Maximizing Energy Efficiency in a Solar Tracking System Using IoT-Based Image Processing

This paper presents an IoT-based solar tracking system that uses image processing to improve solar energy efficiency. A fixed camera is used to capture video frames of the sun, and these frames are processed in real time using the OpenCV library. By applying HSV thresholding and contour detection, the system finds the sun’s bright area and calculates its azimuth and elevation angles.The angles are formatted as JSON and sent to the ThingsBoard IoT platform via MQTT. On the platform, a dashboard shows real-time data using charts, radial gauges, and alarm indicators. The system also uses the Hottel model to calculate theoretical solar radiation and compares it with the image-based results.To show how the system could be used in real life, a control algorithm was written to move a servo motor based on the azimuth angle. This part of the system hasn’t been tested with hardware yet, but the Python code is ready to run on a Raspberry Pi.The simulation results show that the system tracks the sun accurately and provides consistent data when compared to theoretical values. The combination of image processing, IoT communication, and automation makes this system a simple and low-cost solution for solar tracking.