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Deep learning-based forecasting of aggregated CSP production

Author

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  • Segarra-Tamarit, Jorge
  • Pérez, Emilio
  • Moya, Eric
  • Ayuso, Pablo
  • Beltran, Hector

Abstract

This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance values and available weather conditions forecasts for the locations where the main CSP power plants are installed. The performances of the forecast models are analysed and compared by means of the most extended metrics in the literature for a whole year of CSP energy production.

Suggested Citation

  • Segarra-Tamarit, Jorge & Pérez, Emilio & Moya, Eric & Ayuso, Pablo & Beltran, Hector, 2021. "Deep learning-based forecasting of aggregated CSP production," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 306-318.
    • Handle: RePEc:eee:matcom:v:184:y:2021:i:c:p:306-318
    DOI: 10.1016/j.matcom.2020.02.007
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    Cited by:

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    2. Arias, I. & Cardemil, J. & Zarza, E. & Valenzuela, L. & Escobar, R., 2022. "Latest developments, assessments and research trends for next generation of concentrated solar power plants using liquid heat transfer fluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    3. Ouyang, Tiancheng & Pan, Mingming & Huang, Youbin & Tan, Xianlin & Qin, Peijia, 2023. "Thermodynamic design and power prediction of a solar power tower integrated system using neural networks," Energy, Elsevier, vol. 278(PA).
    4. 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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