Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/62343
Título: | Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system |
Autor(es): | Alaiz-Moretón, Héctor Jove, Esteban Casteleiro-Roca, José-Luis Quintián, Héctor López García, Hilario Benítez-Andrades, José Alberto Novais, Paulo Calvo-Rolle, Jose Luis |
Palavras-chave: | fuel cell hydrogen energy intelligent systems hybrid systems Artificial Neural Networks power management |
Data: | 7-Nov-2019 |
Editora: | Multidisciplinary Digital Publishing Institute |
Revista: | Processes |
Citação: | 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. |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/62343 |
DOI: | 10.3390/pr7110825 |
e-ISSN: | 2227-9717 |
Versão da editora: | https://www.mdpi.com/2227-9717/7/11/825 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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processes-07-00825.pdf | 659,86 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons