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Data mining techniques for electricity customer characterization

2021Journal article Open access
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dc.contributor.authorRamos, Sérgio Filipe Carvalho
dc.contributor.authorSoares, João
dc.contributor.authorCembranel, Samuel S.
dc.contributor.authorTavares, Inês
dc.contributor.authorForoozandeh, Z.
dc.contributor.authorVale, Zita
dc.contributor.authorFernandes, Rubipiara
dc.date.accessioned2021-09-17T10:53:38Z
dc.date.available2021-09-17T10:53:38Z
dc.date.issued2021
dc.descriptionMeeting: 14th International Symposium "Intelligent Systems – 2020" (INTELS 2020), Moscow, Russia, December 14–16, 2020pt_PT
dc.description.abstractThe liberalization of electricity markets has been resulted in the emergence of new players, increasing the competitiveness in the markets, standing those can provide better services for better prices. The knowledge of energy consumers’ profile has been an important tool to help players to make decisions in the electrical sectors. In this paper, a characterization model of typical load curves for Low Voltage (LV) customers is proposed and evaluated. The identification of consumption patterns is based on clustering analysis. The clustering methodology is based on seven algorithms, partitional and hierarchical. Also, five clustering validity indices are used to identify the best data partition. With the knowledge obtained in clustering analysis, a classification model is used to classify new customers according to their consumption data. The classification model is used to select the correct class for each customer. To make the model simple, each load curve is represented by three indices which represent load curves shape. The methodology used in this work demonstrates to be an effective tool and can be used in most diverse sectors, highlighting the use of knowledge in the optimization of the energy contracting for low voltage customers. The energy consumption data can be constantly updated to improve the model precision, finding estimates that can better represent consumers and their consumption habits.pt_PT
dc.description.sponsorshipThis work has received funding from FEDER Funds through COMPETE program and from184 National Funds through FCT under the project BENEFICE–PTDC/EEI-EEE/29070/2017 and185 UIDB/00760/2020 under CEECIND/02814/2017 grant.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.procs.2021.04.168pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18404
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1877050921010048?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectKnowledge discovery in Databasespt_PT
dc.subjectData miningpt_PT
dc.subjectClusteringpt_PT
dc.subjectClassificationpt_PT
dc.subjectTypical load profilespt_PT
dc.titleData mining techniques for electricity customer characterizationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage488pt_PT
oaire.citation.startPage475pt_PT
oaire.citation.titleProcedia Computer Sciencept_PT
oaire.citation.volume186pt_PT
person.familyNameCarvalho Ramos
person.familyNameSoares
person.familyNameVale
person.givenNameSérgio Filipe
person.givenNameJoão
person.givenNameZita
person.identifier1043580
person.identifier632184
person.identifier.ciencia-id6D1F-C495-6660
person.identifier.ciencia-id1612-8EA8-D0E8
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-1120-5656
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication9ece308b-6d79-4cec-af91-f2278dcc47eb
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relation.isAuthorOfPublication.latestForDiscoveryf01a54a0-e6c0-4cf3-afd8-5a664bbac7b4

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