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<p>Accumulation of dust on solar panels lowers performance and limits energy production, particularly in dry locations. Dust accumulation on photovoltaic panels diminishes performance and reduces energy output, especially in arid regions. This study uses four identical modules in Roorkee, India, from October to December to examine the impact of cleaning frequency on photovoltaic (PV) performance. The reference panel is cleaned daily, while the remaining panels are cleaned weekly, biweekly, and monthly. Alongside short-circuit current measurements, environmental parameters including global horizontal irradiance, ambient temperature, wind speed, and relative humidity are continuously recorded. In this study, soiling loss (%) is examined as the primary performance indicator under various cleaning intervals to observe dust accumulation progression and its impact on the performance of the solar photovoltaic module. Experimental data are utilized to develop an empirical regression model that describes the trend of dust accumulation. The daily average soiling loss ranges between 0.17 and 0.21%. Furthermore, machine learning models, including Decision Tree, K-Nearest Neighbour, support vector regression, artificial neural network, and a stacking ensemble method, are developed for accurate prediction of soiling loss from environmental variables and cleaning frequency. The stacking model consistently achieves the best performance across all months, with root mean square error as low as 0.03–0.045, mean absolute error below 0.03, and R² = 0.999 compared to other models. Moreover, statistical analyses such as Bland–Altman plots and the Wilcoxon signed-rank test are employed to validate the significance and agreement of the predicted outcomes. The study highlights the benefits of data-driven solutions for predictive operation and maintenance of solar photovoltaic systems and provides valuable insights into the impact of cleaning frequency on reducing soiling losses.</p>

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Machine learning-based prediction of soiling losses in photovoltaic modules under different cleaning frequencies: an experimental investigation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Ashutosh Shukla,
  • Rupendra Kumar Pachauri,
  • Ranjan Walia,
  • Vinay Gupta

摘要

Accumulation of dust on solar panels lowers performance and limits energy production, particularly in dry locations. Dust accumulation on photovoltaic panels diminishes performance and reduces energy output, especially in arid regions. This study uses four identical modules in Roorkee, India, from October to December to examine the impact of cleaning frequency on photovoltaic (PV) performance. The reference panel is cleaned daily, while the remaining panels are cleaned weekly, biweekly, and monthly. Alongside short-circuit current measurements, environmental parameters including global horizontal irradiance, ambient temperature, wind speed, and relative humidity are continuously recorded. In this study, soiling loss (%) is examined as the primary performance indicator under various cleaning intervals to observe dust accumulation progression and its impact on the performance of the solar photovoltaic module. Experimental data are utilized to develop an empirical regression model that describes the trend of dust accumulation. The daily average soiling loss ranges between 0.17 and 0.21%. Furthermore, machine learning models, including Decision Tree, K-Nearest Neighbour, support vector regression, artificial neural network, and a stacking ensemble method, are developed for accurate prediction of soiling loss from environmental variables and cleaning frequency. The stacking model consistently achieves the best performance across all months, with root mean square error as low as 0.03–0.045, mean absolute error below 0.03, and R² = 0.999 compared to other models. Moreover, statistical analyses such as Bland–Altman plots and the Wilcoxon signed-rank test are employed to validate the significance and agreement of the predicted outcomes. The study highlights the benefits of data-driven solutions for predictive operation and maintenance of solar photovoltaic systems and provides valuable insights into the impact of cleaning frequency on reducing soiling losses.