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<p>Dust accumulation on solar panels markedly diminishes their energy conversion efficiency, especially in dry and semi-arid regions such as Saudi Arabia. Conventional maintenance techniques, including manual cleaning and fixed-interval schedules, frequently exhibit inefficiency, high costs, and environmental unsustainability. This paper presents a machine learning approach for real-time dust level detection employing a Random Forest Classifier, trained on a synthetically created dataset that simulates various climatic and operational situations in Dammam, Saudi Arabia. A DJI Matrice 300 RTK drone was utilized to implement this model in practical situations, facilitating airborne, real-time observation of dust deposition on photovoltaic (PV) modules. This UAV-enabled system markedly improves scalability and diminishes the requirement for terrestrial labour. The technology utilizes the normalized voltage-to-irradiance ratio to categorize dust levels and activate automatic cleaning processes accordingly. The results indicate a classification accuracy of 91.3% and a robust AUC score of 0.93, along with significant restoration of power output following cleaning. Confusion matrices and ROC curves assess the system’s efficacy, while cost–benefit and sustainability analyses substantiate its practical feasibility. A dual-axis time-series graph depicting PV production in relation to PM10 dust concentration over time further demonstrates their inverse correlation. This research advances intelligent, resource-efficient solar panel maintenance technologies that optimize energy output and save operational expenses.</p>

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Drone-Based Random Forest Classifier for Intelligent Dust Monitoring on Solar PV Systems in Saudi Arabia

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Lyu Guanghua,
  • Syed Hadi Hussain Shah,
  • Ghulam E Mustafa Abro,
  • Sun Chang jiang,
  • Rizwan Arshad

摘要

Dust accumulation on solar panels markedly diminishes their energy conversion efficiency, especially in dry and semi-arid regions such as Saudi Arabia. Conventional maintenance techniques, including manual cleaning and fixed-interval schedules, frequently exhibit inefficiency, high costs, and environmental unsustainability. This paper presents a machine learning approach for real-time dust level detection employing a Random Forest Classifier, trained on a synthetically created dataset that simulates various climatic and operational situations in Dammam, Saudi Arabia. A DJI Matrice 300 RTK drone was utilized to implement this model in practical situations, facilitating airborne, real-time observation of dust deposition on photovoltaic (PV) modules. This UAV-enabled system markedly improves scalability and diminishes the requirement for terrestrial labour. The technology utilizes the normalized voltage-to-irradiance ratio to categorize dust levels and activate automatic cleaning processes accordingly. The results indicate a classification accuracy of 91.3% and a robust AUC score of 0.93, along with significant restoration of power output following cleaning. Confusion matrices and ROC curves assess the system’s efficacy, while cost–benefit and sustainability analyses substantiate its practical feasibility. A dual-axis time-series graph depicting PV production in relation to PM10 dust concentration over time further demonstrates their inverse correlation. This research advances intelligent, resource-efficient solar panel maintenance technologies that optimize energy output and save operational expenses.