Module-Power Prediction from PL Measurements using Deep Learning
This work addresses a domain-specific challenge in solar energy by enabling power loss analysis from PL images, which is incremental as it extends existing EL-based methods to PL.
The paper tackles the problem of predicting photovoltaic module power from photoluminescence (PL) images, which is harder than from electroluminescence (EL) images, achieving a mean absolute error of 4.4% or 11.7WP using a deep convolutional neural network.
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.