Data mining techniques for electricity customer characterization
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
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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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 |
format |
article |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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