Solar Tracking System: From Traditional to AI-Integrated Solutions

The performance of solar panels is highly dependent on their alignment with the sun. Fixed solar panel systems do not increase energy absorption, while traditional tracking methods often depend on mechanical sensors and predefined algorithms, which may lack pre- cision and adaptability. Different types of solar tracker available are studied and the best mechanism is chosen to built the solar tracker that makes use of machine learn- ing. The proposed model uses historical solar position data, weather parameters, and real-time power output to determine the optimal tilt and azimuth angles. By contin- uously adapting to environmental conditions, the system enhances energy capture, minimizes reliance on costly sensors, and surpasses conventional tracking techniques in performance. Experimental evaluations indicate that ML-driven tracking significantly improves energy effi- ciency and adaptability. This research underscores the role of artificial intelligence in optimizing renewable energy systems and contributes to the development of better renewable energy sources.