Machine Learning (ML) technology for solar photovoltaic (PV) systems has emerged as a good option for increasing energy conversion efficiency under varying environmental conditions. This paper presents an adaptive Maximum Power Point Tracking (MPPT) approach using ML techniques to optimize real-time energy harvesting in PV systems. Traditional MPPT techniques such as Perturb and Observe (P&O) and Incremental Conductance are less efficient under rapidly varying irradiance and temperature. But the proposed ML-based MPPT scheme learns dynamically from environment and system data and forecast and optimize the operating point with a better accuracy. Various supervised learning models are compared on simulated data to identify the most accurate model with respect to accuracy, convergence speed, and computational cost. Experimental validation by MATLAB/Simulink confirms the better performance of the adaptive ML-based MPPT approach compared to conventional approaches. This paper illustrates the potential of intelligent control in improving the robustness and efficiency of PV systems in real-world applications
