A REVIEW OF EXISTING COMMERCIAL ENERGY USE INTENSI1YAND LOAD-SHAPE STUDmS

Hal Akbari, I. Tunel, J 0 Em, K Heinemeie:r;B. Lebot, and Ltl Rainer

Energy Analysis ProgramLawrence Berkeley Laboratory

This paper reviews and compares existing studies of energy use intensities (BUIs) andload shapes (LSs) in the commercial sector, focusing on stud that used californiadata" Our review of EDI studies found fairly good agreement on electric lighting andcooling EUIs.. Other BUIs, notably electric miscellaneous in offices, retail, and foodstores; electric refrigeration in restaurants and warehouses; electric cooking inrestaurants; and electric water heating and ventilation for all types of premisesexhibited the largest variations. The major variations in gas EUls were found inrestaurants (all end uses) and food stores (cooking and water heating)..

OUf review of LS studies, which included existing LSs in use by Southern. CaliforniaEdison (SeE) Company, the california Energy Commission (CEC), and a LawrenceBerkeley Laboratory (LBL) study, uncovered two significant features of existing LSestimates.. First, LSs were generally not consistent between studies (e"g$' SCE andCEC had different load shapes for the same end use in the same type of premises),but these differences could often be related to differences in assumptions foroperating hours$ Second, for a given type of premise, LSs were often identical foreach month and for peak and standard-days, suggesting that, according to somestudies, these end uses were not affected by seasonal or climatic influences..

INTRODUCTIONEnergy use and k demand modeling is an inte-gral part of electricity forecasting grams for bothelectric utilities and various governmental agencies"Supply-side planning more recently, demand-si anning) is based on estimates of the currentand future ene use and peak

Various forecasting models--from simple extrapo-lation of the historical trends to more detailed enduse modeling--are used throughout the country toestimate energy use by sector.. The more detailedmodels the market into major componentssuch as buildings (commercial and residential),

(aSSeIJnt:HV", process), agriculture, and modeland peak demand of each componentThe basic ingredient of all the sub-

models include: estimates of annual energyuse intensity (EDI) (or unit energy consumptions),

estimates of unit peak power demand, estimates ofmarket size (e"g.. , floor area of office buildings), andestimates of saturation of particular end uses ortechnologies (e..g", saturation of fluorescent lightingin small office buildings or saturation of adjustablespeed drives in process industries) ..

For the building sector, end-use energy demandforecasting is data intensive.. End use energy data,either in the form of energy usejntensities (EUls)or load shapes (LSs), are difficult and costly tocollect Etoet at (1990) present a state-of-the-artreview of end-use load shapes data application,collection, and estimation methods. Yet, they are acrucial input to the development of meaningfulforecasts.. In California, major utilities and theCalifornia Energy Commission (CEC) are constantlyimproving the quality of the forecasting models by

Commercial Data, Design, and Technologies 3.7

EUI STUDIES

obtaining more accurate EUI and load-shape data..This paper reviews and compares existing studies ofEUIs and LSs in the commercial sector, focusing onstudies that used California data..

Lighting (Figure 1):

Agreement among studies was generally good. Foodstores, because of both longer hours of operationand higher intensities, had the highest EDI for thisend use, with a range from 10 to 16 kWh/ft2_yr..Except for large offices in the LBL study, officesand retail stores both had similar EDI ranges(505-8.5 kWh/ft2-yr)& The agreement among studieswas not as good for schools, hospitals, and hotels/motels.. Some variation in lighting energy use wasexpected among studies because of differing equip-ment efficiency and usage, but we did not expect asmuch as we found for the latter business types.. Oneexplanation for such wide ranges can be found indifferences between the definition of building typesamong these studies.. For instance, hospitals mayactually be a combination of general health, clinics,and hospitals, all with dissimilar energy usecharacteristics.. Similarly, schools may be a mix ofprimary, secondary, and vocational schools, again allwith different end-use characteristics..

Miscellaneous Electrical Equipment (Figure 2):

This end-use category is fairly difficult to understandand compare.. The difficulty arises mainly because ofthe fact that the definition of this end use is almostarbitrary.. The estimates among building types in thiscategory ranged from about 1.0 to 5..0 kWh/ft2-yr..The SDG&E stud.y (McCollister 1987) estimatedhigher miscellaneous electricity use than the otherstudies for almost all building types..1 When theSDG&E study is removed, the agreement amongstud.ies is improved significantly0

be found

conditional demand3.. Statistical studiesanSllVSllS; and

4" auditor estimates and bill disaggregationfrom on-site visits,

COMPARISON

An review of these methodsin Thriel (1987)&

We have reviewed 12 commercial sector EDI studiesthat have been carried out over the past eight years..Of these, seven were conducted for Californiautilities.. Other studies have been carried out forFlorida Power and Light (FP ortheast Utilities,Wisconsin Power and Light (WEPCO), and NewYork State Electric and Gas (RER 1987, NEU1985, 1986a, 1986b, 1987a, 1987b, McMenamin1986, Parti 1986).. One study was national (Parti1984)..

The methodologies used in these studies can begrouped into four general categories:1.. Submetering of energy using equipment;

2.. Computer simulation of prototypical buildingswith and without reconciliation against themeasured

The above-mentioned studies have developed EUIsfor up to 11 types (small office, large office,

foodVUJl.J.VJ;;,.''V'll JII....Vi,;1 ..,JIl.ll.U,... hotel/motel, and miscellaneous)and 12 electric and gas end uses.. Electric end usesinclude: lighting, miscellaneous, refrigeration,cooking, water heating, ventilation, space cooling,and space gas end uses include: cooking,

water heating, and space heating.. Thedata for all these building s and end uses arepresented and compared in Akbari et aL (1989) .. Inthis we will restrict OUf attention to fourelectrical end uses by presenting and discussing EUIdata for lighting, miscellaneous, refrigeration, and'VV,JJi.... end uses for aU building types..

(Figure 3):

Restaurants and food stores had the highest refrig-eration EUIs0 This is reasonable since large capacityrefrigeration equipment is most prevalent in thesetwo business types.. The EDI for food stores rangedfrom about 10 to 30 kWh/ft2-yr, while the EUI forrestaurants ranged from about 2 to 22 kWh/ft2-yr*The CEC (1987a) study estimates for restaurantrefrigeration in the SDG&E and SeE service terri-tories were low compared to the other studies.. The

1 we speculate that the reason may be the SDG&E's inclusion ofventilation in miscellaneous end uses.

3g8 Akbari at a/.

EJ SOG&E

Ea SDG&E(CEC)

msa:SCE(CEC)

o SCE(SRC)II PG&Em PG&E(CEC)

lEJ PG&E(CEC1)

tJ lBlStudy

NR

IL

s

N lIIll Non Refrigrated R SIl Warehouse

L !III Large Retail, S lIl! Smd RehlJl

20 .........---------------------------------------..........,..19

18

17

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1514

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........N 9.....,

8.c 7 II

4

3

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1o

LARGE RESTAU- RETAIL FOOD- WAREHOUSE SCH:CL cc.u..EGE HOSPITAL HOTBJ MISCEl-OFFICE RANT STORE Iv10lEL LANEOUS

1. Lighting EUIs Comparison

2

o

N flIIl Non Refl'iQf&1ed Waret"toooe, 1m SOG&Ef1 m H8fr1gernted WluGhouse 121 SDG&E(CEC)L lIII large Retail, S lIIll Sma" Retail II see

SCE(CEC) R

0 SCE(SRC)

III PG&Eg PG&E(CEC)

1m PG&E(CEC1)

fJ lBlStudy

S

l I I-".

SMAU.OfRCE

lARGE RESTAU- RETAIL FOOD- WAReiQUSE SCH:CL cc.u..EGE HOSPITAL HOTB../ MISCEL·OFFICE RANT STORE MOTEl LANEOUS

2. Electrical Miscellaneous EUIs Comparison

Commercial Oats, Design, and Technologies 3.9

R Refrigerated Warehouse

G SDG&E

I2J SDG&E(CEC)

Gsa:SCE{CEC)

o SCE(SRC)• PG&E

PG&E{CEC)

I3l PG&E(CEC1)

LBlStudv

R

RESTAU- RETAIL FOOD- WAREHOUSE sa-oa.. CCUEGE HOSPITAL HOTEU MISCEL-RANT WOTB.. lANEOUS

24

32 ----------------............---------------_............_---,

30

28

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bm 20

Q,) 18

;; 16

'*"'" 14...........c 12

.:g:: 10

8

{)

30 Refrigeration EUIs Comparison

from the PG&E to other studies) forthis uilding

In EUls obtained from different studies butfor the same end use and building type, are expectedto differ somewhat The stock of buildings in each...... +-i·l-i+«,,7,t{'O service area will be of varying vintages, aswill be the equipment found within0 Climate vari-ations will affect space heating, cooling, and water

EUIso Floor space definitions will affectEUIs.. The composition of a building type (e.go, fastfood restaurant vSo sit down restaurant) may have alarge affect on EUIso For example, fast foodrestaurants are more energy intensive than sit downrestaurants 0

lWo other major reasons for large variations inEUIs among these studies are the definition of floorareas and end-use categories.. Errors as much as50% have been noted in the reported estimates ofthe floor areas for individual buildings.. Also, enduse definitions for lighting mayor may not includetask lighting; some space heating is included in the'miscellaneous' end use; etc.

warehouse which is a combination of.... ..ina'Jl"ni"I.::.\r1 and buildin ,had thenext although it wasmuch lower than for food stores and restaurants"The of values for warehouse isbecause of differences studies in thedefinition of the end use and floor areas used inestimating EUIso

building the ED! forto be around

and .U.Va.,V.lLl/AJLAVlI.YJ!.1.:)

about 6 Some ofEUIs was because of the

climate the three.11.-""'..."" ........., .. Cll..UU.AI'"J.'-'.8lA""JI..LY'l the definition of floor space

the studies,. For example, the.1.VJ."""",,,VJUl.J.I.:)!&.,_JII 1985) and SDG&E studies used

for andend uses" The difference between the

conditioned and unconditioned floor area inwarehouses may account for the relatively high EDI

3.10 Akbari at a/.

18

16

146

m 12Q)

""'-N 10

-.....t:: 8

6

4

2

o

l m Retmn. S llfII Sma" Reid EI SOO&ENOR Norton Air Foree Base Weather IZl SDG&E(CEC)LAX w loe Angof.e$ Airpot1 Weather II see

m sce(CCC)

0 SCE(SRC)II PG&E

PG&E(CEC)SCNOA) m PG&E(CEC1)

lBlSludyN:IA

L(NOA)

m

S

51 -'

ldn lLI £SMALL LARGE RESTAU- RETAIL FOOD-- WAREHOUSE s:::::H:Cl cx::uEGE HOSPITAL HOTELI MISCEL..OFFICE OFACE RANT STORE MOTS.. lANEOUS

4.. Cooling EUIs Comparison

seasons..

and statistical methods to reconcile the auditinformationwith annual utilitybills$

LoadThble 1 summarizes the data that wehave used in our comparison,; A complete discussionand of these load shapes are presentedelsewhere et at 1989); in this paper, we

focus our attention on studies that haveload shapes for California..

In these load-shape data bases, theshould be noted:

1" LBL and SeE studies have developed standard,non-standard, and peak day load-shape data forall 12 months of the year; CEC data only containone set of load-shape data (for the peakday)..

2" The non-HVAC end-use load shapes for allLBL study, do not change across

Because of added the larger amount ofdata and to a certain extent less historical

there were fewer commercial sector LSstudies available than EDI studieso In a recentAkbari et at (1989) identified and reviewed four

sources of data (three inCalifornia and one outside LBL inte-

and EUI for seE servicebe noted as LBL data in the i"nllliin'll:"llt1"1ifln

SRC simulation for SeE servicearea RC data and CECdemand model load

and selected studiesUtilities

ft"lllOi"hr''II.tiLt''''o,§nt1M,oC' used in end-use loadsimulations of

some augmented withreconciliation of the simulated results againstmeasured data" A few of these studies have also

load survey data

Commercial JlJ",#V'y'f;"jf,lll .. and Technologies 3. 11

Table 1. Load Shape Comparison os Data Summary

Study LBL Southern California California Energy NortheastEdison Commission Utilities

Types of days Peak, Standard Peak, Standard Peak Peak, StandardWeekend Weekend WeekendS

load shapes for 12 Months 12 Months Winter, Summer Winter, Summer

Building Types:1 1Office (large & Small) \9 @ \9

Retail (large & Small) .1,2 .1,2 5@ \9

Restaurant 3• 4D \9 6)

Food Store 5e • @ •Warehouse 4 4

@ @ • eSchool 5.. lSi •College 5

0 • ..Hospital * e $5,8

Medical Office *Hotel/Motel * * @

Miscellaneous 5@ $ *

End Uses:Heating 9

@ @ *Cooling 9• •Ventilation 7• e • •Lighting e @ • @

Cooking (3 Gil eRefrigeration 8.. @ @ @

Water Heating @ • •Other 0 $ • $

Notes:1. Load shapes for large office and department store were simulated with both central and package air

conditioning units.2. Separate load shapes were estimated for small retail and department store.3. Load shapes were estimated only for fastfood restaurant.4. Separate load shapes were estimated for refrigerated and non-refrigerated warehouses.5. We have omitted presentation of data from this category.6. Northeast Utilities load shapes for hospitals included all categories of health buildings.7. Ventilation was included in heating and cooling end uses for all building types but office.8. Refrigeration load shape was only estimated for refrigerated warehouse.9. Load shapes for heating and cooling were calculated using THI matrices.

3.12 Akbar! at al.

3. The cooling load shapes for CEC were calculatedusing typical year weather data and temperaturehumidity index (THI) matricese

The load-shape data from the california data basesdiffer widely in their development and application..In order to establish a common framework forcomparing end-use load shapes of two of thesestudies, first, we calculate daily allocation factorsthat apportion annual end-use consumption to dailyconsumption, then, we apportion daily consumptionto hourly consumption with hourly allocationfactors, whose 24-hour integral adds up to one.

These studies have developed load-shape data forfive electric non-HVAC end uses (lighting,miscellaneous, water heating, cooking, andrefrigeration) and three HVAC end uses (cooling,ventilation, and space heating) for all building typesand all climate regions. The seE data for non-HVAC end uses included monthly peak day,monthly average weekday, and monthly averageweekend-day load shapes for all 12 months of theyear" There is not significant month to monthvariation for these load shapes.. The CEC non-HVAC load-shape data are for the peak day ofthe year. Therefore, there is not a one-to-one

for all months of the The LBLdata have been developed by disaggregating

whole-building hourly loads into end uses..The resulting hourly data have been used toload shapes for standard, non-standard, and

for each month of the year"

We limit our discussion of the load shape data andcomparison to an example for lighting,briefly discussing the of the load shapesfor other end uses, and finally presenting a sampleload of all end uses from the LBL for

office buildings"

b,andThese data foran from and LBL data-bases" Each load shape is divided into two parts.The presents fractional data, so that whenthe ED! is a the reSl:HtiJ:12number is the use for the given month

and the given day type.. The bottom part of thegraph shows hourly load-shape data for three daytypes.. The hourly end-use load is calculated bymultiplying daily consumption by the hourly load-shape fraction.. Solid lines represent peak days,dashed lines represent standard days, and dottedlines represent weekend days..In general, the load shapes are quite different and adetailed comparison is difficult We observed that:

@ The fractions of daily consumptions betweenCEC, SCE, and LBL data were within about 25%of each other, except for the small office, school,and college (note that LBL has not developedload shapes for a few building types includingschools and colleges);

@I The load shapes differed mainly during theshoulder hours.. Hours of full operation variedamong these studies;

@ CEC load shapes indicated zero nighttimelighting for schools and small offices;

@ CEC data showed an almost flat lighting loadshape for warehouses, but a very complicatedload shape for the miscellaneous building;

@ Peak and load shapes for SeE wereexcept for the school and

college..

Miscellaneous End Uses:

The comparison of the load shape data for this enduse category showed more differences thansimilarities (Akbari et at 1989):

• The fraction of daily consumptions among thesestudies was within about 25% of each other,

for the small office, school, and college;

@ Peak and weekday load shapes for SeE were alsonearly identical, except for the school andcollege;

@ CEC uses the same load shape for both theelementary school and college;

@ Load shapes for supermarket (food store),warehouse, school, college, hospital, andhotel/motel differed considerably..

Commercial Data, Design, and Technologies 3" 13

Month

Hour

a)SCE

Month

Hour

c) lBl

3.14 Akbari at a/.

Month

Hour

b)CEC

5* Summary Presentation of LightingLoad-ShapeData. a) SeE, b) CEe, c) LBLData. Each end-use load shapepresentationis divided into two parts. The top partpresents fractional data, so that when theEUI is multiplied by a fraction, the resultingnumber is the daily energy use for the givenmonth and the given day type" The bottompart of the graph shows hourly load-shapedata for three day The hourly end-useload is calculated by multiplying dailyconsumption by the hourly load-shapefraction. Except where peak, standard, andweekend data are shown in separate graphs,solid lines representpeak days, dashed linesrepresent standard days, and dotted linesrepresent weekend days.

Water Heating, Cooking, and Refrigeration:

lD For these end uses, SCE did not give load shapesexcept for supermarket refrigeration (whichexhibited no variation, either diurnal orseasonal). In comparing the load shapes fromthese sources, we observed:

• CEC's load shape for hotels showed very highnighttime water heating energy use;

.. CEC's load shapes for the large and small officeappeared to neglect water heater standby losses;CEC's flat load shape for warehouse cooking wasunexpected;

• CEC uses a flat refrigeration load shape for allbuilding

Ventilation:

As expected, the variations in the ventilation andHVAC end uses among the three reports were evengreater than the ones found for the non-HVAC enduses.. SeE data indicated that there was significantvariation in ventilation and :A.C end uses foreach of S 's four planning CEC reportedheating and cooling load sh s in the form ofweather data and THI matrices" LBL load shapesfor HVAC end uses are developed by first reconcil-ing simulation against the hourly load data for theentire SeE utility service area and then, usingDOE-2 simulations, the load shapes were scaled forthese SeE climate regions"In comparing the ventilation load shapes from thesestudies, we observed:

@ LBL data show a seasonal dependency of theventilation load shapes for most buildings

also the ventilation load shapes for thepeak and standard days are significantly different;

@ For the SeE data, except for large offices, theventilation load shapes for all 12 months of theyear were nearly identical. Also, for most buildingtypes, there was no significant variation betweenpeak and standard day;

@ CEC assumes the same load shape for allDU1.1QIIU!

Cooling:

Comparison of the cooling load shapes (Note:shapes not intensities) among these studies aredifficult. Normalizing load shapes (the area underload shape equal to one) for this end use can bemisleading. Normalized load shapes suppressseasonal and operational effects, which can vary

For example, winter month cooling loadsmay appear more "peaky" than those in the summer,because of shorter cooling hours inAs one would expect, the load shapes for largeoffices exhibited less monthly variation than dothose for other The school load shapeswere interesting because in the month when schoolis not in session, August, the load shape is similarto the weekend load shape" Also, the Januaryweekend load shape appears to be similar to thestandard-day load shape (although much smaller inmagnitude)eThe CEC load shapes were calculated from THImatrices for each building type using typical yearweather data for the four SeE climate regions:Bakersfield, Burbank, Los Angeles Airport, and San

The monthly variation was calculatedfrom the daily cooling degree days for each planningarea, and did not vary from building to building"The load shapes did not vary much from planningarea to planning area" The load shapes for hotelsand hospitals were identical and quite flat, probablybecause of nighttime occupancyo One might expect,however, that the hospital would have a larger day-time peak, because of "office hours" during the day.

Electric Space Heating:

The heating load shapes were the least uniform ofall the load shapes.. This comes in part from the factthat during swing seasons there is little heating use,so little that perhaps random fluctuations in demandare magnified when the load shape is normalized,creating confusing resultso These months are,however, less important since their overallmagnitudes are quite smaltFigure 6 presents the LBL load-shape data for anAugust standard day for all end uses for large office

Commercial Dats, Design, and Technologies 15

2.5 ......,.......----------------.............---------.

/ Interior lighting

..........Cooling....

'to-tT \en""'-J::3: 1.5

=enc:(!)...,E:CI.) 1.0VI;::)

C)a....CDCW

0.5

2.0

Hour

re 6. LBL Load Shapes for Large Office End Uses. The end use load shapes are for an August standard day.Note the high nighttime lighting usage. The peak lighting intensity is comparable to the total airconditioning and ventilation peak intensities.

buildings.. Th.e data indicate that lighting has thehighest energy use intensity for all hours ofoperation" The sum of cooling and ventilation loadsare comparable to the lighting load" The energy useduring the nighttime in large offices is appreciable.The sample of office buildings in the LBLstudy had large whole-building EUIs. In the

of load shapes, the large whole-building EDI resulted in a higher nighttime energyuse, particularly for lighting and equipment.

In summary, our review of the three LS studies inCalifornia uncovered two significant features ofexisting LSs. First, LSs were generally not consistentamong studies, but these differences could often berelated to differences in assumptions and theestimation methodologies of the studies.. Second, fora given building type within one study, only one ofthese studies show significant seasonal variations fornon-HVAC load shapes.

3g 16 Akbari at a/.

CONCLUDING REMARKSCurrent energy use and peak demand forecastingmodels are simple in principle but complicated inapplication. The models estimate energy use bysumming up the products of energy use intensities(for each end use, building type, and end-usetechnology options) and the estimates of the marketsizeo The same method is principally used toestimate the peak energy demand. Thlerance forerrors in the forecasting models is not large.. A10 percent error in forecasting peak electricitydemand in california, for example, could mean fourlarge power plants (4 OW) too many or too few.Classically, to avoid these problems, the results ofthe forecasting models are calibrated with historicalenergy demand.. Better EUI and LS data wouldprobably yield better forecasts and hence wouldrequire less model calibration.

The estimates of the EUIs and LSs show significantdifferences among various studies.. Some of thesedifference are because of inherent variations in thebuilding stock and equipment among utility serviceareas.. Additionally, there are statistical uncertaintiesin the sample designs and in the estimates derivedfrom various models. Some of the uncertainties canbe traced back to lack of quality raw data used indeveloping EUIs and LSs.. Since, most estimatesstart with some sample population data thatcharacterizes the market (on site audits, mailsurveys, sample utility bills, etc.) the resulting EUlsand LSs are associated with some statisticalvariance" At this time, we are not aware of athorough study addressing this sampling variation.A limited attempt by Akbari et at (1989) showedthat their EUI estimates were subject to 10 to 20%statistical error.. Greater relative errors were'lr"A?,\,t"\1l""1"i::30t'1 for the smaller EUIs.. No such wasmade to variance of LSs.

Most LS and EUI estimation methods have utilizedsome, sort of simulation tools with heavy doses of"engineering judgment" to arrive at their results..Reconciliation ofengineering estimates to measuredEUIs and LSs have then been used as a finalcalibration toot Detailed case studies of the energyuse in sometimes have questioned the

of some of these engineering estimates,

indicating that there is a need for improvedestimating methodsll It is not clear that additionalEDI and LS comparison studies will add much toour understanding in this area. Individual utilitieswill still wish to conduct EU! and LS studies fortheir service territories. They should expect similarvariations between their studies and otherso In orderto understand some of the differences discussedwhat is required is more measured data.The quantity of measured data is increasing in size.Many utilities have collected or started to collectdetailed end-use data for their residential andcommercial customers. Eta et at (1990) identified27 metering projects throughout the country41Analysis of these data and development of an inte-grated measured data base can substantially helpto improve our understanding of end-use EUlsand LSSll

ACKNOWLEDGEMENTThe work described in this paper was supported bythe Southern California Edison Company and thecalifornia Energy Commission" Preparation of thepaper was in part supported by the AssistantSecretary for Conservation and Renewable Energy,Office of Buildings and Community Systems, of theU.. S. Department of Energy, under Contract NO$DEoAC03-76SF00098o

REFERENCESAkbari, · Eto, Thriel, I..; Heinemeier, K;Lebot, B..; Nordman, B.; Rainer, L" 1989.. "Integratedestimation of commercial sector end-use load shapeand energy use intensities," Lawrence BerkeleyLaboratory Report LBL-27512, (Jan..).

california Energy Commission (CEC) 1987a."California Energy Demand 1985-2005, Prepared forq>nsideration in the 1987 Electricity ReportProceedings, Electricity Demand Forecast Methods, It

P300-87-004, Volume 2"California Energy Commission, (CEC) 1987b."California Energy Demand 1985-2005, Prepared forConsideration in the 1987 Electricity ReportProceedings, The SeE Planning Area Forms,ftP300-870007"

Commercial Data, Design, and Technologies 3. 17

Restaurants, Warehouses, Hotels(lV1otels andcellaneous Buildings,ff Xenergy, Inc..

ti, M.., Sebald, A, and Parris, K 1984" "EnergyUse and Conservation in the Commercial Sector: AConditional Demand Study," Applied Econometrics,Inc"Parti, M", et at 1986.. ifConditional DemandEstimation of End-Use Consumption For NYSEGCommercialCustomers,11 AppliedEconometrics, Inc.

Regional Economic Research, Inc.. (RER) 1987""Commend Users Group eeting Notes,,"

Schultz, D$ 1984,," d-Use Consumption Patternsand Energy Conservation Savings in CommercialBuildings," ACEEE 1984 Summer Study on EnergyEfficiency in Buildings..Sierra Energy Risk Assessment, Inc$ (SERA) 1983a.."Methodological Elements for Re-Estimation ofHeating and Cooling Energy Use in Buildings,,"prepared for california Energy Commission, SERAReport No.. 83·70..

erra Energy Risk Assessment, Inc.. (SERA) 1983b.."Update and Further evelopment of CaliforniaNonosHVAC Energy Utilization Indices for theCommercial Sector," prepared for CaliforniaEnergy SERA Report No.. 83-39..

Synergic Resources Corporation (SRC) 1987.."End-Use Data Development, Initial Load Shapeand Thchnology Data Base," prepared forSouthern California Edison, SRC Report No..

I.., et at 1987.. "Estimation of Energyby End-Use for Commercial Buildings,"

Vot No.. 6"

1987b"for

Northeast Utilities Service (NEU) 1987a.."End-Use Energy Survey for OfficeBuildings Conservation tl

Management Sciences, Inc$

Northeast Utilities Service ........-v'IUl)l.llJ'U,AJI.'f

"End-Use

Eto, J$; Akbari, H.; Pratt, R ..; Braithwait, S9 (1990)."End-use load shape data application, collection,and estimation, tf Submitted to ACEEE 1990Summer Study on Energy Efficiency in Buildings..

/'''!gnelzi, E, and Thain, 1(. 1984. "Econometricestimation of energy utilization Indices andPersistence of Savings," Cambridge Systematics Inc<lMcCollister, Go et 1985.. "Energy UtilizationIntensities for Major End Uses in the CommercialSector," QED Research, Inc..

McCollister, G., and TIlriel, 1. 198701 "CommercialEnergy Utilization Indices In SDG&Es ServiceThrritory" QED Research Inc..

McMenamin, SOl 1986.. "Summary of CommercialSector End-Use Data Development," RegionalEconomic Research, Inc"Northeast Utilities Service Company (NEU) 1985""New Office Buildi End-Use Energy Consumpostion Survey," Synergic Resources Corporation..

rtheast Utilities Servi Company, (NED) 1986a..etail Buildings d-Use Energy Consumption

Survey," Xenergy, Inc..Northeast Utilities Service Company (NEU) 1986b""Education and Health Buildings Energy

Survey," Resources

3e 18 Akbari et ale