Enhancing anomaly detection in electrical consumption profiles through computational intelligence

[EN] The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The "zero energy buildings" idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of...

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Autores: Luna-Romero, Santiago Felipe, Serrano-Guerrero, Xavier, de Souza, Mauren Abreu, Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
Tipo de recurso: artículo
Fecha de publicación:2024
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:10251/220604
Acceso en línea:https://riunet.upv.es/handle/10251/220604
Access Level:acceso abierto
Palabra clave:Building energy efficiency
Anomaly detection
Computational intelligence method
Zero energy buildings
Energy consumption patterns
Electrical energy consumption
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spelling Enhancing anomaly detection in electrical consumption profiles through computational intelligenceLuna-Romero, Santiago FelipeSerrano-Guerrero, Xavierde Souza, Mauren AbreuEscrivá-Escrivá, Guillermo|||0000-0002-3202-4571Building energy efficiencyAnomaly detectionComputational intelligence methodZero energy buildingsEnergy consumption patternsElectrical energy consumption[EN] The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The "zero energy buildings" idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of CO2 into the environment, is now one of the most significant initiatives connected to building energy efficiency. This article describes a computational intelligence method to detect anomalous variations in a facility's energy use and infer a potential cause of such changes. The model is built using five sets of historical power consumption data from three buildings spread across four nations (Ecuador, Spain, France, and Canada), which are categorized based on the anomaly type each piece of data represents. Through a statistical study of the confidence interval, the proposed method, first determines the consumption patterns for each day of the week in each of the building's data sets. After normalizing the day to be studied toward its "Z" value, it is then cataloged using a machine learning model. The proposed method is evaluated in comparison to a purely statistical method called SAEEC methodology and it is discovered that the proposed method offers a relative improvement in accuracy, false positive rate (FPR), and false negative rate (FNR) of 12.41%, 42, 36%, and 42.45%, respectively, for the detection of atypical values in electrical energy consumption.We acknowledge Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) CNPq/MAI-DAI CP no 12/2020. Additionally, we acknowledge CNPq, by projects 310079/2019-5-PQ2, 4408164/2021-2-Universal and Fundacao Araucaria (FAPPR) - project 51432/2018-PPP.ElsevierDepartamento de Ingeniería EléctricaInstituto Universitario de Investigación de Ingeniería EnergéticaEscuela Técnica Superior de Ingeniería IndustrialConselho Nacional de Desenvolvimento Científico e Tecnológico, BrasilRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-06-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/220604reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengConselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 4408164%2F2021-2Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 51432%2F2018-PPPConselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 310079%2F2019-5-PQ2open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2206042026-06-13T07:49:27Z
dc.title.none.fl_str_mv Enhancing anomaly detection in electrical consumption profiles through computational intelligence
title Enhancing anomaly detection in electrical consumption profiles through computational intelligence
spellingShingle Enhancing anomaly detection in electrical consumption profiles through computational intelligence
Luna-Romero, Santiago Felipe
Building energy efficiency
Anomaly detection
Computational intelligence method
Zero energy buildings
Energy consumption patterns
Electrical energy consumption
title_short Enhancing anomaly detection in electrical consumption profiles through computational intelligence
title_full Enhancing anomaly detection in electrical consumption profiles through computational intelligence
title_fullStr Enhancing anomaly detection in electrical consumption profiles through computational intelligence
title_full_unstemmed Enhancing anomaly detection in electrical consumption profiles through computational intelligence
title_sort Enhancing anomaly detection in electrical consumption profiles through computational intelligence
dc.creator.none.fl_str_mv Luna-Romero, Santiago Felipe
Serrano-Guerrero, Xavier
de Souza, Mauren Abreu
Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
author Luna-Romero, Santiago Felipe
author_facet Luna-Romero, Santiago Felipe
Serrano-Guerrero, Xavier
de Souza, Mauren Abreu
Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
author_role author
author2 Serrano-Guerrero, Xavier
de Souza, Mauren Abreu
Escrivá-Escrivá, Guillermo|||0000-0002-3202-4571
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Eléctrica
Instituto Universitario de Investigación de Ingeniería Energética
Escuela Técnica Superior de Ingeniería Industrial
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Building energy efficiency
Anomaly detection
Computational intelligence method
Zero energy buildings
Energy consumption patterns
Electrical energy consumption
topic Building energy efficiency
Anomaly detection
Computational intelligence method
Zero energy buildings
Energy consumption patterns
Electrical energy consumption
description [EN] The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The "zero energy buildings" idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of CO2 into the environment, is now one of the most significant initiatives connected to building energy efficiency. This article describes a computational intelligence method to detect anomalous variations in a facility's energy use and infer a potential cause of such changes. The model is built using five sets of historical power consumption data from three buildings spread across four nations (Ecuador, Spain, France, and Canada), which are categorized based on the anomaly type each piece of data represents. Through a statistical study of the confidence interval, the proposed method, first determines the consumption patterns for each day of the week in each of the building's data sets. After normalizing the day to be studied toward its "Z" value, it is then cataloged using a machine learning model. The proposed method is evaluated in comparison to a purely statistical method called SAEEC methodology and it is discovered that the proposed method offers a relative improvement in accuracy, false positive rate (FPR), and false negative rate (FNR) of 12.41%, 42, 36%, and 42.45%, respectively, for the detection of atypical values in electrical energy consumption.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-06-01
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/10251/220604
url https://riunet.upv.es/handle/10251/220604
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 4408164%2F2021-2
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 51432%2F2018-PPP
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 310079%2F2019-5-PQ2
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/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 - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
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: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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