Artificial Intelligence-Based Power Optimizer to Enhance the Efficiency of Solar Panels

This paper presents the design and implementation of an AI-based power optimizer to enhance the efficiency of solar panel energy conversion. The system integrates both hardware and software components to intelligently manage energy output under varying environmental conditions. The core development involved a desktop-integrated linear regression model working alongside an Arduino microcontroller, forming a real-time feedback system. The setup includes a 260 W solar panel, an Arduino board, a desktop-based AI model, a buck converter, an inverter, and a connected load. The maximum power output is obtained by continuously monitoring and adjusting the system’s behavior through real-time data acquisition and handling a large number of datasets using the incremental conductance method. The system is designed to continuously operate at or near the maximum power point, thereby significantly improving energy conversion efficiency. The integration of AI and embedded systems in this context demonstrates the potential of intelligent controllers in renewable energy systems, especially in maximizing solar panel efficiency under fluctuating environmental conditions, such as shading and variance in solar irradiance.