基于CNN-BiGRU-Attention的非侵入式负荷分解-豆柴文库

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基于CNN-BiGRU-Attention的非侵入式负荷分解
Title:Non-IntrusiveLoadDisaggregationusingCNN-BiGRU-Attention
Abstract:
Non-intrusiveloaddisaggregation(NILM)isavitaltechniqueinsmartgridapplicationsforextractingindividualapplianceenergyconsumptionfromaggregatedpowersignals.Itenablesuserstomonitorandanalyzeenergyusageatanappliancelevelwithoutrequiringanyadditionalhardware.Thispaperproposesanovelapproach,combiningConvolutionalNeuralNetworks(CNN),BidirectionalGatedRecurrentUnits(BiGRU),andAttentionmechanisms,toaccuratelydisaggregateloads.Theproposedmodelachievesbetterperformanceintermsofaccuracyandcomputationalefficiencycomparedtoexistingmethods.Experimentalresultsonpublicdatasetsdemonstratethesignificantpotentialoftheproposedapproachforreal-worldapplications.
1.Introduction
Theunderstandingandmanagementofindividualappliancelevelenergyconsumptionisessentialforoptimizingenergyusageandimprovingenergyefficiency.However,obtainingsuchdetailedinformationusuallyrequirestheinstallationofmultiplesensorsoneachappliance,whichisimpracticalandintrusive.Non-intrusiveloaddisaggregation(NILM)techniquesaimtoovercomethislimitationbydisaggregatingappliance-levelenergyconsumptionfromtheaggregatedpowersignaloftheentirehousehold.
2.RelatedWork
SeveralNILMmethodshavebeenproposedintheliterature,includingsignalprocessing-basedmethods,statisticalmethods,andmachinelearning-basedmethods.Recentadvancementsindeeplearninghavealsoledtothedevelopmentofneuralnetwork-basedNILMmodelswithimprovedaccuracyandflexibility.
3.ProposedMethod
ThissectionpresentstheproposedCNN-BiGRU-Attentionmodelfornon-intrusiveloaddisaggregation.Themodelconsistsofthreemaincomponents:aCNNforfeatureextraction,aBiGRUforcapturingtemporaldependencies,andanattentionmechanismforenhancingthemodel'sfocusonrelevantappliance-specificfeatures.
4.ExperimentalSetup
Toevaluatetheperformanceoftheproposedmodel,experimentswereconductedonpubliclyavailabledatasetssuchasREDDandREFIT.Theperformancemetricsusedincludeaccuracy,precision,recall,andF1-score.Theresultsarec