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Hybrid models for direct normal irradiance forecasting: a case study of Ghardaia zone (Algeria)

Author

Listed:
  • Boumediene Ladjal

    (Université de Ghardaia)

  • Imad Eddine Tibermacine

    (Sapienza University of Rome)

  • Mohcene Bechouat

    (Université de Ghardaia)

  • Moussa Sedraoui

    (University 8 Mai 1945 Guelma)

  • Christian Napoli

    (Sapienza University of Rome
    Italian National Research Council
    Czestochowa University of Technology)

  • Abdelaziz Rabehi

    (University of Djelfa)

  • Djemoui Lalmi

    (Université de Ghardaia)

Abstract

This study presents a resilient model for accurately predicting annual solar radiation in Ghardaia, Algeria, utilizing a locally-sourced database. The model integrates temperature, humidity, wind speed, and pressure as inputs. A combination of machine learning and deep learning techniques, including convolutional neural networks and conventional neural networks, are employed to forecast direct normal irradiance and diffuse solar radiation. This comprehensive approach uses multivariate regression analysis, validated with established databases for high-resolution analysis in data-scarce regions. The findings highlight the model’s effectiveness in providing precise forecasts and outline potential applications for optimizing solar energy use in similar climates.

Suggested Citation

  • Boumediene Ladjal & Imad Eddine Tibermacine & Mohcene Bechouat & Moussa Sedraoui & Christian Napoli & Abdelaziz Rabehi & Djemoui Lalmi, 2024. "Hybrid models for direct normal irradiance forecasting: a case study of Ghardaia zone (Algeria)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(15), pages 14703-14725, December.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:15:d:10.1007_s11069-024-06837-1
    DOI: 10.1007/s11069-024-06837-1
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    References listed on IDEAS

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