Solar Panel Dust Detection Using Deep Learning Model

Accurate detection of dust particles on solar panels is essential for guaranteeing their maximum efficiency and longevity. Dust deposition can significantly impede the amount of radiation accessing the solar cells, leading to a decrease in total energy production. The diminution in production additionally carries financial implications, particularly in solar power plants, where minimal decreases in efficiency can contribute to significant declines in revenue. Therefore, efficient dust detection is vital for optimizing the performance of solar panels, which in turn maximizes the return on investments and enhances the long-term viability of renewable energy systems. Recent studies have shown that deep learning models, trained adequately on extensive sets of labeled data, can efficiently acquire the ability to identify the presence of dust accumulation on solar panels. In addition, recent studies have shown that using metaheuristic methods for hyperparameter tuning can significantly boost the effectiveness of deep learning models. In this context, this paper aims to construct an effective convolutional neural network (CNN) model with hyperparameter tuning using the Equilibrium optimizer (EO) for accurately recognizing dust on solar panels. The obtained findings show that the suggested model provides high accuracy and achieves early convergence.