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

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dc.contributor.author Devlin, Michael A.
dc.contributor.author Hayes, Barry P.
dc.date.accessioned 2019-07-05T11:05:51Z
dc.date.available 2019-07-05T11:05:51Z
dc.date.issued 2019-05-24
dc.identifier.citation Devlin, 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.2019.2918922 en
dc.identifier.startpage 1 en
dc.identifier.endpage 1 en
dc.identifier.issn 0098-3063
dc.identifier.uri http://hdl.handle.net/10468/8119
dc.identifier.doi 10.1109/TCE.2019.2918922 en
dc.description.abstract This 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.sponsorship Enterprise Ireland (via the IERC project “EnerPort” (contract number TC2013-0002B)) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en
dc.relation.uri https://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.subject Load identification en
dc.subject Non-intrusive load monitoring en
dc.subject Energy disaggregation en
dc.subject Smart metering en
dc.subject Appliance identification en
dc.subject Machine learning en
dc.subject Home appliances en
dc.subject Smart meters en
dc.subject Deep learning en
dc.subject Monitoring en
dc.subject Buildings en
dc.subject Electric variables measurement en
dc.subject Clustering algorithms en
dc.title Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Barry Hayes, Electrical & Electronic Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: barry.hayes@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2019-07-03T09:07:25Z
dc.description.version Accepted Version en
dc.internal.rssid 489841180
dc.contributor.funder Enterprise Ireland en
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle IEEE Transactions on Consumer Electronics en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress barry.hayes@ucc.ie en
dc.identifier.articleid 10 en
dc.internal.bibliocheck In Press. Update citation, add vol. issue, update page nos. en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en


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