Solar Panel Dust Detection for Optimizing Energy Performance by Visual Geometry Group Convolutional Neural Network (VGG-CNN)

The increasing awareness of carbon footprints has led to a steady rise in the demand for renewable energy in recent years. The use of solar energy has surged as a viable and eco-friendly substitute for conventional power sources but has challenges. A major challenge for solar panels is the accumulation of dust and debris, which can diminish their efficiency and performance. Maintaining the constant cleanliness of solar panels can be challenging, but automated detection systems offer a valuable solution. In this research, a novel and efficient image-processing approach is proposed, that uses a convolutional neural network (CNN) with the widely used VGG16 architecture to detect, identify, and extract dust from solar panels. The proposed model achieved an impressive 98% accuracy for dust detection, which could significantly improve solar panel efficiency. Incorporating the early prediction technique for solar panel disruptions using the CNN (VGG16) model into the solar energy monitoring system enables more timely and efficient preventive maintenance. This approach not only lowers operational costs but also greatly enhances the system’s productivity and reliability.