2026-05-05T21:56:15Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/629702025-01-31T08:05:11Zcom_10324_1166com_10324_931com_10324_894col_10324_1338

Mateo Romero, Héctor Felipe Hernández Callejo, Luis González Rebollo, Miguel Ángel Cardeñoso Payo, Valentín Alonso Gómez, Víctor Morales Aragones, José Ignacio Moyo, Ranganai Tawanda 2023-11-15T08:41:49Z 2023-11-15T08:41:49Z 2023 Solar Energy, 2023, vol. 265, 112089 0038-092X https://uvadoc.uva.es/handle/10324/62970 10.1016/j.solener.2023.112089 112089 Solar Energy 265 Producción Científica 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. Ministerio de Ciencia e Innovación de España (PID2020-113533RB-C33) application/pdf eng Elsevier info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2023 The Authors Attribution-NonCommercial-NoDerivatives 4.0 Internacional Renewable and Green Energy PV systems Electroluminescence image Gradient Boosting Recurrent Neural Network Imagen de electroluminiscencia Aumento de gradiente Red neuronal recurrente 3308 Ingeniería y Tecnología del Medio Ambiente Optimized estimator of the output power of PV cells using EL images and I–V curves info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.sciencedirect.com/science/article/pii/S0038092X23007235?via%3Dihub SI