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Machine Learning-Based Optimization of Photovoltaic Panel Cleaning Schedules in Arid Environments: A Case Study of Ryadh, Saudi Arabia

Authors

  • Lyu Guanghua

    Power China Huadong Engineering Corporation Limited, China
    Author

  • Syed Hadi Hussain Shah

    Power China Huadong Engineering Corporation Limited, China
    Author

  • Rizwan Arshad

    Power China Huadong Engineering Corporation Limited, China
    Author

  • Ni Jiahua

    Power China Huadong Engineering Corporation Limited, China
    Author

DOI:

https://doi.org/10.22581/0395

Keywords:

Photovoltaic (PV) Systems, Machine Learning (ML) Models, Dust Accumulation, XGBoost, Dynamic Cleaning Schedules

Abstract

Photovoltaic solar power systems in arid regions offer tremendous energy potential due to high solar irradiance. This study proposes a machine learning framework for predicting the effects of pollution on photovoltaic panels. Environmental data were collected from NASA, the Solar Photovoltaic Information System (PVGIS), and local air quality stations. The XGBoost, Random Forest, and LSTM machine learning models were used in the analysis. The results show that the XGBoost model outperformed the other models with a coefficient of determination (R²) of 0.93, thanks to its ability to accurately predict energy loss due to pollution. The cost-benefit analysis demonstrated that biweekly cleaning maximizes energy recovery and reduces operating costs by up to 15% compared to fixed schedules. The proposed framework integrates real-time dust prediction and dynamic scheduling, providing a scalable solution for solar farms in arid environments. This approach improves the efficiency of photovoltaic solar energy systems, reduces water and labor waste, and supports the deployment of sustainable solar energy in dust-prone areas.

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Author Biographies

  • Lyu Guanghua, Power China Huadong Engineering Corporation Limited, China

    General Manager Power china Huadong Engineering corporation Middle east, IEEE senior Member

  • Syed Hadi Hussain Shah, Power China Huadong Engineering Corporation Limited, China

    Research Scientist at Al-henakiyah 1100 MW PV project KSA

  • Rizwan Arshad, Power China Huadong Engineering Corporation Limited, China

    Site Manager At al-henakiyah 1100 MW Project

  • Ni Jiahua, Power China Huadong Engineering Corporation Limited, China

    Research Scientist at power china

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Published

2026-04-01

Issue

Vol. 45 No. 2 (2026): April Issue

Section

Articles

License

Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Machine Learning-Based Optimization of Photovoltaic Panel Cleaning Schedules in Arid Environments: A Case Study of Ryadh, Saudi Arabia. (2026). Mehran University Research Journal of Engineering and Technology, 45(2), 08-18. https://doi.org/10.22581/0395

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