Development of a Nano-Coating-Based Solution for Surface Dirt Removal on Solar Panels Using Deep Learning

Solar panel efficiency is in fact significantly affected by a number of variables, including their direction, inclination, and atmospheric factors such as dirt buildup on their surfaces. Scientists are researching self-cleaning strategies, including electrostatic, mechanical, and coating methods. Super-hydrophobic coatings that repel water and prevent dirt accumulation show promise for improving energy generation. This paper aims to optimize solar panel cleaning and maintenance for maximizing energy production. By analyzing various studies and exploring different cleaning strategies, including super-hydrophobic coatings, the paper provides insights for developing efficient and sustainable solar panel technologies. It obtained a precision of 96.66% for dusty and clean solar panel data sets, as well as 100% for pituitary, for a total accuracy of 99.5%, which outperformed numerous other existing VGG16 designs, Epoch 50, and state-of-the-art work. We have also implemented MobileNetV2, VGG19, Resnet50, AlexNet, and CNN architectures.