Stanford University Disaggregation: a Survey

Disaggregation:BriefSurveyDisaggregation:BriefSurveyAdrianAlbert

Stanford University Disaggregation: a Survey

WhatShouldWeFocusOn?

Appliances by energy consumed (US) and difficulty to disambiguate from noisy signal. Source: Hart (1992)

Someappliances(heating,refrigerators,lighting)takeupmost ofenergyconsumed(US)

Someapplianceshardertoidentifyasothers:complexphysicaloperationcharacteristics

Data20yearsold;Thinghavechangedinthemeantime(e.g.,morecomputers)

Canwedo

betterthanthis?

>75W loads: 99% of consumed US energy

Stanford University Disaggregation: a Survey

AtaGlance:DisaggregationAlgorithms

Source: Najmeddine et al., 2008

AtaGlance

Steadystatepowerchanges(earlywork)Detectjumpsinactive&reactivepowerspecifictoindividualappliances

Resistive,simpleappliances

G.Hart(1992),F.Sultanem(1991),M.L.Marceau,R.Zmeureanu(2000),C.Laughmanetal.(2003),

HarmonicanalysisUsecurrentharmonicsandpowerthereofasadditionalfeatures

Betteridentifiesnonlinearappliances

Y.Nakano&K.Yoshimoto(2004),L. Farinaccio & R. Zmeureanu (1995)

TransientstateHighfrequencynoiseinducedbychangeinappliancestate(needhighsamplingrate)

Builddatabaseoftransientsignature

L.Norford,S.Leebetal.(1993),S.Shaw(1998),S.Pateletal.,(2007)

RecentworkEmphasizetransient,statechanges

R.Ford/J.McCoullough(DphilThesisOxford,2009):Bayesianframeworkforapplianceclassification

Watersystemdisaggregation(S.Pateletal,2009):98%accuracy

Stanford University Disaggregation: a Survey

PerformanceChart

Algorithm type

Study examples Data & setup specifications Accuracy and performance

Steady-state edge detection (base case)

Hart, Leeb (MIT); Sultanem (EDF)10-20 yr old data

1 Hz average power & voltage custom-built reading instrumentoff-line data analysis (few homes)Manual/automatic training

85% (simple ON/OFF appliances >150W)

Steady state harmonics features SVM classification

Nakano & Hidaka (CRIEPI & TUAT);

1/60 Hz (1 minute)Several test households

65%-80%, larger for smaller appliance subset

Transients analysis: SVM classification

Patel (UW); Schwab, Leeb (MIT)

(Patel) 1MHz current and voltageCustom-built monitoring system6 homes, 4 weeks, ~3000 eventsTrain: 150-350 events; Test: 80-100 events

~40 different appliances of various complexities80%-90%

PerformanceandLimitations

Arethereotheralgorithmicalapproachesthatimproveaccuracy?

Howtodefineaccuracy (%ofappliancesidentified,%ofenergy)?

Stanford University Disaggregation: a Survey

LimitationsofCurrentTechniquesPerformanceandLimitations

Stanford University Disaggregation: a Survey

AccuracyvsSamplingRate:PerformanceandLimitations

What (commercially-achievable) data sampling rate do we need for good disaggregation? Upper limit on achievable accuracy (cutoff sampling rate)?

60

65

70

75

80

85

90

95

100

105

0.01 1 100 10000 1000000

%ofEne

rgyDisaggregated

SamplingRate[Hz, logscale]

AccuracyvsSamplingRate

Whatcurrentresearchsuggests

Optimistic

Pesimistic

?

Stanford University Disaggregation: a Survey

DataandAlgorithms

Sampling rate /

Accuracy70%-80%? 80%-90%? 90%-95%? > 95%?

Hourly ? ? ? ?

15 minutes

? ? ? ?

1 minute Harmonics detection for some non-linear appliances? ? ?

1 s (1 Hz) ? Edge detection for simple on/off appliances? ?

0.01 s (100 Hz)

? ? ? ?

1kHz)

? Transient analysis for relatively complex appliances ? ?

PerformanceandLimitations

Whatkindofdatadoweneedtoachievedifferentdisaggregation accuracies?

Samplingrate,monitoredelectricityparameters

Testdata:typesofhomes,typesofappliances?

Builddatabaseofsignatures:howlargeistrainingset?Howmuchofaproblem?

Stanford University Disaggregation: a Survey

Universities,utilities,smartmetercompaniesputtogetherreferencedataset:differentdataresolutions,parametersmonitoredetc.

Agreeonkeyquestionstobeansweredbasedondataset

NextSteps

Questions,Suggestions

DesignaDisaggregationChallenge similartoe.g.,theInfoVis/IEEEVisualizationChallenge

Academicteamscompeteinanopenchallenge

Awardprizes?E.g.,conferenceparticipationfees

Trainingtheappliancemodel&buildingsignaturedatabase

Useraidedtraining:designsystemthatasksforuserinputforlabelingappliances

UseAItechniquestostudybehavioralandlifestylepatternsofindividualhomes

Customizegeneralpurposeapplianceinformation(signatures)

Stanford University Disaggregation: a Survey

AlgorithmsSurvey

AlgorithmsDetails

Stanford University Disaggregation: a Survey

Smartmeters110Wpowerresolution,95%99%power/voltageaccuracy

Samplingrate1mHz 1Hz:meterscangenerallysampleatfastrates

HomeAreaNetworks:wirelessenabledsmartmeters routers/gateways inhomedisplaydevices

ZWave,ZigBee(IEEE802.15.42003):40250kbpstransferrate,~10meterrange

starorpeertopeer:networkcoordinators,routers,enddevices

Interfaces:Specificationsforsmartmetertocommunicationcard,HANdatatransfer(ZigBeeSE):messagestrings,priceinformation,time,etc.(ANSIC12.19datatablesformat)

AtaGlance:HardwareAtaGlance

Source: ZigBee SE specification manual

Stanford University Disaggregation: a Survey

Detectingjumpsinpower(active/reactive)

Matchapplianceoperationalcharacteristics(DP,DQ)tosignaturesdatabase(edges)

EventsIdentification:SteadyStateEarlyApproaches

Source: Hart (1992)

Work at MIT by Hart, Leeb, Shaw (10-20 years old)

Best for residential: steady, finite state machines (light bulb, toaster)Initial calibration phase for individual home neededUse ~1s data, achieve ~85% accuracy

Stanford University Disaggregation: a Survey

HarmonicsandTransientRegimesFormultiplerelativelysimilarloads,(DP,DQ)spacegetscluttered

Manyappliancesfornonresidentialusetakealongtimetoreachstationarystate

Fourieranalysisofcurrentwaveform:compute"spectralenvelopes"thatsummarizetimevaryingharmoniccontent

Source: Najmeddine et al., 2008

C. Laughman et al (2003)

Detect transient regimes:Computer (capacitors) profile is different than that of lamp (resistor)Collect signatures: initial calibrationUse as feature power in higher harmonics (3, 5, 7) of current waveform

Harmonics

Stanford University Disaggregation: a Survey

Transients:HigherDimensionalFeatureSpace

AIflavoredapproaches:neuralnetworkassupervisedclassifier

J.G.Roosetal,UsingNeuralNetworksforNonintrusiveMonitoringofIndustrialElectricalLoads (1994)

Classifyappliancesaftertheirfunctionality,physicaloperationprincipleetc.

Trainneuralnetusinglabeledprototypesfromeachappliancecategory

Signaturespace(featurevector):Fourierharmonics

Industrial loads: J. G. Roos et al (1994)

Standard machine learning classifier: Support Vector Machines (SVM)

N-dimensional separation hyperplane.Signatures collected in initial calibrationWall switch: 100 Hz 5 KHz noiseInductive loads: 5 kHz 1 MHz continuous noise

Sample rate 1MHz, accuracy 85%-90% for different types of homes

Classification in higher dimensional spaceS.Pateletal., AttheFlickofaSwitch:DetectingandClassifyingUniqueElectricalEventsontheResidentialPowerLine(2007)

Use transient or continuous noise produced by abruptly switching on electrical loads

TransientAnalysis

Stanford University Disaggregation: a Survey

AllIsNotElectricityThatSparks

Measure water flow pressure at main source

Build pressure jumps and harmonics signature database

Train events classifier using multiple features

Accuracy ~98%

Froelich,Patel:HydroSense:InfrastructureMediatedSinglePointSensingofWholeHomeWaterActivity(2009)

Stanford University Disaggregation: a Survey

HouseholdlevelmonitoringanddisaggregationbasedonHart'smethod(eventsidentification):F.Sultanem,USINGAPPLIANCESIGNATURESFORMONITORINGRESIDENTIALLOADSATMETERPANELLEVEL.IEEETransactiononPowerDelivery,Vol.6,No.4,1991G.Hart,NonintrusiveApplianceLoadMonitoring.ProceedingsoftheIEEE,vol.80,no.12,1992

M.L.Marceau,R.Zmeureanu,Nonintrusiveloaddisaggregationcomputerprogramtoestimatetheenergyconsumptionofmajorendusesforresidentialbuildings.EnergyConversion&Management41(2000)13891403

C.Laughmanetal,AdvancedNonintrusiveMonitoringofElectricLoads.IEEEPowerandEnergy,March2003

HarmonicanalysisbasedmethodsY.Nakano,NonIntrusiveElectricAppliancesLoadMonitoringSystemUsingHarmonicPatternRecognition.TechnicalReport,2004.S.Leeb,S.Shaw,J.Kirtley,TransientEventDetectioninSpectralEnvelopeEstimatesforNonintrusiveLoadMonitoring.IEEETransactionsonPowerDelivery,Vol.10.No.3,July1995

TransientsanalysisJ.G.Roosetal,UsingNeuralNetworksforNonintrusiveMonitoringofIndustrialElectricalLoads.IMTC'9S.Pateletal., AttheFlickofaSwitch:DetectingandClassifyingUniqueElectricalEventsontheResidentialPowerLine.UbiComp2007,LNCS4717,pp.271288,2007

R.Cox,S.Leeb,S.Shaw,L.Norford, TransientEventDetectionforNonintrusiveLoadMonitoringandDemandSideManagementUsingVoltageDistortion.IEEE,2006.

HardwareordevicespecificpapersK.D.Lee,EstimationofVariableSpeedDrivePowerConsumptionFromHarmonicContent. IEEETRANSACTIONSONENERGYCONVERSION,VOL.20,NO.3,SEPTEMBER2005S.R.Shaw,C.B.Abler,R.F.Lepard,D.Luo,S.B.Leeb,L.K. Norford,InstrumentationforHighPerformanceNonintrusiveElectricalLoadMonitoring.TransactionsoftheASIVIE,224/Vol.120,AUGUST1998

OverviewandexploratorypapersW.K.Le