Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
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/10316/27849 https://doi.org/10.1016/j.enbuild.2014.06.009 |
Resumo: | Retrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs. |
id |
RCAP_ad68b45dd0043550b1fc8d3598d2e561 |
---|---|
oai_identifier_str |
oai:estudogeral.uc.pt:10316/27849 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an applicationBuilding retrofitMulti-objective optimizationGenetic algorithmArtificial neural networkEnergy efficiencyThermal comfortRetrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs.Elsevier2014-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/27849http://hdl.handle.net/10316/27849https://doi.org/10.1016/j.enbuild.2014.06.009engASADI, Ehsan [et. al] - Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application. "Energy and Buildings". ISSN 0378-7788. Vol. 81 (2014) p. 444–4560378-7788http://www.sciencedirect.com/science/article/pii/S0378778814004915Asadi, EhsanSilva, Manuel Gameiro daAntunes, Carlos HenggelerDias, LuísGlicksman, Leoninfo: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:RCAAP2021-04-21T16:19:03Zoai:estudogeral.uc.pt:10316/27849Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:53:45.115580Repositório 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-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application |
title |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application |
spellingShingle |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application Asadi, Ehsan Building retrofit Multi-objective optimization Genetic algorithm Artificial neural network Energy efficiency Thermal comfort |
title_short |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application |
title_full |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application |
title_fullStr |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application |
title_full_unstemmed |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application |
title_sort |
Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application |
author |
Asadi, Ehsan |
author_facet |
Asadi, Ehsan Silva, Manuel Gameiro da Antunes, Carlos Henggeler Dias, Luís Glicksman, Leon |
author_role |
author |
author2 |
Silva, Manuel Gameiro da Antunes, Carlos Henggeler Dias, Luís Glicksman, Leon |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Asadi, Ehsan Silva, Manuel Gameiro da Antunes, Carlos Henggeler Dias, Luís Glicksman, Leon |
dc.subject.por.fl_str_mv |
Building retrofit Multi-objective optimization Genetic algorithm Artificial neural network Energy efficiency Thermal comfort |
topic |
Building retrofit Multi-objective optimization Genetic algorithm Artificial neural network Energy efficiency Thermal comfort |
description |
Retrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-10 |
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/10316/27849 http://hdl.handle.net/10316/27849 https://doi.org/10.1016/j.enbuild.2014.06.009 |
url |
http://hdl.handle.net/10316/27849 https://doi.org/10.1016/j.enbuild.2014.06.009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ASADI, Ehsan [et. al] - Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application. "Energy and Buildings". ISSN 0378-7788. Vol. 81 (2014) p. 444–456 0378-7788 http://www.sciencedirect.com/science/article/pii/S0378778814004915 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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 |
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 |
|
_version_ |
1799133823782354944 |