Optimizing Solar Panel Performance Through Advanced CNN Architectures for Fault Classification

Optimizing the performance of solar panels is crucial for enhancing energy efficiency and sustainability. This study explores advanced CNN architectures-VGG19, InceptionV3, and ResNet-50-for fault classification in solar panels. Using a dataset of six fault categories, images are preprocessed, resized, and balanced with class weights. Models leveraged pre-trained ImageNet weights, fine-tuning, and the Adam optimizer for efficient training. Performance is evaluated using standard metrics, and experiments are conducted on different hardware setups to assess classification accuracy and computational efficiency. Results provide insights into optimizing solar panel diagnostics for improved maintenance and energy output.