Optimization assessment of the energy performance of a BIPV/T-PCM system using Genetic Algorithms
Autor(a) principal: | |
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Data de Publicação: | 2019 |
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/10400.9/3126 |
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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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) |
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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 |
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1799130228139753472 |