Improving Solar Panel Efficiency by Solar Tracking and Tilt Angle Optimization with Deep Learning

The surge in energy demand over the last few decades has led to the vast depletion of natural sources of energy and calls for active research and development of effective renewable energy systems. Solar energy is the most abundant renewable source of energy available to us and thus, adoption of solar energy over other energy sources can be an efficient way to secure a sustainable future. However, the change in the sun's relative position to earth through the day varies the intensity of sunlight falling on the photo-voltaic cells of a solar panel, leading to a substantial decrease in its overall efficiency. Therefore, development should concentrate on achieving greater efficiencies using solar panel arrays in order to generate maximum solar power. This paper proposes a solution by tracking the sun's relative position to earth continuously and optimizing the tilt angle of the solar panel accordingly with deep learning. The proposed method predicts the tilt and orientation angle of the sun with a 97% accuracy, resulting in the increase of the solar output power by 10.6% when compared to other non-tracking systems.