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Classification of new electricity customers based on surveys and smart metering data

2016Journal article Restricted access
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dc.contributor.authorViegas, Joaquim L.
dc.contributor.authorVieira, Susana M.
dc.contributor.authorMelício, R.
dc.contributor.authorMendes, Victor
dc.contributor.authorSousa, João M. C.
dc.date.accessioned2017-06-07T10:15:51Z
dc.date.available2017-06-07T10:15:51Z
dc.date.issued2016
dc.description.abstractThis paper proposes a process for the classification of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classification task. Firstly, the normalized representative consumption profiles of the population are derived through the clustering of data from households. Secondly, new customers are classified using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of different consumption profiles, enabling the extraction of appropriate models. The results of a case study show that the use of survey data significantly increases accuracy of the classification task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classified with only one week of metering data, with more weeks the accuracy is significantly improved. The use of model-based feature selection resulted in the use of a significantly lower number of features allowing an easy interpretation of the derived models.pt_PT
dc.description.sponsorshipUID/EMS/50022/2013
dc.description.sponsorshipMITP-TB/CS/0026/2013
dc.description.sponsorshipSFRH/BDE/95414/2013
dc.description.sponsorshipIF/00833/2014
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVIEGAS, Joaquim L.; [et al] - Classification of new electricity customers based on surveys and smart metering data. Energy. ISSN 0360-5442. Vol. 107 (2016), pp. 804-817pt_PT
dc.identifier.doi10.1016/j.energy.2016.04.065pt_PT
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.urihttp://hdl.handle.net/10400.21/7158
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttp://ac.els-cdn.com/S0360544216304789/1-s2.0-S0360544216304789-main.pdf?_tid=458ef232-4b69-11e7-91d1-00000aab0f02&acdnat=1496830296_53bd0213cbfed072f039760a36b5d09bpt_PT
dc.subjectData-driven energy efficiencypt_PT
dc.subjectElectricity customer clusteringpt_PT
dc.subjectClassification of new residential customerspt_PT
dc.subjectCustomer feature selectionpt_PT
dc.subjectSmart metering datapt_PT
dc.subjectCustomer surveys datapt_PT
dc.titleClassification of new electricity customers based on surveys and smart metering datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage817pt_PT
oaire.citation.startPage804pt_PT
oaire.citation.titleEnergypt_PT
oaire.citation.volume107pt_PT
person.familyNameMendes
person.givenNameVictor
person.identifier.orcid0000-0002-4599-477X
person.identifier.ridD-2332-2012
person.identifier.scopus-author-id55138675600
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationa86b9291-f23c-4f09-83d7-9ee691696705
relation.isAuthorOfPublication.latestForDiscoverya86b9291-f23c-4f09-83d7-9ee691696705

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