Evolutionary algorithms on reducing energy consumption in buildings: An approach to provide smart and efficiency choices, considering the rebound effect
Autor(a) principal: | |
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Data de Publicação: | 2018 |
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/10400.21/9345 |
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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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 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
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 instacron:RCAAP |
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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) |
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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1799133442898657280 |