Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/10400.22/17110 |
Resumo: | This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system. |
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Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in BuildingsBuilding energy managementCase-based reasoning (CBR)Energy efficiencyMulti-agent systems (MAS)This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.This work was supported in part by the EU's H 2020 research and innovation programme under the Marie SklodowskaCurie Grant Agreement 641794 (project DREAM-GO) and Grant Agreement 703689 (project ADAPT), in part by the FEDER Funds through COMPETE program, and in part by the National Funds through FCT under the Project UID/EEA/00760/2013. (Corresponding author: Tiago Pinto.)IEEERepositório Científico do Instituto Politécnico do PortoPinto, TiagoFaia, RicardoNavarro-Caceres, MariaSantos, GabrielCorchado, Juan ManuelVale, Zita2021-02-24T11:51:53Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/17110eng1932-818410.1109/JSYST.2018.2876933info: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:RCAAP2023-03-13T13:06:26Zoai:recipp.ipp.pt:10400.22/17110Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:36:46.838801Repositório 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 |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings |
title |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings |
spellingShingle |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings Pinto, Tiago Building energy management Case-based reasoning (CBR) Energy efficiency Multi-agent systems (MAS) |
title_short |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings |
title_full |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings |
title_fullStr |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings |
title_full_unstemmed |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings |
title_sort |
Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings |
author |
Pinto, Tiago |
author_facet |
Pinto, Tiago Faia, Ricardo Navarro-Caceres, Maria Santos, Gabriel Corchado, Juan Manuel Vale, Zita |
author_role |
author |
author2 |
Faia, Ricardo Navarro-Caceres, Maria Santos, Gabriel Corchado, Juan Manuel Vale, Zita |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Pinto, Tiago Faia, Ricardo Navarro-Caceres, Maria Santos, Gabriel Corchado, Juan Manuel Vale, Zita |
dc.subject.por.fl_str_mv |
Building energy management Case-based reasoning (CBR) Energy efficiency Multi-agent systems (MAS) |
topic |
Building energy management Case-based reasoning (CBR) Energy efficiency Multi-agent systems (MAS) |
description |
This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2021-02-24T11:51:53Z |
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/10400.22/17110 |
url |
http://hdl.handle.net/10400.22/17110 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1932-8184 10.1109/JSYST.2018.2876933 |
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 |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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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1799131458696118272 |