Solar farms are subject to various environmental factors that can affect their performance and output. One of the major factors is soiling. Consequently, it is essential to monitor and mitigate the effects of soiling on solar panels to ensure optimal performance and maximum profitability of a solar farm. In this paper, we present a novel cleaning intervention strategy that adapts to the present solar panel performance status to detect cleaning requirements for maximizing profit. This algorithm works with onsite insolation and panel data to give live feedback on cleaning requirements. Moreover, unlike other models in the literature, this method does not require historical or a priori insolation, weather, or soiling data. We have comprehensively tested the algorithm through experiment and simulation under various conditions (i.e., seasonal rain, few annual rainfall or sandstorms, unpredictable dust accumulation). The results show that the adaptive cleaning algorithm, even with no historical insolation or soiling information, outperforms optimal periodic cleaning strategies in terms of net revenue. The adaptive cleaning suggestion system shows significant advantages especially in locations with high variability in weather and soiling conditions. The algorithm is also suitable for practical deployment in solar farms near industrial or construction zones where sudden dust accumulation can occur. Our adaptive model will maximize profit in such unpredictable soiling scenarios, while in comparison, it is not even possible to utilize historical information-based cleaning cycle model in such cases.
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