Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/19725 |
Resumo: | In the context of the expected advances with the implementation of Smart Grids, enabling the modernization of the electricity billing system and making the management of residential electric power systems attractive, the detailed knowledge of the residential consumption profile gains importance as an element to subsidize the decision making by Home Energy Management Systems. One of the techniques for detailing the residential electrical consumption profile is Non-Intrusive Load Monitoring (NILM), a low cost installation technique that presents its complexity in the development of the disaggregation algorithm. Given this challenge, this dissertation presents a methodology based on low frequency sampling electrical monitoring to perform the extraction of characteristics of the activated and deactivated electric loads during the operation of the electric network. Through these characteristics, an evaluation of the event classification performance is performed using the Convolutional Neural Network - CNN, a type of artificial intelligence specially used for visual pattern recognition. To evaluate the developed method, a case study is performed using monitoring data from the United Kingdom Domestic Appliance Level Electricity database - UK-DALE, a data source widely used in NILM surveys. The performance of the developed method is evaluated using the Precision, Recall, F1-Score and Accuracy metrics as well as the confusion matrix to present the classification errors. To compare the classification performance obtained by the developed method is also modeled classification method called Decision Tree. Through the performance analysis of the developed method it is observed that it presents some restriction to deal with behaviors not foreseen in the training phase, but presents the ability to learn new behaviors through new training phases. Also, it presented good performance in the classification of low power events, when compared to the Decision Tree classification method. In conclusion, the use of convolutional neural network presents positive performance to be used in the event classification in NILM applications. |
id |
UFSM_8ac9f6ee1fcefbaee7f3339d570911e0 |
---|---|
oai_identifier_str |
oai:repositorio.ufsm.br:1/19725 |
network_acronym_str |
UFSM |
network_name_str |
Biblioteca Digital de Teses e Dissertações do UFSM |
repository_id_str |
|
spelling |
2020-03-04T15:28:44Z2020-03-04T15:28:44Z2019-10-22http://repositorio.ufsm.br/handle/1/19725In the context of the expected advances with the implementation of Smart Grids, enabling the modernization of the electricity billing system and making the management of residential electric power systems attractive, the detailed knowledge of the residential consumption profile gains importance as an element to subsidize the decision making by Home Energy Management Systems. One of the techniques for detailing the residential electrical consumption profile is Non-Intrusive Load Monitoring (NILM), a low cost installation technique that presents its complexity in the development of the disaggregation algorithm. Given this challenge, this dissertation presents a methodology based on low frequency sampling electrical monitoring to perform the extraction of characteristics of the activated and deactivated electric loads during the operation of the electric network. Through these characteristics, an evaluation of the event classification performance is performed using the Convolutional Neural Network - CNN, a type of artificial intelligence specially used for visual pattern recognition. To evaluate the developed method, a case study is performed using monitoring data from the United Kingdom Domestic Appliance Level Electricity database - UK-DALE, a data source widely used in NILM surveys. The performance of the developed method is evaluated using the Precision, Recall, F1-Score and Accuracy metrics as well as the confusion matrix to present the classification errors. To compare the classification performance obtained by the developed method is also modeled classification method called Decision Tree. Through the performance analysis of the developed method it is observed that it presents some restriction to deal with behaviors not foreseen in the training phase, but presents the ability to learn new behaviors through new training phases. Also, it presented good performance in the classification of low power events, when compared to the Decision Tree classification method. In conclusion, the use of convolutional neural network presents positive performance to be used in the event classification in NILM applications.No contexto dos avanços esperados com a implantação das redes elétricas inteligentes, viabilizando a modernização do sistema de tarifação de energia elétrica e tornando atrativo o gerenciamento dos sistemas de energia elétrica residencial, o conhecimento detalhado do perfil de consumo residencial ganha importância como elemento para subsidiar a tomada de decisão pelos sistemas de gerenciamento de energia elétrica residencial. Uma das técnicas para realizar o detalhamento do perfil de consumo das cargas elétricas residenciais é o monitoramento não intrusivo (Non-Intrusive Load Monitoring - NILM), uma técnica de baixo custo de instalação que apresenta sua complexidade no desenvolvimento do algoritmo de desagregação. Diante deste desafio, nesta dissertação é apresentada metodologia baseada em monitoramento elétrico em baixa frequência de amostragem para realizar a extração de características das cargas elétricas ativadas e desativadas durante a operação da rede elétrica. Através destas características é realizada avaliação do desempenho da classificação dos eventos utilizando a rede neural convolucional (Convolutional Neural Network - CNN), uma técnica de inteligência artificial especialmente utilizada para reconhecimento de padrões visuais. Para avaliar o método desenvolvido é realizado estudo de caso utilizando dados de monitoramento do banco de dados público de consumo de cargas elétricas domésticas do Reino Unido (United Kingdom Domestic Appliance Level Electricity - UK-DALE), uma fonte de dados amplamente utilizada em pesquisas NILM. O desempenho do método desenvolvido é avaliado através das métricas Precision, Recall, F1-Score e Accuracy assim como é utilizada a matriz de confusão para apresentar os erros de classificação observados. Para comparar o desempenho de classificação obtida pelo método desenvolvido é também modelado o método de classificação denominado árvore de decisões. Através da análise desempenho do método desenvolvido é observado que este apresenta certa restrição para lidar com comportamentos não previstos na fase de treinamento, porém apresenta capacidade de aprender novos comportamentos através de novas fases de treinamento. Também, apresentou bom desempenho na classificação de eventos de baixo valor de potência, se comparado ao método de classificação árvore de decisões. Concluindo-se que a utilização da rede neural convolucional apresenta desempenho positivo para ser utilizada na classificação de eventos em aplicações NILM.porUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia ElétricaUFSMBrasilEngenharia ElétricaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessCNNMonitoramento não intrusivoNILMRede neural convolucionalCNNNon-intrusive load monitoringNILMConvolutional neural networkCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAClassificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucionalEvent classification in NILM monitoring of residential electrical loads using convolutional neural networkinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisCanha, Luciane Neveshttp://lattes.cnpq.br/6991878627141193Sausen, Paulo Sérgiohttp://lattes.cnpq.br/5358851504202129Barriquello, Carlos Henriquehttp://lattes.cnpq.br/4127396473202565http://lattes.cnpq.br/3354780828942537Medeiros, Aérton Pedra300400000007600ab53fdc5-93b0-417d-b96d-9442b235a1ff5d0aad23-cbd7-4baa-a58b-b6d03bd579153b3df45e-8ffa-411d-aec5-f41ee1d31c6eb9acccb6-a581-4984-9671-9e2b868c4c72reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALDIS_PPGEE_2019_MEDEIROS_AERTON.pdfDIS_PPGEE_2019_MEDEIROS_AERTON.pdfDissertação de Mestradoapplication/pdf9348873http://repositorio.ufsm.br/bitstream/1/19725/1/DIS_PPGEE_2019_MEDEIROS_AERTON.pdff5b4f19d6d1f9f0d1ad586ee80d7126bMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.ufsm.br/bitstream/1/19725/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81956http://repositorio.ufsm.br/bitstream/1/19725/3/license.txt2f0571ecee68693bd5cd3f17c1e075dfMD53TEXTDIS_PPGEE_2019_MEDEIROS_AERTON.pdf.txtDIS_PPGEE_2019_MEDEIROS_AERTON.pdf.txtExtracted texttext/plain207124http://repositorio.ufsm.br/bitstream/1/19725/4/DIS_PPGEE_2019_MEDEIROS_AERTON.pdf.txtadedf2e7b8c462e6aa914a7d842533d6MD54THUMBNAILDIS_PPGEE_2019_MEDEIROS_AERTON.pdf.jpgDIS_PPGEE_2019_MEDEIROS_AERTON.pdf.jpgIM Thumbnailimage/jpeg4150http://repositorio.ufsm.br/bitstream/1/19725/5/DIS_PPGEE_2019_MEDEIROS_AERTON.pdf.jpgbea3af59ef04f5f695091a195b0f39c1MD551/197252020-03-05 03:01:31.998oai:repositorio.ufsm.br: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 Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2020-03-05T06:01:31Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional |
dc.title.alternative.eng.fl_str_mv |
Event classification in NILM monitoring of residential electrical loads using convolutional neural network |
title |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional |
spellingShingle |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional Medeiros, Aérton Pedra CNN Monitoramento não intrusivo NILM Rede neural convolucional CNN Non-intrusive load monitoring NILM Convolutional neural network CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional |
title_full |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional |
title_fullStr |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional |
title_full_unstemmed |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional |
title_sort |
Classificação de eventos em monitoramento NILM de cargas elétricas residenciais utilizando rede neural convolucional |
author |
Medeiros, Aérton Pedra |
author_facet |
Medeiros, Aérton Pedra |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Canha, Luciane Neves |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6991878627141193 |
dc.contributor.referee1.fl_str_mv |
Sausen, Paulo Sérgio |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/5358851504202129 |
dc.contributor.referee2.fl_str_mv |
Barriquello, Carlos Henrique |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/4127396473202565 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3354780828942537 |
dc.contributor.author.fl_str_mv |
Medeiros, Aérton Pedra |
contributor_str_mv |
Canha, Luciane Neves Sausen, Paulo Sérgio Barriquello, Carlos Henrique |
dc.subject.por.fl_str_mv |
CNN Monitoramento não intrusivo NILM Rede neural convolucional |
topic |
CNN Monitoramento não intrusivo NILM Rede neural convolucional CNN Non-intrusive load monitoring NILM Convolutional neural network CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
CNN Non-intrusive load monitoring NILM Convolutional neural network |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
In the context of the expected advances with the implementation of Smart Grids, enabling the modernization of the electricity billing system and making the management of residential electric power systems attractive, the detailed knowledge of the residential consumption profile gains importance as an element to subsidize the decision making by Home Energy Management Systems. One of the techniques for detailing the residential electrical consumption profile is Non-Intrusive Load Monitoring (NILM), a low cost installation technique that presents its complexity in the development of the disaggregation algorithm. Given this challenge, this dissertation presents a methodology based on low frequency sampling electrical monitoring to perform the extraction of characteristics of the activated and deactivated electric loads during the operation of the electric network. Through these characteristics, an evaluation of the event classification performance is performed using the Convolutional Neural Network - CNN, a type of artificial intelligence specially used for visual pattern recognition. To evaluate the developed method, a case study is performed using monitoring data from the United Kingdom Domestic Appliance Level Electricity database - UK-DALE, a data source widely used in NILM surveys. The performance of the developed method is evaluated using the Precision, Recall, F1-Score and Accuracy metrics as well as the confusion matrix to present the classification errors. To compare the classification performance obtained by the developed method is also modeled classification method called Decision Tree. Through the performance analysis of the developed method it is observed that it presents some restriction to deal with behaviors not foreseen in the training phase, but presents the ability to learn new behaviors through new training phases. Also, it presented good performance in the classification of low power events, when compared to the Decision Tree classification method. In conclusion, the use of convolutional neural network presents positive performance to be used in the event classification in NILM applications. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019-10-22 |
dc.date.accessioned.fl_str_mv |
2020-03-04T15:28:44Z |
dc.date.available.fl_str_mv |
2020-03-04T15:28:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/19725 |
url |
http://repositorio.ufsm.br/handle/1/19725 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300400000007 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
ab53fdc5-93b0-417d-b96d-9442b235a1ff 5d0aad23-cbd7-4baa-a58b-b6d03bd57915 3b3df45e-8ffa-411d-aec5-f41ee1d31c6e b9acccb6-a581-4984-9671-9e2b868c4c72 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Elétrica |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Engenharia Elétrica |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Biblioteca Digital de Teses e Dissertações do UFSM |
collection |
Biblioteca Digital de Teses e Dissertações do UFSM |
bitstream.url.fl_str_mv |
http://repositorio.ufsm.br/bitstream/1/19725/1/DIS_PPGEE_2019_MEDEIROS_AERTON.pdf http://repositorio.ufsm.br/bitstream/1/19725/2/license_rdf http://repositorio.ufsm.br/bitstream/1/19725/3/license.txt http://repositorio.ufsm.br/bitstream/1/19725/4/DIS_PPGEE_2019_MEDEIROS_AERTON.pdf.txt http://repositorio.ufsm.br/bitstream/1/19725/5/DIS_PPGEE_2019_MEDEIROS_AERTON.pdf.jpg |
bitstream.checksum.fl_str_mv |
f5b4f19d6d1f9f0d1ad586ee80d7126b 4460e5956bc1d1639be9ae6146a50347 2f0571ecee68693bd5cd3f17c1e075df adedf2e7b8c462e6aa914a7d842533d6 bea3af59ef04f5f695091a195b0f39c1 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1801485168869900288 |