Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms

Detalhes bibliográficos
Autor(a) principal: Pereira, R.
Data de Publicação: 2019
Outros Autores: Aelenei, Laura Elena
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.9/3126
Resumo: ABSTRACT: In this paper, a BIPV/T-PCM installed in an office building façade is investigated to approach the system efficiency optimization using Genetic Algorithm method. Based on an updated mathematical model, theoretical simulation has been conducted for BIPV/T-PCM in this case for the existing system set-up (geometry-air cavity width, ventilation, system layers). Furthermore, field testing for this case has also been performed to validate the model, and then the simulated and experimental results are compared and found in considerably good agreement. The overall energy efficiency of the system was evaluated for winter and summer condition adopting different utilization strategies and optimization variables have been identified. The thermal and electric efficiencies were calculated based on the optimization variables and the results shown that the system can achieve a maximum overall efficiency of 64% with winter configuration and 32% with summer configuration.
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spelling Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic AlgorithmsZero Energy BuildingsBuilding-integrated PhotovoltaicEnergy storagePhase change materialsThermal modelingGenetic AlgorithmsABSTRACT: In this paper, a BIPV/T-PCM installed in an office building façade is investigated to approach the system efficiency optimization using Genetic Algorithm method. Based on an updated mathematical model, theoretical simulation has been conducted for BIPV/T-PCM in this case for the existing system set-up (geometry-air cavity width, ventilation, system layers). Furthermore, field testing for this case has also been performed to validate the model, and then the simulated and experimental results are compared and found in considerably good agreement. The overall energy efficiency of the system was evaluated for winter and summer condition adopting different utilization strategies and optimization variables have been identified. The thermal and electric efficiencies were calculated based on the optimization variables and the results shown that the system can achieve a maximum overall efficiency of 64% with winter configuration and 32% with summer configuration.ElsevierRepositório do LNEGPereira, R.Aelenei, Laura Elena2019-02-06T11:50:42Z2019-01-01T00:00:00Z2019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.9/3126engPereira, R.; Aelenei, L. - Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms. In: Renewable Energy, 2019, Vol. 137, p. 157-1660960-1481info: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:RCAAP2022-09-06T12:28:31Zoai:repositorio.lneg.pt:10400.9/3126Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:36:17.069557Repositório 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 Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
title Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
spellingShingle Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
Pereira, R.
Zero Energy Buildings
Building-integrated Photovoltaic
Energy storage
Phase change materials
Thermal modeling
Genetic Algorithms
title_short Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
title_full Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
title_fullStr Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
title_full_unstemmed Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
title_sort Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
author Pereira, R.
author_facet Pereira, R.
Aelenei, Laura Elena
author_role author
author2 Aelenei, Laura Elena
author2_role author
dc.contributor.none.fl_str_mv Repositório do LNEG
dc.contributor.author.fl_str_mv Pereira, R.
Aelenei, Laura Elena
dc.subject.por.fl_str_mv Zero Energy Buildings
Building-integrated Photovoltaic
Energy storage
Phase change materials
Thermal modeling
Genetic Algorithms
topic Zero Energy Buildings
Building-integrated Photovoltaic
Energy storage
Phase change materials
Thermal modeling
Genetic Algorithms
description ABSTRACT: In this paper, a BIPV/T-PCM installed in an office building façade is investigated to approach the system efficiency optimization using Genetic Algorithm method. Based on an updated mathematical model, theoretical simulation has been conducted for BIPV/T-PCM in this case for the existing system set-up (geometry-air cavity width, ventilation, system layers). Furthermore, field testing for this case has also been performed to validate the model, and then the simulated and experimental results are compared and found in considerably good agreement. The overall energy efficiency of the system was evaluated for winter and summer condition adopting different utilization strategies and optimization variables have been identified. The thermal and electric efficiencies were calculated based on the optimization variables and the results shown that the system can achieve a maximum overall efficiency of 64% with winter configuration and 32% with summer configuration.
publishDate 2019
dc.date.none.fl_str_mv 2019-02-06T11:50:42Z
2019-01-01T00:00:00Z
2019-01-01T00:00:00Z
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/10400.9/3126
url http://hdl.handle.net/10400.9/3126
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Pereira, R.; Aelenei, L. - Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms. In: Renewable Energy, 2019, Vol. 137, p. 157-166
0960-1481
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
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
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