Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage system
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http://hdl.handle.net/2183/24449
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Bioinspired hybrid model to predict the hydrogen inlet fuel cell flow change of an energy storage systemAutor(es)
Data
2019-11-07Cita bibliográfica
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
[Abstract]: 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 3.73 with the validation dataset.
Palabras chave
Fuel Cell
Hydrogen energy
Intelligent systems
Hybrid systems
Artificial neural networks
Power management
Pila de combustible
Energía del hidrógeno
Sistemas inteligentes
Sistemas híbridos
Redes neuronales artificiales
Gestión de potencia
Hydrogen energy
Intelligent systems
Hybrid systems
Artificial neural networks
Power management
Pila de combustible
Energía del hidrógeno
Sistemas inteligentes
Sistemas híbridos
Redes neuronales artificiales
Gestión de potencia
Versión do editor
Dereitos
Creative Commons Attribution (CC BY 4.0)
ISSN
2227-9717