Digital Twin-Based Smart Renewable Energy System Using AI and IoT

In this paper, the design and development of a Digital Twin-Based Smart Renewable Energy System using a dual-axis, image-based tracking algorithm were successfully demonstrated. By applying OpenCV image processing instead of traditional LDR light sensors, the system achieved higher tracking accuracy and robustness against environmental noise [22]. The integration of IoT technology via the INA219 sensor enabled precise, real-time monitoring of DC voltage and current [14]. Simultaneously, the Three.js-based Digital Twin solved the challenge of remote visibility by providing a live, synchronized 3D visualization of the physical hardware [8]. Overall, the system displayed notable enhancements in efficiency and responsiveness. The real-time serial communication and synchronization between the physical Arduino-based edge hardware and the digital browser counterpart occurred seamlessly, demonstrating the powerful synergy of Digital Twin technology and IoT monitoring systems. While the current prototype successfully validates this architecture, there are avenues for future expansion. The reliance on a local camera feed could be upgraded to edge-computing modules (such as a standalone Raspberry Pi) for entirely self-contained processing. Additionally, incorporating advanced Machine Learning models to predict power output based on historical weather APIs would transition the system from reactive tracking to predictive energy management [11], [12]. Ultimately, this project establishes a highly effective, low-cost foundation for next-generation intelligent energy infrastructures.

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