@article{10272/19476,
year = {2020},
month = {12},
url = {http://hdl.handle.net/10272/19476},
abstract = {This work deals with the prediction of variables for a hydrogen energy storage system
integrated into a microgrid. Due to the fact that this kind of system has a nonlinear behaviour,
the use of traditional techniques is not accurate enough to generate good models of the system under
study. Then, a hybrid intelligent system, based on clustering and regression techniques, has been
developed and implemented to predict the power, the hydrogen level and the hydrogen system
degradation. In this research, a hybrid intelligent model was created and validated over a dataset
from a lab-size migrogrid. The achieved results show a better performance than other well-known
classical regression methods, allowing us to predict the hydrogen consumption/generation with a
mean absolute error of 0.63% with the test dataset respect to the maximum power of the system.},
publisher = {MDPI},
title = {Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy},
doi = {10.3390/su122410566},
author = {Casteleiro Roca, José Luis and Vivas Fernández, Francisco José and Calvo Rolle, José Luis and Andújar Márquez, José Manuel},
}