  The individual causes for power loss of photovoltaic modules are investigated
for quite some time. Recently, it has been shown that the power loss of a
module is, for example, related to the fraction of inactive areas. While these
areas can be easily identified from electroluminescense (EL) images, this is
much harder for photoluminescence (PL) images. With this work, we close the gap
between power regression from EL and PL images. We apply a deep convolutional
neural network to predict the module power from PL images with a mean absolute
error (MAE) of 4.4% or 11.7WP. Furthermore, we depict that regression maps
computed from the embeddings of the trained network can be used to compute the
localized power loss. Finally, we show that these regression maps can be used
to identify inactive regions in PL images as well.
