Smart Hybrid Energy Management for Renewable Energy Optimization using Random Forest Algorithm

The present hybrid energy management systems are static rule-based. They are not able to respond to unpredictable alterations of renewable energy sources and internal load dynamics which leads to poor utilization of energy and increases power losses. This work is aimed at developing Smart Hybrid Energy Management System (SHEMS), which has been based on Random Forest Algorithm for optimum control of solar, wind, hydro and piezoelectric energy sources. This SHEMS for monitoring real-time environmental, load requirements, and state-of-charge battery data with optimizing the distribution of the energy dynamically to maximize the efficiency and reliability of the system. Experimental findings indicate an increase in energy efficiency by 15-25%, drastic reductions in power wastage, and system stability improvements. Furthermore, IoT-based monitoring and control are also included in the scheme, which can further improve hassle-free operations in flexibility with future smart energy applications. These results render SHEMS a feasible and scalable solution for further-hybrid energy management in renewable energy systems.