Photovoltaics (PV) are widely used to harvest solar energy, an important form of renewable energy. Photovoltaic arrays consist of multiple solar panels constructed from solar cells. Solar cells in the field are vulnerable to various defects, and electroluminescence (EL) imaging provides effective and non-destructive diagnostics to detect those defects. We use multiple traditional machine learning and modern deep learning models to classify EL solar cell images into different functional/defective categories. Because of the asymmetry in the number of functional vs. defective cells, an imbalanced label problem arises in the EL image data. The current literature lacks insights on which methods and metrics to use for model training and prediction. In this paper, we comprehensively compare different machine learning and deep learning methods under different performance metrics on the classification of solar cell EL images from monocrystalline and polycrystalline modules. We provide a comprehensive discussion on different metrics. Our results provide insights and guidelines for practitioners in selecting prediction methods and performance metrics.

本研究解决了当前文献中关于太阳能电池电致发光图像分类的模型训练和预测方法的不足，特别是在功能性与缺陷细胞数量不平衡的问题。通过比较多种传统和现代的机器学习及深度学习模型，并讨论不同的性能指标，我们为从业者选择合适的预测方法和性能评估提供了指导和见解。

机器学习方法在太阳能电池电致发光图像分类性能的综合案例研究