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

Author

Listed:
  • José-Luis Casteleiro-Roca

    (Department of Industrial Engineering, University of A Coruña, CTC, CITIC, 15405 Ferrol, Spain
    These authors contributed equally to this work.)

  • Francisco José Vivas

    (Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain
    These authors contributed equally to this work.)

  • Francisca Segura

    (Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain
    These authors contributed equally to this work.)

  • Antonio Javier Barragán

    (Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain
    These authors contributed equally to this work.)

  • Jose Luis Calvo-Rolle

    (Department of Industrial Engineering, University of A Coruña, CTC, CITIC, 15405 Ferrol, Spain
    These authors contributed equally to this work.)

  • José Manuel Andújar

    (Department of Electronic Engineering, Computer Systems and Automatic, Campus de El Carmen, University of Huelva, 21071 Huelva, Spain
    These authors contributed equally to this work.)

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.

Suggested Citation

  • José-Luis Casteleiro-Roca & Francisco José Vivas & Francisca Segura & Antonio Javier Barragán & Jose Luis Calvo-Rolle & José Manuel Andújar, 2020. "Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10566-:d:463830
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    References listed on IDEAS

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