Advancements in Solar Tracking: A Comprehensive Review of Image-Processing Techniques

Solar energy is a widely available renewable source suitable for diverse applications, including residential, industrial and aerospace sectors. To maximize energy capture, solar tracking systems adjust panels to maintain perpendicular alignment with sunlight. Various tracking techniques are employed to adjust these trackers, such as sensors, predefined algorithms, deep learning, and image-processing techniques. Image processing-based trackers have gained prominence for their precision and accuracy. This approach uses cameras as sensors to capture real-time sky images and analyze them to detect the sun and its coordinates, orienting solar panels toward its center. This technology can be integrated with other techniques to enhance energy output with high accuracy, minimal tracking error, and low maintenance requirements. This review examines computer vision methods used in solar tracking systems, synthesizing findings from 26 studies published between 2009 and 2024. The paper discusses main system components, methods utilized, and results obtained. Findings demonstrate that the robustness and accuracy of these tracking systems have increased compared to other tracking systems, while tracking error has decreased.