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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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    as
    1. de Souza Dutra, Michael David & Anjos, Miguel F. & Le Digabel, Sébastien, 2019. "A general framework for customized transition to smart homes," Energy, Elsevier, vol. 189(C).
    2. Yang, Chi-Jen & Jackson, Robert B., 2011. "Opportunities and barriers to pumped-hydro energy storage in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 839-844, January.
    3. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    4. Nizami, M.S.H. & Haque, A.N.M.M. & Nguyen, P.H. & Hossain, M.J., 2019. "On the application of Home Energy Management Systems for power grid support," Energy, Elsevier, vol. 188(C).
    5. Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2019. "Time frequency relationship between energy consumption, economic growth and environmental degradation in the United States: Evidence from transportation sector," Energy, Elsevier, vol. 173(C), pages 706-720.
    6. Hache, Emmanuel & Palle, Angélique, 2019. "Renewable energy source integration into power networks, research trends and policy implications: A bibliometric and research actors survey analysis," Energy Policy, Elsevier, vol. 124(C), pages 23-35.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Rafael Vega Vega & Héctor Quintián & Carlos Cambra & Nuño Basurto & Álvaro Herrero & José Luis Calvo-Rolle, 2019. "Delving into Android Malware Families with a Novel Neural Projection Method," Complexity, Hindawi, vol. 2019, pages 1-10, June.
    9. Carrosio, Giovanni & Scotti, Ivano, 2019. "The ‘patchy’ spread of renewables: A socio-territorial perspective on the energy transition process," Energy Policy, Elsevier, vol. 129(C), pages 684-692.
    10. Hall, Peter J. & Bain, Euan J., 2008. "Energy-storage technologies and electricity generation," Energy Policy, Elsevier, vol. 36(12), pages 4352-4355, December.
    11. Franses, Philip Hans, 2016. "A note on the Mean Absolute Scaled Error," International Journal of Forecasting, Elsevier, vol. 32(1), pages 20-22.
    12. Good, Nicholas & Martínez Ceseña, Eduardo A. & Heltorp, Christopher & Mancarella, Pierluigi, 2019. "A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems," Energy, Elsevier, vol. 184(C), pages 165-179.
    13. Chen, Shaoqing & Zhu, Feiyao & Long, Huihui & Yang, Jin, 2019. "Energy footprint controlled by urban demands: How much does supply chain complexity contribute?," Energy, Elsevier, vol. 183(C), pages 561-572.
    14. Hector Alaiz-Moreton & Jose Aveleira-Mata & Jorge Ondicol-Garcia & Angel Luis Muñoz-Castañeda & Isaías García & Carmen Benavides, 2019. "Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol," Complexity, Hindawi, vol. 2019, pages 1-11, April.
    Full references (including those not matched with items on IDEAS)

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    2. 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).
    3. 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.
    4. 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).
    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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