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 indicates a malfunctioning
panel. The problem is 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 accurately detect malfunctioning panels. Moreover,
the proposed approach outperforms existing approaches based
on simple interpolation filters.