A Random Forest-Based Intelligent Duty-Cycle Prediction Framework for High-Efficiency Dc–Dc Converter Control

The increasing penetration of renewable energy sources in modern microgrids has created a growing need for intelligent and efficient power converter control strategies capable of handling dynamic operating conditions. This paper presents a machine-learning-based framework for the optimal design and control of a DC–DC converter in renewable-energy-based microgrid applications. The proposed approach utilizes key operational parameters, including photovoltaic (PV) voltage, PV current, PV power, loa