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Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach

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  • Montero-Sousa, Juan Aurelio
  • Aláiz-Moretón, Héctor
  • Quintián, Héctor
  • González-Ayuso, Tomás
  • Novais, Paulo
  • Calvo-Rolle, José Luis

Abstract

Energy storage is one of the challenges of the electric sector. There are several different technologies available for facing it, from the traditional ones to the most advanced. With the current trend, it is mandatory to develop new energy storage systems that allow optimal efficiency, something that does not happen with traditional ones. Another feature that new systems must meet is to envisage the behaviour of energy generation and consumption. With this aim, the present research deals the hydrogen consumption prediction of a fuel cell based system thanks a hybrid intelligent approach implementation. The work is based on a real testing plant. Two steps have been followed to create a hybrid model. First, the real dataset has been divided into groups whose elements have similar characteristics. The second step, carry out the regression using different techniques. Very satisfactory results have been achieved during the validation of the model.

Suggested Citation

  • Montero-Sousa, Juan Aurelio & Aláiz-Moretón, Héctor & Quintián, Héctor & González-Ayuso, Tomás & Novais, Paulo & Calvo-Rolle, José Luis, 2020. "Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220310938
    DOI: 10.1016/j.energy.2020.117986
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    Cited by:

    1. Chen, Ben & Zhou, Haoran & He, Shaowen & Meng, Kai & Liu, Yang & Cai, Yonghua, 2021. "Numerical simulation on purge strategy of proton exchange membrane fuel cell with dead-ended anode," Energy, Elsevier, vol. 234(C).
    2. Vichos, Emmanouil & Sifakis, Nikolaos & Tsoutsos, Theocharis, 2022. "Challenges of integrating hydrogen energy storage systems into nearly zero-energy ports," Energy, Elsevier, vol. 241(C).
    3. Li, Da & Zhang, Zhaosheng & Zhou, Litao & Liu, Peng & Wang, Zhenpo & Deng, Junjun, 2022. "Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction," Applied Energy, Elsevier, vol. 325(C).
    4. Han, Yuan & Zhang, Houcheng, 2022. "Potentiality of elastocaloric cooling system for high-temperature proton exchange membrane fuel cell waste heat harvesting," Renewable Energy, Elsevier, vol. 200(C), pages 1166-1179.
    5. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

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