Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect

Detalhes bibliográficos
Autor(a) principal: Santos, Ricardo
Data de Publicação: 2018
Outros Autores: Matias, João, Abreu, António, Reis, Francisco
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.21/9345
Resumo: This paper presents a model to promote energy efficiency among household appliances, by supporting the consumer decisions through the maximization of his savings, associated to a set of electrical appliances from the market to be acquired. Not always an efficient equipment from the market, is more expensive than a less efficient one, which can lead the consumer to compromise the expected savings on future. Given the several models/brands available on market and its possible combinations, the problem can be defined as a combinatorial problem, whose complexity can compromise the efficiency of using deterministic algorithms. Genetic algorithms (GM) were therefore included in the model, whose results were compared later with Simplex to verify the quality of the obtained solutions, as well as their performance. In addition, it was performed a statistical analysis of the obtained results, as well as a sensitivity analysis of GAs parameters, to validate their robustness. we conclude that the proposed method can provide several efficient solutions to the problem, as well as sensitize the consumer to their choices made on future, by estimating their corresponding rebound effect.
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spelling Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effectEnergy efficiencyGenetic algorithms (GAs)Simplex methodLife Cycle Cost Analysis (LCCA)Indirect Rebound EffectThis paper presents a model to promote energy efficiency among household appliances, by supporting the consumer decisions through the maximization of his savings, associated to a set of electrical appliances from the market to be acquired. Not always an efficient equipment from the market, is more expensive than a less efficient one, which can lead the consumer to compromise the expected savings on future. Given the several models/brands available on market and its possible combinations, the problem can be defined as a combinatorial problem, whose complexity can compromise the efficiency of using deterministic algorithms. Genetic algorithms (GM) were therefore included in the model, whose results were compared later with Simplex to verify the quality of the obtained solutions, as well as their performance. In addition, it was performed a statistical analysis of the obtained results, as well as a sensitivity analysis of GAs parameters, to validate their robustness. we conclude that the proposed method can provide several efficient solutions to the problem, as well as sensitize the consumer to their choices made on future, by estimating their corresponding rebound effect.ElsevierRCIPLSantos, RicardoMatias, JoãoAbreu, AntónioReis, Francisco2019-01-18T10:34:45Z2018-122018-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/9345engSANTOS, Ricardo; [et al] – Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect. Computers and Industrial Engineering. ISSN 0360-8352. Vol. 126 (2018), pp. 729-7550360-835210.1016/j.cie.2018.09.050metadata only accessinfo: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:RCAAP2023-08-03T09:57:55Zoai:repositorio.ipl.pt:10400.21/9345Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:17:56.114422Repositório 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 Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
title Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
spellingShingle Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
Santos, Ricardo
Energy efficiency
Genetic algorithms (GAs)
Simplex method
Life Cycle Cost Analysis (LCCA)
Indirect Rebound Effect
title_short Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
title_full Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
title_fullStr Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
title_full_unstemmed Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
title_sort Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
author Santos, Ricardo
author_facet Santos, Ricardo
Matias, João
Abreu, António
Reis, Francisco
author_role author
author2 Matias, João
Abreu, António
Reis, Francisco
author2_role author
author
author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Santos, Ricardo
Matias, João
Abreu, António
Reis, Francisco
dc.subject.por.fl_str_mv Energy efficiency
Genetic algorithms (GAs)
Simplex method
Life Cycle Cost Analysis (LCCA)
Indirect Rebound Effect
topic Energy efficiency
Genetic algorithms (GAs)
Simplex method
Life Cycle Cost Analysis (LCCA)
Indirect Rebound Effect
description This paper presents a model to promote energy efficiency among household appliances, by supporting the consumer decisions through the maximization of his savings, associated to a set of electrical appliances from the market to be acquired. Not always an efficient equipment from the market, is more expensive than a less efficient one, which can lead the consumer to compromise the expected savings on future. Given the several models/brands available on market and its possible combinations, the problem can be defined as a combinatorial problem, whose complexity can compromise the efficiency of using deterministic algorithms. Genetic algorithms (GM) were therefore included in the model, whose results were compared later with Simplex to verify the quality of the obtained solutions, as well as their performance. In addition, it was performed a statistical analysis of the obtained results, as well as a sensitivity analysis of GAs parameters, to validate their robustness. we conclude that the proposed method can provide several efficient solutions to the problem, as well as sensitize the consumer to their choices made on future, by estimating their corresponding rebound effect.
publishDate 2018
dc.date.none.fl_str_mv 2018-12
2018-12-01T00:00:00Z
2019-01-18T10:34:45Z
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/10400.21/9345
url http://hdl.handle.net/10400.21/9345
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv SANTOS, Ricardo; [et al] – Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect. Computers and Industrial Engineering. ISSN 0360-8352. Vol. 126 (2018), pp. 729-755
0360-8352
10.1016/j.cie.2018.09.050
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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)
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