Automated Detection of Electric Energy Consumption Load Profile Patterns

[EN] Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of d...

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Detalles Bibliográficos
Autores: Benítez, Ignacio, Diez, José-Luís|||0000-0002-5659-1212
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/198814
Acceso en línea:https://riunet.upv.es/handle/10251/198814
Access Level:acceso abierto
Palabra clave:Time series analysis
Dynamic clustering
User load profiles
INGENIERIA DE SISTEMAS Y AUTOMATICA
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oai_identifier_str oai:riunet.upv.es:10251/198814
network_acronym_str ES
network_name_str España
repository_id_str
spelling Automated Detection of Electric Energy Consumption Load Profile PatternsBenítez, IgnacioDiez, José-Luís|||0000-0002-5659-1212Time series analysisDynamic clusteringUser load profilesINGENIERIA DE SISTEMAS Y AUTOMATICA[EN] Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of dynamic clustering techniques to obtain and visualise temporal patterns characterising the users of electrical energy is deeply studied. The performed review can be used as a guide for those interested in the automatic analysis and groups of behaviour detection within load profile databases. Additionally, a selection of dynamic clustering algorithms have been implemented and the performances compared using an available electric energy consumption load profile database. The results allow experts to easily evaluate how users consume energy, to assess trends and to predict future scenarios.The data analysed has been facilitated by the Spanish Distributor Iberdrola Electrical Distribution S.A. as part of the research project GAD (Active Management of the Demand), national project by DEVISE 2010 funded by the INGENIIO 2010 program and the CDTI (Centre for Industrial Technology Development), Business Public Entity dependent of the Ministry of Economy and Competitiveness of the Government of Spain.MDPI AGDepartamento de Ingeniería de Sistemas y AutomáticaInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería IndustrialMinisterio de Ciencia e InnovaciónUniversitat Politècnica de ValènciaCentro para el Desarrollo Tecnológico IndustrialRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/198814reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1988142026-06-13T07:49:27Z
dc.title.none.fl_str_mv Automated Detection of Electric Energy Consumption Load Profile Patterns
title Automated Detection of Electric Energy Consumption Load Profile Patterns
spellingShingle Automated Detection of Electric Energy Consumption Load Profile Patterns
Benítez, Ignacio
Time series analysis
Dynamic clustering
User load profiles
INGENIERIA DE SISTEMAS Y AUTOMATICA
title_short Automated Detection of Electric Energy Consumption Load Profile Patterns
title_full Automated Detection of Electric Energy Consumption Load Profile Patterns
title_fullStr Automated Detection of Electric Energy Consumption Load Profile Patterns
title_full_unstemmed Automated Detection of Electric Energy Consumption Load Profile Patterns
title_sort Automated Detection of Electric Energy Consumption Load Profile Patterns
dc.creator.none.fl_str_mv Benítez, Ignacio
Diez, José-Luís|||0000-0002-5659-1212
author Benítez, Ignacio
author_facet Benítez, Ignacio
Diez, José-Luís|||0000-0002-5659-1212
author_role author
author2 Diez, José-Luís|||0000-0002-5659-1212
author2_role author
dc.contributor.none.fl_str_mv Departamento de Ingeniería de Sistemas y Automática
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Industrial
Ministerio de Ciencia e Innovación
Universitat Politècnica de València
Centro para el Desarrollo Tecnológico Industrial
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Time series analysis
Dynamic clustering
User load profiles
INGENIERIA DE SISTEMAS Y AUTOMATICA
topic Time series analysis
Dynamic clustering
User load profiles
INGENIERIA DE SISTEMAS Y AUTOMATICA
description [EN] Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of dynamic clustering techniques to obtain and visualise temporal patterns characterising the users of electrical energy is deeply studied. The performed review can be used as a guide for those interested in the automatic analysis and groups of behaviour detection within load profile databases. Additionally, a selection of dynamic clustering algorithms have been implemented and the performances compared using an available electric energy consumption load profile database. The results allow experts to easily evaluate how users consume energy, to assess trends and to predict future scenarios.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/198814
url https://riunet.upv.es/handle/10251/198814
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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