@inproceedings{AlRiyalatdeLeeuwDennenmoseretal.2023,
  author    = {Yaman Al-Riyalat and Jan de Leeuw and Martin Dennenmoser and Kai Saegebarth and Michael Schmidt},
  title     = {Evaluation and Prediction of Soiling on PV Sites and Adaptation of Cleaning Strategies},
  series = {EU PVSEC Proceedings},
  publisher = {WIP Renewables GmbH},
  address   = {M{\"u}nchen},
  isbn      = {3-936338-88-4},
  issn      = {2196-100X},
  doi       = {10.4229/EUPVSEC2023/3AV.3.38},
  pages     = {020233-001 -- 020233-005},
  year      = {2023},
  abstract  = {Soiling is an important issue in the renewable energy sector since it can result in significant yield losses, especially in regions with higher pollution or dust levels. To mitigate the impact of soiling on photovoltaic (PV) plants, it is essential to regularly monitor and clean the panels, as well as develop accurate soiling predictions that can affect cleaning strategies and enhance the overall performance of PV power plants. This research focuses on the problem of soiling loss in photovoltaic power plants and the potential to improve the accuracy of soiling predictions. The study examines how soiling can affect the efficiency and productivity of the modules and how to measure and predict soiling using machine learning (ML) algorithms. The research includes analyzing real data from large-scale ground-mounted PV sites and comparing different soiling measurement methods. It was observed that there were some deviations in the real soiling loss values compared to the expected values for some projects in southern Spain, thus, the main goal of this work is to develop machine learning models that could predict the soiling more accurately. The developed models have a low mean square error (MSE), indicating the accuracy and suitability of the models to predict the soiling rates. The study also investigates the impact of different cleaning strategies on the performance of PV power plants and provides a powerful application to predict both the soiling and the number of cleaning cycles.},
  language  = {en}
}