Optimized estimator of the output power of PV cells using EL images and I–V curves

Abstract

In this work, a method to predict the output power of Photovoltaic (PV) cells using their Electroluminescence (EL) images is presented. The data used includes Electroluminescence Images and the value of the Max Power Point computed from the Current–Voltage Curve of the cells. The method is used as follows: Firstly, the images are preprocessed to improve their quality. After that, a comparison between different Machine Learning methods from Traditional ones, such as Random Forest or Gradient Boosting, to Deep Learning methods, such as Recurrent Neural Networks or Convolutional Neural Networks is performed. Another significant contribution of this paper is that it analyzes the problem of unbalanced data, trying to solve it using Synthetic Images created by a Generative Adversarial Network. Our results show that the best model is the Gradient-Boosting based method using a pre-trained Resnet50 as a feature extraction method with a Mean Absolute Error (MAE) of 0.0341 and a Mean Squared Error (MSE) of 0.00211. The results also shows how the models trained with the unbalanced dataset are capable of obtaining results similar to the models trained with the balanced dataset.