建筑专业外文翻译---用智能数据分析检测建筑物中的能源异常消耗

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1、PDF外文：http:/ 5120 字    EnergyandBuildings39(2007)5258     Usingintelligentdataanalysistodetectabnormalenergyconsumptioninbuildings  JohnE.Seem*  JohnsonControls,Inc.,507EastMichiganStreet,Milwaukee,WI53202,USA Received31October2005；receivedinrevisedform11March2006；accep

2、ted18March2006    Abstract  Thispaperdescribesanovelmethodfordetectingabnormalenergyconsumptioninbuildingsbasedondailyreadingsofenergyconsumptionandpeakenergyconsumption.Themethodusesoutlierdetectiontodetermineiftheenergyconsumptionforaparticulardayissignificantlydifferentthanprevious

3、energyconsumption.Forbuildingswithabnormalenergyconsumption,theamountofvariationfromnormalisdeterminedusingrobustestimatesofthemeanandstandarddeviation.Thisnewdataanalysismethodwillreduceoperatingcostsbydetectingproblemsthatpreviouslywouldhavegoneunnoticed.Also,operatorsshouldsavetimebynothavingtoma

4、nuallydetectfaultsordiagnosefalsealarms.Thenewdataanalysismethodhassuccessfullydetectedhigh-energyconsumptioninmanybuildings.Thispaperpresentsfieldtestresultsforbuildingsthathadthefollowingproblems:(1)chillerfailureandapoorcontrolstrategy,(2)poordesignofventilatingandair-conditioningequipment,and(3)

5、improperoperationofequipmentfollowingachangeintheelectricalpanel. #2006ElsevierB.V.Allrightsreserved.  Keywords:Energyconsumption；Faultdetection；Outlieranalysis；Performancemonitoring；Robuststatistics    1. Introduction  Energymanagementandcontrolsystemscancollectandstoremassivequ

6、antitiesofenergyconsumptiondata.Facilityoperatorscanbeoverwhelmedwiththequantityofdata.Formanyoperators,itisnotpossibletodetectequipment,design,oroperationproblemsbecauseofdataoverload.Modernbuildingmanagementsystemshavetwosystemstohelptheoperatorswiththisdataoverload:alarmandwarningsystemsanddatavi

7、sualizationprograms.Today,operatorsmustselectthethresholds foralarmsandwarnings.Thisis adifficulttask.Ifthethresholdsaretootight,thenanumberoffalsealarmsareissued,andifthethresholdsaretooloose,thenequipmentorsystemfailurescangoundetected.Thedatavisualizationprogramscanhelpbuildingoperatorsdetectandd

8、iagnoseproblems,butalargeamountoftimecanbespentdetectingproblems.Also,theexpertiseofbuildingoperatorsvariesgreatly.Neworinexperiencedoperatorsmayhavedifficultydetectingfaultsandtheperformanceofanoperatorcanvarywiththetimeofdayordayoftheweek.    *Tel.:+14145244677；fax:+14145245810. E-mailad

9、dress:.  0378-7788/$seefrontmatter#2006ElsevierB.V. Allrightsreserved.doi:10.1016/j.enbuild.2006.03.033 Theresearchcommunityhasdevelopedanumberofmethodsfordetectingfaultsinbuildingsandheating,ventilating,andair-conditioningsystems.TwomajorresearcheffortshavebeensponsoredbytheInternationalEnergy

10、Agency:Annex251,2andAnnex343.Therearetwobasicapproachestofaultdetectionanddiagnosticsinbuildings:acomponentlevel(bottom-up)approachandawhole-building(top-down)approach.Thecomponentlevelapproachlooksforfaultsinindividualsystemssuchasvariable-air-volumeboxes,air-handlingunits,chillers,orboilers.Thewho

11、le-buildingapproachlooksforunusualbehaviorinhigh-levelmeasure-mentssuchasthewhole-buildingcooling,heating,orelectricalconsumption. Claridgeetal.4describeanenergyconsumptionreportmethodthathelpsbuildingoperatorsandfacilitymanagersidentifyifthebuildingsystemsareworkingproperly.Thereportcontainsscatter

12、plotsofdailychilledwaterenergyconsumptionversusaveragedailytemperatureanddailyhotwaterconsumptionversusaveragedailytemperaturefora3-monthperiod.Forthelastmonth,thescatterplotusesletters(M,T,W,H,F,S,U)toidentifythedaysoftheweek.Thelettershelpsbuildingoperatorsidentifyoutliersinenergyconsumptionforapa

13、rticularday.Thereportalsocontainstwo-andthree-dimensionaltime seriesplotsofchilledwaterconsumptionand J.E.Seem/EnergyandBuildings39(2007)5258 53    Nomenclature  a2B a isan elementofset B a2=B aisnotanelementofsetB i indexusedinforloopinFig.1n numberofelementsinsetXnout numberofoutlie

14、rsinsetX p righttailareaprobabilityfort-distribution Ri extremestudentizeddeviateforithextreme s standarddeviationforelementsinsetX srobust robustestimate  ofstandarddeviationfor  ele-mentsinsetX tn,pcriticalvalue(tn,p)fortheStudents t-distributionwithndegreesoffreedomandarighttailareaprob

15、abilityofp xe,i valueofithextreme xj valueofjthobservationinsetX x average of elements in set X xrobust robustestimateofaverageofelementsinsetXX setofobservations thatcontainoutliersandnon- outliers Xnon-out  setofobservationsthatcontainnooutliers Xout setofobservationsthatcontainoutliers zm mo

16、difiedz-score(standardscore) setofobservationsorelements j suchthat  Greekletters a probabilityofdeclaringanormalvalueanoutlier li criticalvalueforRosnersgeneralizedESDmany-outlierprocedure throughthetediousprocessofmanuallyinspectinggraphstodetectabnormalenergyconsumption.Instead,theoperatoror

17、maintenanceoperatorcaninvestigateonlybuildingswithabnormalenergyconsumption.Themethodaccountsforweeklyvariationinenergyconsumptionbygroupingdaysoftheweekwithsimilarpowerconsumption.Arobustoutlierdetectionmethodisusedtodetermineiftheenergyconsumptionissignificantlydifferentthanpreviousenergyconsumpti

18、on.Fortimeperiodswithabnormalenergyconsumption,theamountofdeviationfromnormalisdeterminedusingrobuststatistical methods.   2. Overviewofdataanalysismethod  Fig.1showsthemajorstepsrequiredtoidentifyabnormalenergyconsumptioninbuildings.Thefeatureextractionblockdeterminesfeaturessuchastheaver

19、agedailyconsumptionorpeakdemandforadayfromenergydatasuchasthewhole-buildingelectricalconsumption.Thefeaturesarethensortedintogroupsbasedondaysoftheweekwithsimilarenergyconsumptionprofiles.(Inthispaper,thetermdaytypereferstodaysoftheweekwithsimilarconsumptionprofiles.)Afterthedataisgroupedbasedondayt

20、ype,outlieridentificationisusedtodeterminethefeaturesthataresignificantlydifferentfromthefeaturesforthesamedaytype.Ifanyoutliersareidentified,thenamodifiedz-score9isusedtodeterminetheamountanddirectionofvariationfromanormalobservation.(z-Scoresarealsocalledstandardscores10.)Next,detailsonarobustoutl

21、ieridentificationmethodandarobustmethodfordeterminingtheamountofvariationfromnormalarepresented.   whole-buildingelectricconsumption.Byinspectingtheseplots,buildingoperatorscanidentifydaysofabnormalenergyconsumption.HaberlandAbbas5,6reviewseveralnewgraphicaldisplaysforviewingbuildingenergydata.

22、 DodierandKreider7presentamethodfordetectingwhole-buildingenergyproblemsforthefollowingenergyuses:whole-buildingtotalelectricenergy,whole-buildingtotalthermalenergy,HVAC-other-than-chillerelectricenergy,andchillerenergyusage.TheyusedanEnergyConsumptionIndex(ECI)todetermineiftheenergyconsumptionwashi

23、gherthannormal,normal,orlowerthannormal.TheECIistheratioofactualenergyconsumptiontoexpectedenergyconsumptionasdeterminedfrom aneuralnetwork. Iftheratioislargerthananupperlimit(e.g.,1.125)thenthestateofthesystemishigherthannormal.Iftheratioislowerthanalowerlimit(e.g.,0.875),then the stateofthe system

24、 is lowerthan normal. If the ratioisbetweenthelowerlimitandupperlimit,thenthestateofthesystemisnormal.GraphsoftheECIwillhelpbuildingoperatorsidentifymajorchangesinenergyconsumption.FigurespresentedbyDodierandKreider7showaweeklycycleofECI. Thispaperpresentsanintelligentdataanalysismethod8forautomatic

25、allydetectingabnormalenergyconsumptioninbuildings.Withthismethod,operatorswillnothavetogo     Fig.1.Blockdiagramfordetectingabnormalenergyconsumption. 54 J.E.Seem/EnergyandBuildings39(2007)5258  Pn n i 1；p Pn 2 s   3. Outlieridentification:GESDmany-outlierprocedure  Anoutlie

26、risanobservationthatappearstobeinconsistentwiththemajorityofobservations inadataset. For example,in Block1:Setnout= 0.Thisstepisusedtoinitializethenumberofoutlierstozero. Block2:Computeaverage(x)ofelementsinsetX.The averageisdeterminedfrom thedataset1,2, 1,0,3,2,101, 2,theobservation101 appearstobea

27、noutlier.Datasetsmaycontainmorethanoneoutlier.Forexampleinthedataset1,2, 1,0,3,2,101, 2,  x  j1xj n  (1) 96,2,0, 209,theobservations101,96,and209appeartobeoutliers. BarnetandLewis11providedetailsonseveralcommonoutlieridentificationmethods.Aftercomparingseveralpopularoutlieridentificat

28、ionmethods,IglewiczandHoaglin9highlyrecommendthegeneralizedextremestudentizeddeviate(ESD) wherexj isamemberofsetXandnequalsthenumberofelementsinsetX. Block3:Computestandarddeviation(s)ofelementsinsetX. Thestandarddeviationisdeterminedfrom s j1xj x many-outlier procedure that was proposed by Rosner 1

29、2 s becauseitworkswellunderavarietyofconditions. n 1 (2) ThegeneralizedESDmany-outlierprocedurecanidentitytheelementsinasetthatareoutliers.Fig.2isaflowchartfordeterminingoneormoreoutliersfromasetofnobservationsX 2x1,x2,x3,.,xn.Theuserneedstospecifytheprobability,a,ofincorrectlydeclaringoneormoreoutl

30、ierswhennooutliersexistandanupperbound,nu,onthenumberofpotentialoutliers.Careyetal.13saidtheupperbound(nu) Block4:s=0.Thisblockchecksifthestandarddeviationof theelementsinsetXiszero.Ifthestandarddeviationequalszero,thentheelementsinsetXallhavethesame valueandtherearenooutliersintheremainingelementsi

31、nsetX.(Duringfield-testingofthismethod,severaldatasetshadastandarddeviationofzero.)TopreventadividebyzeroinBlock6,executiongoestoBlock10whenthestandard deviationdeterminedinBlock3equalszero. couldbedeterminedbyfindingthelargestintegerthatsatisfies thefollowinginequality:nu0.5(n 1).Followingaredetail

32、s Block5:Findithextreme(xe,i )insetX.Theextremeelement, onthenumberedblocksinFig.2. xe,i,istheelementinsetXthatisfurthestfromx.Ofallthe elementsinsetX,theextremeelementxe,imaximizesthe functionjxj xjwherexi isanelementofsetX. Block6:ComputeithextremestudentizeddeviateRi.The extremestudentizeddeviate

33、isdeterminedfrom  xe；i xj Rij  (3)  whereRiisanormalizedmeasureofhowfartheithextremeisfromtheaveragevalue(x)determinedinBlock2. Block7:Computeithcriticalvalueli.Rosner12developedthefollowingequationfordeterminingthecriticalvalue:   n itn i 1；p  liq (4) n i1n i 1t2  wher

34、etn i 1,pistheStudentst-distributionwith(n i1)degreesandthetailareaprobabilitypisdeterminedfrom a p2n  i1 (5)               Fig.2.FlowchartforimplementingRosnersgeneralizedmany-outlierpro-cedure.  Abramowitzand Stegun 14reviewequations forestimatingtheStu

35、dentst-distribution.   Block8:Ri>li.Thisblockdeterminesiftheithextremestudentizeddeviate,Ri,determinedinBlock6isgreaterthantheithcritical value,li,determinedinBlock7. Block9:Setnout=i.Thisblocksetsthenumberofoutliers, nout,equaltoi. Block10:Removeextremeelementxe,ifromsetX.Theextremeelementxe,iisremovedfromsetXandafterremovingtheextremeelementxe,i,thenumberofelementsinSetXisni.Ifiequalsnu,thenexecutiongoestoBlock11；otherwise,returntotheforlooponi.