Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach

Detalhes bibliográficos
Autor(a) principal: Montero-Sousa, Juan Aurelio
Data de Publicação: 2020
Outros Autores: Aláiz-Moretón, Héctor, Quintián, Héctor, González-Ayuso, Tomás, Novais, Paulo, Calvo-Rolle, José Luis
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/67990
Resumo: Energy storage is one of the challenges of the electric sector. There are several different technologies available for facing it, from the traditional ones to the most advanced. With the current trend, it is mandatory to develop new energy storage systems that allow optimal efficiency, something that does not happen with traditional ones. Another feature that new systems must meet is to envisage the behaviour of energy generation and consumption. With this aim, the present research deals the hydrogen consumption prediction of a fuel cell based system thanks a hybrid intelligent approach implementation. The work is based on a real testing plant. Two steps have been followed to create a hybrid model. First, the real dataset has been divided into groups whose elements have similar characteristics. The second step, carry out the regression using different techniques. Very satisfactory results have been achieved during the validation of the model.
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spelling Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approachANNBHLEnergy managementEnergy storageFuel cellSVMScience & TechnologyEnergy storage is one of the challenges of the electric sector. There are several different technologies available for facing it, from the traditional ones to the most advanced. With the current trend, it is mandatory to develop new energy storage systems that allow optimal efficiency, something that does not happen with traditional ones. Another feature that new systems must meet is to envisage the behaviour of energy generation and consumption. With this aim, the present research deals the hydrogen consumption prediction of a fuel cell based system thanks a hybrid intelligent approach implementation. The work is based on a real testing plant. Two steps have been followed to create a hybrid model. First, the real dataset has been divided into groups whose elements have similar characteristics. The second step, carry out the regression using different techniques. Very satisfactory results have been achieved during the validation of the model.- (undefined)Elsevier LtdUniversidade do MinhoMontero-Sousa, Juan AurelioAláiz-Moretón, HéctorQuintián, HéctorGonzález-Ayuso, TomásNovais, PauloCalvo-Rolle, José Luis20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/67990eng0360-544210.1016/j.energy.2020.117986https://www.sciencedirect.com/science/article/pii/S0360544220310938info: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:RCAAP2023-07-21T12:30:49Zoai:repositorium.sdum.uminho.pt:1822/67990Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:26:04.579759Repositório 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 Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
title Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
spellingShingle Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
Montero-Sousa, Juan Aurelio
ANN
BHL
Energy management
Energy storage
Fuel cell
SVM
Science & Technology
title_short Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
title_full Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
title_fullStr Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
title_full_unstemmed Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
title_sort Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach
author Montero-Sousa, Juan Aurelio
author_facet Montero-Sousa, Juan Aurelio
Aláiz-Moretón, Héctor
Quintián, Héctor
González-Ayuso, Tomás
Novais, Paulo
Calvo-Rolle, José Luis
author_role author
author2 Aláiz-Moretón, Héctor
Quintián, Héctor
González-Ayuso, Tomás
Novais, Paulo
Calvo-Rolle, José Luis
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Montero-Sousa, Juan Aurelio
Aláiz-Moretón, Héctor
Quintián, Héctor
González-Ayuso, Tomás
Novais, Paulo
Calvo-Rolle, José Luis
dc.subject.por.fl_str_mv ANN
BHL
Energy management
Energy storage
Fuel cell
SVM
Science & Technology
topic ANN
BHL
Energy management
Energy storage
Fuel cell
SVM
Science & Technology
description Energy storage is one of the challenges of the electric sector. There are several different technologies available for facing it, from the traditional ones to the most advanced. With the current trend, it is mandatory to develop new energy storage systems that allow optimal efficiency, something that does not happen with traditional ones. Another feature that new systems must meet is to envisage the behaviour of energy generation and consumption. With this aim, the present research deals the hydrogen consumption prediction of a fuel cell based system thanks a hybrid intelligent approach implementation. The work is based on a real testing plant. Two steps have been followed to create a hybrid model. First, the real dataset has been divided into groups whose elements have similar characteristics. The second step, carry out the regression using different techniques. Very satisfactory results have been achieved during the validation of the model.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01T00:00:00Z
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/1822/67990
url http://hdl.handle.net/1822/67990
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0360-5442
10.1016/j.energy.2020.117986
https://www.sciencedirect.com/science/article/pii/S0360544220310938
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 Ltd
publisher.none.fl_str_mv Elsevier Ltd
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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