Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management

Detalhes bibliográficos
Autor(a) principal: Faia, R.
Data de Publicação: 2017
Outros Autores: Pinto, Tiago, Abrishambaf, Omid, Fernandes, Filipe, Vale, Zita, Corchado, Juan Manuel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/17338
Resumo: This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.
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spelling Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy managementArtificial intelligenceCase based reasoningDemand responseEnergy efficiencyIntelligent house energy managementThis paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAMGO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.ElsevierRepositório Científico do Instituto Politécnico do PortoFaia, R.Pinto, TiagoAbrishambaf, OmidFernandes, FilipeVale, ZitaCorchado, Juan Manuel2021-03-09T14:57:31Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/17338eng10.1016/j.enbuild.2017.09.020info: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:RCAAP2023-03-13T13:07:50Zoai:recipp.ipp.pt:10400.22/17338Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:36:56.473822Repositório 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 Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
title Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
spellingShingle Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
Faia, R.
Artificial intelligence
Case based reasoning
Demand response
Energy efficiency
Intelligent house energy management
title_short Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
title_full Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
title_fullStr Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
title_full_unstemmed Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
title_sort Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management
author Faia, R.
author_facet Faia, R.
Pinto, Tiago
Abrishambaf, Omid
Fernandes, Filipe
Vale, Zita
Corchado, Juan Manuel
author_role author
author2 Pinto, Tiago
Abrishambaf, Omid
Fernandes, Filipe
Vale, Zita
Corchado, Juan Manuel
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 Faia, R.
Pinto, Tiago
Abrishambaf, Omid
Fernandes, Filipe
Vale, Zita
Corchado, Juan Manuel
dc.subject.por.fl_str_mv Artificial intelligence
Case based reasoning
Demand response
Energy efficiency
Intelligent house energy management
topic Artificial intelligence
Case based reasoning
Demand response
Energy efficiency
Intelligent house energy management
description This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
2021-03-09T14:57:31Z
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/10400.22/17338
url http://hdl.handle.net/10400.22/17338
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.enbuild.2017.09.020
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 Elsevier
publisher.none.fl_str_mv Elsevier
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
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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