RT info:eu-repo/semantics/article
T1 Optimized estimator of the output power of PV cells using EL images and I–V curves
A1 Mateo Romero, Héctor Felipe
A1 Hernández Callejo, Luis
A1 González Rebollo, Miguel Ángel
A1 Cardeñoso Payo, Valentín
A1 Alonso Gómez, Víctor
A1 Morales Aragonés, José Ignacio
A1 Moyo, Ranganai Tawanda
K1 Renewable and Green Energy
K1 PV systems
K1 Electroluminescence image
K1 Gradient Boosting
K1 Recurrent Neural Network
K1 Imagen de electroluminiscencia
K1 Aumento de gradiente
K1 Red neuronal recurrente
K1 3308 Ingeniería y Tecnología del Medio Ambiente
AB 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.
PB Elsevier
SN 0038-092X
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/62970
UL https://uvadoc.uva.es/handle/10324/62970
LA eng
NO Solar Energy, 2023, vol. 265, 112089
NO Producción Científica
DS UVaDOC
RD 17-jul-2024