  Automated inspection plays an important role in monitoring large-scale
photovoltaic power plants. Commonly, electroluminescense measurements are used
to identify various types of defects on solar modules but have not been used to
determine the power of a module. However, knowledge of the power at maximum
power point is important as well, since drops in the power of a single module
can affect the performance of an entire string. By now, this is commonly
determined by measurements that require to discontact or even dismount the
module, rendering a regular inspection of individual modules infeasible. In
this work, we bridge the gap between electroluminescense measurements and the
power determination of a module. We compile a large dataset of 719
electroluminescense measurementsof modules at various stages of degradation,
especially cell cracks and fractures, and the corresponding power at maximum
power point. Here,we focus on inactive regions and cracks as the predominant
type of defect. We set up a baseline regression model to predict the power from
electroluminescense measurements with a mean absolute error of 9.0+/-3.7$W_P$
(4.0+/-8.4%). Then, we show that deep-learning can be used to train a model
that performs significantly better (7.3+/-2.7$W_P$ or 3.2+/-6.5%) and propose a
variant of class activation maps to obtain the per cell power loss, as
predicted by the model. With this work, we aim to open a new research topic.
Therefore, we publicly release the dataset, the code and trained models to
empower other researchers to compare against our results. Finally, we present a
thorough evaluation of certain boundary conditions like the dataset size and an
automated preprocessing pipeline for on-site measurements showing multiple
modules at once.
