Abstract
Soiling losses, caused by the accumulation of dust and particulate matter on PV module surfaces,
are a major environmental factor reducing solar power generation efficiency. In South Africa, this
issue is especially pronounced near unpaved gravel roads, where heavy vehicle traffic produces
high concentrations of airborne dust, leading to significant revenue losses. The key operational
challenge is determining an optimal, cost-effective cleaning schedule.
This study evaluated the impact of gravel road proximity on PV soiling at a utility-scale
installation in the North West Province. A quantitative, comparative design analysed four PV string
zones at controlled distances (11 m, 39 m, 67 m, and 200 m) over an extended period (March–May
2025). Data were collected via SCADA systems for energy yield, on-site weather stations for
environmental conditions, and security cameras for daily traffic intensity. These variables were
used to train and validate ML models (Random Forest, XGBoost, and an Ensemble) in R Studio
to predict soiling-related energy losses.
Results showed that PV strings closest to the gravel road experienced the highest cumulative
soiling losses, with degradation decreasing consistently with distance. The Ensemble, using the
results of both models, provided the most accurate predictions, capturing the complex relationship
between energy output, traffic, and distance from the dust source. This demonstrates a
reproducible, data-driven methodology that enables operators to adopt dynamic, predictive
cleaning strategies, focusing maintenance only on heavily soiled panels. Such an approach
optimises costs, conserves water, and maximises energy yield.
The study confirms the hypothesis that integrating dust source distance and traffic data with ML
predictions can guide more efficient cleaning schedules, improving operational management and
performance forecasting. The methodology is transferable to other PV installations in high-dust
environments, supporting improved energy forecasting and day-to-day operational planning.