Data mining techniques for electricity customer characterization

Detalhes bibliográficos
Autor(a) principal: Ramos, Sérgio Filipe Carvalho
Data de Publicação: 2021
Outros Autores: Soares, João, Cembranel, Samuel S., Tavares, Inês, Foroozandeh, Z., Vale, Zita, Fernandes, Rubipiara
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/18404
Resumo: Meeting: 14th International Symposium "Intelligent Systems – 2020" (INTELS 2020), Moscow, Russia, December 14–16, 2020
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spelling Data mining techniques for electricity customer characterizationKnowledge discovery in DatabasesData miningClusteringClassificationTypical load profilesMeeting: 14th International Symposium "Intelligent Systems – 2020" (INTELS 2020), Moscow, Russia, December 14–16, 2020The 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.This 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.ElsevierRepositório Científico do Instituto Politécnico do PortoRamos, Sérgio Filipe CarvalhoSoares, JoãoCembranel, Samuel S.Tavares, InêsForoozandeh, Z.Vale, ZitaFernandes, Rubipiara2021-09-17T10:53:38Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/18404eng10.1016/j.procs.2021.04.168info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:09:41Zoai:recipp.ipp.pt:10400.22/18404Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:37:51.872169Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Data mining techniques for electricity customer characterization
title Data mining techniques for electricity customer characterization
spellingShingle Data mining techniques for electricity customer characterization
Ramos, Sérgio Filipe Carvalho
Knowledge discovery in Databases
Data mining
Clustering
Classification
Typical load profiles
title_short Data mining techniques for electricity customer characterization
title_full Data mining techniques for electricity customer characterization
title_fullStr Data mining techniques for electricity customer characterization
title_full_unstemmed Data mining techniques for electricity customer characterization
title_sort Data mining techniques for electricity customer characterization
author Ramos, Sérgio Filipe Carvalho
author_facet Ramos, Sérgio Filipe Carvalho
Soares, João
Cembranel, Samuel S.
Tavares, Inês
Foroozandeh, Z.
Vale, Zita
Fernandes, Rubipiara
author_role author
author2 Soares, João
Cembranel, Samuel S.
Tavares, Inês
Foroozandeh, Z.
Vale, Zita
Fernandes, Rubipiara
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Ramos, Sérgio Filipe Carvalho
Soares, João
Cembranel, Samuel S.
Tavares, Inês
Foroozandeh, Z.
Vale, Zita
Fernandes, Rubipiara
dc.subject.por.fl_str_mv Knowledge discovery in Databases
Data mining
Clustering
Classification
Typical load profiles
topic Knowledge discovery in Databases
Data mining
Clustering
Classification
Typical load profiles
description Meeting: 14th International Symposium "Intelligent Systems – 2020" (INTELS 2020), Moscow, Russia, December 14–16, 2020
publishDate 2021
dc.date.none.fl_str_mv 2021-09-17T10:53:38Z
2021
2021-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/18404
url http://hdl.handle.net/10400.22/18404
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.procs.2021.04.168
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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