Synthèse
Volume 30, Numéro 2, Pages 33-37
2025-12-01

Multi-task Convolutional Neural Networks For Damage Prediction On Solar Panels



Authors : Oualha Chiheb Eddine . Hadji Mohammed Lakhdar .

Abstract

Maintaining solar panels in the solar energy industry is crucial for ensuring consistent energy production. Experts often rely on elec- troluminescence (EL) imaging to identify defects in solar panels ; however, this conventional approach is manual, time-consuming, and struggles to keep pace with the growing scale and complexity of modern solar installations. In this work, we propose a multitask deep convolutional neural network (multi-task CNN) that not only classifies solar panels as Monocrystalline or Polycrystalline but also predicts the probability of defects and damage on the panel surface. The performance of the model is evaluated on a dataset of 2624 EL solar cell images [2], where each image is annotated by type and defect probability. The motivation behind this work is to investigate the capability of multitask deep learning to enhance classification and defect prediction results, while also highlighting potential limi- tations of AI in handling complex, real-world scenarios. By automa- ting these tasks, the proposed method addresses critical challenges in solar farm maintenance, including scalability, cost reduction, and the need for consistent and accurate inspections.

Keywords

Deep Learning ; Convolutional neural ; network ; Solar energy ; Multitask learning

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