Synthetic Dataset of Electroluminescence Images of Photovoltaic Cells by Deep Convolutional Generative Adversarial Networks

Héctor Felipe Mateo Romero (), Luis Hernández-Callejo (), Miguel Ángel González Rebollo, Valentín Cardeñoso-Payo, Victor Alonso Gómez, Hugo Jose Bello, Ranganai Tawanda Moyo and Jose Ignacio Morales Aragonés
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Héctor Felipe Mateo Romero: Departamento Fisica de la Materia Condensada, Universidad de Valladolid, 47011 Valladolid, Spain
Luis Hernández-Callejo: Departamento Ingeniería Agrícola y Forestal, Universidad de Valladolid, 47002 Valladolid, Spain
Miguel Ángel González Rebollo: Departamento Fisica de la Materia Condensada, Universidad de Valladolid, 47011 Valladolid, Spain
Valentín Cardeñoso-Payo: Departamento Informatica, Universidad de Valladolid, 47011 Valladolid, Spain
Victor Alonso Gómez: Departamento de Física, Universidad de Valladolid, 47002 Valladolid, Spain
Hugo Jose Bello: Departamento de Matemática Aplicada, Universidad de Valladolid, 47011 Valladolid, Spain
Ranganai Tawanda Moyo: Department of Mechanical Engineering, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa
Jose Ignacio Morales Aragonés: Departamento de Física, Universidad de Valladolid, 47002 Valladolid, Spain

Sustainability, 2023, vol. 15, issue 9, 1-20

Abstract: Affordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to use advanced artificial intelligence techniques, which critically depend on big amounts of data. In this work, a model based on Deep Convolutional Generative Adversarial Neural Networks (DCGANs) was trained in order to generate a synthetic dataset made of 10,000 electroluminescence images of photovoltaic cells, which extends a smaller dataset of experimentally acquired images. The energy output of the virtual cells associated with the synthetic dataset is predicted using a Random Forest regression model trained from real IV curves measured on real cells during the image acquisition process. The assessment of the resulting synthetic dataset gives an Inception Score of 2.3 and a Fréchet Inception Distance of 15.8 to the real original images, which ensures the excellent quality of the generated images. The final dataset can thus be later used to improve machine learning algorithms or to analyze patterns of solar cell defects.

Keywords: generative adversarial neural networks; photovoltaics; artificial intelligence; synthetic data; electroluminescence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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