Energy disaggregation using multi-objective genetic algorithm designed neural networks
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/18672 |
Resumo: | Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices. |
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Energy disaggregation using multi-objective genetic algorithm designed neural networksNon-intrusive load monitoring (NILM)Energy disaggregationNeural networksMulti-objective genetic algorithmLow frequency power dataConvex hull algorithmsEnergy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.72581/2020UID/EMS/50022/2020MDPISapientiaHabou Laouali, InoussaGomes, IsaíasRuano, MariaBennani, Saad DosseFadili, Hakim ElRuano, Antonio2022-12-20T09:57:33Z2022-11-302022-12-09T20:23:07Z2022-11-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/18672engEnergies 15 (23): 9073 (2022)10.3390/en152390731996-1073info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:30:54Zoai:sapientia.ualg.pt:10400.1/18672Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:08:21.988410Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Energy disaggregation using multi-objective genetic algorithm designed neural networks |
title |
Energy disaggregation using multi-objective genetic algorithm designed neural networks |
spellingShingle |
Energy disaggregation using multi-objective genetic algorithm designed neural networks Habou Laouali, Inoussa Non-intrusive load monitoring (NILM) Energy disaggregation Neural networks Multi-objective genetic algorithm Low frequency power data Convex hull algorithms |
title_short |
Energy disaggregation using multi-objective genetic algorithm designed neural networks |
title_full |
Energy disaggregation using multi-objective genetic algorithm designed neural networks |
title_fullStr |
Energy disaggregation using multi-objective genetic algorithm designed neural networks |
title_full_unstemmed |
Energy disaggregation using multi-objective genetic algorithm designed neural networks |
title_sort |
Energy disaggregation using multi-objective genetic algorithm designed neural networks |
author |
Habou Laouali, Inoussa |
author_facet |
Habou Laouali, Inoussa Gomes, Isaías Ruano, Maria Bennani, Saad Dosse Fadili, Hakim El Ruano, Antonio |
author_role |
author |
author2 |
Gomes, Isaías Ruano, Maria Bennani, Saad Dosse Fadili, Hakim El Ruano, Antonio |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Habou Laouali, Inoussa Gomes, Isaías Ruano, Maria Bennani, Saad Dosse Fadili, Hakim El Ruano, Antonio |
dc.subject.por.fl_str_mv |
Non-intrusive load monitoring (NILM) Energy disaggregation Neural networks Multi-objective genetic algorithm Low frequency power data Convex hull algorithms |
topic |
Non-intrusive load monitoring (NILM) Energy disaggregation Neural networks Multi-objective genetic algorithm Low frequency power data Convex hull algorithms |
description |
Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-20T09:57:33Z 2022-11-30 2022-12-09T20:23:07Z 2022-11-30T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/18672 |
url |
http://hdl.handle.net/10400.1/18672 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Energies 15 (23): 9073 (2022) 10.3390/en15239073 1996-1073 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133329440636928 |