Sistema híbrido para la predicción del funcionamiento de una celda de combustible basada en hidrógeno, empleada en el almacenamiento de energía

  1. Juan-Aurelio Montero-Sousa 1
  2. Esteban Jove Pérez 1
  3. José-Luis Casteleiro-Roca 1
  4. Héctor Quintián Pardo 1
  5. José Luis Calvo Rolle 1
  6. Héctor Alaiz Moretón 2
  7. Tomás González Ayuso 3
  1. 1 Universidade da Coruña

    Universidade da Coruña

    La Coruña, España


  2. 2 Universidad de León

    Universidad de León

    León, España


  3. 3 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas

    Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas

    Madrid, España


XL Jornadas de Automática: libro de actas. Ferrol, 4-6 de septiembre de 2019
  1. José Luis Calvo Rolle (coord.)
  2. José-Luis Casteleiro-Roca (coord.)
  3. Isabel Fernández-Ibáñez (coord.)
  4. Óscar Fontenla Romero (coord.)
  5. Esteban Jove Pérez (coord.)
  6. Alberto J. Leira-Rejas (coord.)
  7. José Antonio López Vázquez (coord.)
  8. Vanesa Loureiro-Vázquez (coord.)
  9. María-Carmen Meizoso-López (coord.)
  10. Francisco Javier Pérez Castelo (coord.)
  11. Andrés José Piñón Pazos (coord.)
  12. Héctor Quintián Pardo (coord.)
  13. Juan Manuel Rivas Rodríguez (coord.)
  14. Benigno Antonio Rodríguez Gómez (coord.)
  15. Rafael A. Vega-Vega (coord.)

Publisher: Servizo de Publicacións ; Universidad de La Coruña

ISBN: 978-84-9749-716-9

Year of publication: 2019

Pages: 200-210

Congress: Jornadas de Automática (40. 2019. Ferrol)

Type: Conference paper


Currently, largely due to the rise of the electric vehicle, energy storage systems are becoming agreater need, being both electric batteries and fuel cells, the two most developed technologies in recent years. However, it is not enough just to develop energy storage system, but it is essential to maximize the efficiency of them, in order to take the maximum advantage of the stored energy. To reach this goal, one of the most relevant aspects is to predict with enough accuracy and in advance both the generation and consumption of energy that will be made on the storage device. For this reason, the present research focuses on the development of a hybrid system for modeling a fuel cell using unsupervised learning techniques for clustering combined with regression techniques for modeling. Finally, the models generated on a real dataset, coming from an experimental real generation and storage system of energy by means of a hydrogen cell, are validated obtaining highly satisfactory results.