Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data

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dc.contributor.authorDevlin, Michael A.
dc.contributor.authorHayes, Barry P.
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2019-07-05T11:05:51Z
dc.date.available2019-07-05T11:05:51Z
dc.date.issued2019-05-24
dc.date.updated2019-07-03T09:07:25Z
dc.description.abstractThis paper develops an approach for household appliance identification and classification of household Activities of Daily Living (ADLs) using residential smart meter data. The process of household appliance identification, i.e. decomposing a mains electricity measurement into each of its constituent individual appliances, is a very challenging classification problem. Recent advances have made deep learning a dominant approach for classification in fields such as image processing and speech recognition. This paper presents a deep learning approach based on multi-layer, feedforward neural networks that can identify common household electrical appliances from a typical household smart meter measurement. The performance of this approach is tested and validated using publicly-available smart meter data sets. The identified appliances are then mapped to household activities, or ADLs. The resulting ADL classifier can provide insights into the behaviour of the household occupants, which has a number of applications in the energy domain and in other fields.en
dc.description.sponsorshipEnterprise Ireland (via the IERC project “EnerPort” (contract number TC2013-0002B))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid10en
dc.identifier.citationDevlin, M. A. and Hayes, B. P. (2019) 'Non-Intrusive Load Monitoring and Classification of Activities of Daily Living using Residential Smart Meter Data', IEEE Transactions on Consumer Electronics, In Press, doi: 10.1109/TCE.2019.2918922en
dc.identifier.doi10.1109/TCE.2019.2918922en
dc.identifier.endpage1en
dc.identifier.issn0098-3063
dc.identifier.journaltitleIEEE Transactions on Consumer Electronicsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/8119
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://ieeexplore.ieee.org/document/8721550
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectLoad identificationen
dc.subjectNon-intrusive load monitoringen
dc.subjectEnergy disaggregationen
dc.subjectSmart meteringen
dc.subjectAppliance identificationen
dc.subjectMachine learningen
dc.subjectHome appliancesen
dc.subjectSmart metersen
dc.subjectDeep learningen
dc.subjectMonitoringen
dc.subjectBuildingsen
dc.subjectElectric variables measurementen
dc.subjectClustering algorithmsen
dc.titleNon-intrusive load monitoring and classification of activities of daily living using residential smart meter dataen
dc.typeArticle (peer-reviewed)en
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