total 40 429 414 Residential 35 178 170  $1,000 per kW AC, up to $5,000  Retail Net Metering  Replaced Retail Net Metering with Wholesale Net Metering ◦ Credit at Avg. Monthly Spot Market Energy Price  Rebate: 10 Years Worth of Estimated Cost Savings, Up to 5 kW AC of Installed Capacity  Transmission Cost Savings  Forward Capacity Market Cost Savings

408

ford.PDF  

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

FORD RANGER EV FORD RANGER EV LEAD ACID BATTERIES MARCH 1998 Urban Range (On Urban Pomona Loop - see other side for map) Range (mi.) Without Aux. Loads With Aux . Lo a ds Pay load ( lb.) Maximum 640 Minimum 140 UR1 UR2 UR3 UR4 58.3 58.7 60.1 72.1 Test UR1 UR2 UR3 UR4 Payload (lb.) 140 140 640 640 AC kWh Recharge 29.11 28.16 28.20 28.23 AC kWh/mi. 0.40 0.47 0.48 0.48 Range (mi.) 72.1 60.1 58.7 58.3 Avg. Ambient Temp. 79° F 61° F 69° F 64° F State of Charge Meter (Urban Range Test) 0 10 20 30 40 50 60 70 80 0 0.5 1 1.5 2 2.5 3 3.5 4 State of Charge (4=F, 0=E) Miles Driven Miles Driven Miles Remaining * * Initial "Miles Remaining" depend on driving economy before recharge Freeway Range (On Freeway Pomona Loop - see other side for map) Range (mi.) Without Aux. Loads With Au x . L o a ds 51.6 57.2 60 66.4

409

Toyota_RAV4.PDF  

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

1999 Inductive version tested. 1999 Inductive version tested. Test Date: June 1999 / Revised: 10/07/99 1999 TOYOTA RAV4-EV* (NIMH BATTERIES) PERFORMANCE CHARACTERIZATION SUMMARY ELECTRIC TRANSPORTATION DIVISION Urban Range (On Urban Pomona Loop - see other side for map) Payload (lb) 92.8 89.5 84.8 Range Without Aux. loads With Aux. loads Maximum 760 Minimum 160 UR1 UR2 UR3 UR4 68.9 Test UR1 UR2 UR3 UR4 Payload (lb.) 160 160 766 766 AC kWh Recharge 31.80 33.96 32.72 32.22 AC kWh/mi. 0.329 0.394 0.360 0.434 Range (mi.) 92.8 84.8 89.5 68.9 Avg. Ambient Temp. 68.5°F 75.3°F 80.0°F 87.0°F Note: A/C fluctuating and may have impacted A/C tests. UR1 Urban Range Test, Min Payload, No Auxiliary Loads UR2 Urban Range Test, Min Payload, A/C on High, Headlights on Low, Radio On UR3 Urban Range Test, Max Payload, No Auxiliary Loads UR4

410

SLCA/IP Hydro Generation Estimates Month Forecast Generation  

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

42014 15:46 SLCAIP Hydro Generation Estimates Month Forecast Generation less losses (kWh) Less Proj. Use (kWh) Net Generation (kWh) SHP Deliveries (kWh) Firming Purchases (kWh)...

411

SLCA/IP Hydro Generation Estimates Month Forecast Generation  

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

13 16:39 SLCAIP Hydro Generation Estimates Month Forecast Generation less losses (kWh) Less Proj. Use (kWh) Net Generation (kWh) SHP Deliveries (kWh) Firming Purchases (kWh)...

412

Seagate Crystal Reports - RADCM  

Office of Environmental Management (EM)

Mexico Mexico SITE: LosAlamos PROGRAM: DP WASTE TYPE: Low Level Waste OPERATIONS OFFICE: Albuquerque Operations Office % of Stream LosAlamos - Low Level Waste - LLW-PCB WASTE STREAM CODE: 02015 STREAM NAME:LLW-PCB MPC NAME:Soil/Debris TOTAL CURIES: Approved Volume : Future Volume Avg: Future Volume Lower Limit: Future Volume Upper Limit: 100.000 LLW -PCB Isotopes Plutonium-239 Avg Concentration: Low Limit Concent: Upper Limit Concent:1.0000E+002 nCi/g Americium-241 Avg Concentration: Low Limit Concent: Upper Limit Concent:1.0000E+002 nCi/g Cesium-137 Avg Concentration: Low Limit Concent: Upper Limit Concent:8.6400E+002 nCi/g Uranium-238 Avg Concentration: Low Limit Concent: Upper Limit Concent:4.8900E+002 nCi/g Plutonium-238 Avg Concentration: Low Limit Concent: Upper Limit Concent:1.0000E+002 nCi/g

413

Seagate Crystal Reports - RADCM  

Office of Environmental Management (EM)

South Carolina South Carolina SITE: Savannah PROGRAM: DP WASTE TYPE: Low Level Waste OPERATIONS OFFICE: Savannah River Operations Office % of Stream Savannah - Low Level Waste - Intermediate Level Waste WASTE STREAM CODE: 00539 STREAM NAME:Intermediate Level Waste MPC NAME:Solids TOTAL CURIES: Approved Volume : Future Volume Avg: Future Volume Lower Limit: Future Volume Upper Limit: 100.000 Intermediate Level W aste Isotopes Nickel-59 Avg Concentration: Low Limit Concent: Upper Limit Concent:8.3000E-002 Ci/ft3 Uranium-234 Avg Concentration: Low Limit Concent: Upper Limit Concent:4.7000E-004 Ci/ft3 Carbon-14 Avg Concentration: Low Limit Concent: Upper Limit Concent:4.7000E-004 Ci/ft3 Hydrogen-3 Avg Concentration: Low Limit Concent: Upper Limit Concent:3.0000E+001 Ci/ft3 Technetium-99 Avg Concentration: Low Limit Concent:

414

Seagate Crystal Reports - RADCM  

Office of Environmental Management (EM)

Colorado Colorado SITE: GrJuncOff PROGRAM: EM WASTE TYPE: 11e(2) Byproduct Waste OPERATIONS OFFICE: Idaho Operations Office % of Stream GrJuncOff - 11e(2) Byproduct Waste - RRM Contaminated Soil WASTE STREAM CODE: 01091 STREAM NAME:RRM Contaminated Soil MPC NAME:Soil TOTAL CURIES: Approved Volume : 30.000 Future Volume Avg: 0.000 Future Volume Lower Limit: Future Volume Upper Limit: 100.000 RRM Contaminated Soil Isotopes Radium-226 Avg Concentration: Low Limit Concent: Upper Limit Concent: Uranium-234 Avg Concentration: Low Limit Concent: Upper Limit Concent: Uranium-238 Avg Concentration: Low Limit Concent: Upper Limit Concent: Thorium-230 Avg Concentration: Low Limit Concent: Upper Limit Concent: Uranium-235 Avg Concentration: Low Limit Concent: Upper Limit Concent: % of Stream GrJuncOff - 11e(2) Byproduct Waste - RRM Contaminated Rubble/Debris

415

Seagate Crystal Reports - RADCM  

Office of Environmental Management (EM)

Cs/Sr Capsules Cs/Sr Capsules WASTE STREAM CODE: 02115 STREAM NAME:Cs/Sr Capsules MPC NAME:Salt Waste TOTAL CURIES: Approved Volume : Future Volume Avg: Future Volume Lower Limit: Future Volume Upper Limit: 100.000 Cs/Sr Capsules Isotopes Barium-137m Avg Concentration: 2.5941E+007 Ci/m3 Low Limit Concent: Upper Limit Concent: Cesium-137 Avg Concentration: 2.7391E+007 Ci/m3 Low Limit Concent: Upper Limit Concent: Yttrium-90 Avg Concentration: 1.1840E+007 Ci/m3 Low Limit Concent: Upper Limit Concent: Strontium-90 Avg Concentration: 1.1840E+007 Ci/m3 Low Limit Concent: Upper Limit Concent: % of Stream Hanford - High Level Waste - HLW to Treatment WASTE STREAM CODE: 03857 STREAM NAME:HLW to Treatment MPC NAME:Aqueous Liquids/Slurries TOTAL CURIES: Approved Volume : Future Volume Avg: Future Volume Lower Limit:

416

MonthlyReport  

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

Braking Energy Recovery (%) 14% City Trips ( < 5 stopsmile & <37 mph avg) DC electrical energy consumption (DC Whmi) 380 Number of trips 106 Distance traveled (mi) 237 Percent...

417

MonthlyReport  

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

Braking Energy Recovery (%) 15% City Trips ( < 5 stopsmile & <37 mph avg) DC electrical energy consumption (DC Whmi) 414 Number of trips 152 Distance traveled (mi) 131 Percent...

418

MonthlyReport  

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

Braking Energy Recovery (%) 15% City Trips ( < 5 stopsmile & <37 mph avg) DC electrical energy consumption (DC Whmi) 410 Number of trips 94 Distance traveled (mi) 307 Percent of...

419

ARM - Instrument - ecmwfdiag  

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

entire coverage ecmwften : ECMWF: total and physical tendencies for met and cloud vars, entire coverage, 1-hr avg ecmwfvar : ECMWF: model met. and cloud variables at...

420

postkwonTable1.xls  

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

programsCSEQterrestrialpostkwon2000postkwon2000.html Site history Years since agriculture Soil sample depth (cm) Rate of change (g m -2 y -1 ) Reference MAX AVG Cool...

Note: This page contains sample records for the topic "kwh daily avg" 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

Power Grid Proposal Motivation  

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

and Aggregation Line, Curve, Cluster representat ion (min,max,avg,v ar) Direct Query Alarm correlation Aggregated alarm 15 Technical Accomplishments: Protocols *...

422

Office of the Chief Financial Officer Annual Report 2009  

E-Print Network (OSTI)

Avg. Total Science Sites PNNL Brookhaven FY2003 FY2004National Laboratory PLF PNNL PPPL R&D SLAC SNAP SNL SPO

Fernandez, Jeffrey

2010-01-01T23:59:59.000Z

423

ARE Update Volume 10, Number 5  

E-Print Network (OSTI)

Table 1. Global Biofuel Production by Feedstock Avg. YieldAs land devoted to biofuel production increases, it willas they move into biofuel production. They may uncertain.

Sumner, Dan; Sexton, Steven E.; Rajagopal, Deepak; Zilberman, David D; Roland-Holst, David; Martin, Philip

2007-01-01T23:59:59.000Z

424

U.S. Department of Energy Natural Gas Imports and Exports Form...  

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

Natural Gas Imports and Exports Form FE-746R Expiration Date: 7312016 AVG Burden: 3.0 hours MonthYear: Exporter (Authorization Holder):...

425

Natural Gas - U.S. Energy Information Administration (EIA ...  

U.S. Energy Information Administration (EIA)

Prices at the Chicago Citygate normally are very close to Henry Hub prices; ... *Avg. of NGI's reported prices for: Malin, PG&E citygate, and ...

426

ARM - Instrument - okm  

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

: Oklahoma Mesonet (OKM): 15-min averages, meteorological data from 111 stations 30okm : Oklahoma Mesonet (OKM): meteorological data from 111 stations, 30-min avg Primary...

427

Roadway Powered Electric Vehicle Project Parametric Studies: Phase 3D Final Report  

E-Print Network (OSTI)

replacement Wholesale cost of energy per kwh Retail price ofbefore replacement Cost of energy per kwh Retail price ofbefore replacement Cost of energy per kwh Retail price of

Systems Control Technology

1996-01-01T23:59:59.000Z

428

Seagate Crystal Reports - Radcm  

Office of Environmental Management (EM)

Dry Active Waste Dry Active Waste WASTE STREAM CODE: 01585 STREAM NAME:Dry Active Waste MPC NAME:Solids TOTAL CURIES: 87,675.000 Approved Volume : Future Volume Avg: Future Volume Lower Limit: Future Volume Upper Limit: 100.000 Dry Active W aste Isotopes Hafnium-181 Avg Concentration: 1.4222E-007 Ci/m3 Low Limit Concent: Upper Limit Concent: Iridium-192 Avg Concentration: 1.2220E-003 Ci/m3 Low Limit Concent: Upper Limit Concent: Molybdenum-99 Avg Concentration: 3.6706E-008 Ci/m3 Low Limit Concent: Upper Limit Concent: Protactinium-234m Avg Concentration: 5.1907E-009 Ci/m3 Low Limit Concent: Upper Limit Concent: Lead-212 Avg Concentration: 1.6569E-009 Ci/m3 Low Limit Concent: Upper Limit Concent: Silver-111 Avg Concentration: 3.2355E-007 Ci/m3 Low Limit Concent: Upper Limit Concent: Arsenic-73 Avg Concentration: 1.4091E-010 Ci/m3

429

Category:Properties | Open Energy Information  

Open Energy Info (EERE)

Category Category Edit History Facebook icon Twitter icon » Category:Properties Jump to: navigation, search This category uses the form Property. Subcategories This category has only the following subcategory. T [×] Tech Potential Properties‎ 30 pages Pages in category "Properties" The following 200 pages are in this category, out of 1,075 total. (previous 200) (next 200) A Property:Abbreviation Property:Achievement Date Property:AdditionalRef Property:Affiliated Companies Property:Applicant Property:Area Property:AreaGeology Property:Author Property:Available Personnel Types Property:Available Sensors Property:AvgAnnlGrossOpCpcty Property:AvgGeoFluidTemp Property:AvgReservoirDepth Property:AvgTempGeoFluidIntoPlant Property:AvgWellDepth B Property:Bandwidth(kHz)

430

Study Design And Realization Of Solar Water Heater  

Science Conference Proceedings (OSTI)

Solar is one of the most easily exploitable energy, it is moreover inexhaustible. His applications are many and are varied. The heating of the domestic water is one of the most immediate, simplest and also of most widespread exploitation of the solar energy. Algeria, from its geographical situation, it deposits one of the largest high sun surface expositions in the world. The exposition duration of the almost territory exceeds 2000 hours annually and can reach the 3900 hours (high plateaus and Sahara). By knowing the daily energy received by 1 m{sup 2} of a horizontal surface of the solar thermal panel is nearly around 1700 KWh/m{sup 2} a year in the north and 2263 KWh/m{sup 2} a year in the south of the country, we release the most important and strategic place of the solar technologies in the present and in the future for Algeria. This work consists to study, conceive and manufacture solar water heating with the available local materials so, this type of the energy will be profitable for all, particularly the poor countries. If we consider the illumination duration of the panel around 6 hours a day, the water heat panel manufactured in our laboratory produce an equivalent energy of 11.615 KWh a day so, 4239 KWh a year. These values of energy can be easily increased with performing the panel manufacture.

Lounis, M. [LAAR Laboratory-Physics Department-USTOMB 31000 Oran (Algeria); Boudjemaa, F.; Akil, S. Kouider [Genie Climatic Department-CUKM 44000-Khemis Miliana (Algeria)

2011-01-17T23:59:59.000Z

431

Tradeoffs between Costs and Greenhouse Gas Emissions in the Design of Urban Transit Systems  

E-Print Network (OSTI)

of veh (kWh/veh-km) Cost per kWh ($/kWh) Operating cost ($/of veh (kWh/veh-km) Cost per kWh ($/kWh) Operating cost ($/

Griswold, Julia Baird

2013-01-01T23:59:59.000Z

432

Alternative Transportation Technologies: Hydrogen, Biofuels,...  

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

Current PHEV Battery Pack Cost* Estimates Compared (kWh nameplate) * 700-1500kWh (McKinsey Report) * 1000kWh (Carnegie Mellon University) * 800-1000kWh (Pesaran et al) *...

433

--No Title--  

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

7122013 10:37 SLCAIP Hydro Generation Estimates Month Forecast Generation less losses (kWh) Less Proj. Use (kWh) Net Generation (kWh) SHP Deliveries (kWh) Firming Purchases...

434

Conservation screening curves to compare efficiency investments to power plants: Applications to commercial sector conservation programs  

E-Print Network (OSTI)

7/kWh Gas Turbine 5/kWh Combined-Cycle Oil Baseload Coal7/kWh Gas Turbine 5/kWh Combined-Cycle Oi Baseload Coalof Supply Technologies CT Combined- Cycle Oil Baseload Coal

Koomey, Jonathan; Rosenfeld, Arthur H.; Gadgil, Ashok J.

2008-01-01T23:59:59.000Z

435

Distributed Energy Resources On-Site Optimization for Commercial Buildings with Electric and Thermal Storage Technologies  

E-Print Network (OSTI)

electricity ($/kWh) demand ($/kW) Natural Gas $/kWh fixed (electricity ($/kWh) demand ($/kW) Natural Gas $/kWh fixed (demand via utility purchases and burns natural gas to meet

Stadler, Michael

2008-01-01T23:59:59.000Z

436

Data:3bb770e7-d439-4615-b6fe-0f9414538f93 | Open Energy Information  

Open Energy Info (EERE)

General Service Demand Metered Three Phase Industrial Sector: Commercial Description: kWh Tax (Effective May 1, 2001) First 2000 kWh tax is 0.00465 per kWh Next 13000 kWh tax is...

437

Data:30a93170-c929-4cd0-804a-9a98fc59045d | Open Energy Information  

Open Energy Info (EERE)

General Service Demand Metered Three Phase Commercial Sector: Commercial Description: kWh Tax (Effective May 1, 2001) First 2000 kWh tax is 0.00465 per kWh Next 13000 kWh tax is...

438

Data:51627f7e-b021-4268-8860-f5435f62169b | Open Energy Information  

Open Energy Info (EERE)

General Service without Demand Three Phase Commercial Sector: Commercial Description: kWh Tax (Effective May 1, 2001) First 2000 kWh tax is 0.00465 per kWh Next 13000 kWh tax is...

439

Clean energy funds: An overview of state support for renewable energy  

E-Print Network (OSTI)

public benefits, net cost per kWh, advancement of commercialbenefits, net cost per kWh, commercial potential, leverage

Bolinger, Mark; Wiser, Ryan; Milford, Lew; Stoddard, Michael; Porter, Kevin

2001-01-01T23:59:59.000Z

440

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

E-Print Network (OSTI)

Residential Electricity Electricity Price Price 2000$/kWh 2000$/kWh Electric Carbon Emissions Electric Heat Rate

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

2003-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

Conservation screening curves to compare efficiency investments to power plants: Applications to commercial sector conservation programs  

E-Print Network (OSTI)

kW. 9/kWh 7/kWh Gas Turbine 5/kWh Combined-Cycle Oilhigh operating costs (such as gas turbines) during those fewtechnology. 9/kWh 7/kWh Gas Turbine 5/kWh Combined-Cycle

Koomey, Jonathan; Rosenfeld, Arthur H.; Gadgil, Ashok J.

2008-01-01T23:59:59.000Z

442

Power Technologies Energy Data Book: Fourth Edition, Chapter...  

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

Table 5.10 - Top 10 U.S. Investor-Owned Utilities & Power Marketers 2004 Rank Million kWh Rank Million kWh Rank Million kWh Rank Million kWh Rank Million kWh Rank Million kWh...

443

Seagate Crystal Reports - RADCM  

Office of Environmental Management (EM)

Packaged Low-level Waste from SNF Activities Packaged Low-level Waste from SNF Activities WASTE STREAM CODE: 00265 STREAM NAME:Packaged Low-level Waste from SNF Activities MPC NAME:Solids TOTAL CURIES: 2.900 Approved Volume : 0.000 Future Volume Avg: 18.500 Future Volume Lower Limit: Future Volume Upper Limit: 100.000 Hot Cell W aste Isotopes Strontium-90 Avg Concentration: 1.5800E+000 Ci/m3 Low Limit Concent: Upper Limit Concent: Curium-244 Avg Concentration: 9.1700E-002 Ci/m3 Low Limit Concent: Upper Limit Concent: Europium-152 Avg Concentration: 4.6400E-001 Ci/m3 Low Limit Concent: Upper Limit Concent: Plutonium-238 Avg Concentration: 2.4100E-002 Ci/m3 Low Limit Concent: Upper Limit Concent: Europium-154 Avg Concentration: 4.0100E-001 Ci/m3 Low Limit Concent: Upper Limit Concent: Plutonium-240 Avg Concentration: 8.4500E-003 Ci/m3

444

EIA - Daily Report 9/29/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

49.51. Gasoline (c/gal ... of its interstate natural gas transmission systems located in the Gulf Coast area and identified minimal damage from Hurricane ...

445

Reconstruction of a Daily Large-Pan Evaporation Dataset over China  

Science Conference Proceedings (OSTI)

Land surface evaporation is an important component of the earths surface hydrological cycle, as well as in the atmospheric energy and water balances. In China, different instruments have been used over time to measure evaporation. A small pan ...

An-Yuan Xiong; Jie Liao; Bin Xu

2012-07-01T23:59:59.000Z

446

Comparisons of Daily Sea Surface Temperature Analyses for 200708  

Science Conference Proceedings (OSTI)

Six different SST analyses are compared with each other and with buoy data for the period 200708. All analyses used different combinations of satellite data [for example, infrared Advanced Very High Resolution Radiometer (AVHRR) and microwave ...

Richard W. Reynolds; Dudley B. Chelton

2010-07-01T23:59:59.000Z

447

A Daily Soil Temperature Dataset and Soil Temperature Climatology of the Contiguous United States  

Science Conference Proceedings (OSTI)

Although affected by atmospheric circulations, variations in soil temperature result primarily from the radiation and sensible and latent heat exchanges at the surface and heat transfer in the soils of different thermal properties. Thus, soil ...

Qi Hu; Song Feng

2003-08-01T23:59:59.000Z

448

EIA - Daily Report 10/17/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

... Regulatory Commission to construct a new receipt point at Texas Eastern Transmission's Larose compressor station in Lafourche Parish, ...

449

Daily Journal -California's Largest Legal News Provider High speed rail project squeezes by in key  

E-Print Network (OSTI)

. Aghaian of McKenna Long & Aldridge LLP Environmental Hydraulic fracking: we can decide now While hydraulic fracking in an environmentally responsible way. States are a major source of regulation of the fracking, and site restoration. State laws on fracking vary widely. Some states, including California, do not even

Barrett, Jeffrey A.

450

EIA - Daily Report 10/7/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

There are 14 pipelines with interconnections at the Henry Hub. As a result of these changes at the Henry Hub, the ...

451

Daily/Hourly Hydrosystem Operation : How the Columbia River System Responds to Short-Term Needs.  

SciTech Connect

The System Operation Review, being conducted by the Bonneville Power Administration, the US Army Corps of Engineers, and the US Bureau of Reclamation, is analyzing current and potential future operations of the Columbia River System. One goal of the System Operations Review is to develop a new System Operation Strategy. The strategy will be designed to balance the many regionally and nationally important uses of the Columbia River system. Short-term operations address the dynamics that affect the Northwest hydro system and its multiple uses. Demands for electrical power and natural streamflows change constantly and thus are not precisely predictable. Other uses of the hydro system have constantly changing needs, too, many of which can interfere with other uses. Project operators must address various river needs, physical limitations, weather, and streamflow conditions while maintaining the stability of the electric system and keeping your lights on. It takes staffing around the clock to manage the hour-to-hour changes that occur and the challenges that face project operators all the time.

Columbia River System Operation Review (U.S.)

1994-02-01T23:59:59.000Z

452

Daily natural gas and power price differences in Mid-Atlantic ...  

U.S. Energy Information Administration (EIA)

tags: electricity natural gas New Jersey Pennsylvania pipelines prices spot prices states transmission transportation weather. Email Updates. RSS Feeds. Facebook.

453

DayRec: United States Record-Maximum/Minimum Daily Temperatures  

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

the Subcommittee on Global Change Research, 11-33. Peterson, T. C., P. A. Stott and S. Herring, Editors, 2012: Explaining extreme events of 2011 from a climate perspective. Bull....

454

Supporting lifelong learners to build personal learning ecologies in daily physical spaces  

Science Conference Proceedings (OSTI)

This paper presents the results from a questionnaire filled out by 147 lifelong learners with the aim to analyse learning practices of adults, and to recognise patterns of lifelong learners in order to support them with technology. These patterns capture ...

Bernardo Tabuenca; Stefaan Ternier; Marcus Specht

2013-10-01T23:59:59.000Z

455

A Numerical Daily Air Quality Forecast System for The Pacific Northwest  

Science Conference Proceedings (OSTI)

A real-time photochemical air quality forecast system has been implemented for the Puget Sound region to support public awareness of air quality issues. The Air Indicator Report for Public Access and Community Tracking (AIRPACT) forecast system ...

Joseph Vaughan; Brian Lamb; Chris Frei; Rob Wilson; Clint Bowman; Cristiana Figueroa-Kaminsky; Sally Otterson; Mike Boyer; Cliff Mass; Mark Albright; Jane Koenig; Alice Collingwood; Mike Gilroy; Naydene Maykut

2004-04-01T23:59:59.000Z

456

EIA - Daily Report 9/20/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

According to Chevron, it has repaired a hurricane-damaged product berth at its Pascagoula, MS, refinery and is now ...

457

Stability of supply function equilibria: Implications for daily versus hourly bids  

E-Print Network (OSTI)

market. Ross Baldick Department of Electrical and Computer Engineering The University of Texas at Austin an electricity market where generating rms bid to supply energy, Analyze the e ect of requiring that the bid

Baldick, Ross

458

Stability of supply function equilibria: Implications for daily versus hourly bids  

E-Print Network (OSTI)

market Ross Baldick Department of Electrical and Computer Engineering The University of Texas at Austin bid to supply energy and we analyze the e ect of requiring that the bid remains xed throughout a time

Baldick, Ross

459

Modeling and Forecasting the Daily Maximum Temperature Using Abductive Machine Learning  

Science Conference Proceedings (OSTI)

The abductory induction mechanism (AIM) is a modern machine-learning modeling tool that draws from the fields of neural networks, abductive networks, and multiple regression analysis. This paper introduces AIM as a useful weather modeling and ...

R. E. Abdel-Aal; M. A. Elhadidy

1995-06-01T23:59:59.000Z

460

Predicting Daily Maximum Temperatures Using Linear Regression and Eta Geopotential Thickness Forecasts  

Science Conference Proceedings (OSTI)

The relationship between forecast geopotential thickness and observed maximum temperature is investigated, and regression equations are calculated using numerical model thickness forecasts for Nashville. Model thickness forecast accuracy is shown ...

Darrell R. Massie; Mark A. Rose

1997-12-01T23:59:59.000Z

Note: This page contains sample records for the topic "kwh daily avg" 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

Assessing Maturity in Sweet Sorghum Hybrids and its Role in Daily Biomass Supply  

E-Print Network (OSTI)

Sweet sorghum is a highly versatile C4 grass noted for its improved drought tolerance and water use efficiency relative to sugarcane. Sweet sorghum is well suited for ethanol production due to a rapid growth rate, high biomass production, and a wide range of adaptation. Unlike the 12-18 month growth cycle of sugarcane, sweet sorghum produces a harvestable crop in three to five months. Sweet sorghum and sugarcane crops are complementary and in combination can extend the sugar mill seasons in many regions of the world to an estimated 8 months. Seasonal growth and weather patterns both optimize and restrict production of each crop to specific times of the year, however these are different for the two crops. In addition to temporally spacing the date of harvest between crops, the genetic variability of maturity within the crops may also be used to extend the mill seasons; specific hybrids can be used and selected to maximize yield throughout the harvest season. Under favorable growing environments, sweet sorghum hybrids of all maturity groups produced sugar yields ranging from 2.8 to 4.9 MT/ha. Early/medium, late, and very late maturity hybrids planted during April, May, and June planting dates are necessary to maximize the mill season. In this study, early/medium maturity hybrids planted during April and May matured for harvest between late July and mid-August. June planting dates were unfavorable for early/medium maturity hybrids. In addition, late and very late maturity hybrids planted during April matured for harvest in late August; the additional growing season thus resulted in higher sugar yields. Timely planting of late and very late maturity hybrids in April, May, and June produce the maximum yields for harvests after mid August. Intermittent use of late and very late maturity hybrids can therefore extend sugar milling seasons into mid November if so desired.

Burks, Payne

2012-05-01T23:59:59.000Z

462

Estimating Summer Design Temperatures from Daily Maximum Temperatures in New Mexico  

Science Conference Proceedings (OSTI)

Many climatological locations report only maximum and minimum temperatures. However, in certain applications, such as estimation of design temperatures, the frequency distribution of hourly temperatures is required. For this reason, a method is ...

Kenneth E. Kunkel

1986-04-01T23:59:59.000Z

463

A user-friendly tool for detecting the stress level in a person's daily life  

Science Conference Proceedings (OSTI)

Mental health care represents over a third of the cost of health care to all EU nations and, in USA, it is estimated to be around the 2.5% of the gross national product. Depression and Stress related disorders are the most common mental illnesses. The ... Keywords: depression, mental health, prevention, stress detection, usability

Irene Zaragoz; Beatriz Rey; Cristina Botella; Rosa Baos; Ins Moragrega; Diana Castilla; Mariano Alcaiz

2011-07-01T23:59:59.000Z

464

Introduction Unlike most objects studied in astrophysics, the Sun has a considerable influence on our daily  

E-Print Network (OSTI)

to variations in the solar energy output. Solar activity is believed to be closely related to the solar rotation is to provide us with an almost constant and totally predictable source of energy, and that any changes, this is far from the case. One of the most obvious solar changes to affect us is the variation in the number

Schou, Jesper

465

EIA - Daily Report 10/24/05 - Hurricane Impacts on U.S. Oil ...  

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

24, 4:00 pm Shut-in Status Date Shut-in Oil (bbld) % of Total Federal GOM Shut-in Gas (mmcfd) % of Total Federal GOM 10242005 1,018,478 64.6% 5,472 54.2% 10212005 986,805...

466

Feasibility of compressed air energy storage to store wind on monthly and daily basis.  

E-Print Network (OSTI)

??The storage volumes are often limited in terms of availability. Since volume is expensive, optimizing its use is very important to make reasonable net earnings. (more)

Riaz, Muhammad Ali

2010-01-01T23:59:59.000Z

467

EIA - Daily Report 9/30/05 - Hurricane Impacts on U.S. Oil &...  

Gasoline and Diesel Fuel Update (EIA)

(MMBtu) 13.92 14.20 -0.28 12.32 6.80 Petroleum As Hurricane Rita approached, 16 refineries along the Gulf Coast shut down as a precautionary measure and to allow employees to...

468

Incorporating Satellite Observations of No Rain in an Australian Daily Rainfall Analysis  

Science Conference Proceedings (OSTI)

Geostationary satellite observations can be used to distinguish potential rain-bearing clouds from nonraining areas, thereby providing surrogate observations of no rain over large areas. The advantages of including such observations are the ...

Elizabeth E. Ebert; Gary T. Weymouth

1999-01-01T23:59:59.000Z

469

Spatial Interpolation of Daily Potential Evapotranspiration for New Zealand Using a Spline Model  

Science Conference Proceedings (OSTI)

Potential evapotranspiration (PET) is an important component of water balance calculations, and these calculations form an equally important role in applications such as irrigation scheduling, pasture productivity forecasts, and groundwater ...

Andrew Tait; Ross Woods

2007-06-01T23:59:59.000Z

470

The integration of disabled people in the daily working life in the German primary labor market.  

E-Print Network (OSTI)

??The aim of this study was to examine and bring to light any issues currently confronting the disabled community when looking to integrate themselves into (more)

Milcher, Magdalena

2013-01-01T23:59:59.000Z

471

An Improved QC Process for Temperature in the Daily Cooperative Weather Observations  

Science Conference Proceedings (OSTI)

TempVal is a spatial component of data quality assurance algorithms applied by the National Climatic Data Center (NCDC), and it has been used operationally for about 4 yr. A spatial regression test (SRT) approach was developed at the regional ...

Kenneth G. Hubbard; Nathaniel B. Guttman; Jinsheng You; Zhirong Chen

2007-02-01T23:59:59.000Z

472

A New Perspective on Recent Global Warming: Asymmetric Trends of Daily Maximum and Minimum Temperature  

Science Conference Proceedings (OSTI)

Monthly mean maximum and minimum temperatures for over 50% (10%) of the Northern (Southern) Hemisphere landmass, accounting for 37% of the global landmass, indicate that the rise of the minimum temperature has occurred at a rate three times that ...

Thomas R. Karl; Richard W. Knight; Kevin P. Gallo; Thomas C. Peterson; Philip D. Jones; George Kukla; Neil Plummer; Vyacheslav Razuvayev; Janette Lindseay; Robert J. Charlson

1993-06-01T23:59:59.000Z

473

Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting  

Science Conference Proceedings (OSTI)

This paper investigates the effectiveness of the genetic algorithm (GA) evolved neural network for rainfall-runoff forecasting and its application to predict the runoff in a catchment located in a semi-arid climate in Morocco. To predict the runoff at ... Keywords: Back propagation, Catchment, Genetic algorithm, Neural network, Rainfall-runoff, Semi-arid climate

A. Sedki; D. Ouazar; E. El Mazoudi

2009-04-01T23:59:59.000Z

474

EIA - Daily Report 10/4/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

... and 1 shut down or attempting to restart in the Houston/Texas City /Galveston ... the Calcasieu Ship Channel and Industrial Canal have been ...

475

EIA - Daily Report 10/12/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

... weather conditions, the size and efficiency of individual homes and their heating equipment, and thermostat settings. Prices for petroleum and natural gas ...

476

EIA - Daily Report 10/26/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

... 239,000-bbl/d Bayway refinery has reportedly been restarted as of yesterday following the 45-minute power outage. No timetable has ...

477

Daily Journal -California's Largest Legal News Provider presiding judge of the San Diego Superior Court  

E-Print Network (OSTI)

place. Our state department recognizes Abkhazia as land within Georgia, whose own behavior has not been questions that apply well beyond this region of the Caucasus - in Western Europe, in the Middle East, many ethnic Georgians were forced to leave Abkhazia and now live in Georgia. They have their own

Rose, Michael R.

478

UR Self-Service Hours of Operation Available 24/7, except for daily updates  

E-Print Network (OSTI)

5) Choose the Term 6) Search for courses by subject, and narrow down your search by course number for Classes 4) Select a Term To Add a Course 1) Scroll down to Add Class Worksheet 2) Enter your CRN 2) Class Schedules 3) Class Sections Requiring Staff Registration 4) Select a term 5) Search

Argerami, Martin

479

Surface circulation types and daily maximum and minimum temperatures in southern La Plata Basin  

Science Conference Proceedings (OSTI)

La Plata Basin is one of the most important agriculture and hydropower producing regions worldwide. Extreme climate events such as cold and heat waves and frost events have a significant socio-economic impact. This work analyzes the influence of ...

Dr. Olga Clorinda Penalba; Dr. Mara Laura Bettolli; Pablo Andrs Krieger

480

Habitat: awareness of daily routines and rhythms over a distance using networked furniture  

E-Print Network (OSTI)

distant partners in just such a situation. The project particularly focuses on conveying the patterns explores the potential of addressing these issues by using household furniture as a network of distributed - London/Berlin 2003). 1 #12;Figure 1: Habitat being used to link two distant partners. 3 Technology

Haddadi, Hamed

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481

Predicting daily streamflow using rainfall forecasts, a simple loss module and unit hydrographs: Two Brazilian catchments  

Science Conference Proceedings (OSTI)

The performance of a simple, spatially-lumped, rainfall-streamflow model is compared with that of a more complex, spatially-distributed model. In terms of two model-fit statistics it is shown that for two catchments in Brazil (about 30,000km^2 and 34,000km^2) ... Keywords: Brazil, Hydropower, Rainfall forecasts, River Paran, Streamflow forecasts, Unit hydrographs

I. G. Littlewood; R. T. Clarke; W. Collischonn; B. F. W. Croke

2007-09-01T23:59:59.000Z

482

Daily Journal -California's Largest Legal News Provider U.S. firms to enter that territory.  

E-Print Network (OSTI)

for the BCS bowl system. By Rodney K. Smith of Thomas Jefferson School of Law Technology & Science Test The Bowl Championship Series: It's about power Academic values and student welfare are no longer priorities

Rose, Michael R.

483

Daily Precipitation Statistics for South America: An Intercomparison between NCEP Reanalyses and Observations  

Science Conference Proceedings (OSTI)

In this study, the authors document the extent to which the precipitation statistics of the new CFS reanalysis (CFSR) represent an improvement over the earlier reanalyses: the NCEPNCAR reanalysis (R1) and the NCEPDOE Second Atmospheric Model ...

Viviane B. S. Silva; Vernon E. Kousky; R. Wayne Higgins

2011-02-01T23:59:59.000Z

484

INTRODUCTION Daily irradiance in near-surface waters can vary over an intensity  

E-Print Network (OSTI)

of sciaenid visual systems to respond to colored light stimuli. The output of a Cermax Xenon fiberoptic light of sciaenid visual systems was assessed via flicker fusion frequency (FFF) experiments with the white light monitored the ability of a visual system to track light flickering in logarithmically increasing frequencies

Hartley, Troy W.

485

Climatological Estimates of Daily Local Nontornadic Severe Thunderstorm Probability for the United States  

Science Conference Proceedings (OSTI)

The probability of nontornadic severe weather event reports near any location in the United States for any day of the year has been estimated. Gaussian smoothers in space and time have been applied to the observed record of severe thunderstorm ...

Charles A. Doswell III; Harold E. Brooks; Michael P. Kay

2005-08-01T23:59:59.000Z

486

EIA - Daily Report 9/19/05 - Hurricane Katrina's Impact on U.S ...  

U.S. Energy Information Administration (EIA)

While the peak crude oil production loss from Hurricane Katrina ... ExxonMobil, located in Chalmette, LA; and Murphy Oil, ... Total Gulf Coast Region ...

487

EIA - Daily Report 9/16/05 - Hurricane Katrina's Impact on U.S ...  

U.S. Energy Information Administration (EIA)

While the peak crude oil production loss from Hurricane Katrina ... ExxonMobil, located in Chalmette, LA; and Murphy Oil, ... Total Gulf Coast Region ...

488

Viable alternative for conducting cost-effective daily atmospheric soundings in developing countries  

Science Conference Proceedings (OSTI)

Radiosonde-collected data are of vital importance to a wide variety of studies that aim at understanding the interaction between land-surface and the atmosphere, amongst others. However, atmospheric measurements in developing countries, some of which ...

Thomas Lafon; Jennifer Fowler; John Fredy Jimnez; Gabriel Jaime Tamayo Cordoba

489

Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications  

E-Print Network (OSTI)

al. , 1980), such as the BIOME-BGC (Hunt and Running, 1992;big-leaf methodology used by BIOME-BGC needs to be furtherformed in the same way as in BIOME-BGC. These functions are

Chen, J.M; Liu, J; Cihlar, J; Goulden, M.L

1999-01-01T23:59:59.000Z

490

The Daily Cloud-to-Ground Lightning Flash Density in the Contiguous United States and Finland  

Science Conference Proceedings (OSTI)

A method is developed to quantify thunderstorm intensity according to cloud-to-ground lightning flashes (hereafter ground flashes) determined by a lightning-location sensor network. The method is based on the ground flash density ND per ...

Antti Mkel; Pekka Rossi; David M. Schultz

2011-05-01T23:59:59.000Z

491

EIA - Daily Report 9/22/05 - Hurricane Impacts on U.S. Oil &...  

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

gas in storage increased to 2,832 Bcf, which is 3.4 percent above the 5-year average inventory level. The implied net addition of 74 Bcf is nearly 8 percent below the 5-year...

492

EIA - Daily Report 9/29/05 - Hurricane Impacts on U.S. Oil &...  

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

gas in storage increased to 2,885 Bcf, which is 2.4 percent above the 5-year average inventory level. The implied net injection during the report week was 53 Bcf, which is about...

493

A transitional home in the city : rebuilding the layers of daily life  

E-Print Network (OSTI)

This thesis explores how the architecture of a place can be informed by an understanding of psychological needs. The project is the design of a transitional home in the South End of Boston. A transitional home is a place ...

Steinberg, Shira Judith

1990-01-01T23:59:59.000Z

494

Spatial Bayesian Model for Statistical Downscaling of AOGCM to Minimum and Maximum Daily Temperatures  

Science Conference Proceedings (OSTI)

Atmosphereocean general circulation models (AOGCMs) are useful for assessing the state of the climate at large scales. Unfortunately, they are not tractable for the finer-scale applications (e.g., hydrometeorological variables). Downscaling ...

Dominique Fasbender; Taha B. M. J. Ouarda

2010-10-01T23:59:59.000Z

495

Long-Term Daily Climate Records from Stations Across the Contiguous...  

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

National Climatic Data Center (NCDC) in collaboration with the Department of Energy's Carbon Dioxide Information Analysis Center (CDIAC). The USHCN project dates to the mid-1980s...

496

Bayesian Changepoint Analysis of the Annual Maximum of Daily and Subdaily Precipitation over South Korea  

Science Conference Proceedings (OSTI)

Bayesian changepoint analysis is applied to detect a change point in the 30-year (19762005) time series of the area-averaged annual maximum precipitation (A3MP) for the six accumulated time periods (1, 3, 6, 12, 24, and 48 h) over South Korea. ...

Chansoo Kim; Myoung-Seok Suh; Ki-Ok Hong

2009-12-01T23:59:59.000Z

497

EIA - Daily Report 10/24/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

Heating Oil (c/gal) 179.73. 186.65 ... Williams is conducting a second open season for capacity on its Discovery Pipe in order to free up stranded gas ...

498

EIA - Daily Report 10/5/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

75.4%. 9/24/2005: 1,500,898. 96.1%. 7,488. 72.0%. source: Minerals Management Service figure data. Prices figure data figure ...

499

EIA - Daily Report 9/27/05 - Hurricane Impacts on U.S. Oil ...  

U.S. Energy Information Administration (EIA)

75.5%. 9/26/2005: 1,527,630. 97.8%. 7,843. 75.4%. 9/25/2005: 1,501,863. 96.2%. 8,047. 77.4%. 9/24/2005: 1,500,898. 96.1%. 7,488. 72.0%. 9/23/2005: 1,486,877. 95.2% ...

500

Question of the Week: What Is Your Daily Commute Like? | Department...  

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

Innovation Science & Technology Science Education Innovation Energy Sources Energy Usage Energy Efficiency Mission News & Blog Maps Data About Us For Staff & Contractors Offices...