Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach

Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On the other hand, the accumulation of solar panels dust is a significant challenge for PV-based systems. The accumulation of solar panels dust results in a significant reduction in the amount of energy produced. Because of the country's low wind velocity and rainfall, frequent cleaning of solar panels is necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs and increase the power output of solar panels that have been projected to be affected by dust accumulation. In this paper, we develop a deep belief network model to detect the dust particles in the solar panels installed as a large unit. The study takes into account various input metrics that includes solar irradiance, temperature level, and dust level on the panels. These metrics are used for the estimation of the level of dust present in the atmosphere and how often the panels can be cleaned at regular intervals. The simulation is conducted to test the efficacy of the model in cleaning the panels. The results are estimated in terms of accuracy, precision, recall, and F-measure. The results of the simulation show that the proposed model achieves higher accuracy rate of more than 99% than other methods.