# Module-Power Prediction from PL Measurements using Deep Learning

arXiv: https://arxiv.org/abs/2108.13640 · HTML: https://mlsanity.com/paper/2108.13640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images](https://mlsanity.com/paper/1807.02894/) (2018)
- [Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm](https://mlsanity.com/paper/2001.11248/) (2020)
- [Deep Learning-based Pipeline for Module Power Prediction from EL Measurements](https://mlsanity.com/paper/2009.14712/) (2020)

## Abstract (arXiv)

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.