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<p>This paper presents an innovative AI-driven solution for the early detection of dust accumulation on solar energy modules, leveraging computer vision and machine learning techniques. The study identifies two significant gaps in existing literature: the impact of dataset quality on research outcomes and the predominance of binary classification models, which limit the analysis of dust levels on photovoltaic (PV) modules. To address these gaps, we propose a sophisticated system that utilizes a visual dataset of continuously monitored images captured by a Raspberry Pi camera, alongside a raw dataset from the inverter tracking real-time energy production metrics. Our model is trained using machine learning algorithms to optimize cleaning patterns dynamically, maximizing energy output while minimizing operational costs. The results indicate that the AI-powered system enhances PV performance and contributes to cost reductions, ultimately promoting sustainability in solar energy production. The cleaning classifier automates condition-based cleaning, reducing the need for routine inspections and preventing up to 30% energy loss due to dust accumulation. The system achieves a cleaning efficiency of 1.23, reflecting a 23% increase in energy production compared to traditional periodic cleaning methods, resulting in a cost saving of $2,023. Furthermore, the implementation of the WattsUp mobile application facilitates user interaction by allowing monitoring of solar panel status and maintenance instructions, enhancing user engagement with the system. The application design emphasizes user trust and participation, featuring onboarding elements that highlight the importance of solar energy management in combating climate change. This comprehensive approach not only demonstrates the feasibility of a dynamic cleaning model but also establishes a payback period of less than a year, underscoring the economic viability of the proposed system in real-world applications.</p>

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Early detection of dust accumulation on solar energy modules using computer vision and machine learning techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月12日

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  • Sara Hesham,
  • Mohamed Elgohary,
  • Mariam Massoud,
  • Nouran Adel,
  • Omar Elmahy,
  • Sameh Abdellatif

摘要

This paper presents an innovative AI-driven solution for the early detection of dust accumulation on solar energy modules, leveraging computer vision and machine learning techniques. The study identifies two significant gaps in existing literature: the impact of dataset quality on research outcomes and the predominance of binary classification models, which limit the analysis of dust levels on photovoltaic (PV) modules. To address these gaps, we propose a sophisticated system that utilizes a visual dataset of continuously monitored images captured by a Raspberry Pi camera, alongside a raw dataset from the inverter tracking real-time energy production metrics. Our model is trained using machine learning algorithms to optimize cleaning patterns dynamically, maximizing energy output while minimizing operational costs. The results indicate that the AI-powered system enhances PV performance and contributes to cost reductions, ultimately promoting sustainability in solar energy production. The cleaning classifier automates condition-based cleaning, reducing the need for routine inspections and preventing up to 30% energy loss due to dust accumulation. The system achieves a cleaning efficiency of 1.23, reflecting a 23% increase in energy production compared to traditional periodic cleaning methods, resulting in a cost saving of $2,023. Furthermore, the implementation of the WattsUp mobile application facilitates user interaction by allowing monitoring of solar panel status and maintenance instructions, enhancing user engagement with the system. The application design emphasizes user trust and participation, featuring onboarding elements that highlight the importance of solar energy management in combating climate change. This comprehensive approach not only demonstrates the feasibility of a dynamic cleaning model but also establishes a payback period of less than a year, underscoring the economic viability of the proposed system in real-world applications.