Data-driven soiling detection in PV modules

TL;DR

This paper introduces a data-driven method for estimating soiling in PV modules that does not require labeled data or generic formulas, improving accuracy over existing methods.

Contribution

The authors develop a novel, label-free algorithm for soiling estimation in PV modules that adapts to specific installations using minimal measurements.

Findings

Significantly outperforms current state-of-the-art methods.

Does not require labeled data or generic analytical formulas.

Uses minimal measurements available to PV operators.

Abstract

Soiling is the accumulation of dirt in solar panels which 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 photo-voltaic (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 labelled data, i.e., periods of explicitly monitoring the soiling in each park, and without relying on generic analytical formulas which 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.