Deep Learning in Solar Panel Maintenance: A Model Comparison for Automated Cleaning

Automated solar panel cleaning robots are transforming the maintenance landscape for solar energy systems, addressing critical issues like contamination due to dust, snow and bird droppings. These not only reduce the energy output but also increases the frequencies for cleaning which is time-consuming and can be expensive especially when manual cleaning is adopted. To tackle this issue, new advanced automated cleaning systems are used that incorporate sensor technologies and AI platforms to evaluate the cleanliness of the solar panels and decide whether cleaning is required. At the core of these systems are CNN integrations for fault detection purposes. In this study, several CNN architectures have been investigated, and the results indicate that EfficientNetB0 achieves high performance-efficiency trade-off. It stands out in identifying outer conditions on the substrates of solar panels so that they can be easily maintained. DenseNet121 also reveals great performance in feature extraction, which helps to improve the robot's evaluation of cleanliness. Other models like Inception V3 and NASNet are also taken into consideration. Inception V3‧s efficiency could be influenced by training schemes where as NASNet tends to be problematic when works in a small database often, thereby affecting its performance. The validation accuracy of 87% achieved in most of the DenseNet121 training configurations points to the model's suitability as a tool for predicting cleaning needs in automated systems. Such model identification is helpful in driving efficiency to advance cleaning of solar panels, owing to efficient energy generation and increased utilization of energy from the sun hence revolutionizing solar energy production. In other words, these forms of automated systems directly add value to renewable sources of energy, namely solar panel functionality and effectiveness.