Assessment of the robustness of photovoltaic panel cleaning schedules to forecast uncertainties under limited resource conditions

Abstract

This study evaluates the robustness of photovoltaic panel cleaning schedules to forecast uncertainty under conditions of limited water resources. The accumulation of dust, mineral particles, and atmospheric contaminants on photovoltaic surfaces reduces solar irradiance transmission and consequently decreases electricity generation efficiency. However, in water-stressed regions, frequent panel cleaning may require excessive water consumption and increase operational costs, making conventional maintenance schedules less sustainable. The research considers surface soiling dynamics, precipitation probability, water requirements, energy losses, and meteorological forecast errors as interconnected factors influencing cleaning decisions. Particular attention is given to the ability of alternative cleaning schedules to maintain acceptable performance when predicted environmental conditions differ from actual conditions. Robustness assessment provides an opportunity to identify cleaning intervals that balance water conservation with the prevention of excessive energy losses caused by soiling. The findings contribute to the development of adaptive and resource-efficient maintenance strategies for photovoltaic systems operating in arid and water-limited environments. The proposed approach can also support more reliable operational planning by incorporating uncertainty directly into the decision-making process rather than relying exclusively on deterministic weather forecasts.