Next Article in Journal
Drug Leaching Properties of Vancomycin Loaded Mesoporous Hydroxyapatite as Bone Substitutes
Previous Article in Journal
Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform
Previous Article in Special Issue
A Review of Computational Methods for Clustering Genes with Similar Biological Functions
Open AccessFeature PaperArticle

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

1
Department of Electrical and Systems Engineering, University of León, 24071 León, Spain
2
Department of Industrial Engineering, University of A Coruña, 15405 Ferrol, Spain
3
Department of Electrical, Electronic, Computers and Systems Engineering, University of Oviedo, 33204 Gijón, Spain
4
Department of Informatics/Algoritmi Center, University of Minho, 4710-057 Braga, Portugal
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Processes 2019, 7(11), 825; https://doi.org/10.3390/pr7110825
Received: 16 October 2019 / Revised: 1 November 2019 / Accepted: 5 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Bioinformatics Applications Based On Machine Learning)
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. View Full-Text
Keywords: fuel cell; hydrogen energy; intelligent systems; hybrid systems; Artificial Neural Networks; power management fuel cell; hydrogen energy; intelligent systems; hybrid systems; Artificial Neural Networks; power management
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop