Predictive Maintenance of Photovoltaic Panels via Deep Learning

We apply convolutional neural networks (CNN) for monitoring the operation of photovoltaic panels. In particular, we predict the daily electrical power curve of a photovoltaic panel based on the power curves of neighboring panels. An exceptionally large deviation between predicted and actual (observed) power curve can be used to indicate a malfunctioning panel. The problem is quite challenging because the power curve depends on many factors such as weather conditions and the surrounding objects (causing shadows with a regular time pattern). We demonstrate, by means of numerical experiments, that the proposed method is able to predict accurately the power curve of a functioning panel. Moreover, the proposed approach outperforms the existing approaches that are based on simple interpolation filters.