Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy

José-Luis Casteleiro-Roca, Francisco José Vivas, Francisca Segura, Antonio Javier Barragán, Jose Luis Calvo-Rolle and José Manuel Andújar
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José-Luis Casteleiro-Roca: Department of Industrial Engineering, University of A Coruña, CTC, CITIC, 15405 Ferrol, Spain
Francisco José Vivas: Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain
Francisca Segura: Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain
Antonio Javier Barragán: Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain
Jose Luis Calvo-Rolle: Department of Industrial Engineering, University of A Coruña, CTC, CITIC, 15405 Ferrol, Spain
José Manuel Andújar: Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain

Sustainability, 2020, vol. 12, issue 24, 1-18

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.

Keywords: clustering; prediction; regression; hydrogen-based systems; renewable sources-based microgrid; hybrid model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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