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p-Index From 2021 - 2026
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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Camelia Putri Lestari
STMIK IKMI Cirebon
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Implementation of Deep Learning Based on Convolutional Neural Network for Detecting Images of Solar Panel Damage in Smart Grid Systems Camelia Putri Lestari; Nining Rahaningsih; Irfan Ali; Dodi Solihudin; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2225

Abstract

This study aims to implement Deep Learning based on Convolutional Neural Network (CNN) in detecting solar panel damage using thermal images as part of a Smart Grid system. The main problem addressed is the difficulty of early automatic identification of solar panel cell damage using conventional methods. Through the CNN approach, this study developed a classification model to distinguish between damaged (Defective) and undamaged (Non-Defective) solar panel conditions. The research stages included thermal image dataset collection, pre-processing, model training, and performance evaluation. The results showed that the CNN model was able to achieve an accuracy of over 87% with stable performance on the validation data. Visualization using the Grad-CAM method helps interpret the damaged areas that are the focus of the model's decision.
Co-Authors Dodi Solihudin Irfan Ali Nining Rahaningsih Tati Suprapti