Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance

[EN] As the analysis of electrical loads is reaching data measured from low voltage power distribution networks, there is a need for the main agents involved in the operation and management of the power grids to segment the end users as a function of their shapes of daily energy consumption or load...

Descripción completa

Detalles Bibliográficos
Autores: Benítez Sánchez, Ignacio Javier, Delgado Espinos, Ignacio, Diez, José-Luís|||0000-0002-5659-1212, Quijano-Lopez, Alfredo|||0000-0001-7916-8698
Tipo de recurso: artículo
Fecha de publicación:2016
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:10251/87902
Acceso en línea:https://riunet.upv.es/handle/10251/87902
Access Level:acceso abierto
Palabra clave:Dynamic clustering
Data mining
Load profilesa
INGENIERIA ELECTRICA
INGENIERIA DE SISTEMAS Y AUTOMATICA
id ES_d5fed32db552b89b9addbbadd9332ca7
oai_identifier_str oai:riunet.upv.es:10251/87902
network_acronym_str ES
network_name_str España
repository_id_str
spelling Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distanceBenítez Sánchez, Ignacio JavierDelgado Espinos, IgnacioDiez, José-Luís|||0000-0002-5659-1212Quijano-Lopez, Alfredo|||0000-0001-7916-8698Dynamic clusteringData miningLoad profilesaINGENIERIA ELECTRICAINGENIERIA DE SISTEMAS Y AUTOMATICA[EN] As the analysis of electrical loads is reaching data measured from low voltage power distribution networks, there is a need for the main agents involved in the operation and management of the power grids to segment the end users as a function of their shapes of daily energy consumption or load profiles, and to obtain patterns that allow to classify the users in groups based on how they consume the energy. However, this analysis is usually limited to the analysis of single days. Since the smart metering data are time series formed by sequential measurements of energy through each hour or quarter of hour of the day, and also through each day, thanks to the implementation of Advanced Metering Infrastructure (AMI) and the Smart Grid technologies, it becomes clear that the analysis of the data needs to be extended to consider the dynamic evolution of the consumption patterns through days, weeks, months, seasons, and even years. This is the objective of the present work. A new framework is presented that addresses the dynamic clustering, visualization and identification of temporal patterns in load profiles time series, fulfilling the detected gap in this area. The present development is a generic framework that allows the clustering and visualization of load profiles time series applying different classical clustering algorithms. A novel dynamic clustering algorithm is also presented, based on an initial segmentation of the energy consumption time series data in smaller surfaces, and the computation of a similarity measure among them applying the Hausdorff distance. Following, these developments are presented and tested on two dataset of energy consumption load profiles from a sample of residential users in Spain and London.The data set for the Spanish case used in this work has been provided by the Spanish DSO Iberdrola Distribucion Electrica S.A. as part of the works developed in the Spanish R&D project GAD. The GAD or "Active Demand Management" (in Spanish) project was a project financed by the INGENIO 2010 program and supported by the CDTI (Technological Development Centre of the Ministry of Science and Innovation of Spain).ElsevierDepartamento de Ingeniería de Sistemas y AutomáticaInstituto de Tecnología EléctricaDepartamento de Ingeniería EléctricaInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería IndustrialCentro para el Desarrollo Tecnológico IndustrialMinisterio de Ciencia e InnovaciónRepositorio Institucional de la Universitat Politècnica de València Riunet20162016-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/87902reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/879022026-06-13T07:49:27Z
dc.title.none.fl_str_mv Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
title Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
spellingShingle Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
Benítez Sánchez, Ignacio Javier
Dynamic clustering
Data mining
Load profilesa
INGENIERIA ELECTRICA
INGENIERIA DE SISTEMAS Y AUTOMATICA
title_short Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
title_full Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
title_fullStr Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
title_full_unstemmed Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
title_sort Dynamic clustering of residential electricity consumption time seriesdata based on Hausdorff distance
dc.creator.none.fl_str_mv Benítez Sánchez, Ignacio Javier
Delgado Espinos, Ignacio
Diez, José-Luís|||0000-0002-5659-1212
Quijano-Lopez, Alfredo|||0000-0001-7916-8698
author Benítez Sánchez, Ignacio Javier
author_facet Benítez Sánchez, Ignacio Javier
Delgado Espinos, Ignacio
Diez, José-Luís|||0000-0002-5659-1212
Quijano-Lopez, Alfredo|||0000-0001-7916-8698
author_role author
author2 Delgado Espinos, Ignacio
Diez, José-Luís|||0000-0002-5659-1212
Quijano-Lopez, Alfredo|||0000-0001-7916-8698
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería de Sistemas y Automática
Instituto de Tecnología Eléctrica
Departamento de Ingeniería Eléctrica
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Industrial
Centro para el Desarrollo Tecnológico Industrial
Ministerio de Ciencia e Innovación
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Dynamic clustering
Data mining
Load profilesa
INGENIERIA ELECTRICA
INGENIERIA DE SISTEMAS Y AUTOMATICA
topic Dynamic clustering
Data mining
Load profilesa
INGENIERIA ELECTRICA
INGENIERIA DE SISTEMAS Y AUTOMATICA
description [EN] As the analysis of electrical loads is reaching data measured from low voltage power distribution networks, there is a need for the main agents involved in the operation and management of the power grids to segment the end users as a function of their shapes of daily energy consumption or load profiles, and to obtain patterns that allow to classify the users in groups based on how they consume the energy. However, this analysis is usually limited to the analysis of single days. Since the smart metering data are time series formed by sequential measurements of energy through each hour or quarter of hour of the day, and also through each day, thanks to the implementation of Advanced Metering Infrastructure (AMI) and the Smart Grid technologies, it becomes clear that the analysis of the data needs to be extended to consider the dynamic evolution of the consumption patterns through days, weeks, months, seasons, and even years. This is the objective of the present work. A new framework is presented that addresses the dynamic clustering, visualization and identification of temporal patterns in load profiles time series, fulfilling the detected gap in this area. The present development is a generic framework that allows the clustering and visualization of load profiles time series applying different classical clustering algorithms. A novel dynamic clustering algorithm is also presented, based on an initial segmentation of the energy consumption time series data in smaller surfaces, and the computation of a similarity measure among them applying the Hausdorff distance. Following, these developments are presented and tested on two dataset of energy consumption load profiles from a sample of residential users in Spain and London.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-11-01
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/10251/87902
url https://riunet.upv.es/handle/10251/87902
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
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
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
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
_version_ 1869420774915309568
score 15,300719

Similares en LA Referencia