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Evaluation of Empirical Daily Solar Radiation Models for the Northeast Coast of the Iberian Peninsula

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  • Anton Vernet

    (Department of Mechanical Engineering, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain)

  • Alexandre Fabregat

    (Department of Mechanical Engineering, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain)

Abstract

The ability to accurately predict daily solar radiation reaching the earth’s surface is essential in applications such as solar power generation. Given their ease of use, many empirical models have been proposed based on different dependent variables such as cloud cover, daily temperature range, etc. In this study we evaluate 23 of these models for the prediction of daily solar radiation in the northern coastal zone of the Iberian Peninsula. Daily measurements during the period 2000–2018 from 16 meteorological stations spread over this area are used to adjust the parameters of each model, whose predictive capacity is then evaluated using measurements made between 2019 and 2022. Using different statistical metrics to assess their predictive performance, it was found that models based on hours of sunshine and level of cloudiness are significantly more accurate than those based on maximum and minimum daily temperature and day of the year. Specifically, the sunshine-based model by SBM3 obtained the highest Global Performance Indicator at 5.05. The results offer insight on the ability of each type of empirical model to accurately predict daily solar radiation in the Mediterranean region.

Suggested Citation

  • Anton Vernet & Alexandre Fabregat, 2023. "Evaluation of Empirical Daily Solar Radiation Models for the Northeast Coast of the Iberian Peninsula," Energies, MDPI, vol. 16(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2560-:d:1091476
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    References listed on IDEAS

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    Cited by:

    1. Chih-Chiang Wei & Yen-Chen Yang, 2023. "A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models," Energies, MDPI, vol. 16(23), pages 1-18, November.
    2. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2023. "Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods," Energies, MDPI, vol. 16(17), pages 1-30, August.

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