Data-Driven Soiling Detection in PV Modules

Soiling is the accumulation of dirt in solar panels that leads to a decreasing trend in solar energy yield and may be the cause of vast revenue losses. The effect of soiling can be reduced by washing the panels, which is, however, a procedure of non-negligible cost. Moreover, soiling monitoring systems are often unreliable or very costly. We study the problem of estimating the soiling ratio in photovoltaic (PV) modules, i.e., the ratio of the real power output to the power output that would be produced if solar panels were clean. A key advantage of our algorithms is that they estimate soiling, without needing to train on labeled data, i.e., periods of explicitly monitoring the soiling in each park, and without relying on generic analytical formulas that do not take into account the peculiarities of each installation. We consider as input a time series comprising a minimum set of measurements that are available to most PV park operators. Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.