AI驱动下的电力重构：美国数据中心能源需求新图景

WhitePaper

2026

PoweringIntelligence2026

UpdatedScenariosofU.S.DataCenterElectricityUseandPowerStrategies

2|PoweringIntelligence2026February2026

TABLEOFCONTENTS

EXECUTIVESUMMARY 3

INTRODUCTION 10

UNDERSTANDINGKEYDATACENTERPOWERMETRICS 10

NominalCapacity 11

NameplateCapacityandPowerUsageEffectiveness(PUE) 11

AnnualandPeakElectricityUse 11

NOMINALCAPACITY:HISTORYANDFUTUREPROJECTIONS 12

FutureScenarios 12

ANNUALANDPEAKELECTRICITYUSE:HISTORYANDFUTUREPROJECTIONS 15

SUMMARYOFFUTUREPROJECTIONS 19

ComparisontoOtherStudiesandForecasts 20

DATACENTERLOADGROWTHINCONTEXT 22

DataCenterSharesofTotalElectricityDemand 22

ComparisontoEVChargingLoad 24

GENERATIONANDCAPACITYIMPACTSOFDATACENTERLOAD 25

CONCLUSIONS 27

REFERENCES 28

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EXECUTIVESUMMARY

KeyMessages

•Datacenters,industrialonshoring,andtransportelectrificationaredrivingrenewedregionalloadgrowth.IntheUnitedStatesandothernations,

clustersofnew,largeloadsaretestingutilities’

abilitytokeeppaceandarespurringtechnicalandbusinessinnovation.

•ArtificialIntelligence(AI)playsagrowing,but

uncertainroleindatacenterload.AIworkloads

areestimatedtoaccountfor15–25%ofdatacen-terelectricitytodayandthatshareisrisingrapidlyevenasnon-AIdatacenterdemandcontinues

steadygrowth.However,thepaceandscaleofAIadoption,thepowerintensityofAIhardwareandalgorithms,andpowersystemandsupplychain

constraintsremainhighlyuncertain.

•EPRIprojectsdatacenterstoconsume9%to

17%ofU.S.electricityby2030,upfrom4%to

5%today.EPRIdevelopedLow,Medium,andHighgrowthscenariosofU.S.state-leveldatacenter

powerdemandthrough2030usingcommercial

projectdevelopmentdata.TheLowscenario

assumesmostprojectsunderconstructionand

one-fourthofthoseinadvancedplanningarefully

operationalby2030.TheHighScenarioassumesallprojectsunderconstructionorinadvancedplan-

ningplus30%ofthoseinearlyplanningquickly

overcomesupplychainandprocessconstraintstobeoperationalby2030.

•EPRI’srevisedprojectionsareabout60%higherthanits

2024report

estimates.Theincreaseisdrivenprimarilybyrecordlevelsofdevelopmentoverthepast18months.Therevisedrangeis

roughlyconsistentthrough2028withLawrence

BerkeleyNationalLab’sprojectionsintheirDe-

cember2024reporttoCongress,whichwasbaseduponprojectedchipandequipmentshipments

ratherthancommercialdevelopmentdata.

•State-levelloadgrowthprojectionsvarywidely,

creatinglocalizedchallenges.Today,Virginiaistheonlystatewheredatacentersconsumeover20%ofelectricity;thissharecouldincreaseto39%to

57%by2030.IntheMediumscenario,sevenad-ditionalstatescouldexceeda20%shareby2030.

•Energyprocurementstrategiesdrivesharplydif-ferentgenerationbuildoutstomeetthisdemand.

AssumingcurrentU.S.stateandfederalenergy

policies,least-costprocurementstrategiesfavor

naturalgas,withbuildratesintheHighscenario

morethandoubletherecentaverage.Incontrast,meetingalldatacenterloadwithhourly-matched,carbon-freeenergywouldfavorrenewableand

batteryadditions,withnewnucleargeneration

comingonlinewherefeasible.Supplybottlenecksaswellasdelaysinpermittingandsitingcouldcon-strainthemodeledadditionsofbothgeneration

andtransmissioncapacity.

•Acceleratedcollaborationisessentialtosupport

theselevelsofdatacentergrowth.Collabora-

tionbetweendatacenterdevelopers,energyand

equipmentproviders,policymakers,andcommuni-tiesiskeytodatacenterbuildout.EffortslikeEPRI’s

DCFlex

initiative,whichisworkingwithmorethan60companiestoaddressgridreliability,flexibility,affordability,andotherissuescentraltotransform-ingdatacentersintogridassets,canhelpacceler-ateprogress.

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AIPowerDemandsAreAcceleratingDataCenterLoadGrowth

Datacentershavebecomethefastest-growingsourceof

U.S.electricitydemand,andregionalclustersoffacilities

aretransforminglocalgriddynamics,testingutilities’abilitytokeeppaceandspurringtechnicalandbusinessinnova-

tion.Forecastingfuturedatacenter(DC)loadgrowthises-sentialforpowersystemplanningbutremainsdifficultbe-causepublicreportingislimited,manyannouncedprojectsarespeculative,andthereisfundamentaluncertaintyabouttheadoptionofgenerativeAIandsuccessortechnologies.

AIapplicationsaremuchmoreenergyintensivethanthe

streaming,communications,search,andothertraditional

datacenterworkloads.WhileAIworkloadsareestimatedtoconsumeonly15─25%ofdatacenterelectricitytoday(IEA(2025),JLL(2026)),thatshareisrisingevenasnon-AIdatacenterdemandscontinuetogrowsteadily.

U.S.State-LevelDataCenterLoadProjections

Drawinguponstate-leveldataonoperationalcapacity,

constructioninprogress,andannouncedplans,EPRIdevel-opedthreescenariosforU.S.datacentercapacitygrowththrough2030(FigureES-1):

•Lowgrowth.Assumesthatmostprojectsundercon-

structionandone-fourthofthoseinadvancedplanningarefullyoperationalby2030.

•Mediumgrowth.Assumesthatallprojectsundercon-struction,75%ofthoseinadvancedplanning,and10%inearlyplanningarefullyoperationalby2030.

•Highgrowth.Assumesthatallprojectsunderconstruc-tionorinadvancedplanningplus30%ofthoseinearlyplanningarefullyoperationalby2030.

FigureES-1.DatacenternominalITcapacitybystate.Innercirclesshowcapacityin2021(gray)and2024(blue).Outerbandshows

scenariorangeofprojectedcapacityin2030(orange).CircleareaisproportionaltonominalITcapacity.1Estimatesincludesmall-and

large-scaledatacentersaswellascryptocurrencymining.Resultshighlightcontinuedconcentrationinestablishedmarkets(e.g.,Virginia,Texas)alongsideemerginggrowthinnewstatesasdevelopersdiversifygeographically.

1Nominalcapacityofadatacentertypicallyreferstothemaximumpotentialinformationtechnology(IT)load(i.e.serversandotherITequipment,exclusiveofancillaryenergyuseforcoolingandinfrastructure).

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Thesescenariosweredesignedtoreflectuncertaintyabouttherealizationofplannedprojectsandarenotnecessarilyequallylikely.2TheLowgrowthscenarioservesasafloor,

assumingactiveconstructionsitesareaccuratelyestimatedandcomeonline,whiletheHighgrowthscenarioassumesthatpower,supplychain(materialsandlabor),andprocessconstraintsformanyprojectsstillinearlyplanningcan

bequicklyovercome.TheMediumgrowthscenariofallsroughlyhalfwaybetweentheothertwo.

In2024,totalU.S.datacenternominalITcapacityisesti-

matedtobe35to44GW,arangethatreflectsuncertaintyacrossaccesseddatasources.By2030,EPRI’sscenariosfortherealizationofplannedandannouncedprojectsresultinarangeof56to132GWU.S.totalnominalcapacity.

Theresultsshowwidevariationsacrossstatesinboththehistoricestimatesandprojectedexpansion(FigureES-1).

CapacitycontinuestoaccumulateinVirginia,Texas,andotherprimarydatacentermarkets,buttheemergenceof

newcapacityinotherstatessuchasOhio,Indiana,Pennsyl-vania,Louisiana,andMississippi,wherethereiscurrently

littleoperatingcapacity,suggestsincreasedprioritizationofpoweraccessandlandavailability,particularlyforlargeAItrainingcenters.3

TranslatingnominalITcapacityestimatestopeakelec-

tricloadandannualelectricitydemandrequiresmaking

assumptionsaboutcoolingandotheron-siteloads,data

centerannualloadfactors(i.e.,variationsindemandthatdifferbydatacentertypeandfunction),andtheramprateforoperationalizingnominalcapacity.In2024,totalU.S.

datacenterelectricityuseisestimatedat177─192TWh,

growingtoroughly380to790TWhby2030.Theestimated2030rangeisaround60%higherthanEPRI’sprioresti-

matespublishedin2024,largelyreflectingtheacceleratedpaceofdatacenterdevelopmentoverthepast18months(FigureES-2).

FigureES-2.ComparisonofU.S.datacenterannualelectricityconsumptionprojections.ProjectionsinthisstudyspanasimilarrangetotheLBNL(2024)report.ShadedbandshowsscenariosfromLBNL(2024);linesshowrecentexternalestimates,includingBCG(2024),

BloombergNEF(2025),EPRI(2024a),IEA(2025),Jefferies(2024),McKinsey(2024),S&P(2024).EPRIestimatesincludesmall-andlarge-scaledatacentersaswellascryptocurrencymining.

3Notethatashortcomingoftheestimationapproachusedinthis

2SeetheNominalCapacitysectionofthefullreportformoreprecisedefinitionsofthesescenariosandthedataunderlyingthem.

paperisthatitreliesheavilyonpublicannouncementsofprojects.Forvariousreasons,thestageofprojectdevelopmentwherepublicannouncementsaremadecandifferbystateanddevelopertype.

6|PoweringIntelligence2026February2026

FigureES-2showsprojectionsofannualU.S.datacenterannualenergyusethrough2030relativetootherstudies.

EPRI’snewscenariosspanarangeconsistentwithprojec-

tionsdevelopedbyLBNL(2024),despitefundamental

differencesinhowtheywerecreated.ThestartingpointforLBNL’sestimatesisshipmentsofprocessorchipsandotherITequipment,versustheEPRIstartingpointofdatacentersitedevelopmentdata.4

TheaggregatepeakloadforoperatingU.S.datacentersis

estimatedtobe21─22GWin2024,withprojectedgrowthby2030tobetween45GW(Low),71GW(Medium),and

94GW(High),lowerthanthecorrespondingnominalIT

capacityestimates.5Theanalysisshowsthatannounced

nominalMWfordatacenterprojectsshouldbetreatedasapipelineindicatorratherthananear-termpeakforecast,asnon-ITloads,ramp-up,loadshapes,onsiteenergyassets,andloadflexibilitymateriallyimpactpeakeffects.2025

FERCforecasts6ofpeakdemandgrowthalignwellwithEPRI’sHighscenarioinmostregions.

GrowingDataCenterShareofElectricityDemand

FigureES-3showstheupdatedEPRIprojectionsinthe

contextofmodeledscenariosforeconomy-wideelectricityloadgrowth.ThedatacentershareofU.S.totalelectricitydemandin2030rangesfrom9%to17%,anincreasefrom4─5%today.Atthestatelevel,continueddevelopmentof

thelargestDCmarketinVirginiaimpliesashareincreasingtobetween39%and57%by2030,reflectingthemanyproj-ectscurrentlyunderconstructionorinadvancedplanning.

Thefigurealsohighlightswidevariationsacrossstatesin

theirpaceofdatacenterdevelopment.Today,Virginiaistheonlystatewheredatacentersconsumeover20%ofelectric-ity.By2030,sevenadditionalstates—Oregon,Iowa,Nebras-ka,Nevada,Wyoming,Arizona,andIndiana—couldseedatacentersexceedinga20%share(Mediumscenario).Other

states,suchasWashingtonandNewJersey,areabovethe

U.S.averagetodaybuthaverelativelylittleestimatedcapac-ityinconstructionorplanning(baseduponaccessedpublicsources)andhencerelativelylowincreasesintheirdatacen-tersharesby2030.Ontheotherhand,severalstateswith

someexistingcapacity(e.g.NewMexico,Ohio,andPenn-

sylvania)andotherswithverylittle(e.g.Indiana,Louisiana,andMississippi)areprojectedtoemergeasnewareasof

concentrateddevelopmentwith2030sharesexceeding10%.

FigureES-3.Datacentershareoftotalelectricitydemandby

state.Estimatesincludesmall-andlarge-scaleDCaswellas

cryptocurrencymining.DCscenariosfor2030arebasedonEPRI

analysisofarangeofindustrysources.Non-DCelectricitydemandisbasedonUS-REGENmodelscenarioswithcurrentfederaland

statepolicies.

4TheLBNLstudy’sprojectionsalsoexcludecryptocurrencyload,whichisincludedinEPRI’sestimates.

5Theseestimatesreflectprojectedpeakdatacenterloadbeforeany

potentialflexibledemandresponse.EPRI’sDCFlexinitiative,de-

scribedlaterinthissummary,isdemonstratingdatacenterdemandflexibilityasastrategytospeedaccesstopower,delayorreducegridbuildout,andimprovegridreliability.

6UtilitiesandbalancingareasreportpeakloadforecaststhroughFERCForm714.

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FigureES-4.ChangeinU.S.capacitytomeetnewdatacenterload.ResultsfromUS-REGENmodelscenarioswithreferencepolicies(“Ref”)and24/7carbon-freeenergytargets(“CFE”).

TechnologiesforPoweringNear-termDataCenterGrowth

Electricsystemresponsestomeetdatacenterdemand

dependstronglyonthepolicyenvironmentanddatacenterenergyprocurementobjectives.7FigureES-4illustratesthatundercurrentstateandfederalpolicies,naturalgasmay

dominatenear-termincrementalsupply.Ifalldatacentersadoptandachieve24/7carbon-freeenergy(CFE)targets,portfoliosshifttowardslow-emittinggenerationandenergystorage.

Projectedannualnaturalgascapacitybuildsfrom2025to2030rangefrom6.6to13.7GWperyearwithreference

policies,higherthantheaverageof5.7GWperyearover

thepastfiveyears.Theseimpactsareadeparturefrom

EPRI’s2024analysis(EPRI2024b),whichincludedsignifi-

cantlyhigherwindandsolardeploymenttomeetgrowingDCloadsduetotheInflationReductionAct’sproduction

andinvestmenttaxcredits,whichhavebeencurtailedfor

manytechnologiesunderthe2025budgetbill(EPRI,2026).

Under24/7CFEtargets,generationandcapacityresponsestohigherDCloadcomefromwind,solar,nuclear,anden-

ergystorage.Nuclearandenergystorageremaineligibleforinvestmenttaxcreditsundercurrentpolicy,increasingtheirvalueinCFE-constrainedportfolios.Portfoliosofincremen-talsupplyvarybyregion.

Modeledresultsprovideanindicationofthescaleofnew

resourcesneededtomeetprojecteddatacenterload

growth.However,supplychainbottlenecksfromequipmentmanufacturingtopermittingandsitingprocessescould

constrainadditionsofbothgenerationandtransmissioncapacity.

7Thispaperisgenerallyagnosticaboutownershipandlocationofgen-erationandstoragetomeetdatacenterdemand.Foradiscussionofthetrade-offsofstrategiesrangingfromtraditionalpassivegridcon-nectionstooff-gridpower,seeEPRI’sreport:

ReconcilingtheValue

ofGridInterconnectionandSpeedtoPower:StrategiesforPowering

DataCentersintheAIEra

.

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ActionstoSupportRapid,Reliable,AffordableDataCenterExpansion

Datacenterexpansionofthescaleprojectedherefaces

manychallenges.Atthelocalandregionallevel,challengesarisefromthescaleofthecentersthemselvesandmis-

matchesininfrastructuretiming.Atypicalnewdatacenterof100to1000megawattsrepresentsaloadequaltothatofanewneighborhoodof80,000to800,000averagehomes.Whileneighborhoodsandthegridrequiremanyyearsto

planandbuild,datacenterscanbedevelopedandconnect-edinafewyears.Addedtothetimingchallengearethe

supplychainissuesassociatedwiththescaleofthisgrowth.ITandpowerequipmentandskilledlaborarebothregionalandnational-levelchallenges.

Essentialstrategiestosupportrapid,reliable,andafford-abledatacenterexpansioninclude:

•Improvedshort-andlonger-termloadforecasting

forlargeloads.Bettertoolsareessentialtoenable

effectiveresponsestoshort-termsystemdisruptionsandtoinformlong-terminvestmentsinthegrid.Thisreporthighlightsbothnewapproachestounderstanddatacenterloadgrowthandthemanydatagapsanduncertaintiesthathavehinderedtheeffort.Withgriddevelopmenttimesreachingyearstodecades,rapidadvancesareneededtoguideefficientinvestments.

•Moreeffectiveutilizationofexistinggridassets.

Identifyinglocationswithunderusedassets,increaseddatacenterandgridloadflexibility,generationcapacityincreasesatexistingsites,andgrid-enhancingtech-

nologiesthatquicklyreducegridcongestionareamongthekeystrategiestobetterutilizetheexistinggridto

acceleratecost-effectivedatacenterloadgrowth.

•Closercollaborationbetweendatacenterdevelopers,energyandequipmentproviders,policymakersand

communities.Collaborationisessentialtomaintain

andenhancegridreliabilityandtoaddressaffordabilityandcommunityimpactsasdatacentersconnecttothegrid.EPRI-ledcollaborativeeffortsinclude:

-

DCFlex

.EPRIisworkingwithmorethan60compa-niesthroughtheDCFlexinitiativetoaddressgrid

reliability,flexibility,

affordability

andotherissuescentraltotransformingdatacentersintogridas-sets.

-

GETSET

.EPRI’sGrid-EnhancingTechnologiesforaSmartEnergyTransition(

GETSET

)Initiativesup-portsutilityimplementationoftechnologiesandstrategiestomaximizeexistinggridassets.

-

Mercury

.EPRIispartneringwithKrakenonthe

MercuryConsortium

toadvancegrid-edgetechnol-ogies,includingflexibleloaddevices,andexpand

customerchoice.

-

DistributedDataCenters

.EPRIrecentlyannouncedanewcollaborationwithPrologis,NVIDIA,and

InfraPartnerstostudysmaller-scaledatacentersdesignedfordistributedinference.

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INTRODUCTION

Datacentershavebecomethefastest-growingsourceof

U.S.electricitydemand,andregionalclustersoffacili-

tiesaretransforminglocalgriddynamics,testingutilities’

abilitytokeeppaceandspurringtechnicalandbusiness

innovation.Computationalservicedemandshaveincreasedrapidly,fueledbyincreasedconsumerdemandforstream-ingandotherdata-intensiveservices,cryptocurrency,andartificialintelligence(AI).Datacenter(DC)capacityhas

grownrapidlytomeetthesesurgingdemands.WhileU.S.

datacenterdevelopmenthashistoricallybeenconcen-

tratedinseveralregions,therecentprioritizationofpoweravailability,cleanpower,andlargetractsoflandarecausingdevelopmenttospread.

Amajoruncertaintyinprojectingdatacenterloadgrowth

isthebroademergenceofAItechnologies,highlightedby

therapidadoptionofgenerativeAImodelssinceNovember2022.AIapplicationsaremuchmoreenergyintensivethanthestreaming,communications,search,andothertradi-

tionaldatacenterworkloads.Anearlyruleofthumbheld

thatanAIqueryrequiredroughlytentimesthepowerof

atraditionalsearchquery;thatcomparisonisincreasingly

outdatedbecauseofhardwareandalgorithmicadvances

andchanginguserbehavior.Verbalrequests(ratherthan

text),contextualinputs(forexamplephotosorreports),

andcontentgenerationsuchasimagesandvideoallrequireadditionalprocessingandhavenoanalogtoearlieruser

requests.Theseinteractionsarebecomingmorecommonandmorepowerintensive.WhileAIworkloadsareestimat-edtoconsume15─25%ofdatacenterelectricitytoday

(IEA(2025),JLL(2026)),thatshareisrisingevenasnon-AIdatacenterdemandscontinuetogrowsteadily.

Datacenterusagewasoncelimitedbyhumanattention

—howmanymoviesapersoncanwatchatonetimeor

howmanyqueriestheycantype.Advancessuchasdeep

researchandagenticAImeanthatusageisincreasingly

drivenbyhundredsorthousandsofAI-generatedrequeststhatrequireonlyoccasionalhumanoversight.With5.3

billionglobalinternetusers,widespreadadoptionofthesetoolscouldproduceastepchangeinpowerrequirements.Ontheotherhand,historyhasshownthatdemandfor

increasedprocessinghaslargelybeenoffsetbydatacenterefficiencygains.

AlthoughDCelectricitydemandisnotpubliclyreported,

DC-drivenloadgrowthisapparentinaggregatestatistics

incertainstates,andutilitiesacrossthenationandaroundtheworldareexperiencingsharpincreasesinservice

connectionrequests.Forecastingfuturedatacenter(DC)

loadgrowthisessentialforpowersystemplanningbut

remainsdifficultbecausepublicreportingislimited,manyannouncedprojectsarespeculative,andthereisfunda-

mentaluncertaintyabouttheadoptionofgenerativeAI

andsuccessortechnologies.SeveralrecentEPRIreports

havediscussedthekeydriversofDCelectricitydemand

anddevelopedestimatesofcurrentandprojectedfuture

datacenterloadatthestatelevelfortheU.S.8ThisreportupdatesEPRI’spriorestimates,usingnewdataonplannedandannouncedprojectsratherthanextrapolatinghistoricaltrends.ItalsoaddsdeepermodelingofkeyDCelectricity

metrics:nominalITcapacity,non-ITdatacenterpoweruse,capacityutilization,andannualandpeakenergyuse.

UNDERSTANDINGKEYDATACENTERPOWERMETRICS

Thereisanimportantdistinctionandrelationshipbetweendatacentercapacity,whichreferstotheamountofpoweradatacentercansupplytoitsequipment—ordemandfromthegrid—inagivenmoment(measuredinwatts),anddatacenterannualenergyuse,whichreferstotheactualcon-

sumptionofpoweroverthecourseofayear(measuredinwatt-hours).Bothmetricsdescribethescaleofdatacenteractivityandelectricitydemand,buttheyhavedifferent

implicationsforelectricsystemplanning.Capacityaffects

peaksupplyandgridupgrades,whileannualenergyaffectsgeneration,fueluse,andemissions.Estimatingbothisun-certainbecausepublicdataarelimited,announcedprojectsmaynotproceedatplannedscaleorpace,andprojecting

futurestate-levelloadsfromavailabledatasetsanddevel-operconstructionannouncementsisinherentlyimprecise.

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PoweringIntelligence

(EPRI,2024a)presentsdatacenterload

growthprojectionsanddiscussestechnologyadvancesthatcould

slowgrowth.

PoweringDataCenters

(EPRI,2024b)updatestheloadprojectionsandanalyzesenergyandemissionsimpacts.

ScalingIntel-

ligence

(EPRI,2025a)exploresthedriversofAIelectricitydemandsandtheimpactondatacentersize,and

SpeedtoPower

(EPRI,

2025b)discussesstrategiesforpoweringdatacentersrangingfrom

off-gridtotraditionalgrid-connectedpower.

EPRI’sDCFlex

initiative,acollaborationbetweenutilities,developers,andotherstakeholders,hasproducedseveralreportsonregionalloadforecasting,intercon-nectionrequirements,systemimpacts,andopportunities/needsfordatacenterflexibility.

10|PoweringIntelligence2026February2026

NominalCapacity

Nominalcapacityofadatacentertypicallyreferstothemaximumpotentialinformationtechnology(IT)load(i.e.

serversandotherITequipment,exclusiveofancillary

energyuseforcoolingandinfrastructure).Becauseelectricpowerissoessentialtodatacenteroperation,thismetricisusedtocharacterizedatacentersizeandisoftenpublishedbyDCdevelopersinprojectannouncements.Althoughit

isperhapsthemostvisibleandpubliclytrackablemetric

available,nominalITcapacityisnotalwayspubliclyreport-ed.Whennominalcapacitydataarenotavailableeither

throughpublicorprivatesources,itcanbeestimatedbaseduponproxiessuchassquarefootageofbuildingsonasite

combinedwithinformationonthedatacenter’sfunction,airpermitsforbackupgeneration,andaerialassessments.

NameplateCapacityandPowerUsageEffectiveness(PUE)

Nameplatecapacityofadatacentertypicallyreferstothemaximumpotentialtotalloadatthefacility,whichin-

cludesbothITandnon-ITload(cooling,powerconversion,lighting,etc.),andcorrespondstowhatthedatacenter

requestsforserviceinterconnection.Non-ITloadisoftensummarizedbyPUE,theratiooftotalloadtoIT-onlyload.

PUEcanvarydependingoncoolingtechnology,regional

climate,datacenterarchitectureandscale,chipdesign,

computationalusecase,andotherfactors.Ancillaryenergyuseforcoolingandotherinfrastructureneedswithinadatacenter,whiledecliningsignificantlyoverthepastdecades,stillaccountsforasignificantshareoftotaldatacenter

energyuse.PUEandnameplatecapacityarelessfrequentlyreportedinpublicdatathannominalcapacitybutcanin

somecasesbeestimatedfromsitecharacteristics.

AnnualandPeakElectricityUse

Translatingnameplatecapacitytoannualelectricityuseandpeaksystemloaddependsonadditionalassumptionsabouttheramp-upofactiveoroperationalcapacityandpatternsofcapacityutilization.Newfacilitiestypicallyrampupop-

erationalcapacityovertimeasdatahalls,