Bridging the gap between energy consumption and distribution through non-technical loss detection

The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount...

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Detalhes bibliográficos
Autores: Coma Puig, Bernat|||0000-0003-3944-797X, Carmona Vargas, Josep|||0000-0001-9656-254X
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/177598
Acesso em linha:https://hdl.handle.net/2117/177598
https://dx.doi.org/10.3390/en12091748
Access Level:acceso abierto
Palavra-chave:Energy industries
Energy consumption
Machine learning
Fraud detection
Supervised systems
Indústries energètiques
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia elèctrica
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oai_identifier_str oai:upcommons.upc.edu:2117/177598
network_acronym_str ES
network_name_str España
repository_id_str
spelling Bridging the gap between energy consumption and distribution through non-technical loss detectionComa Puig, Bernat|||0000-0003-3944-797XCarmona Vargas, Josep|||0000-0001-9656-254XEnergy industriesEnergy consumptionMachine learningFraud detectionSupervised systemsIndústries energètiquesEnergia -- ConsumAprenentatge automàticÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticÀrees temàtiques de la UPC::Energies::Energia elèctricaThe application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides.Peer Reviewed20192019-05-0120202020-02-13journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/177598https://dx.doi.org/10.3390/en12091748reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-86727-C2-1-R MODELOS Y METODOS BASADOS EN GRAFOS PARA LA COMPUTACION EN GRAN ESCALAopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1775982026-05-27T15:37:01Z
dc.title.none.fl_str_mv Bridging the gap between energy consumption and distribution through non-technical loss detection
title Bridging the gap between energy consumption and distribution through non-technical loss detection
spellingShingle Bridging the gap between energy consumption and distribution through non-technical loss detection
Coma Puig, Bernat|||0000-0003-3944-797X
Energy industries
Energy consumption
Machine learning
Fraud detection
Supervised systems
Indústries energètiques
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia elèctrica
title_short Bridging the gap between energy consumption and distribution through non-technical loss detection
title_full Bridging the gap between energy consumption and distribution through non-technical loss detection
title_fullStr Bridging the gap between energy consumption and distribution through non-technical loss detection
title_full_unstemmed Bridging the gap between energy consumption and distribution through non-technical loss detection
title_sort Bridging the gap between energy consumption and distribution through non-technical loss detection
dc.creator.none.fl_str_mv Coma Puig, Bernat|||0000-0003-3944-797X
Carmona Vargas, Josep|||0000-0001-9656-254X
author Coma Puig, Bernat|||0000-0003-3944-797X
author_facet Coma Puig, Bernat|||0000-0003-3944-797X
Carmona Vargas, Josep|||0000-0001-9656-254X
author_role author
author2 Carmona Vargas, Josep|||0000-0001-9656-254X
author2_role author
dc.subject.none.fl_str_mv Energy industries
Energy consumption
Machine learning
Fraud detection
Supervised systems
Indústries energètiques
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia elèctrica
topic Energy industries
Energy consumption
Machine learning
Fraud detection
Supervised systems
Indústries energètiques
Energia -- Consum
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia elèctrica
description The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-05-01
2020
2020-02-13
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://hdl.handle.net/2117/177598
https://dx.doi.org/10.3390/en12091748
url https://hdl.handle.net/2117/177598
https://dx.doi.org/10.3390/en12091748
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-86727-C2-1-R MODELOS Y METODOS BASADOS EN GRAFOS PARA LA COMPUTACION EN GRAN ESCALA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
https://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
Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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