Artificial Intelligence-Based Models for Estimating and Extrapolating Soiling Effects on Photovoltaic Systems in Spain

Environmental and temporal conditions, particularly dust accumulation, can significantly impact the performance of photovoltaic solar panels, potentially reducing their efficiency by up to 20%, and thereby affecting profitability. Accurately estimating these losses is crucial for optimising maintenance and avoiding unforeseen losses. Various models have been proposed in the literature for this purpose. In this context, four machine learning models were developed using meteorological and air quality data from the Solar Energy Research Center (CIESOL). A Gradient-Boosting model (LightGBM) and a neural network achieved RMSE values of 0.68% and 0.88% of soiling loss, and R2 values of 0.86 and 0.76 between measured and estimated values, respectively, on their test sets. The generalisation capability of these models was tested by extrapolating them to other regions in Spain. To enhance robustness across locations, a global artificial neural network (ANN) model was trained using combined data from two sites, achieving an RMSE of 1.02% when estimating soiling losses. This result highlights a significant improvement over models trained on a single location and tested elsewhere, demonstrating the global model’s stronger ability to generalise across different geographic settings.