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Using artificial intelligence for global solar radiation modeling from meteorological variables

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Listed:
  • Zaim, Salma
  • El Ibrahimi, Mohamed
  • Arbaoui, Asmae
  • Samaouali, Abderrahim
  • Tlemcani, Mouhaydine
  • Barhdadi, Abdelfettah

Abstract

Long-term quantification of solar energy variables at ground level is not easily achievable in many locations. In order to overcome this limitation, use of artificial intelligence such as the application of machine learning methods is commonly used for solar irradiance prediction.

Suggested Citation

  • Zaim, Salma & El Ibrahimi, Mohamed & Arbaoui, Asmae & Samaouali, Abderrahim & Tlemcani, Mouhaydine & Barhdadi, Abdelfettah, 2023. "Using artificial intelligence for global solar radiation modeling from meteorological variables," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008017
    DOI: 10.1016/j.renene.2023.118904
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
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