Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system
Autor(a) principal: | |
---|---|
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/1822/62343 |
Resumo: | The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>3.73</mn> </mrow> </semantics> </math> </inline-formula> with the validation dataset. |
id |
RCAP_13532eb340d6be1a511d917ce26303af |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/62343 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage systemfuel cellhydrogen energyintelligent systemshybrid systemsArtificial Neural Networkspower managementScience & TechnologyThe present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>3.73</mn> </mrow> </semantics> </math> </inline-formula> with the validation dataset.This work has been funded by Consejería de Educación (Junta de Castilla y León) through the LE078G18 project (UXXI2018/000149. U-220).Multidisciplinary Digital Publishing InstituteUniversidade do MinhoAlaiz-Moretón, HéctorJove, EstebanCasteleiro-Roca, José-LuisQuintián, HéctorLópez García, HilarioBenítez-Andrades, José AlbertoNovais, PauloCalvo-Rolle, Jose Luis2019-11-072019-11-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/62343engAlaiz-Moretón, H.; Jove, E.; Casteleiro-Roca, J.-L.; Quintián, H.; López García, H.; Benítez-Andrades, J.A.; Novais, P.; Calvo-Rolle, J.L. Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System. Processes 2019, 7, 825.2227-971710.3390/pr7110825https://www.mdpi.com/2227-9717/7/11/825info: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:RCAAP2023-07-21T12:31:54Zoai:repositorium.sdum.uminho.pt:1822/62343Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:27:12.503134Repositório 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 |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
title |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
spellingShingle |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system Alaiz-Moretón, Héctor fuel cell hydrogen energy intelligent systems hybrid systems Artificial Neural Networks power management Science & Technology |
title_short |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
title_full |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
title_fullStr |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
title_full_unstemmed |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
title_sort |
Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
author |
Alaiz-Moretón, Héctor |
author_facet |
Alaiz-Moretón, Héctor Jove, Esteban Casteleiro-Roca, José-Luis Quintián, Héctor López García, Hilario Benítez-Andrades, José Alberto Novais, Paulo Calvo-Rolle, Jose Luis |
author_role |
author |
author2 |
Jove, Esteban Casteleiro-Roca, José-Luis Quintián, Héctor López García, Hilario Benítez-Andrades, José Alberto Novais, Paulo Calvo-Rolle, Jose Luis |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Alaiz-Moretón, Héctor Jove, Esteban Casteleiro-Roca, José-Luis Quintián, Héctor López García, Hilario Benítez-Andrades, José Alberto Novais, Paulo Calvo-Rolle, Jose Luis |
dc.subject.por.fl_str_mv |
fuel cell hydrogen energy intelligent systems hybrid systems Artificial Neural Networks power management Science & Technology |
topic |
fuel cell hydrogen energy intelligent systems hybrid systems Artificial Neural Networks power management Science & Technology |
description |
The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>3.73</mn> </mrow> </semantics> </math> </inline-formula> with the validation dataset. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-11-07 2019-11-07T00:00:00Z |
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/1822/62343 |
url |
http://hdl.handle.net/1822/62343 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Alaiz-Moretón, H.; Jove, E.; Casteleiro-Roca, J.-L.; Quintián, H.; López García, H.; Benítez-Andrades, J.A.; Novais, P.; Calvo-Rolle, J.L. Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System. Processes 2019, 7, 825. 2227-9717 10.3390/pr7110825 https://www.mdpi.com/2227-9717/7/11/825 |
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 |
Multidisciplinary Digital Publishing Institute |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
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_ |
1799132762460913664 |